🌦️ Australia Rainfall Prediction

🔍 Project Overview

Australia's highly variable climate makes rainfall prediction crucial for agriculture, water management, and disaster preparedness. This project leverages historical weather data and machine learning techniques to develop a predictive model for rainfall, helping stakeholders make informed decisions. By analyzing meteorological variables such as temperature, humidity, wind speed, and atmospheric pressure, the model aims to generate accurate rainfall forecasts.

🛠 Tools & Technologies

• Programming: Python (Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn)
• Machine Learning: Random Forest, Support Vector Machines, Gradient Boosting
• Data Processing: Feature engineering, data cleaning, normalization
• Visualization: Matplotlib & Seaborn for exploratory analysis
• Jupyter Notebook: Interactive data exploration & model development

📂 Folder Structure

• data/: Spatial datasets (kaggle australia rainfall data)
• notebooks/: Analysis workflow in Jupyter
• outputs/: Final maps and visuals
• src/: Custom scripts

📊 Key Findings

• Feature Engineering Improves Accuracy: Selecting optimal features significantly improves model performance.

• Weather Trends Influence Predictions: Temperature & humidity show strong correlations with rainfall occurrence.

• 85% Accuracy Achieved: The final model provides robust predictions with an accuracy score of 85%.

🚀 Run the Project

git clone https://github.com/FisanUJI/australia-rainfall-prediction.git
cd australia-rainfall-prediction
pip install -r requirements.txt
jupyter notebook

🎯 Results & Insights

• Interactive charts displaying historical rainfall patterns

• Model evaluation metrics including precision, recall, and accuracy

• Predicted rainfall values vs. actual recorded data

📞 Contact

Oluwatobi – LinkedIn |Portfolio