mtaimoork / australian-rainfall-forecast

Discover 'Precision in Projections' by Muhammad Taimoor Khan, unraveling Australian rainfall forecasts. Leveraging advanced models, the project attains high accuracies, with Logistic Regression leading at 83.7%. A vital resource for data-driven decisions in Australia's ever-changing climate.

Australian Rainfall Prediction

Author: Muhammad Taimoor Khan

Overview

Welcome to the "Australian Rainfall Prediction" project! This repository contains the code and analysis for predicting rainfall patterns in Australia. Leveraging machine learning models, the project aims to provide accurate and actionable insights for weather forecasting.

Project Structure

• Australian_Rainfall_Forecast.ipynb: Notebook that contains Python scripts for data preprocessing and machine learning model training.
• weather_data.csv: Dataset used in the analysis.
• README.md: Overview of the project, its goals, and key findings.

Getting Started

1. Clone the Repository:

   git clone https://github.com/MTaimoorK/australian-rainfall-prediction.git
   

2. Install Dependencies

   pip install -r requirements.txt
   

Explore the Code

• Review the Jupyter Notebooks in the code/ directory for detailed analysis.
• Execute scripts for data preprocessing and model training.

Results

Linear Regression:

• Mean Absolute Error: 0.256
• Mean Squared Error: 0.116
• R2 Score: 0.427

K-Nearest Neighbour:

• Accuracy Score: 0.818
• Jaccard Index: 0.425
• F1-Score: 0.597
• Log Loss: N/A

Decision Tree:

• Accuracy Score: 0.756
• Jaccard Index: 0.401
• F1-Score: 0.572
• Log Loss: N/A

Logistic Regression:

• Accuracy Score: 0.837
• Jaccard Index: 0.509
• F1-Score: 0.675
• Log Loss: 0.380

Support Vector Machine:

• Accuracy Score: 0.846
• Jaccard Index: 0.535
• F1-Score: 0.697
• Log Loss: N/A

Future Enhancements

Explore potential areas for improvement and future enhancements, such as incorporating real-time data or exploring additional machine learning algorithms.

Contribution

Feel free to contribute by forking the repository and submitting pull requests. Bug fixes, feature enhancements, and additional analyses are all welcome!