3 Ways Data Analysis Can Make U Advance : EMV - Python + Tableau
3 Ways Data Analysis Can Make U Advance : EMV - Python + Tableau Laxman Singh Follow Dec 20 · 5 min read

Extract , Model and Visualize in Interactive Dashboard : Give Meaning to Raw Data

Data is huge around the globe and extracting the meaning from the data, visualizing that makes it appealing and easier to understand by others. Here, we will use famous Yelp Data set to do our analysis and visualization. Social networking data is growing at very rapid rate and it influences business growth in case it is utilized properly to extract meaning.

About Dataset

Yelp facilitates the platform where consumers post their user user experience about businesses, products or services through free-form text and numeric ratings of 1 to 5. A star rating of 1 to 3 means that it was not a good user experience while a rating of 4 or 5 means a good user experience.

Therefore, it is vital for businesses to focus on receiving high reviews from their customers. One important question for many businesses is whether there is a way to encourage higher ratings through targeted alterations to their offerings. Dataset contains business, review, user, check-in and tips.

Yelp dataset comprises of user reviews, business data, check-ins and tips which shorter reviews. This dataset is in Json format.

More detail about Dataset

Our Goal

To assess the public perception of restaurants on Yelp via exploratory data analysis To build a machine learning model which accurately predicts the sentiment of reviews

Let us do together in three steps : EDA, Machine Learning and Visualization

EDA (Exploratory Data Analysis)

We have following files: business

User : User data including the user’s friend mapping and all the metadata associated with the user.

Review : Contains full review text data including the user_id that wrote the review and the business_id the review is written for. Ratings are between 1–5.

Business : Contains business data including location data, attributes, and categories.

Tip : Tips written by a user on a business. Tips are shorter than reviews and tend to convey quick suggestions.

Checkin : Checkins on a business

Not considering the photos for this analysis.

B usiness and Review dataset are related with business_id field and can be joined to further analysis.

Data is spread across different reviews. Three different visualization to show more about data: 1. Review ratings 2. Review against industries 3. Geo spread of Reviews.

Data Spread as per ratings

Top Categories in Reviews
['Laxman Singh']
['Machine Learning', 'Data Visualization', 'Data Science', 'Python', 'Technology']