https://github.com/dawoudtormos/rain_predicition_ml
This repository contains a machine learning project for rain prediction using historical meteorological data. The goal is to predict whether it will rain the next day based on weather measurements like atmospheric water vapor, cloud formation, and past rainfall records.
https://github.com/dawoudtormos/rain_predicition_ml
ai machine-learning machine-learning-algorithms matplotlib numpy seaborn sklearn
Last synced: about 1 month ago
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This repository contains a machine learning project for rain prediction using historical meteorological data. The goal is to predict whether it will rain the next day based on weather measurements like atmospheric water vapor, cloud formation, and past rainfall records.
- Host: GitHub
- URL: https://github.com/dawoudtormos/rain_predicition_ml
- Owner: DawoudTormos
- Created: 2025-06-06T18:40:30.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-06-06T18:53:49.000Z (about 1 year ago)
- Last Synced: 2025-06-06T19:43:33.207Z (about 1 year ago)
- Topics: ai, machine-learning, machine-learning-algorithms, matplotlib, numpy, seaborn, sklearn
- Language: Jupyter Notebook
- Homepage: https://colab.research.google.com/drive/1VoVsrEU6L7gXqqVNC8_hmA5Cz47FQ6o5?usp=sharing
- Size: 1.83 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Rain Detection using Machine Learning
🌧️ **Predict whether it will rain tomorrow using weather data**
This project implements a **machine learning model** to forecast rainfall based on historical meteorological data. Using daily weather observations (e.g., humidity, cloud cover, past rainfall), the model predicts the likelihood of rain the following day.
## 📌 Key Features
- **Classification model** (e.g., Logistic Regression, Random Forest, or Neural Networks)
- **Dataset**: Publicly available weather data from **Australia’s Bureau of Meteorology**
- **Preprocessing**: Handles missing data, feature engineering, and normalization
- **Evaluation**: Compares model accuracy against traditional forecasting methods
## 🚀 Applications
- **Agriculture**: Helps farmers plan irrigation and harvesting
- **Disaster Management**: Early warning for floods or droughts
- **Urban Planning**: Improves water resource management
## 📂 Dataset
The dataset includes:
- **Atmospheric conditions** (humidity, pressure, temperature)
- **Cloud cover & wind speed**
- **Historical rainfall records**
- **other factors**
🔗 **Source**: [Bureau of Meteorology (Australia)](http://www.bom.gov.au/climate/data/)
## 🛠️ Setup & Usage
1. Clone the repo:
```bash
git clone https://github.com/yourusername/rain_detection_ML.git
```
2. Run the Jupyter notebook for training & evaluation:
```bash
jupyter notebook rain_prediction_model.ipynb
```
## 📊 Results
- Model achieves **85.54% accuracy** (varies by algorithm)
- Feature importance analysis reveals key weather indicators
🤝 **Contributions welcome!** Open to optimizations, new models, or expanded datasets.
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🔍 *A practical ML project for weather prediction, reducing reliance on manual forecasting.*