This repository showcases the R projects completed as part of my training with Edvancer Eduventures, where I honed my data science and machine learning skills. Each project demonstrates real-world problem-solving using various statistical and machine learning techniques, along with the effective use of R for data analysis, visualization, and modeling.

1. Real Estate Price Prediction

Objective: Predict property prices based on features like area, architecture, and environmental ratings.
Key Highlights:

• Implemented Ridge and Lasso regression techniques to handle multicollinearity and improve model performance.
• Used feature engineering and exploratory data analysis (EDA) to uncover insights about the dataset.
• Datasets: housing_train.csv and housing_test.csv.

2. Employee Turnover Prediction

Objective: Predict whether valuable employees are likely to leave prematurely.
Key Highlights:

• Built a Random Forest classifier model to submit probability scores for employee turnover.
• Conducted feature selection and importance analysis to identify top factors influencing attrition.
• Datasets: hr_train.csv and hr_test.csv.