Loan Approval Prediction¶
Executive Summary¶
This project builds supervised machine learning models to predict loan approval based on borrower features.
Key Steps: • Data preprocessing and missing value handling • Categorical encoding and scaling • Class imbalance handling using SMOTE • Model comparison (Logistic Regression & Random Forest)
Results: • Random Forest achieved 83% accuracy. • ROC-AUC score: 0.79 • SMOTE improved class balance and recall performance.
Business Insight: Random Forest model is recommended for deployment with threshold adjustment to reduce risky approvals.
In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score, confusion_matrix
from imblearn.over_sampling import SMOTE
In [5]:
df = pd.read_csv("C:\\Users\\DeLL\\OneDrive\\Desktop\\Alfido Tech Internship\\loan_prediction.csv")
df.head()
Out[5]:
| 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 |
In [7]:
df.shape
df.info()
df.isnull().sum()
df['Loan_Status'].value_counts()
<class 'pandas.core.frame.DataFrame'> Index: 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: 67.2+ KB
Out[7]:
Loan_Status Y 422 N 192 Name: count, dtype: int64
Removing Loan_ID as it does not contribute to prediction.
In [10]:
df.drop(columns=['Loan_ID'], inplace=True)
In [14]:
categorical_cols = ['Gender','Married','Dependents','Self_Employed','Credit_History']
for col in categorical_cols:
df[col] = df[col].fillna(df[col].mode()[0])
In [16]:
numerical_cols = ['LoanAmount','Loan_Amount_Term']
for col in numerical_cols:
df[col] = df[col].fillna(df[col].median())
In [18]:
df.isnull().sum()
Out[18]:
Gender 0 Married 0 Dependents 0 Education 0 Self_Employed 0 ApplicantIncome 0 CoapplicantIncome 0 LoanAmount 0 Loan_Amount_Term 0 Credit_History 0 Property_Area 0 Loan_Status 0 dtype: int64
In [20]:
df['Loan_Status'] = df['Loan_Status'].map({'Y':1, 'N':0})
In [22]:
df['Loan_Status'].value_counts()
Out[22]:
Loan_Status 1 422 0 192 Name: count, dtype: int64
In [24]:
df = pd.get_dummies(df, drop_first=True)
In [26]:
df.head()
df.shape
Out[26]:
(614, 15)
In [28]:
X = df.drop('Loan_Status', axis=1)
y = df['Loan_Status']
In [30]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y,
test_size=0.2,
random_state=42,
stratify=y
)
In [32]:
X_train.shape
X_test.shape
Out[32]:
(123, 14)
In [34]:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
In [36]:
from imblearn.over_sampling import SMOTE
smote = SMOTE(random_state=42)
X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)
In [38]:
pd.Series(y_train_smote).value_counts()
Out[38]:
Loan_Status 1 337 0 337 Name: count, dtype: int64
In [40]:
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(max_iter=1000)
lr.fit(X_train_smote, y_train_smote)
y_pred_lr = lr.predict(X_test)
In [42]:
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(random_state=42)
rf.fit(X_train_smote, y_train_smote)
y_pred_rf = rf.predict(X_test)
In [44]:
from sklearn.metrics import classification_report, roc_auc_score
print("Logistic Regression Report")
print(classification_report(y_test, y_pred_lr))
print("ROC-AUC:", roc_auc_score(y_test, y_pred_lr))
print("\nRandom Forest Report")
print(classification_report(y_test, y_pred_rf))
print("ROC-AUC:", roc_auc_score(y_test, y_pred_rf))
Logistic Regression Report
precision recall f1-score support
0 0.70 0.68 0.69 38
1 0.86 0.87 0.87 85
accuracy 0.81 123
macro avg 0.78 0.78 0.78 123
weighted avg 0.81 0.81 0.81 123
ROC-AUC: 0.7773993808049535
Random Forest Report
precision recall f1-score support
0 0.74 0.68 0.71 38
1 0.86 0.89 0.88 85
accuracy 0.83 123
macro avg 0.80 0.79 0.80 123
weighted avg 0.83 0.83 0.83 123
ROC-AUC: 0.7891640866873064
In [46]:
y_prob = rf.predict_proba(X_test)[:,1]
custom_threshold = 0.6
y_custom = (y_prob >= custom_threshold).astype(int)
print(classification_report(y_test, y_custom))
precision recall f1-score support
0 0.63 0.71 0.67 38
1 0.86 0.81 0.84 85
accuracy 0.78 123
macro avg 0.75 0.76 0.75 123
weighted avg 0.79 0.78 0.78 123
Business Interpretation¶
Random Forest performed slightly better than Logistic Regression with higher accuracy and ROC-AUC.
However, recall for rejected loans is relatively low (0.68), meaning some risky applications may still be approved.
For deployment, a higher decision threshold (e.g., 0.6) can be used to reduce financial risk, even if it slightly lowers recall.
Conclusion¶
The Random Forest model achieved the best overall performance with 83% accuracy and ROC-AUC of 0.79. While both models performed well in predicting approved loans, further tuning and threshold adjustment is recommended before deployment.