Select a categorical data with at least 3 factors. Use any technique that we have covered in the course to analyze your data. Prepare a three-page report explaining your analyses and results. Please include a brief explanation of the data along with your results and conclusions. You may consider the following points in your analysis:

• Describing your data and reporting source of the data.
• Explaining why it is a categorical data and why it is important to analyze it.
• Examining different models which could fit to your data.
• Using a model selection criterion to select the best model among the models that were fitting to your data.
• Explaining the meaning of your selected model in the context of the data.
• Summarizing your findings from the analysis.
• Explaining your final conclusion and discussing the results.

Your report may include the following sections:

• Introduction: Statement of the problem.
• Material and Methods: Description of your data and the methods you have used for the analysis.
• Results: Explanation of the results of your analyses. You can cut and paste some of your computer outputs and refer to them in explaining your results.
• Conclusion and Discussion: Highlighting the main points and discussing.

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These solutions may offer step-by-step problem-solving explanations or good writing examples that include modern styles of formatting and construction of bibliographies out of text citations and references. Students may use these solutions for personal skill-building and practice. Unethical use is strictly forbidden.

Recommendation Modeling (Categorical Data)

Introduction: Statement of the problem.
Dressify is a garment company, which deals the latest fashion trends around the world. They have one of the best creative designers, and now they plan to use data science too. They have captured few data points for style, price, size, fabric type, pattern and different dress attributes over the last couple of days.
For Dressify we would like to identify the most recommended dresses based on the datapoints that they have gathered over the past days. Some of these variables will be very useful in identifying the most recommended dress, which then can be a possible sale lead.
Finally after fitting the model we will predict the Recommendation for some of their Articles, which then Dressify will recommend to its customers
Material and Methods: Description of your data and the methods you have used for the analysis.
Since, our response variable and most of the independent variables are categorical, we will use Chi-Square test of independence to retain/testing the importance of the variables in the model, and t-test for testing the importance/variation of any continuous variable with the response variable.

Description of the Data:
Style: The Style of Dress for a particular piece of clothing.
Price: Price of the Dress
Size: Mostly, dress of size medium and large has highest sell compare to small, XL and XXL.
Rating: Rating of the article.
Season: Recommendation of sale is also based on season. For example: Sale increases of woollen dress in winter compare to other season.
NeckLine: Neckline is a type of Neck for a particular piece of article.
SleeveLength: Type of Sleevelength
Waiseline: Type of Waiseline
Material: Type of Material used to create the article
FabricType: Type of Fabric used to create the article
Decoration: Material Decorated with
Pattern: Pattern on the Article
Area: Area from where the customer bought the Product...

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