data science

Developed an end-to-end data science project to build a predictive model using real-world data. The project included data collection, preprocessing, exploratory data analysis (EDA), feature engineering, model building, and evaluation.

Utilized Python libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, and Seaborn to analyze data and implement machine learning algorithms. Applied supervised learning techniques (e.g., Logistic Regression, Decision Trees, or Random Forest) to generate accurate predictions.