Shoplytics is an intelligent analytics platform that uses machine learning to analyze user behavior, predict purchase intent, forecast product demand, and identify customer churn in e-commerce environments.
The system processes historical transaction data and user interaction patterns to generate predictive insights that help businesses optimize marketing strategies, inventory management, and customer engagement.
-
Purchase Prediction
- Predicts the probability of a user purchasing a product using behavioral signals.
-
Customer Churn Prediction
- Identifies customers likely to stop engaging with the platform.
-
Demand Forecasting
- Forecasts future product demand using historical sales data.
-
Product Recommendation Engine
- Suggests products to users based on interaction and purchase patterns.
-
Modular ML Pipeline
- Supports training, evaluation, and deployment of multiple predictive models.
The system trains multiple predictive models:
| Model | Purpose |
|---|---|
| Purchase Model | Predicts purchase probability |
| Churn Model | Detects at-risk customers |
| Demand Model | Forecasts product sales |
| Recommendation Model | Suggests relevant products |
Models are serialized using Pickle and loaded by the backend for inference.
User Interaction Data
↓
Data Processing & Feature Engineering
↓
Model Training Pipeline
↓
ML Models (Purchase, Churn, Demand, Recommendation)
↓
FastAPI Backend
↓
Prediction APIs
↓
Frontend Dashboard / Analytics Interface
Backend
- Python
- FastAPI
- Uvicorn
Machine Learning
- Scikit-learn
- Pandas
- NumPy
- XGBoost
Data Processing
- Feature engineering pipelines
- Model training scripts
Version Control
- Git
- GitHub
Shoplytics
│
├── backend
│ ├── data
│ │ ├── users.csv
│ │ └── sales_history.csv
│ │
│ ├── models
│ │ ├── purchase_model.pkl
│ │ ├── churn_model.pkl
│ │ ├── demand_model.pkl
│ │ └── recommendation_model.pkl
│ │
│ ├── main.py
│ ├── train_models.py
│ └── requirements.txt
│
└── frontend
Clone the repository:
git clone https://github.com/swdeveloperabhi/Shoplytics.git
cd Shoplytics
Create a virtual environment:
python -m venv venv
Activate it:
venv\Scripts\activate
Install dependencies:
pip install -r backend/requirements.txt
Run the backend:
uvicorn backend.main:app --reload
- Real-time event streaming for predictions
- Advanced recommendation algorithms
- Model retraining pipeline
- Interactive analytics dashboard
- Cloud deployment with scalable inference APIs
Abhishek Kumar Computer Science Student | AI & Systems Enthusiast
Interested in building intelligent systems combining machine learning, backend engineering, and scalable architectures.