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Loan Eligibility Prediction For Customer

Loan prediction for customers using decision tree model and random forest model for comparison.

Loading the Libraries and Dataset

Let’s start by importing the required Python libraries and our dataset.

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split

Importing the dataset

The dataset consists of 614 rows and 13 features, including credit history, marital status, loan amount, and gender. Here, the target variable is Loan_Status, which indicates whether a person should be given a loan or not.

In [2]:
# Importing dataset
Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area Loan_Status
0 LP001002 Male No 0 Graduate No 5849 0.0 NaN 360.0 1.0 Urban Y
1 LP001003 Male Yes 1 Graduate No 4583 1508.0 128.0 360.0 1.0 Rural N
2 LP001005 Male Yes 0 Graduate Yes 3000 0.0 66.0 360.0 1.0 Urban Y
3 LP001006 Male Yes 0 Not Graduate No 2583 2358.0 120.0 360.0 1.0 Urban Y
4 LP001008 Male No 0 Graduate No 6000 0.0 141.0 360.0 1.0 Urban Y

Data Preprocessing

Now, comes the most crucial part of any data science project – data preprocessing and feature engineering. In this section, I will be dealing with the categorical variables in the data and also imputing the missing values. I will impute the missing values in the categorical variables with the mode, and for the continuous variables, with the mean (for the respective columns). Also, we will be label encoding the categorical values in the data.

In [3]:
# Data Preprocessing and null values imputation
# Label Encoding
df['Education']=df['Education'].map({'Graduate':1,'Not Graduate':0})

#Null Value Imputation

Creating Train and Test Sets

Now, let’s split the dataset in an 80:20 ratio for training and test set respectively:

In [4]:
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)

Here is a look of the shape of the created train and test sets below:

In [5]:
print('Shape of X_train=>',X_train.shape)
print('Shape of X_test=>',X_test.shape)
print('Shape of Y_train=>',Y_train.shape)
print('Shape of Y_test=>',Y_test.shape)
Shape of X_train=> (491, 11)
Shape of X_test=> (123, 11)
Shape of Y_train=> (491,)
Shape of Y_test=> (123,)

Building and Evaluating the Model with Decision Tree

Since we have both the training and testing sets, it’s time to train our models and classify the loan applications. First, we will train a decision tree on this datase. Next, we will evaluate this model using F1-Score.

In [6]:
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(criterion = 'entropy', random_state = 42)
dt.fit(X_train, Y_train)
dt_pred_train = dt.predict(X_train)

Evaluatiion on Training set

In [7]:
dt_pred_train = dt.predict(X_train)
print('Training Set Evaluation with  Decision Tree F1-Score=>',f1_score(Y_train,dt_pred_train))
Training Set Evaluation with  Decision Tree F1-Score=> 1.0

Evaluation on Testing set

In [8]:
dt_pred_test = dt.predict(X_test)
print('Testing Set Evaluation with  Decision Tree F1-Score=>',f1_score(Y_test,dt_pred_test))
Testing Set Evaluation with  Decision Tree F1-Score=> 0.7953216374269005

Here, you can see that the decision tree performs well on in-sample evaluation, but its performance decreases drastically on out-of-sample evaluation.

Building and Evaluating the Model with Random Forest Classifier

In [9]:
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(criterion = 'entropy', random_state = 42)
rfc.fit(X_train, Y_train)
/home/webtunix/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
  "10 in version 0.20 to 100 in 0.22.", FutureWarning)
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',
                       max_depth=None, max_features='auto', max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=10,
                       n_jobs=None, oob_score=False, random_state=42, verbose=0,

Evaluating on Training set

In [10]:
rfc_pred_train = rfc.predict(X_train)
print('Training Set Evaluation with  Random Forest F1-Score=>',f1_score(Y_train,rfc_pred_train))
Training Set Evaluation with  Random Forest F1-Score=> 0.992679355783309

Evaluating on Test set

In [11]:
rfc_pred_test = rfc.predict(X_test)
print('Testing Set Evaluation with  Random Forest F1-Score=>',f1_score(Y_test,rfc_pred_test))
Testing Set Evaluation with  Random Forest F1-Score=> 0.7951807228915662

Here, we can clearly see that the random forest model performed much better than the decision tree in the out-of-sample evaluation. Let’s discuss the reasons behind this in the next section.

Graph representation of random forest model and decision tree model.

Random forest leverages the power of multiple decision trees. It does not rely on the feature importance given by a single decision tree. Let’s take a look at the feature importance given by different algorithms to different features:

In [12]:

index = np.arange(len(feature_importance))
fig, ax = plt.subplots(figsize=(18,8))
rfc_feature=ax.barh(index,feature_importance['rfc'],0.4,color='purple',label='Random Forest')
dt_feature=ax.barh(index+0.4,feature_importance['dt'],0.4,color='lightgreen',label='Decision Tree')

Loan Eligibility Prediction using Random Forest

As you can clearly see in the above graph, the decision tree model gives high importance to a particular set of features. But the random forest chooses features randomly during the training process. Therefore, it does not depend highly on any specific set of features. This is a special characteristic of random forest over bagging trees.


Therefore, the random forest can generalize over the data in a better way. This randomized feature selection makes random forest much more accurate than a decision tree.

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