[SOLVED] Aiml 231 assignment 2: machine learning pipeline
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tools for machine learning. The purpose is to implement common data handling methods
on real-world observations. To validate the effectiveness of the implemented methods, you
are also required to perform data analysis tasks to draw useful conclusions.In particular,
the following topics should be reviewed:
• CRISP-DM
• Machine Learning Pipeline
• Exploratory Data Analysis (EDA)
• Data Preprocessing
• Feature Selection and Feature ConstructionIt requires use of python, numpy, matplotlib, scipy, and scikit-learn, and serves as an
introduction to all those tools. You can run Python based on the template Jupyter
notebook and Python code templates provided.In this assignment, your task is to build a machine learning pipeline to predict customers’
credit risks (good or bad) of a German bank. The dataset Credit and the feature descriptions can be downloaded from the Assignment page.The first part of this assignment is to explore the data and to define the machine learning
task. You should:
1. Perform EDA as an initial step to analyse the Credit dataset. The analyses should
be conducted on the whole dataset, i.e., on the “Data.csv” in the data folder. The
analyses should explore the data from the four different aspects:
AIML231: Assignment 2 Machine learning pipeline
• Describe the summary statistics of the data. This should include the number
of instances and number of features. Report the number of categorical and
numerical features separately.• Identify the top three numerical features with the highest correlation with the
target variable Credit Risk according to the Pearson correlation, and report
their correlation values.• Plot the distributions of these three numerical features identified in the previous
question and the target variable using histograms. One histogram for each
feature/variable. Describe how to determine the number of bins to draw the
histograms. Based on the histograms, describe the shape of their distributions
(i.e., Positive or Negative or Zero) with respect to their skewness and kurtosis
(use Scipy for obtaining skewness and kurtosis values).• Check for missing values. Write a paragraph to briefly summarise how many
features containing missing values and the percentage of missing values for each
incomplete feature.2. Among the three machine learning tasks: classification, regression, and clustering,
which one does this problem belong to? Justify your answer.
Provide answers to above questions in your report. Submit your Jupyter Notebook file
(.ipynb) or your Python file (.py) that shows how you get the answers.It is crucial to partition the data prior to preprocessing in any supervised learning task
to prevent data leakage. Therefore, we must split the whole dataset into a training set
and a test data. Any preprocessing model must be trained on the training set only. The
trained preprocssing model, then, can be applied to process both the training set and the
test set.You should determine the appropriate approaches to perform the following preprocessing
steps.
• Encoding categorical data to numerical data.
• Handling missing data.
• Normalising/standardising the data.Describe your chosen approaches and and your rationale for selecting them in your report.
Show the preprocessing steps in the preprocess() function which takes the original training
and test sets as its input and outputs the processed training and test sets. All following
questions (Parts 4 and 5) should use the processed sets.A straightforward feature selection approach is to rank features based on their relevance
to the target output. Then, we can select the top-ranked features for use in our machine
learning task. In this part of the assignment, you will use Mutual Information to rank
features. The higher the mutual information score, the better the feature.
You must use sklearn.feature_selection.mutual_info_classif to calculate the mutual information between each feature and the target variable.You should:
1. Implement the feature_ranking() method that takes a training set as its input
and outputs a feature subset containing top five features.
2. Write a short report that includes:
• Report the top five features selected by the aforementioned feature selection
process.• Evaluate and compare the performance on the test set using the subset containing the top five features and the original feature set. Determine which one
is better and provide your justification.
• Use a heatmap to show the Pearson correlation between the top five features.
In your report, you should show your heatmap, provide an analysis of the
visualisation, and interpret how the features relate to each other.Sequential Forward Feature Selection (SFFS) is a well-known feature selection method.
The task of this part is to implement the SFFS algorithm in Python based on the
provided code template and then examine the selected features. The pseudocode
of the algorithm can be seen in Algorithm 1.
Algorithm 1 Sequential Forward Feature Selection1: Input: Training set with D features, number of selected features d
2: Output: A selected feature set S
3: Initialize set of selected features: S ← ∅
4: Initialize set of remaining features: R ← {f1, f2, …, fD}
5: for i = 1 to d do
6: Find the feature f
∗
in R that achieves the best score when combined with S
7: Remove f
∗
from R
8: Add f
∗
to S
9: end for
10: return S
5.1 ImplementationYou will need to complete two methods: sequential_feature_selection() and sequential_score():
• sequential_feature_selection() is a method that takes a training set and the number
of features d that you wish to select. The method starts with an empty set, and
iteratively adds one feature at a time to the set until d features are selected in the set.
The method finally outputs the selected feature set. sequential_feature_selection()
will use sequential_score() to determine which feature to add at each step.• sequential_score() is a method that takes a feature subset S and a training set. It
uses 10-fold cross validation to evaluate the classification performance of the feature
subset S. In this part, we will use KNN(K = 3) as the classifier.5.2 Report
Write a concise report that includes:
1. Is the implemented SFFS algorithm a filter, embedded, or wrapper feature selection
approach? Justify your answer.
2. Is the implemented SFFS algorithm a feature ranking or feature subset selection
approach? Justify your answer.3. Set the number of selected features d to five and run the sequential selection algorithm.
• Report the five selected features and the testing performance achieved when
using these five features.
• Use a heatmap to show the Pearson correlation between the five selected features. Provide an analysis of the visualisation, interpreting how the features
relate to each other.• Compare the testing performance of the implemented SFFS algorithm and the
testing performance of the feature ranking algorithm. Discuss which one is
better and provide your justification.Assessment
• Format: You can use any font to write the report, with a minimum of single spacing
and 11-point size (handwriting is not permitted without approval from the course
coordinator). Reports are expected to be at most 8 pages that cover all the questions
described above.• Late Penalties: Late submissions for assignments will be managed under the
“Three Late Day Policy”. You will have three automatic extension days, which
can be applied to any assignments throughout the course. No formal application
is required; instead, any remaining late hours will be automatically deducted when
submitting assignments after the due date. You have the flexibility to use only a
portion of your late day and retain the remainder for future use. Please note that
these three days are for the whole course, not for each assignment. The penalty
for assignments that are handed in late without prior arrangement (or use of “late
days”) is one grade reduction per day. Assignments that are more than one week late
will not be marked. If you require any extension due to exceptional circumstances
(like medical), you need to email the course coordinator.