Its a data analysis project where google playstore's data is being analysed on different factors focusing on the rating of the apps provided.
- To do so numpy, pandas, matplot, seaborn liberary python is been used.
- numpy is used for any numercal support required while analysing
- pandas is used for reading, and manipulating dataset for the good
- matplot and seaborn is used for ploting the required graph
Google Play Store team is about to launch a new feature where in certain apps that are promising are boosted in visibility. The boost will manifest in multiple ways – higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in visibility in search results. This feature will help bring more attention to newer apps that have potential. The task is to understand what makes an app perform well - size? price? category? multiple factors together? Analyze the data and present your insights in a format consumable by business – the final output of the analysis would be presented to business as insights with supporting data/visualizations.
- python is required
- numpy
- pandas
- matplot
- seaborn