💻 End-to-End Machine Learning Projects

Welcome to my collection of End-to-End Machine Learning Projects, where I’ve applied key ML concepts to solve real-world problems using Python and scikit-learn. Each project in this repository is designed to showcase my hands-on expertise—from data preprocessing and exploratory data analysis (EDA) to model building, tuning, and evaluation.

🧠 What I’ve Implemented:

• Supervised Learning: Linear Regression, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors
• Unsupervised Learning: K-Means Clustering, Hierarchical Clustering
• Model Optimization: GridSearchCV, RandomizedSearchCV, VIF for multicollinearity
• Evaluation Metrics: RMSE, MAE, R² Score, Accuracy Score, Confusion Matrix
• Distance Metrics: Euclidean, Manhattan, Minkowski (for KNN)

📂 Project Highlights:

• Insurance Cost Prediction (Linear Regression): Predicted medical insurance charges using regression models, EDA, and outlier handling.
• Banking Customer Subscription (Logistic Regression): Built a classifier to predict whether a bank client would subscribe to a term deposit.
• Heart Disease Prediction (Decision Tree): Used pruning and tree-depth control to classify patients by heart disease risk.
• Telecom Churn Prediction (Random Forest + GridSearchCV): Improved customer churn accuracy using hyperparameter tuning and ensemble models.
• Disease Diagnosis (KNN): Predicted disease based on symptoms using K-Nearest Neighbors with 98.47% accuracy.
• Cricket Player Segmentation (K-Means Clustering): Clustered international players based on performance metrics.
• Customer Segmentation (Hierarchical Clustering): Used dendrograms and agglomerative clustering to identify 5 distinct customer groups.

📎 Tools Used:

• Python, Jupyter Notebook, Google Colab
• scikit-learn, pandas, numpy
• matplotlib, seaborn
• Data preprocessing, Label Encoding, PCA, StandardScaler

🚀 Explore the full repository on GitHub:

github.com/ruthikan/End-to-End-Machine-Learning-Projects