Update README.md

Added detailed project overview, dataset source, dependencies, instructions to run the notebook, and a results table with sample accuracy and AUC scores for all models in the Rainfall Prediction Classifier project.

Author: pathananas2007

README.md

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-# Rainfall-Prediction-Classifier-Using-Machine-Learning
-End-to-end Rainfall Prediction pipeline using Python. Implements data cleaning, feature engineering (Season), preprocessing, and multiple ML models (Random Forest, XGBoost, SVM, KNN, Logistic Regression, Gradient Boosting) with hyperparameter tuning, evaluation, and model comparison.
+# Rainfall Prediction Classifier Using Machine Learning
+
+**Author:** [@pathananas2007](https://github.com/pathananas2007)  
+
+## 📄 Project Overview
+This project implements an **end-to-end machine learning pipeline** to predict rainfall in Melbourne using historical weather data. The workflow includes:  
+- Data cleaning and filtering  
+- Feature engineering (Date → Season)  
+- Preprocessing pipelines (scaling + encoding)  
+- Model building and hyperparameter tuning for:  
+  - Random Forest  
+  - Logistic Regression  
+  - SVM  
+  - KNN  
+  - Gradient Boosting  
+  - XGBoost  
+- Model evaluation using **accuracy, confusion matrices, classification reports, ROC curves, and AUC scores**  
+- Comparison of models to select the best performer  
+
+## 📊 Dataset
+- **File:** `data final.csv`  
+- **Source:** [Australian Government Bureau of Meteorology – Climate Data Online](http://www.bom.gov.au/climate/dwo/)  
+- Contains cleaned weather data for Melbourne, Melbourne Airport, and Watsonia.  
+
+## 🛠 Dependencies
+All required libraries are listed in `requirements.txt`. Key packages:  
+- pandas  
+- numpy  
+- scikit-learn  
+- xgboost  
+- matplotlib  
+- seaborn  
+
+Install dependencies with:  
+```bash
+pip install -r requirements.txt