Machine Learning Practice Projects 🚀

This repository contains a collection of machine learning projects developed during my internship at Elevvo Pathways. Each project showcases practical applications of core ML concepts such as regression, classification, clustering, and model evaluation using real-world datasets.

🧠 Projects Overview

1. 🎓 Student Performance Prediction

• Explored the impact of study habits, motivation, sleep, and parental involvement on exam scores.
• Performed EDA, data preprocessing, and built a Polynomial Regression model.
• Evaluated model performance using MAE, MSE, and R² metrics.
• Visualized actual vs predicted values.

2. 🛍️ Customer Segmentation

• Applied K-Means clustering on mall customers based on income, age, and spending score.
• Used Label Encoding, Standard Scaling, and the Elbow Method.
• Visualized clusters and insights for business segmentation.

3. 🌲 Forest Cover Type Classification

• Built multi-class classification models (Decision Tree, Random Forest) to predict forest cover types.
• Tuned hyperparameters using GridSearchCV.
• Analyzed feature importances and model accuracy using classification metrics.

4. 💸 Loan Approval Prediction

• Built classification models (Logistic Regression, SVM, Decision Tree) to predict loan approval.
• Handled missing values, encoded categorical features, and addressed class imbalance.
• Evaluated using confusion matrix, precision, recall, and F1-score.

🛠️ Technologies & Tools Used

• Languages & Libraries: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
• Techniques: Polynomial Regression, K-Means Clustering, Decision Tree, Random Forest, Logistic Regression
• Extras: Feature Engineering, Model Evaluation, GridSearchCV, EDA, Data Cleaning

📌 Useful Links

• 📊 Kaggle Profile

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Thanks to the Elevvo Pathways team for this opportunity to apply machine learning to real challenges!