This project involves cleaning and analyzing a dataset using Pandas, and visualizing the results using Matplotlib and Seaborn. The dataset used contains air quality measurements from various cities.
Real-world datasets often contain missing or inconsistent values. Effective data wrangling is essential to clean the data and derive meaningful insights.
- Load the air quality dataset.
- Handle missing values by replacing them with the mean of the column.
- Group the data by city and calculate the average Air Quality Index (AQI).
- Visualize the average AQI per city using Seaborn.
- Cleaned dataset with missing values handled.
- A bar chart showing the average AQI for each city.
- Install the required libraries: Pandas, Matplotlib, Seaborn.
- Load the air quality dataset.
- Run the script to clean the data and generate visualizations.
This project is licensed under the MIT License.