EV Population ↔ ML Forecasting 🚗⚡

This repository contains a machine learning forecasting project designed to predict the on-road population of electric vehicles (EVs) for the next 5 years. The project leverages Python and relevant libraries to analyze historical data and generate future projections.

📂 Project Files

1. forecasting.ipynb
   Jupyter Notebook containing the complete code for data preprocessing, model training, forecasting, and visualization.

2. Electric_Vehicle_Population_Data.csv
   Historical dataset used for training and validating the forecasting model. It includes details about the EV population growth over time.

🚀 Features

• Data Analysis: Insights into historical trends of electric vehicle adoption.
• Forecasting Model: Time series forecasting using machine learning techniques.
• Visualization: Graphical representation of past trends and future projections.

🛠️ Technologies Used

• Python (Pandas, NumPy, Prophet)
• Jupyter Notebook
• Plotly for visualization

📊 Forecast Objective

• Predict the number of electric vehicles on the road for the next 5 years based on historical data.
• Analyze growth trends to support planning and decision-making for EV infrastructure and policy.

📈 How to Use

1. Clone this repository:

   git clone https://github.com/Soumyadipta2020/ml_forecasting.git
   cd ml_forecasting

2. Install dependencies:

   pip install -r requirements.txt

3. Open the Jupyter Notebook:

   jupyter notebook forecasting.ipynb

4. Run each cell to preprocess the data, train the model, and visualize the results.

💡 Contribution

Contributions are welcome! If you have ideas to enhance the app or fix issues, feel free to fork the repository, make changes, and submit a pull request.

Steps to Contribute:

1. Fork this repository.
2. Create a new branch: git checkout -b feature-name
3. Commit your changes: git commit -m "Add feature-name"
4. Push to your branch: git push origin feature-name
5. Open a Pull Request.

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Happy Forecasting! 🌍🔌