https://github.com/raghavendranhp/e_cart_intelligence

A comprehensive machine learning and AI-driven solution built for the E-Cart platform. This project provides personalized product recommendations, forecasts weekly inventory demand, segments customers using RFM analysis, and generates executive business insights using LLMs
https://github.com/raghavendranhp/e_cart_intelligence

pickle python streamlit

Last synced: 23 days ago
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A comprehensive machine learning and AI-driven solution built for the E-Cart platform. This project provides personalized product recommendations, forecasts weekly inventory demand, segments customers using RFM analysis, and generates executive business insights using LLMs

• Host: GitHub
• URL: https://github.com/raghavendranhp/e_cart_intelligence
• Owner: raghavendranhp
• Created: 2026-03-11T12:47:28.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2026-03-11T13:46:49.000Z (4 months ago)
• Last Synced: 2026-03-11T19:44:05.097Z (4 months ago)
• Topics: pickle, python, streamlit
• Language: Jupyter Notebook
• Homepage:
• Size: 9.81 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# E-Cart Intelligent Product Recommendation & Demand Prediction

A comprehensive machine learning and AI-driven solution built for the E-Cart platform. This project provides personalized product recommendations, forecasts weekly inventory demand, segments customers using RFM analysis, and generates executive business insights using LLMs.

## Live Demo

![E-Cart Platform Demo](live_demo.gif)

---

## Key Features

* **Recommendation Engine:** Collaborative filtering model (Cosine Similarity) to suggest relevant products based on user interaction history.

* **Demand Prediction:** Time-series forecasting using a Random Forest Regressor to predict next week's product demand and prevent stockouts.

* **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.

* **AI Business Insights:** Automated executive summaries and marketing strategies powered by Llama 3.1 via the Groq API.

* **Interactive Dashboard:** A clean, user-friendly Streamlit interface for exploring models, predictions, and reports.

---

## Project Structure

* `data/`: Contains raw dummy datasets and processed features ready for inference.

* `notebooks/`: Jupyter notebooks covering data cleaning, EDA, feature engineering, and model training.

* `models/`: Serialized machine learning model artifacts (`.pkl` files).

* `prompts/`: Text-based system prompts utilized for LLM generation.

* `app/`: Contains the Streamlit dashboard (`app.py`) and the connecting backend logic (`inference.py`).

---

## Getting Started

**1. Install Dependencies**

Ensure you have Python installed, then run:

```bash

pip install -r requirements.txt

```

**2. Environment Setup**

Create a `.env` file in the root directory and add your Groq API key to enable the AI insights and recommendation text generation:

```text

GROQ_API_KEY=your_groq_api_key_here

```

**3. Run the Application**

Navigate to the project root in your terminal and launch the Streamlit dashboard:

```bash

streamlit run app/app.py

```