📈 End-to-End ML Sales Predictor Application

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🎯 Application Features

• Interactive Data Upload: Users can effortlessly upload their own datasets.
• Automated Data Preprocessing: Clean and preprocess data for training or inference.
• Multiple Regression Models: Train and compare various models like Linear Regression, Decision Tree, Random Forest, SVR, and K-Neighbors Regressor.
• Instant Predictions: Generate predictions based on real-time user input.
• Model Evaluation: Easily visualize model performance metrics (MAE, MSE, R²).

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📂 Project Structure

src/
└── salespredictor/
    ├── api/
    ├── components/
    │   ├── data_ingestion.py
    │   ├── data_processing.py
    │   ├── model_training.py
    │   └── model_inference.py
    ├── dashboard/
    ├── pipeline/
    ├── utils/
    └── __init__.py
requirements.txt
setup.py
README.md

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⚙️ Installation

1. Clone the Repository

git clone git@github.com:Anand-puthiyapurayil/AI_Sales_Project.git
cd AI_Sales_Project

2. Set Up the Environment

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

Install dependencies:

pip install -r requirements.txt

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🚀 Running the Application

Run Streamlit Interface

streamlit run src/salespredictor/dashboard/app.py

Start FastAPI Backend

uvicorn src.salespredictor.api.main:app --reload

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💡 How to Use

1. Ingest Data: Upload your CSV file via Streamlit and preprocess.
2. Train Models: Select to train multiple models with your data.
3. Make Predictions: Input real-time data to get instant predictions.
4. Evaluate: View detailed metrics and performance visuals of your trained models.

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🛠️ Technology Stack

• Frontend: Streamlit
• Backend: FastAPI, Uvicorn
• Machine Learning Libraries: Scikit-learn, Pandas, NumPy, Joblib
• Visualization: Matplotlib, Seaborn

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📜 License

Distributed under the MIT License.

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🤝 Contributions

Contributions are welcome! To contribute:

• Fork the repository.
• Create a new feature branch (git checkout -b feature/my-feature).
• Commit your changes (git commit -m 'Add feature').
• Push your branch (git push origin feature/my-feature).
• Open a Pull Request.

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📞 Contact

• Email: anand.nelliot@gmail.com
• LinkedIn: Anand Puthiyapurayil

Happy Predicting! 🚀✨