🤖 Predict programming problem difficulty with AI using text analysis and machine learning for accurate complexity scoring.

Modular ML expense tracker for Indian students — classifies UPI transactions, nets reimbursements, and tracks SIP goals using Random Forest + Fuzzy Matching.

Collection of Machine Learning and Data Science projects including EDA, classification, regression and model deployment.

Modelo preditivo de Machine Learning para identificar evasão de clientes (Churn) no setor de telecomunicações e propor estratégias de retenção.

Built and deployed a Basketball Lineup Analytics Engine — a sports-tech decision-support tool that ranks 5-man lineups, recommends substitutions, and evaluates matchup counters.

A modular machine learning pipeline comparing 6 classifiers for diabetes prediction, featuring SMOTEENN balancing, KNN imputation, and XGBoost optimization with 91%+ recall.

Database system using ORM with AI/ML preventive maintenance and dashboards for key metrics.

🚀 My journey into deep learning for audio using Python and TensorFlow through music genre classification.

A machine learning–based spam email detection system

This repository contains the code, data, and analysis for a project investigating the relationship between energy consumption and population growth in four select regions: India, the United States, the European Union, and Canada. The analysis spans 34 years of data sourced from the World Bank.

"AI-based tool to predict programming problem difficulty using Random Forest."

金融挖掘系统，集成数据分析，爬虫，机器学习

Machine learning–based classification of movement commands for a wall-following mobile robot (SCITOS-G5) using ultrasound sensor data from the UCI ML Repository.

This project analyzes movie reviews and classifies them as Positive or Negative using Natural Language Processing (NLP) techniques. 💻 Technologies

Simple tool to calculate customs and export costs

This project is an end-to-end machine learning pipeline for predicting housing prices in California.

This is a comprehensive collection of Python implementations covering fundamental machine learning algorithms, data preprocessing techniques, model evaluation methods, and practical applications