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Deloitte Consulting
Data Scientist
June 16, 2026 – Present
medcare-drug_interaction
December 7, 2025 – December 7, 2025
medcare-drug_interaction — GitHub repository
View Projectdeept-agl.github.io
December 6, 2025 – December 10, 2025
deept-agl.github.io — GitHub repository
View Projectinsurance-claims-data-visualization
November 16, 2025 – November 16, 2025
Tableau dashboard project analyzing insurance claim statistics. Includes visual exploration of policy types, claim rates, customer demographics, settlement trends, and region-wise performance. Interactive charts provide insights into claim behavior and help identify patterns for improving insurance decision-making.
View Projectstatistical-methods-and-decision-making-project
November 16, 2025 – November 16, 2025
SMDM project applying descriptive statistics, variability measures, outlier detection, and boxplot analysis on wholesale customer spending. Includes probability & contingency table analysis for student survey data, and hypothesis testing (one-sample and two-sample t-tests) on moisture levels in shingles A & B to evaluate quality standards.
View Projectanova-and-pca-advanced-statistics_project
November 16, 2025 – November 16, 2025
Advanced Statistics project performing one-way and two-way ANOVA to study salary differences across education and occupation, including interaction effects. Also applies PCA on college data (777 rows, 18 variables) with scaling, covariance/correlation analysis, eigenvalues, scree plot, and interpretation of principal components.
View Projectdata-mining-clustering-and-classification
November 16, 2025 – November 16, 2025
Data Mining project with two parts: customer segmentation using hierarchical clustering, agglomerative clustering, and K-Means; and tour insurance claim prediction using CART, Random Forest, and ANN. Includes full EDA, scaling, model tuning, performance evaluation, and insights for marketing and risk decisions.
View Projectpredictive-modelling-linear-logistic-lda-project
November 16, 2025 – November 16, 2025
Predictive Modelling project with two parts: Linear Regression to estimate cubic zirconia prices using 27k records, and Logistic Regression & LDA to predict holiday package opt-ins for 872 employees. Includes EDA, encoding, VIF checks, train–test split, performance metrics, model comparison, and business insights.
View Projectelection-vote-prediction-ml-nlp_project
November 16, 2025 – November 16, 2025
Machine learning and NLP project analyzing voter survey data and US presidential speeches. Includes EDA, encoding, multicollinearity checks, and models like Logistic Regression, LDA, KNN, Naive Bayes, Random Forest, AdaBoost, and Gradient Boosting. Gradient Boost delivers best accuracy for exit poll prediction.
View ProjectWine_Sales_Time_Series_Forecasting_Project
November 16, 2025 – November 16, 2025
Time series forecasting project analyzing Rose and Sparkling Wine sales (1980–1995). Includes EDA, decomposition, stationarity checks, exponential smoothing, ARIMA/SARIMA modeling, and model comparison. Best models used to predict future 12-month sales with insights for business planning.
View ProjectFinancial_Risk_Analytics_Project
November 16, 2025 – November 16, 2025
Credit risk prediction project analyzing 3,478 companies using financial ratios, growth metrics, and balance-sheet indicators. Includes EDA, outlier treatment, logistic regression, random forest, and LDA models to identify default risks and key drivers, achieving strong predictive accuracy.
View ProjectCultural Fit Analysis
The candidate demonstrates a strong passion for data science through a diverse portfolio of personal projects covering various domains (finance, healthcare, insurance, sales). This indicates a proactive and self-driven learning attitude. The breadth of projects suggests adaptability and a willingness to explore different problem spaces. However, the lack of team-based projects or contributions to open-source initiatives makes it difficult to assess collaborative cultural fit. The future start date for the current role also limits the assessment of professional cultural alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a structured approach to problem-solving and a focus on deriving business insights. However, without psychometric test results or interview data, it is difficult to assess soft skills like teamwork, stress handling, or communication clarity in a professional setting. The future start date for the current role makes it challenging to assess real-world operational fit.