https://github.com/tanay-dwivedi/weather-data-analysis

The project involves comprehensive analysis of a weather dataset to uncover trends, correlations, and insights, facilitating informed decision-making in various sectors reliant on meteorological data.
https://github.com/tanay-dwivedi/weather-data-analysis

dataanalysis matplotlib-pyplot plotly python seaborn visualization weatherdata

Last synced: 8 days ago
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The project involves comprehensive analysis of a weather dataset to uncover trends, correlations, and insights, facilitating informed decision-making in various sectors reliant on meteorological data.

• Host: GitHub
• URL: https://github.com/tanay-dwivedi/weather-data-analysis
• Owner: Tanay-Dwivedi
• Created: 2024-03-09T18:27:10.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2024-05-08T14:25:02.000Z (8 months ago)
• Last Synced: 2024-11-07T03:31:04.779Z (about 2 months ago)
• Topics: dataanalysis, matplotlib-pyplot, plotly, python, seaborn, visualization, weatherdata
• Language: Jupyter Notebook
• Homepage:
• Size: 1.69 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Weather Data Analysis

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## Problem Statement

The project aims to conduct comprehensive **data analysis** on a weather dataset comprising variables such as **temperature (Temp_C)**, **dew point temperature (Dew Point Temp_C)**, **relative humidity (Rel Hum_%)**, **wind speed (Wind Speed_km/h)**, **visibility (Visibility_km)**, **pressure (Press_kPa)**, and **weather conditions (Weather_condition)**. Through this analysis, the objective is to uncover insights and patterns in the data.

The project utilizes **matplotlib**, **seaborn**, and **plotly** for data visualization to provide a comprehensive understanding of the weather dataset.

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## Identify the Data

[Dataset](https://github.com/Tanay-Dwivedi/Weather-Data-Analysis/blob/master/weather.csv)

The dataset comprises various weather parameters including temperature, humidity, wind speed, and atmospheric pressure. It also includes categorical data describing weather conditions, enabling comprehensive analysis of meteorological trends and patterns.

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## Aim of the analysis

1. **Understanding Weather Patterns:** The analysis aims to unravel trends and patterns within the weather dataset, providing insights into temperature fluctuations, humidity levels, wind speed variations, and other meteorological phenomena.

2. **Identifying Correlations:** By examining the relationships between different weather parameters, the analysis seeks to identify correlations and dependencies, shedding light on how these variables interact and influence each other.

3. **Informing Decision Making:** Through data-driven insights, the analysis aims to assist in informed decision-making processes, such as urban planning, agricultural management, and emergency preparedness, by providing a deeper understanding of prevailing weather conditions and their potential impacts.

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