🌧️ Rainfall Prediction Using Machine Learning

📌 Overview

This project predicts daily rainfall using historical weather data from Austin (2019–2023). Multiple machine learning regression models are trained, cross-validated, and compared to select the most accurate model.

---

🎯 Objective

• Predict rainfall (precip) using meteorological features
• Compare baseline and advanced ML models
• Select the best-performing model using cross-validation

---

📊 Dataset

• File: Austin_2019_2023.csv
• Location: Austin, USA
• Time Period: January 2019 – July 2023
• Features:
  • tempmax
  • tempmin
  • humidity
  • dew
• Target:
  • precip (Rainfall)

---

🧠 Models Used

• Linear Regression (Baseline)
• Random Forest Regressor
• Support Vector Regressor (SVR)
• XGBoost Regressor

---

🔬 Methodology

1. Data preprocessing and missing value handling
2. Train–test split (80/20)
3. 5-fold cross-validation
4. Model evaluation using RMSE and R²
5. Model comparison and selection

---

🚀 How to Run

pip install -r requirements.txt
python src/train_models.py