This project focuses on performing Exploratory Data Analysis (EDA) on an hourly weather dataset using Python and Pandas. The dataset contains time-series weather observations including temperature, dew point, relative humidity, wind speed, visibility, atmospheric pressure, and weather conditions.
The objective of this analysis is to clean and preprocess the data, handle missing and inconsistent values, and explore statistical patterns and relationships between different weather parameters. Various data analysis techniques such as descriptive statistics, correlation analysis, and time-based trend analysis are applied to gain insights into weather behavior over time. Visualization libraries like Matplotlib and Seaborn are used to identify seasonal patterns, variations, and anomalies in weather conditions.
This project demonstrates practical skills in data cleaning, exploratory analysis, and interpreting real-world time-series datasets using Python.