Summary
Completed a Data Science Internship, gaining practical experience in machine learning model development, data analysis, and predictive modeling for real-world applications.
Highlights
Engineered a machine learning model to classify emails as spam or not spam, demonstrating proficiency in classification techniques.
Applied advanced text classification and binary classification techniques, leveraging Natural Language Processing (NLP) for robust model performance.
Improved data preprocessing, feature extraction, and model evaluation methodologies, optimizing model efficiency and accuracy.
Conducted comprehensive Exploratory Data Analysis (EDA) on a large Airbnb dataset, identifying critical trends and patterns in pricing and property types.
Cleaned and preprocessed extensive datasets, effectively resolving missing values, duplicates, and inconsistencies to ensure data integrity for accurate analysis.
Developed compelling visualizations using Matplotlib and Seaborn to illustrate pricing patterns, property types, and high-performing neighborhoods, informing strategic decisions.
Built a machine learning model to accurately predict house prices, integrating key features such as square footage and number of bedrooms.
Performed extensive data preprocessing, effectively managing missing values, outliers, and categorical variables to enhance model reliability.
Evaluated and optimized multiple regression models (Linear, Decision Tree, Random Forest) using MAE, MSE, and RMSE metrics, significantly improving predictive accuracy through rigorous tuning and feature engineering.