diff --git a/credit-card-fraud-detection-eda/Renu Prakash - FAAI - Assignment1 - EDA_final.ipynb b/credit-card-fraud-detection-eda/Renu Prakash - FAAI - Assignment1 - EDA_final.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2566e179-5b40-49b0-8789-5ce5922f6208",
+ "metadata": {
+ "id": "2566e179-5b40-49b0-8789-5ce5922f6208"
+ },
+ "source": [
+ "# ๐งช Assignment #1: Exploratory Data Analysis (EDA) on Fraud Transactions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd30bf11-1df8-4650-a923-a5356537502b",
+ "metadata": {
+ "id": "fd30bf11-1df8-4650-a923-a5356537502b"
+ },
+ "source": [
+ "## ๐ Objective:\n",
+ "Perform exploratory data analysis (EDA) using a real-world fraud dataset to uncover insights, spot anomalies, and build intuition about fraud patterns."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c6c9b4e1-6a65-4b46-aa30-4aa3e3670783",
+ "metadata": {
+ "id": "c6c9b4e1-6a65-4b46-aa30-4aa3e3670783"
+ },
+ "source": [
+ "### ๐ง What You'll Do:\n",
+ "- Load the dataset\n",
+ "\n",
+ "- Explore the data structure and summary statistics\n",
+ "\n",
+ "- Visualize fraud-related patterns using:\n",
+ "\n",
+ " - Boxplot\n",
+ "\n",
+ " - Histogram\n",
+ "\n",
+ " - Heatmap"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7abd9777-e4bd-44d8-9cc3-a9694833f119",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 400
+ },
+ "collapsed": true,
+ "executionInfo": {
+ "elapsed": 6729,
+ "status": "error",
+ "timestamp": 1748368132805,
+ "user": {
+ "displayName": "Renu Prakash",
+ "userId": "14561974129657939890"
+ },
+ "user_tz": 420
+ },
+ "id": "7abd9777-e4bd-44d8-9cc3-a9694833f119",
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "outputId": "0c21edd0-ceaf-4416-e754-ed0631efde3c",
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Time V1 V2 V3 V4 V5 V6 V7 \\\n",
+ "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n",
+ "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n",
+ "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n",
+ "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n",
+ "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n",
+ "\n",
+ " V8 V9 ... V21 V22 V23 V24 V25 \\\n",
+ "0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n",
+ "1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n",
+ "2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n",
+ "3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n",
+ "4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 \n",
+ "\n",
+ " V26 V27 V28 Amount Class \n",
+ "0 -0.189115 0.133558 -0.021053 149.62 0 \n",
+ "1 0.125895 -0.008983 0.014724 2.69 0 \n",
+ "2 -0.139097 -0.055353 -0.059752 378.66 0 \n",
+ "3 -0.221929 0.062723 0.061458 123.50 0 \n",
+ "4 0.502292 0.219422 0.215153 69.99 0 \n",
+ "\n",
+ "[5 rows x 31 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Load the dataset (update path if needed)\n",
+ "df = pd.read_csv('creditcard.csv')\n",
+ "\n",
+ "# Preview the data\n",
+ "print(df.head())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "96565c67-4666-4f26-9d0a-b26b5ac3af9d",
+ "metadata": {
+ "id": "0b9241b3-0abc-4188-8d7d-1df48a5d7207"
+ },
+ "source": [
+ "# ๐งช Assignment #1: Exploratory Data Analysis on Credit Card Fraud Data\n",
+ "\n",
+ "## ๐ฏ Objective\n",
+ "In this assignment, youโll perform exploratory data analysis (EDA) on a **real-world credit card fraud dataset**. Your goal is to develop a clear understanding of the datasetโs structure, key variables, and any potential signs of fraud using summary statistics and data visualizations.\n",
+ "\n",
+ "This is your first hands-on opportunity to apply Python and data science techniques to a real fraud detection problem.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐ Dataset: [Credit Card Fraud Detection Dataset (Kaggle)](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)\n",
+ "\n",
+ "- Transactions made by European cardholders in September 2013\n",
+ "- Features are PCA-transformed (`V1`โ`V28`) plus `Time`, `Amount`, and `Class`\n",
+ "- `Class = 1` indicates a fraudulent transaction\n",
+ "\n",
+ "You may already have this dataset preloaded for the assignment. If not, download it from the Kaggle link above.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐ง Your Task\n",
+ "\n",
+ "1. **Import the Dataset**\n",
+ " - Load the dataset into a Pandas DataFrame\n",
+ " - Use `df.head()` to preview the first few rows\n",
+ "\n",
+ "2. **Summary Statistics**\n",
+ " - Use `df.describe()` to examine key metrics\n",
+ " - Highlight and interpret any differences between fraudulent and non-fraudulent transactions\n",
+ "\n",
+ "3. **Boxplot**\n",
+ " - Plot a boxplot comparing the **Amount** variable by `Class` (fraud vs. non-fraud)\n",
+ " - Identify any outliers or unusual distributions\n",
+ "\n",
+ "4. **Histogram**\n",
+ " - Create a histogram of transaction amounts\n",
+ " - Use color or grouping to compare fraud vs. non-fraud\n",
+ "\n",
+ "5. **Correlation Heatmap**\n",
+ " - Generate a correlation matrix of all numeric features\n",
+ " - Plot it using `seaborn.heatmap()` and interpret any strong relationships\n",
+ "\n",
+ "6. **Observations**\n",
+ " - Provide a short summary of what you've discovered\n",
+ " - Use Markdown to document: suspicious trends, high-risk variables, or visual insights\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐ฏ Grading Rubric (100 Points)\n",
+ "\n",
+ "| Category | Points | Details |\n",
+ "|----------------------------------------------|--------|-------------------------------------------------------------------------|\n",
+ "| ๐ฅ Dataset Loading & Head Output | 10 pts | Dataset is correctly loaded and displayed |\n",
+ "| ๐ Summary Statistics (`describe`) | 10 pts | Key metrics are shown and explained |\n",
+ "| ๐ฆ Boxplot of Amount by Class | 20 pts | Boxplot created with meaningful labels and insights |\n",
+ "| ๐ Histogram with Class Overlay | 20 pts | Histogram clearly compares fraud vs. non-fraud |\n",
+ "| ๐ฅ Correlation Heatmap | 20 pts | Accurate matrix and clear heatmap generated |\n",
+ "| ๐ Observations & Markdown Commentary | 20 pts | Thoughtful insighoor formatting, hard-to-read, or broken code |\n",
+ "\n",
+ "> ๐งฎ **Total: 100 Points**\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐ Optional Extra Credit (Up to +50 Points)\n",
+ "\n",
+ "Looking to challenge yourself or build your portfolio? Choose any of the following:\n",
+ "\n",
+ "| Extra Credit Task | Points |\n",
+ "|---------------------------------------------------------|--------|\n",
+ "| ๐ Plot fraud rate over time using `Time` column | +10 pts |\n",
+ "| ๐งฎ Use `groupby()` to explore fraud by amount bins | +10 pts |\n",
+ "| ๐ Create a pairplot with Seaborn for high-correlation variables | +10 pts |\n",
+ "| ๐ป Try violin or KDE plots to explore distributions | +10 pts |\n",
+ "| ๐งพ Use z-score or IQR for outlier detection | +10 pts |\n",
+ "\n",
+ "> You can earn **up to +50 extra points**, which will be added to your final score. These tasks are optional and great for resume-worthy exploration.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐งต Submission Guidelines\n",
+ "\n",
+ "- Submit your `.ipynb` Jupyter Notebook via the course platform\n",
+ "- Include inline comments and Markdown explanations\n",
+ "- Your notebook should be clean, organized, and runnable\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## โ
Example Code Snippet to Get You Started\n",
+ "\n",
+ "```python\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seabord dataset\n",
+ "df = pd.read_csv(\"creditcard.csv\")\n",
+ "print(df.head())\n",
+ "\n",
+ "# Summary statistics\n",
+ "print(df.describe())\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b5ff5f0-569a-40e8-a89f-99f70eee31f2",
+ "metadata": {},
+ "source": [
+ "### Task1 : Import the Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "66d93ee1-90ae-4678-9c1c-b33bc610b237",
+ "metadata": {
+ "collapsed": true,
+ "id": "66d93ee1-90ae-4678-9c1c-b33bc610b237",
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "outputId": "ac5fb72d-00e8-44c9-e9af-52b09682d057",
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Time V1 V2 V3 V4 V5 V6 V7 \\\n",
+ "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n",
+ "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n",
+ "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n",
+ "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n",
+ "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n",
+ "\n",
+ " V8 V9 ... V21 V22 V23 V24 V25 \\\n",
+ "0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n",
+ "1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n",
+ "2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n",
+ "3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n",
+ "4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 \n",
+ "\n",
+ " V26 V27 V28 Amount Class \n",
+ "0 -0.189115 0.133558 -0.021053 149.62 0 \n",
+ "1 0.125895 -0.008983 0.014724 2.69 0 \n",
+ "2 -0.139097 -0.055353 -0.059752 378.66 0 \n",
+ "3 -0.221929 0.062723 0.061458 123.50 0 \n",
+ "4 0.502292 0.219422 0.215153 69.99 0 \n",
+ "\n",
+ "[5 rows x 31 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import necessary libraries \n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Load dataset\n",
+ "\n",
+ "df = pd.read_csv(\"creditcard.csv\")\n",
+ "\n",
+ "print(df.head()) # Display top 5 rows of the dataframe\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "712391c8-3737-43e1-a9c1-039c8446308d",
+ "metadata": {},
+ "source": [
+ "### Task 2: Summary Statistics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b8c6463b-98d3-4831-8408-cdc2c4f4199f",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Time V1 V2 V3 V4 \\\n",
+ "count 284807.000000 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 \n",
+ "mean 94813.859575 1.168375e-15 3.416908e-16 -1.379537e-15 2.074095e-15 \n",
+ "std 47488.145955 1.958696e+00 1.651309e+00 1.516255e+00 1.415869e+00 \n",
+ "min 0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00 \n",
+ "25% 54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01 \n",
+ "50% 84692.000000 1.810880e-02 6.548556e-02 1.798463e-01 -1.984653e-02 \n",
+ "75% 139320.500000 1.315642e+00 8.037239e-01 1.027196e+00 7.433413e-01 \n",
+ "max 172792.000000 2.454930e+00 2.205773e+01 9.382558e+00 1.687534e+01 \n",
+ "\n",
+ " V5 V6 V7 V8 V9 \\\n",
+ "count 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 \n",
+ "mean 9.604066e-16 1.487313e-15 -5.556467e-16 1.213481e-16 -2.406331e-15 \n",
+ "std 1.380247e+00 1.332271e+00 1.237094e+00 1.194353e+00 1.098632e+00 \n",
+ "min -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01 \n",
+ "25% -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01 \n",
+ "50% -5.433583e-02 -2.741871e-01 4.010308e-02 2.235804e-02 -5.142873e-02 \n",
+ "75% 6.119264e-01 3.985649e-01 5.704361e-01 3.273459e-01 5.971390e-01 \n",
+ "max 3.480167e+01 7.330163e+01 1.205895e+02 2.000721e+01 1.559499e+01 \n",
+ "\n",
+ " ... V21 V22 V23 V24 \\\n",
+ "count ... 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 \n",
+ "mean ... 1.654067e-16 -3.568593e-16 2.578648e-16 4.473266e-15 \n",
+ "std ... 7.345240e-01 7.257016e-01 6.244603e-01 6.056471e-01 \n",
+ "min ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00 \n",
+ "25% ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01 \n",
+ "50% ... -2.945017e-02 6.781943e-03 -1.119293e-02 4.097606e-02 \n",
+ "75% ... 1.863772e-01 5.285536e-01 1.476421e-01 4.395266e-01 \n",
+ "max ... 2.720284e+01 1.050309e+01 2.252841e+01 4.584549e+00 \n",
+ "\n",
+ " V25 V26 V27 V28 Amount \\\n",
+ "count 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 284807.000000 \n",
+ "mean 5.340915e-16 1.683437e-15 -3.660091e-16 -1.227390e-16 88.349619 \n",
+ "std 5.212781e-01 4.822270e-01 4.036325e-01 3.300833e-01 250.120109 \n",
+ "min -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01 0.000000 \n",
+ "25% -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02 5.600000 \n",
+ "50% 1.659350e-02 -5.213911e-02 1.342146e-03 1.124383e-02 22.000000 \n",
+ "75% 3.507156e-01 2.409522e-01 9.104512e-02 7.827995e-02 77.165000 \n",
+ "max 7.519589e+00 3.517346e+00 3.161220e+01 3.384781e+01 25691.160000 \n",
+ "\n",
+ " Class \n",
+ "count 284807.000000 \n",
+ "mean 0.001727 \n",
+ "std 0.041527 \n",
+ "min 0.000000 \n",
+ "25% 0.000000 \n",
+ "50% 0.000000 \n",
+ "75% 0.000000 \n",
+ "max 1.000000 \n",
+ "\n",
+ "[8 rows x 31 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Descriptive statistics Summary for entire dataset\n",
+ "\n",
+ "print(df.describe())\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39299788-dcf3-4a4a-b344-61bcb48ad1b9",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "source": [
+ "Interpretation of summary statistics:\n",
+ "\n",
+ "1. Class Imbalance - Fraud Cases: Only ~0.17% (Class column mean = 0.001727), \n",
+ "indicating a highly imbalanced dataset whereas Non-Fraud Cases are in Majority.\n",
+ "\n",
+ "2. Amount Column: Mean = $88.35, StdDev = $250.12, Max = $25691.16 that means data is right-skewed.\n",
+ "\n",
+ "The distribution of amounts for fraud cases is more concentrated at the lower end, \n",
+ "while non-fraudulent transactions span a wider range and include higher values. \n",
+ "This means most high-value transactions are legitimate, and fraud is more common in smaller transactions\n",
+ "\n",
+ "3. 75% of transactions are below $77.17 โ confirms long-tail distribution.\n",
+ "\n",
+ "4. Time Column: Ranges from 0 to ~172792.00 seconds (โ 48 hours) โ reflects the time elapsed \n",
+ "since the first transaction."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7cdfddfb-6b77-4dc6-8918-5c9c878bc3a8",
+ "metadata": {},
+ "source": [
+ "#### Interpret any differences between fraudulent and non-fraudulent transactions "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e3de5301-b2ee-4c64-8efd-e68dccc218f9",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Time | \n",
+ " V1 | \n",
+ " V2 | \n",
+ " V3 | \n",
+ " V4 | \n",
+ " V5 | \n",
+ " V6 | \n",
+ " V7 | \n",
+ " V8 | \n",
+ " V9 | \n",
+ " ... | \n",
+ " V21 | \n",
+ " V22 | \n",
+ " V23 | \n",
+ " V24 | \n",
+ " V25 | \n",
+ " V26 | \n",
+ " V27 | \n",
+ " V28 | \n",
+ " Amount | \n",
+ " Class | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 284315.000000 | \n",
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\n",
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+ " 94838.202258 | \n",
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+ " -0.000131 | \n",
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\n",
+ " \n",
+ " | std | \n",
+ " 47484.015786 | \n",
+ " 1.929814 | \n",
+ " 1.636146 | \n",
+ " 1.459429 | \n",
+ " 1.399333 | \n",
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+ " 1.329913 | \n",
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+ " 0.716743 | \n",
+ " 0.723668 | \n",
+ " 0.621541 | \n",
+ " 0.605776 | \n",
+ " 0.520673 | \n",
+ " 0.482241 | \n",
+ " 0.399847 | \n",
+ " 0.329570 | \n",
+ " 250.105092 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 0.000000 | \n",
+ " -56.407510 | \n",
+ " -72.715728 | \n",
+ " -48.325589 | \n",
+ " -5.683171 | \n",
+ " -113.743307 | \n",
+ " -26.160506 | \n",
+ " -31.764946 | \n",
+ " -73.216718 | \n",
+ " -6.290730 | \n",
+ " ... | \n",
+ " -34.830382 | \n",
+ " -10.933144 | \n",
+ " -44.807735 | \n",
+ " -2.836627 | \n",
+ " -10.295397 | \n",
+ " -2.604551 | \n",
+ " -22.565679 | \n",
+ " -15.430084 | \n",
+ " 0.000000 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 54230.000000 | \n",
+ " -0.917544 | \n",
+ " -0.599473 | \n",
+ " -0.884541 | \n",
+ " -0.850077 | \n",
+ " -0.689398 | \n",
+ " -0.766847 | \n",
+ " -0.551442 | \n",
+ " -0.208633 | \n",
+ " -0.640412 | \n",
+ " ... | \n",
+ " -0.228509 | \n",
+ " -0.542403 | \n",
+ " -0.161702 | \n",
+ " -0.354425 | \n",
+ " -0.317145 | \n",
+ " -0.327074 | \n",
+ " -0.070852 | \n",
+ " -0.052950 | \n",
+ " 5.650000 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 84711.000000 | \n",
+ " 0.020023 | \n",
+ " 0.064070 | \n",
+ " 0.182158 | \n",
+ " -0.022405 | \n",
+ " -0.053457 | \n",
+ " -0.273123 | \n",
+ " 0.041138 | \n",
+ " 0.022041 | \n",
+ " -0.049964 | \n",
+ " ... | \n",
+ " -0.029821 | \n",
+ " 0.006736 | \n",
+ " -0.011147 | \n",
+ " 0.041082 | \n",
+ " 0.016417 | \n",
+ " -0.052227 | \n",
+ " 0.001230 | \n",
+ " 0.011199 | \n",
+ " 22.000000 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 139333.000000 | \n",
+ " 1.316218 | \n",
+ " 0.800446 | \n",
+ " 1.028372 | \n",
+ " 0.737624 | \n",
+ " 0.612181 | \n",
+ " 0.399619 | \n",
+ " 0.571019 | \n",
+ " 0.326200 | \n",
+ " 0.598230 | \n",
+ " ... | \n",
+ " 0.185626 | \n",
+ " 0.528407 | \n",
+ " 0.147522 | \n",
+ " 0.439869 | \n",
+ " 0.350594 | \n",
+ " 0.240671 | \n",
+ " 0.090573 | \n",
+ " 0.077962 | \n",
+ " 77.050000 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 172792.000000 | \n",
+ " 2.454930 | \n",
+ " 18.902453 | \n",
+ " 9.382558 | \n",
+ " 16.875344 | \n",
+ " 34.801666 | \n",
+ " 73.301626 | \n",
+ " 120.589494 | \n",
+ " 18.709255 | \n",
+ " 15.594995 | \n",
+ " ... | \n",
+ " 22.614889 | \n",
+ " 10.503090 | \n",
+ " 22.528412 | \n",
+ " 4.584549 | \n",
+ " 7.519589 | \n",
+ " 3.517346 | \n",
+ " 31.612198 | \n",
+ " 33.847808 | \n",
+ " 25691.160000 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
8 rows ร 31 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Time | \n",
+ " V1 | \n",
+ " V2 | \n",
+ " V3 | \n",
+ " V4 | \n",
+ " V5 | \n",
+ " V6 | \n",
+ " V7 | \n",
+ " V8 | \n",
+ " V9 | \n",
+ " ... | \n",
+ " V21 | \n",
+ " V22 | \n",
+ " V23 | \n",
+ " V24 | \n",
+ " V25 | \n",
+ " V26 | \n",
+ " V27 | \n",
+ " V28 | \n",
+ " Amount | \n",
+ " Class | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " ... | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.000000 | \n",
+ " 492.0 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 80746.806911 | \n",
+ " -4.771948 | \n",
+ " 3.623778 | \n",
+ " -7.033281 | \n",
+ " 4.542029 | \n",
+ " -3.151225 | \n",
+ " -1.397737 | \n",
+ " -5.568731 | \n",
+ " 0.570636 | \n",
+ " -2.581123 | \n",
+ " ... | \n",
+ " 0.713588 | \n",
+ " 0.014049 | \n",
+ " -0.040308 | \n",
+ " -0.105130 | \n",
+ " 0.041449 | \n",
+ " 0.051648 | \n",
+ " 0.170575 | \n",
+ " 0.075667 | \n",
+ " 122.211321 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 47835.365138 | \n",
+ " 6.783687 | \n",
+ " 4.291216 | \n",
+ " 7.110937 | \n",
+ " 2.873318 | \n",
+ " 5.372468 | \n",
+ " 1.858124 | \n",
+ " 7.206773 | \n",
+ " 6.797831 | \n",
+ " 2.500896 | \n",
+ " ... | \n",
+ " 3.869304 | \n",
+ " 1.494602 | \n",
+ " 1.579642 | \n",
+ " 0.515577 | \n",
+ " 0.797205 | \n",
+ " 0.471679 | \n",
+ " 1.376766 | \n",
+ " 0.547291 | \n",
+ " 256.683288 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 406.000000 | \n",
+ " -30.552380 | \n",
+ " -8.402154 | \n",
+ " -31.103685 | \n",
+ " -1.313275 | \n",
+ " -22.105532 | \n",
+ " -6.406267 | \n",
+ " -43.557242 | \n",
+ " -41.044261 | \n",
+ " -13.434066 | \n",
+ " ... | \n",
+ " -22.797604 | \n",
+ " -8.887017 | \n",
+ " -19.254328 | \n",
+ " -2.028024 | \n",
+ " -4.781606 | \n",
+ " -1.152671 | \n",
+ " -7.263482 | \n",
+ " -1.869290 | \n",
+ " 0.000000 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 41241.500000 | \n",
+ " -6.036063 | \n",
+ " 1.188226 | \n",
+ " -8.643489 | \n",
+ " 2.373050 | \n",
+ " -4.792835 | \n",
+ " -2.501511 | \n",
+ " -7.965295 | \n",
+ " -0.195336 | \n",
+ " -3.872383 | \n",
+ " ... | \n",
+ " 0.041787 | \n",
+ " -0.533764 | \n",
+ " -0.342175 | \n",
+ " -0.436809 | \n",
+ " -0.314348 | \n",
+ " -0.259416 | \n",
+ " -0.020025 | \n",
+ " -0.108868 | \n",
+ " 1.000000 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 75568.500000 | \n",
+ " -2.342497 | \n",
+ " 2.717869 | \n",
+ " -5.075257 | \n",
+ " 4.177147 | \n",
+ " -1.522962 | \n",
+ " -1.424616 | \n",
+ " -3.034402 | \n",
+ " 0.621508 | \n",
+ " -2.208768 | \n",
+ " ... | \n",
+ " 0.592146 | \n",
+ " 0.048434 | \n",
+ " -0.073135 | \n",
+ " -0.060795 | \n",
+ " 0.088371 | \n",
+ " 0.004321 | \n",
+ " 0.394926 | \n",
+ " 0.146344 | \n",
+ " 9.250000 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 128483.000000 | \n",
+ " -0.419200 | \n",
+ " 4.971257 | \n",
+ " -2.276185 | \n",
+ " 6.348729 | \n",
+ " 0.214562 | \n",
+ " -0.413216 | \n",
+ " -0.945954 | \n",
+ " 1.764879 | \n",
+ " -0.787850 | \n",
+ " ... | \n",
+ " 1.244611 | \n",
+ " 0.617474 | \n",
+ " 0.308378 | \n",
+ " 0.285328 | \n",
+ " 0.456515 | \n",
+ " 0.396733 | \n",
+ " 0.826029 | \n",
+ " 0.381152 | \n",
+ " 105.890000 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 170348.000000 | \n",
+ " 2.132386 | \n",
+ " 22.057729 | \n",
+ " 2.250210 | \n",
+ " 12.114672 | \n",
+ " 11.095089 | \n",
+ " 6.474115 | \n",
+ " 5.802537 | \n",
+ " 20.007208 | \n",
+ " 3.353525 | \n",
+ " ... | \n",
+ " 27.202839 | \n",
+ " 8.361985 | \n",
+ " 5.466230 | \n",
+ " 1.091435 | \n",
+ " 2.208209 | \n",
+ " 2.745261 | \n",
+ " 3.052358 | \n",
+ " 1.779364 | \n",
+ " 2125.870000 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
8 rows ร 31 columns
\n",
+ "