A web-based Loan Approval Prediction app built with Flask and Python. Users enter personal and financial details, and the app computes a Risk Score, predicts Loan Approval or Rejection, and displays results in a dashboard with progress bars, history, charts, and an interactive results page for better insights.

📊 This Python project analyzes the top 100 highest-rated movies of the decade to uncover trends in ratings, box office collections, and voter demographics. Using libraries like Pandas, Matplotlib, and Seaborn, it provides visual insights into movies, actors, and audience preferences. 🎥📈

This repository contains a Cab Fare Prediction Web App that uses machine learning to predict fares based on distance, time, and location, with Google Maps API integration for route and fare calculations. It features a user-friendly interface and secure authentication, powered by a Kaggle dataset.

A repo with basic ops of numpy and pandas

Logical Rhythm is annual Machine learning competition hosted on Kaggle under Avishkar (Official Technical Fest of MNNIT)

🚀 Natural Language to SQL AI Tool This project enables organizations to query internal structured databases using plain English — no SQL required. It uses Natural-SQL-7B, LangChain, FAISS, and RAG to convert natural language into accurate, schema-aware SQL queries.

This project focuses on predicting retail sales using historical sales data and time-series regression techniques. It leverages Python, Scikit-learn, and XGBoost to build predictive models capable of forecasting sales trends. The goal is to provide actionable insights to retailers for inventory and sales strategy planning.

news analysis dashboard with sentiment analysis, topic modeling, interactive visualization

The SMS Spam Detection System is a Django-based application that classifies messages as spam or ham using machine learning and keyword-based filtering. It detects spam by analyzing suspicious words, patterns, and NLP techniques to improve accuracy.

We analyse GPS fleet data to evaluate driver behavior, detect risky patterns, and identify route anomalies using real world telematics from California. The goal is to have an end-to-end analytics pipeline that will transform raw trip data into actionable insights for fleet safety, operational efficiency, and proactive risk management.