🔮 EV Adoption Forecasting Dashboard

An interactive Streamlit web application that forecasts county-level Electric Vehicle (EV) adoption for the next 3 years using historical data and machine learning.
This project was developed as part of AICTE Internship Cycle 2.

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📌 Features

• County Selection – View historical & forecasted EV adoption trends for any county.
• 3-Year Forecast – Predict cumulative EV adoption using a trained regression model.
• Multi-County Comparison – Compare adoption growth across up to 3 counties.
• Growth Insights – Display forecasted growth percentages.
• Downloadable Forecast – Export forecast data as CSV.

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🛠 Tools & Technologies

1. Python – Core programming language.
2. Pandas & NumPy – Data preprocessing & feature engineering.
3. scikit-learn & Joblib – Machine learning model training and persistence.
4. Streamlit & Matplotlib – Interactive UI & visualization.
5. OS & Caching – File handling and st.cache_data for performance.

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📊 Methodology

1. Data Collection – Load historical EV adoption data.
2. Feature Engineering – Create lag, rolling average, and growth rate features.
3. Model Training – Train and validate a Random Forest Regressor model.
4. Forecasting – Predict EV adoption trends for the next 36 months.
5. Deployment – Build an interactive Streamlit dashboard with export options.

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🎯 Learning Objectives

1. Perform data preprocessing & feature engineering for time-series forecasting.
2. Train and deploy a machine learning regression model.
3. Visualize historical vs forecasted trends interactively.
4. Enable multi-county comparisons with growth metrics.
5. Build a robust, user-friendly web app.

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📂 Project Structure

📁 EV-Adoption-Forecast

• app.py # Main Streamlit app
• forecasting_ev_model.pkl # Trained ML model
• preprocessed_ev_data.csv # Preprocessed dataset
• requirements.txt # Dependencies
• README.md # Project documentation
• images/ # Images for app UI