{"id":15132654,"url":"https://github.com/ayaanjawaid/google_playstore_data_analysis","last_synced_at":"2026-02-24T08:35:40.397Z","repository":{"id":257164594,"uuid":"857497561","full_name":"Ayaanjawaid/Google_Playstore_Data_Analysis","owner":"Ayaanjawaid","description":"This project provides an in-depth analysis of Google Play Store apps and user reviews, focusing on understanding app performance, user sentiment, and key trends in app categories. Using Python, I performed data cleaning, feature engineering, and exploratory data analysis (EDA) on app data and reviews.","archived":false,"fork":false,"pushed_at":"2024-09-14T20:30:23.000Z","size":14135,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-26T08:51:36.179Z","etag":null,"topics":["data-analysis","eda","html","numpy","pandas-dataframe","plotly","python","vizualisation"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Ayaanjawaid.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-14T20:05:44.000Z","updated_at":"2024-09-15T09:49:21.000Z","dependencies_parsed_at":null,"dependency_job_id":"14a1fc91-0a5e-49ad-96f0-002d046659cc","html_url":"https://github.com/Ayaanjawaid/Google_Playstore_Data_Analysis","commit_stats":null,"previous_names":["ayaanjawaid/google_playstore_data_analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Ayaanjawaid/Google_Playstore_Data_Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FGoogle_Playstore_Data_Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FGoogle_Playstore_Data_Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FGoogle_Playstore_Data_Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FGoogle_Playstore_Data_Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ayaanjawaid","download_url":"https://codeload.github.com/Ayaanjawaid/Google_Playstore_Data_Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FGoogle_Playstore_Data_Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274297375,"owners_count":25259050,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-09T02:00:10.223Z","response_time":80,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","eda","html","numpy","pandas-dataframe","plotly","python","vizualisation"],"created_at":"2024-09-26T04:22:08.121Z","updated_at":"2025-10-30T21:48:58.540Z","avatar_url":"https://github.com/Ayaanjawaid.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n\n# 1. Data Loading and Preprocessing\nThe datasets used for analysis include:\n\nPlay Store Data (apps_df)\nUser Reviews Data (reviews_df)\nMissing data was handled effectively:\nDropping rows with missing values in critical columns (e.g., Rating).\nFilling missing values in other columns with the mode.\nRemoving duplicate entries.\nEnsuring that the Rating values were capped at 5.\nTransformation of data included converting Installs and Price to numerical formats and computing log-transformed versions of these features.\n\nHighlights:\nProper handling of missing data ensures a clean dataset.\nData types were adjusted accordingly, ensuring smooth processing for further analysis.\n\n\n# 2. Feature Engineering\nCreated a Revenue column based on the formula Price * Installs.\nApplied sentiment analysis on the Translated_Review using the VADER lexicon to compute sentiment polarity scores for the reviews.\nExtracted the year from the Last Updated column to understand trends over time.\n\nHighlights:\nCreation of a new Revenue metric adds business insights.\nSentiment analysis of user reviews helps in understanding customer feedback.\n\n# 3. Exploratory Data Analysis (EDA)\nVarious visualizations were created using Plotly to explore app categories, types, reviews, and ratings. Key plots include:\n\nCategory Analysis: Bar chart showing top categories by app count.\n\nApp Type Distribution: Pie chart demonstrating the distribution of free vs paid apps.\n\nRating Distribution: Histogram showing the skewness towards higher ratings.\n\nSentiment Distribution: Bar chart visualizing the sentiment scores from user reviews.\n\nInstalls by Category: Horizontal bar chart showing installs by app category.\n\nRevenue by Category: Bar chart showing revenue across app categories.\n\nHighlights:\nThe visualizations are clean and informative, with clear titles and insights.\nThe EDA covers multiple aspects, including app type, rating, installs, and sentiments, providing a comprehensive overview of the data.\n\n# 4. Key Insights from Visualizations\n\nTop Categories: Tools, entertainment, and productivity apps dominate the Play Store.\n\nApp Type: Most apps are free, indicating a strategy to attract users and monetize through ads or in-app purchases.\n\nRating Trends: Ratings are skewed towards higher values, showing a general satisfaction from users.\n\nSentiment: User sentiments are slightly inclined towards positive feedback.\n\nInstalls and Revenue: Categories like social and communication apps have the highest installs, while business and productivity apps generate the most revenue.\n\nHighlights:\nThe insights generated from the analysis provide a clear understanding of user behavior and app performance in different categories.\n\n# 5. Web Dashboard Creation\nA dynamic HTML dashboard was generated using Plotly, allowing the plots to be displayed interactively.\nThe dashboard includes all the key visualizations, with the functionality to view insights by clicking on the plot containers.\n\nHighlights:\nThe creation of a web-based dashboard offers an engaging way to present the findings.\nThe dashboard provides an interactive experience, enhancing the usability of the analysis.\n\n\n# 7. Technical Strengths\nEfficient use of Python libraries such as pandas, NumPy, Plotly, and scikit-learn.\nApplication of NLP techniques for sentiment analysis using the VADER lexicon.\nVisual aesthetics in the plots are maintained with consistent colors, fonts, and background themes.\nThe automation of dashboard creation using HTML templates demonstrates good coding practices and makes it easier to share the results.\n\n# Conclusion:\nThis project effectively analyzes Google Play Store data, delivering meaningful insights through data cleaning, feature engineering, exploratory analysis, and visualization. The combination of sentiment analysis, revenue calculation, and a dashboard presentation makes it a well-rounded project. \n\n## DASHBOARD\n\n![ss-1](https://github.com/user-attachments/assets/9f09770f-f3d1-4600-994f-08dd26ffca51)\n\n![ss-2](https://github.com/user-attachments/assets/14c25d13-1108-48f7-95f1-b3356efe296a)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayaanjawaid%2Fgoogle_playstore_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fayaanjawaid%2Fgoogle_playstore_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayaanjawaid%2Fgoogle_playstore_data_analysis/lists"}