In this project, I applied data preprocessing steps on the Iris dataset and built a classification model using XGBoost.
Key steps included:
- Handling missing values and data cleaning
- Feature engineering and data scaling
- Splitting the dataset into training and testing sets
- Training and predicting with the XGBoost model
- Evaluating model performance (accuracy, confusion matrix, etc.)
- This project was developed as a hands-on practice to demonstrate data preprocessing and machine learning model development.