https://github.com/neuraladitya/electric_vehicle_sales_predictor
EV Sales Prediction in India using Machine Learning. Forecasts electric vehicle sales across Indian states with interactive visualizations and a modern web UI.
https://github.com/neuraladitya/electric_vehicle_sales_predictor
dashboard data-science data-visualization electric-vehicles ev-sales flask india machine-learning matplotlib prediction-model python random-forest sales-analysis
Last synced: about 2 months ago
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EV Sales Prediction in India using Machine Learning. Forecasts electric vehicle sales across Indian states with interactive visualizations and a modern web UI.
- Host: GitHub
- URL: https://github.com/neuraladitya/electric_vehicle_sales_predictor
- Owner: NeuralAditya
- Created: 2025-04-18T19:29:51.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-18T19:45:56.000Z (6 months ago)
- Last Synced: 2025-04-24T00:58:29.179Z (6 months ago)
- Topics: dashboard, data-science, data-visualization, electric-vehicles, ev-sales, flask, india, machine-learning, matplotlib, prediction-model, python, random-forest, sales-analysis
- Language: Python
- Homepage:
- Size: 661 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ⚡ Electric Vehicle Sales Predictor – India
---
## 🚘 Overview
A **machine learning-powered web app** that predicts **electric vehicle (EV) sales** in **Indian states** based on user input features.
This dynamic **web application** predicts electric vehicle (EV) sales trends using a **custom-trained machine learning model**. Users can **upload new datasets**, **train or retrain models** directly from the dashboard, and explore **powerful insights** through **interactive visualizations** and **downloadable reports**. Built for **analysts, policy makers, and auto manufacturers** to decode the future of EV markets—state by state, type by type, and over time.
## 📷 UI Preview
1. Dashboard :
2. Prediction Page :
## 🎯 Prediction Output Screenshot
Here’s a sample output :
---
## 🔑 Features
### 🎯 Add-Ons Like Never Before
1. Dynamic Dropdowns auto-filled from dataset (vehicle types, classes, brands, etc.)
2. Light/Dark Mode switch for better accessibility and modern feel
3. Real-time graph updates post prediction or training
4. Upload your own CSV to retrain the model from the dashboard
### 🔍 EV Sales Prediction
- Inputs:
- Vehicle Type
- Brand/Model
- State
- Year
- Uses a trained model to predict EV sales volume
### 📈 Dynamic Visualizations
Graphs include:
- EV Sales by State
- EV Sales Trends over Years
- Vehicle Type Distribution
- Brand-wise Sales Share (Future)
- Correlation Matrix (Future)
- Custom graphs rendered from user selection (Future)
### 🛠️ Model Management
- Train new models using uploaded .csv via the dashboard
- Upload new training data directly
- Train model on-the-fly with one click
- Models saved as .pkl files for future predictions
### 📄 PDF Report
- Downloadable report with:
- Prediction result
- Embedded analysis graphs
- Copyright
---
## 🧠 Tech Stack
| Layer | Tech |
|--------------|-------------------------------------------|
| Backend | Python, Flask |
| ML/Processing| scikit-learn, pandas, NumPy |
| Text Features| TF-IDF Vectorization |
| Visualization| matplotlib, seaborn |
| Frontend | HTML, CSS, JavaScript (custom styles) |
---
## 🗂️ Project Structure
```
ELECTRIC_VEHICLE_SALES/
│
├── app.py
├── train_model.py
├── extract_dropdown_data.py
├── graphs.py
├── requirements.txt
│
├── data/
│ └── EV_sales_india.csv
│
├── model/
│ ├── model.pkl
│ ├── features.pkl
│ └── dropdown_data.pkl
│
├── static/
│ ├── styles.css
│ ├── graphs.css
│ └── graphs/
│ └── *.png
│
├── templates/
│ ├── index.html
│ ├── result.html
│ └── dashboard.html
│
└── README.md
```
---
## 🚀 How to Run the App
1. Install dependencies:
```bash
pip install -r requirements.txt
```
2. Create these folders and files:
```bash
create model folder
create model.pkl , features.pkl & dropdown_data.pkl
keep all .pkl files empty
(req to save trained models)
```
3. Train the model (Optional):
```bash
python train_model.py
```
4. Run the Flask app:
```bash
python app.py
```
5. Open browser at:
```
http://localhost:5000
```
---
## 🔄 Example Workflow
1. Open the app in browser
2. Select vehicle, state, year, etc.
3. Click Predict
4. View results and interactive charts
5. Head to Dashboard tab to:
- Upload new data
- Retrain model
- Refresh dropdowns
---
## 👨💻 Developer
Made with ❤️ by [Aditya Arora](https://www.linkedin.com/in/NeuralAditya)
© 2025 Aditya Arora. All rights reserved.
---