🌦️ Weather Forecasting using Machine Learning

📌 Overview

This project aims to predict daily rainfall probability based on historical weather data using machine learning models. The dataset includes weather parameters such as temperature, humidity, wind speed, cloud cover, and pressure. The final model predicts whether it will rain on a given day, helping businesses and individuals make data-driven decisions.

🚀 Features

• Data Preprocessing: Handles missing values, outliers, and feature scaling.
• Exploratory Data Analysis (EDA): Visualizes trends and correlations.
• Machine Learning Model: Trains multiple models and selects the best one.
• Hyperparameter Tuning: Uses GridSearchCV for optimization.
• Real-time Predictions: Designed for API integration with IoT sensor data.

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📂 Project Structure

📦 Weather-Forecasting
│── weather_data.csv       # Raw & Processed Datasets
│── Intellihack_q1.ipynb   # Jupyter Notebooks for EDA, Data cleaning, feature engineering, & Model Training
│── models/                # Saved ML Models
│── requirements.txt       # Required Python Packages
│── README.md              # Project Documentation
│── report.pdf             # Detailed Report (if applicable)

📊 Dataset

The dataset consists of 296+ daily observations with the following features:

• avg_temperature (°C)
• humidity (%)
• avg_wind_speed (km/h)
• cloud_cover (%)
• pressure (hPa)
• rain_or_not (Binary: 1 = Rain, 0 = No Rain)

📌 Correlation Insights:

• Humidity & temperature are highly correlated with rain.
• Wind speed has minimal impact on rain prediction.

📌 Installation & Setup

🔧 1️⃣ Prerequisites

Ensure you have Python 3.8+ installed. Then, install the required dependencies:

pip install -r requirements.txt

⚙️ System Architecture

The system is designed to work with real-time IoT sensor data.

🔹 Data Flow:

1️⃣ IoT Sensors & APIs → 2️⃣ Data Cleaning & Storage → 3️⃣ ML Model Prediction → 4️⃣ API & Dashboard

📌 (Check the report.pdf for a detailed system diagram.)

🔮 Future Improvements

• 🔹 Deploy the model using Flask/FastAPI for real-time predictions
• 🔹 Implement LSTM for time-series forecasting
• 🔹 Optimize data pipeline for large-scale weather data

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.