Loan Status Prediction Machine Learning Model Exercise

Predicting whether a loan application will be approved or rejected is a critical task for financial institutions. In this blog, we’ll walk through a hands-on machine learning exercise to build a loan status prediction model using real-world data. By the end, you’ll understand how to clean the data, select the right features, and train a model that can help automate loan decisions with accuracy and confidence.

workflow: data > data pre processing > split the data into train test split > SVM( supervised learning :Machine learning model)

new data > trained support vector machine model > approved or rejected

Import the dependencies

• import numpy as np
• import pandas as pd
• import seaborn as sns
• from sklearn import svm
• from sklearn.metrics import accuracy_score
• from sklearn.model_selection import train_test_split

Data Collection and Data Preprocessing

• #loading the dataset to pandas dataframe
• loan_dataset =pd.read_csv(‘/content/loan_dataset.csv’)
• type(loan_dataset)

link to practice the dataset :

https://www.kaggle.com/datasets/altruistdelhite04/loan-prediction-problem-dataset

• # printing the first 5 rows of the dataframe
• loan_dataset.head()

• # no of rows and columns
• loan_dataset.shape

• # statistical measures
• loan_dataset.describe()

• # number of missing values in each column
• loan_dataset.isnull().sum()

• # dropping the missing values
• loan_dataset = loan_dataset.dropna()

• loan_dataset.isnull().sum()

label encoding

loan_dataset.replace({“Loan_Status”:{‘N’:0,’Y’:1}},inplace=True)

loan_dataset.head()

• # dependent column values
• loan_dataset[‘Dependents’].value_counts()

• # replacing the value of 3+ to 4
• loan_dataset = loan_dataset.replace(to_replace=’3+’, value=4)

• # dependent values
• loan_dataset[‘Dependents’].value_counts()

Data Visualisation

• # education and loan status
• sns.countplot(x=’Education’,hue=’Loan_Status’,data = loan_dataset)

• # married and loan status
• sns.countplot(x=’Married’,hue=’Loan_Status’,data = loan_dataset)

• # convert categorical data into numerical data
• loan_dataset.replace({‘Married’:{‘No’:0,’Yes’:1},’Gender’:{‘Male’:1,’Female’:0},’Self_Employed’:{‘No’:0,’Yes’:1},’Property_Area’:{‘Rural’:0,’Semiurban’:1,’Urban’:2},’Education’:{‘Graduate’:1,’Not Graduate’:0}},inplace=True)
• loan_dataset.head()

• # separating the data and label
• X = loan_dataset.drop(columns=[‘Loan_ID’,’Loan_Status’],axis=1)
• Y = loan_dataset[‘Loan_Status’]
• print(x)
• print(y)

Train Test Split

• X_train, X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.1,stratify=Y,random_state=2)
• print(X.shape, X_train.shape, X_test.shape)

Training the model : Support Vector Machine

• classifier = svm.SVC(kernel=’linear’)
• #training the support Vector Macine model
• classifier.fit(X_train,Y_train)

Model evaluation

• # accuracy score on training data
• X_train_prediction = classifier.predict(X_train)
• training_data_accuray = accuracy_score(X_train_prediction,Y_train)
• print(‘Accuracy on training data : ‘, training_data_accuray)

• # accuracy score on testing data
• X_test_prediction = classifier.predict(X_test)
• test_data_accuracy = accuracy_score(X_test_prediction,Y_test)
• print(‘Accuracy on testing data : ‘, training_data_accuracy)

Making a predictive system

• input_data = (1,1,1,1,0,4583,1508.0,128.0,360.0,1.0,0)

• # changing the input_data to numpy array
• input_data_as_numpy_array = np.asarray(input_data)

• # reshape the array as we are predicting for one instance
• input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

• # standardize the input data
• # scaler.transform(input_data_reshaped)
• prediction = classifier.predict(input_data_reshaped)
• print(prediction)

• if (prediction[0] == 0):
• print(‘The person is not eligible for loan’)
• else:
• print(‘The person is eligible for loan’)

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