tayyab885 / google-playstore-data-analysis

Google Playstore Data Analysis

This notebook provides a detailed analysis of the Google Play Store dataset using Python and various data analysis and machine learning techniques. The main objective of this notebook is to extract insights from the dataset and provide useful information for app developers, marketers, and other stakeholders.

Dataset

The dataset used in this analysis is the Google Play Store Apps dataset available on Kaggle. It contains information about various apps available on the Google Play Store, such as app name, category, rating, reviews, installs, price, and other features.

Contents

The notebook is divided into five main sections:

1. Data Preprocessing: This section deals with data cleaning and transformation. The author has used Python's pandas library to clean the data and handle missing and duplicate values.

2. Exploratory Data Analysis (EDA): In this section, the author has analyzed various features of the dataset using visualizations such as bar charts, histograms, and scatterplots. The author has also used statistical measures such as mean, median, and standard deviation to summarize the data.

3. Feature Engineering: This section involves creating new features based on the existing features. The author has created features such as app size group, app type, and content rating group.

Conclusion

The notebook provides a comprehensive analysis of the Google Play Store dataset and showcases various techniques for data cleaning, exploratory data analysis, feature engineering, statistical analysis, and machine learning. The notebook can serve as a useful reference for anyone looking to analyze similar datasets or gain insights from the Google Play Store data.

Requirements

The notebook requires Python 3.x and various data analysis and machine learning libraries such as pandas, numpy, matplotlib and seaborn. These libraries can be installed using pip or conda package manager. The notebook can be run on Jupyter Notebook or JupyterLab.