Rohini B

Developed a desktop Expense Tracker application in Python to analyze and predict user spending patterns using Pandas, NumPy, and Matplotlib for data manipulation and visualization. Performed end-to-end data cleaning, preprocessing, and exploratory data analysis (EDA) on real-world datasets using Python in Jupyter Notebook. Implemented supervised machine learning models including Linear Regression, Logistic Regression, and Decision Trees using Scikit-learn to uncover predictive patterns. Applied descriptive statistics and data interpretation techniques to extract actionable business insights from structured datasets.

Completed end-to-end data science projects involving data preprocessing, statistical analysis, and implementation of supervised machine learning models using Python (Pandas, NumPy, Scikit-learn). Performed feature engineering and exploratory data analysis to improve model accuracy and derive meaningful insights from structured datasets. Communicated data-driven recommendations to support business decision-making and delivered actionable reports.