From 7c95894a65b6f8abe3a02d38217e9db5359f9ab7 Mon Sep 17 00:00:00 2001
From: Ansh0611 <62184938+Ansh0611@users.noreply.github.com>
Date: Thu, 11 Jun 2020 10:37:05 +0530
Subject: [PATCH 1/2] Add files via upload

---
 Student_Performance.ipynb | 5589 +++++++++++++++++++------------------
 1 file changed, 2801 insertions(+), 2788 deletions(-)

diff --git a/Student_Performance.ipynb b/Student_Performance.ipynb
index 60985ca..473465d 100644
--- a/Student_Performance.ipynb
+++ b/Student_Performance.ipynb
@@ -1,2841 +1,2854 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Student Performance.ipynb",
-      "version": "0.3.2",
-      "provenance": [],
-      "collapsed_sections": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/roshank1605A04/Student-s-Performance-Analytics/blob/master/Student_Performance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/roshank1605A04/Student-s-Performance-Analytics/blob/master/Student_Performance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "metadata": {
-        "id": "G7ZVqj3wV1Ry",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "cell_type": "code",
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "metadata": {
-        "id": "RiBBc-sEWTN5",
-        "colab_type": "code",
-        "outputId": "f0b50be4-b473-4383-d381-33c057fb8fb6",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 225
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data = pd.read_csv('drive/My Drive/Projects/Students Performance/StudentsPerformance.csv')\n",
-        "\n",
-        "print(data.shape)\n",
-        "\n",
-        "data.head()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "(1000, 8)\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>gender</th>\n",
-              "      <th>race/ethnicity</th>\n",
-              "      <th>parental level of education</th>\n",
-              "      <th>lunch</th>\n",
-              "      <th>test preparation course</th>\n",
-              "      <th>math score</th>\n",
-              "      <th>reading score</th>\n",
-              "      <th>writing score</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group B</td>\n",
-              "      <td>bachelor's degree</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>none</td>\n",
-              "      <td>72</td>\n",
-              "      <td>72</td>\n",
-              "      <td>74</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group C</td>\n",
-              "      <td>some college</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>completed</td>\n",
-              "      <td>69</td>\n",
-              "      <td>90</td>\n",
-              "      <td>88</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group B</td>\n",
-              "      <td>master's degree</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>none</td>\n",
-              "      <td>90</td>\n",
-              "      <td>95</td>\n",
-              "      <td>93</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>male</td>\n",
-              "      <td>group A</td>\n",
-              "      <td>associate's degree</td>\n",
-              "      <td>free/reduced</td>\n",
-              "      <td>none</td>\n",
-              "      <td>47</td>\n",
-              "      <td>57</td>\n",
-              "      <td>44</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>male</td>\n",
-              "      <td>group C</td>\n",
-              "      <td>some college</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>none</td>\n",
-              "      <td>76</td>\n",
-              "      <td>78</td>\n",
-              "      <td>75</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   gender race/ethnicity parental level of education         lunch  \\\n",
-              "0  female        group B           bachelor's degree      standard   \n",
-              "1  female        group C                some college      standard   \n",
-              "2  female        group B             master's degree      standard   \n",
-              "3    male        group A          associate's degree  free/reduced   \n",
-              "4    male        group C                some college      standard   \n",
-              "\n",
-              "  test preparation course  math score  reading score  writing score  \n",
-              "0                    none          72             72             74  \n",
-              "1               completed          69             90             88  \n",
-              "2                    none          90             95             93  \n",
-              "3                    none          47             57             44  \n",
-              "4                    none          76             78             75  "
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 168
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "EGC_GP4-Wll0",
-        "colab_type": "code",
-        "outputId": "5236fee8-f525-40ab-8cbd-aa8f9dee483d",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 223
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data.tail()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>gender</th>\n",
-              "      <th>race/ethnicity</th>\n",
-              "      <th>parental level of education</th>\n",
-              "      <th>lunch</th>\n",
-              "      <th>test preparation course</th>\n",
-              "      <th>math score</th>\n",
-              "      <th>reading score</th>\n",
-              "      <th>writing score</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>995</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group E</td>\n",
-              "      <td>master's degree</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>completed</td>\n",
-              "      <td>88</td>\n",
-              "      <td>99</td>\n",
-              "      <td>95</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>996</th>\n",
-              "      <td>male</td>\n",
-              "      <td>group C</td>\n",
-              "      <td>high school</td>\n",
-              "      <td>free/reduced</td>\n",
-              "      <td>none</td>\n",
-              "      <td>62</td>\n",
-              "      <td>55</td>\n",
-              "      <td>55</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>997</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group C</td>\n",
-              "      <td>high school</td>\n",
-              "      <td>free/reduced</td>\n",
-              "      <td>completed</td>\n",
-              "      <td>59</td>\n",
-              "      <td>71</td>\n",
-              "      <td>65</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>998</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group D</td>\n",
-              "      <td>some college</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>completed</td>\n",
-              "      <td>68</td>\n",
-              "      <td>78</td>\n",
-              "      <td>77</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>999</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group D</td>\n",
-              "      <td>some college</td>\n",
-              "      <td>free/reduced</td>\n",
-              "      <td>none</td>\n",
-              "      <td>77</td>\n",
-              "      <td>86</td>\n",
-              "      <td>86</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "     gender race/ethnicity parental level of education         lunch  \\\n",
-              "995  female        group E             master's degree      standard   \n",
-              "996    male        group C                 high school  free/reduced   \n",
-              "997  female        group C                 high school  free/reduced   \n",
-              "998  female        group D                some college      standard   \n",
-              "999  female        group D                some college  free/reduced   \n",
-              "\n",
-              "    test preparation course  math score  reading score  writing score  \n",
-              "995               completed          88             99             95  \n",
-              "996                    none          62             55             55  \n",
-              "997               completed          59             71             65  \n",
-              "998               completed          68             78             77  \n",
-              "999                    none          77             86             86  "
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 169
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "yQEEsbpSWpdG",
-        "colab_type": "code",
-        "outputId": "c664fcb3-0563-483a-8b79-d2f072a9f47e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 261
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data.info()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 1000 entries, 0 to 999\n",
-            "Data columns (total 8 columns):\n",
-            "gender                         1000 non-null object\n",
-            "race/ethnicity                 1000 non-null object\n",
-            "parental level of education    1000 non-null object\n",
-            "lunch                          1000 non-null object\n",
-            "test preparation course        1000 non-null object\n",
-            "math score                     1000 non-null int64\n",
-            "reading score                  1000 non-null int64\n",
-            "writing score                  1000 non-null int64\n",
-            "dtypes: int64(3), object(5)\n",
-            "memory usage: 62.6+ KB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "p8qPdumvWsFi",
-        "colab_type": "code",
-        "outputId": "7e538d81-7631-42b2-875f-b053da212534",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 300
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data.describe()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>math score</th>\n",
-              "      <th>reading score</th>\n",
-              "      <th>writing score</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>1000.00000</td>\n",
-              "      <td>1000.000000</td>\n",
-              "      <td>1000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>66.08900</td>\n",
-              "      <td>69.169000</td>\n",
-              "      <td>68.054000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>15.16308</td>\n",
-              "      <td>14.600192</td>\n",
-              "      <td>15.195657</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>0.00000</td>\n",
-              "      <td>17.000000</td>\n",
-              "      <td>10.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>57.00000</td>\n",
-              "      <td>59.000000</td>\n",
-              "      <td>57.750000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>66.00000</td>\n",
-              "      <td>70.000000</td>\n",
-              "      <td>69.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>77.00000</td>\n",
-              "      <td>79.000000</td>\n",
-              "      <td>79.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>100.00000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "       math score  reading score  writing score\n",
-              "count  1000.00000    1000.000000    1000.000000\n",
-              "mean     66.08900      69.169000      68.054000\n",
-              "std      15.16308      14.600192      15.195657\n",
-              "min       0.00000      17.000000      10.000000\n",
-              "25%      57.00000      59.000000      57.750000\n",
-              "50%      66.00000      70.000000      69.000000\n",
-              "75%      77.00000      79.000000      79.000000\n",
-              "max     100.00000     100.000000     100.000000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 171
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "2t2utISaWuH0",
-        "colab_type": "code",
-        "outputId": "c9c4dcd2-1b0b-49f5-9c57-15d93c8f874c",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 186
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data.isnull().sum()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "gender                         0\n",
-              "race/ethnicity                 0\n",
-              "parental level of education    0\n",
-              "lunch                          0\n",
-              "test preparation course        0\n",
-              "math score                     0\n",
-              "reading score                  0\n",
-              "writing score                  0\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 172
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "b-u05ntqWw2y",
-        "colab_type": "code",
-        "outputId": "7efae39c-7258-47de-976f-941f969144bc",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 396
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# visualising the number of male and female in the dataset\n",
-        "\n",
-        "data['gender'].value_counts(normalize = True)\n",
-        "data['gender'].value_counts(dropna = False).plot.bar(color = 'magenta')\n",
-        "plt.title('Comparison of Males and Females')\n",
-        "plt.xlabel('gender')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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AgM9wLMgvvfRSjRs3ToMGDdK+fft01113qbKy0vO+MeaU851u+onCwkIUEOBfZ7UCdSUy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\nTkSuQmCC\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f323f59f208>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "NQiPqVqiW4Wa",
-        "colab_type": "code",
-        "outputId": "e5a583af-483d-4e11-e649-94feaf2a5b3a",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 401
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# visualizing the different groups in the dataset\n",
-        "\n",
-        "data['race/ethnicity'].value_counts(normalize = True)\n",
-        "data['race/ethnicity'].value_counts(dropna = False).plot.bar(color = 'cyan')\n",
-        "plt.title('Comparison of various groups')\n",
-        "plt.xlabel('Groups')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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q1UpTp07Vhg0b1LhxYyUnJyskJESTJk1SamqqHA6Hxo4dK7fbvCNjAAD8wWHV5KT0Tcau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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f32360a69e8>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "HuHz4ouB-McA",
-        "colab_type": "code",
-        "outputId": "17a911b5-7543-439a-9c69-1c4f9b6218ed",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data['race/ethnicity'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "group C    319\n",
-              "group D    262\n",
-              "group B    190\n",
-              "group E    140\n",
-              "group A     89\n",
-              "Name: race/ethnicity, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 175
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "G5B9KxHRW_D0",
-        "colab_type": "code",
-        "outputId": "c0a96572-aa93-4161-8ed9-1f8f48945ac2",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 449
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# visualizing the differnt parental education levels\n",
-        "\n",
-        "data['parental level of education'].value_counts(normalize = True)\n",
-        "data['parental level of education'].value_counts(dropna = False).plot.bar()\n",
-        "plt.title('Comparison of Parental Education')\n",
-        "plt.xlabel('Degree')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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iIqJKw2yb1tu3b4/PPvsMAFC9enUUFhYiPj4ePXv2BAB0794dsbGxOHXqFFxdXWFnZwcb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\nrkJggg==\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f323605e160>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "r8temzw0XFd1",
-        "colab_type": "code",
-        "outputId": "80a975e8-482b-4b43-9db1-970dd89e5221",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 422
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# visualizing different types of lunch \n",
-        "\n",
-        "data['lunch'].value_counts(normalize = True)\n",
-        "data['lunch'].value_counts(dropna = False).plot.bar(color = 'yellow')\n",
-        "plt.title('Comparison of different types of lunch')\n",
-        "plt.xlabel('types of lunch')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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svv76a12+fFljxoxRWFiYpk2bptLSUtntdi1btkw+Pj5KTEzU+vXr5eHhoSFD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\naWSakSl3AAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f32360642e8>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "CgO6Upe9XOMs",
-        "colab_type": "code",
-        "outputId": "5545fb2d-a20b-411f-af85-0f6a6b2b13e9",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 627
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# visualizing maths score\n",
-        "\n",
-        "data['math score'].value_counts(normalize = True)\n",
-        "data['math score'].value_counts(dropna = False).plot.bar(figsize = (18, 10))\n",
-        "plt.title('Comparison of math scores')\n",
-        "plt.xlabel('score')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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O6urOrcrQ1P7lOcxYmej87+SYtpzR1LVNmak9XduUmdrTtS3in0Vz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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3236089438>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "nwoEGMVuTU89",
-        "colab_type": "code",
-        "outputId": "80fb69df-b0ba-4b29-b274-b32e1ddda724",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 627
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# visualizing reading score score\n",
-        "\n",
-        "data['reading score'].value_counts(normalize = True)\n",
-        "data['reading score'].value_counts(dropna = False).plot.bar(figsize = (18, 10), color = 'orange')\n",
-        "plt.title('Comparison of math scores')\n",
-        "plt.xlabel('score')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235eda588>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "04Y4oXmfdpnN",
-        "colab_type": "code",
-        "outputId": "65f0de4b-2e08-42cf-9df2-931af2ce18fb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 627
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# visualizing writing score\n",
-        "\n",
-        "data['math score'].value_counts(normalize = True)\n",
-        "data['math score'].value_counts(dropna = False).plot.bar(figsize = (18, 10), color = 'pink')\n",
-        "plt.title('Comparison of math scores')\n",
-        "plt.xlabel('score')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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SygYAAAAgKWUDAAAAkJSyAQAAAEhK2QAAAAAkVdHZAwAAANunqqXL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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235d92160>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "pfAaNUKvVGJY",
-        "colab_type": "code",
-        "outputId": "22ec3315-902c-4199-d5ef-72660cbb35fd",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 318
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# gender vs race/etnicity \n",
-        "\n",
-        "x = pd.crosstab(data['gender'], data['race/ethnicity'])\n",
-        "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (4, 4))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3235b65240>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 181
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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kZs2aebEqcdSAAQMYMGCAt8vwOn196KSEhARqamrqvGYymfjyyy+9VJHI+VMQ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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235b7b9e8>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "oM5B03VaXH2f",
-        "colab_type": "code",
-        "outputId": "5bdb1a15-0aaa-4874-ff09-27b2b7adc182",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 324
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# comparison of race/ethnicity and parental level of education\n",
-        "\n",
-        "x = pd.crosstab(data['race/ethnicity'], data['parental level of education'])\n",
-        "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = 'True', figsize = (7, 4) )"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3235a99668>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 182
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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HLm7/Dc9WHfDt9hjRP35lGiMrMY5mw8biUKMWRxZNp9GAV7DzrMGlHb9zacfveLbpXOR4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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235bc3390>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "hw0nW8fYZbkB",
-        "colab_type": "code",
-        "outputId": "62244083-effc-4763-cfb8-c042f64a7b7c",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 418
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# comparison of parental degree and test course\n",
-        "\n",
-        "sns.countplot(x = 'parental level of education', data = data, hue = 'test preparation course', palette = 'dark')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
-            "  stat_data = remove_na(group_data[hue_mask])\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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85E5ERP8Ifn6zdV1CteIZOhERkQww0ImIiGSAgU5ERCQDDHQiIiIZYKATERHJAAOdiIhI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\nBjoREZEMMNCJiIhk4P8A8uyvYM1bcn0AAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f32360be5c0>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "metadata": {
-        "id": "SG8Eojinal_D",
-        "colab_type": "code",
-        "outputId": "c886e57c-ba8a-46ba-ae31-21a465328cb0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 418
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# comparison of race/ethnicity and test preparation course\n",
-        "\n",
-        "sns.countplot(x = 'race/ethnicity', data = data,  hue = 'test preparation course', palette = 'bright')\n",
-        "plt.show()"
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "G7ZVqj3wV1Ry"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "colab_type": "code",
+    "id": "RiBBc-sEWTN5",
+    "outputId": "f0b50be4-b473-4383-d381-33c057fb8fb6"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 8)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race/ethnicity</th>\n",
+       "      <th>parental level of education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test preparation course</th>\n",
+       "      <th>math score</th>\n",
+       "      <th>reading score</th>\n",
+       "      <th>writing score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
-            "  stat_data = remove_na(group_data[hue_mask])\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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url56aYAkacCAwZo4cYwqVKiooKDyxbrvRcE0prhn/PGDq+C9/GCYOXOqnn66lZo1a+7o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\nkAMAYGAEOQAABkaQAwBgYP8fUyF79ksBpHIAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f32360cd438>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+      "text/plain": [
+       "   gender race/ethnicity parental level of education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test preparation course  math score  reading score  writing score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  \n",
+       "2                    none          90             95             93  \n",
+       "3                    none          47             57             44  \n",
+       "4                    none          76             78             75  "
       ]
-    },
-    {
-      "metadata": {
-        "id": "5vtrMoiVb2KB",
-        "colab_type": "code",
-        "outputId": "fd215592-6b92-4974-dbc2-a83aa0c9c6a9",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 252
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# feature engineering on the data to visualize and solve the dataset more accurately\n",
-        "\n",
-        "# setting a passing mark for the students to pass on the three subjects individually\n",
-        "passmarks = 40\n",
-        "\n",
-        "# creating a new column pass_math, this column will tell us whether the students are pass or fail\n",
-        "data['pass_math'] = np.where(data['math score']< passmarks, 'Fail', 'Pass')\n",
-        "data['pass_math'].value_counts(dropna = False).plot.bar(color = 'black', figsize = (5, 3))\n",
-        "\n",
-        "plt.title('Comparison of students passed or failed in maths')\n",
-        "plt.xlabel('status')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
+     },
+     "execution_count": 168,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('drive/My Drive/Projects/Students Performance/StudentsPerformance.csv')\n",
+    "\n",
+    "print(data.shape)\n",
+    "\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 223
+    },
+    "colab_type": "code",
+    "id": "EGC_GP4-Wll0",
+    "outputId": "5236fee8-f525-40ab-8cbd-aa8f9dee483d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race/ethnicity</th>\n",
+       "      <th>parental level of education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test preparation course</th>\n",
+       "      <th>math score</th>\n",
+       "      <th>reading score</th>\n",
+       "      <th>writing score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group E</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>88</td>\n",
+       "      <td>99</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>high school</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>62</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>high school</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>59</td>\n",
+       "      <td>71</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group D</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>68</td>\n",
+       "      <td>78</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group D</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>77</td>\n",
+       "      <td>86</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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L4mOgvxYSEoLw8HDs2rULXbt2RUBAAFQqFUJDQzF//nzIZDIsXLgQGg3HZYjI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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f32360a06a0>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+      "text/plain": [
+       "     gender race/ethnicity parental level of education         lunch  \\\n",
+       "995  female        group E             master's degree      standard   \n",
+       "996    male        group C                 high school  free/reduced   \n",
+       "997  female        group C                 high school  free/reduced   \n",
+       "998  female        group D                some college      standard   \n",
+       "999  female        group D                some college  free/reduced   \n",
+       "\n",
+       "    test preparation course  math score  reading score  writing score  \n",
+       "995               completed          88             99             95  \n",
+       "996                    none          62             55             55  \n",
+       "997               completed          59             71             65  \n",
+       "998               completed          68             78             77  \n",
+       "999                    none          77             86             86  "
       ]
-    },
-    {
-      "metadata": {
-        "id": "qsOJMDZkg3K5",
-        "colab_type": "code",
-        "outputId": "0069d22e-e62a-4b34-ff0f-3f4a5d6cca1e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data['pass_math'].value_counts()"
+     },
+     "execution_count": 169,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 261
+    },
+    "colab_type": "code",
+    "id": "yQEEsbpSWpdG",
+    "outputId": "c664fcb3-0563-483a-8b79-d2f072a9f47e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 8 columns):\n",
+      "gender                         1000 non-null object\n",
+      "race/ethnicity                 1000 non-null object\n",
+      "parental level of education    1000 non-null object\n",
+      "lunch                          1000 non-null object\n",
+      "test preparation course        1000 non-null object\n",
+      "math score                     1000 non-null int64\n",
+      "reading score                  1000 non-null int64\n",
+      "writing score                  1000 non-null int64\n",
+      "dtypes: int64(3), object(5)\n",
+      "memory usage: 62.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "colab_type": "code",
+    "id": "p8qPdumvWsFi",
+    "outputId": "7e538d81-7631-42b2-875f-b053da212534"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>math score</th>\n",
+       "      <th>reading score</th>\n",
+       "      <th>writing score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.00000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>66.08900</td>\n",
+       "      <td>69.169000</td>\n",
+       "      <td>68.054000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>15.16308</td>\n",
+       "      <td>14.600192</td>\n",
+       "      <td>15.195657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>57.00000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>57.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>66.00000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>77.00000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>100.00000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Pass    960\n",
-              "Fail     40\n",
-              "Name: pass_math, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 186
-        }
+      "text/plain": [
+       "       math score  reading score  writing score\n",
+       "count  1000.00000    1000.000000    1000.000000\n",
+       "mean     66.08900      69.169000      68.054000\n",
+       "std      15.16308      14.600192      15.195657\n",
+       "min       0.00000      17.000000      10.000000\n",
+       "25%      57.00000      59.000000      57.750000\n",
+       "50%      66.00000      70.000000      69.000000\n",
+       "75%      77.00000      79.000000      79.000000\n",
+       "max     100.00000     100.000000     100.000000"
       ]
-    },
-    {
-      "metadata": {
-        "id": "YlFzqNvVeaeS",
-        "colab_type": "code",
-        "outputId": "b4740ae9-6131-422f-addf-2029630cac13",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 252
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# creating a new column pass_math, this column will tell us whether the students are pass or fail\n",
-        "data['pass_reading'] = np.where(data['reading score']< passmarks, 'Fail', 'Pass')\n",
-        "data['pass_reading'].value_counts(dropna = False).plot.bar(color = 'brown', figsize = (5, 3))\n",
-        "\n",
-        "plt.title('Comparison of students passed or failed in maths')\n",
-        "plt.xlabel('status')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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iXbt22Ldvn+W9MhqN6NWrV53vx71yc3PRrVs3qNVqXLhwAadPn67zugsICMDnn3+O0tJS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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235861eb8>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "execution_count": 171,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 186
+    },
+    "colab_type": "code",
+    "id": "2t2utISaWuH0",
+    "outputId": "c9c4dcd2-1b0b-49f5-9c57-15d93c8f874c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender                         0\n",
+       "race/ethnicity                 0\n",
+       "parental level of education    0\n",
+       "lunch                          0\n",
+       "test preparation course        0\n",
+       "math score                     0\n",
+       "reading score                  0\n",
+       "writing score                  0\n",
+       "dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "HyHpREyVhBdM",
-        "colab_type": "code",
-        "outputId": "99f80a0c-b1c9-4d6c-8baa-5b6cbe7aee52",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data['pass_reading'].value_counts(dropna = False)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Pass    974\n",
-              "Fail     26\n",
-              "Name: pass_reading, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 188
-        }
+     },
+     "execution_count": 172,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 396
+    },
+    "colab_type": "code",
+    "id": "b-u05ntqWw2y",
+    "outputId": "7efae39c-7258-47de-976f-941f969144bc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AgM9wLMgvvfRSjRs3ToMGDdK+fft01113qbKy0vO+MeaU851u+onCwkIUEOBfZ7UCdSUy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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f323f59f208>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "e_GAFUo0gZLF",
-        "colab_type": "code",
-        "outputId": "910f6067-933c-40a1-cd22-356a8c951005",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 252
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# creating a new column pass_math, this column will tell us whether the students are pass or fail\n",
-        "data['pass_writing'] = np.where(data['writing score']< passmarks, 'Fail', 'Pass')\n",
-        "data['pass_writing'].value_counts(dropna = False).plot.bar(color = 'blue', figsize = (5, 3))\n",
-        "\n",
-        "plt.title('Comparison of students passed or failed in maths')\n",
-        "plt.xlabel('status')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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nZTAY0KtXr1o/jztlZ2ejW7duUKvVOH/+PE6dOlXreefn54cvv/wSxcXFAIDt27dj165d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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235852668>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualising the number of male and female in the dataset\n",
+    "\n",
+    "data['gender'].value_counts(normalize = True)\n",
+    "data['gender'].value_counts(dropna = False).plot.bar(color = 'magenta')\n",
+    "plt.title('Comparison of Males and Females')\n",
+    "plt.xlabel('gender')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 401
+    },
+    "colab_type": "code",
+    "id": "NQiPqVqiW4Wa",
+    "outputId": "e5a583af-483d-4e11-e649-94feaf2a5b3a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q1UpTp07Vhg0b1LhxYyUnJyskJESTJk1SamqqHA6Hxo4dK7fbvCNjAAD8wWHV5KT0Tcau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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f32360a69e8>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "W5qlGHHJhUDP",
-        "colab_type": "code",
-        "outputId": "5b0b792e-c0c5-48df-c4c7-adb00bf14b80",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 539
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# computing the total score for each student\n",
-        "\n",
-        "data['total_score'] = data['math score'] + data['reading score'] + data['writing score']\n",
-        "\n",
-        "data['total_score'].value_counts(normalize = True)\n",
-        "data['total_score'].value_counts(dropna = True).plot.bar(color = 'cyan', figsize = (40, 8))\n",
-        "\n",
-        "plt.title('comparison of total score of all the students')\n",
-        "plt.xlabel('total score scored by the students')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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mzZopJSVFV155pdq0aaNvv/02+HGUAdZ+Yh1n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\nDnNzc7Vq1aqQa5w9e3bEstJPe5/33ntP7733nlauXKmsrCx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s4bXXXos3+/z5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\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235824c50>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing the different groups in the dataset\n",
+    "\n",
+    "data['race/ethnicity'].value_counts(normalize = True)\n",
+    "data['race/ethnicity'].value_counts(dropna = False).plot.bar(color = 'cyan')\n",
+    "plt.title('Comparison of various groups')\n",
+    "plt.xlabel('Groups')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 129
+    },
+    "colab_type": "code",
+    "id": "HuHz4ouB-McA",
+    "outputId": "17a911b5-7543-439a-9c69-1c4f9b6218ed"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "group C    319\n",
+       "group D    262\n",
+       "group B    190\n",
+       "group E    140\n",
+       "group A     89\n",
+       "Name: race/ethnicity, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "0Jpy1FwCjJbG",
-        "colab_type": "code",
-        "outputId": "abaad66f-b7d7-4ae2-b6dc-cc0ef454e03e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 621
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# computing percentage for each of the students\n",
-        "# importing math library to use ceil\n",
-        "from math import * \n",
-        "\n",
-        "data['percentage'] = data['total_score']/3\n",
-        "\n",
-        "for i in range(0, 1000):\n",
-        "  data['percentage'][i] = ceil(data['percentage'][i])\n",
-        "\n",
-        "data['percentage'].value_counts(normalize = True)\n",
-        "data['percentage'].value_counts(dropna = False).plot.bar(figsize = (16, 8), color = 'red')\n",
-        "\n",
-        "plt.title('Comparison of percentage scored by all the students')\n",
-        "plt.xlabel('percentage score')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
-            "A value is trying to be set on a copy of a slice from a DataFrame\n",
-            "\n",
-            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-            "  \n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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Lq23bto3rr79+revvueeeYpUEAABgE1G0lwEDAABAUwmrAAAAJEdYBQAAIDnC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\nAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f323d406390>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "execution_count": 175,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['race/ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 449
+    },
+    "colab_type": "code",
+    "id": "G5B9KxHRW_D0",
+    "outputId": "c0a96572-aa93-4161-8ed9-1f8f48945ac2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iIqJKw2yb1tu3b4/PPvsMAFC9enUUFhYiPj4ePXv2BAB0794dsbGxOHXqFFxdXWFnZwcb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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f323605e160>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "JP86Ohe7oBZA",
-        "colab_type": "code",
-        "outputId": "3de2b683-6ded-4e6e-f49f-aabcf13a27ea",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 251
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# checking which student is fail overall\n",
-        "\n",
-        "data['status'] = data.apply(lambda x : 'Fail' if x['pass_math'] == 'Fail' or \n",
-        "                           x['pass_reading'] == 'Fail' or x['pass_writing'] == 'Fail'\n",
-        "                           else 'pass', axis = 1)\n",
-        "\n",
-        "data['status'].value_counts(dropna = False).plot.bar(color = 'gray', figsize = (3, 3))\n",
-        "plt.title('overall results')\n",
-        "plt.xlabel('status')\n",
-        "plt.ylabel('count')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235864fd0>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing the differnt parental education levels\n",
+    "\n",
+    "data['parental level of education'].value_counts(normalize = True)\n",
+    "data['parental level of education'].value_counts(dropna = False).plot.bar()\n",
+    "plt.title('Comparison of Parental Education')\n",
+    "plt.xlabel('Degree')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 422
+    },
+    "colab_type": "code",
+    "id": "r8temzw0XFd1",
+    "outputId": "80a975e8-482b-4b43-9db1-970dd89e5221"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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svv76a12+fFljxoxRWFiYpk2bptLSUtntdi1btkw+Pj5KTEzU+vXr5eHhoSFD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\naWSakSl3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f32360642e8>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "3druUjNTuCpg",
-        "colab_type": "code",
-        "outputId": "45f553fe-63c1-4c31-c5af-8d87d337bb08",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 148
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# Assigning grades to the grades according to the following criteria :\n",
-        "# 0  - 40 marks : grade E\n",
-        "# 41 - 60 marks : grade D\n",
-        "# 60 - 70 marks : grade C\n",
-        "# 70 - 80 marks : grade B\n",
-        "# 80 - 90 marks : grade A\n",
-        "# 90 - 100 marks : grade O\n",
-        "\n",
-        "def getgrade(percentage, status):\n",
-        "  if status == 'Fail':\n",
-        "    return 'E'\n",
-        "  if(percentage >= 90):\n",
-        "    return 'O'\n",
-        "  if(percentage >= 80):\n",
-        "    return 'A'\n",
-        "  if(percentage >= 70):\n",
-        "    return 'B'\n",
-        "  if(percentage >= 60):\n",
-        "    return 'C'\n",
-        "  if(percentage >= 40):\n",
-        "    return 'D'\n",
-        "  else :\n",
-        "    return 'E'\n",
-        "\n",
-        "data['grades'] = data.apply(lambda x: getgrade(x['percentage'], x['status']), axis = 1 )\n",
-        "\n",
-        "data['grades'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "B    260\n",
-              "C    252\n",
-              "D    223\n",
-              "A    156\n",
-              "O     58\n",
-              "E     51\n",
-              "Name: grades, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 193
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing different types of lunch \n",
+    "\n",
+    "data['lunch'].value_counts(normalize = True)\n",
+    "data['lunch'].value_counts(dropna = False).plot.bar(color = 'yellow')\n",
+    "plt.title('Comparison of different types of lunch')\n",
+    "plt.xlabel('types of lunch')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 627
+    },
+    "colab_type": "code",
+    "id": "CgO6Upe9XOMs",
+    "outputId": "5545fb2d-a20b-411f-af85-0f6a6b2b13e9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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O6urOrcrQ1P7lOcxYmej87+SYtpzR1LVNmak9XduUmdrTtS3in0Vz\nx6S6ts3t35GvbXP7y31tm7quKWek2p9yhn+OlW9Ge/p77Ts5Zkf4e21zx+Tx99oTLvx5yfVfTDsx\nyf7WZEl1TBFnFDFTHjOKmCmPGUXMlMeMImZ6J8fkWjZ84AMfiK9//etx3HHHxapVq+LUU0+NhQsX\nRqdOnUrur6/f2KLzrl27oVU5Wrs/jxlFzJTHjCJmymNGETPlMaOImfKYUcRMecwoYqY8ZhQxUx4z\nipgpjxlFzJTHjCJmymNGUTJVV3du9Xlbe0wRZxQxUx4zipgpjxlFzJTHjCJmau6Y5gqIXB+j6NWr\nVxx//PFRUVER++67b+yxxx6xZs2aPCMAAAAAZZZr2TBv3rz44Q9/GBERa9eujXXr1kWvXr3yjAAA\nAACUWa6PUdTU1MRFF10Uv/3tb2PLli1x+eWXN/kIBQAAANA+5Vo27LbbbnHDDTfkORIAAADIma++\nBAAAAJJSNgAAAABJKRsAAACApJQNAAAAQFLKBgAAACApZQMAAACQlLIBAAAASErZAAAAACRV2dYB\nAACA5p27aEyT702vmVpyffkZp73xerv1PjffUnL/9VMWl1w/u3Zw8+EASnBnAwAAAJCUsgEAAABI\nStkAAAAAJKVsAAAAAJJSNgAAAABJKRsAAACApJQNAAAAQFLKBgAAACApZQMAAACQlLIBAAAASErZ\nAAAAACSlbAAAAACSUjYAAAAASSkbAAAAgKQq2zoAAADQPq18dMIbr7db3/fQupL7xz64ouT65CN6\nl1wfNWVRk7Nn1ta06pim9p+7aEzJ9ek1U5ucvfyM0954vd16n5tvafIYeLdxZwMAAACQlLIBAAAA\nSErZAAAAACSlbAAAAACSUjYAAAAASSkbAAAAgKSUDQAAAEBSygYAAAAgKWUDAAAAkJSyAQAAAEhK\n2QAAAAAkpWwAAAAAklI2AAAAAElVtnUAAACAHdn1UxaXXD+7dnDJ9ZWPTnjj9d+9t++hdSWPGfvg\nipLrk4/oXXJ91JRFJddn1taUXH+nx/Du5c4GAAAAICllAwAAAJCUsgEAAABIStkAAAAAJKVsAAAA\nAJJSNgAAAABJKRsAAACApJQNAAAAQFLKBgAAACApZQMAAACQlLIBAAAASErZAAAAACSlbAAAAACS\nUjYAAAAASVW2dQAAAAB2POcuGlNyfXrN1JLry8847Y3Xf/den5tvKXnM9VMWl1w/u3ZwyfWVj054\n4/V26/seWldyf0TE2AdXlFyffETvkuujpiwquT6ztqZV+5s7JtW1beq6RrT+2v49dzYAAAAASSkb\nAAAAgKSUDQAAAEBSygYAAAAgKWUDAAAAkJSyAQAAAEhK2QAAAAAkpWwAAAAAklI2AAAAAEkpGwAA\nAICkKvMeOHny5PjDH/4QFRUVMXbs2PjIRz6SdwQAAACgjHItG/7nf/4n/vrXv8acOXPiqaeeirFj\nx8acOXPyjAAAAACUWa6PUTzwwANxzDHHRETEhz70oXjxxRfj5ZdfzjMCAAAAUGYVWZZleQ0bN25c\nDBo0qLFwGDFiREyaNCn222+/vCIAAAAAZdamHxCZY88BAAAA5CTXsqFnz57x/PPPN/783HPPRXV1\ndZ4RAAAAgDLLtWz4xCc+EQsWLIiIiGXLlkXPnj1jt912yzMCAAAAUGa5fhvFYYcdFgceeGAMHz48\nKioq4tvf/nae4wEAAIAc5PoBkQAAAMCOr00/IBIAAADY8SgbAAAAgKRy/cwGAAAAoO1kWRYVFRXN\n7nnllVcav0myuro6qqqqWj1H2QAAAAA7oPvuuy8mTZoU3bt3j0suuSTGjx8fzz33XOy6664xYcKE\n+NjHPvam/X/84x9j0qRJ8dJLL0W3bt0iy7J47rnnolevXlFXVxf7779/i2d3vPzyyy9P/Pskdc89\n98QHPvCBiIhYv359XHnllXHzzTfHsmXL4qCDDopddtnlLcesW7cupk+fHr/61a+iqqoq9tlnn8b3\nJkyYEIMGDXrT/m3btsX8+fNj1qxZcccdd8S8efPikUceiYqKisbZ/8j+iIjNmzfHr3/963jppZdi\n7733jrvuuituv/32WLlyZfTt2zcqK1ve+1x11VVx5JFHvmW9rq4u9thjj+jVq1eLzlNfXx+33XZb\nPP3009G3b9+48cYbY8aMGbFs2bI44IAD3nJtN2zYELfeemv85S9/ib59+8bs2bPjP//zP2PFihXR\nr1+/6NSpU4t/h4iIW265JQ455JCyzLjwwgtj6NChJd+76aabYp999mnx16629jq9/nv87ne/i/32\n2y9eeuml+P73vx+33nprs8c0pdR1et3vf//7mDt3bvzqV7+KxYsXx4oVK6JLly7RvXv3t+x94okn\norq6OiL+76/HW2+9Ne64445YuXJlHHDAAW/5a3DLli3xxBNPRK9evWLLli0xZ86cuPPOO2PlypWx\n//77t/iv2ZEjR8ZJJ51U8r3XXnstfvzjH8d9990XPXr0iG7duv3/9s48Koori8M/cA0m0SCSqBEj\nGnUkwdEEEYwLiKgYMYiCCyiBMK5oNAbFEdEko4kZdQwjrjEa1xw1riOKg8ElLDLiQhw0ihvIjgiy\nRaDv/OHpmm6qGnkFtE7mfudwjlbfV3d779brV1Wvpc8iIiJgZ2en2O7s2bPYs2cPDh48iKioKFy6\ndAmmpqbo0KFDnW1SU3NEx55ojQLEY6WmTonGtaKiAnv37sXGjRvx/fffY//+/Th79ixKSkrQrVs3\nmJrqv6knOvYqKytx/Phx5OXloUOHDvjpp59w9OhRZGdno0uXLrLzq7FJjQ7RsSRa10T7k1ZHfdUc\nQ9cYJWqqtaK5UOODmvFanZrqwdNQipWa2ikaK7VziqKiIsTFxSExMRH//ve/kZeXBwsLi1pfW2uK\nlZq6ZozaaYia+rnItVW0D6qZUzT0/A5o+LmwmtyJ6lAzhxRtI1qnRMe2Gh3GmLcoUVM9UDt3bshc\nGMuPhv4eqiZW8+fPx4YNG+Dg4AB/f3+Eh4cjODgYTk5O+Pzzz+Hp6akn//HHH+Mvf/kLgoKC4Onp\nCU9PT0yaNAndu3fHF198IZOvief+1ygmTZqE77//HsCTSU23bt0wePBgJCQk4MyZM1i/fr2sjb+/\nPwYPHgxzc3Ps2rULffv2xYwZM2Tn07J48WK0bdsW/fr1w7lz50BE6NmzJ3788Ue8+uqrmD9/fp3k\ngSdJe+GFF5CXl4eOHTvi4cOHGDx4MK5cuYKMjAysWbNGT76srMxgTAIDA7Fjxw7ZcXd3d7z11lso\nLi6Gj4+PbJVK6Tw9e/ZETk4O8vPz0alTJ7i6uuLKlSuIiYnB5s2b9eSnTp2KXr16obCwEElJSejd\nuzccHR2RnJyMlJQUfPPNNzXqq45SLtTocHZ2lh4D0nZn7QTKxMQE0dHRevJDhw5Fx44d8cYbb8DX\n1/epX1BF4wQAH374Idzc3DB27Fh88skn6NKlC9577z1cvXoV0dHR2LRpU53iBACrV69Gfn4++vXr\nh9jYWLRs2RIdOnTAoUOH4OrqCj8/P4PnCQsLg4mJCZycnHD+/HlkZWVh5cqVevKzZs1C9+7dMX36\ndISFhUGj0aBfv364evUq7t27J+uzANC9e3dYWlqiSZMmUi5yc3PRpk0bxVzMnDkTVlZWMDc3x6FD\nhxAQEIAPPvigRr+XLl2KoqIiODs7SxO/7OxsREVFoWPHjrLxJ2qTmpojOvZEa5SaWInWKdG4AsCc\nOXNgZWUFJycntG7dGkSE7OxsnDhxAkVFRVixYoWevOjYmzdvHszMzFBUVASNRgNTU1M4ODggOTkZ\nVVVVWL58eZ1tUqNDdCyJ1jXR/gSI1xw11xjRWiuaCzV1U3S8itYDNbFSUztFYyU6pwCAffv2Ydu2\nbejduzfMzc0lHRcvXkRQUBBGjBhRp1ipqWsNXTvV9PO6XFtr0wfVzCkaen4HNPxcWE3/ENWhZg4p\n2ka0TomObTU6jDFvEa0HavpgQ+fCWH409PdQNbHS1evq6oqoqCjpM19fX2zfvl1Pfty4cdizZ49M\n79M+U4Sec3x9faV/T5o0Se8zHx8fxTa6x6uqqmju3LkUHh5usE31Y5MnT5b+PWbMmDrL67apqKig\nQYMGUVVVlfTZxIkTZfI2Njbk5OSk9+fs7ExOTk5ka2tbo45bt27RkiVLyN3dnRYtWkQ7duygY8eO\nyeS1sdVoNOTq6lqjj7ryRETDhg0z+Jkuffv2Vfyzt7cnGxubetGxe/du8vf3p8uXL0vHvLy8FGWJ\n/utbbGwsTZkyhSZPnkzr1q2jn376Se8c1fXWNk5E+v2gusz48eNl8qJxUjqvv78/ERFVVlaSh4dH\njfLV+5ySH2PHjpX+PWHCBL3PlPosEdGZM2fIx8eHjh8/Lh2rTS6IiEpKSmjy5Mm0f/9+gzYRKcev\nps9EbapLzant2BOtUdWP1yZWonVKNK5EhvuBoc9Ex56uDy4uLgY/qw+bRHSIjiXRuiban4jEa46a\na4xorRXNhagPROLjVbQeEInHSk3tVNtvazunIHriZ3l5uex4cXExeXt7y47XpZ6L1rWGqp11mUtp\nedq1VbQPqplTNPT8Tul4fc+F63rdq40ONXNI0TaidUp0bKvRYYx5i9q5lEgfbOhcEBnHj4b+Hqpr\nF1HtYjVz5kxatWoVhYaGUkBAAIWGhlJUVBStWLGCZs+eLZNftmwZTZkyhfbu3UvR0dEUHR1NP/zw\nA/n7+9PKlSsVbTLEc/9rFAUFBTh9+jROnz6Npk2b4tq1awCAtLQ0gyvWjRs3xokTJ0BEMDU1xddf\nf420tDQsWrQIJSUlMnkiwrlz51BYWIiDBw+iefPmAJ48lqSEqDzw5DGqkpISNG7cGLNmzZIem8rN\nzcVvv/0mkw8ODsbIkSNx6tQp6S86OhqnTp2Cra2tog7tHadOnTohLCwM+/btw/Dhw1FcXIwLFy7I\n5CsrK3H//n2YmJhg0aJF0vFr166hoqJCUf7u3bu4ePEiCgsLcenSJQBAamqqojwAeHp6IigoCHFx\ncXp/8fHx6NWrV73oGDduHL7++mvs3r0bS5cuxaNHj2rc8ET7mYODA9avX4+vvvoKrVu3xqlTp7Bu\n3bo6xwkArKyssGzZMiQnJ8Pe3h7Hjh1DXl4efvzxR+nx67rECXjyGO2tW7cAAP/6179QVVUFALh5\n86aifFlZGVJTU3Hz5k2Ym5sjLS0NwJNHsZTGRcuWLbFt2zY8ePAA/fr1w5UrVwAACQkJaNasmaKO\n/v3749tvv8X169cxY8YMpKWl1ZgLjUaDX375BQBgZmaGiIgIHDlyBOvXr0dlZaXBNlevXpUd1z5y\nVleb1NQc0bFnqEaFhoYq5kLrt0isROuUobheuHDBYLxMTEwQFRWlNw4eP36Mw4cPKz6uKjr2tHUz\nIyMDRUVFSE9PB/AkR48fP64Xm9To0I6lGzdu1GosidY10f4EiNccNdcYNbVWJBeiPgD/Ha8xMTG1\nGq+i9UBNrNTUTrX9trZzCgCoqqoyWCs0Go3suGisGjdujOPHjwvVtYaunWr6uei1VbQPqplTNPT8\nDtC/Zhw6dKje58KG+oehubkaHWrmkKJtROuUiYkJTpw4UeuxrUaHaB/U2gXUvk+J1gM1fbCuuYiM\njHzqNcMYfojWQtGxpyZWX331FSwtLdG3b19s3rwZ7777Ln7++WdYWFhg2bJlMvmQkBAEBAQgIyMD\nMTExiImJQU5ODmbOnIm5c+catEsRoaWJZ8CCBQv0/uLi4ig/P59mzpxJCQkJim0yMzNpwYIFVFZW\nJh3Lz8+nw4cPK67ep6am0rRp08jNzY3mzJlDGRkZlJ6eTqtWraJbt249VT4rK4vy8/NpzZo1dPv2\nbUWboqOjyc/PT+/YmTNnaODAgXTmzBnFNgcPHqSSkhI9H4iI1q1bpyg/a9Ys2TFtGyUuXrwoW806\nduwYjRw5kn755ReZfGJiIo0ePZo++ugjunnzJk2ePJn+8Ic/kLu7O128eFFRh0ajoQ0bNuj5oeXz\nzz9/qo5JkyaRg4MDjRgxwqAOXeLj42nixImyVT5dDK1uG6J6nCoqKui7774jNzc3xThpZXbu3Ekf\nffQRDR8+nIYNG0Zubm60YcMGvX6pRRun0tJSvWNEynEiIkpKSiJ3d3dycHAgLy8vunHjBhERhYSE\n0IULF2TyPj4+5OvrSz4+PuTj40MnT54kIiI/Pz+KjIyUyT969IhWrFhBw4cPJzs7O7K1taWhQ4dS\nWFhYjf1Ky+3bt+lPf/oTDR482KDMtWvXyMfHR69//Pbbb7Ru3Tp67733FNukpKSQr68vOTs7k4eH\nB33wwQc0aNAg8vf3p5s3bz7Vpo8++oh69OhBlZWVijLVa865c+coPT29xppTfexVVFRQeno6VVRU\nKMpnZmbS/Pnzpb6glT9w4IBijSL6b6yKi4ulY5WVlRQREaEYK22dGjFihFSnYmNjKTw8XLFOpaSk\n0KRJk6S4enh4PDWu2lrr7OxMjo6O5ODgQC4uLhQaGkq5ubkyeaUVfW0/V+LEiRM0YMAAcnd3p4SE\nBHJ3d6eRI0fSwIEDKTo6ulY29enTh7p3704LFy6knJwcRR39+/enkSNHUkJCAo0cOfKpOrRjSTue\n/vnPf5JGo6EPP/xQcSxVr2t+fn7Up08fg7VTqZbHxsYajBORcs0ZP348bdy4UfFuNhHRgQMHpLGn\n22cNXWN0SUhIIB8fH9mdHl20uRg8eDA5OjqSo6Njjf2jug8uLi7k5OREa9euVaybRMpzhNjYWAoK\nCjI4XrVoa9SQIUOe6u/BgwepuLiY8vPzKS8vTzquFCtt7XRzcyM7Ozvq2bMnubq61lg7dWPVvXt3\neuutt6RYKfVbpTlFTEwMDRo0yOCc4tChQzR06FD69NNPafny5bR8+XKaO3cuubq60okTJwz6rtFo\n6PLlyzR58uQa67nu3Es3TobmXkT/7esajUYWW0M6dGsn0ZOxUZMO3X6ulScyPJdKSkqiUaNGkaOj\no3Rt1Wg0tHDhQkpKSpLJi85TleZekZGRNGrUKINzCtH5XVJSEs2ePVsvrlFRUTXqULpmaOe2tZkL\nZ2ZmEhHRN998Q4mJiTJ50bl5dR2TJk2irKwsIiIKDw9XtCkxMZE8PT2F5qnaNv7+/pSYmEh+fn7k\n6OhI7u7uivnW1qnAwEByc3Oj4cOH08SJE2njxo2Kdap6HXzadbK6jtrMIbV9Lzg4mAIDA+nIkSNE\nRBQUFETx8fGKOnTHnpLNNVGb+Z3odwwi/Vxor5UODg7k7u6u2Keq52LYsGHk4+NDGzdufKoPGo1G\ndg1QaqPkx8mTJw3apD3PggULZNfeQ4cOyZ6gJKp5LCk99UlEdP78edm8Qhur2nxfMibP/a9RDBs2\nDNHR0fjss88QFxeHkK0EeFYAABBMSURBVJAQtGjRAqWlpQZXQq9fv44mTZqgefPmiIuLw8KFC/Hi\niy+ipKQEoaGhMvldu3YhIiICABAbG4sJEybAwsIC+fn5ePfdd9GpUyc9+bS0NFhYWCAiIgJxcXEY\nN26cZJOtra3iZh6ffPIJRo8ejfz8fLRu3RoAYG9vj+joaDRq1Egmf/r0aVy4cAGjRo2SfNDqUPIB\nAEaPHo3FixdLsdJts3jxYgwaNEhPvrCwEC+//DIAyORzc3Nl5y8pKYGNjY10/rt378La2hqPHj1C\nQUGBok3vvPMOPDw8UFZWJvu5FN0VQi3Hjx/H/v37ATzJxb1799C+fXvk5+ejqKhIUUfv3r3h4eGB\n6dOnw97eHr1790ZKSoqiLABcvnwZn3/+OaZPny7loiaOHj2Kv/3tb5JNf/7zn2FhYYGysjI8ePBA\nsc3PP/+Ma9euYdOmTXqx3bNnD7p27SrLRWxsLA4cOIDTp0/LdoldunSpoo6AgADJb10/lFYoAeCX\nX37B6NGjZfLfffedovyAAQPg4eGB7du31ypOwJN+qx2vmZmZ+PXXX2FqagpnZ2fFPpibm4u8vDwE\nBgZKfufm5sLMzAx//etfFXVMmDABHh4eWLFihXQ3z9zc3OCGaF988YXU1zIyMnDz5k28/vrrGDJk\nCJYuXYr+/fvrybdo0UKS1813fn4+ysvLFXXorqBXb6OkY/Pmzfjyyy/15Nu0aYO8vDwY2rN3/Pjx\n8PDwQHl5OVq0aAEAaNSoEaZNm4Zp06bJ5K9cuQJXV1fpp43i4uIQERGB6dOn49KlS7I6lZeXh5yc\nHFhaWiI4OBiffvopKioqkJ6ejry8PHTu3FmmQ1tro6OjpX7erFkznDt3Ds7OzrJ8T5kyBcOHD9fb\nDVmbb6XdkM3MzGBmZoYXX3wRzZs3h6mpKW7duoXXXntNikF1Nm/eLO2zoI2tlZUV4uLiMGzYMNnd\njvnz5+uNi0OHDuHBgwd45ZVXFDfu0vpRfVfnJUuWwMzMTG/jTi3p6enw8fEB8GSHZ3d3d6Snp+PD\nDz/EnTt3ZBvADho0CAcPHpT+T0RYt24dpk+fDgDSXh26HD16FGZmZnBzc5OOrVu3Dm3atEFkZKSs\nje64qN5nDfXB6rtZl5SUICsrC8OGDcPSpUthb2+vJx8fHw97e3u9vEZERKB37944d+6czKYvv/wS\nixYtwoQJE/Rs2rdvH95++23ZOAIg6dT288zMTClWGRkZMnnduAJP5hhr166VjivF9vbt24iMjMTm\nzZuRnp6Ozp07o7CwEDY2NggJCZHJX758GadOnYK5uTmWLVsm9fP4+Hi4ubkpvhtdVlaGgoICvPDC\nC2jevDmsra1RVFQk7SVSnaZNmyIrKwsTJ06U+mB6ejpeeuklg3dL3d3dMWTIEFy+fBn5+fkAAEtL\nS9ja2io+cXH79m189dVXuH//PtLT02FtbQ0iwscff4yQkBDZxnJaH8aOHSvFqaioCD169FDcQwIA\nZs+ejalTp0o6dNso6YiPj0ffvn1x/PhxAPpjY9y4cbLza/OqfT9ZV/61115TtKmkpAS//fabtFfN\n/Pnzpeuxu7u7TH7o0KFYvnw52rZti4ULF2LevHmoqqpCaWkpSktLZfJ37tzRG+O6Nt24cQM2Njay\nNh4eHhg6dKhMR1lZGcLCwmQbzrVs2RLl5eVSrdHtsxYWFop+37t3D6mpqWjbti2mTp2KwMBASUfP\nnj1lc+HU1FSkpKSgtLQUb731lrTxb1BQkOJ76deuXUNSUhKmTp0q+aDRaKQ5qhImJibSXjpXrlzB\njBkzJD+UfoKvuLgYjx49wosvvoiqqio8ePBA2jywsLBQUYe5uTksLCxw//59BAYGwtraGmZmZujU\nqRPatWsnk09LS8OZM2eQmZmJjIwMWFtbIzs7G1evXkVhYaF0V7q636+//jpCQkIkv8+ePYvBgwcr\nboz5888/Y9u2bWjbti1Wr16NTz/9FFVVVdizZw+6desmaxMYGIgVK1ZI4+jBgwdYs2YNbGxsDG4w\n+P7778PJyQllZWUYOHAgQkNDpRwGBwfL8hcTE6PXzzMzM0FEcHZ2VuyDDx8+REpKCvz8/GTjQvtT\nitUpKChAfn4+0tLSsHHjRoSHh0s2KfWpmJgYbNq0CaWlpRg0aJCeD4b2ATl58iSWLVsm+b1y5Urp\n+6SS37m5ubh8+TL69u0rxcnFxQUuLi4GdSQnJyM+Ph4DBw7Ui627uzv27t0rk7e2tpa+h+piaCwB\ngJ2dnfR9CdCfyxtq88x4dusctWP06NHSyt/EiRPp3r17RET04MEDvfci69JG9073hAkTJPmcnBzF\n93jU2OTj40Pnz5+nyZMn04IFC+j8+fMG73oay++Gllfjt2gu1Ogwhk2isRo3bhxlZ2fTr7/+Svb2\n9pSSkkJEROnp6QbfO2tov0Xl1fqdk5PT4H5rqU3+1ORbtI2oTdo2In67uLjQmDFjKDw8XPobMGCA\n9O/qVM/FtWvXiKjmXDR0P1djk2hs1fRzUbuUcjFw4ECDuRDNnZo2avpg9fzVt99qbBL1W01sfX19\nJVtSU1NpyZIlRER0+vRpxad1ROOkVodo7Xz8+DHt3r2bgoKCyNvbm7y9vWnWrFm0d+9exSe9RG0S\nlVfTxhj5Fo2tl5cX3b9/nxITE8nJyUmSz83NJU9Pz3qxSVSHmlyI6hgzZgwVFBRQVVUV/fDDD+Tl\n5UVFRUVEpPwUm+j51fhhDB3GsMkY+dbN3549e+o9f2r8FrVJtA/WVUdt5J/WRunp6h07dhj8M/T0\noJo2z4rn/smGyspK6Q7WSy+9JP18SKtWraRdROujjRbtrsPAk7uVSndL1ZzfxMQEdnZ22Lp1K5KT\nk7F3716EhoaiRYsWaN26NTZu3Gh0v+sq3759+3r3W5fa5EKNDmPYJBqrJk2awNLSEpaWlnj55ZfR\nvXt3AED79u0Vn3wxht9q4qTG7zZt2qBNmzYN6reW2uZPrXxt26ixSdTvo0ePIiIiAtevX8eCBQvQ\nvn17nD17FjNnzlQ8f/VcaH9DuaZcNHQ/V2OTaGzV9HNRu0RzISqvpo2aPlg9f/XttxqbjBHbx48f\nS7a88cYbuH79OoAnT3+Fh4fL5EXjpFaHaO0MDg6GlZUV/P39Zbvih4SEyHbFF7VJVF5NG2PkWzS2\nTZs2Rbt27dCuXTtYWlpK8hYWFopPjKixSVSHmlyI6mjUqBFatWoFAPDy8oK5uTkCAgKwfv16xXfg\nRc+vxg9j6DCGTcbIt27+vL290bp163rNnxq/RW0S7YN11VEb+ae1UWLr1q1wcHCApaWl7DND+5ep\nafOseO4XG7Q/79avXz+0atUK06dPR69evZCQkICxY8fWS5sbN25g9uzZICLcvXsXkZGRGD58OLZs\n2YKXXnqpXmzSnXi//fbbePvttwEAOTk5iq8sGMPvusrPmDGj3v0WzYUaHcawSTRWLVu2xOrVq1FQ\nUAArKyssXrwY/fv3x6VLlwy+wtDQfovKP69+i+ZPTb6NoUPU72bNmmHOnDm4desWPvvsM/Tq1Uvx\ncWwtanLR0PlWY5NobNX0c1G7RHMhKq+mjZo+2NB+q7HJGLHt2rUr5s6dC1tbW5w9e1Z6dWPhwoXo\n0qVLneNkLB25ublYvXq13jErKyvY2dlJr/nUxSZReTVtjJFv0di2bt0a3377LQICAqSfg8vKysKW\nLVsUX9VQY5OoDjW5ENXRu3dvTJkyBWvWrEHz5s3h4uKCZs2awc/PDw8fPqzz+dX4YQwdxrDJGPlu\n6Pyp8VvUJlH551XH2rVrpVcbq78Gl5CQoKhDTZtnhQk97Vb/c8DDhw8RGxuL+/fvg4hgYWGBfv36\nyd7lU9vm/Pnzev/v2LEjXn31VRw5cgTOzs6K7waL2rRv3z6MGTPmufLbGPKifqvJhagOY9gEiMWq\ntLQUBw4cwCuvvAI3NzccPnwYSUlJ6NixI7y9vRXfTWxov9X0WeD581s0f2rybQwdavOh5eDBgzh9\n+rTsy4YWNbkAGjbfamwSja2auKqNlZan5aKu8rVpo6YPNrTfamutiA418kSE6Oho3LlzB127dsWA\nAQMAPHkXu1u3brK7W2riZAwdvr6+8PX1hZOTE5o0aQLgyR3REydO4MCBA9iyZUudbBKVV9tGl4bI\nt2hsy8vLcerUKb39Uq5evYrExESMHz/e4F1cEZtEdaiJqxo/EhIS0KdPH73zFRcX49ixY/Dy8qrz\n+UX9MIYOY9hkjHwDDZs/teNCxCY18s+rjrKyMjRr1ky2V9TVq1cV93FR2+ZZ8D+x2MAwDMMwDMOo\nJysrC2vWrMH58+eln8Nr0aIFHBwcMHPmTMXHcRmGYRimLvBiA8MwDMMwzP8xz93u5QzDMMzvgud+\nzwaGYRiGYRimbuzcudPgZ9nZ2Ua0hGEYhvl/gRcbGIZhGIZhfuf8L+1ezjAMw/w+4MUGhmEYhmGY\n3zn/S7uXMwzDML8PeM8GhmEYhmGY/wP+V3YvZxiGYX4f8GIDwzAMwzAMwzAMwzD1iunTRRiGYRiG\nYRiGYRiGYWoPLzYwDMMwDMMwDMMwDFOv8GIDwzAMwzAMwzAMwzD1Ci82MAzDMAzDMAzDMAxTr/BP\nXzIMwzAMI0x2djbmzZsHACgvL4e3tzfeffddhIaGQqPRoFmzZli+fDleffVVREREICYmBo0bN8ab\nb76JRYsWITs7G9OmTUPXrl3x5ptvYurUqVi1ahWSkpJQXl4OOzs7BAcHw8TE5Bl7yjAMwzCMGvjJ\nBoZhGIZhhImMjIS1tTW2b9+OHTt2oLy8HGFhYQgICMDOnTvh6emJyMhIXLx4EVFRUdi5cyd27dqF\ngoICHD16FACQmpqKGTNmYOrUqYiMjER2djZ27NiBffv24d69e/jpp5+esZcMwzAMw6iFn2xgGIZh\nGEaY/v37Y9euXViwYAEGDhwIb29vrFy5En369AEAjBgxAgCwdetW2NnZoUmTJgCAPn36IDk5GXZ2\ndmjZsiWsra0BAAkJCbh06RJ8fX0BAI8ePUJ6evoz8IxhGIZhmPqAFxsYhmEYhhGmc+fO+Mc//oHE\nxEQcP34c27ZtAwBoNBo9ueqvQRCRdEy7AAEATZs2hZeXFwICAhrYcoZhGIZhjAG/RsEwDMMwjDBH\njhxBcnIyHB0dERYWhszMTNja2uLs2bMAgGPHjmHVqlX44x//iISEBFRUVAAA4uLi0LNnT9n53nnn\nHZw8eRKVlZUAgL///e+4c+eO0fxhGIZhGKZ+4ScbGIZhGIYRpkuXLggLC0PTpk1BRAgMDMR7772H\n0NBQ7Nq1C40bN8ayZcvQtm1bjBgxAhMnToSpqSlsbGzw/vvvIyMjQ+98rq6uuHTpEsaNG4dGjRqh\nR48e6NChwzPyjmEYhmGYumJCRPSsjWAYhmEYhmEYhmEY5vcDv0bBMAzDMAzDMAzDMEy9wosNDMMw\nDMMwDMMwDMPUK7zYwDAMwzAMwzAMwzBMvcKLDQzDMAzDMAzDMAzD1Cu82MAwDMMwDMMwDMMwTL3C\niw0MwzAMwzAMwzAMw9QrvNjAMAzDMAzDMAzDMEy98h/YWMAK0A52OgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3236089438>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "7eKwrPxG02aF",
-        "colab_type": "code",
-        "outputId": "3d6bc2f6-82d7-4519-d7f9-d1b76675e5be",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 401
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# plotting a pie chart for the distribution of various grades amongst the students\n",
-        "\n",
-        "labels = ['Grade 0', 'Grade A', 'Grade B', 'Grade C', 'Grade D', 'Grade E']\n",
-        "sizes = [58, 156, 260, 252, 223, 51]\n",
-        "colors = ['yellow', 'gold', 'lightskyblue', 'lightcoral', 'pink', 'cyan']\n",
-        "explode = (0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001)\n",
-        "\n",
-        "patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90)\n",
-        "plt.legend(patches, labels)\n",
-        "plt.axis('equal')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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VTYVnZBQ+bYHz5lldScHI9daDp4vwqVSKO++8k3vvvXfsxO2nnnqKBx54gEcf\nfTTH1Rxv7dq1LFiwIKfPqUAjZ800TX6z5f/moun/L2XefqvLKXnBVJ/VJRSkH/Yp5BWkzl6oroCp\n2nbAClu3biUUCo2FGYDbbrtt7Eym1atXYxgGfX19rF27lrvvvptoNEosFmPNmjU0NTWxceNG1q9f\nT0NDAz6fj1AoRCqVYs2aNbS2tpJMJlm1ahVLly6dlNekOTRy1v64bTP+xCZm1X5sdSkClHn6iRyc\nYnUZBWVOJMLipNpNBeuz/TCsozussGfPHhYuXJh1zel0Zp3jVFlZyRNPPEFXVxc33ngjGzZs4K67\n7uLpp5/GNE0ef/xxnn32WZqbm2lpyexYvmnTJurr69mwYQPr1q3jwQcfnLTXpBEaOSsHWj9l90ev\n8K3P/5vVpchREt1emG51FYXjm+2dVpcgp5JOZ/anWXweTPBgQhkfp9NJMnlkL4M77riDoaEhwuEw\nL774IgBNTU0A1NXV8eSTT/LMM88Qj8cJBAJEIhGCwSC1tbUALF68GIDt27ezbdu2scMqR0dHicfj\neDyerPv/5Cc/yZpD8+CDDzJz5swJvSYFGhm3WCzKG7/5V754zst4Db27KiTV7g4So24MrzZdAbhV\nq5sK3/AI7GqFBbOtrqSkhEIhnn/++bHPm5ubAbjqqqtIpzNH0huGAcBzzz1HQ0MDjzzyCDt27ODh\nhx8GyDod+3CryjAMbr/9dpYvX37K++djDo0isYzbG7/5V2ZUvs2Mmk+sLkWOYbjiRPZPs7qMgjCr\nr49L1G4qDu1d0K05YJNpyZIlhMNhXnvttbFrO3fuZHh4OKvtBBCJRJg1axYAmzdvJpFIUFVVxeDg\nIAMDAyQSibERmUWLFrFlyxYAenp6eOyxxybpFWmERsZp92fv0tu5jWsv+qXVpchJGMNxq0soCGo3\nFZldLVBVDm4t5Z4MDoeD9evXc//997Nu3ToMwyAQCNDc3IzP58t67HXXXcc999zDq6++ys0338xL\nL73ECy+8wMqVK1mxYgXTp08nFAoBsGzZMt566y1uuukmUqkUK1euPOH9j205XXbZZSd97Bm/JvPw\nOJHIaYxEh3jx5//Al+Y/z/Tqz6wuR05hqKGSsmrrV57tjc1jnm+3Jfd+Y+u7LE2mLbm3nKXpU2D+\nLKurkCKllpOcsa2//VdmVf9BYaYIDB+ssroES83s61eYKUYHO2FA857k7CjQyBn57JN36O96l4vn\nqNVUDMrNHtIlvM/hsnadF1S0Pm3J7CYsMk4KNHJa0eFB3n37VS6e+zoe96jV5cgZCBhDRA6W7uTg\nH0QGrS5BztbwCLSGra5CipDoCvw1AAAgAElEQVQCjZzWG7/9V6r9u3TwZJFJR0pzcuX0/n4uT+gd\nflFraYeY3jzJ+CjQyCl9+tHbhNs/45K5r1pdioxTjdHOaNRrdRmTbplWNxW/dBp2t1pdhRQZBRo5\nqfhojO3bfsWCae9RHdSchGLjcqboa22wuoxJ94NetZtsobsPeq1fqSfFQ4FGTuoPv/8FTrOfphmv\nW12KnCVfLGp1CZOqcWCALye0usk2drVmRmtEzoACjZxQb087+3a/x6KZr+PT8QZFq9LbTX9njdVl\nTJpvaHWTvYzEMku5Rc6AAo0cxzRN3n5jE9XBLuY3bLO6HJmgWEe51SVMmlt61G6ynZY2GNXu13J6\nCjRynD27/khX134unvMKTodWixS7SkcnqaTD6jLyburAIF9Ru8l+UmnYe9DqKqQIKNBIllQqyR+3\n/Yo5dZ/SUNFidTmSAz73CJHWRqvLyLtvtGvvEtvq6IGh0poPJuOnQCNZtr/zK0aig3xuxq+tLkVy\nacD+I203a3WTve1vt7oCKXAKNDJmcCDCpx/9nrn1H1IV6LK6HMmhGk+YkaHA6R9YpBoGB7k6rnaT\nrXVFMrsIi5yEAo2M+cObm3CQ5sIZv7G6FMkxpyPNwP56q8vIm6+3aXVTSWjRKI2cnAKNANDVuZ+2\nA58yp24Hlf4eq8uRPAgkBqwuIW9W6Oym0tDVC1GN0siJKdAIANvf2YzL5eTC6b+1uhTJk3JvhL52\n+43S1A8N8dXREj5avNRolEZOQoFGCLfvpSO8h7n171Hu77W6HMmjeJff6hJy7mtqN5WWzl6Ixqyu\nQgqQAo3w/rtbcLscGp0pAVWuDpJxe53CvULn/ZQerXiSE1CgKXHhtt10hPcxr/6PlPn6rC5H8szj\nGrXVnjR1Q8N8LaZ2U8np6MkciyByFAWaEvfeu6/hdjm5YPq/W12KTBLnYMLqEnLmq+1hnA7774Is\nJ6C5NHIMBZoS1nZgF50d+5hV+yFlXg3bl4pqbwfDffY43+nmHvuu3JLT6OyFkVGrq5ACokBTwt7f\nvgWXy2Dh1N9bXYpMIqfDZOhg8Z/AXTM8zLJY0uoyxCqmqbk0kkWBpkQd2P8JXZ2t1AQPUld+wOpy\nZJKVpSKYRb6x7lfb1G4qeR09ENMojWQo0JSoDz/4HS6XW6MzJSroGSDS1mB1GRPyZzq7SUwTDmjZ\nvmQo0JSgSG8HHe178BmDzKrdaXU5YpFkj8fqEs5adTTKNSP2mdwsE9DRC+kiH26UnFCgKUE73/8t\nLpdBqOEdXE59IyhVNe4wiVHD6jLOylfbwrjVbhKAZBK6teWEKNCUnER8lNaWnTgdSeZP2WZ1OWIh\ntytBpGWq1WWclZt6tCpPjtLeZXUFUgAUaErMBzv+nVQqzey6D/B7hq0uRyxmFOGy16polG+NaHWT\nHKVvUJODRYGmlJimyd7PtuN0OjUZWACo9nYy2FNldRnj8qdtHWo3yfHau62uQCymQFNC9u1+n+Hh\nPmqCbdQEw1aXIwUi2l5pdQnj8h/UbpIT6ejOrHqSkqVAU0I++egtXC6DuXXvWV2KFJAKs5t0kRyH\nVDkywrVa3SQnMpoAHVRa0hRoSkR310E6O/fjIK2l2pLFbwzTe6A4Dqy8qi2MoXaTnIzaTiVNgaZE\nfLRzK26XwbSq3ZoMLMcx+4rjW4HaTXJKvf0Q1wheqSqO72IyIalUirYDnwEwp+59i6uRQlRjtBMb\n9lldxilVxGJ8O6ofVnIKpglhjdKUKgWaErBv93vER0dwu0aZUf2x1eVIAXI5U/S3TrG6jFP6E7Wb\n5Ewo0JQsBZoSsG/vDlwuN7NqPsTt0v4dcmK+0cJuRX6/R7vByhkYGc3sSyMlR4HG5uLxGOG23QDM\nqdthcTVSyCq9PfR31FpdxgmVx2J8R+0mOVMapSlJCjQ298mHb2Ga4PcMMKVin9XlSIGLdZRZXcIJ\nfaUtjAe1m+QM9fRpT5oSpEBjc637P8bpdDKndgdOh/6By6lVOTtJJQvv28L3u7W6ScYhmYL+Iaur\nkElWeN+5JGcGBnrp7moFYEbNJxZXI8XA6x6hd/80q8vIEhwd5TsjcavLkGKjOVclR4HGxj758E1c\nTjced5TasgNWlyNFwjlQWCN5X2kL41O7ScZLuwaXHAUamzJNk7bWT3A4HDRW7lK7Sc5YtbedkYGg\n1WWM+V633mnLWYjGdAJ3iVGgsalIb5j+vsxM/8bqzyyuRoqJ02EycKDO6jKATLvpxqjaTXKWtLN0\nSVGgsam9u/6Iy23gIM20ql1WlyNFJpgsjB8EV7SH8avdJGerV6N7pUSBxqY6O1pwOBzUlbfidces\nLkeKTJmnj0ib9TsHf1/tJpmIvkFIFclR8jJhCjQ2FI/H6OnKTAKeXv2pxdVIsUp0W3u2kz8e53vD\najfJBKRNiGjX4FKhQGNDuz/bDo7Ml7axSvNn5OxUu8Ik427L7n9Fm9pNkgNa7VQyFGhsKNy2G6fT\nSdDbR1Wgy+pypEgZrjgRC/ekubE7Ytm9xUYUaEqGAo3NpNNpujr3AzBNozMyQa4ha1o+/nic76vd\nJLkwGoehqNVVyCRQoLGZcNtuRmOZU5MbylssrkaKXbW3g+G+8km/75faw5Sp3SS5olGakqBAYzP7\n932Iy2UAUF+x3+JqpNg5HDB0sGbS73tjl1Y3SQ5pP5qSoEBjM10dmVGZMm8vAY9m98vElad7MdOT\ndz9fIsF/GNYOr5JDg8OQnsS/xGIJBRobGY1F6Yt0ADClQu0myY2AMUjk4NRJu9/lajdJrpkmDGs/\nLrtToLGRln07cTgzX9KwewnvDX6XWKpwzuSR4pXqnbzl29/t0uomyYOhYasrkDyzbpMJybnuzv04\nnS4AdlX8kI/df8UrsSSB+H7q039ktuu3LPT/glqvRm9kfKqNMPGYB48vvyuPvIkENw+NgkZoJNcG\no2DdLgQyCRRobKS3px2AtNNL0lubueh0E/XNo4V5tHADv03/A57+LqqTO5nh2ErI90tm+d7CqbE6\nOQW3M0lHyywaFuZ3ovkX2zsoV5iRfBjUCI3dKdDYxGh8lD17d1NTVc5o+fTM8pSTiHvq6fB8hQ6+\nwjb+GudwlIr4J0zlD8zzvMaCwC/xufSPX7J5YyN5v8d3u3rzfg8pUcMjmYnBevdmWwo0NrH/YJhh\nd4jergE8FTPHNTkq7Q7Q576IPi7iY/53Xo4lCcZbqEv9kdnu33Ju4CVqPK15q12KQ5W3i4Huairq\n8jPHxZtI8GdqN0m+mGYm1JRrXqFdKdDYRFu4k/LySsrLKxmpncOEzpd1uhn2ncMw59DCd/ht6r/i\n6e+kJvkB051vEPL9G3P8b+WqdCkiI+0VeQs0S8IdVCnMSD4NRhVobEyBxia6e4/8kEn7K3P+/HHP\nFMKeqwhzFdv4G5yDw1SOtam2sCDwS7yu/LckxFqVji7SKQdOl5nz5/6OVjdJvg0NA/VWVyF5okBj\nA6ZpjgUa0+nC9JTl/Z5pd5CIezERFvMRP+YXsSTB0Rbq09vH2lTVngN5r0Mml88dpbu1kbo5bTl9\nXiOZZMVQDLWbJK8GdaaTnSnQ2MDQcJTh6Agej0HaV3nKCcF543Qz7M+0qfbxXX6T+hmevg5qkh8w\nw/kG8/2/ZI7/7cmvS3KvL/d/v5a0d1BtKsxInmlisK0p0NjA/rZ2XO7M/jNpX+7bTWcr7m0g7G0g\nzJ/yDmtwDQ5REf+Eabw9tprK49LuncWm2tNObNiPL5i7FuN3tLpJJoMmBtuaAo0N9Eb6cB16x5GP\n+TO5knKXEXFfTISL+ZA7IJYkGN/HlPS7zHb9hoWBX1DtyW0rQ3LP5UzT3ToF37m52aDRSCa5Re0m\nmSyDwwo0NqVAYwN9/UcOoSykEZrTcroZ9s1nL/PZy/f4dfK/4Y12Up3cwQznGyzwv8Is/zarq5QT\n8I/m7uDTS8Od1KjdJJNF82hsS4HGBvoGBsb+u6gCzbEcDkbH2lRX8w734hocojL+MVN5+9Bqql+p\nTVUAKry99IXrqJraPeHn+k5nTw4qEjlDUX3/sCsFmiKXSqXoHxgEhwPT5cE0fFaXlFMpdxm97i/Q\nyxf4kD/HEUsQHN1LvbmdOe7fcq7/F1SqTWWJ0c4ATPAQbiOZ5JbB0dwUJHImRvN7HplYR4GmyPX2\n9RNPJDMrnDwBq8vJO9NpMORfwBAL2Mv3eT353/BGO6hJvc9M5xuEfK8y0/+u1WWWhGpnJ6mEE5eR\nPuvn+EJHJ3U5rEnktOKJzORgK1aDSl4p0BS51rYwhpH5MpqeEpzo5nAw6p1KO1Np52u8zd/iGhyk\nMv4x03ibczybmR/4FR6X3pXlmscdo2v/DOrPOfv9hm5Qu0kmm2lmQo3XY3UlkmMKNEWuv38Qx6F3\nGulSDDQnkHKX0+u+hF4uYSd/MdammmK+y2z3bzg38AsqjbDVZdqCc/DsD9lwp1L8YFCrm8QCo3EF\nGhtSoClyQ9EjM/bNEmg5nY2j21R7uInXEybe4TC1yR3McG1lge8VZvj/aHWZRanaGybaX0agcmjc\nf/bicCdTtLpJrKB5NLakQFPkjg40GqE5Qw4Ho95ptHmn0cbXeJu/wzUwQFXiY6Y5fs88zxbm+zer\nTXUGnA6TwQO1ZxVoru9Su0ksokBjSwo0RW54+OgRGgWas5UyKugxLqWHS/mAOw+1qfbQkH6XOcav\nWRh4mQqjw+oyC1Iw1TfuP+NKpbh1YAS1m8QSsYTVFUgeKNAUsVQqxfBIDJfr0C7BajnlTKZNtZAh\nFrKb/8CWhIl3uJ3a5A5mHmpTTfe/Z3WZBaHM00/k4BSqp3ee8Z+5uKOTqWo3iVXiGqGxIwWaIjYw\nNEwymcTl8mA63eDSJLe8cTgY9TbS5m2kja/ze+4/1Kb6iGmO3zPfs5n5/i24XaX5zi/R7YXpZ/74\nb3fq7CaxUEyBxo4UaIpYZ3cv7kOHUppue22oVwwybarL6OEyPmAVjlicstHdTDHfZc6h1VTlRpfV\nZU6KancHiVE3hjd52se60ml+MJC7gy1Fxk1zaGxJgaaI9Q3043IdDjQanbGa6fQw6D+PQc5jNzez\nJZHGN9xObep9Zji3stD3Co3+HVaXmReGK07n/plMCbWe9rGf7+hkujkJRYmcjDbXsyUFmiIWHT5y\nJompdlPhcTiJeadzkOkcZBm/5+9xD/RTmfiIRn7POd5fMd//um3aVMbwmb3rvb5Dq5ukAIzGwee1\nugrJIQWaIhYdPSrQaISmKCSNSnqMJfSwhB38JY6RUcrie2gwtzHb/WvODbxMuTHxAx+tUO3rYChS\nSVl1/0kf40qnuXVQ7SYpAKMJBRqbUaApYvGjZuqbLv3DLEamyzvWptrFikNtqjZqk+9nVlP5X6bR\nt9PqMs/Y8MGqUwaaps4uZpz90U8iuaOVTrajQFPERo+a2KYRGptwOIl5Z3DQO4ODXMNb5gO4B/qp\nSnxII28x37uZeYHXcDvP/siBfCo3e0inwOk68e9f31Gco09iQykla7tRoClio/Gj5l5oDo1tJY1K\nuo2ldLOU9/k/cURHKY/vZoq5jbnu1zk38DJBozCWQQeMIQ60TGPGvPbjfs+ZTvOD/ijaTE8KQloz\n0+1GgaaIxRNHAo3pVsupVJguLwP+8xngfHZxC79KpPENHaQu9T4zXb9jof9lpvo+sqy+VOTEwzNN\nnV3M1mZ6UihMBRq7UaApUqZpZlpOh34+mE59KUuWw0nMN5MDzOQA3+RNcy3ugQjViY+Y5niL+d5f\nMc//60lrUzX62hmJBvAHolk/NK7tVLtJCkhaLSe70U/BIpVMpUgmk7iNQ19Ch9PagqSgJI1quowv\n0sUXeZ+7cERjlMd302C+wxz3rzk3+DJBdyQv9zZcKQ62zmTOwk8wDwUaRzrND/u1ukkKiEZobEeB\npkjFYqOkzPSRL6ACjZyC6fIx4L+AAS7gM27lV/E0/sGD1KbeY5brdyz0/4IG3yc5u18wNggcmS3z\nua5u5uoNsRQSzaGxHQWaIhUdiWW9wTAVaGQ8HE5GxtpUy3nD/C+H2lQf0uh4i3O8v+Ic/69xOc8u\nhdR72+jubIBDB8Bfp9VNUmjUcrIdBZoilUgkcTqPmmCpLbxlgjJtqsvp4nLe4+5DbapdTDXfYY6R\nWU0VcJ98j5ljRQ6U4TkvhSOd5tb+aB4rFzkLajnZjgJNkUokEziODjFOjdBIbmXaVBcywIV8yg/5\nt3ga/+AB6tJ/zLSpfC8zxffpSf98oztMV3oaF3T3cI7eDEuhUcvJdhRoilQylcSZNSqjQCN55nAy\n4ptFK7No5Vq2mg/j7u+lOplpU81z/4L5ZW+OtamCnmF2t03j2pjaTVKATKVsu1GgKVLJZCprhMbU\nCI1YIOmpocvzJbr4Eu/xVziGoxix3dSk3+Nz3tdxGQf50ciw1WWKHE8jNLajQFOkEscEGu2+KoXA\ndAeIl32OMJ8jzApqu/dw7Y5XOFhWAcEyXP4gHl+AoNeH121YXa6UMs2hsR0FmiKVTh0baPSPUwpP\nfeQgvsF+GMyeTBwDBj1eEuUVpIIVOAJBDH8Qv89PwPDi0oij5JtGaGxHgaZIpY75x+hIpxVppODU\nDHSc9PeM+ChGTxf0dI1dSwMDQLysgmRZOWawHJc/iNcfwO/149eojuSKlm3bjgJNkTKPndBmFubp\ny1LayiInDzQn4wR8QwMwNJB1PQ5E3Qbx8kpSZeU4AmUY/iA+n5+Ax4dbozoyHurS244CjV3o3YYU\noNp4bo87cCcTuCPdEDmycsoEhoB4oIxEWQVmWWZUx3NoVCdg6CR6OQHXiQ9RleKlQFOkXK7sd6MO\njdBIgXGkk5Qn45NzL8AbHcIbHYLOI9cTQI/LTby8glRZBY5gGW5/EJ/XT8Drw3Dqh1rJcuvHn93o\nK1qkXE4XpmkemRisPRWkwFT3HqQQ4oI7lcTd1wt9vVnXh4G4P0CyrIJ0sAJn4Miojt/wHLPPk9iO\nuxD+dkouKdAUKcNwkzZNXIe/6aY1QiOFpa53v9UlnJID8I5E8Y5EoSs8dj0J9Dldh0Z1yiFQjtsf\nxOv3E/D48ahVYQ8KNLajQFOkPIYHM22ObRDs0AiNFJia/vDpH1SgXOkU/v4I9Eeyro8A/V4fifJK\n0mXlOANlGL7A2HJzjeoUEQUa21GgKVIew31opdOhf5TppKX1iByrerj39A8qQp7RGJ7RGHQfWcGV\nAvocDuJlmbk6ZrAcdyCI1xcg4PXhdelbbcHRHBrb0Ve0SBmGQfqovWgcyVELqxE5XuXIkNUlTCqX\naeI/ySaCA4c2EUwHK3AGM6M6meXmXlwOLTe3hEZobEeBpkhlRmgUaKRApdOUJ/R38jBPfBTPiTYR\ndDgYDZaPjeq4jhrV8WkTwfxSoLEdBZoi5Xa7s44+UKCRQlLV16ZvLmfAaZr4T7CJ4Cgw7DYYragk\nHSzHESwfG9UJenw6GiIXFGhsR99zipTP58V51F40CjRSSAp9hVMxcCcTuHu7oTd7E8FBDm0iWF4B\nwczEZM+hUR2/NhE8c5pDYzv6ihYpw+3GOOofpCM1ORuYiZyJYl7hVOiyNhE8SuZoCPfYxOQjmwgG\nCHq9uLWJYDaN0NiOAk2Rcjgc+DweYvFMkNEIjRSS6qEeq0soSe7kiTcRHALi/iCJ8sxcncwmgsHM\nqI7bk9W+LgkuJ5Taay4BCjRFzOtVoJHCVDUyaHUJcpTMJoLDeEeGgfax6wlgxOU6MqoTKBtbbh70\n+DDsuomgRmdsSYGmiHm8nkxDncy5OaST4NSXVKynFU7Fw51K4T7BJoJRIO7zZzYRPDxXxx/InINV\n7EdDaP6MLemrWsR8x0wAdMRHMH3lFlUjklHR145x1JYCUrw8sRE8sZGsoyEymwg6iZeXkyyrxBEs\nw+Uvsk0EPVoSb0dF8DdPTsbn9WZ97owPkVKgEYtphZP9ucw0/oF+GDjBJoJeH4myisyoTrAMwx/E\n781sIlgwozp+7+kfI0VHgaaIebzHjtAMW1SJyBG1fe2nf5DY1tjRED2dY9dSQL/DQbysnERZRebA\nz0BwbLn5pG8iqEBjSwo0RSxwzD9K56gCjVivSiuc5AScpolvcADf4PGbCA4ZHhLllaQOHfjp9gcz\nmwga3vxsIuhToLEjBZoiVl1ZSSqVwnVoJYJTIzRSAKpGBk7/IJGjGIk4Rm8X9B45GsIEBoB4sJxk\neQVmsAyX/9Amgj4//omM6vh9E65ZCo8CTRFrqKvNCjSOeGkdBiiFqSIes7oEsQkn4BsehOHsbQAy\nmwgaxMuPLDc3/EG8h46GcJ9uVMenHZXtSIGmiJWXBXEftfxQIzRitbKBTjxa4SSTwJ1M4I70QCS7\nxTkExANBEmWVmGWZUR3D56e6qgZnKo3DY4Bd99cpcQo0RczpdFIWDBAdybwjdqQSkIyDW+8+xBp1\nPS1WlyAlLnM0xDDe6DAcmZfMEIDXh+fCC/GzyKLqJJ90ZGuRCwYDWZ87R7VDq1hHK5ykoI3GyMzO\nETtSoCly5YFjAk2s/ySPFMm/6qHu0z9IxELOujqrS5A8UaApckEFGikgVVGtcJLCpkBjXwo0Ra6i\nLIh51CRM54gCjVhHK5yk0LmmTLG6BMkTBZoi1zi1gUQiOfa5RmjEKoGhHrxm2uoyRE7KEQzirKiw\nugzJEwWaIldXU4XHOGrpdnIURzxqYUVSqrTCSQqdq7HR6hIkjxRoipzL5aK8LPtASudIn0XVSCmr\n7WuzugSRU3JNm2Z1CZJHCjQ2UF2lQCPWqxnUCicpbBqhsTcFGhuoOqYn7BqJWFSJlLLKqOZvSWFT\noLE3BRobaKivI5VKjX3uHNY7ZZl8lfERq0sQOSlHWRnO8vLTP1CKlgKNDcyaPpV0+qil28lRHDHt\nGCyTxxvtw5fWCicpXBqdsT8FGhvw+3yUB4NZ11wapZFJVK8VTlLgXNOnW12C5JkCjU3U1VRlfe4a\n7rKoEilFdZGDVpcgckruOXOsLkHyTIHGJupra7I+d+lMHZlE1VrhJIXMMDRCUwIUaGxizszpJOJH\n7RgcH8KR0CRNmRxVUW0VIIXLPXMmDpfL6jIkzxRobGJaQz1eryfrmlY7yWSpGFV4lsLlUrupJCjQ\n2ITT6aS+tjrrmtpOMhk8sSEC6dTpHyhiEffcuVaXIJNAgcZGjp9H02FRJVJK6rr3Wl2CyMl5PFqy\nXSIUaGxkxrSpJJJH5tG4YgM44sMWViSloK5PK5ykcLlnzcLh1I+6UqCvso3MntmIy5k98c010G5R\nNVIqqge0RYAULrWbSocCjY0Ybjf1NdnzaNwKNJJnVTrDSQqYe8ECq0uQSaJAYzONU6dkfe4a7IR0\n8iSPFpm4ylG1NaUwOWtrcdXVWV2GTBIFGpsJzZtNIpEY+9xhpnANqSUg+WHEowRSWuEkhcm9cKHV\nJcgkUqCxmWlT6ik79lwntZ0kT2q79+GwugiRkzAUaEqKAo3NOBwOGhuy206aRyP5ojOcpFA5gkFc\nM2daXYZMIgUaG5o1fSqpo9oAzvgwzhFtTS+5VzPQaXUJIifkDoVwODR+WEoUaGxo4fx5OBzZX1p3\nZL9F1Yid6QwnKVRqN5UeBRob8hgGDXW1Wdfcfa0WVSN2VhnTCicpQIaB+5xzrK5CJpkCjU1Nn9aA\naZpjnzvjwziHeyysSOzGFY8RSGlLACk8xnnn4TAMq8uQSaZAY1OLzl9AKpXOuqa2k+RSXW+LvoFI\nQTKamqwuQSyg70c2VV5WxtQp2RtKuftawUyf5E+IjE9t5IDVJYgcx1FWpuMOSpQCjY3NnjE9u+2U\njGmTPcmZmn6tcJLCY3zuczqMskTpq25ji85fgJk2s66p7SS5UjUcsboEkeN41G4qWQo0Nhbw+487\n28ndf0BnO0lO6AwnKTTOKVNwTZ1qdRliEQUam5s3ewbp9JF5M45UQku4ZcKcyThlycTpHygyiTQ6\nU9oUaGzuwoULcBxz2o7RvduiasQuanv265uHFBanU6ubSpy+J9mcx2Mwo7Eh65or2otzRPMf5Oxp\nhZMUGuPcc3GWl1tdhlhIgaYEXLAwRCqZvVzb6N5jUTViBzUDHVaXIJLFc8klVpcgFlOgKQGhubMp\nLwtkXXNHWiClORBydqqHeq0uQWSMc8oU3HPmWF2GWEyBpgQ4HA7mz52dtSeNI53UEm45azrDSQqJ\nRmcEFGhKxsVNF2Bmb0mD0aPJwTJ+jnSSsmTc6jJEMrxerW4SQIGmZAQDfmZPn5Z1zTXSh1M7B8s4\n1fS04rK6CJFDPIsW4fB4rC5DCoACTQm58LwFJJOprGuezo8tqkaKVW1E+xhJ4VC7SQ5ToCkh82bN\noLIie1mja6Ad50i/RRVJMart1wonKQzuBQtw1dWd/oFSEhRoSojD4WDhOXOydw4GDI3SyDhohZMU\nCu+Xv2x1CVJAFGhKzBcWXYjb7c665o7sxxHXqhU5M5WxIatLEME1dy7uGTOsLkMKiAJNifEYBgvP\nmZu9hBsTo+tTC6uSopFOUZYYtboKEXxf+pLVJUiBUaApQUsWL4Jjz3fq2QNJ/aCSU6uKHMR9+oeJ\n5JVr+nTc8+ZZXYYUGAWaEhQM+Jk/Z2bWNUc6hdG9y6KKpFjU92ozRrGeV6MzcgIKNCXqssWLSKWy\nz3fydH0K2jBNTqGmP2x1CVLinFOm4F640OoypAAp0JSomqpKZs9ozLrmSCXwdH5iUUVSDLTCSazm\nveIKHA7H6R8oJUeBpoRd9vnPHbfRntH1KY7EiEUVSaHTCiexkquxEeP8860uQwqUAk0JmzZ1CtOn\nNWRdc5gpjI6PLKpIClo6TYVWOImFfFdfrdEZOSkFmhL3pS9clLXRHoDRvRvHqN6JS7bK/nbcx55w\nKjJJ3Oecg3vuXKvLkAKmQFPipk2dwqzpx8ylwcQT/sCiiqRQ1fVohZNYx3f11VaXIAVOgUb48mUX\nHzdK447sxznSZ1FFUqlTitoAABUBSURBVIhqBrTCSaxhNDXhmjrV6jKkwCnQCLXVVZwze9YxuweD\np32HdUVJwakZ7LG6BClFLhe+P/kTq6uQIqBAIwB8+bLFHLt7sHugHVd/mzUFScGpHBm0ugQpQZ5L\nLsFZVWV1GVIEFGgEgIrycs6bPy9rlAbAe3A7pFMn+VNSSioSMatLkBLjCAbxXXml1WVIkVCgkTFf\nunQxLqcr65ozPoyhzfZKXll/B4ZWOMkk8119NQ6fz+oypEgo0MgYn8/LRReeRyqVPSLj6fgIR3zY\noqqkENT3tFhdgpQY14wZGIsWWV2GFBEFGsly2eImKivKs645zBTeg3+0qCIpBLX97VaXIKXE4cD/\nzW9qEz0ZFwUayeJ0OrniskuOOxLB3X8Q14B+qJWq6sFuq0uQEuK59FIt05ZxU6CR48ybPYO5M6dr\ngrCMqRoZsLoEKRGO8nIt05azokAjJ/Qnl1+K85jhXufoEB6d81SSKuJa4SSTw/+Nb+Dweq0uQ4qQ\nAo2cUEV5OZ+/8DxSx57z1PERzpGIRVWJFYKDXXi0wkkmgXHBBTpNW86aAo2c1JLFi6gIBrOuOTDx\n7v8DmOmT/CmxmzqtcJLJEAjgu+Yaq6uQIqZAIyflcrm4cumlxy3jdo30YXR8bFFVMtlq+zQZXPIv\n8K1v4QwErC5DipgCjZzSvNkzCM2dc9wEYU/Hh2o9lQitcJJ8M5qaMM491+oypMgp0MhpXf3lpfiO\nmaTnMNN4W97WqqcSoBVOkk9mMIh/2TKryxAbUKCR0/J4DK5cegmpVPa8GVesH0/4Q4uqkslSER+x\nugSxseC3v63jDSQnFGjkjCyYN4dzZs88rvVkdH6Mc6jToqok3/zDvfjSmgAu+WEsXowxf77VZYhN\nKNDIGfvqFV/Ef2zrCRPfvrdw6CRmW6rr1gonyZPaWvzf+IbVVYiNKNDIGfN6PXzl8stIH/OO3ZmM\n4d3/e9BeJbZT29dmdQliQ2mXi7KbbsJhGFaXIjaiQCPjMn/OLM6dP++4UOMe7MDo1FJuu6kZ0gon\nyb3gtdfiqquzugyxGQUaGbc//dISqiorjrvuaf8Ap34A2kpVtN/qEsRmHBdeiKepyeoyxIYUaGTc\nXC4X37z6ShzHnPXkwMTX8iYkRy2qTHKtYlQrnCR3UlVVlF97rdVliE0p0MhZqa2qOuFSbmdiBN/+\ntzWfxga8IwP4tc+Q5Eja5aLy5ps1b0byRoFGztqFC0OE5s0+fj7NQDue8E6LqpJcqe/eZ3UJYhMm\nmf1mNG9G8kmBRibka1dcTmV5+XHXjY4PcUdaLahIcqWm76DVJYhNuJYswXPhhVaXITanQCMT4na7\nuOZPr8B53Hwa8O5/G2e015rCZMJqBrusLkFsIDFrFmVf+5rVZUgJUKCRCauvreGKJZeQPmY+jcNM\n4du7FUdCE0uLUdWwVjjJxMTKyqhdseK4BQQi+aBAIzlx4bkhms5feOJJwnu36hDLIlQZj1pdghSx\nuNtN3W23aRKwTBoFGsmZK5dewszGqced9+SK9uJtfceiquRsGLEh/CmFUDk7KYeDihUrcFVWWl2K\nlBAFGskZh8PBt772J1SUlR33e0akBUMncxeNup4W1CSQs2EC7q9/He/s2VaXIiVGgUZyynC7ue7r\nf4rhdh/3e97wB7i7d1tQlYxXXUQrnOTsJC69lIrLLrO6DClBCjSSc1WV5XzjT758XOsJwHvgXVx9\nByyoSsajeqDT6hKkCA2FQtQvW2Z1GVKiFGgkL2bPaORLly4+bpJw5niEt3ANdlhUmZyJ6mif1SVI\nkYlMncr0P/szq8uQEqZAI3lz0YXnc9GF55E6ZnKpw0zj27tVe9QUsP+/vTt9juLM7wD+ffqY7p5L\nGo00QuhCQpYwGLAx2IAxsN41hrUh2XhtknW8PrK84C9x8iLXq9RWzqq8TqVSdtXGu97YGC/GBgPm\nsMUhQOhEYkbSCEkzfeaFBMuuRhICST098/1UTUkU1xdK9Hx5+vf0E89zhxM9vExFBZqOHPE7BpU5\nFhpaVi8+/yyebFs763gE4drQrx+HyI37lIzmopiTiDi23zEoIEbDYTQePQpJ4tsJ+YtfgbSshBB4\nec9ONDfUzyo1kp2Hcf0YBJ93UlS4w4keVlbTsProUSia5ncUIhYaWn5CCLz2oz2orameNSgsmZMw\nrn0KYU74lI7+WHWGQ9u0sDFVRc3Ro1ALPKaByA8sNLQiZFnGn/34ZVTG4wVKzQSMa5+x1BQJ7nCi\nhYyqKiqPHIHOB+dREWGhoRWjKgp++to+RIzwrO+TzAkYVz+FyN/1IRk9iDucaD4ZVUX8nXcQr6nx\nOwrRH2ChoRVl6DreOLgPhqbPXqmxJqdXavIcFPZTPMeVMioso6qIvPUWEvX1fkchmoWFhlZcLBrF\nm4deQSRssNQUGcnKcYcTFZRWVcTefhspHmlARYqFhnwRi0bx5sEDiITDBUrN1PTtp9yYT+nKV036\nFi8KNMudUAjxn/8c1Y2NfkchmhOvXeSbaCSMw4cOIBopUGrsHMJXP4V0d9indOUpOcIdTvSH+jQN\nFe++i+qGBr+jEM2LhYZ8FQkbOHzoAGKRyKxSIxwTRtfnPPtpBVVleSQF/d71cBi177+P6ro6v6MQ\nLYiFhnwXNgwc/pMDiEejs0uN50C/eQLq8FWf0pWXygnucCLAA/BdLIY1776LZCrldxyih8JCQ0XB\n0HUcPnQAyUTl7GMSAGh9ZxHqP+9PuDJSkeO2+XLnADhfVYUN772HBLdmU4Cw0FDR0HUNhw8dQOPq\nulmlBgBCQ53Qur8CXKfAz6bHJdkmorbldwzykSkEztfV4blf/ALxRMLvOESLwkJDRUWWZfzp/h9i\nXVvrrFO6AUAd6YZ+/Thg531IV9qqMtzhVM7GJQmXWlvx4vvvQzcMv+MQLRqvX1R0hBDYt+cFPPfM\npoIrNcrdIYSvfAJpivMeS4lnOJWvQUXBjc2bsftnP4OsKH7HIXokLDRUtHY8+zT27nxu1qAwMHNU\nwpXfQhm55UOy0sQdTuXpsqYh+8IL2HXwICSJbwkUXPzqpaK26ckO/PilPZDE7C9V4TnQu09ODwt7\ns1dyaHESEyN+R6AVZAM4FYkgum8ftu3dCyGE35GIHgsLDRW9tWsa8eah/Qgbs49KAKaHhTlX8/i4\nw6l8TEoSTlZVYf0bb2D9li1+xyFaEiw0FAjVVQn85esHUZeqgeMUmKsZvz0zV8NVhkchXBtRy/Q7\nBq2AQUXBNw0N2P3OO1jNc5mohLDQUGCEVBWvv7oPzzy1ruCw8L25GnX4ig/pgq0q0wvZ7xC0rFwA\nFzUN/Rs34sDbbyMWj/sdiWhJsdBQoAghsHv7Nuzb8wKkAvf8hedC6zsHvetzCCvnQ8JgSmZ6/I5A\ny2hCknA8EkF0717sPXiQO5moJLHQUCCta2vFG6/tR2SOuRplfBDG5V9Dzg76kC54qsb491SqekMh\n/K6qCk//5CfYvH07h3+pZAmv0LsBUUDk8yY+/uwL3OjphSzPvmniAbBq2mHWbQQk3lSZy4FPf4k1\n42m/Y9ASsoTAecPAZH099r72GqK8xUQljoWGSsK5S504ceoM3Dm+nB2jErnm7fB0XtQL+fNf/S0S\nFneJlYohRcE5w0Dzli3Yuns3ny9DZYGFhkpGenQUv/rkc6THxiAXuIB7QoK5agOsVAdQ4Lk2Zct1\ncOSjvwGnKoLPFAKXDAO343Hs3LcPjS0tfkciWjEsNFRSXNfFZye+xsXL1yBJhWcFHCOBfNM2uEbl\nCqcrTpWZHvzFF//pdwx6TIOqinOahsSaNdjz6qvQdN3vSEQrioWGStL17l58cvwE8qZZcAjSg4BV\nuw5m7fqyn61pu/Y7vPzdMb9j0CMyhcDFcBgDuo7NO3Zg/ZYtHPylssRCQyVrKpfDb46dwPWeXigF\nBoYBwNWiyDdshRNLrXC64vHc6f/Cs/2X/Y5Bj6A3FMK3qopEUxN27d/PZ8tQWWOhoZLXee0GPj95\nap7VGsBONMNcvQmeaqx8QJ/t/+yf0ZId9jsGLcKYLOOCYSBrGHh6506s27yZqzJU9lhoqCzk8yY+\nOf4lrt3sLri9GwA8SYFZ+ySsmvayug11+H//DlUmH0IYBKYQ6DQMXJdl1DU3Y9crryAcjfodi6go\nsNBQWblxqxefnfga4xMTc25ldUMR5FdvhlPZsMLp/HHkww+g8DJQ1DwA3ZqG73Udnqbh2RdfRPtT\nT/kdi6iosNBQ2bFtB5+fPI1Ll69CzLETCgDsaA3M+mdKejdUfKQfbx3/D79j0DzSioKL4TBGhcCa\n9nY894MfQDfK79Yo0UJYaKhspUdHcezEKdzqH5hzaNiDgJ1sgVm7Hl4ovMIJl19r10m8cun//I5B\nBYzJMjoNAwOShGQqhedfegk1dXV+xyIqWiw0VPa6unvwxVffYDSbnXu+Rkiwkmth1a4rqcHhbWf+\nG1t7v/c7Bj1gXJJwORxGv6IgpOt4escOtG/cyKFfogWw0BBh+oF8p89fwpnzl2DZ9pxvHp6QYVW3\nwUp1wFOD/+Cyfcf+BWvHhvyOQQAmJQlXDAM9oRA8AG0bNmDb7t1QVNXvaESBwEJD9IBcLo9jJ0/h\ncteNec+/8SQFVnUbzFQHoGgrmHBpvfnx3yOZn/I7RlmbkiRc03V0axpsx0FDayue3bULlcmk39GI\nAoWFhqiA9Mgovjx1Fl23eiHLCxcbq+aJQN6K+qsPP0CIlwBfZGUZXbqOvlAItutiVUMDtuzahZpV\nq/yORhRILDRE8xhOZ3Di9Fl09/RBmmO+BpiesbErG2GlOgKzKyo6dhtvH/tXv2OUnTuKgi5dx1Ao\nBMe2UVNXh6d37sTqpia/oxEFGgsN0UO4PZzGl6fP4mZvPxRl/ofu2dEUrFQHnNgqoIgHOVtufI39\nFz7xO0ZZ8AAMqCquGQbGFAW2ZaGqpgabd+xAc1ub3/GISgILDdEiDNwexonTZ9E7MDjnjqh7XC0O\nM9UOO9EESMoKJXx4W8/+D7b1XPI7RknLCYFbmoZbmoYpWYZt26hZtQobtm5Fc1sbdy4RLSEWGqJH\nMHQnja/PXcSN7h5AYN43Jk9SYSeaYCVb4IarVjDl/H50/N/wxMig3zFKjgdgWFHQreu4rarwhIBj\n26hvacHGbdtQW1/vd0SiksRCQ/QYJqem8NWZC7jcdR15y4I8z84oAHD0CtjJVliJJt93R/30439A\nTX7S1wylJP/AasykLOPepbW5rQ2bt29HRVXxlFmiUsRCQ7QEbNvBuUvf42LnVYxmxxecs/GEBLui\nHnZVC5xYChDzF6Hl8P6HH0DjP//HYgMYDIXQHwph6N5qjONANww0t7dj8/PPwwiX3hOmiYoRCw3R\nEvI8D1dvdONi51X09g9CSGLBOQlX0eFU1MOubIQTrV6RchMeT+OdT3+57L9PKXIADKkq+kKh6Z1K\nQsDzPLiOg9r6ejzx1FNoWbdu3ucYEdHSY6EhWiYTk1M4c+E7dN3swejYGBR14cFgV9Fmyk0DnOjy\nrdw0dX+DV7/9eFl+7VLkAEjPlJjBUAj2TEl1bBt6OIymtWuxYetWxCuDsWWfqBSx0BAtM8/zcKuv\nHxc7r+LGrT64nvtQ/3v35ND0bamK1dPlRl66R+Bv+fYjPN99fsl+vVI0JQSGZm4lDasqnJkS47ou\nPM/DqoYGtK5bh7Xr13M1hqgIsNAQrSDTsnD+u8u42dOH/tvTZyg9VLmBgBtJwo6tghOrhRtOPNbq\nzQ+/+He0ZwYe+eeXIg/AiKLgtqpiSFWRVX6/ouZ5HhzHQaquDg0tLWjftAm6EbwnQxOVMhYaIp/k\n8yYudF5ZdLkBZlZvYik4sVVwoil4WnRRv/frv/5HpHITi85cSjxMHz+QVhSkVRVpRYH1wN+/53lw\nbBvJVAr1LS3o2LQJkVjMv8BENC8WGqIiYJoWLl6+ghs9fegfGILruQs+uO9BrqLBDSfhRJJww1Vw\nwlXz3qJ676O/hu66SxE9MGwAo4qCzMxrRFFg/1GBdBwHQghU19YiNTPgW5FI+BOYiBaFhYaoyJiW\nhSvXu9HbP4DBoWGMjE1vA1/MU2U9AK5eATeShBOugqNXwNPigKLCmBjBu7/9p+X7AxSBnBDIyjKy\nioKsLGNMUXBXkgoeRWGZJsKxGGrr67G6qQktHR1QQyEfUhPR42ChISpyY9lxdHZdx8DgMAaH08jl\n8lDUh1+9AYBsJoPKXBZGqhGyHELHnVuIOg6ijoOI42Dpxo1XjgcgJ0mYkCRMShLuyvL9EpOf59ad\nY9uQZRmJVAo1tbVY09GBmro6HkNAFHAsNEQB4rou+gaHcLO3D+n0CO5kRjE+cReyLM87f5Pp6cGa\nCmPOHxNyXRgzL/2Bz++9NNfFSu7j8QBYQiAvBExJQl6I6fIiy5iUJEzOfHQXKCGe58G2LGi6jkR1\nNZK1tVjd3Iy6pqZF3dIjouLHQkMUcON3J9DV3YPhO2kMZ0aRGRmFaVlQVeX+qsN4bzcaKx9voFX2\nPKieB9V1pz8+8FI8D8Lz7pceCYDwPAjg/ssF4AgBB4ArxKzPHywwphDwFrli4nkeLNNEKBRCPJFA\nrLISlckkGltbkayt5QoMUYljoSEqMY7jYCidQd/AbYyMZjE6nsVA53eokAVcz4OiKIF+c7+36gIA\nRjiMeCKBimQS8cpK1Dc3o7K6ms+FISpDLDREZcIyTWSGhzHU34+JbBaTExPITU7e/2jm8xBCQFFV\nXwvPve3SjuNAURQY0SjCkQgisRjCsRgi0SiSqRQSNTV8FgwR3cdCQ0TwPA/5qSmMZjIYuXMHUxMT\nMHM5mKYJyzRhWRZs07z/bds0Yds24HnTp0rPnGeEmcvJvcuKJEkQQkBIEiRJgqIokGbmfRRFQcgw\noGkaVE2DpuvTL8NArKICsYoKRONxyMrCR0YQEbHQENEj8zxv+igA14XrefDufT7zjBtZUe6XmCDf\n5iKi4sdCQ0RERIHHyTkiIiIKPBYaIiIiCjwWGiIiIgo8FhoiIiIKPBYaIiIiCjwWGiIiIgo8Fhoi\nIiIKPBYaIiIiCjwWGiIiIgo8FhoiIiIKPBYaIiIiCjwWGiIiIgo8FhoiIiIKPBYaIiIiCjwWGiIi\nIgo8FhoiIiIKPBYaIiIiCjwWGiIiIgo8FhoiIiIKPBYaIiIiCrz/B7j+LQajBpprAAAAAElFTkSu\nQmCC\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f32359bd908>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing maths score\n",
+    "\n",
+    "data['math score'].value_counts(normalize = True)\n",
+    "data['math score'].value_counts(dropna = False).plot.bar(figsize = (18, 10))\n",
+    "plt.title('Comparison of math scores')\n",
+    "plt.xlabel('score')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 627
+    },
+    "colab_type": "code",
+    "id": "nwoEGMVuTU89",
+    "outputId": "80fb69df-b0ba-4b29-b274-b32e1ddda724"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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x5ZZbZtNNN83IkSPT0NCQE088sTOXBwAAADpBp75BJAAAAPDu16Vv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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235eda588>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "qSTFx72A27Hp",
-        "colab_type": "code",
-        "outputId": "5dd0f3f9-2cc3-41f9-af64-5dc96cb85394",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 426
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# comparison parent's degree and their corresponding grades\n",
-        "\n",
-        "x = pd.crosstab(data['parental level of education'], data['grades'])\n",
-        "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (9, 5))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3235957e80>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 195
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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jdWIUZybjUEt7XlY6QAWopS3Vgu1pXGxP43nUbVlukeHk5ISUlP/1IiQlJcHR0REAcPLk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\nSUVORK5CYII=\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f323588a8d0>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing reading score score\n",
+    "\n",
+    "data['reading score'].value_counts(normalize = True)\n",
+    "data['reading score'].value_counts(dropna = False).plot.bar(figsize = (18, 10), color = 'orange')\n",
+    "plt.title('Comparison of reading scores')\n",
+    "plt.xlabel('score')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 627
+    },
+    "colab_type": "code",
+    "id": "04Y4oXmfdpnN",
+    "outputId": "65f0de4b-2e08-42cf-9df2-931af2ce18fb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SygYAAAAgKWUDAAAAkJSyAQAAAEhK2QAAAAAkVdHZAwAAANunqqXL\nWm6//lg/oKak6/PIaGv9th4D7zSubAAAAACSUjYAAAAASSkbAAAAgKSUDQAAAEBSygYAAAAgKWUD\nAAAAkJSyAQAAAEhK2QAAAAAkpWwAAAAAklI2AAAAAEkpGwAAAICklA0AAABAUsoGAAAAIKmKzh4A\nAABgR1a1dFnL7dcf6wfUdGj9thyTav22HsM7lysbAAAAgKSUDQAAAEBSygYAAAAgKWUDAAAAkJSy\nAQAAAEhK2QAAAAAkpWwAAAAAklI2AAAAAEkpGwAAAICklA0AAABAUsoGAAAAICllAwAAAJCUsgEA\nAABIStkAAAAAJFXR2QMAAACw46lauqzl9uuP9QNqOrR+W45JtT6PjB3lvP8/VzYAAAAASSkbAAAA\ngKSUDQAAAEBSygYAAAAgKWUDAAAAkJSyAQAAAEhK2QAAAAAkpWwAAAAAklI2AAAAAEkpGwAAAICk\nKvIOvPTSS+MPf/hDlJWVxfnnnx8f/OAH8x4BAAAAKKFcy4b//M//jL/+9a8xe/bseOqpp+L888+P\n2bNn5zkCAAAAUGK53kaxcOHCOO644yIi4v3vf3+8+OKL8fLLL+c5AgAAAFBiZVmWZXmFTZ48OYYM\nGdJcOIwZMyamTZsW++23X14jAAAAACXWqb8gMseeAwAAAMhJrmVD3759Y/Xq1c3bzz//fFRVVeU5\nAgAAAFBiuZYNRx99dMyfPz8iIpYsWRJ9+/aN3XbbLc8RAAAAgBLL9d0oDj300DjwwANj9OjRUVZW\nFt/97nfzjAcAAABykOsviAQAAAB2fJ36CyIBAACAHY+yAQAAAEgq19/ZAAAAAHSeLMuirKxsq2te\neeWV5neSrKqqisrKyg7nKBsAAABgB/Twww/HtGnTolevXjFx4sSYMmVKPP/887HrrrvGxRdfHB/+\n8IdbrP/jH/8Y06ZNi5deeil69uwZWZbF888/H9XV1VFbWxs1NTXtzu5y0UUXXZT4fJJ68MEH473v\nfW9ERKxduzauvPLK+Md//MdYsmRJHHTQQbHLLrtsccyaNWvixhtvjF/84hdRWVkZe++9d/PnLr74\n4hgyZEiL9Zs3b4558+bFbbfdFj/96U9j7ty58bvf/S7Kysqas9/O+oiIDRs2xH333RcvvfRS7LXX\nXnHvvffGrFmzYvny5dGvX7+oqGh/73PVVVfFUUcdtcX+2tra6NOnT1RXV7freRoaGuL222+Pp59+\nOvr16xc333xzTJ8+PZYsWRIDBgzY4rVtbGyMGTNmxF/+8pfo169fzJw5M/7lX/4lnnzyyejfv390\n7dq13ecQEfGTn/wkDjnkkJJkfPvb344RI0a0+rlbbrkl9t5773a/7WpHX6c3zuM3v/lN7LfffvHS\nSy/FddddFzNmzNjqMW1p7XV6w+9///uYM2dO/OIXv4gHHnggnnzyyejevXv06tVri7VLly6Nqqqq\niPjbn8cZM2bET3/601i+fHkMGDBgiz+DGzdujKVLl0Z1dXVs3LgxZs+eHXfffXcsX748ampq2v1n\nduzYsXHSSSe1+rnXXnstfvazn8XDDz8cvXv3jp49ezZ/7oc//GEcccQRrR730EMPxaxZs+Kee+6J\nBQsWxO9///soLy+PffbZ523PtC3fczr6d6+j36MiOv5abcv3qY6+rhs3boyf/exnccstt8SMGTPi\nzjvvjIceeiheeeWVqKmpifLylnfqdfTv3qZNm+K+++6L1atXxz777BP3339//PznP49Vq1bFBz7w\ngS2ef1tm2paMjv5d6uj3tY7+eXojI9X3nLb+G9OarX2v7ejXYlvOYVv+vv5/W/t+8FZae6225Xtn\nR1+rbf2Z4qWXXoqFCxfGo48+GkuXLo3Vq1dHnz592v3f1q29VtvyfS2P751t2dqf8478t7Wjfwa3\n5WeKUv98F1H6n4W35WvX0Yxt+Rmyo8d09PtUR/9ub0tGHj+3tGZr3w+29WfnUn4t8jqPUv87dFte\nq4kTJ8bNN98cgwcPjtNPPz2uv/76mDBhQgwdOjSmTp0ao0aNarF+/PjxMW3atBg3blyMGjUqRo0a\nFaeeemr069cvLrnkki3Wb03h343i1FNPjRkzZkTE336oqampieHDh0ddXV38+te/jptuummLY04/\n/fQYPnx49OrVK+6444448sgj4+tf//oWz/eG2tra2HPPPePoo4+Ohx9+OLIsi4MPPjjuuuuuqK6u\njokTJ76t9RF/+6LtsssusXr16th3331j7dq1MXz48HjsscfimWeeieuuu67F+vXr17f5mpxxxhkx\nc+bMLfZ/+tOfjoEDB8bLL78cp5xyyhYtVWvPc/DB/9vemQdFceVx/IsXLmo0iCRqghGNcSXiSkQE\nDw4RESMGD1ABQZAVFXQ1XrgimmxhNKuuYUWjxmh5pjTrueKxGPDgXFFECo3iBXIfgnIoML/9w5re\nGaYH6QZGN/v7VFGlPb/Xv+u9X7953f1mEAoKClBcXIzevXvDyckJN2/eRExMDHbt2qUmHxgYiMGD\nB6OsrAwpKSmwsLCAjY0N0tLSkJGRge+++65BffURy4UcHQ4ODsJjQMrurJxA6enpITo6Wk1+7Nix\n6NWrFz766CN4e3u/9guq1DgBwKxZs+Di4oKpU6fiyy+/RN++fTFixAikp6cjOjoaO3fubFKcAGDz\n5s0oLi7G8OHDERcXh86dO+PDDz/EiRMn4OTkBF9fX63nCQsLg56eHuzt7ZGUlIS8vDxs3LhRTX7B\nggXo378/5s2bh7CwMCgUCgwfPhzp6el4/PixRp8FgP79+8PY2Bht27YVclFYWIhu3bqJ5iIoKAgm\nJiYwNDTEiRMn4O/vjy+++KJBv9euXYvy8nI4ODgIE7/8/HycP38evXr10hh/Um2SU3Okjj2pNUpO\nrKTWKalxBYBFixbBxMQE9vb26Nq1K4gI+fn5OHfuHMrLy7FhwwY1ealjb8mSJTAwMEB5eTkUCgVa\ntWoFa2trpKWloa6uDuvWrWuyTXJ0SB1LUuua1P4ESK85cq4xUmut1FzIqZtSx6vUeiAnVnJqp9RY\nSZ1TAMDRo0exd+9eWFhYwNDQUNBx/fp1BAcHY/z48U2KlZy61tK1U04/b8q1tTF9UM6coqXnd0DL\nz4Xl9A+pOuTMIaW2kVqnpI5tOTp0MW+RWg/k9MGWzoWu/Gjp76FyYqWq18nJCefPnxc+8/b2xr59\n+9Tkp02bhsOHD2vofd1notBbjre3t/DvmTNnqn3m5eUl2kb1eF1dHS1evJgiIiK0tql/zMfHR/j3\nlClTmiyv2qampobs7Oyorq5O+MzT01ND3szMjOzt7dX+HBwcyN7enszNzRvUcf/+fVqzZg25urrS\nqlWraP/+/XTmzBkNeWVsFQoFOTk5NeijqjwRkbOzs9bPVBk2bJjon5WVFZmZmTWLjkOHDpGfnx+l\npqYKx9zd3UVlif7rW1xcHM2ZM4d8fHxo27Zt9Msvv6ido77exsaJSL0f1JeZPn26hrzUOImd18/P\nj4iIamtryc3NrUH5+n1OzI+pU6cK/54xY4baZ2J9lojo0qVL5OXlRWfPnhWONSYXREQVFRXk4+ND\nP//8s1abiMTj19BnUm1qSs1p7NiTWqPqH29MrKTWKalxJdLeD7R9JnXsqfrg6Oio9bPmsEmKDqlj\nSWpdk9qfiKTXHDnXGKm1VmoupPpAJH28Sq0HRNJjJad2yu23jZ1TEL3ys7q6WuP48+fPycPDQ+N4\nU+q51LrWUrWzKXMpJa+7tkrtg3LmFC09vxM73txz4aZe9xqjQ84cUmobqXVK6tiWo0MX8xa5cykp\nfbClc0GkGz9a+nuoql1EjYtVUFAQbdq0iUJDQ8nf359CQ0Pp/PnztGHDBlq4cKGGfHh4OM2ZM4eO\nHDlC0dHRFB0dTT/99BP5+fnRxo0bRW3Sxlv/axSlpaWIjY1FbGws2rVrh9u3bwMAsrKytK5Yt2nT\nBufOnQMRoVWrVvj222+RlZWFVatWoaKiQkOeiHDlyhWUlZXh+PHjaN++PYBXjyWJIVUeePUYVUVF\nBdq0aYMFCxYIj00VFhbixYsXGvLLli3DhAkTcPHiReEvOjoaFy9ehLm5uagO5R2n3r17IywsDEeP\nHsW4cePw/PlzXLt2TUO+trYWT548gZ6eHlatWiUcv337NmpqakTlHz16hOvXr6OsrAw3btwAAGRm\nZorKA8DkyZMRHByM+Ph4tb+EhAQMHjy4WXRMmzYN3377LQ4dOoS1a9fi2bNnDW54ovzM2toa27dv\nx/r169G1a1dcvHgR27Zta3KcAMDExATh4eFIS0uDlZUVzpw5g6KiIvzjH/8QHr9uSpyAV4/R3r9/\nHwDw73//G3V1dQCAe/fuicpXVVUhMzMT9+7dg6GhIbKysgC8ehRLbFx07twZe/fuRUlJCYYPH46b\nN28CABITE6Gvry+qY+TIkfjhhx9w584dzJ8/H1lZWQ3mQqFQ4NatWwAAAwMDREZG4tSpU9i+fTtq\na2u1tklPT9c4rnzkrKk2yak5UseethoVGhoqmgul31JiJbVOaYvrtWvXtMZLT08P58+fVxsHL1++\nxMmTJ0UfV5U69pR1MycnB+Xl5cjOzgbwKkcvX75sFpvk6FCOpbt37zZqLEmta1L7EyC95si5xsip\ntVJyIdUH4L/jNSYmplHjVWo9kBMrObVTbr9t7JwCAOrq6rTWCoVCoXFcaqzatGmDs2fPSqprLV07\n5fRzqddWqX1Qzpyiped3gPo148SJE80+F9bWP7TNzeXokDOHlNpGap3S09PDuXPnGj225eiQ2geV\ndgGN71NS64GcPtjUXERFRb32mqELP6TWQqljT06s1q9fD2NjYwwbNgy7du3CkCFDcPXqVRgZGSE8\nPFxDPiQkBP7+/sjJyUFMTAxiYmJQUFCAoKAgLF68WKtdokhamngDrFixQu0vPj6eiouLKSgoiBIT\nE0Xb5Obm0ooVK6iqqko4VlxcTCdPnhRdvc/MzKS5c+eSi4sLLVq0iHJycig7O5s2bdpE9+/ff618\nXl4eFRcX05YtW+jBgweiNkVHR5Ovr6/asUuXLpGtrS1dunRJtM3x48epoqJCzQciom3btonKL1iw\nQOOYso0Y169f11jNOnPmDE2YMIFu3bqlIZ+cnEyTJk2i2bNn071798jHx4d+//vfk6urK12/fl1U\nh0KhoO+//17NDyVff/31a3XMnDmTrK2tafz48Vp1qJKQkECenp4aq3yqaFvd1kb9ONXU1NCPP/5I\nLi4uonFSyhw4cIBmz55N48aNI2dnZ3JxcaHvv/9erV8qUcapsrJS7RiReJyIiFJSUsjV1ZWsra3J\n3d2d7t69S0REISEhdO3aNQ15Ly8v8vb2Ji8vL/Ly8qILFy4QEZGvry9FRUVpyD979ow2bNhA48aN\nI0tLSzI3N6exY8dSWFhYg/1KyYMHD+iPf/wjjR49WqvM7du3ycvLS61/vHjxgrZt20YjRowQbZOR\nkUHe3t7k4OBAbm5u9MUXX5CdnR35+fnRvXv3XmvT7NmzacCAAVRbWysqU7/mXLlyhbKzsxusOfXH\nXk1NDWVnZ1NNTY2ofG5uLi1fvlzoC0r5Y8eOidYoov/G6vnz58Kx2tpaioyMFI2Vsk6NHz9eqFNx\ncXEUEREhWqcyMjJo5syZQlzd3NxeG1dlrXVwcCAbGxuytrYmR0dHCg0NpcLCQg15sRV9ZT8X49y5\nczRq1ChydXWlxMREcnV1pQkTJpCtrS1FR0c3yqahQ4dS//79aeXKlVRQUCCqY+TIkTRhwgRKTEyk\nCRMmvFaHciwpx9O//vUvUigUNGvWLNGxVL+u+fr60tChQ7XWTrFaHhcXpzVOROI1Z/r06bRjxw7R\nu9lERMeOHRPGnmqf1XaNUSUxMZG8vLw07vSooszF6NGjycbGhmxsbBrsH/V9cHR0JHt7e9q6dato\n3SQSnyPExcVRcHCw1vGqRFmjxowZ81p/jx8/Ts+fP6fi4mIqKioSjovFSlk7XVxcyNLSkgYNGkRO\nTk4N1k7VWPXv358+/fRTIVZi/VZsThETE0N2dnZa5xQnTpygsWPH0tKlS2ndunW0bt06Wrx4MTk5\nOdG5c+e0+q5QKCg1NZV8fHwarOeqcy/VOGmbexH9t68rFAqN2GrToVo7iV6NjYZ0qPZzpTyR9rlU\nSkoKTZw4kWxsbIRrq0KhoJUrV1JKSoqGvNR5qtjcKyoqiiZOnKh1TiF1fpeSkkILFy5Ui+v58+cb\n1CF2zVDObRszF87NzSUiou+++46Sk5M15KXOzevrmDlzJuXl5RERUUREhKhNycnJNHnyZEnzVGUb\nPz8/Sk5OJl9fX7KxsSFXV1fRfCvrVEBAALm4uNC4cePI09OTduzYIVqn6tfB110n6+tozBxS2feW\nLVtGAQEBdOrUKSIiCg4OpoSEBFEdqmNPzOaGaMz8Tup3DCL1XCivldbW1uTq6irap+rnwtnZmby8\nvGjHjh2v9UGhUGhcA8TaiPlx4cIFrTYpz7NixQqNa++JEyc0nqAkangsiT31SUSUlJSkMa9Qxqox\n35d0yVv/axTOzs6Ijo7GV199hfj4eISEhKBDhw6orKzUuhJ6584dtG3bFu3bt0d8fDxWrlyJjh07\noqKiAqGhoRryBw8eRGRkJAAgLi4OM2bMgJGREYqLizFkyBD07t1bTT4rKwtGRkaIjIxEfHw8pk2b\nJthkbm4uupnHl19+iUmTJqG4uBhdu3YFAFhZWSE6OhqtW7fWkI+NjcW1a9cwceJEwQelDjEfAGDS\npElYvXq1ECvVNqtXr4adnZ2afFlZGd555x0A0JAvLCzUOH9FRQXMzMyE8z969AimpqZ49uwZSktL\nRW367LPP4ObmhqqqKo2fS1FdIVRy9uxZ/PzzzwBe5eLx48fo2bMniouLUV5eLqrDwsICbm5umDdv\nHqysrGBhYYGMjAxRWQBITU3F119/jXnz5gm5aIjTp0/jb3/7m2DTn//8ZxgZGaGqqgolJSWiba5e\nvYrbt29j586darE9fPgw+vXrp5GLuLg4HDt2DLGxsRq7xK5du1ZUh7+/v+C3qh9iK5QAcOvWLUya\nNElD/scffxSVHzVqFNzc3LBv375GxQl41W+V4zU3Nxe//vorWrVqBQcHB9E+WFhYiKKiIgQEBAh+\nFxYWwsDAAH/9619FdcyYMQNubm7YsGGDcDfP0NBQ64Zof/nLX4S+lpOTg3v37uGDDz7AmDFjsHbt\nWowcOVJNvkOHDoK8ar6Li4tRXV0tqkN1Bb1+GzEdu3btwjfffKMm361bNxQVFUHbnr3Tp0+Hm5sb\nqqur0aFDBwBA69atMXfuXMydO1dD/ubNm3BychJ+2ig+Ph6RkZGYN28ebty4oVGnioqKUFBQAGNj\nYyxbtgxLly5FTU0NsorXpwkAAA3eSURBVLOzUVRUhD59+mjoUNba6OhooZ/r6+vjypUrcHBw0Mj3\nnDlzMG7cOLXdkJX5FtsN2cDAAAYGBujYsSPat2+PVq1a4f79+3j//feFGNRn165dwj4LytiamJgg\nPj4ezs7OGnc7li9frjYuTpw4gZKSErz77ruiG3cp/ai/q/OaNWtgYGCgtnGnkuzsbHh5eQF4tcOz\nq6srsrOzMWvWLDx8+FBjA1g7OzscP35c+D8RYdu2bZg3bx4ACHt1qHL69GkYGBjAxcVFOLZt2zZ0\n69YNUVFRGm1Ux0X9PqutD9bfzbqiogJ5eXlwdnbG2rVrYWVlpSafkJAAKysrtbxGRkbCwsICV65c\n0bDpm2++wapVqzBjxgw1m44ePYqBAwdqjCMAgk5lP8/NzRVilZOToyGvGlfg1Rxj69atwnGx2D54\n8ABRUVHYtWsXsrOz0adPH5SVlcHMzAwhISEa8qmpqbh48SIMDQ0RHh4u9POEhAS4uLiIvhtdVVWF\n0tJS/O53v0P79u1hamqK8vJyYS+R+rRr1w55eXnw9PQU+mB2djY6deqk9W6pq6srxowZg9TUVBQX\nFwMAjI2NYW5uLvrExYMHD7B+/Xo8efIE2dnZMDU1BRHhT3/6E0JCQjQ2llP6MHXqVCFO5eXlGDBg\ngOgeEgCwcOFCBAYGCjpU24jpSEhIwLBhw3D27FkA6mNj2rRpGudX5lX5frKq/Pvvvy9qU0VFBV68\neCHsVbN8+XLheuzq6qohP3bsWKxbtw7du3fHypUrsWTJEtTV1aGyshKVlZUa8g8fPlQb46o23b17\nF2ZmZhpt3NzcMHbsWA0dVVVVCAsL09hwrnPnzqiurhZqjWqfNTIyEvX78ePHyMzMRPfu3REYGIiA\ngABBx6BBgzTmwpmZmcjIyEBlZSU+/fRTYePf4OBg0ffSb9++jZSUFAQGBgo+KBQKYY4qhp6enrCX\nzs2bNzF//nzBD7Gf4Hv+/DmePXuGjh07oq6uDiUlJcLmgWVlZaI6DA0NYWRkhCdPniAgIACmpqYw\nMDBA79690aNHDw35rKwsXLp0Cbm5ucjJyYGpqSny8/ORnp6OsrIy4a50fb8/+OADhISECH5fvnwZ\no0ePFt0Y8+rVq9i7dy+6d++OzZs3Y+nSpairq8Phw4fxySefaLQJCAjAhg0bhHFUUlKCLVu2wMzM\nTOsGg59//jns7e1RVVUFW1tbhIaGCjlctmyZRv5iYmLU+nlubi6ICA4ODqJ98OnTp8jIyICvr6/G\nuFD+lGJ9SktLUVxcjKysLOzYsQMRERGCTWJ9KiYmBjt37kRlZSXs7OzUfNC2D8iFCxcQHh4u+L1x\n40bh+6SY34WFhUhNTcWwYcOEODk6OsLR0VGrjrS0NCQkJMDW1lYttq6urjhy5IiGvKmpqfA9VBVt\nYwkALC0the9LgPpcXlubN8abW+doHJMmTRJW/jw9Penx48dERFRSUqL2XmRT2qje6Z4xY4YgX1BQ\nIPoejxybvLy8KCkpiXx8fGjFihWUlJSk9a6nrvxuaXk5fkvNhRwdurBJaqymTZtG+fn59Ouvv5KV\nlRVlZGQQEVF2drbW985a2m+p8nL9LigoaHG/lTQmf3LyLbWNVJuUbaT47ejoSFOmTKGIiAjhb9So\nUcK/61M/F7dv3yaihnPR0v1cjk1SYyunn0u1SywXtra2WnMhNXdy2sjpg/Xz19x+y7FJqt9yYuvt\n7S3YkpmZSWvWrCEiotjYWNGndaTGSa4OqbXz5cuXdOjQIQoODiYPDw/y8PCgBQsW0JEjR0Sf9JJq\nk1R5OW10kW+psXV3d6cnT55QcnIy2dvbC/KFhYU0efLkZrFJqg45uZCqY8qUKVRaWkp1dXX0008/\nkbu7O5WXlxOR+FNsUs8vxw9d6NCFTbrIt2r+Dh8+3Oz5k+O3VJuk9sGm6miM/OvaiD1dvX//fq1/\n2p4elNPmTfHWP9lQW1sr3MHq1KmT8PMhXbp0EXYRbY42SpS7DgOv7laK3S2Vc349PT1YWlpiz549\nSEtLw5EjRxAaGooOHTqga9eu2LFjh879bqp8z549m91vVRqTCzk6dGGT1Fi1bdsWxsbGMDY2xjvv\nvIP+/fsDAHr27Cn65Isu/JYTJzl+d+vWDd26dWtRv5U0Nn9y5RvbRo5NUv0+ffo0IiMjcefOHaxY\nsQI9e/bE5cuXERQUJHr++rlQ/oZyQ7lo6X4uxyapsZXTz6XaJTUXUuXltJHTB+vnr7n9lmOTLmL7\n8uVLwZaPPvoId+7cAfDq6a+IiAgNealxkqtDau1ctmwZTExM4Ofnp7ErfkhIiMau+FJtkiovp40u\n8i01tu3atUOPHj3Qo0cPGBsbC/JGRkaiT4zIsUmqDjm5kKqjdevW6NKlCwDA3d0dhoaG8Pf3x/bt\n20XfgZd6fjl+6EKHLmzSRb5V8+fh4YGuXbs2a/7k+C3VJql9sKk6GiP/ujZi7NmzB9bW1jA2Ntb4\nTNv+ZXLavCne+sUG5c+7DR8+HF26dMG8efMwePBgJCYmYurUqc3S5u7du1i4cCGICI8ePUJUVBTG\njRuH3bt3o1OnTs1ik+rEe+DAgRg4cCAAoKCgQPSVBV343VT5+fPnN7vfUnMhR4cubJIaq86dO2Pz\n5s0oLS2FiYkJVq9ejZEjR+LGjRtaX2Foab+lyr+tfkvNn5x860KHVL/19fWxaNEi3L9/H1999RUG\nDx4s+ji2Ejm5aOl8y7FJamzl9HOpdknNhVR5OW3k9MGW9luOTbqIbb9+/bB48WKYm5vj8uXLwqsb\nK1euRN++fZscJ13pKCwsxObNm9WOmZiYwNLSUnjNpyk2SZWX00YX+ZYa265du+KHH36Av7+/8HNw\neXl52L17t+irGnJskqpDTi6k6rCwsMCcOXOwZcsWtG/fHo6OjtDX14evry+ePn3a5PPL8UMXOnRh\nky7y3dL5k+O3VJukyr+tOrZu3Sq82lj/NbjExERRHXLavCn06HW3+t8Cnj59iri4ODx58gREBCMj\nIwwfPlzjXT65bZKSktT+36tXL7z33ns4deoUHBwcRN8NlmrT0aNHMWXKlLfKb13IS/VbTi6k6tCF\nTYC0WFVWVuLYsWN499134eLigpMnTyIlJQW9evWCh4eH6LuJLe23nD4LvH1+S82fnHzrQofcfCg5\nfvw4YmNjNb5sKJGTC6Bl8y3HJqmxlRNXubFS8rpcNFW+MW3k9MGW9lturZWiQ448ESE6OhoPHz5E\nv379MGrUKACv3sX+5JNPNO5uyYmTLnR4e3vD29sb9vb2aNu2LYBXd0TPnTuHY8eOYffu3U2ySaq8\n3DaqtES+pca2uroaFy9eVNsvJT09HcnJyZg+fbrWu7hSbJKqQ05c5fiRmJiIoUOHqp3v+fPnOHPm\nDNzd3Zt8fql+6EKHLmzSRb6Bls2f3HEhxSY58m+rjqqqKujr62vsFZWeni66j4vcNm+C/4nFBoZh\nGIZhGEY+eXl52LJlC5KSkoSfw+vQoQOsra0RFBQk+jguwzAMwzQFXmxgGIZhGIb5P+at272cYRiG\n+U3w1u/ZwDAMwzAMwzSNAwcOaP0sPz9fh5YwDMMw/y/wYgPDMAzDMMxvnP+l3csZhmGY3wa82MAw\nDMMwDPMb539p93KGYRjmtwHv2cAwDMMwDPN/wP/K7uUMwzDMbwNebGAYhmEYhmEYhmEYpllp9XoR\nhmEYhmEYhmEYhmGYxsOLDQzDMAzDMAzDMAzDNCu82MAwDMMwDMMwDMMwTLPCiw0MwzAMwzAMwzAM\nwzQr/NOXDMMwDMNIJj8/H0uWLAEAVFdXw8PDA0OGDEFoaCgUCgX09fWxbt06vPfee4iMjERMTAza\ntGmDjz/+GKtWrUJ+fj7mzp2Lfv364eOPP0ZgYCA2bdqElJQUVFdXw9LSEsuWLYOent4b9pRhGIZh\nGDnwkw0MwzAMw0gmKioKpqam2LdvH/bv34/q6mqEhYXB398fBw4cwOTJkxEVFYXr16/j/PnzOHDg\nAA4ePIjS0lKcPn0aAJCZmYn58+cjMDAQUVFRyM/Px/79+3H06FE8fvwYv/zyyxv2kmEYhmEYufCT\nDQzDMAzDSGbkyJE4ePAgVqxYAVtbW3h4eGDjxo0YOnQoAGD8+PEAgD179sDS0hJt27YFAAwdOhRp\naWmwtLRE586dYWpqCgBITEzEjRs34O3tDQB49uwZsrOz34BnDMMwDMM0B7zYwDAMwzCMZPr06YN/\n/vOfSE5OxtmzZ7F3714AgEKhUJOr/xoEEQnHlAsQANCuXTu4u7vD39+/hS1nGIZhGEYX8GsUDMMw\nDMNI5tSpU0hLS4ONjQ3CwsKQm5sLc3NzXL58GQBw5swZbNq0CX/4wx+QmJiImpoaAEB8fDwGDRqk\ncb7PPvsMFy5cQG1tLQDg73//Ox4+fKgzfxiGYRiGaV74yQaGYRiGYSTTt29fhIWFoV27diAiBAQE\nYMSIEQgNDcXBgwfRpk0bhIeHo3v37hg/fjw8PT3RqlUrmJmZ4fPPP0dOTo7a+ZycnHDjxg1MmzYN\nrVu3xoABA/Dhhx++Ie8YhmEYhmkqekREb9oIhmEYhmEYhmEYhmF+O/BrFAzDMAzDMAzDMAzDNCu8\n2MAwDMMwDMMwDMMwTLPCiw0MwzAMwzAMwzAMwzQrvNjAMAzDMAzDMAzDMEyzwosNDMMwDMMwDMMw\nDMM0K7zYwDAMwzAMwzAMwzBMs8KLDQzDMAzDMAzDMAzDNCv/AWsOl6+8/9tMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235d92160>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "Om9KcvtS6zoC",
-        "colab_type": "code",
-        "outputId": "7653c751-cecc-455a-ac69-26d4a231693e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 418
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# for better visualization we will plot it again using seaborn\n",
-        "\n",
-        "sns.countplot(x = data['parental level of education'], data = data, hue = data['grades'], palette = 'pastel')\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
-            "  stat_data = remove_na(group_data[hue_mask])\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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BAB4eHoiKipKraSIiIpMlW3AvWrQIAQEB+t+zsrKg0WgAAHZ2dkhKSpKraSIiIpMly8Vp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\n9BiOiXFcAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235b85b70>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing writing score\n",
+    "\n",
+    "data['math score'].value_counts(normalize = True)\n",
+    "data['math score'].value_counts(dropna = False).plot.bar(figsize = (18, 10), color = 'pink')\n",
+    "plt.title('Comparison of writing scores')\n",
+    "plt.xlabel('score')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 318
+    },
+    "colab_type": "code",
+    "id": "pfAaNUKvVGJY",
+    "outputId": "22ec3315-902c-4199-d5ef-72660cbb35fd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3235b65240>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "V6TBXagn5F7H",
-        "colab_type": "code",
-        "outputId": "de6f5eff-5403-4084-9b95-4085e19482d1",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 418
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# comparing the distribution of grades among males and females\n",
-        "\n",
-        "sns.countplot(x = data['grades'], data = data, hue = data['gender'], palette = 'cubehelix')\n",
-        "#sns.palplot(sns.dark_palette('purple'))\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
-            "  stat_data = remove_na(group_data[hue_mask])\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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3b55Gjx5tdSSf8+677+rGG29Ujx49dOjQIaWnp1sdyScdOHBAV199dZlpNptN\nl19+uQ4dOmRNKIO0bdtWP/zwg9UxYKADBw7oqquuKjMtMDDQ9w+ri3PkVaJVq1aKi4uTJO3fv1+P\nPfaY3nzzTdntrN5fbN68WWPHjpW/v7/69eunLVu26N5777U6ls+x2WwqKSn5zXTX/54DRvlyc3NZ\nT+fxy87GL1q1aqVZs2ZZmMj3XOi9ZwKapopFRkaqdu3aOnHihJo1a2Z1HJ9w8uRJpaamat68ebLZ\nbCooKFBwcDBFfh6tW7fWP//5zzLTXC6XfvjhB7Vs2dKaUAbZs2fPb/aqUHZnA+fXunVrrVu3rsy0\nwsJCHTp0SFdccYVFqSqHQ+tVLDs7W06nUw0bNrQ6is/YvHmzhg8froSEBL311lt69913dfr0aR05\ncsTqaD6ne/fuOnbsmLZv/78bRaxevVpdunThE8YVOHLkiFavXq1Ro0ZZHQUG6t69u9LS0vT+++9L\nkkpLS7Vw4UJt2bLF4mQV48puv9Ovv9px9uxZPfjgg+77q0MaNGiQ5s+fX+Z/tcuWLZOfn5/+9re/\nWZjMNzmdTk2fPl0nT56Uy+VS27Zt9eSTTyooKMjqaD7l3PdeYWGhSkpK9Pjjj6t79+5WR/MpF/r6\n2cSJE9W+fXuLUvmmjIwMTZs2TRkZGQoICFC3bt308MMPy8/Pt/d5KXIAAAzm2//NAAAA5aLIAQAw\nGEUOAIDBKHIAAAxGkQMAYDCKHMAlee6557RkyRKrYwA1HkUOAIDBuEQrUIO4XC7NmjVLqampatCg\ngRo1aqSQkBDFxcXprrvuUmlpqaZOnarp06frwIEDKiwsVIcOHRQTEyPp573wDz74QI0bN9Yf/vAH\nRUZGSpJ27NihZcuWyeVyyW63KzY2Vs2aNdMzzzyjHTt2KCAgQA0bNtT8+fONuAkFYBKKHKhBkpKS\ntGvXLr3++us6e/asBg4cqP79+ysvL0833HCDunfvrqysLLVp00axsbGSpH79+mnv3r2qVauWNm3a\npHfffVd+fn4aPHiwIiMjlZ+fr+nTp2v9+vWqX7++tm3bpgULFmj27Nlat26dvvjiC/n7+2vLli3K\nzMxURESExWsBqF4ocqAG+fbbbxUVFSV/f38FBQWpR48ekn7eU+/cubMk6bLLLtOJEyc0dOhQBQQE\nyOl0KisrS9nZ2brmmmvce9RRUVGSpH379snpdGr8+PGSpJKSEtlsNtWrV089evTQiBEj1KdPH91y\nyy1q1KiRBa8aqN4ocqAGKS0tLXPd6HN/rlWrliTp7bff1u7du7Vu3TrZ7Xbdcccdkn4ue5vNVmZZ\nkhQQEKCIiIjz3l1r8eLF2r+77vAcAAABfElEQVR/v7Zv364RI0ZoyZIl3J0MqGJ82A2oQVq3bq2v\nvvpKLpdL+fn5+vjjj3/zmFOnTqlVq1ay2+3as2ePjhw5osLCQkVGRuqbb75RYWGhioqK9Nlnn0mS\nWrZsqaysLO3du1eS9Pnnn2v9+vU6evSoVq9ercjISN13333q06ePvvvuO6++XqAm4KYpQA1SXFys\nSZMm6dChQ2rcuLHq1aunxo0ba+nSpfr6669lt9t14sQJjRkzRsHBwercubMCAwP11ltv6V//+peW\nL1+uHTt2KCIiQnXr1lWzZs00fvx4ffrpp3ruuedUu3ZtSdKsWbPUtGlTxcTE6MCBA6pTp47q1aun\nuXPnqk6dOhavBaB6ociBGiQnJ0fbtm3TwIEDZbPZNGbMGA0YMEADBgywOhqAS8Q5cqAGqVOnjlJS\nUvTqq6+qdu3aatWqlfr162d1LAC/A3vkAAAYjA+7AQBgMIocAACDUeQAABiMIgcAwGAUOQAABqPI\nAQAw2P8A+tnNgQIRYB0AAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<matplotlib.figure.Figure at 0x7f3235bbf7b8>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     },
+     "execution_count": 181,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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SSDV4atC3b19iY2NJSkr69ZFMyaSmphISEkJCQgJTpkzhL3/5C4ZhcPnllzNs2DBP1C0i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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235b7b9e8>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "fSEMQE4W87U4",
-        "colab_type": "code",
-        "outputId": "2d415ca5-5dd8-4c3f-d6ab-050405826c33",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 348
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data.head()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>gender</th>\n",
-              "      <th>race/ethnicity</th>\n",
-              "      <th>parental level of education</th>\n",
-              "      <th>lunch</th>\n",
-              "      <th>test preparation course</th>\n",
-              "      <th>math score</th>\n",
-              "      <th>reading score</th>\n",
-              "      <th>writing score</th>\n",
-              "      <th>pass_math</th>\n",
-              "      <th>pass_reading</th>\n",
-              "      <th>pass_writing</th>\n",
-              "      <th>total_score</th>\n",
-              "      <th>percentage</th>\n",
-              "      <th>status</th>\n",
-              "      <th>grades</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group B</td>\n",
-              "      <td>bachelor's degree</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>none</td>\n",
-              "      <td>72</td>\n",
-              "      <td>72</td>\n",
-              "      <td>74</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>218</td>\n",
-              "      <td>73.0</td>\n",
-              "      <td>pass</td>\n",
-              "      <td>B</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group C</td>\n",
-              "      <td>some college</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>completed</td>\n",
-              "      <td>69</td>\n",
-              "      <td>90</td>\n",
-              "      <td>88</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>247</td>\n",
-              "      <td>83.0</td>\n",
-              "      <td>pass</td>\n",
-              "      <td>A</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>female</td>\n",
-              "      <td>group B</td>\n",
-              "      <td>master's degree</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>none</td>\n",
-              "      <td>90</td>\n",
-              "      <td>95</td>\n",
-              "      <td>93</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>278</td>\n",
-              "      <td>93.0</td>\n",
-              "      <td>pass</td>\n",
-              "      <td>O</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>male</td>\n",
-              "      <td>group A</td>\n",
-              "      <td>associate's degree</td>\n",
-              "      <td>free/reduced</td>\n",
-              "      <td>none</td>\n",
-              "      <td>47</td>\n",
-              "      <td>57</td>\n",
-              "      <td>44</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>148</td>\n",
-              "      <td>50.0</td>\n",
-              "      <td>pass</td>\n",
-              "      <td>D</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>male</td>\n",
-              "      <td>group C</td>\n",
-              "      <td>some college</td>\n",
-              "      <td>standard</td>\n",
-              "      <td>none</td>\n",
-              "      <td>76</td>\n",
-              "      <td>78</td>\n",
-              "      <td>75</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>Pass</td>\n",
-              "      <td>229</td>\n",
-              "      <td>77.0</td>\n",
-              "      <td>pass</td>\n",
-              "      <td>B</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   gender race/ethnicity parental level of education         lunch  \\\n",
-              "0  female        group B           bachelor's degree      standard   \n",
-              "1  female        group C                some college      standard   \n",
-              "2  female        group B             master's degree      standard   \n",
-              "3    male        group A          associate's degree  free/reduced   \n",
-              "4    male        group C                some college      standard   \n",
-              "\n",
-              "  test preparation course  math score  reading score  writing score pass_math  \\\n",
-              "0                    none          72             72             74      Pass   \n",
-              "1               completed          69             90             88      Pass   \n",
-              "2                    none          90             95             93      Pass   \n",
-              "3                    none          47             57             44      Pass   \n",
-              "4                    none          76             78             75      Pass   \n",
-              "\n",
-              "  pass_reading pass_writing  total_score  percentage status grades  \n",
-              "0         Pass         Pass          218        73.0   pass      B  \n",
-              "1         Pass         Pass          247        83.0   pass      A  \n",
-              "2         Pass         Pass          278        93.0   pass      O  \n",
-              "3         Pass         Pass          148        50.0   pass      D  \n",
-              "4         Pass         Pass          229        77.0   pass      B  "
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 198
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# gender vs race/etnicity \n",
+    "\n",
+    "x = pd.crosstab(data['gender'], data['race/ethnicity'])\n",
+    "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (4, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 324
+    },
+    "colab_type": "code",
+    "id": "oM5B03VaXH2f",
+    "outputId": "5bdb1a15-0aaa-4874-ff09-27b2b7adc182"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3235a99668>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "du9CiE7c8ER7",
-        "colab_type": "code",
-        "outputId": "d2ffd396-abd0-42fb-d0a2-548a78401a24",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 300
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data.describe()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>math score</th>\n",
-              "      <th>reading score</th>\n",
-              "      <th>writing score</th>\n",
-              "      <th>total_score</th>\n",
-              "      <th>percentage</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>1000.00000</td>\n",
-              "      <td>1000.000000</td>\n",
-              "      <td>1000.000000</td>\n",
-              "      <td>1000.000000</td>\n",
-              "      <td>1000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>66.08900</td>\n",
-              "      <td>69.169000</td>\n",
-              "      <td>68.054000</td>\n",
-              "      <td>203.312000</td>\n",
-              "      <td>68.105000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>15.16308</td>\n",
-              "      <td>14.600192</td>\n",
-              "      <td>15.195657</td>\n",
-              "      <td>42.771978</td>\n",
-              "      <td>14.258095</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>0.00000</td>\n",
-              "      <td>17.000000</td>\n",
-              "      <td>10.000000</td>\n",
-              "      <td>27.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>57.00000</td>\n",
-              "      <td>59.000000</td>\n",
-              "      <td>57.750000</td>\n",
-              "      <td>175.000000</td>\n",
-              "      <td>59.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>66.00000</td>\n",
-              "      <td>70.000000</td>\n",
-              "      <td>69.000000</td>\n",
-              "      <td>205.000000</td>\n",
-              "      <td>69.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>77.00000</td>\n",
-              "      <td>79.000000</td>\n",
-              "      <td>79.000000</td>\n",
-              "      <td>233.000000</td>\n",
-              "      <td>78.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>100.00000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>300.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "       math score  reading score  writing score  total_score   percentage\n",
-              "count  1000.00000    1000.000000    1000.000000  1000.000000  1000.000000\n",
-              "mean     66.08900      69.169000      68.054000   203.312000    68.105000\n",
-              "std      15.16308      14.600192      15.195657    42.771978    14.258095\n",
-              "min       0.00000      17.000000      10.000000    27.000000     9.000000\n",
-              "25%      57.00000      59.000000      57.750000   175.000000    59.000000\n",
-              "50%      66.00000      70.000000      69.000000   205.000000    69.000000\n",
-              "75%      77.00000      79.000000      79.000000   233.000000    78.000000\n",
-              "max     100.00000     100.000000     100.000000   300.000000   100.000000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 199
-        }
+     },
+     "execution_count": 182,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HLm7/Dc9WHfDt9hjRP35lGiMrMY5mw8biUKMWRxZNp9GAV7DzrMGlHb9zacfveLbpXOR4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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235bc3390>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "TGVw87JkX0rJ",
-        "colab_type": "code",
-        "outputId": "4947d499-85d4-4297-edda-959560307770",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "from sklearn.preprocessing import LabelEncoder\n",
-        "\n",
-        "# creating an encoder\n",
-        "le = LabelEncoder()\n",
-        "\n",
-        "# label encoding for test preparation course\n",
-        "data['test preparation course'] = le.fit_transform(data['test preparation course'])\n",
-        "data['test preparation course'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "1    642\n",
-              "0    358\n",
-              "Name: test preparation course, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 200
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# comparison of race/ethnicity and parental level of education\n",
+    "\n",
+    "x = pd.crosstab(data['race/ethnicity'], data['parental level of education'])\n",
+    "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = 'True', figsize = (7, 4) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 418
+    },
+    "colab_type": "code",
+    "id": "hw0nW8fYZbkB",
+    "outputId": "62244083-effc-4763-cfb8-c042f64a7b7c"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
+      "  stat_data = remove_na(group_data[hue_mask])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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85E5ERP8Ifn6zdV1CteIZOhERkQww0ImIiGSAgU5ERCQDDHQiIiIZYKATERHJAAOdiIhI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\nBjoREZEMMNCJiIhk4P8A8uyvYM1bcn0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f32360be5c0>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "23lY5N_PY-U-",
-        "colab_type": "code",
-        "outputId": "67fbccbd-c127-4047-ffd0-3d9dcfb023bd",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for lunch\n",
-        "\n",
-        "data['lunch'] = le.fit_transform(data['lunch'])\n",
-        "data['lunch'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "1    645\n",
-              "0    355\n",
-              "Name: lunch, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 201
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# comparison of parental degree and test course\n",
+    "\n",
+    "sns.countplot(x = 'parental level of education', data = data, hue = 'test preparation course', palette = 'dark')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 418
+    },
+    "colab_type": "code",
+    "id": "SG8Eojinal_D",
+    "outputId": "c886e57c-ba8a-46ba-ae31-21a465328cb0"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
+      "  stat_data = remove_na(group_data[hue_mask])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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url56aYAkacCAwZo4cYwqVKiooKDyxbrvRcE0prhn/PGDq+C9/GCYOXOqnn66lZo1a+7o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\nkAMAYGAEOQAABkaQAwBgYP8fUyF79ksBpHIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f32360cd438>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "f_L3U8clZMuO",
-        "colab_type": "code",
-        "outputId": "b72d7ce0-52f7-43cd-fd00-812ed7ced612",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for race/ethnicity\n",
-        "# we have to map values to each of the categories\n",
-        "\n",
-        "data['race/ethnicity'] = data['race/ethnicity'].replace('group A', 1)\n",
-        "data['race/ethnicity'] = data['race/ethnicity'].replace('group B', 2)\n",
-        "data['race/ethnicity'] = data['race/ethnicity'].replace('group C', 3)\n",
-        "data['race/ethnicity'] = data['race/ethnicity'].replace('group D', 4)\n",
-        "data['race/ethnicity'] = data['race/ethnicity'].replace('group E', 5)\n",
-        "\n",
-        "data['race/ethnicity'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "3    319\n",
-              "4    262\n",
-              "2    190\n",
-              "5    140\n",
-              "1     89\n",
-              "Name: race/ethnicity, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 202
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# comparison of race/ethnicity and test preparation course\n",
+    "\n",
+    "sns.countplot(x = 'race/ethnicity', data = data,  hue = 'test preparation course', palette = 'bright')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 252
+    },
+    "colab_type": "code",
+    "id": "5vtrMoiVb2KB",
+    "outputId": "fd215592-6b92-4974-dbc2-a83aa0c9c6a9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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L4mOgvxYSEoLw8HDs2rULXbt2RUBAAFQqFUJDQzF//nzIZDIsXLgQGg3HZYjI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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f32360a06a0>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "PpowtC99ZdPM",
-        "colab_type": "code",
-        "outputId": "eda1d827-15ab-4dd8-c3ed-85f56d55d2ff",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 148
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for parental level of education\n",
-        "\n",
-        "data['parental level of education'] = le.fit_transform(data['parental level of education'])\n",
-        "data['parental level of education'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "4    226\n",
-              "0    222\n",
-              "2    196\n",
-              "5    179\n",
-              "1    118\n",
-              "3     59\n",
-              "Name: parental level of education, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 203
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# feature engineering on the data to visualize and solve the dataset more accurately\n",
+    "\n",
+    "# setting a passing mark for the students to pass on the three subjects individually\n",
+    "passmarks = 40\n",
+    "\n",
+    "# creating a new column pass_math, this column will tell us whether the students are pass or fail\n",
+    "data['pass_math'] = np.where(data['math score']< passmarks, 'Fail', 'Pass')\n",
+    "data['pass_math'].value_counts(dropna = False).plot.bar(color = 'black', figsize = (5, 3))\n",
+    "\n",
+    "plt.title('Comparison of students passed or failed in maths')\n",
+    "plt.xlabel('status')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "qsOJMDZkg3K5",
+    "outputId": "0069d22e-e62a-4b34-ff0f-3f4a5d6cca1e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pass    960\n",
+       "Fail     40\n",
+       "Name: pass_math, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "geX7js-LbEgB",
-        "colab_type": "code",
-        "outputId": "be192129-00b1-49b5-e66e-e4e2de1b446b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for gender\n",
-        "\n",
-        "data['gender'] = le.fit_transform(data['gender'])\n",
-        "data['gender'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0    518\n",
-              "1    482\n",
-              "Name: gender, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 204
-        }
+     },
+     "execution_count": 186,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['pass_math'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 252
+    },
+    "colab_type": "code",
+    "id": "YlFzqNvVeaeS",
+    "outputId": "b4740ae9-6131-422f-addf-2029630cac13"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iXbt22Ldvn+W9MhqN6NWrV53vx71yc3PRrVs3qNVqXLhwAadPn67zugsICMDnn3+O0tJS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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235861eb8>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "q_-OubIg-dOv",
-        "colab_type": "code",
-        "outputId": "8b9b2f10-decc-4900-9544-be7b790fdbd9",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for pass_math\n",
-        "\n",
-        "data['pass_math'] = le.fit_transform(data['pass_math'])\n",
-        "data['pass_math'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "1    960\n",
-              "0     40\n",
-              "Name: pass_math, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 205
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# creating a new column pass_math, this column will tell us whether the students are pass or fail\n",
+    "data['pass_reading'] = np.where(data['reading score']< passmarks, 'Fail', 'Pass')\n",
+    "data['pass_reading'].value_counts(dropna = False).plot.bar(color = 'brown', figsize = (5, 3))\n",
+    "\n",
+    "plt.title('Comparison of students passed or failed in reading')\n",
+    "plt.xlabel('status')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "HyHpREyVhBdM",
+    "outputId": "99f80a0c-b1c9-4d6c-8baa-5b6cbe7aee52"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pass    974\n",
+       "Fail     26\n",
+       "Name: pass_reading, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "SEdTTzYK-y51",
-        "colab_type": "code",
-        "outputId": "0b1a7ae1-3d30-4434-ac6e-28674d542197",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for pass_reading\n",
-        "\n",
-        "data['pass_reading'] = le.fit_transform(data['pass_reading'])\n",
-        "data['pass_reading'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "1    974\n",
-              "0     26\n",
-              "Name: pass_reading, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 206
-        }
+     },
+     "execution_count": 188,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['pass_reading'].value_counts(dropna = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 252
+    },
+    "colab_type": "code",
+    "id": "e_GAFUo0gZLF",
+    "outputId": "910f6067-933c-40a1-cd22-356a8c951005"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nZTAY0KtXr1o/jztlZ2ejW7duUKvVOH/+PE6dOlXreefn54cvv/wSxcXFAIDt27dj165d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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235852668>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "c_DXag2R_OIp",
-        "colab_type": "code",
-        "outputId": "43f88b31-2adc-4d2a-8f18-62068cdb4cca",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for pass_writing\n",
-        "\n",
-        "data['pass_writing'] = le.fit_transform(data['pass_writing'])\n",
-        "data['pass_writing'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "1    968\n",
-              "0     32\n",
-              "Name: pass_writing, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 207
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# creating a new column pass_math, this column will tell us whether the students are pass or fail\n",
+    "data['pass_writing'] = np.where(data['writing score']< passmarks, 'Fail', 'Pass')\n",
+    "data['pass_writing'].value_counts(dropna = False).plot.bar(color = 'blue', figsize = (5, 3))\n",
+    "\n",
+    "plt.title('Comparison of students passed or failed in writing')\n",
+    "plt.xlabel('status')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 539
+    },
+    "colab_type": "code",
+    "id": "W5qlGHHJhUDP",
+    "outputId": "5b0b792e-c0c5-48df-c4c7-adb00bf14b80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mzZopJSVFV155pdq0aaNvv/02+HGUAdZ+Yh1n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\nDnNzc7Vq1aqQa5w9e3bEstJPe5/33ntP7733nlauXKmsrCx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72PQ1dqx5Oxd7H/RZJ599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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235824c50>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "ItRtcvtDACEh",
-        "colab_type": "code",
-        "outputId": "0b6dd4e9-dfe5-4994-866a-742516b3c178",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for status\n",
-        "\n",
-        "data['status'] = le.fit_transform(data['status'])\n",
-        "data['status'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "1    949\n",
-              "0     51\n",
-              "Name: status, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 208
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# computing the total score for each student\n",
+    "\n",
+    "data['total_score'] = data['math score'] + data['reading score'] + data['writing score']\n",
+    "\n",
+    "data['total_score'].value_counts(normalize = True)\n",
+    "data['total_score'].value_counts(dropna = True).plot.bar(color = 'cyan', figsize = (40, 8))\n",
+    "\n",
+    "plt.title('comparison of total score of all the students')\n",
+    "plt.xlabel('total score scored by the students')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 621
+    },
+    "colab_type": "code",
+    "id": "0Jpy1FwCjJbG",
+    "outputId": "abaad66f-b7d7-4ae2-b6dc-cc0ef454e03e"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Lq23bto3rr79+revvueeeYpUEAABgE1G0lwEDAABAUwmrAAAAJEdYBQAAIDnC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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f323d406390>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "Ktnb-_N1AUbZ",
-        "colab_type": "code",
-        "outputId": "515e73d6-a1cb-4d2b-c27d-bc8a6b09871f",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# label encoding for grades\n",
-        "# we have to map values to each of the categories\n",
-        "\n",
-        "data['grades'] = data['grades'].replace('O', 0)\n",
-        "data['grades'] = data['grades'].replace('A', 1)\n",
-        "data['grades'] = data['grades'].replace('B', 2)\n",
-        "data['grades'] = data['grades'].replace('C', 3)\n",
-        "data['grades'] = data['grades'].replace('D', 4)\n",
-        "data['grades'] = data['grades'].replace('E', 5)\n",
-        "\n",
-        "data['race/ethnicity'].value_counts()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "3    319\n",
-              "4    262\n",
-              "2    190\n",
-              "5    140\n",
-              "1     89\n",
-              "Name: race/ethnicity, dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 209
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# computing percentage for each of the students\n",
+    "# importing math library to use ceil\n",
+    "from math import * \n",
+    "\n",
+    "data['percentage'] = data['total_score']/3\n",
+    "\n",
+    "for i in range(0, 1000):\n",
+    "  data['percentage'][i] = ceil(data['percentage'][i])\n",
+    "\n",
+    "data['percentage'].value_counts(normalize = True)\n",
+    "data['percentage'].value_counts(dropna = False).plot.bar(figsize = (16, 8), color = 'red')\n",
+    "\n",
+    "plt.title('Comparison of percentage scored by all the students')\n",
+    "plt.xlabel('percentage score')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 251
+    },
+    "colab_type": "code",
+    "id": "JP86Ohe7oBZA",
+    "outputId": "3de2b683-6ded-4e6e-f49f-aabcf13a27ea"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235864fd0>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "9fcJDzBNJZHT",
-        "colab_type": "code",
-        "outputId": "27ddb324-ce80-4535-b84e-566493b8c5df",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 36
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "data.shape"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(1000, 15)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 210
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# checking which student is fail overall\n",
+    "\n",
+    "data['status'] = data.apply(lambda x : 'Fail' if x['pass_math'] == 'Fail' or \n",
+    "                           x['pass_reading'] == 'Fail' or x['pass_writing'] == 'Fail'\n",
+    "                           else 'pass', axis = 1)\n",
+    "\n",
+    "data['status'].value_counts(dropna = False).plot.bar(color = 'gray', figsize = (3, 3))\n",
+    "plt.title('overall results')\n",
+    "plt.xlabel('status')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 148
+    },
+    "colab_type": "code",
+    "id": "3druUjNTuCpg",
+    "outputId": "45f553fe-63c1-4c31-c5af-8d87d337bb08"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "B    260\n",
+       "C    252\n",
+       "D    223\n",
+       "A    156\n",
+       "O     58\n",
+       "E     51\n",
+       "Name: grades, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "tfaXk6JBbiNZ",
-        "colab_type": "code",
-        "outputId": "f233545e-91ef-4f6a-f6ed-4b1886603b53",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 54
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# splitting the dependent and independent variables\n",
-        "\n",
-        "x = data.iloc[:,:14]\n",
-        "y = data.iloc[:,14]\n",
-        "\n",
-        "print(x.shape)\n",
-        "print(y.shape)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "(1000, 14)\n",
-            "(1000,)\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 193,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Assigning grades to the grades according to the following criteria :\n",
+    "# 0  - 40 marks : grade E\n",
+    "# 41 - 60 marks : grade D\n",
+    "# 60 - 70 marks : grade C\n",
+    "# 70 - 80 marks : grade B\n",
+    "# 80 - 90 marks : grade A\n",
+    "# 90 - 100 marks : grade O\n",
+    "\n",
+    "def getgrade(percentage, status):\n",
+    "  if status == 'Fail':\n",
+    "    return 'E'\n",
+    "  if(percentage >= 90):\n",
+    "    return 'O'\n",
+    "  if(percentage >= 80):\n",
+    "    return 'A'\n",
+    "  if(percentage >= 70):\n",
+    "    return 'B'\n",
+    "  if(percentage >= 60):\n",
+    "    return 'C'\n",
+    "  if(percentage >= 40):\n",
+    "    return 'D'\n",
+    "  else :\n",
+    "    return 'E'\n",
+    "\n",
+    "data['grades'] = data.apply(lambda x: getgrade(x['percentage'], x['status']), axis = 1 )\n",
+    "\n",
+    "data['grades'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 401
+    },
+    "colab_type": "code",
+    "id": "7eKwrPxG02aF",
+    "outputId": "3d6bc2f6-82d7-4519-d7f9-d1b76675e5be"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VTYVnZBQ+bYHz5lldScHI9daDp4vwqVSKO++8k3vvvXfsxO2nnnqKBx54gEcf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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f32359bd908>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "CLHjid6FY13N",
-        "colab_type": "code",
-        "outputId": "348964a1-6a7b-4096-8603-4a531fe873e5",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 92
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# splitting the dataset into training and test sets\n",
-        "\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 45)\n",
-        "\n",
-        "print(x_train.shape)\n",
-        "print(y_train.shape)\n",
-        "print(x_test.shape)\n",
-        "print(y_test.shape)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "(750, 14)\n",
-            "(750,)\n",
-            "(250, 14)\n",
-            "(250,)\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting a pie chart for the distribution of various grades amongst the students\n",
+    "\n",
+    "labels = ['Grade 0', 'Grade A', 'Grade B', 'Grade C', 'Grade D', 'Grade E']\n",
+    "sizes = [58, 156, 260, 252, 223, 51]\n",
+    "colors = ['yellow', 'gold', 'lightskyblue', 'lightcoral', 'pink', 'cyan']\n",
+    "explode = (0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001)\n",
+    "\n",
+    "patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90)\n",
+    "plt.legend(patches, labels)\n",
+    "plt.axis('equal')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 426
+    },
+    "colab_type": "code",
+    "id": "qSTFx72A27Hp",
+    "outputId": "5dd0f3f9-2cc3-41f9-af64-5dc96cb85394"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3235957e80>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "ciauOTq_b04w",
-        "colab_type": "code",
-        "outputId": "de29f2bf-2e9b-4f8d-c089-f00e4d676d01",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# importing the MinMaxScaler\n",
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "\n",
-        "# creating a scaler\n",
-        "mm = MinMaxScaler()\n",
-        "\n",
-        "# feeding the independent variable into the scaler\n",
-        "x_train = mm.fit_transform(x_train)\n",
-        "x_test = mm.transform(x_test)\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
-            "  return self.partial_fit(X, y)\n"
-          ],
-          "name": "stderr"
-        }
+     },
+     "execution_count": 195,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jdWIUZybjUEt7XlY6QAWopS3Vgu1pXGxP43nUbVlukeHk5ISUlP/1IiQlJcHR0REAcPLk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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f323588a8d0>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "t3e-fhJPKtFm",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "cell_type": "code",
-      "source": [
-        "# applying principal components analysis\n",
-        "\n",
-        "from sklearn.decomposition import PCA\n",
-        "\n",
-        "# creating a principal component analysis model\n",
-        "#pca = PCA(n_components = None)\n",
-        "\n",
-        "# feeding the independent variables to the PCA model\n",
-        "#x_train = pca.fit_transform(x_train)\n",
-        "#x_test = pca.transform(x_test)\n",
-        "\n",
-        "# visualising the principal components that will explain the highest share of variance\n",
-        "#explained_variance = pca.explained_variance_ratio_\n",
-        "#print(explained_variance)\n",
-        "\n",
-        "# creating a principal component analysis model\n",
-        "#pca = PCA(n_components = 2)\n",
-        "\n",
-        "# feeding the independent variables to the PCA model\n",
-        "#x_train = pca.fit_transform(x_train)\n",
-        "#x_test = pca.transform(x_test)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "metadata": {
-        "id": "7KJx14EzKk91",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "**Modelling**"
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# comparison parent's degree and their corresponding grades\n",
+    "\n",
+    "x = pd.crosstab(data['parental level of education'], data['grades'])\n",
+    "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = True, figsize = (9, 5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 418
+    },
+    "colab_type": "code",
+    "id": "Om9KcvtS6zoC",
+    "outputId": "7653c751-cecc-455a-ac69-26d4a231693e"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
+      "  stat_data = remove_na(group_data[hue_mask])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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BAB4eHoiKipKraSIiIpMlW3AvWrQIAQEB+t+zsrKg0WgAAHZ2dkhKSpKraSIiIpMly8Vp\n//nPf9ChQwc8//zzBv8uhJC0HltbK5ibqyuztJop5pGkxeztrSu12YSr2ZXbblpy5a6PKo7blDTs\nJ+lMpa+qgCzBfeTIEcTFxeHIkSO4f/8+NBoNrKyskJ2dDUtLSyQkJMDBwaHc9aSkZMpRHpUiKSm9\nktco7aryym638l8HVRS3KWOt3zT7SZ42jNNXZSnvnwRZgnvlypX6n9esWYMmTZrg9OnTiIiIwLvv\nvosDBw7A3d1djqaJiIhMWpXdgGX8+PH4z3/+A29vb6SmpsLLy6uqmiYiIjIZst+AZfz48fqfQ0ND\n5W6OiIjIKE5JvBags43dM7XDW54SEREpCIObiIhIQRjcRERECsLgJiIiUhAGNxERkYIwuImIiBSE\nwU1ERKQgDG4iIiIFYXATEREpCIObiIhIQRjcRERECsLgJiIiUhBJwR0QEFDisZEjR1Z6MURERFS2\nMmcH2717N7Zt24Zr165h2LBh+se1Wi0ePHgge3FUtX7WHpa03FsWvWSuhEwFtymiyldmcL/zzjvo\n1q0bJk+eXGx6TjMzMzRv3lz24oiIiKi4cufjdnR0xKZNm5Ceno7U1FT94+np6ahfv76sxREREVFx\n5QY3AMybNw87duxAgwYNIIQAAKhUKkRGRspaHBERERUnKbijo6Nx/Phx1KpVS+56iIiIqAySrip3\ncXFhaBMREVUDkkbcjRo1wrBhw9C5c2eo1Wr94xMnTpStMCIiIipJUnDXr18f3bt3l7sWIiIiKoek\n4B43bpzcdRAREZEEkoK7devWUKlU+t9VKhWsra0RHR0tW2FERERUkqTgvnz5sv7n3NxcREVF4cqV\nK7IVRURERIY99SQjGo0GPXv2xLFjx+Soh4iIiMogacQdHh5e7Pf79+8jISFBloKIiIiodJKC+9Sp\nU8V+r1u3LlauXClLQURERFQ6ScEdHBwMAEhNTYVKpYKNjY2sRREREZFhkoL7r7/+gp+fHzIyMiCE\nQP369bFkyRK4ubnJXR8REREVISm4ly1bhq+++gotW7YEAFy8eBHz58/Hli1bZC2OiIiIipMU3GZm\nZvrQBgq+11301qfGsi9GSFquXzNV+QsREREpgKSvg5mZmSEiIgKPHz/G48ePsW/fvmoR3ERERDWN\npBH3nDlzEBQUhOnTp8PMzAyurq6YN2+e3LURERHREySNuI8dOwaNRoOTJ08iOjoaQgj8+uuvctdG\nRERET5AU3Lt378batWv1v4eEhGDPnj2yFUVERESGSTpUrtPpip3TVqlUEELahWFUtlNpyZKW62xj\nJ3MlRESkBJKCu1evXhgyZAg6d+6M/Px8HD9+HG+++WaZz8nKykJAQACSk5ORk5ODcePGwdXVFX5+\nftDpdLC3t8eSJUug0Wgq5YUQERHVBJLn43755Zdx7tw5qFQqzJo1Cx06dCjzOb/88gvatm2Ljz/+\nGPHx8fjoo4/QqVMneHt7o2/fvli+fDnCw8Ph7e1dKS+EiIioJpAU3ADQpUsXdOnSRfKK+/Xrp//5\n3r17cHR0RHR0NObMmQMA8PDwQEhICIObiIjoKUgO7ooaMmQI7t+/j3Xr1uHDDz/UHxq3s7NDUlJS\nmc+1tbWCuXkZ3xePeSSpBnt7a8n1VjmJ57if6TVI7CeppNaScDW7UtdXJX1F0nCbksZIn1GK6yfA\nNPqqij6jZA/ubdu24dKlS5gyZUqxC9qkXNyWkpJZKTUkJaVXynqMqTq9Bum1WFTy+qSpTn1F0tT0\nbary12+a/SRPG1XfV+Wtq7xgl/R1sIo4f/487t27BwB46aWXoNPpUKdOHWRnF/x3k5CQAAcHB7ma\nJyIiMkmyBfeff/6JkJAQAMCDBw+QmZmJHj16ICIiAgBw4MABuLu7y9U8ERGRSZLtUPmQIUMQGBgI\nb29vZGdnY+bMmWjbti38/f0RFhYGJycneHl5ydU8ERGRSZItuC0tLbFs2bISj4eGhsrVJBERkcmT\n7VA5ERERVT4GNxERkYIwuImIiBRE9u9xExERVZaftYclLfeWRS+ZKzEejriJiIgUhMFNRESkIAxu\nIiIiBWFwExERKQiDm4iISEEY3ERERArC4CYiIlIQBjcREZGCMLiJiIgUhMFNRESkIAxuIiIiBWFw\nExERKQiDm4iISEEY3ERERArC4CYiIlIQBjcREZGCMLiJiIgUhMFNRESkIAxuIiIiBWFwExERKQiD\nm4iISEEY3ERERArC4CYiIlIQBjcREZGCMLiJiIgUhMFNRESkIObGLqAq/Kw9LGm5tyx6yVwJERHR\ns+GIm4iISEEY3ERERArC4CYiIlIQBjcREZGCyHpx2uLFi3Hq1Cnk5eVh9OjRcHNzg5+fH3Q6Hezt\n7bFkyRJoNBo5SyAiIjIpsgX38ePHce3aNYSFhSElJQXvvfceunfvDm9vb/Tt2xfLly9HeHg4vL29\n5SqBiIjI5Mh2qLxr165YtWoVAKBevXrIyspCdHQ0evfuDQDw8PBAVFSUXM0TERGZJNmCW61Ww8rK\nCgAQHh6O1157DVlZWfpD43Z2dkhKSpKreSIiIpMk+w1YDh06hPDwcISEhODNN9/UPy6EKPe5trZW\nMDdXl75AzKPKKFHP3t66UtcnSVqypMWeqTYj9VPC1exKXV+V9BVJw21KGon9VNnbrOL6CTCNbaqK\nPqNkDe7ffvsN69atw4YNG2BtbQ0rKytkZ2fD0tISCQkJcHBwKPP5KSmZcpZXQlJSepW29zSqU23S\na7Go5PVJU536iqSp6dtU5a/fNPvpaVTnbaq8dZUX7LIdKk9PT8fixYuxfv161K9fHwDQo0cPRERE\nAAAOHDgAd3d3uZonIiIySbKNuPft24eUlBRMmjRJ/9jChQsxffp0hIWFwcnJCV5eXnI1T0REZJJk\nC+7Bgwdj8ODBJR4PDQ2Vq0kiUijLI3ulLehkmv/scyIkehq8cxoREZGCMLiJiIgUhMFNRESkIAxu\nIiIiBWFwExERKQiDm4iISEEY3ERERArC4CYiIlIQBjcREZGCMLiJiIgUhMFNRESkIAxuIiIiBWFw\nExERKYhss4OZsoSr0iZed2yplbkSIqKKq107UtqCaR3kLUQBJPVVFfUTR9xEREQKwuAmIiJSEAY3\nERGRgvAcN1EF/Kw9LGm5tyx6yVwJEdU0HHETEREpCIObiIhIQRjcRERECsLgJiIiUhBenEZUxL4Y\nIWk5M2eZC3kGp9KSJS3X2cZO5kqISA4ccRMRESkIg5uIiEhBGNxEREQKwnPcRVge2SttQScveQsh\nIiIqBUfcRERECsLgJiIiUhAGNxERkYLwHDc9NV4LQERkPBxxExERKQiDm4iISEEY3ERERArC4CYi\nIlIQBjcREZGCyBrcV69ehaenJzZv3gwAuHfvHnx8fODt7Y2JEyciNzdXzuaJiIhMjmzBnZmZiaCg\nIHTv3l3/2OrVq+Ht7Y2tW7fCxcUF4eHhcjVPRERkkmQLbo1Gg2+//RYODg76x6Kjo9G7d28AgIeH\nB6KiouRqnoiIyCTJdgMWc3NzmJsXX31WVhY0Gg0AwM7ODklJSWWuw9bWCubm6tIXiHn0zHXKyd7e\nuvyF0pIrb12lqeb9VLt2pLQF0zpIWqw69dUz1VJR3Kakvza5+8pI21PC1exKbVeq6rRNVee+etbP\nBaPdOU0IUe4yKSmZVVCJfJKS0qvlukxddeqr6lTLk6pzbc+qsl9bdekr6XVYyFpHaapLPwHVu6/K\nq628YK/Sq8qtrKyQnV3w301CQkKxw+hERERUvioN7h49eiAiIgIAcODAAbi7u1dl80RERIon26Hy\n8+fPY9GiRYiPj4e5uTkiIiKwdOlSBAQEICwsDE5OTvDy4iQURERET0O24G7bti02bdpU4vHQ0FC5\nmiRSpISr0s6xObbUylwJESkB75xGRESkIAxuIiIiBWFwExERKQiDm4iISEEY3ERERArC4CYiIlIQ\nBjcREZGCMLiJiIgUxGiTjBDVBJZH9pa/kBPvIEhE0nHETUREpCAMbiIiIgVhcBMRESkIg5uIiEhB\nGNxEREQKwuAmIiJSEAY3ERGRgjC4iYiIFIQ3YCEik1O7dqS0BdM6yFsIkQw44iYiIlIQBjcREZGC\nMLiJiIgUhOe4ZSTpPBvPsRERVTpJE/wAipzkhyNuIiIiBWFwExERKQiDm4iISEF4jpuISCFM+bwt\nSccRNxERkYIwuImIiBSEwU1ERKQgDG4iIiIFYXATEREpCIObiIhIQRjcRERECsLgJiIiUhAGNxER\nkYJU+Z3TFixYgLNnz0KlUmHatGlo165dVZdARESkWFUa3CdOnEBsbCzCwsIQExODadOmISwsrCpL\nICIiUrQqPVQeFRUFT09PAECzZs2QlpaGx48fV2UJREREilalwf3gwQPY2trqf2/QoAGSkpKqsgQi\nIiJFUwkhRFU1NmPGDPTs2VM/6h46dCgWLFiAF154oapKICIiUrQqHXE7ODjgwYMH+t8TExNhb29f\nlSUQEREpWpUG9yuvvIKIiAgAwIULF+Dg4IC6detWZQlERESKVqVXlXfq1Alt2rTBkCFDoFKpMGvW\nrKpsnoiISPGq9Bw3ERERPRveOY2IiEhBGNxEREQKUi2De+fOnVi0aFGFnx8dHY0JEyZIWvbOnTsY\nOHCg5PWuWbPmqWr55ZdfEBAQ8FTPqUl8fHxw9epVrFmzBps3bzZ2ORVy9+5dnDt3TvLyPj4+T7X+\njIwM9OrV62nLqpaOHj2KrVu3lvr38vrSFPbB0j6f5s+fj7i4uFKf16tXL2RkZDxz+5Wxr3Xr1u2Z\n65BLaZ/p33zzDU6fPl3q8wo/i57Vs+YXUP57XeX3KicyNcePH0dmZibvuy/Ba6+9Vubfa3JfBgYG\nGrsEk/bJJ58Yu4RKU22D+86dO/j4449x//59fPDBBxg0aBB2796NzZs3w8zMDC1atEBQUBC0Wi0C\nAgIQHx+PWrVqYfHixQAKRimTJ0/GlStX0KdPH/j6+uL69euYO3cuVCoV6tSpg4ULFxZrMzo6GitW\nrIC5uTkcHR0RHByMPXv24OjRo0hMTMSKFSvQokUL3L17F1OmTIGZmRl0Oh2WLFmCJk2a6Ndz5coV\n+Pv7w8bGBs7OzvrHt2zZgp9++glmZmbw9PTERx99hPv372PixImwsLBAly5dcOrUKWzatAlvvvkm\nWrdujVdeeQUdO3YsUXe9evUMrq80hmp2cHDAzJkzERcXh9zcXEyYMAGvvvoqPD098f777+Pnn3+G\ni4sL2rRpo/952bJlSEhIQGBgILRaLdRqNebNmwcnJ6di7c2bNw/nzp2DWq3GnDlz0LJlSyxevBh/\n/fUXdDodhg0bBi8vL4O1rlixAn/++Sd0Oh2GDx+O/v374/LlywgICIC1tTXatm2LlJQULFy48Kn6\noKidO3fi5MmTSElJwbVr1/DZZ59hz549iImJwdKlS9G+fXsEBwfj3LlzyMnJwdChQ/HPf/4Tv//+\nO1auXAlLS0vY2dlh1qxZWLt2LczNzdG4cWO4uLiUeK8ePXqEKVOmwMrKCsOHD8eiRYug1WoxZcoU\nJCUlITc3F+PHjy8Wao8fP8b48eORk5ODzp076x//888/sXz5cn17QUFBUKlUmDJlCu7evYuOHTti\n//79OHr0KHx8fNCiRQsAwOeff45p06YhLS0NOp0O06dPh6urq359QME+5+zsjJycHMyYMQPt2rXD\nN998g4MHD8LMzAweHh4YM2aMwccM7TsqlarEvnns2DFcu3YN/v7+Jfq3d+/e5fZlu3btFLsPFmXo\n88nHxwczZsxAvXr1DNZTWP+vv/4KnU6HDRs2FPs67ZPb5tKlS5GYmIiAgADodDo4OTnpR4JXr17F\n6NGjcevWLQQGBuK1117Dvn378N1330GtVqNNmzaYPn060tPTERAQgEePHiEvLw/Tp09HmzZtJL3G\noqr680cIgVmzZuHvv/9GmzZtEBQUhICAAPTp0wddunTBhAkTkJ2djZ49e2L79u04fPgwAGD//v2Y\nP38+UlNT8fXXXxdb78WLFzFnzhxoNBpoNBqsWLECADB58mQ8fvwY1tbW+n0pMTER48ePx/Xr1zFy\n5EgMGjSo1H3EUB+US1RDO3bsEP379xe5ubni4cOHwt3dXeTn54tt27aJtLQ0IYQQ3t7e4vLly2L7\n9u1iwYIFQggh9uzZI7Zs2SLnYDWHAAAU00lEQVSOHz8uevbsKTIzM8Xjx49Ft27dhBBCjBgxQty8\neVMIIcTmzZvFV199JeLi4sR7770nhBCiT58+4u7du0IIIebMmSPCw8PFjh07xPvvvy/y8/P19YWE\nhIi1a9cKIYQ4f/68OH36dLH6J0yYIA4ePCiEEGLmzJnC399f3L59WwwfPlzk5+eL/Px8MXjwYBEf\nHy+Cg4NFaGioEEKIRYsWieHDhwshhHB1dRVXr14tte7S1lcaQzXv2rVLzJw5UwghxP3798Wbb74p\nhBDCw8ND/PbbbyI/P1+89tprYt++fUIIIXr27CnS0tLE1KlTxbFjx4QQQhw5ckQEBgYWa+vYsWPi\n008/FUIIceLECbFixQpx4sQJMWrUKCGEEBkZGaJ3794iPT1dDB8+XFy5ckWsXr1abNq0SZw8eVJ8\n8cUXQgghcnJyRL9+/URWVpbw9fUVBw4c0PdvWX0qxY4dO8SQIUNEfn6+CAsLE/379xd5eXli+/bt\nYt68eSI7O1t8//33QgghsrKyxCuvvCKEEGL06NHi5MmTQgghIiIiRGJior720t6ruLg40b59e/Hw\n4UN9++fPnxcjRowQQgiRlpYmdu/eXay+zZs3i/nz5wshhNi7d6/w8PAQQgjx7rvvipSUFCFEwfby\n448/isjISDFmzBghhBCHDx8WrVq1EkIIMXz4cLF161YhhBBr164V27dvF0IIce3aNfGvf/2r2Ppu\n3Lghxo4dK3788Ufxxx9/CF9fXyGEEN26dRNarVbk5+eLLVu2lPqYoX3H0L65Y8cOsXDhwlL7t7y+\nLKTEfbBQaZ9PhftCafV4eHiIw4cPCyGE+Oyzz/Svr5ChbfOLL74Qhw4d0q/rzJkzYvXq1WL8+PFC\nCCGOHj0qxo4dKx4/fiw8PT3F48eP9euKiooSa9asEevXrxdCCHHu3DkxbNgwIYQQL7/8crmvs6iq\n/PyJi4sTHTp0EImJiUKn0wl3d3eRlpYm/P39xeHDh8UPP/wggoKChBAF72XhvjV8+HD9trd06VL9\ne1AoKChI7Nq1SwghxB9//CGuX78uli9frt+OQ0NDxcGDB8WOHTvEP//5T5GXlydiYmLEO++8I4Qw\nvI+U1QeF74Uh1XbE3alTJ1hYWMDW1hZ169ZFSkoKbGxsMG7cOABATEwMUlNTceHCBXTv3h0A8Pbb\nbwMoGDm3bt0atWvXBlDw3xcAnDt3DjNmzAAA5Obmws3NTd9eamoqVCoVGjduDKDgHM7JkyfRunVr\nuLm5QaVS6Zd95ZVX4Ovri/T0dPTp0wcdO3YsVntMTAw6deqkX8/Ro0fx999/IzY2FiNGjABQ8B93\nfHw8YmJi0K9fPwAF5zX+/vtvAEDt2rX1oyVDdZe2vif/8yyr5j179ujPVTk6OkKj0SA1NRUA0K5d\nO6hUKtjZ2aF169YACu4tn56ejtOnT+PmzZv4+uuvodPp0KBBg2JtXbhwQf/6u3btiq5duyI0NBRd\nu3YFAFhZWaF58+aIjY0tUedff/2Fs2fP6s8D5+fnIykpqVif9urVC1FRUU/dB09q27YtVCoV7O3t\n0apVK6jVajRs2BB//fUXatWqhbS0NAwZMgQWFhZISUkBALz11luYNWsWBgwYgLfffrvEnf9K28ae\nf/75Yvfpf/HFF5GRkYEpU6bgjTfe0G+7hWJiYvT99fLLLwMouNd/bGwsxo8fDwDIzMyEra0tEhIS\n9H3Ts2dPmJv/b7cuPOR8+vRpPHz4ELt37wYAZGVlFVtfXl4eYmJicPnyZdja2sLKygoA0KdPH3z4\n4Yfo378/3nnnHYOPlbbv5OXlldg3d+7cCQCl9q+UvgSUuQ8WZejzqWjthuoBoD/64ujoiPT09GLP\nM7RtXrx4UX8I3s/PD0DBdQaFfVO4nlu3bsHFxQV16tQBULDNXbp0CefPn8fYsWMBAG5ubgb3WSmq\n8vMHAJydnfX7ZsOGDYv1VUxMjH6f6t27NzZu3GiwfwtrKdS7d2/Mnj0bt27dQr9+/dCsWTNcvHgR\nEydOBAD861//AlCwjbdv3x5qtVrfv6XtI4U/G+qDslTb4C4alEDBB/jcuXPx448/wt7eHqNHjwYA\nqNVq5Ofnl3h+0Q+vQrVr18YPP/xQbN137tzRt1d0B9JqtfrlLCwsiq2nZcuW+PHHH3Hs2DEsX74c\n//jHP4od9hVC6J9bWJuFhQVef/11zJ07t9i61q9fr1+2aF1F2zRU98GDBw2urzSGai6stVBubi7M\nzAquV1Sr1frHi/4shICFhQVWrVoFBwcHg20Zek+efD+1Wq2+raI0Gg0GDRqkf3+LtvtkP5XWp1IV\n3UaK/iyEwIkTJ3D8+HFs2rQJFhYW+mDw8vKCu7s7Dh06hLFjx2LVqlXF1lnaNvbkNlS7dm1s374d\nf/31F3bt2oVffvkFwcHBxWoo7J+i25CDg4P+sGmhb775Rv8ePdnPhe1aWFhgxowZxQIuLS1Nv761\na9eic+fOmDx5Mv7++2/9Kac5c+YgJiYG+/fvh4+PD/7v//6vxGMbN240uO+Utm8CKLV/y+vLQkrc\nB4sy9PlkqPYnX/uT+2JRhrZNtVpdYjlD7Rv6/KtVq1aJx0t7P8tTlZ8/Tz7nyXaK7ltP07/du3dH\neHi4/mJHPz8/yflTWr4IIUrtg7JUy6vKAeDMmTPQ6XR4+PAhsrKyoFaroVarYW9vj3v37uH8+fPQ\narVwc3PD8ePHARRcPbpu3bpS1+nq6oqjR48CAPbu3YuoqCj932xsbKBSqXD37l0ABR8sbdu2Nbie\nvXv34tq1a/D09MTEiRNx/vz5Yn9/4YUX9I9FR0cDANq0aYPo6GhkZWVBCIF58+YhOzsbzs7O+mUL\na5NSd2nrK42hmt3c3PT13bt3D2ZmZqhXr16p6yjUvn17HDp0CEDBVK0//fRTsb8XXW/heaG2bdvq\nH8vIyMDt27fh4uJSYt3t2rXDL7/8gvz8fOTk5CAoKAgADPbT0/bB00hJSUGjRo1gYWGByMhI6HQ6\n5Obm4ssvv4S5uTkGDx6Mfv36ISYmBiqVCnl5eQDK3saKunDhAn766Sd06dIFs2fPRkxMTLG/G9qG\nbGxsAADXr18HAGzatAmXL18u1je///47dDpdifaKvmfXr19HaGhosfWlpKQgPj4ely9fxqFDh6DV\napGeno61a9eiWbNm8PX1hY2NDRISEko8ZmZmZnDfKWvfLK1/pfalEvdBqaTUY4ihbbNt27b692DV\nqlX4448/DD63adOmiI2N1U+zXPQ9LOy/M2fO6I9APK2q/PwpT0X7d/PmzUhNTcU777yDDz74AJcu\nXSrWv9u2bcOuXbsMPre0fKloH1TbEfeLL76IiRMnIjY2FpMmTYKtrS1eeeUV/OMf/4CrqytGjRqF\n4OBg7Nq1C3/88QeGDx8Oc3NzLFq0CLdu3TK4zsDAQMyYMQPffvstatWqhWXLlhWbDzwoKAhffPEF\nzM3N8fzzz+Ptt9/WH1osqmnTppg1axasrKygVqsxffr0Yn8fO3Yspk6dih9++AHPP/88tFotnJyc\nMGLECAwbNgxqtRqenp6wtLTEiBEjMGnSJERERKB9+/YG/9syVHf9+vUNrq80hmp2cXHBiRMn4OPj\nA61WK3nk4Ovri2nTpmHv3r1QqVTFRopAweHxyMhIeHt7AwBmzZqFVq1aoW3bthg2bBjy8vLwxRdf\n6A/HFtWpUyd069YNgwcPhhBCv46xY8di+vTp+P7779G8eXOkp6eX2qeVoUePHvj2228xfPhweHp6\n4vXXX8fs2bPRtWtXfPjhh6hXrx7q1auHDz/8EHXq1IG/vz8aNGhQ7jZW6LnnnsPy5csRFhYGtVqN\nkSNHFvu7l5cXPv30U3zwwQfFLk6bP38+pk6dqh99Dx48GC+88AJ27NiBoUOH4uWXX0b9+vVLtDd8\n+HBMnToV3t7eyM/P1x8+LVxfbm4u4uLikJKSAh8fH+zZswcHDhxASkoKBg0aBCsrK3Ts2BFNmjQp\n8Vj9+vUN7jv5+fkl9s1jx46V2b9vv/12mX1ZSIn7oFRS6jHEycmpxLbp5uaGqVOnYuvWrWjcuDF8\nfX1x6tSpEs+1srKCn58fRo0aBTMzM3Tu3BldunSBq6srpk2bhhEjRkAIgZkzZ1boNVXl50953nvv\nPYwbNw4+Pj7o0aOH5P51dnbGxIkTYW1tDY1Gg+DgYNSqVQt+fn7w8fFBnTp1sHTpUhw4cMDg8w3t\nIwAq1Ae85amRXbt2DY8ePULnzp2xZ88eREdH60eZ9D9nzpyBpaUlXF1dsX79egghMGbMGGOXVS2k\npqYiOjoaffr0QUJCAj744AP8/PPPxi5LMarbPljd6jE18fHxuHHjBtzd3XH69GmsWbMGISEhxi7r\nqVTbEXdNUadOHcycORMqlQpmZmZP/d9jTaHRaBAYGAhLS0tYWloWG33VdHXq1MH+/fuxceNG5Ofn\nY+rUqcYuSVGq2z5Y3eoxNdbW1vjuu+/w5ZdfAlDm9+c54iYiIlKQantxGhEREZXE4CYiIlIQBjcR\nEZGCMLiJqolff/213Lsm+fj4lPgubnR0NIYOHVqptbRq1Ur/fery6HQ6DB06FIMHD4ZWq33qtlas\nWPHUM36V58cffwQAJCUlSZ4pkEgpGNxE1cR3332HtLQ0Y5fx1BITExEbG4uwsLASd4gzBp1Oh6++\n+goAYG9vj9WrVxu5IqLKxa+DERkQHR2NlStXwsnJCfHx8bC2tsaKFStQt25drFq1Sn8Xr0aNGmHJ\nkiWwsLBAp06dMGjQIOTn52P69OnYtGkT9u/fD51OhxdffBGzZs3CgwcPMHbsWLz66qs4d+4cMjIy\nsH79ekRGRuLPP//E5MmTERwcjJs3b2LDhg3QaDTQ6XRYvHgxnnvuuXLrvnv3LubMmYOsrCxkZmbi\n888/h729PXx9fREREQGg4A5N77//Po4cOYKIiAhs3rwZQgg0aNAA8+bNK3ZP9aIyMzMxY8YM3L9/\nH3l5eXj33Xfh7e2NqVOn4tGjR/rbn2o0Gv1zDPWBpaUlVqxYgV9++QWNGzdG7dq10axZMwAFI/0L\nFy7A3NwcO3fuxB9//IGlS5fi7NmzWLBgASwsLGBjY4NFixbBzMwM/v7+SE1NRUZGBt566y188skn\nmDZtGuLj4/HRRx9h7ty58Pb2xtGjR/HgwQMEBgYiMzMTubm5GDVqFN544w2sWbMGqampuH//PmJj\nY9GtWzf9fcmJqqVSpx8hqsGOHz8u3NzcxP3794UQQkyePFl8//33QqvVivXr1wudTieEEOKjjz7S\nz9jUqlUr8fvvvwshhDh79qzw8fHRzyo3f/588cMPP4i4uDjx0ksv6WedCggI0M9C5OHhIW7duiWE\nECI8PFw/09S6devEwoULhRAFMxgVzoxUtNYhQ4YIIYT4+OOPRVRUlBBCiMTEROHh4SG0Wq145513\nxKVLl4QQQmzcuFEsXLhQ3L17VwwYMEDk5OQIIYT47rvvRHBwsBBCiJYtWwqtVlusnXXr1onZs2cL\nIQpm9PLw8BC3b98WcXFxwt3dvUQfltYHN27cEB4eHiInJ0dotVrh5eUlVq9eXaLdHTt26GeKe+ON\nN8SVK1eEEAWzMO3Zs0fcvn1bP1tTTk6O6NSpk0hPTy9WT9GfZ8yYIb799lshhBAPHjwQPXr0EOnp\n6WL16tViyJAhIi8vT2RlZYkOHTqI1NTUEq+HqLrgiJuoFM2bN4ejoyOAgluxXrp0Cebm5jAzM4O3\ntzfMzc1x48YN/cxWQgj9rEvR0dG4ffu2fuaozMxM/cQDtra2+ns+Ozk5GTyv3bBhQ/j7+0MIgaSk\nJIOTcBgSHR2NjIwM/c0lzM3NkZycjAEDBiAiIgKurq7Yt28fgoKCcPr0aSQlJelvt5qbm1vmqP7s\n2bMYOHAgAMDS0hJt27bFhQsXSr2nf2l9cPXqVbRp00Y/Mu/SpUuZr+nhw4d49OgRWrZsCeB/szBl\nZmbi1KlT2LZtGywsLJCTk1PmNQJnz57VXwtgZ2cHR0dH3Lx5E0DBrFCF8yHY2toiLS1Nfy93ouqG\nwU1UCvHEjEIqlQqnTp3Cjh07sGPHDlhZWZW48KnwHK9Go0GvXr1K3Nv5zp07Zc5cBBTMHDRp0iTs\n2rULTZs2xebNm0tMolEajUaDNWvWlJjqsH///hg1ahQGDhyInJwcvPTSS4iPj0e7du2wfv16Set+\nciYlUWQWq9JqMdQHP//8c7HnlTbjVOGFbk/OrFTo+++/R25uLv79739DpVLpp0eUWn/Rx8p7T4iq\nE16cRlSKGzduIDExEQBw6tQptGrVCsnJyWjSpAmsrKwQHx+PM2fOIDc3t8RzO3XqhKNHjyIjIwMA\nsGXLFpw+fbrM9gpnxsrIyICZmRmaNGmCnJwcREZGGmzDkM6dO2P//v0ACkaq8+fPB1BwLt7W1hYb\nN27Uz6vt5uaGc+fOISkpCQCwf/9+/axLhrRv3x6//fYbgILR7oULF9CmTZtSly+tDwrnMc7NzYVW\nq8WJEyf0z6lbty7u3bsH4H+zetna2qJ+/fo4d+4cACAkJARbtmxBcnIymjVrBpVKhcjISGRnZ+un\nRTR0RXzR+hMSEpCYmIgXXnihvC4lqnY44iYqRfPmzbF8+XLExsbCxsYGXl5eEEIgJCQEQ4cORYsW\nLTB+/Hh8+eWXJUZ7bm5uGDZsGHx8fFCrVi04ODhg4MCBSE5OLrW9V199FWPGjMGiRYvQv39/DBo0\nCE5OThg5ciT8/Pz0gVyWwMBAzJw5E3v37kVubi7Gjh2r/9uAAQMwd+5cfTg7OjoiMDAQo0ePRu3a\ntWFpaYlFixaVum4fHx/MmDEDw4YNQ25uLsaNG4fnnntOP6f9k0rrg9q1a8PT0xPvv/8+nJyc8NJL\nL+mf88knn2DkyJFwcXGBq6urPsSXLFmCBQsWwNzcHNbW1liyZAni4uLw+eef4/fff0fv3r0xYMAA\nTJ48Gdu3b0fDhg0xcODAYq9nwoQJCAwMhI+Pj37K2Dp16pTbp0TVDe9VTmRA4VXl//73v41dChFR\nMTxUTkREpCAccRMRESkIR9xEREQKwuAmIiJSEAY3ERGRgjC4iYiIFITBTUREpCAMbiIiIgX5f63s\n9BiOiXFcAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235b85b70>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "P6fR-dCXN4SK",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "**Logistic Regression**"
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# for better visualization we will plot it again using seaborn\n",
+    "\n",
+    "sns.countplot(x = data['parental level of education'], data = data, hue = data['grades'], palette = 'pastel')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 418
+    },
+    "colab_type": "code",
+    "id": "V6TBXagn5F7H",
+    "outputId": "de6f5eff-5403-4084-9b95-4085e19482d1"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
+      "  stat_data = remove_na(group_data[hue_mask])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AANUWV3YDAMBgFDkAAAajyAEAMBhFDgCAwShyAAAMRpEDAGAwihwAAINR5AAAGIwiBwDA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\nAQAw2P8A+tnNgQIRYB0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3235bbf7b8>"
       ]
-    },
-    {
-      "metadata": {
-        "id": "EVJHYo1IhJ2k",
-        "colab_type": "code",
-        "outputId": "eed70f37-79b7-4aa6-8041-66e6c86cdd9f",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 149
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "from sklearn.linear_model import  LogisticRegression\n",
-        "\n",
-        "# creating a model\n",
-        "model = LogisticRegression()\n",
-        "\n",
-        "# feeding the training data to the model\n",
-        "model.fit(x_train, y_train)\n",
-        "\n",
-        "# predicting the test set results\n",
-        "y_pred = model.predict(x_test)\n",
-        "\n",
-        "# calculating the classification accuracies\n",
-        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
-        "print(\"Testing Accuracy :\", model.score(x_test, y_test))\n"
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# comparing the distribution of grades among males and females\n",
+    "\n",
+    "sns.countplot(x = data['grades'], data = data, hue = data['gender'], palette = 'cubehelix')\n",
+    "#sns.palplot(sns.dark_palette('purple'))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 348
+    },
+    "colab_type": "code",
+    "id": "fSEMQE4W87U4",
+    "outputId": "2d415ca5-5dd8-4c3f-d6ab-050405826c33"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race/ethnicity</th>\n",
+       "      <th>parental level of education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test preparation course</th>\n",
+       "      <th>math score</th>\n",
+       "      <th>reading score</th>\n",
+       "      <th>writing score</th>\n",
+       "      <th>pass_math</th>\n",
+       "      <th>pass_reading</th>\n",
+       "      <th>pass_writing</th>\n",
+       "      <th>total_score</th>\n",
+       "      <th>percentage</th>\n",
+       "      <th>status</th>\n",
+       "      <th>grades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>218</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>pass</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>247</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>pass</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>278</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>pass</td>\n",
+       "      <td>O</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>148</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>pass</td>\n",
+       "      <td>D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>Pass</td>\n",
+       "      <td>229</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>pass</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Training Accuracy : 0.7786666666666666\n",
-            "Testing Accuracy : 0.768\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
-            "  FutureWarning)\n",
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
-            "  \"this warning.\", FutureWarning)\n"
-          ],
-          "name": "stderr"
-        }
+      "text/plain": [
+       "   gender race/ethnicity parental level of education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test preparation course  math score  reading score  writing score pass_math  \\\n",
+       "0                    none          72             72             74      Pass   \n",
+       "1               completed          69             90             88      Pass   \n",
+       "2                    none          90             95             93      Pass   \n",
+       "3                    none          47             57             44      Pass   \n",
+       "4                    none          76             78             75      Pass   \n",
+       "\n",
+       "  pass_reading pass_writing  total_score  percentage status grades  \n",
+       "0         Pass         Pass          218        73.0   pass      B  \n",
+       "1         Pass         Pass          247        83.0   pass      A  \n",
+       "2         Pass         Pass          278        93.0   pass      O  \n",
+       "3         Pass         Pass          148        50.0   pass      D  \n",
+       "4         Pass         Pass          229        77.0   pass      B  "
       ]
-    },
-    {
-      "metadata": {
-        "id": "3aK-UV7jRE2D",
-        "colab_type": "code",
-        "outputId": "5b67c319-18a8-4268-d78d-232a81e159b1",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# printing the confusion matrix\n",
-        "\n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "\n",
-        "# creating a confusion matrix\n",
-        "cm = confusion_matrix(y_test, y_pred)\n",
-        "\n",
-        "# printing the confusion matrix\n",
-        "print(cm)"
+     },
+     "execution_count": 198,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "colab_type": "code",
+    "id": "du9CiE7c8ER7",
+    "outputId": "d2ffd396-abd0-42fb-d0a2-548a78401a24"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>math score</th>\n",
+       "      <th>reading score</th>\n",
+       "      <th>writing score</th>\n",
+       "      <th>total_score</th>\n",
+       "      <th>percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.00000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>66.08900</td>\n",
+       "      <td>69.169000</td>\n",
+       "      <td>68.054000</td>\n",
+       "      <td>203.312000</td>\n",
+       "      <td>68.105000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>15.16308</td>\n",
+       "      <td>14.600192</td>\n",
+       "      <td>15.195657</td>\n",
+       "      <td>42.771978</td>\n",
+       "      <td>14.258095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>57.00000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>57.750000</td>\n",
+       "      <td>175.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>66.00000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>77.00000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>233.000000</td>\n",
+       "      <td>78.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>100.00000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>300.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[[ 2  9  0  0  0  0]\n",
-            " [ 0 15 17  0  0  0]\n",
-            " [ 0  2 56 14  0  0]\n",
-            " [ 0  0  6 44  8  0]\n",
-            " [ 0  0  0  2 59  0]\n",
-            " [ 0  0  0  0  0 16]]\n"
-          ],
-          "name": "stdout"
-        }
+      "text/plain": [
+       "       math score  reading score  writing score  total_score   percentage\n",
+       "count  1000.00000    1000.000000    1000.000000  1000.000000  1000.000000\n",
+       "mean     66.08900      69.169000      68.054000   203.312000    68.105000\n",
+       "std      15.16308      14.600192      15.195657    42.771978    14.258095\n",
+       "min       0.00000      17.000000      10.000000    27.000000     9.000000\n",
+       "25%      57.00000      59.000000      57.750000   175.000000    59.000000\n",
+       "50%      66.00000      70.000000      69.000000   205.000000    69.000000\n",
+       "75%      77.00000      79.000000      79.000000   233.000000    78.000000\n",
+       "max     100.00000     100.000000     100.000000   300.000000   100.000000"
       ]
-    },
-    {
-      "metadata": {
-        "id": "ujNMPnsXKqfT",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "**Random Forest**"
+     },
+     "execution_count": 199,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "TGVw87JkX0rJ",
+    "outputId": "4947d499-85d4-4297-edda-959560307770"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    642\n",
+       "0    358\n",
+       "Name: test preparation course, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "st1bVJlgPvr2",
-        "colab_type": "code",
-        "outputId": "1288b059-af96-4f25-ce18-c5a6ba6da90e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 112
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "from sklearn.ensemble import RandomForestClassifier\n",
-        "\n",
-        "# creating a model\n",
-        "model = RandomForestClassifier()\n",
-        "\n",
-        "# feeding the training data to the model\n",
-        "model.fit(x_train, y_train)\n",
-        "\n",
-        "# predicting the x-test results\n",
-        "y_pred = model.predict(x_test)\n",
-        "\n",
-        "# calculating the accuracies\n",
-        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
-        "print(\"Testing Accuracy :\", model.score(x_test, y_test))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Training Accuracy : 1.0\n",
-            "Testing Accuracy : 0.996\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
-            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
-          ],
-          "name": "stderr"
-        }
+     },
+     "execution_count": 200,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "# creating an encoder\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# label encoding for test preparation course\n",
+    "data['test preparation course'] = le.fit_transform(data['test preparation course'])\n",
+    "data['test preparation course'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "23lY5N_PY-U-",
+    "outputId": "67fbccbd-c127-4047-ffd0-3d9dcfb023bd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    645\n",
+       "0    355\n",
+       "Name: lunch, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "17rfXUPzQlvk",
-        "colab_type": "code",
-        "outputId": "f4bec713-a072-4588-8cca-e6069a3bdbd3",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# printing the confusion matrix\n",
-        "\n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "\n",
-        "# creating a confusion matrix\n",
-        "cm = confusion_matrix(y_test, y_pred)\n",
-        "\n",
-        "# printing the confusion matrix\n",
-        "print(cm)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[[11  0  0  0  0  0]\n",
-            " [ 0 32  0  0  0  0]\n",
-            " [ 0  0 72  0  0  0]\n",
-            " [ 0  0  0 58  0  0]\n",
-            " [ 0  0  0  0 61  0]\n",
-            " [ 0  0  0  0  1 15]]\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 201,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for lunch\n",
+    "\n",
+    "data['lunch'] = le.fit_transform(data['lunch'])\n",
+    "data['lunch'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 129
+    },
+    "colab_type": "code",
+    "id": "f_L3U8clZMuO",
+    "outputId": "b72d7ce0-52f7-43cd-fd00-812ed7ced612"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    319\n",
+       "4    262\n",
+       "2    190\n",
+       "5    140\n",
+       "1     89\n",
+       "Name: race/ethnicity, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "-h5wuSOfVHk4",
-        "colab_type": "code",
-        "outputId": "4d0561c3-4dfe-4428-dc56-e48a55048cbd",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# k fold cross validation \n",
-        "\n",
-        "from sklearn.model_selection import cross_val_score\n",
-        "\n",
-        "# computing accuracies for 20 different model's accuracy\n",
-        "accuracies = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 20)\n",
-        "print(accuracies)\n",
-        "\n",
-        "# computing mean of accuaracies obtained by all the models\n",
-        "print(\"mean accuracy :\", accuracies.mean())\n",
-        "\n",
-        "# computing the standard variance of the models\n",
-        "print(\"mean standard variance :\", accuracies.std())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-            "mean accuracy : 1.0\n",
-            "mean standard variance : 0.0\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 202,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for race/ethnicity\n",
+    "# we have to map values to each of the categories\n",
+    "\n",
+    "data['race/ethnicity'] = data['race/ethnicity'].replace('group A', 1)\n",
+    "data['race/ethnicity'] = data['race/ethnicity'].replace('group B', 2)\n",
+    "data['race/ethnicity'] = data['race/ethnicity'].replace('group C', 3)\n",
+    "data['race/ethnicity'] = data['race/ethnicity'].replace('group D', 4)\n",
+    "data['race/ethnicity'] = data['race/ethnicity'].replace('group E', 5)\n",
+    "\n",
+    "data['race/ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 148
+    },
+    "colab_type": "code",
+    "id": "PpowtC99ZdPM",
+    "outputId": "eda1d827-15ab-4dd8-c3ed-85f56d55d2ff"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    226\n",
+       "0    222\n",
+       "2    196\n",
+       "5    179\n",
+       "1    118\n",
+       "3     59\n",
+       "Name: parental level of education, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "NLUnNmY_RLmN",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "**Decision Forest**"
+     },
+     "execution_count": 203,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for parental level of education\n",
+    "\n",
+    "data['parental level of education'] = le.fit_transform(data['parental level of education'])\n",
+    "data['parental level of education'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "geX7js-LbEgB",
+    "outputId": "be192129-00b1-49b5-e66e-e4e2de1b446b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    518\n",
+       "1    482\n",
+       "Name: gender, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "cZvzebMFQcZO",
-        "colab_type": "code",
-        "outputId": "ec3c6d7b-90a8-4b96-dec1-2746eaa41308",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 54
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "from sklearn.tree import DecisionTreeClassifier\n",
-        "\n",
-        "# creating a model\n",
-        "model = DecisionTreeClassifier()\n",
-        "\n",
-        "# feeding the training data to the model\n",
-        "model.fit(x_train, y_train)\n",
-        "\n",
-        "# predicting the x-test results\n",
-        "y_pred = model.predict(x_test)\n",
-        "\n",
-        "# calculating the accuracies\n",
-        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
-        "print(\"Testing Accuracy :\", model.score(x_test, y_test))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Training Accuracy : 1.0\n",
-            "Testing Accuracy : 1.0\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 204,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for gender\n",
+    "\n",
+    "data['gender'] = le.fit_transform(data['gender'])\n",
+    "data['gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "q_-OubIg-dOv",
+    "outputId": "8b9b2f10-decc-4900-9544-be7b790fdbd9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    960\n",
+       "0     40\n",
+       "Name: pass_math, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "MYo6K7puRj6_",
-        "colab_type": "code",
-        "outputId": "263bcb1c-4de3-49d8-c32c-cdfb6c2e7eb4",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# printing the confusion matrix\n",
-        "\n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "\n",
-        "# creating a confusion matrix\n",
-        "cm = confusion_matrix(y_test, y_pred)\n",
-        "\n",
-        "# printing the confusion matrix\n",
-        "print(cm)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[[11  0  0  0  0  0]\n",
-            " [ 0 32  0  0  0  0]\n",
-            " [ 0  0 72  0  0  0]\n",
-            " [ 0  0  0 58  0  0]\n",
-            " [ 0  0  0  0 61  0]\n",
-            " [ 0  0  0  0  0 16]]\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 205,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for pass_math\n",
+    "\n",
+    "data['pass_math'] = le.fit_transform(data['pass_math'])\n",
+    "data['pass_math'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "SEdTTzYK-y51",
+    "outputId": "0b1a7ae1-3d30-4434-ac6e-28674d542197"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    974\n",
+       "0     26\n",
+       "Name: pass_reading, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "U0hMQ5FiSzoh",
-        "colab_type": "code",
-        "outputId": "779752b9-981d-4d78-b4e4-a8780f019422",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 73
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# k fold cross validation \n",
-        "\n",
-        "from sklearn.model_selection import cross_val_score\n",
-        "\n",
-        "# computing accuracies for 20 different model's accuracy\n",
-        "accuracies = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 20)\n",
-        "print(accuracies)\n",
-        "\n",
-        "# computing mean of accuaracies obtained by all the models\n",
-        "print(\"mean accuracy :\", accuracies.mean())\n",
-        "\n",
-        "# computing the standard variance of the models\n",
-        "print(\"mean standard variance :\", accuracies.std())\n",
-        "   "
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-            "mean accuracy : 1.0\n",
-            "mean standard variance : 0.0\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 206,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for pass_reading\n",
+    "\n",
+    "data['pass_reading'] = le.fit_transform(data['pass_reading'])\n",
+    "data['pass_reading'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "c_DXag2R_OIp",
+    "outputId": "43f88b31-2adc-4d2a-8f18-62068cdb4cca"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    968\n",
+       "0     32\n",
+       "Name: pass_writing, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "-qX9Y2FWQkza",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "**Support Vector Machine**"
+     },
+     "execution_count": 207,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for pass_writing\n",
+    "\n",
+    "data['pass_writing'] = le.fit_transform(data['pass_writing'])\n",
+    "data['pass_writing'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "ItRtcvtDACEh",
+    "outputId": "0b6dd4e9-dfe5-4994-866a-742516b3c178"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    949\n",
+       "0     51\n",
+       "Name: status, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "kgbwZfCAVeS6",
-        "colab_type": "code",
-        "outputId": "55dcaf90-0f11-4cb2-81ff-6bad274da5bd",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 112
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "from sklearn.svm import SVC\n",
-        "\n",
-        "# creating a model\n",
-        "model = SVC()\n",
-        "\n",
-        "# feeding the training data to the model\n",
-        "model.fit(x_train, y_train)\n",
-        "\n",
-        "# predicting the x-test results\n",
-        "y_pred = model.predict(x_test)\n",
-        "\n",
-        "# calculating the accuracies\n",
-        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
-        "print(\"Testing Accuracy :\", model.score(x_test, y_test))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Training Accuracy : 0.732\n",
-            "Testing Accuracy : 0.76\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
-            "  \"avoid this warning.\", FutureWarning)\n"
-          ],
-          "name": "stderr"
-        }
+     },
+     "execution_count": 208,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for status\n",
+    "\n",
+    "data['status'] = le.fit_transform(data['status'])\n",
+    "data['status'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 129
+    },
+    "colab_type": "code",
+    "id": "Ktnb-_N1AUbZ",
+    "outputId": "515e73d6-a1cb-4d2b-c27d-bc8a6b09871f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    319\n",
+       "4    262\n",
+       "2    190\n",
+       "5    140\n",
+       "1     89\n",
+       "Name: race/ethnicity, dtype: int64"
       ]
-    },
-    {
-      "metadata": {
-        "id": "QNhhAW-PVq6P",
-        "colab_type": "code",
-        "outputId": "94861392-0397-4d1f-ba53-4b74b8780d1c",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 129
-        }
-      },
-      "cell_type": "code",
-      "source": [
-        "# printing the confusion matrix\n",
-        "\n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "\n",
-        "# creating a confusion matrix\n",
-        "cm = confusion_matrix(y_test, y_pred)\n",
-        "\n",
-        "# printing the confusion matrix\n",
-        "print(cm)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[[ 0 11  0  0  0  0]\n",
-            " [ 0  9 23  0  0  0]\n",
-            " [ 0  0 64  8  0  0]\n",
-            " [ 0  0  5 50  3  0]\n",
-            " [ 0  0  0 10 51  0]\n",
-            " [ 0  0  0  0  0 16]]\n"
-          ],
-          "name": "stdout"
-        }
+     },
+     "execution_count": 209,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# label encoding for grades\n",
+    "# we have to map values to each of the categories\n",
+    "\n",
+    "data['grades'] = data['grades'].replace('O', 0)\n",
+    "data['grades'] = data['grades'].replace('A', 1)\n",
+    "data['grades'] = data['grades'].replace('B', 2)\n",
+    "data['grades'] = data['grades'].replace('C', 3)\n",
+    "data['grades'] = data['grades'].replace('D', 4)\n",
+    "data['grades'] = data['grades'].replace('E', 5)\n",
+    "\n",
+    "data['race/ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 36
+    },
+    "colab_type": "code",
+    "id": "9fcJDzBNJZHT",
+    "outputId": "27ddb324-ce80-4535-b84e-566493b8c5df"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 15)"
       ]
+     },
+     "execution_count": 210,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 54
+    },
+    "colab_type": "code",
+    "id": "tfaXk6JBbiNZ",
+    "outputId": "f233545e-91ef-4f6a-f6ed-4b1886603b53"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 14)\n",
+      "(1000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# splitting the dependent and independent variables\n",
+    "\n",
+    "x = data.iloc[:,:14]\n",
+    "y = data.iloc[:,14]\n",
+    "\n",
+    "print(x.shape)\n",
+    "print(y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 92
+    },
+    "colab_type": "code",
+    "id": "CLHjid6FY13N",
+    "outputId": "348964a1-6a7b-4096-8603-4a531fe873e5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(750, 14)\n",
+      "(750,)\n",
+      "(250, 14)\n",
+      "(250,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# splitting the dataset into training and test sets\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 45)\n",
+    "\n",
+    "print(x_train.shape)\n",
+    "print(y_train.shape)\n",
+    "print(x_test.shape)\n",
+    "print(y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "colab_type": "code",
+    "id": "ciauOTq_b04w",
+    "outputId": "de29f2bf-2e9b-4f8d-c089-f00e4d676d01"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
+      "  return self.partial_fit(X, y)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# importing the MinMaxScaler\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "# creating a scaler\n",
+    "mm = MinMaxScaler()\n",
+    "\n",
+    "# feeding the independent variable into the scaler\n",
+    "x_train = mm.fit_transform(x_train)\n",
+    "x_test = mm.transform(x_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "t3e-fhJPKtFm"
+   },
+   "outputs": [],
+   "source": [
+    "# applying principal components analysis\n",
+    "\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "# creating a principal component analysis model\n",
+    "#pca = PCA(n_components = None)\n",
+    "\n",
+    "# feeding the independent variables to the PCA model\n",
+    "#x_train = pca.fit_transform(x_train)\n",
+    "#x_test = pca.transform(x_test)\n",
+    "\n",
+    "# visualising the principal components that will explain the highest share of variance\n",
+    "#explained_variance = pca.explained_variance_ratio_\n",
+    "#print(explained_variance)\n",
+    "\n",
+    "# creating a principal component analysis model\n",
+    "#pca = PCA(n_components = 2)\n",
+    "\n",
+    "# feeding the independent variables to the PCA model\n",
+    "#x_train = pca.fit_transform(x_train)\n",
+    "#x_test = pca.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "7KJx14EzKk91"
+   },
+   "source": [
+    "**Modelling**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "P6fR-dCXN4SK"
+   },
+   "source": [
+    "**Logistic Regression**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 149
+    },
+    "colab_type": "code",
+    "id": "EVJHYo1IhJ2k",
+    "outputId": "eed70f37-79b7-4aa6-8041-66e6c86cdd9f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy : 0.7786666666666666\n",
+      "Testing Accuracy : 0.768\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import  LogisticRegression\n",
+    "\n",
+    "# creating a model\n",
+    "model = LogisticRegression()\n",
+    "\n",
+    "# feeding the training data to the model\n",
+    "model.fit(x_train, y_train)\n",
+    "\n",
+    "# predicting the test set results\n",
+    "y_pred = model.predict(x_test)\n",
+    "\n",
+    "# calculating the classification accuracies\n",
+    "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
+    "print(\"Testing Accuracy :\", model.score(x_test, y_test))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 129
+    },
+    "colab_type": "code",
+    "id": "3aK-UV7jRE2D",
+    "outputId": "5b67c319-18a8-4268-d78d-232a81e159b1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2  9  0  0  0  0]\n",
+      " [ 0 15 17  0  0  0]\n",
+      " [ 0  2 56 14  0  0]\n",
+      " [ 0  0  6 44  8  0]\n",
+      " [ 0  0  0  2 59  0]\n",
+      " [ 0  0  0  0  0 16]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# printing the confusion matrix\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "# creating a confusion matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "# printing the confusion matrix\n",
+    "print(cm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ujNMPnsXKqfT"
+   },
+   "source": [
+    "**Random Forest**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 112
+    },
+    "colab_type": "code",
+    "id": "st1bVJlgPvr2",
+    "outputId": "1288b059-af96-4f25-ce18-c5a6ba6da90e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy : 1.0\n",
+      "Testing Accuracy : 0.996\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
+      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "# creating a model\n",
+    "model = RandomForestClassifier()\n",
+    "\n",
+    "# feeding the training data to the model\n",
+    "model.fit(x_train, y_train)\n",
+    "\n",
+    "# predicting the x-test results\n",
+    "y_pred = model.predict(x_test)\n",
+    "\n",
+    "# calculating the accuracies\n",
+    "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
+    "print(\"Testing Accuracy :\", model.score(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 129
+    },
+    "colab_type": "code",
+    "id": "17rfXUPzQlvk",
+    "outputId": "f4bec713-a072-4588-8cca-e6069a3bdbd3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[11  0  0  0  0  0]\n",
+      " [ 0 32  0  0  0  0]\n",
+      " [ 0  0 72  0  0  0]\n",
+      " [ 0  0  0 58  0  0]\n",
+      " [ 0  0  0  0 61  0]\n",
+      " [ 0  0  0  0  1 15]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# printing the confusion matrix\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "# creating a confusion matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "# printing the confusion matrix\n",
+    "print(cm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "-h5wuSOfVHk4",
+    "outputId": "4d0561c3-4dfe-4428-dc56-e48a55048cbd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      "mean accuracy : 1.0\n",
+      "mean standard variance : 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# k fold cross validation \n",
+    "\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# computing accuracies for 20 different model's accuracy\n",
+    "accuracies = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 20)\n",
+    "print(accuracies)\n",
+    "\n",
+    "# computing mean of accuaracies obtained by all the models\n",
+    "print(\"mean accuracy :\", accuracies.mean())\n",
+    "\n",
+    "# computing the standard variance of the models\n",
+    "print(\"mean standard variance :\", accuracies.std())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "NLUnNmY_RLmN"
+   },
+   "source": [
+    "**Decision Forest**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 54
+    },
+    "colab_type": "code",
+    "id": "cZvzebMFQcZO",
+    "outputId": "ec3c6d7b-90a8-4b96-dec1-2746eaa41308"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy : 1.0\n",
+      "Testing Accuracy : 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "# creating a model\n",
+    "model = DecisionTreeClassifier()\n",
+    "\n",
+    "# feeding the training data to the model\n",
+    "model.fit(x_train, y_train)\n",
+    "\n",
+    "# predicting the x-test results\n",
+    "y_pred = model.predict(x_test)\n",
+    "\n",
+    "# calculating the accuracies\n",
+    "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
+    "print(\"Testing Accuracy :\", model.score(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 129
+    },
+    "colab_type": "code",
+    "id": "MYo6K7puRj6_",
+    "outputId": "263bcb1c-4de3-49d8-c32c-cdfb6c2e7eb4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[11  0  0  0  0  0]\n",
+      " [ 0 32  0  0  0  0]\n",
+      " [ 0  0 72  0  0  0]\n",
+      " [ 0  0  0 58  0  0]\n",
+      " [ 0  0  0  0 61  0]\n",
+      " [ 0  0  0  0  0 16]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# printing the confusion matrix\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "# creating a confusion matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "# printing the confusion matrix\n",
+    "print(cm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "colab_type": "code",
+    "id": "U0hMQ5FiSzoh",
+    "outputId": "779752b9-981d-4d78-b4e4-a8780f019422"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      "mean accuracy : 1.0\n",
+      "mean standard variance : 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# k fold cross validation \n",
+    "\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# computing accuracies for 20 different model's accuracy\n",
+    "accuracies = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 20)\n",
+    "print(accuracies)\n",
+    "\n",
+    "# computing mean of accuaracies obtained by all the models\n",
+    "print(\"mean accuracy :\", accuracies.mean())\n",
+    "\n",
+    "# computing the standard variance of the models\n",
+    "print(\"mean standard variance :\", accuracies.std())\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "-qX9Y2FWQkza"
+   },
+   "source": [
+    "**Support Vector Machine**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 112
+    },
+    "colab_type": "code",
+    "id": "kgbwZfCAVeS6",
+    "outputId": "55dcaf90-0f11-4cb2-81ff-6bad274da5bd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy : 0.732\n",
+      "Testing Accuracy : 0.76\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "# creating a model\n",
+    "model = SVC()\n",
+    "\n",
+    "# feeding the training data to the model\n",
+    "model.fit(x_train, y_train)\n",
+    "\n",
+    "# predicting the x-test results\n",
+    "y_pred = model.predict(x_test)\n",
+    "\n",
+    "# calculating the accuracies\n",
+    "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
+    "print(\"Testing Accuracy :\", model.score(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 129
+    },
+    "colab_type": "code",
+    "id": "QNhhAW-PVq6P",
+    "outputId": "94861392-0397-4d1f-ba53-4b74b8780d1c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0 11  0  0  0  0]\n",
+      " [ 0  9 23  0  0  0]\n",
+      " [ 0  0 64  8  0  0]\n",
+      " [ 0  0  5 50  3  0]\n",
+      " [ 0  0  0 10 51  0]\n",
+      " [ 0  0  0  0  0 16]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# printing the confusion matrix\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "# creating a confusion matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "# printing the confusion matrix\n",
+    "print(cm)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "name": "Student Performance.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From e1120b086c7473076400fc697d517232185ab06d Mon Sep 17 00:00:00 2001
From: Ansh0611 <62184938+Ansh0611@users.noreply.github.com>
Date: Thu, 11 Jun 2020 10:41:50 +0530
Subject: [PATCH 2/2] Add files via upload