AI-Driven-Business-Performance-Analytics-System

Overview

This project is a full-stack AI-driven Business Analytics platform that helps organizations monitor, forecast, and analyze key performance indicators (KPIs) interactively. It combines data simulation, KPI calculation, machine learning forecasting, and interactive visualization to support decision-making for business leaders.

The platform allows users to:

Automatically calculate business KPIs like Revenue, Expenses, Profit, ROI, Churn Rate, and Customer Growth

Forecast future KPIs using ARIMA and XGBoost models

Detect anomalies in KPIs

Interactively visualize trends and forecasts via a Streamlit dashboard

Download forecasted data for further analysis

** Features Core Features

Automated Data Simulation & KPI Calculation

Generates realistic monthly business data

Calculates derived KPIs like Profit, ROI, Expenses, Churn, Customer Growth

Machine Learning Forecasting

ARIMA for time-series forecasting

XGBoost for regression-based predictions

Performance metrics (MAE, RMSE) for model comparison

Interactive Dashboard (Streamlit)

Select KPIs dynamically (Revenue, Profit, ROI)

Visualize actual vs forecast trends

Highlight anomalies in red

Display automatic insights for the latest month

Download data as CSV

Advanced Features

Anomaly Detection

Flags unusual KPI deviations automatically

Automated Insights

Generates human-readable summaries of KPI changes

Forecast Comparison

Compare ARIMA and XGBoost predictions side by side

** Project Structure ai-business-performance-analytics/ │ ├── data/ # Raw or external datasets (optional) ├── notebooks/ │ └── business_performance_pipeline.ipynb # Full data prep & modeling notebook ├── results/ │ └── business_kpi_data.csv # Exported KPI & forecast results ├── src/ │ ├── dashboard_app.py # Streamlit interactive dashboard │ ├── data_preprocessing.py # (optional, reusable preprocessing scripts) │ ├── model_training.py # (optional, modular ML training scripts) │ └── auto_report.py # (optional, auto PDF report generator) ├── requirements.txt # Python dependencies └── README.md # Project documentation

** Tech Stack

Python – Data processing and ML modeling

Pandas & NumPy – Data manipulation

Statsmodels (ARIMA) – Time series forecasting

XGBoost – Regression forecasting

Matplotlib & Plotly – Visualization

Streamlit – Interactive dashboard

Git & GitHub – Version control and collaboration

** Key Insights

The platform provides:

Monthly trends of business KPIs

Comparative model forecasts (ARIMA vs XGBoost)

Detection of anomalies (spikes or drops in KPIs)

Automatically generated insights for easy interpretation

Exportable forecast data for reporting

⚙️ Setup Instructions

1. Clone the Repository git clone https://github.com//ai-business-performance-analytics.git cd ai-business-performance-analytics

2. Install Dependencies pip install -r requirements.txt

3. Run the Streamlit Dashboard streamlit run src/dashboard_app.py

4. Explore

Select KPIs in the sidebar

Adjust forecast months

Download CSV of KPI and forecast data

** Example Dashboard Preview

(You can add screenshots or GIFs here showing Revenue trends, Forecast charts, and KPI anomaly highlights.)

** Future Improvements

Add multi-company or regional comparison

Integrate LSTM or Prophet forecasting models

Generate automated PDF reports with charts and insights

Deploy to Streamlit Cloud or any cloud hosting for public access

** Author

Ishan Dhar Pawar M.Sc. Data Science – Business Analytics, SRH University, Germany

LinkedIn:(https://www.linkedin.com/in/ishandharpawarid/)

** This project demonstrates a complete pipeline: from data generation → KPI calculation → ML forecasting → dashboard visualization → report export, making it a strong addition to your portfolio, thesis, or professional application.