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National Datathon 2023 winner and data analyst with hands-on experience building end-to-end ML systems. Developed a customer churn prediction platform achieving 0.93 ROC-AUC, combining Python, SQL, and Streamlit into a production-ready analytics tool. NPTEL Top 1% in Data Mining. Seeking a role where I can turn complex data into clear business decisions.
Institute of Technology and Management Gwalior
B.Tech · Data Science
September 12, 2022 – June 29, 2026
Customer Churn Prediction & Decision Support System
April 14, 2026 – April 19, 2026
- Built an end-to-end churn prediction and decision-support system achieving 0.93 ROC-AUC for customer churn risk classification and proactive retention analysis. - Developed ML pipelines with feature engineering, categorical encoding, and SMOTE-based imbalance handling across 20+ customer and service attributes. - Created an interactive Streamlit dashboard with 10+ KPI and churn analytics visualisations for customer segmentation and business-focused retention insights. - Implemented a real-time what-if simulation engine enabling scenario analysis across pricing, contract type, payment method, and service subscriptions. - Integrated SQL stored procedures to identify high-risk customer segments and analyse revenue-at-risk patterns for data-driven retention strategies.
View ProjectCredit Risk Prediction System with Explainable AI
April 2, 2026 – April 12, 2026
- Built an end-to-end machine learning system for loan default prediction (~0.74 ROC-AUC)- Handled class imbalance using SMOTE; optimized for high recall (~65%) to detect risky customers. - Designed threshold-based decision system (Approve / Review / Reject) aligned with business strategy. - Implemented SHAP-based explanations for interpretable predictions. - Deployed via Streamlit for real-time risk scoring and decision support. - Reduced model bias by excluding sensitive demographic features and focusing on financial behavior variables
View ProjectSales Performance Dashboard
January 31, 2026 – February 7, 2026
- Built an interactive Power BI dashboard to analyse sales, profitability, and regional performance, enabling data-driven pricing and market strategy decisions. - Identified underperforming markets, high-value customer segments, and weak discount-profit correlation to support targeted business strategies. - Developed DAX-based KPIs to track revenue trends, 14% profit margins, and seasonal sales patterns for performance monitoring and forecasting.
View ProjectThe candidate achieved a 72% score, indicating a good grasp of fundamental Python programming and regular expressions. This score suggests a solid base but also highlights opportunities for growth in more advanced or specialized topics covered in the assessment, such as specific Django patterns or intricate privacy protection implementations.
Strengths
Limitations
Cultural Fit Analysis
The psychometric test score of 67% suggests an average alignment with desired work attitudes and team dynamics, with potential areas for growth in stress handling or specific aspects of collaboration. Conversely, the candidate's initiative in co-founding and leading a data science community ('DataVibe Network') demonstrates a strong proactive attitude, passion for learning, and ability to foster collaboration and problem-solving among peers. This indicates a positive cultural fit for an organization that values initiative, continuous learning, and community engagement. The candidate appears adaptable and results-oriented, which aligns well with dynamic technical environments.
Soft Skills & Operational Fit
The psychometric test score of 67% indicates average performance in areas like logical reasoning, work attitude, stress handling, and team collaboration, suggesting potential areas for development. However, the candidate's resume highlights strong soft skills such as Analytical Thinking, Problem-Solving, Team Collaboration, and a Proactive Attitude. Their role as Vice President & Founding Member of a college data science club further supports their ability to collaborate, lead, and organize, indicating a positive operational fit within a team-oriented environment. There is a slight discrepancy between the psychometric score and self-reported/demonstrated soft skills, which could be explored in an interview.