A data-driven project integrating Python, Machine Learning, and Power BI to forecast business performance and visualize insights through interactive dashboards. This project is part of my Emerging Talent ELO2 Capstone, designed to strengthen my applied data science and business intelligence skills.
The main goal of this project is to develop a forecasting and analytics platform capable of transforming raw business data into actionable insights. It focuses on helping decision-makers anticipate trends, improve operational efficiency, and plan strategically using predictive analytics.
Key Questions:
- How can machine learning improve the accuracy of business performance forecasting?
- Which KPIs are most predictive of future operational or sales outcomes?
- How can BI dashboards enhance decision-making in real time?
Target Audience:
- Business analysts and data-driven managers
- Operations and planning departments
- Organizations seeking to enhance forecasting capabilities
Call to Action: Encourage professionals to use open-source, automated analytics solutions to make smarter, faster, and more transparent decisions.
This project focuses on the Business Intelligence and Forecasting domain, particularly applied to operational and sales data. The dataset will be sourced from publicly available repositories such as:
Modeling Approach:
- Data preprocessing and feature engineering in Python (Pandas, Scikit-learn)
- Predictive modeling using Random Forest Regressor / XGBoost
- Visualization and trend tracking in Power BI
Possible Shortcomings:
- Limited access to private corporate data
- Forecast accuracy depends on the quality of available open datasets
- Model performance may vary across industries and regions
The analysis will include:
- Exploratory Data Analysis (EDA): Understanding data trends and patterns
- Feature Selection & Model Training: Using ML algorithms for forecasting
- Performance Evaluation: Comparing models using RMSE, MAE, and R² metrics
- Visualization: Power BI dashboard summarizing insights and predictions
Expected Outcomes:
- Improved short-term and long-term forecasting accuracy
- Interactive BI dashboards for business users
- Actionable insights for process optimization and strategic planning
Interpretation: The results will demonstrate how integrated ML + BI systems can bridge the gap between data science and management decision-making, empowering professionals to move from reactive to proactive business strategies.
Who: This project is aimed at business leaders, data analysts, and decision-makers interested in improving forecasting accuracy.
Message: Demonstrate that combining machine learning with Power BI can turn complex datasets into intuitive and impactful business insights.
Why This Format: Power BI dashboards and Jupyter notebooks ensure the analysis is both transparent and actionable, bridging technical modeling with real-world usability.
“Without data, you're just another person with an opinion.” — W. Edwards Deming