- Predicting whether it will rain tomorrow using Machine Learning.
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This project aims to predict Rainfall (Yes/No) using weather data.
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Since the output is binary, we use Logistic Regression, which is ideal for classification problems.
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The model is trained on important weather features such as:
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🌡️ Temperature (Max & Min)
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💧 Precipitation
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🌬️ Wind Speed
The goal is to build a simple and accurate ML model that helps forecast rainfall.
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To analyze historical weather data
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To train a machine learning model
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To predict if it will rain the next day
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To deploy the model using Streamlit/GitHub/Railway (optional)
1️⃣ Data Preprocessing
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Handled missing values
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Selected required numerical features
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Standardized data
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Split into Train/Test sets
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2️⃣ Model Used
✔ Logistic Regression (Binary Classifier) It works well because the target variable has only two classes: Rain or No Rain.
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3️⃣ Evaluation
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Accuracy Score
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Confusion Matrix
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Classification Report
The model performs well for basic prediction on weather datasets.
- Python 3.x
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-Learn
- Streamlit (for web app)
├── dataset.csv
├── app.py
├── model.pkl
├── requirements.txt
├── README.md
🚀 How to Run the Project Locally 🔧 Step 1: Install Dependencies pip install -r requirements.txt
▶ Step 2: Run Streamlit App streamlit run app.py
- This project can be deployed on:
- Railway
- Render
- Streamlit Cloud
- GitHub Pages (only frontend)
- Max Temperature
- Min Temperature
- Rainfall (Precipitation)
- Wind Speed
- The app predicts:
This project demonstrates how simple weather features can be used to predict rainfall using machine learning. Logistic Regression provides a reliable and efficient solution for binary classification.
You can try the deployed Rainfall Prediction System here:
👉 Live App:https://rainfall-prediction-app-4cff.onrender.com