Loan Approval Prediction Using Random Forest Classifier

This post presents a comprehensive machine learning project for loan approval prediction, focusing on data preprocessing, feature engineering, and classification using a Random Forest model.

💼 Loan Approval Prediction using Random Forest

📌 Project Overview

Automating loan eligibility is a critical task for financial institutions.

In this project, I developed a machine learning pipeline to predict loan approvals based on applicant profiles. The model uses a Random Forest Classifier to evaluate factors such as:

• Credit history
• Income
• Employment status

This helps determine whether a loan should be approved or not.

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🛠️ Technical Stack

The project was built using the Python data science ecosystem:

• Data Manipulation: pandas
• Data Visualization: matplotlib, seaborn
• Machine Learning: scikit-learn

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📊 Data Processing Workflow

🔹 1. Data Cleaning & Imputation

Real-world data often contains missing values. These were handled using:

• Categorical Features: Mode
  • gender, married, dependents, self_employed
• Numerical Features: Median
  • loanamount, loan_amount_term
• Credit History: Mode (most frequent value)

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🔹 2. Feature Engineering

To prepare the dataset for modeling:

• Removed Irrelevant Data:
  • Dropped loan_id (no predictive value)
• Label Encoding:
  • Converted loan_status into numeric format
• One-Hot Encoding:
  • Applied pd.get_dummies() to categorical variables

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🤖 Model Development

A Random Forest Classifier was selected due to:

• High accuracy
• Resistance to overfitting
• Ability to handle non-linear relationships

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

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💻 Implementation (Python Code)

Below is the complete implementation of the project:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Load dataset
df = pd.read_csv("Loan Approval Prediction dataset.csv")

# Basic info
print(df.head())
print(df.info())

# Handling missing values
for col in ['gender', 'married', 'dependents', 'self_employed']:
    df[col].fillna(df[col].mode()[0], inplace=True)

df['loanamount'].fillna(df['loanamount'].median(), inplace=True)
df['loan_amount_term'].fillna(df['loan_amount_term'].median(), inplace=True)
df['credit_history'].fillna(df['credit_history'].mode()[0], inplace=True)

# Drop unnecessary column
df.drop(columns=['loan_id'], inplace=True)

# Visualization
plt.hist(df["applicantincome"])
plt.title("Applicant Income Distribution")
plt.xlabel("Income")
plt.ylabel("Count")
plt.show()

sns.countplot(data=df, x="loan_status")
plt.title("Loan Status Distribution")
plt.show()

# Encoding
le = LabelEncoder()
df['loan_status'] = le.fit_transform(df['loan_status'])

df = pd.get_dummies(df, drop_first=True)

# Splitting data
X = df.drop("loan_status", axis=1)
y = df["loan_status"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Model training
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# Predictions
y_pred = rf.predict(X_test)

# Evaluation
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))

# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)

sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
plt.title("Random Forest Confusion Matrix")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.show()
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📈 Results

• Achieved an accuracy of 78% on test data
• Model performed well in predicting loan approvals
• Confusion matrix shows balanced classification performance

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✅ Conclusion

This project demonstrates how machine learning can automate loan approval decisions efficiently.
The Random Forest model provided reliable predictions and handled the dataset effectively.

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