Weather Data Analysis with Python 🌦️ 📌 Project Overview
This project analyzes a Weather Dataset using Python and Pandas. The dataset is a time-series dataset with hourly information about weather conditions at a specific location.
We explore and analyze key weather attributes such as:
🌡️ Temperature
🌫️ Dew Point Temperature
💧 Relative Humidity
🌬️ Wind Speed
👁️ Visibility
📊 Atmospheric Pressure
⛅ Weather Conditions
The goal is to perform Exploratory Data Analysis (EDA) and extract meaningful insights from raw weather data.
📂 Dataset
The dataset (Weather Dataset.csv) contains hourly records of weather conditions.
⚙️ Tools & Libraries
Python 🐍
Pandas (data analysis & manipulation)
NumPy (numerical operations)
Matplotlib / Seaborn (data visualization)
🔍 Analysis Performed
Some of the key steps covered in this project:
Inspecting the dataset (head(), shape, columns, info()).
Exploring data types and missing values.
Analyzing distributions of temperature, humidity, and wind speed.
Grouping and aggregating data for meaningful statistics (e.g., average temperature by condition).
Visualizing trends in weather conditions over time.
📊 Example Insights
What is the average temperature when it’s Snowy?
How does Wind Speed vary across different weather conditions?
Relationship between Visibility and Weather Condition.
Distribution of Humidity across the dataset.
🚀 How to Run
Clone this repository:
git clone https://github.com/talhayaseen0/weather-data-analysis.git cd weather-data-analysis
Install dependencies (recommended: use a virtual environment):
pip install pandas numpy matplotlib seaborn
Open the Jupyter Notebook:
jupyter notebook Weather_Data_Analysis_With_Python.ipynb
📌 Future Improvements
Add interactive visualizations using Plotly or Power BI.
Apply machine learning models for weather prediction.
Automate reporting dashboards.
🧑💻 Author
Talha Yaseen
🎓 Master’s in Web & Data Science (Universität Koblenz)