This project uses machine learning to identify fraudulent transactions from credit card data. It involves data preprocessing, handling imbalances, and building models for accurate predictions. The goal is to create a system to reduce financial losses caused by fraud.

This repository implements a simulated tweet engagement analysis using Python, showcasing key data science techniques like data generation, cleaning, visualization, and statistical analysis with pandas, NumPy, seaborn, and matplotlib. It highlights patterns across tweet categories (e.g., Fitness, Travel, Health) to discern which types garner the most likes and engagement.

This project delivers a volunteer community platform built with TypeScript and JavaScript, featuring user registration, profile management, opportunity discovery, messaging, and dashboards for tracking volunteer activities. It supports multiple database setups—including Supabase, SQLite, and PostgreSQL—and integrates authentication, real-time updates, and robust security measures.

This project builds a salary prediction model using a synthetic dataset simulating years of experience and corresponding salary data. It leverages regression techniques—specifically linear regression—alongside exploratory data analysis and evaluation metrics like Mean Squared Error and R² Score to illustrate the relationship between experience and salary.

The Crop Recommendation System is a machine learning-based project designed to suggest the best crop for cultivation based on specific input parameters like soil composition, climate conditions, and location. It employs Logistic Regression as the primary algorithm for predictions.

The Titanic Survival Prediction project uses machine learning to analyze passenger data and predict their chances of survival during the Titanic disaster. It involves data preprocessing, exploratory data analysis, and training classification models like Logistic Regression or Random Forest. This project is a great starting point for learning binary classification and feature engineering techniques.

The Movie Rating Prediction project leverages machine learning to predict user ratings for movies based on features like user preferences, movie genres, and historical data. Techniques like collaborative filtering, content-based filtering, or hybrid recommendation systems are commonly used. This project helps in understanding recommendation systems and regression or ranking algorithms.

A Flipkart clone built using HTML, CSS, and JavaScript is a front-end project that mimics the user interface of an e-commerce platform. It includes features like product listings, a shopping cart design, and responsive layouts. The project focuses on creating visually appealing and interactive web pages.