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Data Analyst with less than a year in SQL, Python, and Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Economics graduate with hands-on analytics experience across broadband, healthcare, retail, EV, and supply chain domains. Skilled in SQL, Python, and Power BI — with a track record of quantified business impact. Identified ₹8.77M in revenue leakage and ₹12.86M in recovery opportunity in a single telecom project. Strong ML foundation: trained and deployed classification, regression, time series, and ensemble models across 8+ end-to-end projects.
Imarticus Learning
Postgraduate Program · Data Science & Analytics
August 1, 2025 – June 30, 2026
Kalindi College, University of Delhi
B.A. (Honours) · Economics
August 1, 2022 – June 30, 2026
Broadband Revenue Leakage & Customer Risk Analysis
January 1, 2025 – June 1, 2026
Identified ₹8.77M revenue leakage and ₹12.86M recovery opportunity by analyzing 250K+ telecom records across 10 relational tables. Built SQL queries with CTEs, window functions, and subqueries to detect billing anomalies, payment delays, and high-risk customer segments. Developed 5-dashboard Power BI report tracking revenue collection rate, churn risk index, and operational KPIs for executive decision-making.
ME/CFS and Depression Prediction - Clinical ML App
January 1, 2025 – June 1, 2026
Built a deployed Streamlit web app predicting ME/CFS, Depression, or comorbidity from 22 clinical and lifestyle features. Benchmarked Logistic Regression, Decision Tree, and Random Forest - Random Forest achieved 99.7% accuracy with 99% sensitivity on ME/CFS detection; serialized winning model with Pickle for deployment.
House Price Prediction with PCA Dimensionality Reduction
January 1, 2025 – June 1, 2026
Preprocessed 1,460-row, 81-feature Ames Housing dataset: domain-specific null imputation (No Basement, No Garage, No Fireplace categories), outlier clipping, and log-transformation of target for normality. Applied PCA to reduce 184 encoded features to 63 principal components (75% variance retained); reduced overfitting — Linear Regression Test R² improved from near-zero to meaningful score after PCA. Computed loadings matrix from eigen vectors and values; visualized Scree plot for component selection; demonstrated SVD equivalence of PCA.
E-commerce Conversion Funnel Analysis
January 1, 2025 – June 1, 2026
Analyzed 19K+ user interactions to identify a 10.49% overall funnel conversion rate and ₹45M in potential revenue leakage from abandonment. Built stage-validated funnel (view → cart → checkout → purchase) using CTEs and window functions across raw event logs. Segmented drop-off by device type, product category, and price band - enabling targeted conversion optimization recommendations.
Cultural Fit Analysis
The candidate's academic projects show a diverse interest in data analysis and machine learning applications across different domains (telecom, e-commerce, healthcare, housing). This breadth of exposure suggests adaptability and a willingness to tackle varied challenges. However, the lack of professional experience makes it challenging to fully assess cultural fit in a corporate environment.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking and problem-solving skills through their project work, particularly in identifying revenue leakage and optimizing conversion funnels. Their ability to build and deploy ML models suggests a proactive and results-oriented approach. However, without direct work experience, it's difficult to assess operational fit, teamwork, or stress handling capabilities.