Data Science App Analysis

This repository contains an analysis of mobile applications using Python and Jupyter Notebook. The analysis explores various aspects of app data extracted from the Google Play Store dataset.

Introduction

The analysis focuses on understanding the relationship between app ratings, number of reviews, and number of downloads. We explore hypotheses related to app categories, pricing, and user engagement metrics.

Content

1. Data Preparation

We import the necessary libraries and load the Google Play Store dataset to begin our analysis.

2. Exploratory Data Analysis (EDA)

We conduct exploratory data analysis to gain insights into the dataset's structure and contents. This includes visualizing distributions, correlations, and summary statistics.

3. Hypothesis Testing

We test several hypotheses related to app categories, pricing strategies, and user engagement metrics. Hypothesis testing involves statistical analysis to validate or refute our assumptions.

4. Machine Learning

We utilize machine learning techniques to predict app ratings based on user engagement metrics. This includes feature engineering, model training, and evaluation.

5. Clustering Analysis

We perform clustering analysis to identify distinct groups of apps based on their characteristics. This helps in understanding patterns and trends within the dataset.

Usage

The Jupyter Notebook file (data_science_app_analysis.ipynb) contains the complete code and analysis steps. You can run the notebook on your local environment to reproduce the results.

Requirements

• Python 3.x
• Jupyter Notebook
• Pandas
• Matplotlib
• NumPy
• SciPy
• Scikit-learn