asdhamidi/global-warming-project

An analytical project using machine learning to discover and evaluate temperature trends in climate data.

Climate Data Analysis

This project focuses on a detailed analysis of climate data for the top 20 cities in the United States over a 50-year period. The primary objective is to investigate changes in temperature, extreme temperature events, and other climate-related phenomena. The analysis employs statistical and machine learning techniques to gain insights from the data.

Table of Contents

• Features
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Data Preparation
  • Exploratory Data Analysis
  • Statistical Analysis
  • Machine Learning
• Tech Stack
• Contributing
• License

Features

• In-depth analysis of climate data for 20 major US cities.
• Examination of temperature trends and extreme temperature events.
• Application of statistical and machine learning techniques.

Getting Started

Prerequisites

Before running this project, ensure you have the following software and libraries installed on your system:

• Python
• Required Python libraries (NumPy, Matplotlib, Pandas, Scikit-Learn)

Installation

1. Clone this repository:

   git clone https://github.com/asdhamidi/global-warming-project.git

2. Navigate to the project directory:

   cd global-warming-project

3. Install the required Python libraries:

   pip install numpy matplotlib pandas scikit-learn

Usage

Data Preparation

1. Prepare your climate data in the following format:

   CITY,TEMPERATURE,DATE
   SEATTLE,9.7,19610107
   SEATTLE,7.2,19610108
   # Add records for all 20 cities and over 50 years.
   

Exploratory Data Analysis

1. Use the provided Python scripts to load and explore the climate data.
2. Perform data visualization to understand temperature trends, extreme events, and other statistical characteristics.

Statistical Analysis

1. Employ statistical methods, including linear regression, to establish relationships between variables and identify temperature trends.
2. Calculate relevant statistical metrics to quantify changes in climate parameters.

Machine Learning

1. Utilize machine learning algorithms for time series analysis and forecasting of temperature trends.
2. Evaluate the performance of machine learning models using statistical measures such as R-squared and root mean square error (RMSE).

Tech Stack

• Python for data analysis and machine learning.
• NumPy for numerical operations.
• Pandas for data manipulation.
• Matplotlib for data visualization.
• Scikit-Learn for machine learning algorithms.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please create a GitHub issue or submit a pull request.

License

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