Overview This repository contains the code and documentation for my capstone project titled "Exploratory Data Analysis (EDA) of Play Store Data." In this project, I conducted an in-depth exploratory data analysis on a dataset containing information about apps available on the Google Play Store. The primary goal was to gain insights and uncover trends related to app categories, user reviews, ratings, and more.
Table of Contents
Introduction The Google Play Store is a hub for millions of mobile applications across various categories. Exploring this vast dataset can provide valuable insights into app trends, user preferences, and factors influencing app ratings and reviews. This project aims to perform a comprehensive exploratory data analysis on the Play Store dataset to uncover interesting patterns and trends.
Dataset The dataset used for this project is sourced from Kaggle. It contains information about different apps available on the Play Store, including details like app name, category, rating, reviews, installs, and more.
Project Goals The main objectives of this capstone project were as follows:
- To perform data preprocessing to clean and prepare the dataset for analysis.
- To explore the distribution of apps across different categories and analyze their popularity.
- To investigate the relationship between app ratings, reviews, and installs.
- To identify any potential trends or insights that can be derived from the data.
Methods Used
- Data Cleaning and Preprocessing
- Descriptive Statistics
- Data Visualization (using Python libraries like Matplotlib and Seaborn)
- Exploratory Data Analysis
Insights
- The distribution of app categories on the Play Store.
- The most popular app categories based on the number of installs.
- Popular Genres in popular categories.
- Ideal prices for paid apps.
- The correlation between different features.
Conclusion This capstone project provided a comprehensive analysis of the Play Store dataset, revealing valuable insights into app categories, user preferences, and ratings. The findings can be useful for app developers, marketers, and anyone interested in understanding the dynamics of the mobile app market.