This project is a comprehensive data analysis and visualization endeavor aimed at exploring, cleaning, and extracting insights from a dataset. Through this project, I tackle various tasks, including data preprocessing, feature engineering, statistical analysis, and visualization techniques, using Python libraries such as pandas, matplotlib, and seaborn.

The project begins with downloading the dataset ( "data1" ) and reading it into a pandas DataFrame. We then proceed to perform data cleaning tasks, such as changing column names to capitalize and renaming specific columns for clarity. Additionally, we address missing values by applying suitable imputation strategies.

Next, conduct a thorough analysis of the dataset, examining descriptive statistics, identifying the most frequent values, and exploring the distribution of variables. then delve into grouping and aggregation operations to calculate average salaries by year and city, as well as determining the most common level of education among the dataset.

Visualization plays a crucial role in this project, as we leverage Matplotlib and seaborn to create various types of plots, including histograms, bar charts, scatter plots, and box plots. These visualizations offer valuable insights into the dataset's characteristics, trends, and relationships between variables.

Furthermore, a bonus task is to incorporate an external dataset containing GDP information for specific years and merge it with our original dataset while maintaining its original size.

Overall, this project serves as an exemplary demonstration of data analysis and visualization techniques, providing a structured approach to exploring and understanding complex datasets. Through clear documentation and code transparency, it aims to facilitate learning and inspire further exploration in the field of data science and analytics.