Weather Prediction model

This repository contains a project aimed at analyzing and predicting weather patterns using machine learning. The core functionality is implemented in a Jupyter notebook, and weather data is provided in a CSV file.

Contents

• predict.ipynb: A Jupyter notebook containing the implementation of the weather prediction model.
• weather.csv: A dataset with historical weather data, used for training and evaluating the model.

Getting Started

Prerequisites

To run this project, you need to have the following installed:

• Python (>= 3.7)
• Jupyter Notebook
• Necessary Python packages (listed below)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/weather-prediction.git
   cd weather-prediction

2. Install the required packages:

   pip install pandas numpy scikit-learn matplotlib seaborn

3. Launch the Jupyter notebook:

   jupyter notebook predict.ipynb
   

Dataset

The file weather.csv contains historical weather data. Ensure the dataset is in the same directory as the notebook or update the file path in the notebook accordingly.

Usage

1. Open the predict.ipynb notebook in Jupyter.
2. Run the cells sequentially to:

• Load and preprocess the dataset.
• Train a weather prediction model.
• Evaluate the model's performance.
• Visualize predictions and insights.

Features

• Data preprocessing and visualization.
• Training a machine learning model for weather prediction.
• Performance evaluation metrics and visualizations.