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Weather Data Analysis (2020–2025)

Project Overview

This project analyzes weather data from 2020 to 2025 to identify patterns and trends in temperature, precipitation, wind speed, and weather conditions.

Objectives

  • Analyze temperature trends over time
  • Study monthly and yearly weather patterns
  • Analyze precipitation and wind speed
  • Understand the distribution of weather conditions
  • Explore relationships between different weather variables

Tools & Libraries

  • Python
  • Pandas
  • Matplotlib
  • Seaborn

Analysis

The project includes data cleaning, exploratory data analysis (EDA), statistical analysis, and data visualization.

Dataset

The dataset contains weather observations covering the period from 2020 to 2025.

Visualizations

The analysis includes:

  • Temperature trends
  • Temperature distribution
  • Correlation analysis
  • Precipitation analysis
  • Wind speed analysis
  • Weather condition distribution
  • Yearly and monthly comparisons

Data Structure

The dataset contains weather observations from 2020 to 2025.

The main variables include:

  • date – Date of the weather observation
  • temp_max – Maximum temperature
  • temp_min – Minimum temperature
  • precipitation – Precipitation recorded
  • wind_speed – Wind speed
  • weather – Weather condition
  • weather_code – Numerical code representing the weather condition

Conclusion

The analysis provides an overview of weather patterns and relationships between key weather variables during the period 2020–2025.

About

Weather data analysis from 2020–2025 using Python, Pandas, NumPy, Matplotlib, and Seaborn to explore weather trends, patterns, and insights through data visualization.

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