This repository contains four machine learning projects covering both supervised and unsupervised learning techniques.
Developed a supervised learning model using Random Forest, Logistic Regression, and other classifiers to predict gender based on name-based features such as suffixes, length, and vowel patterns.
Implemented data preprocessing, feature engineering, and machine learning models including Random Forest, Gradient Boosting, and Neural Networks to predict passenger survival with optimized performance. Applied hyperparameter tuning and model evaluation techniques.
Performed clustering analysis using K-Means and PCA on banking data to segment customers for more effective marketing strategies. Analyzed key features to enhance customer targeting and campaign success rates.
Explored heart disease prediction using Logistic Regression and Random Forest classifiers. Conducted data preprocessing, feature engineering, and clustering analysis to identify key risk factors. Evaluated model performance and feature importance to improve diagnostic accuracy.