data analysis of google play store applications data

In this project, I analyzed a dataset containing information about apps from the Google Play Store, including categories, ratings, reviews, and installs. Using Jupyter Python note book and its data analysis libraries, I cleaned and transformed the data to prepare it for meaningful insights.

Key Steps and Achievements:

Data Wrangling:

Removed duplicates and handled missing values.

Transformed and standardized columns such as Size, Installs, Price, and Genres for consistency.

Exploratory Data Analysis (EDA):

Created visualizations to explore trends and relationships in the data.

Compared free and paid apps, showing that free apps are more prevalent but paid apps tend to have higher average ratings.

Analyzed top-performing genres, with "Tools" being the most common.

Insights and Visualizations:

Visualized the distribution of content ratings and identified that most apps are suitable for general audiences.

Examined category-wise app distributions, highlighting "Family" as the largest category.

Built a correlation heatmap to reveal strong relationships, such as between installs and reviews.