Primary aim of this project is to build machine learning model that should be able to predict the solar power output of the 12 different location of the Northern Hemisphere according to the provided dataset.
💧Water Potability Prediction .🔬 AI-Powered Analysis: Predicts water safety using machine learning. 📊 Key Metrics: Evaluates pH, sulfate, and other attributes. 🌐Interactive Interface: Flask-based with Home, Blog, and Prediction Form. ✅ Accurate Results: Ensures reliable insights for water safety.
Sales and pricing data that is subject to noise and skewness are managed with difficulty thanks to the Copper Industry Sales and Leads Prediction Project. In the industry, manual forecasts can be inaccurate and time-consuming. The creation of machine learning models is the main goal of this project in order to overcome these obstacles.
EDA ,data-Pipelines, Randamforest, ROC curve
This project leverages Random Forest Classification to predict heart disease based on clinical parameters such as age, sex, blood pressure, and cholesterol levels. It achieves high accuracy and provides insights into feature importance, aiding early detection and prevention of heart disease.
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