https://github.com/gunjangyl/rainfall_prediction_system
The Rainfall Prediction System is a machine learning-based web application that forecasts rainfall based on weather parameters like precipitation, temperature, and wind speed.
https://github.com/gunjangyl/rainfall_prediction_system
bootstrap css django html javascript jupyter-notebook numpy pandas python
Last synced: 2 months ago
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The Rainfall Prediction System is a machine learning-based web application that forecasts rainfall based on weather parameters like precipitation, temperature, and wind speed.
- Host: GitHub
- URL: https://github.com/gunjangyl/rainfall_prediction_system
- Owner: gunjangyl
- Created: 2025-02-15T15:55:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-17T14:49:47.000Z (about 1 year ago)
- Last Synced: 2025-04-18T05:28:52.137Z (about 1 year ago)
- Topics: bootstrap, css, django, html, javascript, jupyter-notebook, numpy, pandas, python
- Language: Jupyter Notebook
- Homepage: http://127.0.0.1:8000/
- Size: 57.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Rainfall_Prediction_System
## 📖 Overview
The Rainfall Prediction System is a machine learning-based project designed to predict rainfall using meteorological data. It leverages various machine learning models to analyze factors such as temperature, humidity, wind speed, and atmospheric pressure to estimate rainfall probability.
## 📌 Features
- ✅ Data Preprocessing & Cleaning
- ✅ Exploratory Data Analysis (EDA)
- ✅ Machine Learning Models (Linear Regression, Random Forest, etc.)
- ✅ Model Evaluation & Performance Metrics
- ✅ Web Interface (Optional for deployment)
## 🏗️ Technologies Used
- **Programming Language**: Python
- **Libraries**: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
- **Jupyter Notebook** for analysis & model training
- **Flask (Optional)** for deploying a web interface
## 📊 Dataset
The dataset includes various meteorological parameters such as:
- 🌡️ Temperature
- 💧 Humidity
- 💨 Wind Speed
- 🔽 Pressure
- 🌧️ Precipitation
## 🔧 Installation & Setup
1️⃣ Clone the repository:
```bash
git clone https://github.com/gunjangyl/Rainfall_Prediction_System.git
cd Rainfall_Prediction_System
cd myproject
```
2️⃣ Install dependencies:
```bash
pip install -r requirements.txt
```
3️⃣ Run the system:
```bash
python mmanage.py runserver
```
## 🏋️♂️ Model Training
To train the model, run:
```bash
python src/train_model.py
```
This will process the dataset and train the model using selected algorithms.
## 📜 Results
- Model accuracy: **85-90%** (may vary based on dataset and parameters)
- Evaluation Metrics: Mean Squared Error (MSE), R-Squared, etc.
- Visualization of feature importance and model predictions.
## DEMONSTRATION
Rainfall Prediction Dashboard
User Input Page(For Input Data Example 1)
Forecast Output Page
User Input Page(For Input Data Example 2)
Forecast Output Page
