⚡ Energy Consumption Prediction – Data Science Project

A smart analytics solution that forecasts future energy consumption using historical usage patterns. Ideal for energy management systems, smart grids, and sustainability-focused platforms.

📈 Project Highlights
• Time series forecasting of energy usage
• Data preprocessing & feature engineering
• Models: Linear Regression, Random Forest, etc.
• Built using Python, Pandas, Matplotlib, Scikit-learn
• Valuable for energy analytics and smart infrastructure

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Energy Consumption Prediction – View Now

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☀️ Solar Energy Radiation Prediction – Data Science Project

A renewable energy forecasting solution that predicts solar radiation levels using machine learning. Perfect for researchers, developers, and smart energy planners aiming to optimize solar power utilization.

🔍 Project Highlights
• Predicts solar radiation based on weather metrics
• Involves regression models & time series analysis
• Includes data visualization & performance metrics
• Built using Python, Pandas, Matplotlib, Sklearn
• Supports smart energy system development

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🔗 Solar Radiation Prediction – View Now

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💧 Water Quality Prediction – Data Science Project

A machine learning–based solution to assess and predict the safety of water based on physical and chemical parameters. An essential project for environmental monitoring and public health applications.

🔬 Project Highlights
• Predicts water potability using supervised ML models
• Involves data cleaning, visualization & feature engineering
• Algorithms: Logistic Regression, Random Forest, SVM, etc.
• Built with Python, Pandas, Scikit-learn, Matplotlib
• Ideal for environmental science and public safety solutions

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Water Quality Prediction – View Now

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📊 Insurance Claim Prediction – Data Science Project

Build a smart system that predicts whether a customer is likely to claim insurance using historical data and machine learning models. Perfect for those exploring data science in the insurance domain.

🚀 Key Highlights:
✅ Logistic Regression, Random Forest, and XGBoost models
✅ Data preprocessing, encoding & feature selection
✅ Imbalanced data handling techniques
✅ Performance evaluation using accuracy, precision, recall, F1-score
✅ Clean Python code with visualization

🔗 Project Link:
Insurance Claim Prediction

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