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🌦️ Weather Data Analysis in Szeged

This repository contains an analysis of historical weather data from Szeged, Hungary. The project leverages Python for data preprocessing, visualization, and statistical analysis to extract meaningful insights from the dataset.

✨ Features

  • 🔍 Data Exploration: Cleaning, handling missing values, and analyzing data distributions.
  • 📊 Visualizations: Line plots, scatter plots, and heatmaps for trend analysis and correlations.
  • 📈 Statistical Insights: Key observations regarding weather conditions, temperature, and other parameters.

🛠️ Tools and Technologies

  • 🐍 Python: Core programming language for data manipulation and visualization.
  • 📘 Jupyter Notebook: Original analysis platform for iterative development.
  • 📦 Libraries Used: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn.

🗂️ File Structure

  • 📒 weather_analysis_in_szeged.ipynb: Jupyter Notebook containing the original code and visualizations.
  • 🐍 weather_analysis_in_szeged.py: Converted Python script for streamlined execution.
  • 📄 README.md: Project overview and instructions.

📂 Dataset

The dataset used in this analysis is publicly available and includes daily weather measurements such as temperature, humidity, wind speed, and precipitation. Ensure you have the dataset in the appropriate directory before running the analysis.

📊 Results

  • 🌡️ Identified key trends in temperature variations over time.
  • 💧 Explored correlations between weather parameters like humidity and precipitation.
  • 🖼️ Created visualizations to highlight significant patterns in the data.

🚀 Future Work

  • 🤖 Extend the analysis to include predictions using machine learning models.
  • 🖥️ Build an interactive dashboard for dynamic visualization of weather data.

🛠️ Getting Started

Getting Started

To get started with the project, follow these steps:

  1. Clone the repository:
    Use the following command to clone this repository to your local machine:

    git clone https://github.com/usk2003/weather-data-analysis-in-szeged.git

  2. Download the dataset:
    Ensure the dataset is available in the content of gooogle colab. Place the weather dataset file in the content/ folder or as specified in the script.

  3. Execute in Google colab or Jupyter Notebook:
    Execute the ipynb file to analyze the weather data

  4. View the results:
    Explore the visualizations and statistical outputs generated in the analysis.