Data Scientist with 1+ years in Machine Learning & Data Visualization.
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M.Tech. Data Sciences student with a strong foundation in data analysis, machine learning, and visualization tools like Tableau and Python. Proven ability to develop data-driven solutions for real-world problems, including customer segmentation, recommendation systems, and predictive modeling, as demonstrated through academic projects.
COEP
M.Tech. · Data Sciences
August 1, 2024 – June 30, 2026
Genba Sopanrao Moze College of Engineering, Balewadi
UG · Information Technology
August 1, 2019 – June 30, 2023
Deogiri college of Science
Class XII
June 1, 2018 – May 31, 2018
Chate School
Class X
June 1, 2016 – May 31, 2016
Retail Customer Insights and Product Recommendation System
April 1, 2025 – May 1, 2025
Performed customer segmentation using K-Means clustering on retail transaction data to identify distinct customer groups based on purchase frequency, total spend, and recency. Engineered meaningful features such as Recency, Frequency, and Monetary (RFM) values to create accurate customer profiles and enhance cluster quality. Designed a cluster-driven recommendation system to suggest the top 3 most relevant products per customer by excluding previously purchased items. Generated a customer-wise recommendation table for marketing teams to enable targeted product promotions and cross-selling strategies. Visualized key insights and purchasing patterns using Matplotlib and Seaborn to support data-driven marketing decisions.
Airbnb NYC Interactive Dashboard for Rental Insights using Tableau
February 1, 2025 – March 1, 2025
Created an interactive Tableau dashboard to analyze Airbnb NYC listings by room type, price, neighborhood, and availability trends. Visualized geospatial and temporal patterns using heatmaps, bar charts, donut charts, and KPIs to uncover pricing and booking behavior. Enabled dynamic filtering and drill-downs for business users to explore seasonal trends and identify high-demand areas for strategic decisions.
Retail Analysis Using Python And SQL
December 1, 2024 – January 1, 2025
Conducted data cleaning, preprocessing, and exploratory data analysis (EDA) using SQL and Python (Pandas) to uncover sales trends, customer behavior, and operational efficiencies. Generated key insights into regional sales patterns, product demand, and discount impacts, supporting optimized inventory management and pricing strategies. Developed and executed SQL queries to extract actionable business intelligence, including high-performing product categories and seasonal sales trends.
Heart Disease Prediction Using Machine Learning
December 1, 2022 – May 1, 2023
Developed a supervised machine learning model to predict heart disease from structured clinical data, achieving 85%+ accuracy using Random Forest and Logistic Regression algorithms. Applied robust data preprocessing, feature selection, and hyperparameter tuning techniques to optimize predictive performance on healthcare diagnostic datasets. Performed comprehensive exploratory data analysis (EDA) and correlation analysis to identify key risk factors such as cholesterol, blood pressure, and maximum heart rate. Implemented a clean and modular machine learning workflow including model training, k-fold cross-validation, and performance evaluation using confusion matrix and ROC-AUC score.
Data Visualization with Python
IBM SkillsBuild
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects are diverse within the data science domain (retail, healthcare, Airbnb analytics), indicating a broad interest in applying data science to different industries. The academic focus aligns with a learning-driven environment. However, the lack of professional experience makes it difficult to assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on structured problems, analyze data, and present findings. The academic nature of projects suggests a learning-oriented individual. However, there is no information to assess stress handling, team collaboration, or communication clarity in a professional setting.