sophiechin / exploratory-data-analysis-eda-instagram-data

In this repository, I practice proceeding the EDA process in python using the sample data of Instagram Reach Data.

Exploratory-Data-Analysis-EDA-Instagram-Data

Exploratory Data Analysis (EDA) is a fundamental aspect of Data Science, involving the thorough examination of a dataset to unveil underlying patterns, trends, and associations. This process enhances our comprehension of the dataset's content and aids in making informed choices and devising effective approaches for addressing tangible business challenges. In this notebook, I will lead you through a practical application of Exploratory Data Analysis utilizing the Python programming language.

To begin with the EDA process, I will use the dataset about Instagram Reach and this dataset could be found HERE.

Overall, the dataset consists of a total of 119 rows and 13 columns with the column details as the following:

1. Impressions: Count of post views (Reach)
2. Home Reach: Reach originating from the home feed
3. Hashtag Reach: Reach resulting from hashtags
4. Explore Reach: Reach generated by appearing on the Explore page
5. Other Reach: Reach from sources other than the aforementioned
6. Saves: Total saves of the post
7. Comments: Total comment count
8. Shares: Total number of shares
9. Likes: Total number of likes received
10. Profile Visits: Count of visits to the profile via the post
11. Follows: Number of new followers gained through the post
12. Caption: Text accompanying the post
13. Utilized Hashtags: Hashtags employed in the post

Check this repository in Kaggle

Disclaimer: This notebook is for my learning and practice purpose only, and the source code I run here is referenced from THIS GUIDE