In this project, I analyze data from the Google Play Store using Python, marking my first foray into data analysis. With over ten thousand apps available, I explore the Android app market across various categories. Through Exploratory Data Analysis (EDA), I aim to uncover insights to drive growth and retention strategies. The dataset comprises two files: "playstore_data.csv," providing details about each app, and "user_reviews.csv," containing sentiment-labeled reviews. By examining app attributes and user sentiments, I seek to understand user preferences and trends in the market. This analysis will help developers and stakeholders make informed decisions to optimize app performance and user satisfaction. Overall, this project serves as a foundational step in my journey to harness the power of data for decision-making and problem-solving in real-world scenarios
The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market. Each app (row) has values for category, rating, size, and more. Another dataset contains customer reviews of the android apps. Explore and analyze the data to discover key factors responsible for app engagement and success.
Importing Libraries and loading two Datasets (Playstore and user reviews) DataCleaning Data Wranglig Data Visualization Merging Two Dataset Data Visualization of two DataSets
To boost app success, focus on popular genres like Tools, Action, Photography, and Entertainment. Offer engaging free versions to attract users, considering most apps are free. Keep app sizes small for user convenience. Tailor content to Everyone and Teen categories, as Mature categories have less interest. Invest in revenue-generating categories like Lifestyle and Finance. Ensure compatibility with the latest Android versions. Engage users in popular categories like Games and Health. Continuously update based on user feedback, emphasizing positive experiences and addressing negatives.
I looked at the Play Store app data using Python and found important things about what makes apps successful. I used graphs and explanations to understand how users feel about apps, what kinds of apps they like, and how updates affect apps. From this, I made suggestions for the client to make their apps better, like focusing on making users happy, fixing problems quickly, and making apps that are small and updated often. By using data to guide decisions, the client can do well in the Android app market.