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devExplore
Loan Status Prediction
DATA SCIENCE
DATA VISUALIZATION
PYTHON
MACHINE LEARNING
FEATURE ENGINEERING
KAGGLE
AUTHOR
Pratik Kumar
PUBLISHED
April 25, 2021
The goal of this project is to develop an automated system for predicting loan eligibility based on customer details provided through an online application form. The company aims to streamline and optimize their loan approval process by leveraging data to identify customer segments that are most likely to be eligible for a loan. This will enable targeted marketing and more efficient processing of applications.
Problem Statement
The company has provided a dataset with customer information, including the following features: - Gender: The gender of the applicant. - Marital Status: Whether the applicant is married or not. - Education: The education level of the applicant. - Number of Dependents: The number of people financially dependent on the applicant. - Income: The applicant’s monthly income. - Loan Amount: The amount of loan applied for. - Credit History: A record of the applicant’s past credit behavior. - Others: Any additional relevant features.
The task is to use this data to identify which customers are eligible for a loan. This involves segmenting the customers into groups based on their likelihood of being approved for a loan. The dataset provided is partial, and the challenge is to work with the available data to build a predictive model.
Importing Libraries
import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline
from sklearn.model_selection import train_test_split from sklearn import feature_selection from sklearn import model_selection from sklearn.metrics import accuracy_score from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier from sklearn.neighbors import KNeighborsClassifier import warnings warnings.filterwarnings('ignore')
Importing data
train = pd.read_csv('../input/loan-prediction-problem-dataset/train_u6lujuX_CVtuZ9i.csv') test = pd.read_csv('../input/loan-prediction-problem-dataset/test_Y3wMUE5_7gLdaTN.csv')
print (train.shape, test.shape)
(614, 13) (367, 12)
Data Exploration
train.head()
Loan_IDGenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_AreaLoan_Status0LP001002MaleNo0GraduateNo58490.0NaN360.01.0UrbanY1LP001003MaleYes1GraduateNo45831508.0128.0360.01.0RuralN2LP001005MaleYes0GraduateYes30000.066.0360.01.0UrbanY3LP001006MaleYes0Not GraduateNo25832358.0120.0360.01.0UrbanY4LP001008MaleNo0GraduateNo60000.0141.0360.01.0UrbanY
train.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 614 entries, 0 to 613 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Loan_ID 614 non-null object 1 Gender 601 non-null object 2 Married 611 non-null object 3 Dependents 599 non-null object 4 Education 614 non-null object 5 Self_Employed 582 non-null object 6 ApplicantIncome 614 non-null int64 7 CoapplicantIncome 614 non-null float64 8 LoanAmount 592 non-null float64 9 Loan_Amount_Term 600 non-null float64 10 Credit_History 564 non-null float64 11 Property_Area 614 non-null object 12 Loan_Status 614 non-null object dtypes: float64(4), int64(1), object(8) memory usage: 62.5+ KB
train.isnull().sum()
Loan_ID 0 Gender 13 Married 3 Dependents 15 Education 0 Self_Employed 32 ApplicantIncome 0 CoapplicantIncome 0 LoanAmount 22 Loan_Amount_Term 14 Credit_History 50 Property_Area 0 Loan_Status 0 dtype: int64
test.head()
Loan_IDGenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_Area0LP001015MaleYes0GraduateNo57200110.0360.01.0Urban1LP001022MaleYes1GraduateNo30761500126.0360.01.0Urban2LP001031MaleYes2GraduateNo50001800208.0360.01.0Urban3LP001035MaleYes2GraduateNo23402546100.0360.0NaNUrban4LP001051MaleNo0Not GraduateNo3276078.0360.01.0Urban
test.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 367 entries, 0 to 366 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Loan_ID 367 non-null object 1 Gender 356 non-null object 2 Married 367 non-null object 3 Dependents 357 non-null object 4 Education 367 non-null object 5 Self_Employed 344 non-null object 6 ApplicantIncome 367 non-null int64 7 CoapplicantIncome 367 non-null int64 8 LoanAmount 362 non-null float64 9 Loan_Amount_Term 361 non-null float64 10 Credit_History 338 non-null float64 11 Property_Area 367 non-null object dtypes: float64(3), int64(2), object(7) memory usage: 34.5+ KB
test.isnull().sum()
Loan_ID 0 Gender 11 Married 0 Dependents 10 Education 0 Self_Employed 23 ApplicantIncome 0 CoapplicantIncome 0 LoanAmount 5 Loan_Amount_Term 6 Credit_History 29 Property_Area 0 dtype: int64
Data Preparation / Processing
data = [train,test] for dataset in data: #Filter categorical variables categorical_columns = [x for x in dataset.dtypes.index if dataset.dtypes[x]=='object'] # Exclude ID cols and source: categorical_columns = [x for x in categorical_columns if x not in ['Loan_ID' ]] #Print frequency of categories for col in categorical_columns: print ('\nFrequency of Categories for variable %s'%col) print (train[col].value_counts())
Frequency of Categories for variable Gender Gender Male 489 Female 112 Name: count, dtype: int64 Frequency of Categories for variable Married Married Yes 398 No 213 Name: count, dtype: int64 Frequency of Categories for variable Dependents Dependents 0 345 1 102 2 101 3+ 51 Name: count, dtype: int64 Frequency of Categories for variable Education Education Graduate 480 Not Graduate 134 Name: count, dtype: int64 Frequency of Categories for variable Self_Employed Self_Employed No 500 Yes 82 Name: count, dtype: int64 Frequency of Categories for variable Property_Area Property_Area Semiurban 233 Urban 202 Rural 179 Name: count, dtype: int64
Gender
sns.countplot(train['Gender'])

pd.crosstab(train.Gender, train.Loan_Status, margins = True)
Loan_StatusNYAllGenderFemale3775112Male150339489All187414601
The male are in large number as compared to female applicants.Also many of them have positive Loan Status. Further Binarization of this feature should be done,
train.Gender = train.Gender.fillna(train.Gender.mode()) test.Gender = test.Gender.fillna(test.Gender.mode()) sex = pd.get_dummies(train['Gender'] , drop_first = True ) train.drop(['Gender'], axis = 1 , inplace =True) train = pd.concat([train , sex ] , axis = 1) sex = pd.get_dummies(test['Gender'] , drop_first = True ) test.drop(['Gender'], axis = 1 , inplace =True) test = pd.concat([test , sex ] , axis = 1)
Dependants
plt.figure(figsize=(6,6)) labels = ['0' , '1', '2' , '3+'] explode = (0.05, 0, 0, 0) size = [345 , 102 , 101 , 51] plt.pie(size, explode=explode, labels=labels, autopct='%1.1f%%', shadow = True, startangle = 90) plt.axis('equal') plt.show()

train.Dependents.value_counts()
Dependents 0 345 1 102 2 101 3+ 51 Name: count, dtype: int64
pd.crosstab(train.Dependents , train.Loan_Status, margins = True)
Loan_StatusNYAllDependents010723834513666102225761013+183351All186413599
The applicants with highest number of dependants are least in number whereas applicants with no dependance are greatest among these.
train.Dependents = train.Dependents.fillna("0") test.Dependents = test.Dependents.fillna("0") rpl = {'0':'0', '1':'1', '2':'2', '3+':'3'} train.Dependents = train.Dependents.replace(rpl).astype(int) test.Dependents = test.Dependents.replace(rpl).astype(int)
Credit History
pd.crosstab(train.Credit_History , train.Loan_Status, margins = True)
Loan_StatusNYAllCredit_History0.0827891.097378475All179385564
train.Credit_History = train.Credit_History.fillna(train.Credit_History.mode()[0]) test.Credit_History = test.Credit_History.fillna(test.Credit_History.mode()[0])
Self Employed
sns.countplot(train['Self_Employed'])

pd.crosstab(train.Self_Employed , train.Loan_Status,margins = True)
Loan_StatusNYAllSelf_EmployedNo157343500Yes265682All183399582
train.Self_Employed = train.Self_Employed.fillna(train.Self_Employed.mode()) test.Self_Employed = test.Self_Employed.fillna(test.Self_Employed.mode()) self_Employed = pd.get_dummies(train['Self_Employed'] ,prefix = 'employed' ,drop_first = True ) train.drop(['Self_Employed'], axis = 1 , inplace =True) train = pd.concat([train , self_Employed ] , axis = 1) self_Employed = pd.get_dummies(test['Self_Employed'] , prefix = 'employed' ,drop_first = True ) test.drop(['Self_Employed'], axis = 1 , inplace =True) test = pd.concat([test , self_Employed ] , axis = 1)
Married
sns.countplot(train.Married)

pd.crosstab(train.Married , train.Loan_Status,margins = True)
Loan_StatusNYAllMarriedNo79134213Yes113285398All192419611
train.Married = train.Married.fillna(train.Married.mode()) test.Married = test.Married.fillna(test.Married.mode()) married = pd.get_dummies(train['Married'] , prefix = 'married',drop_first = True ) train.drop(['Married'], axis = 1 , inplace =True) train = pd.concat([train , married ] , axis = 1) married = pd.get_dummies(test['Married'] , prefix = 'married', drop_first = True ) test.drop(['Married'], axis = 1 , inplace =True) test = pd.concat([test , married ] , axis = 1)
Loan Amount Term and Loan Amount
train.drop(['Loan_Amount_Term'], axis = 1 , inplace =True) test.drop(['Loan_Amount_Term'], axis = 1 , inplace =True) train.LoanAmount = train.LoanAmount.fillna(train.LoanAmount.mean()).astype(int) test.LoanAmount = test.LoanAmount.fillna(test.LoanAmount.mean()).astype(int)
sns.distplot(train['LoanAmount'])

We observe no outliers in the continuous variable Loan Amount
Education
sns.countplot(train.Education)

train['Education'] = train['Education'].map( {'Graduate': 0, 'Not Graduate': 1} ).astype(int) test['Education'] = test['Education'].map( {'Graduate': 0, 'Not Graduate': 1} ).astype(int)
Property Area
sns.countplot(train.Property_Area)

train['Property_Area'] = train['Property_Area'].map( {'Urban': 0, 'Semiurban': 1 ,'Rural': 2 } ).astype(int) test.Property_Area = test.Property_Area.fillna(test.Property_Area.mode()) test['Property_Area'] = test['Property_Area'].map( {'Urban': 0, 'Semiurban': 1 ,'Rural': 2 } ).astype(int)
Co-Applicant income and Applicant income
sns.distplot(train['ApplicantIncome'])

sns.distplot(train['CoapplicantIncome'])

Target Variable : Loan Status
train['Loan_Status'] = train['Loan_Status'].map( {'N': 0, 'Y': 1 } ).astype(int)
Dropping the ID column
train.drop(['Loan_ID'], axis = 1 , inplace =True)
View the datasets
train.head()
DependentsEducationApplicantIncomeCoapplicantIncomeLoanAmountCredit_HistoryProperty_AreaLoan_StatusMaleemployed_Yesmarried_Yes00058490.01461.001TrueFalseFalse11045831508.01281.020TrueFalseTrue20030000.0661.001TrueTrueTrue30125832358.01201.001TrueFalseTrue40060000.01411.001TrueFalseFalse
test.head()
Loan_IDDependentsEducationApplicantIncomeCoapplicantIncomeLoanAmountCredit_HistoryProperty_AreaMaleemployed_Yesmarried_Yes0LP00101500572001101.00TrueFalseTrue1LP00102210307615001261.00TrueFalseTrue2LP00103120500018002081.00TrueFalseTrue3LP00103520234025461001.00TrueFalseTrue4LP0010510132760781.00TrueFalseFalse
Visualizing the correlations and relation
Plot between LoanAmount, Applicant Income, Employement and Gender
What is the relation of Loan taken between men and women? Did the employed ones were greater in number to take Loan ? What is distribution of Loan Amount and Income?
Corrected code using height instead of size g = sns.lmplot(x='ApplicantIncome', y='LoanAmount', data=train, col='employed_Yes', hue='Male', palette=["Red", "Blue", "Yellow"], aspect=1.2, height=3) g.set(ylim=(0, 800)) # Relation between the male or female applicant's income, loan taken, and self-employment status.

Above graph tells:
The male applicants take more amount of loan than female.
The males are higher in number of “NOT self employed” category.
The amount is still larger in the income range in (0 to 20000).
Also we observe that majority of applicants are NOT self employed.
Highest Loan amount taken is by the female applicant of about 700 which is NOT self employed.
The majority of income taken is about 0-200 with income in the range 0-20000.
The line plotted shows that with increase in income the amount of loan increases with almost same slope for the case of women in both the cases but a slightely lesser slope in the case of men in Self- Employed category as compared to non-self employed.
Boxplots for relation between Property area, amount of Loan and Education qualification
Further we analyse the relation between education status,loan taken and property area
Property_Area:
Urban :0
Semiurban :1
Rural :2
plt.figure(figsize=(5,2)) sns.boxplot(x="Property_Area", y="LoanAmount", hue="Education",data=train, palette="coolwarm")

The above boxplot signifies that,
In the Urban area the non graduates take slightly more loan than graduates.
In the Rural and semiurban area the graduates take more amount of Loan than non graduates
The higher values of Loan are mostly from Urban area
The semiurban area and rural area both have one unusual Loan amount close to zero.
Crosstab for relation between Credit History and Loan status.
train.Credit_History.value_counts()
Credit_History 1.0 525 0.0 89 Name: count, dtype: int64
lc = pd.crosstab(train['Credit_History'], train['Loan_Status']) lc.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)

The credit history vs Loan Status indicates:
The good credit history applicants have more chances of getting Loan.
With better credit History the Loan amount given was greater too.
But many were not given loan in the range 0-100
The applicant with poor credit history were handled in the range 0-100 only.
plt.figure(figsize=(9,6)) sns.heatmap(train.drop('Loan_Status',axis=1).corr(), vmax=0.6, square=True, annot=True)

Prediction
The problem is of Classification as observed and concluded from the data and visualisations.
X = train.drop('Loan_Status' , axis = 1 ) y = train['Loan_Status'] X_train ,X_test , y_train , y_test = train_test_split(X , y , test_size = 0.3 , random_state =102)
from sklearn.linear_model import LogisticRegression logmodel = LogisticRegression() logmodel.fit(X_train , y_train) pred_l = logmodel.predict(X_test) acc_l = accuracy_score(y_test , pred_l)*100 acc_l
83.78378378378379
random_forest = RandomForestClassifier(n_estimators= 100) random_forest.fit(X_train, y_train) pred_rf = random_forest.predict(X_test) acc_rf = accuracy_score(y_test , pred_rf)*100 acc_rf
80.54054054054053
knn = KNeighborsClassifier(n_neighbors = 3) knn.fit(X_train, y_train) pred_knn = knn.predict(X_test) acc_knn = accuracy_score(y_test , pred_knn)*100 acc_knn
61.08108108108108
gaussian = GaussianNB() gaussian.fit(X_train, y_train) pred_gb = gaussian.predict(X_test) acc_gb = accuracy_score(y_test , pred_gb)*100 acc_gb
82.16216216216216
svc = SVC() svc.fit(X_train, y_train) pred_svm = svc.predict(X_test) acc_svm = accuracy_score(y_test , pred_svm)*100 acc_svm
70.27027027027027
gbk = GradientBoostingClassifier() gbk.fit(X_train, y_train) pred_gbc = gbk.predict(X_test) acc_gbc = accuracy_score(y_test , pred_gbc)*100 acc_gbc
82.16216216216216
Arranging the Accuracy results models = pd.DataFrame({ 'Model': ['Logistic Regression', 'Random Forrest','K- Nearest Neighbour' , 'Naive Bayes' , 'SVM','Gradient Boosting Classifier'], 'Score': [acc_l , acc_rf , acc_knn , acc_gb ,acc_svm ,acc_gbc ]}) models.sort_values(by='Score', ascending=False)
ModelScore0Logistic Regression83.7837843Naive Bayes82.1621625Gradient Boosting Classifier82.1621621Random Forrest80.5405414SVM70.2702702K- Nearest Neighbour61.081081
The highest classification accuracy is shown by Logistic Regression of about 83.24 %
Let us Check th feature importance,
importances = pd.DataFrame({'Features':X_train.columns,'Importance':np.round(random_forest.featureimportances,3)}) importances = importances.sort_values('Importance',ascending=False).set_index('Features') importances.head(11)
ImportanceFeaturesCredit_History0.248ApplicantIncome0.216LoanAmount0.211CoapplicantIncome0.122Dependents0.053Property_Area0.052Education0.027married_Yes0.026Male0.024employed_Yes0.021
importances.plot.bar()

Credit History has the maximum importance and empoloyment has the least!
Summarizing
The Loan status has better relation with features such as Credit History, Applicant’s Income, Loan Amount needed by them, Family status(Depenedents) and Property Area which are generally considered by the loan providing organisations. These factors are hence used to take correct decisions to provide loan status or not. This data analysis hence gives a realisation of features and the relation between them from the older decision examples hence giving a learning to predict the class of the unseen data.
Finally the we predict over unseen dataset using the Logistic Regression and Random Forest model(Ensemble Learning):
df_test = test.drop(['Loan_ID'], axis = 1)
df_test.head()
DependentsEducationApplicantIncomeCoapplicantIncomeLoanAmountCredit_HistoryProperty_AreaMaleemployed_Yesmarried_Yes000572001101.00TrueFalseTrue110307615001261.00TrueFalseTrue220500018002081.00TrueFalseTrue320234025461001.00TrueFalseTrue40132760781.00TrueFalseFalse
p_log = logmodel.predict(df_test)
p_rf = random_forest.predict(df_test)
predict_combine = np.zeros((df_test.shape[0])) for i in range(0, test.shape[0]): temp = p_log[i] + p_rf[i] if temp>=2: predict_combine[i] = 1 predict_combine = predict_combine.astype('int')
submission = pd.DataFrame({ "Loan_ID": test["Loan_ID"], "Loan_Status": predict_combine }) submission.to_csv("results.csv", encoding='utf-8', index=False)
Thank you
Author: Pratik Kumar
About
License
Trademark