Building intelligent business analytics systems that turn raw transactional data into actionable customer insights. This repository houses the data-science, business-intelligence, and predictive-analytics solutions developed by AdamAI-Systems.
An end-to-end customer analytics platform combining segmentation, lifetime-value forecasting, and churn prediction with an interactive dashboard.
- Tech Stack: Python, Pandas, scikit-learn, XGBoost, lifetimes (BG/NBD + Gamma-Gamma), SHAP, Streamlit, Plotly.
- Key Features:
- RFM segmentation with K-Means clustering (5 customer segments).
- Probabilistic CLV via BG/NBD + Gamma-Gamma models (3/6/12-month horizons).
- Churn prediction with XGBoost + SHAP explainability.
- Multi-page Streamlit dashboard with custom CSV upload and per-customer drill-down.
To run any of the projects locally, navigate to the specific project directory and follow the instructions in its respective README.md.
Most projects follow a common workflow:
- Create a virtual environment (
python -m venv .venv) and activate it. - Install dependencies:
pip install -r requirements.txt. - (Optional) Download the full dataset:
python scripts/download_data.py. - Train the models:
python scripts/train_all.py. - Launch the dashboard:
streamlit run app/streamlit_dashboard.py.
Note: Trained models (
*.pkl) and raw datasets are intentionally not committed to this repository. Each project ships with a small sample dataset so the dashboard can be explored immediately.
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