Google-Playstore-Case-Study

This project is a small case study aimed at learning data cleaning and visualization using Python, with a focus on the libraries matplotlib and seaborn.

Overview

The Google Playstore Case Study involves cleaning a dataset and visualizing the data using various charts. This project covers essential skills in data preprocessing and data visualization, which are critical for any data analysis task.

Objectives

• Learn and apply data cleaning techniques.
• Use matplotlib for creating various types of plots.
• Use seaborn for creating advanced visualizations.
• Understand the process of exploratory data analysis (EDA).

Data Cleaning

Data cleaning steps include:

• Handling missing values.
• Converting data types.
• Removing or replacing invalid entries.
• Standardizing data formats.

Data Visualization

Various charts and plots are created using matplotlib and seaborn to visualize the cleaned data:

• Box plots: To understand the distribution and outliers in numerical data.
• Histograms: To visualize the distribution of a single numerical variable.
• Scatter plots: To examine relationships between two numerical variables.
• Bar charts: To compare categorical data.

Tools and Libraries

• Python: The main programming language used.
• pandas: For data manipulation and cleaning.
• matplotlib: For basic plotting.
• seaborn: For advanced visualizations.

Conclusion

This case study serves as a practical introduction to data cleaning and visualization using Python. By working through this project, you will gain hands-on experience with some of the essential tools and techniques used in data analysis.

---