🔥 Build a complete AI-based Shopping Decision System using Python, Machine Learning & Streamlit in just 45 minutes (One Shot)!
📌 Part of: Super Sunday Project Series 🚀
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A smart, interactive AI-powered Shopping Decision App built with Python and Streamlit. This application helps users decide whether they should buy a product or not based on factors like price, rating, budget, and need.
- 🧠 AI Decision Making: Predict whether you should buy a product or not.
- 📊 ML Model Integration: Trained model using real-world shopping logic.
- 💡 Smart Inputs: Based on price, rating, urgency, and budget.
- ⚡ Instant Predictions: Get results in real-time.
- 🎨 Clean UI: Simple and user-friendly interface using Streamlit.
- 📁 Data Simulation: Includes dataset generation script.
Check out the live application here: 👉 Coming Soon
- Python 3.8+
- pip (Python package manager)
-
Clone the repository:
git clone [https://github.com/YOUR_USERNAME/smart-shopping-ai.git](https://github.com/YOUR_USERNAME/smart-shopping-ai.git)
-
Go to project folder:
cd smart-shopping-ai -
Install dependencies:
pip install -r requirements.txt
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Run the application:
streamlit run app.py
To use this system, provide inputs like:
- Product Price
- Salary
- Expenses
- Usage/Frequency
- Product Category
- Frontend/Hosting: Streamlit
- Machine Learning: Scikit-learn
- Data Handling: Pandas, NumPy
- Model Storage: Pickle (
.pkl)
├── app.py # Streamlit UI
├── model.py # ML model training
├── data_generation.py # Dataset creation
├── shopping_data.csv # Dataset
└── model.pkl # Trained model