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📦 Amazon Sales Amount Predictor using MLP Regressor

A Streamlit-based web application that predicts the expected sales amount for Amazon orders using a Multilayer Perceptron (MLP) regression model. The model is trained on historical e-commerce transaction data and incorporates categorical and numerical features such as category, fulfillment method, shipping details, and currency.

✅ Key Features

  • 🔍 User-driven input form for key product/shipping parameters
  • 🧠 Trained MLPRegressor neural network for continuous prediction
  • ⚙️ Feature encoding (Label + One-Hot) and scaling pipeline
  • 📈 Model training and automatic persistence (joblib)
  • 🌐 Streamlit UI for browser-based prediction
  • 🧼 Preprocessed dataset (cleaned_amazon_sales.csv) with outliers removed

🧠 Tech Stack

  • Frontend: Streamlit
  • Backend/ML: Python, scikit-learn
  • Model: MLPRegressor (Multilayer Perceptron)
  • Encoding: OneHotEncoder + LabelEncoder
  • Scaling: StandardScaler
  • Persistence: Joblib

🚀 Getting Started

🔧 Installation

git clone https://github.com/your-username/amazon-mlp-sales-predictor.git
cd amazon-mlp-sales-predictor
pip install -r requirements.txt

▶️ Run the App

streamlit run mlp_sales_app.py

📂 Project Structure

mlp_sales_app.py           # Unified training + prediction Streamlit app
cleaned_amazon_sales.csv   # Preprocessed input dataset
mlp_model.pkl              # Saved MLPRegressor model
scaler.pkl                 # Saved StandardScaler
encoder.pkl                # Saved OneHotEncoder
requirements.txt           # Project dependencies
README.md                  # Project overview

📸 Output Screenshot

Sales Predictor Output

📊 Sample Use Case

Predict the likely sales amount (₹) for a new Amazon product based on selected features:

  • Product category
  • Fulfillment type
  • Shipping location
  • Sales channel
  • Currency

🤝 Contributing

Pull requests are welcome. For major changes, open an issue first to discuss what you'd like to change.

📜 License

This project is open-source and available under the MIT License.

📬 Contact

Authors: [Balamurugan & Vijay Kumar]
GitHub: @codestobecreated