A Python-based project that suggests similar movies based on user input. 🌟 It uses a pre-calculated similarity matrix from a dataset of 4,800+ movies to display the top 30 recommendations. 📈 The engine efficiently handles user input errors for an optimal experience. 🎥🍿

This project predicts **Car Prices** with a **92% accuracy** using a **Random Forest model**. It takes you through data cleaning, feature engineering, and model evaluation. Perfect for anyone looking to explore how factors like year, mileage, and engine size impact car prices 🚗📊.

🔍 This repo focuses on detecting Parkinson's Disease using machine learning techniques on vocal features. The project includes data preprocessing, analysis, and model training, achieving a remarkable 99.6% accuracy with the Random Forest Classifier. 🧠

The Sales Forecasting in Data Science project develops a predictive model for sales based on product and store attributes. Using a dataset of 8,500+ entries, it employs data cleaning, EDA, and machine learning techniques to enhance sales predictions and provide actionable insights for retail decision-making. 📊