amirsoahil101/Rainfall_Prediction_Dashboard

🌧️ Rainfall Prediction Dashboard using Machine Learning

A production-grade, highly intuitive Streamlit web application that predicts the likelihood of rainfall based on climate and atmospheric features. The underlying engine utilizes a robust Logistic Regression pipeline, achieving an impressive accuracy metric on test evaluations.

📱 Application Interface

Here is a preview of the interactive dashboard layout:

🚀 Key Features

• Production-Ready UI: Clean, split-column layout with helpful descriptive tooltips for users.
• Resilient Architecture: Embedded error handling (try-except chains) to capture missing project dependencies or serialization asset errors gracefully without breaking the layout skeleton.
• Preprocessed Compatibility: Seamlessly integrates standard scaler mappings directly on user entry streams.

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📊 Model Performance

• Algorithm: Logistic Regression
• Model Accuracy: 92%
• Features Included: Temperature, Dew Point, Humidity, Sea Level Pressure, Wind Direction, Solar Radiation, and Wind Speed.

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📁 Repository Structure

├── app.py              # Main Streamlit Application UI Code
├── UI.png              # Screenshot of the Application Interface
├── model.pickle        # Trained Logistic Regression Model Weights
├── scale.pickle        # Fitted StandardScaler Instance
├── column.pickle       # Reference list of verified training columns
└── requirements.txt    # Application Dependency Manifest

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clone command

git clone https://huggingface.co/amirsoahil101/Rainfall_Prediction_Dashboard