✅ What You Have Done in This Project
✅ Imorted and explored the dataset (Rainfall.csv)
✅ Performed Data Preprocessing Handled missing values Encoded categorical features (if any) Scaled/normalized data for model training
✅ Exploratory Data Analysis (EDA) Visualized rainfall trends using Matplotlib/Seaborn Checked correlations between features Identified class imbalance in target labels
✅ Dealt with Imbalanced Data Used RandomOverSampler from imblearn to balance the dataset
✅ Built and Trained Machine Learning Models Logistic Regression XGBoost Classifier SVC
✅ Evaluated Model Performance Accuracy Score Classification Report (Precision, Recall, F1-Score) Confusion Matrix for detailed prediction analysis
✅ Compared Models to check which performed better