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https://github.com/ultrasage-danz/weather-data-analysis
Weather Data Analysis notebook project. Created using Google collab
https://github.com/ultrasage-danz/weather-data-analysis
collaboration data-analysis data-science dataset google google-colab-notebook project
Last synced: 8 days ago
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Weather Data Analysis notebook project. Created using Google collab
- Host: GitHub
- URL: https://github.com/ultrasage-danz/weather-data-analysis
- Owner: ultrasage-danz
- License: mit
- Created: 2024-06-30T20:50:40.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-07-01T22:53:48.000Z (7 months ago)
- Last Synced: 2024-12-02T12:16:08.762Z (2 months ago)
- Topics: collaboration, data-analysis, data-science, dataset, google, google-colab-notebook, project
- Language: Jupyter Notebook
- Homepage:
- Size: 190 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Weather Data Analysis
## Introduction
This Jupyter Notebook is designed to perform an in-depth analysis of weather data. The notebook includes various data processing, visualization, and analysis techniques to explore weather patterns and insights.
## Table of Contents
1. [Introduction](#introduction)
2. [Installation](#installation)
3. [Usage](#usage)
4. [Features](#features)
5. [Dependencies](#dependencies)
6. [Configuration](#configuration)
7. [Documentation](#documentation)
8. [Examples](#examples)
9. [Troubleshooting](#troubleshooting)
10. [Contributors](#contributors)
11. [License](#license)
## Installation
To use this notebook, you need to have Jupyter Notebook installed. You can install it using the following command:
```bash
pip install notebook
```
## Usage
1. Clone the repository or download the notebook file.
2. Open the notebook using Jupyter Notebook:
```bash
jupyter notebook Weather_Data_Analysis.ipynb
```
3. Run the cells sequentially to perform the data analysis.
## Features
- **Data Loading**: Load weather data from CSV files.
- **Data Cleaning**: Handle missing values and incorrect data types.
- **Data Visualization**: Create various plots to visualize weather trends and patterns.
- **Statistical Analysis**: Perform statistical analysis on the weather data.
## Dependencies
The notebook requires the following Python libraries:
- pandas
- numpy
- matplotlib
- seaborn
You can install these dependencies using the following command:
```bash
pip install pandas numpy matplotlib seaborn
```
## Configuration
Ensure that your CSV data file is in the same directory as the notebook or provide the correct path to the file in the notebook.
## Documentation
The notebook includes detailed comments and explanations for each step of the analysis process. Refer to the markdown cells within the notebook for more information.
## Examples
Below are examples of some visualizations and analyses performed in the notebook:
- Temperature trends over the year
- Correlation between different weather parameters
- Visualization of weather conditions
## Troubleshooting
If you encounter any issues while running the notebook, ensure that all dependencies are correctly installed and that the data file path is correct. Additionally, ensure that you are using compatible versions of the libraries.
## Contributors
- **riles** - Weather Data Analysis
## License
This project is licensed under the MIT License - see the LICENSE file for details.