🌍 Climate Change Modeling - Streamlit App 📌 Project Overview This project predicts climate change indicators using machine learning techniques and a user-friendly Streamlit web interface. It uses historical climate-related data (likesCount, commentsCount, etc.) to generate predictions such as engagement metrics or environmental trends.

🛠 Tools & Technologies Python – Data processing and model building

Pandas & NumPy – Data manipulation

Scikit-learn – Machine Learning (Random Forest)

Streamlit – Web app deployment

Pickle – Model serialization

Matplotlib / Seaborn – Data visualization

📂 Project Structure bash Copy Edit 📁 Climate Change Modeling │-- app.py > Streamlit web application │-- climate_model.pkl > Trained ML model │-- features.pkl > Feature list used by the model │-- climate_nasa.csv > Dataset │-- requirements.txt > Python dependencies │-- README.md > Project documentation 🚀 How to Run the Project Clone the repository

bash Copy Edit git clone https://github.com/your-username/climate-change-modeling.git cd climate-change-modeling Install dependencies

bash Copy Edit pip install -r requirements.txt Run the app

bash Copy Edit streamlit run app.py 📊 Methodology Data Collection & Preprocessing – Handle missing values, select relevant features

Model Training – Random Forest Regressor

Evaluation – MAE, MSE, R² score

Deployment – Streamlit app for real-time prediction

Results & Insights Built a model to predict climate change indicators

Achieved good prediction accuracy on test data

Created an interactive web app for easy use

📢 Conclusion This project demonstrates how machine learning can be applied to climate-related datasets for prediction and decision-making support. It also shows how data science projects can be deployed as interactive web applications.