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CHE1148H - Data Mining in Engineering

创建日期：2024-09-10 17:08:38

所属分类：计算机computer science

标签： CHE1148H

Data Mining in Engineering

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CHE1148H - Data Mining in Engineering

1 Supervised learning

With the features built in Assignment #2, you are now asked to build a model that pre

dicts clients response to a promotion campaign using 3 MLlib algorithms. This is a typical

classification problem in the retail industry, but the formulation of the problem is similar to

industries such as fraud detection, marketing and manufacturing.

The clients responses are stored in the Retail Data Response.csv file from Kaggle. The

responses are binary: 0 for clients who responded negatively to the promotional campaign

and 1 for clients who responded positively to the campaign.

You will explore solving the classification problem with two different sets of features (i.e.

annual and monthly) and three different algorithms as shown in the image below.

Retail response

classification problem

Annual features

Logistic

Regression

with L1

regularization

Decision

Tree

Random

Forests

Monthly features

Logistic

Regression

with L1

regularization

Decision

Tree

Random

Forests

1.1 Import the monthly and annual data and join

In Assignment #2, you created five different feature families that capture annual and monthly

aggregations. Here, you will model the retail problem with two approaches: using annual

and monthly features. Therefore, you need to create the joined tables based on the following

logic:

1Table

annual features outputs

monthly features outputs

annual features.xlsx

mth rolling features.xlsx

annual day of week counts pivot.xlsx

mth day counts.xlsx

days since last txn.xlsx

Retail Data Response.csv

In both the annual and monthly features approach, you need to join at the end with table

#4, the clients responses. This is simply a table that contains the binary response of the

client to our marketing effort as described above and that is the output or label or target

that makes this a supervised learning problem.

1.2 Steps for each method (15 points)

Important note 1: When you set up a new cluster in Databricks make sure that you select

a runtime version that supports ML (any 10.x ML should work).

Important note 2: The Learning Spark book github has many useful notebooks in Chapter

10 relevant to ML pipelines.

1. Separate the inputs X and the output y in two data frames.

2. Split the data in train and test set. Use a test size value of 2/3 and set the random state

equal to 1147 for consistency (i.e. the course code value). Use the following names for

consistency.

Annual

X train annual y train annual

X test annual y test annual

Monthly

X train monthly y train monthly

X test monthly y test monthly

3. Pre-process (if necessary for the method).

4. Fit the training dataset and optimize the hyperparameters of the method.

5. Plot coefficient values or feature importance.

6. Plot probability distribution for test set.

7. Plot confusion matrix and ROC curves of train/test set. Calculate precision/recall.

1.3 Comparison of methods (5 points)

Compare the two feature engineering (annual and monthly) and the three modeling ap

proaches (L1 log-reg, tree, forests) in terms of the outcomes of steps 5-7. Which combina

tion of feature engineering and modeling approach do you select as the best to deploy in a

production environment and why? Tabularize your findings in steps 5-7 to summarize the

results and support your decision (how to organize information with tables in Markdown).

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