📊 End-to-End Machine Learning Projects

This repository contains a comprehensive collection of end-to-end machine learning projects covering the core algorithms of Supervised, Unsupervised, and Ensemble Learning. Each project includes:

• ✅ Clean, well-commented Python code
• ✅ Step-by-step implementation
• ✅ Simulated real-world datasets
• ✅ Preprocessing, Feature Engineering, Model Training, Evaluation, and Tuning
• ✅ Model comparison and final insights

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1️⃣ Supervised Learning

🔢 Regression (Predicting numeric values)

• Linear Regression – Predict house prices
• Polynomial Regression – Car price prediction
• Decision Tree Regression – Predict car engine efficiency

🏷 Classification (Predicting categories)

• Logistic Regression – Customer churn prediction
• K-Nearest Neighbors (KNN) – Predict diabetes risk
• Decision Tree Classifier – Predict loan approval
• Random Forest – Employee attrition detection
• Support Vector Machine (SVM) – Email spam detection
• Naive Bayes – News article classification

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2️⃣ Unsupervised Learning

🔍 Clustering (Group similar items)

• K-Means Clustering – Customer segmentation
• Hierarchical Clustering – College applicant grouping
• DBSCAN – Detecting noise/outliers in spatial data

🔄 Dimensionality Reduction (Simplify features)

• PCA (Principal Component Analysis) – Compress image data
• t-SNE – Visualize high-dimensional user behavior
• LDA (Linear Discriminant Analysis) – Class separation on text data

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3️⃣ Ensemble Learning

🧱 Bagging

• Random Forest – Improve churn prediction accuracy

🔥 Boosting

• AdaBoost – Simple classification with weak learners
• Gradient Boosting – Predict student performance
• XGBoost – Click-through prediction
• LightGBM – Insurance policy prediction
• CatBoost – Telecom plan upgrade prediction

🧠 Stacking

• Combine Random Forest, KNN, and Logistic Regression with SVM as a meta-model for better prediction (Student pass/fail prediction)

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📁 Structure

Each project includes:

• 📌 Problem Statement
• 📊 Data Understanding
• 🧼 Data Cleaning & Preprocessing
• 🔍 Feature Engineering
• 🤖 Model Training
• 📈 Evaluation Metrics (Accuracy, Precision, Recall, F1, Confusion Matrix)
• 🔧 Hyperparameter Tuning
• ✅ Final Model Summary & Suggestions