Weather Prediction System using Machine Learning

Project Overview

This project predicts weather conditions using historical meteorological data. The goal of the project is to analyze weather patterns and build a machine learning model that can predict weather-related outcomes based on different environmental factors.

The project demonstrates the complete machine learning workflow including data preprocessing, model training, evaluation, and visualization.

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Dataset

The dataset contains historical farm weather data with the following features:

• Date – Observation date
• MaxT – Maximum temperature
• MinT – Minimum temperature
• WindSpeed – Wind speed recorded
• Humidity – Humidity percentage
• Precipitation – Rainfall measurement

This dataset helps the model learn relationships between temperature, humidity, wind speed, and rainfall patterns.

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Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Jupyter Notebook / Google Colab

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Machine Learning Workflow

1. Data Collection
2. Data Cleaning and Preprocessing
3. Feature Selection
4. Model Training using Scikit-learn
5. Model Evaluation
6. Data Visualization

Run the Project

You can run this project directly on Google Colab:

Open in Google Colab