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This project provides personalized product recommendations, forecasts weekly inventory demand, segments customers using RFM analysis, and generates executive business insights using LLMs.\n\n## Live Demo\n\n![E-Cart Platform Demo](live_demo.gif)\n\n---\n\n## Key Features\n\n* **Recommendation Engine:** Collaborative filtering model (Cosine Similarity) to suggest relevant products based on user interaction history.\n* **Demand Prediction:** Time-series forecasting using a Random Forest Regressor to predict next week's product demand and prevent stockouts.\n* **Customer Segmentation:** K-Means clustering to categorize users into actionable segments (e.g., High-Value Customers, Occasional Buyers) based on Recency, Frequency, and Monetary (RFM) metrics.\n* **AI Business Insights:** Automated executive summaries and marketing strategies powered by Llama 3.1 via the Groq API.\n* **Interactive Dashboard:** A clean, user-friendly Streamlit interface for exploring models, predictions, and reports.\n\n---\n\n## Project Structure\n\n* `data/`: Contains raw dummy datasets and processed features ready for inference.\n* `notebooks/`: Jupyter notebooks covering data cleaning, EDA, feature engineering, and model training.\n* `models/`: Serialized machine learning model artifacts (`.pkl` files).\n* `prompts/`: Text-based system prompts utilized for LLM generation.\n* `app/`: Contains the Streamlit dashboard (`app.py`) and the connecting backend logic (`inference.py`).\n\n---\n\n## Getting Started\n\n**1. Install Dependencies**\nEnsure you have Python installed, then run:\n```bash\npip install -r requirements.txt\n\n```\n\n**2. Environment Setup**\nCreate a `.env` file in the root directory and add your Groq API key to enable the AI insights and recommendation text generation:\n\n```text\nGROQ_API_KEY=your_groq_api_key_here\n\n```\n\n**3. Run the Application**\nNavigate to the project root in your terminal and launch the Streamlit dashboard:\n\n```bash\nstreamlit run app/app.py\n\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraghavendranhp%2Fe_cart_intelligence","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraghavendranhp%2Fe_cart_intelligence","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraghavendranhp%2Fe_cart_intelligence/lists"}