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Data Analyst with less than a year in Python & Machine Learning
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Detail-oriented and results-driven Data Analyst with hands-on experience in Python, SQL, Machine Learning, and Business Intelligence tools including Power BI. Skilled in exploratory data analysis (EDA), statistical analysis, data mining, data visualization, and predictive modeling using XGBoost, Decision Tree, and Ridge Regression. Proven ability to transform complex, large-scale datasets into actionable business insights that support data-driven decision-making. Experienced in supply chain analytics, financial transaction analysis, and pricing strategy optimization. Adept at building interactive dashboards and communicating findings to both technical and non-technical stakeholders.
SGT University
Bachelor of Technology · Computer Science
July 1, 2022 – July 1, 2026
Laptop Price Prediction & Market Analysis
July 1, 2022 – Present
Developed an end-to-end machine learning pipeline to analyze laptop pricing trends across 1,300+ product SKUs using EDA, data mining, PCA, and advanced feature engineering techniques. Built and optimized multiple predictive models including XGBoost, Ridge Regression, and Decision Tree with GridSearchCV hyperparameter tuning, achieving over 88% prediction accuracy on test data. Engineered 15+ domain-specific features from raw product specifications to significantly improve model performance and reduce prediction error by ~22%. Delivered data-driven pricing strategy insights that enabled more informed product positioning and competitive benchmarking decisions for stakeholders.
Credit Card Transaction Analysis Dashboard
July 1, 2022 – Present
Designed and deployed an interactive Power BI dashboard to analyze 10,000+ credit card transactions, visualizing customer spending behavior across multiple dimensions. Built KPI reporting views covering revenue, transaction volume, customer demographics, and expenditure patterns, enabling real-time stakeholder decision-making. Conducted week-over-week revenue trend analysis using DAX calculated measures to identify behavioral shifts and customer engagement patterns over time. Segmented customer insights by card category, income group, age group, and job sector to enable targeted, data-driven financial strategies - improving reporting efficiency by 35%.
E-Commerce Supply Chain Data Analysis
July 1, 2022 – Present
Analyzed pricing and revenue trends across 5+ product categories using Python (Pandas, NumPy) to optimize sales strategies and identify top-performing SKUs. Evaluated sales performance by product type and shipping carrier, uncovering cost reduction opportunities that could lower logistics spend by an estimated 18%. Optimized inventory planning through SKU-level revenue and order quantity analysis, reducing stock inefficiencies and overstock risk across the supply chain. Identified shipping defect patterns and quality control gaps through data-driven root cause analysis, driving process improvements that reduced product return rates by ~12%. Delivered actionable supply chain insights via structured Python reports and visualizations, supporting data-driven inventory and procurement decisions.
NSDC Certification
NSDC
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a proactive approach to learning and applying diverse data analysis techniques across different domains (e-commerce, finance, product pricing). The GitHub portfolio indicates an interest in open-source contributions and continuous learning, which aligns with a culture of innovation and knowledge sharing. However, the lack of professional experience means cultural fit is primarily inferred from academic engagement and stated interests.
Soft Skills & Operational Fit
The candidate's project descriptions highlight strong analytical thinking, problem-solving, attention to detail, and data-driven decision-making. The mention of 'communicating findings to both technical and non-technical stakeholders' and 'Cross-functional Collaboration' indicates a good operational fit for roles requiring teamwork and stakeholder engagement. However, without direct work experience, the practical application of these soft skills in a professional setting is yet to be validated.