Final-year Computer Science student with hands-on experience in SQL, Python, Excel, workflow automation, and dashboard
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github

Harshil Nagwani  • Peerlist

<h3>Project Overview</h3>
<p>This project provides an end-to-end data pipeline and predictive analytics system designed to estimate Scope-3 emissions, identify high-risk suppliers, and deliver actionable insights through an interactive Power BI dashboard. It addresses the challenge of quantifying Scope-3 emissions, particularly Category 1 (Purchased Goods &amp; Services), by mapping procurement spend to emission factors and developing a supplier-level risk score.</p>
<h3>Key Features:</h3>
<ul>
<li><strong>ETL Pipeline:</strong> Processes raw procurement data, maps categories to NAICS codes, joins with EPA emission factors, handles imputation for missing data, and calculates line-level emissions.</li>
<li><strong>Supplier Risk Scoring:</strong> Generates a composite score based on normalized emissions and spend to prioritize suppliers.</li>
<li><strong>Machine Learning Model:</strong> Utilizes a Random Forest model, achieving high accuracy (97.6% R² = 0.9968) in predicting supplier emissions. Other models like Linear Regression, Ridge Regression, and Gradient Boosting were also evaluated.</li>
<li><strong>Data Visualization:</strong> Presents findings through a comprehensive 4-page Power BI dashboard, including an executive overview, supplier and category analysis, and supplier drilldown views.</li>
<li><strong>Data Sources:</strong> Leverages procurement data (e.g., from Kaggle), US EPA USEEIO emission factors, and NAICS codes for accurate estimation.</li>
<li><strong>Methodology:</strong> Employs spend-based emissions calculation aligned with the GHG Protocol Scope 3 Standard and US EPA USEEIO methodology.</li>
</ul>
<h3>Tech Stack:</h3>
<ul>
<li><strong>Data Engineering:</strong> Python 3.11, Pandas, NumPy</li>
<li><strong>Machine Learning:</strong> scikit-learn (Random Forest, Ridge, Gradient Boosting)</li>
<li><strong>Visualization:</strong> Matplotlib, Seaborn, Plotly</li>
<li><strong>Business Intelligence:</strong> Power BI Desktop, DAX</li>
<li><strong>SQL Analytics:</strong> PostgreSQL-compatible queries</li>
</ul>
<p>The project includes detailed notebooks for the ETL pipeline and ML model training, SQL query examples, and clear instructions on how to run the system.</p>

Harshil Nagwani

Final-year Computer Science student with hands-on experience in SQL, Python, Excel, workflow automation, and dashboard d