Introduction to Machine Learning on Weather Data

This repository contains a directory of projects that cover using logistic regression to analyze and predict various aspects of weather data. The projects include information on data loading and preprocessing, model training, model evaluation, and prediction. The directory "python-sklearn-logistic-regression-2" contains the projects.

Overview

The goal of this repository is to provide an introduction to the application of machine learning on weather data. By using logistic regression, we can analyze and predict various aspects of weather such as temperature, precipitation, and storm occurrence. These projects can serve as a starting point for further research and analysis on weather data.

Requirements

• Python 3.x
• Jupyter Notebook
• pandas
• numpy
• sklearn

Getting Started

1. Clone or download this repository to your local machine.
2. Open a terminal or command prompt and navigate to the directory where you downloaded the repository.
3. Install the required libraries by running pip install -r requirements.txt.
4. Navigate to the directory "python-sklearn-logistic-regression-2"
5. Explore the projects and the results of the logistic regression models.