Exploratory data analysis uses various data visualization techniques, such as Matplotlib, Seaborn, Pandas, etc., to compare multiple sets and highlight their key aspects.
In the previous months, Shark Tank India has captured all of our interests. While my LinkedIn feed was loaded with influencers debating which shark is the greatest, I was busy studying the season. Kaggle Dataset is what was used.
If you enjoy Shark Tank India, you may be interested in
Maximum number of sharks in a deal
Startups that declined sharks' offer
Total amount borrowed or invested by all sharks
Collections of various sharks
Businesses that received the same valuation as was sought
Which businesses (as of pitching day) have no revenue or pre-revenue?
The project's step-by-step outline is shown below:
Putting in the Required Libraries and Importing.
Data set being downloaded.
Getting Understanding from a Dataset.
Visualization of data.
Setting Up the Necessary Libraries
Installing necessary libraries like Pandas, NumPy, Matplotlib, and Seaborn comes first.
We have completed this stage after importing the necessary library, and are now prepared to continue on to the following section of downloading the dataset. Save the data set. The next step is to get the Kaggle dataset.
Searching for Insights Regarding the Data
Season 1- Shark Tank India
The same technology is utilized and the same kind of visualization has been developed to determine the industry-wise allocation for various shark species.
You can get solutions to various problems from this blog. For any other web scraping services, get in touch