🌦️ Weather Data Analysis using Python

This project analyzes weather data to understand temperature, humidity, wind, and precipitation trends over time.
All analysis was performed in Jupyter Notebook using Python libraries like pandas, matplotlib, and seaborn.

📘 Project Overview

The main objective of this project is to explore how weather parameters such as temperature, humidity, wind speed, visibility, and pressure change with different conditions (rainy, cloudy, or clear days).
The goal is to identify seasonal patterns and correlations between these factors for better understanding and forecasting.

🎯 Objectives

1. Temperature Variation Analysis — Examine how temperature and apparent temperature change over time.
2. Humidity & Temperature Relationship — Study how humidity levels affect comfort and temperature.
3. Impact of Wind & Visibility — Determine how wind and visibility vary across weather types.
4. Pressure & Precipitation Correlation — Analyze how atmospheric pressure influences rainfall or snowfall.
5. Weather Pattern Detection — Identify the most frequent weather summaries and their seasonal behavior.

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🧹 Data Preprocessing

• Removed missing and duplicate values.
• Corrected date formats.
• Handled outliers in temperature and humidity.
• Converted all units into standard formats (°C, km/h, etc.).

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📊 Key Analysis & Insights

No.	Question	Visualization Used	Key Insight
1	What are the basic statistics of the dataset?	Summary table	Most columns have balanced values — no major issues.
2	What is the average temperature?	Simple number/stat	The average temperature shows a mild climate trend.
3	How many times did each weather condition occur?	Bar chart	Rain is most common, followed by Clear weather.
4	How does temperature change over time?	Line chart	Temperature rises in summer and falls in winter.
5	What is the relationship between humidity and temperature?	Scatter plot	Hotter days tend to have lower humidity.
6	How does wind speed affect temperature?	Scatter plot	Wind speed shows no strong relation with temperature.
7	What is the distribution of temperature values?	Histogram	Most days fall within a normal temperature range.
8	Correlation between temperature and apparent temperature	Scatter plot	Very strong correlation — both rise and fall together.
9	Which month had the highest temperature?	Line chart	June–August are warmest; Dec–Feb are coolest.
10	How does humidity vary with precipitation type?	Box plot	Rainy/snowy days have higher humidity than clear days.

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🧠 Conclusions

• Weather data is clean and reliable.
• Rain is the most frequent precipitation type.
• Temperature and apparent temperature are highly correlated.
• Clear seasonal patterns observed — hot summers, cool winters.
• Humidity behaves predictably, higher during rainy periods.

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💼 Business Impact

This analysis supports:

• Agricultural planning — better irrigation and crop scheduling.
• Energy companies — forecast power demand based on temperature.
• Transportation — plan operations under poor visibility/wind.
• Weather forecasting — data-backed insights for improved prediction.

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🚀 Future Enhancements

• Use Machine Learning to predict temperature or rainfall probability.
• Automate data collection for continuous weather tracking.
• Integrate dashboards for real-time weather insights.

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🧰 Technologies Used

• Python
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook

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📂 Project Files

• weather_analysis.ipynb – Main analysis notebook
• cleaned_weather_data.csv – Preprocessed dataset
• Weather_Data_Analysis_Report.docx – Final report
• plots/ – Folder containing all visualization images
• README.md – Project documentation

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📜 Author

Pradeep C S
Data Analysis Project — 2025

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