This project involves analyzing the "My MobApp Studio" dataset to uncover valuable insights into the mobile app landscape. The challenge lies in navigating the dataset, cleaning and preprocessing the data, visualizing trends, and interpreting the correlations and relationships between attributes.
In this analysis, we explore the "My MobApp Studio" dataset step by step. We begin by loading the dataset and understanding its structure. Then, we clean and preprocess the data to ensure accuracy. Our journey continues with visualizing data distribution and uncovering correlations among attributes. We use scatter plots to visualize relationships, and we focus on analyzing top-paid family apps and high-review apps to gain insights into user preferences.
To run this analysis, follow these steps:
- Clone this repository.
- Navigate to the project directory in your terminal.
- Install the required Python libraries (if not already installed) using the following command: pip install pandas matplotlib seaborn
- Run the analysis script: python analyze_mobapp_data.py
jupyter notebook http://web-XXXXXXXXX.docode.XX.qwasar.io/
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