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https://github.com/vijaymakkad/rainfall-prediction-using-ml
Machine Learning Project
https://github.com/vijaymakkad/rainfall-prediction-using-ml
Last synced: 11 days ago
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Machine Learning Project
- Host: GitHub
- URL: https://github.com/vijaymakkad/rainfall-prediction-using-ml
- Owner: VijayMakkad
- Created: 2024-03-05T11:31:57.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-03-15T08:24:30.000Z (10 months ago)
- Last Synced: 2024-11-06T23:22:52.725Z (2 months ago)
- Language: Jupyter Notebook
- Size: 5.01 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
**Rainfall Prediction using Logistic Regression**
This repository contains code for predicting rainfall using logistic regression. The main file is an IPython Notebook named `LogisticRegression_RainFall_Prediction.ipynb`. Additionally, it includes a pre-trained model saved as a joblib file named `aussie_rain`, and the dataset used for training and testing the model named `weatherAUS.csv`.
### Files Included:
1. `LogisticRegression_RainFall_Prediction.ipynb`:
- This IPython Notebook contains the code for data preprocessing, model training, evaluation, and prediction using logistic regression for rainfall prediction.
2. `aussie_rain.joblib`:
- This file contains a pre-trained logistic regression model for predicting rainfall. It can be loaded using joblib library in Python for direct use in applications without retraining.
3. `weatherAUS.csv`:
- This CSV file contains the dataset used for training and testing the logistic regression model. It includes various weather-related features along with the target variable indicating whether it rained the next day or not.
### Dependencies:
- Python 3.x
- Jupyter Notebook
- pandas
- numpy
- scikit-learn
### Usage:
1. Clone the repository:
```
git clone https://github.com/VijayMakkad/RainFall-Prediction-using-ML.git
```
2. Install dependencies:
```
pip install -r requirements.txt
```
3. Open and run the `LogisticRegression_RainFall_Prediction.ipynb` notebook in Jupyter environment. This notebook guides through the entire process of rainfall prediction using logistic regression.
4. To directly use the pre-trained model, load `aussie_rain.joblib` file in your Python code using joblib library:
```python
import joblib
# Load the pre-trained model
model = joblib.load('aussie_rain.joblib')
# Use the model for prediction
prediction = model.predict(test_data)
```
### Dataset:
The `weatherAUS.csv` dataset is sourced from [Australian Weather Data](https://www.kaggle.com/jsphyg/weather-dataset-rattle-package) available on Kaggle. It contains weather-related features collected from various weather stations across Australia.
### Acknowledgments:
- This project is developed for educational purposes and inspired by real-world applications of machine learning in weather prediction.
- The dataset used in this project is sourced from Kaggle and provided by Joe Young.
### Contributors:
- [Vijay Makkad](https://github.com/VijayMakkad)
### License:
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.