Machine Learning Projects Built with:

• Python for backend logic and model training
• Pandas & NumPy for data handling and transformation
• Scikit-Learn for machine learning modeling (Logistic Regression, Decision Trees, SVM, KNN)
• TensorFlow & PyTorch for deep learning experiments
• Hyperparameter Optimization using GridSearchCV and RandomizedSearchCV
• Confusion Matrices & ROC Curves for performance evaluation

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Overview

This repository contains machine learning projects, where models are trained for classification, regression, and clustering tasks. Each project includes data preprocessing, feature engineering, model training, evaluation, and visualizations to support findings.

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Core Topics Covered:

• Supervised Learning → Logistic Regression, Support Vector Machines, Decision Trees
• Unsupervised Learning → K-Means Clustering, DBSCAN, Hierarchical Clustering
• Feature Selection & Engineering → Standardization, OneHotEncoding, PCA
• Hyperparameter Optimization → Cross-validation, tuning models for best accuracy
• Model Interpretability → confusion matrices, ROC/AUC curves

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Projects Included:

Obesity Classification → Multi-class prediction using Logistic Regression & SVM

Rainfall Prediction → Forecasting rain occurrence using Random Forest & Logistic Regression

Telecom Customer Churn → Predicting churn risk with Decision Trees & KNN

Credit Card Fraud Detection → Classifying fraudulent transactions with anomaly detection

Titanic Survival Prediction → Determining survival probability using logistic regression

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Dependencies

pip install pandas numpy matplotlib seaborn scikit-learn tensorflow torch tqdm

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License

This repository is licensed under the MIT License.