https://github.com/soumyadipta2020/ml_forecasting

ML Forecasting on EV Population
https://github.com/soumyadipta2020/ml_forecasting

data-science eda exploratory-data-analysis holt-winters-forecasting jupyter-notebook machine-learning modelling prophet python python3 sarima statistical-analysis statistical-models visualization

Last synced: 9 days ago
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ML Forecasting on EV Population

• Host: GitHub
• URL: https://github.com/soumyadipta2020/ml_forecasting
• Owner: Soumyadipta2020
• License: mit
• Archived: true
• Created: 2024-08-07T16:09:50.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-03T17:49:17.000Z (10 months ago)
• Last Synced: 2025-03-15T04:45:16.300Z (7 months ago)
• Topics: data-science, eda, exploratory-data-analysis, holt-winters-forecasting, jupyter-notebook, machine-learning, modelling, prophet, python, python3, sarima, statistical-analysis, statistical-models, visualization
• Language: Jupyter Notebook
• Homepage: https://connect.posit.cloud/Soumyadipta2020/content/019131da-9fc1-ae2b-821a-b865ab76df9c
• Size: 7.66 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# EV Population ↔ ML Forecasting 🚗⚡

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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:

   ```bash

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

   cd ml_forecasting

   ```

2. Install dependencies:

   ```bash

   pip install -r requirements.txt

   ```

3. Open the Jupyter Notebook:

   ```bash

   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.

---

Happy Forecasting! 🌍🔌