Developed a machine learning solution to predict customer churn using a telecom customer dataset. Performed exploratory data analysis to understand customer behavior, identified missing values and data quality issues, and applied appropriate preprocessing techniques. Encoded categorical features and prepared the dataset for machine learning by splitting it into training and testing sets. Implemented and compared Logistic Regression, Decision Tree, and Random Forest models using evaluation metrics such as accuracy, precision, recall, and F1-score. Analyzed model performance to identify a suitable approach for predicting customers who are likely to leave the service. Tech: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn

Developed an AI and data platform focused on processing, analyzing, and deriving insights from business data. Built data ingestion and processing pipelines to handle structured datasets efficiently and performed data cleaning, transformation, and validation using Python and Pandas. Designed an analytics pipeline to generate meaningful insights from the processed data and worked on an AI-powered support assistant for querying and understanding business information. Used Python, SQL, Pandas, and machine learning concepts to build an end-to-end data and AI workflow. Tech: Python, SQL, Pandas, Machine Learning, Data Analytics