*Imagine you are working as a Data Scientist for an Online Wine Shop named “The Wine Land” *As the name suggests, the online store specializes in selling different varieties of wines. *The online store receives a decent amount of traffic and reviews from its users. *Leverage the “reviews” data and draw actionable insights from it.
*Build a predictive model for predicting the wine “variety”. Provide the output along with all features to a CSV file. Both Training & test data is provided here *Submit the source code used for building models in a zip or share the link to the GitHub repository. *Also submit a short summary: Model used, features extracted, Model accuracy in train. Along with some visualization of data and top 5 actionable Insights from the Data.
*user_name - user_name of the reviewer *country -The country that the wine is from. *review_title - The title of the wine review, which often contains the vintage. *review_description - A verbose review of the wine. *designation - The vineyard within the winery where the grapes that made the wine are from. *points - ratings given by the user. The ratings are between 0 -100. *price - The cost for a bottle of the wine *province - The province or state that the wine is from. *region_1 - The wine-growing area in a province or state (ie Napa). *region_2 - Sometimes there are more specific regions specified within a wine-growing area (ie Rutherford inside the Napa Valley), but this value can sometimes be blank. *winery - The winery that made the wine *variety - The type of grapes used to make the wine. Dependent variable for task 2 of the assignment
Keras & TF (1.15) Numpy, Pandas, NLTK (Natural Languge Processing) Scikit learn, re, and seaborn/matplotlib.pyplot