📦 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.

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✅ 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

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🧠 Tech Stack

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

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🚀 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

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📂 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

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📸 Output Screenshot

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📊 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

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🤝 Contributing

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

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📜 License

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

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📬 Contact

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