In this project, I aimed to assess the immediate impact of public response on product sales, focusing on online reviews as a key metric. Using a Google Play Store apps dataset sourced from Kaggle, I formulated analytical questions to explore app performance, user behavior, and market trends.
I started the project with the process of data cleaning including identifying and replacing missing values, rechecking and making sure correct datatypes were being used for smooth analysis. After a throuogh process of data cleaning i moved onto the next process which is data analysis and was able to uncover meaningful insights.
- We find a prevalent rating range of 4.0 to 4.5, with categories like education and games consistently receiving top ratings.
- A linear correlation between ratings and downloads plateaus at 4.5 to 4.8, suggesting user preferences.
- Additionally, smaller-sized apps tend to have higher download rates, reflecting efficiency preferences.
- The dominance of utility-oriented apps, notably in the Tools category, highlights their influence on overall downloads. Through concise visualizations and analysis, our project offers valuable insights into the app ecosystem dynamics
- Looking back, I recognize that improvements in the area of visualizations could've been done to enhance reader engagement. Exploring beyond a single visualization package to leverage the strengths of various tools could have made the analysis more dynamic and insightful.
- Additionally, optimization remains an ongoing area for improvement, particularly in maximizing the utilization of NumPy over pandas for enhanced efficiency and performance.
- For example the following graph is rather shabby and only muddles the insights of this project to readers.
Playstore Reviews: Unveiling App Insights An article diving deep into the processes and insights uncovered by this project.