This project analyzes a publicly available dataset of app ratings from the Google Play Store using Python and Pandas. The project includes code for loading and cleaning the data, calculating basic statistics, creating visualizations, and drawing conclusions based on the analysis.

Getting Started

To get started with this project, you will need to download the dataset from Kaggle and have Python and Pandas installed on your machine.

import pandas as pd

df = pd.read_csv("googleplaystore.csv")

print(df.head())

Cleaning the Data : The dataset may contain duplicates or missing values, so it is important to clean the data before analyzing it. To remove duplicates and missing values, you can use the following code:

df.drop_duplicates(subset="App", inplace=True)

df.dropna(inplace=True)

Analyzing the Data : Once the data is clean, you can start analyzing it. The code in this project includes examples of how to calculate basic statistics such as the average rating and number of downloads for each app category:

category_stats = df.groupby("Category").agg({"Rating": "mean", "Installs": "sum"}) category_stats.rename(columns={"Rating": "Average Rating", "Installs": "Total Downloads"}, inplace=True)

print(category_stats)

import matplotlib.pyplot as plt

df.hist(column="Rating")

plt.scatter(df["Installs"], df["Rating"]) plt.xlabel("Number of Downloads") plt.ylabel("Rating")

Based on the analysis, you can draw several conclusions about the dataset. For example, you can see that the average rating is highest for apps in the "Events" and "Education" categories, while the total number of downloads is highest for apps in the "Communication" and "Social" categories. You can also see that there is a positive correlation between the number of downloads and the app rating, although the relationship is not linear.

This project demonstrates how to use Python and Pandas to analyze app rating data and draw meaningful insights. With further analysis and refinement, this data could be used to optimize app development and marketing strategies.

This project is based on a publicly available dataset from Kaggle. The code in this project was inspired by tutorials and examples from the Python and Pandas documentation.