• 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

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

• 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

• 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

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

This repository is licensed under the MIT License.