Homework week 2_for submission

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Georgia Institute Of Technology *

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6501

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Marketing

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Jan 9, 2024

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Question 3.1 Using the same data set ( credit_card_data.txt or credit_card_data-headers.txt ) as in Question 2.2, use the ksvm or kknn function to find a good classifier: (a) using cross-validation (do this for the k-nearest-neighbors model; SVM is optional); and Ans: Starting with K-nearest-neighbors model: Followed by Cross Validation: Using K-fold: Values from KKNN: (last 5 rows) Values from K-fold: (last 5 rows)

(b) splitting the data into training, validation, and test data sets (pick either KNN or SVM; the other is optional). Ans: 1. Data splitting 2. Using SVM: Below was the prediction from SVM: 3. Using KNN model: below was the data validation from KNN: 4. Testing for accuracy:

Question 4.1 Describe a situation or problem from your job, everyday life, current events, etc., for which a clustering model would be appropriate. List some (up to 5) predictors that you might use. Ans: Being in the e-commerce industry, an appropriate scenario to use clustering model would be using it to do customer segmentation from our existing customer data. An example would be segmenting our customer base into distinct groups to target them more effectively. The aim is to understand the different purchasing behaviors and preferences and tailoring marketing campaigns, product recommendations and promotions based on their cluster group. Suggested Predictors: 1. Purchase History: This includes understanding the frequency, recency and average order value of their transactions, it would also show their purchasing power with us, enabling us to group customers based on high, medium and low spenders. 2. Demographics: Such as age, gender, location and income. These data could provide us insights into the preferences and needs of different customer groups. 3. Engagement metrics: Data that shows how customers interact with our website, for example, time spent with us, page views, no. of add to carts and how many of them made it to check out. 4. Product Category Preferences: Information such as the type of products/brand that each customer tends to purchase. Eg. Electronics, Fashion or Toys & Games. This helps to group customers based on their interests. 5. Purchase Behavior: Understanding how customers buying habits change during holidays or during sales period. This temporal variable can help the us create promotions and product recommendations based that would tailored based on their likeliness to purchase in our marketing messaging. By applying clustering model such as K-means or hierarchical clustering on the above suggested predictors, any e-commerce platform will be able to create customer segments. For example, we might identify a segment of “deal hunters”, who mainly purchase during sales or a segment of audiophiles, who would camp for tech launches to purchase the latest audio technology available to them. Tailoring marketing efforts to these segments can lead to more effective campaigns that translates to higher sales.

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Question 4.2 The iris data set iris.txt contains 150 data points, each with four predictor variables and one categorical response. The predictors are the width and length of the sepal and petal of flowers and the response is the type of flower. The data is available from the R library datasets and can be accessed with iris once the library is loaded. It is also available at the UCI Machine Learning Repository ( https://archive.ics.uci.edu/ml/datasets/Iris ). The response values are only given to see how well a specific method performed and should not be used to build the model. Use the R function kmeans to cluster the points as well as possible. Report the best combination of predictors, your suggested value of k, and how well your best clustering predicts flower type. Ans: The ideal number of cluster is 3 using the elbow method.

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