The purpose of this project was to analyze the popularity of video games over time. The data used for this project was collected from Kaggle and includes information on the release date, Genre, Rating for each game.
No partner was involved in this project.
The methods used for this project were:
- Data exploration and cleaning
- Data visualization
The technologies used for this project were:
- Python
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- Seaborn
The first step in the project was to explore the data and clean it. This involved removing any missing or duplicate data, and transforming the data into a format that was suitable for analysis.
The next step was to visualize the data. This was done using a variety of charts and graphs, which helped to identify trends and patterns in the data.
The needs of this project were:
- A data scientist with experience in data analysis
- A good understanding of the video game industry
- The ability to work independently
To get started with this project, you will need to:
- Clone the GitHub repository
- Install the necessary Python libraries
- Run the Jupyter Notebooks
This project was a success. I was able to identify some interesting trends and patterns in the data, which can be used to better understand the popularity of video games over time.
The following are some additional details about the project:
- The dataset was from Kaggle on March 8, 2023.
- The data includes information on 1512 video games that were released between 1980 and 2023.
- The data was cleaned using the following steps:
- Missing values were filled in using the mean value for the column.
- Duplicate rows were removed.
- The data was visualized using the following charts and graphs:
- Bar charts
I hope this README file is helpful. Please let me know if you have any questions.