Analyze Clothing E-Commerce Dataset for Visualization, Charting, and Sentiment Analysis Techniques

Dataset Description

This ECA requires you to analyze a clothing E-Commerce dataset that includes customer reviews. The data is available from the Kaggle website:

https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews

The dataset has been anonymized and the references to the company in the review text and title were replaced with a generic term “retailer”. You are required to download the dataset (Womens Clothing E-Commerce Reviews.csv). Each row of the dataset corresponds to a customer review.

Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed

Age: Positive Integer variable of the reviewers age

Title: String variable for the title of the review

Review Text: String variable for the review body

Rating: Positive Ordinal Integer variable for the product score granted by the customer from 1 Worst, to 5 Best

Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended

Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive

Division Name: Categorical name of the product high level division

Department Name: Categorical name of the product department name

Class Name: Categorical name of the product class name

(a) Imagine you are tasked to prepare a visualization analysis using the given dataset. Identify one (1) possible business problem that can be addressed by your visualization analysis. Explain how to clean and prepare the dataset in an analyzable form. Explain the benefits and challenges of using the given dataset from the Kaggle website. Use up to 200 words for your answer. (20 marks)

(b) Discuss the types of charts that are recommended to address the business problem identified in (a). Produce the charts and the dashboard using Tableau. Explain how your visualization analysis addressesthe problem mentioned in Part (a). Using up to 250 words for your answer. (20 marks)

(c) Identify the appropriate data mining technique and tools to predict customer sentiment (target outcome - whether the sentiment is positive, negative or neutral) using the nontextual data in the given dataset. Explain the critical steps to ensure successful application of the technique and tools to achieve the objective here. Using up to 150 words for your answer. (15 marks)

(d) The dataset includes textual data (i.e., review text/title). You would like to apply text mining in order to understand the customers better. Identify five (5) challenges when analyzing the given textual data. State the solutions that can be applied to address the each of challenges identified. Using up to 200 words for your answer.