Initial commit

Author: SilentProgrammer-max

.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

README.md

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+# My Streamlit App – Explore ML Models on Popular Datasets
+
+Welcome to my interactive **Machine Learning Model Explorer** built using **Streamlit**. This app allows users to:
+
+- Select different datasets
+- Choose between popular ML models (SVM, KNN, Random Forest)
+- Tune hyperparameters through the sidebar
+- Visualize results in 2D using PCA
+- View model accuracy scores instantly
+
+---
+
+## Features
+
+### Select from popular datasets:
+- Iris 
+- Breast Cancer
+- Wine 
+
+### Choose and tune classifiers:
+- **K-Nearest Neighbors (KNN)** – Adjust the number of neighbors  
+- **Support Vector Machine (SVM)** – Control the regularization parameter `C`  
+- **Random Forest** – Set `n_estimators` and `max_depth`
+
+### Real-time insights:
+- Shows dataset shape and number of classes
+- Displays accuracy of the selected model
+- Visualizes data in 2D using **PCA** with color-coded target labels
+
+---
+
+## Tech Stack
+
+- **Python**
+- **Streamlit**
+- **Scikit-learn**
+- **Matplotlib**
+- **NumPy**
+
+---
+
+## App Preview
+
+![App Screenshot](app_screenshort.PNG)
+
+---
+
+## How to Run This App Locally
+
+```bash
+# 1. Clone the repository
+git clone https://github.com/YourUsername/My-Streamlit-App.git
+
+# 2. Navigate into the project directory
+cd My-Streamlit-App
+
+# 3. Install the dependencies
+pip install -r requirements.txt
+
+# 4. Run the Streamlit app
+streamlit run app.py

app.py

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+# import libraries
+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.decomposition import PCA
+from sklearn.svm import SVC 
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+# app heading
+st.write(""" 
+# Explore different ML Models and Datasets
+Let's see which one is best from these?
+""")
+
+# make sidebar
+dataset_name = st.sidebar.selectbox(
+    'Select Dataset',
+    ("Iris", "Breast Cancer", "Wine")
+)
+
+# classifier selection
+classifier_name = st.sidebar.selectbox(
+    "Select Classifier",
+    ("KNN", "SVM", "Random Forest")
+)
+
+# import datasets
+def get_dataset(dataset_name):
+    data = None
+    if dataset_name == "Iris":
+        data = datasets.load_iris()
+    elif dataset_name == "Wine":
+        data = datasets.load_wine()
+    else: 
+        data = datasets.load_breast_cancer()
+    x = data.data
+    y = data.target
+    return x, y 
+
+# get selected dataset
+X, y = get_dataset(dataset_name)
+
+# dataset info
+st.write("Shape of dataset:", X.shape)
+st.write("Number of classes:", len(np.unique(y)))
+
+# classifier parameters from UI
+def add_parameter_ui(classifier_name):
+    params = dict()
+    if classifier_name == "SVM":
+        C = st.sidebar.slider('C', 0.01, 10.0, 1.0)
+        params['C'] = C
+    elif classifier_name == "KNN":
+        K = st.sidebar.slider("K", 1, 15)
+        params['K'] = K
+    else: 
+        max_depth = st.sidebar.slider('max_depth', 2, 15)
+        params['max_depth'] = max_depth
+        n_estimators = st.sidebar.slider('n_estimators', 1, 100)
+        params['n_estimators'] = n_estimators
+    return params
+
+# get classifier params from UI
+params = add_parameter_ui(classifier_name)
+
+# create classifier based on selected model
+def get_classifier(classifier_name, params):
+    clf = None
+    if classifier_name == "SVM":    
+        clf = SVC(C=params['C'])
+    elif classifier_name == "KNN":
+        clf = KNeighborsClassifier(n_neighbors=params['K'])
+    else: 
+        clf = RandomForestClassifier(
+            n_estimators=params['n_estimators'],
+            max_depth=params['max_depth'],
+            random_state=1234
+        )
+    return clf
+
+# get the classifier
+clf = get_classifier(classifier_name, params)
+
+# split data into training and testing
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)
+
+# train the model
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+
+# show results
+acc = accuracy_score(y_test, y_pred)
+st.write(f'Classifier = {classifier_name}')
+st.write(f'Accuracy = {acc:.2f}')
+
+# plot PCA 2D
+pca = PCA(2)
+X_projected = pca.fit_transform(X)
+
+x1 = X_projected[:, 0]
+x2 = X_projected[:, 1]
+
+fig = plt.figure()
+plt.scatter(x1, x2, c=y, alpha=0.8, cmap='viridis')
+plt.xlabel('Principal Component 1')
+plt.ylabel('Principal Component 2')
+plt.colorbar()
+st.pyplot(fig)

app_screenshort.PNG

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+matplotlib==3.5.3
+numpy==1.22.4
+scikit_learn==0.24.2
+streamlit==1.40.1
+click==8.1.8