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Data Science with less than a year in Machine Learning & Python, focusing on predictive modeling and
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Entry-level Data Scientist with a strong foundation in Python programming, including libraries such as NumPy, Pandas, and Matplotlib, along with proficiency in SQL (MySQL) for data management and analysis. Possesses a solid understanding of machine learning, artificial intelligence, and data structures and algorithms. Demonstrates the ability to analyze data, build basic predictive models, and derive meaningful insights. Seeking an opportunity to apply analytical and technical skills in a data-driven environment while continuing to develop expertise in data science.
ANAND INSTITUTE OF HIGHER TECHNOLOGY - ANNA UNIVERSITY
B.E · Electronics & Communication Engineering
January 1, 2020 – January 1, 2024
Commonwealth Bank
Virtual Internship - Introduction to Data Science Job Simulation
April 1, 2026 – April 1, 2026
India
Accenture
Virtual Internship - Software Engineering Job Simulation
February 1, 2025 – February 1, 2025
India
House Price Prediction using Machine Learning
June 1, 2026 – Present
Developed a machine learning model to predict housing prices using the California housing dataset (20K+ records). Performed data cleaning and preprocessing, including handling missing values using mean imputation. Conducted exploratory data analysis (EDA) using histograms and correlation heatmaps to identify key features. Identified median income as the strongest predictor of house prices through correlation analysis. Applied one-hot encoding to transform categorical feature (ocean proximity) into numerical variables. Implemented and compared multiple regression models: Linear Regression, Decision Tree, and Random Forest. Evaluated model performance using Root Mean Squared Error (RMSE). Achieved best performance with Random Forest (RMSE ≈ 48K) outperforming other models. Split dataset into training and testing sets using train-test split (80/20). Saved the trained model using joblib for deployment and reuse.
Degrees of Separation using BFS (CS50 AI Project)
June 1, 2026 – Present
Built a graph-based program to compute the shortest connection between two individuals using real-world movie dataset. Mapped relationships as a graph structure, representing actors as nodes and shared movies as edges. Created Breadth-First Search (BFS) to efficiently find the shortest path between two nodes. Designed and utilized queue-based frontier and visited tracking to optimize graph traversal. Built a system to reconstruct and display the exact chain of connections (actor → movie → actor). Ensured optimal performance with BFS guaranteeing the minimum degree of separation. Applied core concepts of graph theory, search algorithms, and data structures. Demonstrated real-world applications in social network analysis and recommendation systems.
Customer Churn Prediction using Machine Learning
June 1, 2026 – Present
Built a classification model to predict telecom customer churn using a dataset of 7,000+ records and 20+ features. Performed data cleaning and preprocessing, including converting Total Charges to numeric and handling missing values. Conducted EDA using Seaborn/Matplotlib, identifying key churn drivers such as contract type, tenure, and monthly charges. Observed that short-tenure and high-paying customers have higher churn probability. Encoded categorical variables using one-hot encoding, transforming dataset into 30+ numerical features. Executed multiple models: Logistic Regression and Random Forest Classifier. Addressed class imbalance using class weight='balanced' to improve churn detection. Evaluated models using accuracy, precision, recall, and F1-score. Attained ~79% accuracy with Random Forest and improved churn recall using balanced Logistic Regression. Resolved real-world issues like model convergence warnings and categorical data handling.
5 Day Capstone Project Certificate
Kaggle
June 1, 2026 – Present
Numerical Ability, Verbal Ability, and Logical Reasoning
Tcs Nqt
June 1, 2026 – Present
Virtual Internship - Introduction to Data Science Job Simulation
Commonwealth Bank
June 1, 2026 – Present
Network Essentials
Cisco
June 1, 2026 – Present
Virtual Internship - Software Engineering Job Simulation
Accenture
June 1, 2026 – Present
CS50 Artificial Intelligence with Python
Harvard University
June 1, 2026 – Present
Generative AI, Computer Ethics, Artificial Intelligence
LinkedIn & Microsoft
June 1, 2026 – Present
TCS National Qualifier Test (TCS NQT)
TCS - NQT
June 1, 2026 – Present
Cultural Fit Analysis
The candidate shows a strong interest in data science and AI, aligning well with a data-driven culture. Their academic project (CS50 AI) and virtual internships indicate a proactive and self-driven learning approach. The diversity of projects (housing price, churn prediction, graph traversal) suggests a broad interest in applying ML/AI concepts. However, the experience is primarily academic and personal projects, lacking exposure to diverse team dynamics or corporate environments, which might require some ramp-up for cultural integration.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving, critical thinking, and attention to detail through project descriptions. Their involvement in virtual internships suggests an ability to adapt and learn in structured environments. However, direct experience in team collaboration and communication in a professional setting is limited to self-reported soft skills.