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Data Science with less than a year in machine learning & data analysis.
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Data Science and Machine Learning enthusiast with hands-on experience in machine learning, data analysis, and statistics. Proficient in programming and implementing solutions using various datasets. Seeking opportunities to apply data-driven solutions and build predictive models for impactful projects.
Visva-Bharati University
M.Sc. in Statistics · Statistics
August 1, 2023 – June 30, 2025
Visva-Bharati University
B.Sc. in Statistics (Hons) · Statistics
August 1, 2020 – June 30, 2023
Data Management and Problem Solve Using MySQL
October 1, 2025 – December 1, 2025
Created a database and import a Retail_Sales table in SQL, defining table structure, primary keys, and relevant data types for sales transactions. Executed data cleaning operations by detecting and deleting null or missing values to maintain dataset integrity. Performed data analysis through complex SQL queries, including sales by category, top customers, monthly performance ranking, and shift-based transaction insights..
Car Price Prediction using Ridge and Lasso Regression
July 1, 2025 – September 1, 2025
Performed exploratory data analysis on a complete car dataset, visualized feature correlations using a correlation heatmap, applied label encoding to categorical variables, and use log transformation for numerical values. Assessed multicollinearity using VIF values, and due to multicollinearity built both Ridge and Lasso regression models, evaluating with R2, MSE, and MAE. Ridge Regression achieved best performance (R² ≈ 0.89, MSE ≈ 0.03, MAE ≈ 0.13); validated linearity and homoscedasticity using residuals vs. fitted plots, and ensured residual normality with Q-Q plot and histogram of residuals.
Term Deposit Prediction Using Machine Learning
June 1, 2025 – July 1, 2025
Analyzed a bank marketing dataset (45,211 rows, 18 columns) to predict term deposit subscriptions using machine learning. Used undersampling (88%/12%), Applied Decision Tree, SVM, Random Forest and Adaboost to classify and predict term deposit subscriptions. Achieved 86% precision and 85% recall and 0.85 F1 score with Adaboost, which suggest that Adaboost is best model for prediction.
Sine-Generated Shifted Lindley Distribution :Application and properties
February 1, 2025 – May 1, 2025
Developed and validated the SGSL distribution by applying a sine transformation to the shifted Lindley CDF, analyzing its PDF, CDF, moments, and hazard functions for modeling survival times. Gained parameter estimates θ 2.7827 and μ 0.7502 with biases θ 0.2827 and μ 0.0002 using maximum likelihood estimation and Newton-Raphson methods for simulation data. Analyzed survival times of growth hormone medication data, achieving the best fit with SGSL, gaining AIC of 162.34 and BIC of 161.42, along with log-likelihood metrics, demonstrating superior modeling for biomedical applications.
Time Series Analysis of Amazon & EBay Stock Prices
February 1, 2023 – May 1, 2023
Processed and analyzed monthly stock price data (Open, High, Low, Close, Volume) for Amazon and EBay from March 2000 - April 2023, identifying trends and seasonality impacted by COVID-19. Checked stationarity with the Augmented Dickey-Fuller test and used ACF and PACF plots, applying log transformation and differencing for model optimization. Implemented Decomposition, Moving Average, and Holt-Winters Exponential Smoothing models, showing Amazon's stock recovery post-pandemic.
Training workshop on "Python Programming"
Department of Statistics, Visva-Bharati University
July 1, 2025 – Present
Presented: "A Statistical Point of View of DLS"
Student Seminar
November 1, 2024 – Present
Cultural Fit Analysis
The candidate's involvement in organizing a Statistics Reunion and participating in NSS activities suggests a proactive and community-oriented individual. Representing school and Bhavan teams in cricket tournaments indicates a team player attitude. The diversity of academic and personal projects, combined with extracurricular activities, suggests a well-rounded individual who could contribute positively to a collaborative work environment. The academic background and project work align well with a data-driven culture.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work independently on self-projects and collaborate on academic projects. Participation in organizing events and volunteering suggests teamwork and organizational skills. The academic focus on statistics and machine learning aligns well with the analytical demands of a Data Science role. However, without professional experience, the operational fit in a corporate environment is yet to be proven.