Applying Classification Algorithms to Past Loan Data
Last Updated on July 5, 2022 by Editorial Team
Author(s): Gencay I.
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KNN, Decision Tree, Support Vector Machine, Logistic Regression

In this data set, I am going to conduct classification machine learning analysis on past loan data whichย are;
- K Nearest Neighbor(KNN)
- Decision Tree
- Support Vectorย Machine
- Logistic Regression
Content Table
ยท Data Visualization
ยท One hot encoding
ยท Feature Selection
ยท Normalize Data
ยท Classification
โ K Nearest Neighbor
โ Evaluation Metrics of KNN
โ Decision Tree
โ Evaluation Metrics of Decision Tree
โ Support Vector Machine
โ Evaluation Metrics of SVM
โ Logistic Regression
โ Evaluation Metrics of Logistic Regression
โ Model Evaluation using a Test set
โ Jaccard Scores
โ F1 Scores
โ Final Evaluation
Let's load the necessary libraries;

The Loan_train.csv data set includes details of 346 customers whose loans are already paid off or defaulted.

Lets loadย data;

It is always efficient to look shape of data, to see the bigย picture.

Now let's fix the data frames columnย type.

Data Visualization
Let's see how many of each class is in our dataย set

Let's plot some columns to understand better


Let's look at the day of week people get theย loan

We see that people who get the loan at the end of the week don't pay it off, so let's use Feature binarization to set threshold values less than dayย 4

Now it is time to change categorical features to numerical because we will use machine learning algorithms.

86 % of females pay their loans while only 73 % of males pay theirย loan
Let's convert male to 0 and female toย 1:

One hotย encoding
Now letโs look education column.


We use dummies to transform education from categorical to numerical.

Feature Selection
Letโs define features;

Now it is time to define ourย label;

Normalize Data

Classification
These are the classification techniques that I will use in thisย Dataset.
- K Nearest Neighbor(KNN)
- Decision Tree
- Support Vectorย Machine
- Logistic Regression
K Nearestย Neighbor
Now it is time to split train and test data, as usual, 0.2โ0.8ย portion.



Now it is time to look into the accuracy of test and trainย data.

To define bestย K;

As we can see result 7 is the best K for ourย data.



Evaluation Metrics ofย KNN

Decision Tree
Now let's try using Decision Tree algorithms.


To define the best of theย depth;

5 is the best depth score according to accuracyย scores.

Letโs conduct our algorithm then and evaluate;
Evaluation Metrics of Decisionย Tree

Support Vectorย Machine
Now letโs useย SVM.

To find out the best model inย SVM;



Evaluation Metrics ofย SVM

Logistic Regression
Now it is time to use Logistic Regression.
Lets lock andย load;

Train-test split;

Find the bestย solver;


Evaluation Metrics of Logistic Regression

Model Evaluation using a Testย set


Data processing;

Jaccard Scores




F1 Scores




Final Evaluation

Thanks, IBM for Machine Learning Tutorial which gets meย there.
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