This project is a Python script for analyzing data from the Google Play Store. It performs various tasks on the data, such as data manipulation, filtering, and analysis. The code is written in a Jupyter Notebook environment.
To get started with this project, follow the instructions below.
Before running the code, you need to have the following Python libraries installed:
- NumPy
- Pandas
- Scikit-Learn
You can install these libraries using the following commands:
pip install numpy
pip install pandas
pip install scikit-learnRunning the Code Clone the repository to your local machine:
git clone https://github.com/imSanko/PlayStore_Analysis.git
Open the Jupyter Notebook (IPython) file named "Playstore_Analysis.ipynb" in a Jupyter Notebook environment.
Execute the cells in the notebook one by one to perform the data analysis tasks.
The notebook includes tasks such as data import, data imputation, and filtering apps based on categories and ratings.
The results of the analysis are displayed within the notebook.
The following analysis tasks are performed in the notebook:
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Data is imported from the "googleplaystore.csv" file.
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Missing values in the "Rating" column are imputed with the mean value.
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The notebook identifies the number of free apps in the "ART_AND_DESIGN" category.
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The notebook identifies the number of apps in the "ART_AND_DESIGN" category with a rating greater than 4.5.
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The notebook identifies the number of free apps in the "ART_AND_DESIGN" category with a rating greater than 4.5.
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Finally, the notebook lists the names of free apps in the "ART_AND_DESIGN" category with a rating greater than 4.5.
Contributions to this project are welcome. You can open issues or submit pull requests if you have any improvements or suggestions.
This project is licensed under the MIT License - see the LICENSE.md file for details.
You can copy and paste this markdown into a readme.md file for your project. Feel free to modify it as needed.