Weather Forecast Prediction

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🌤️ Project Overview

This project aims to predict weather conditions (Clear, Cloudy, or Rainy) using machine learning models based on environmental parameters such as temperature, humidity, pressure, wind speed/direction, and precipitation.
The pipeline includes data cleaning, model training, web deployment with Streamlit, and interactive visualization with Power BI.

The final solution enables users to input weather values and receive real-time predictions, while also exploring insightful patterns and trends using an interactive dashboard.

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Features ✨

🧹 Data Cleaning & Preprocessing

• Removed missing values, handled duplicates, and outliers for robust model training.
• Converted raw wind data into wind vector components (wind_x, wind_y) for better prediction accuracy.
• Engineered new features such as:
  • hour, day, month from datetime
  • is_rain binary flag from precipitation
• Final dataset exported and used for both model training and visualization.

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🤖 Machine Learning Model

• Model Used: Random Forest Classifier
• Target Classes: Clear (☀️), Cloudy (☁️), Rainy (🌧️)
• Input Features: Temperature, Humidity, Pressure, Wind Speed/Direction, Precipitation, Cloud Coverage, Time & Date
• Performance Metrics: Accuracy, Confusion Matrix, and Real-time classification via Streamlit
• Model Export: Trained model (weather_model.pkl) and scaler (scaler.pkl) saved using Joblib

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🌐 Streamlit App

• User Interface Features:
  • Clean dark-themed layout with animated weather icons
  • Input widgets for temperature, humidity, wind, etc.
  • Date and time selectors for context-aware prediction
  • Real-time predictions with dynamic summaries and GIF-based feedback
• Output: Displays predicted weather condition with visual cues and weather summary

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📊 Power BI Dashboard

• KPIs Tracked:
  • Weather condition frequency (Clear, Cloudy, Rainy)
  • Time-based distribution (hourly, daily, monthly)
  • Precipitation and pressure trends
• Visualizations:
  • Weather pattern trends
  • Condition distribution over time
  • Interactive filters for date and conditions
• Interactive Elements: Slicers for time, condition, and metrics comparison

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Tools & Technologies Used 🛠️

Purpose	Tools / Libraries
Data Processing	Python (Jupyter Notebook), Pandas, NumPy
Modeling	Scikit-learn (Random Forest Classifier)
Deployment	Streamlit, Joblib
Visualization	Power BI
Interface Design	Streamlit widgets, HTML/CSS in markdown

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🔍 Key Insights

• Wind vector direction and humidity played a crucial role in rain prediction.
• Cloud coverage and pressure showed strong correlation with cloudy weather.
• Power BI dashboard helped identify daily and seasonal weather variations.

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How to Use 🚀

1. Machine Learning Model

• Open MachineLearningModel.ipynb in Jupyter Notebook to view the full data preprocessing and model training process.
• Evaluate predictions, check accuracy, and export the final model and scaler using Joblib.

2. Streamlit Web App

• Run WeatherPredictionApp.py to launch the live prediction interface.

streamlit run app.py

• Enter values and get instant weather predictions!

3. Power BI Dashboard

• Open weather_forecast.pbix in Power BI Desktop.
• Explore different KPIs and analyze patterns in weather behavior.

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📁 Project Files

File Name	Description
MachineLearningModel.ipynb	Jupyter notebook with preprocessing and ML training
WeatherPredictionApp.py	Streamlit app for live weather prediction
weather_model.pkl	Trained Random Forest model
scaler.pkl	Scaler used for model input normalization
Streamlit.png	Screenshot of deployed web interface
weather_forecast.pbix	Power BI dashboard for insights and data storytelling

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Acknowledgments 🙌

• [Akash Singh Rathore]: Data Cleaning
• [Aditya]: Model Development, Streamlit Integration
• [Your Teammates (if any)]: Power BI Dashboard, Data Engineering
• Special thanks to the hackathon mentors & organizers for support and guidance!

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📌 Final Note

This project showcases the power of combining machine learning, real-time prediction, and visual storytelling to help people understand and anticipate weather patterns.
Whether for planning events, optimizing logistics, or just grabbing an umbrella — this app brings data to life. ☁️🌞🌧️

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