GeeksforGeeks Data Science Challenge

In this project, I've taken on the role of a data scientist to analyze and derive insights from two datasets: Swiggy Restaurants Data and GeeksforGeeks YouTube Channel Data.

To view the analysis and results:

1. Swiggy Restaurants Analysis
2. GeeksforGeeks Youtube Channel Analysis

About the Project

In this project, I've tackled two separate analyses: Swiggy Restaurants Analysis and GeeksforGeeks YouTube Channel Analysis. These analyses involve extracting meaningful information from the provided datasets and answering a series of questions related to each dataset.

Swiggy Restaurants Analysis

In this part of the project, I delved into Swiggy's restaurant data to uncover valuable insights about the relationship between restaurants and customers. I addressed various questions such as:

• Identifying the cities where Swiggy has restaurants listed.
• Finding the most expensive cities in the dataset.
• Listing the top-rated and least-rated restaurants.
• Analyzing the popularity of restaurant chains and more.

The analysis is performed using Python programming and data manipulation libraries, showcasing my skills in data preprocessing, visualization, and analytical thinking.

Link to Swiggy Analysis Notebook

GeeksforGeeks YouTube Channel Analysis

For the GeeksforGeeks YouTube Channel Analysis, I scraped data from the GeeksforGeeks YouTube channel using the Google API to gain insights into their video publications. The analysis included:

• Determining the number of videos uploaded in the past 6 months.
• Creating a pandas DataFrame with video details.
• Identifying the most viewed and longest videos.
• Visualizing the relationship between views and video length.

The scraping and analysis were done using Python, illustrating my web scraping abilities and proficiency in data manipulation and visual analytics.

Link to GfG YouTube Analysis Notebook

How to Use This Repository

If you're interested in exploring my analysis, you can follow these steps:

1. Clone this repository to your local machine.
2. Navigate to the relevant analysis folders (code and data).
3. Inside these folders, you will find the required data files and notebook files containing the code and explanations for the respective analyses.
4. Open the Jupyter Notebook in a suitable environment to view the analysis.

Feel free to reach out if you have any questions or suggestions about this project.

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