Part 1
Datasets Used: googleplaystore.csv, googleplaystore_user_reviews.csv
Datasets link: Click to view dataset
Visualization: The dataset, googleplaystore_user_reviews.csv is used in this part helps to identify the co-relation between various sentimental factors amongst various users when it comes to rating playstore apps. Users ratings play a very critical role in determining the popularity of the app. Hence, the visualizations help to determine the analysis of Sentiment Polarity and Subjectivity.
Sentiment Polarity identifies the depth of the positive/negative/neutral sentiment whereas the Sentiment Subjectivity score helps to determine the extent of subjectiveness in the review. Subjectivity is the quality of being based on or influenced by personal feelings, tastes, or opinions. For instance, if a person has given a positive review, mentioning, The app is very useful to help him maintain his diet, this review will have a high subjective score as it may not be necessary that the app may help any other individual.
The dataset googleplaystore.csv is used to find co-relation analysis between reviews and installs and also most common apps.
Part 2
Datasets used: googleplaystore.csv
Datasets link: Click to view dataset
Visualization: The datasets selected for displaying the various visualizations are interconnected to each other that helps us to gain a brief overview about the various trends of the Google playstore data. The googleplaystore.csv file is used to arrive at the visualizations. The dataset stores the details of the applications on Google Play. It has 13 features for each application, that allows us to explore the various applications in depth and perform analysis out of the dataset to derive significant trends from the heat map and bar plot visualizations.
Part 3
Datasets Used: ign.csv, Applestore.csv
Datasets link: Click to view dataset
Visualization: The data sets in this part of the project are used to display visualizations that compare the popularity of the data that is back dated to 20 years. The pie chart in the visualization helps to determine the distribution of the top Genre’s of games over the past 20 years. The data set is also useful to perform a competitive analysis of the comparison of the popularity of the games between the Apple playstore and Android playstore. This helps us to understand a broader overview about various factors within games.
