Project goal
Predict loan approval using applicant data.
Business value
- Faster decisions
- Lower default risk
- Clear interview story
Dataset
Use the common Loan Prediction dataset from analytics practice platforms.
Target
Loan_Status
Y approved
N rejected
Tech stack
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
Step 1. Import libraries
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
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
Step 2. Load data
df = pd.read_csv("loan_prediction.csv")
df.head()
Step 3. Basic checks
df.shape
df.info()
df.isnull().sum()
Step 4. Data cleaning
Fill missing values
df['LoanAmount'].fillna(df['LoanAmount'].median(), inplace=True)
df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mode()[0], inplace=True)
df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)
categorical_cols = ['Gender','Married','Dependents','Self_Employed']
for col in categorical_cols:
df[col].fillna(df[col].mode()[0], inplace=True)
Step 5. Exploratory Data Analysis
Credit history vs approval
sns.countplot(x='Credit_History', hue='Loan_Status', data=df)
plt.show()
Income distribution.python
sns.histplot(df['ApplicantIncome'], kde=True)
plt.show()
Insight
Applicants with credit history have far higher approval rates.
Step 6. Feature engineering
Create total income.
df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']
# Log transform loan amount
df['LoanAmount_log'] = np.log(df['LoanAmount'])
Step 7. Encode categorical variables
le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
df[col] = le.fit_transform(df[col])
Step 8. Split features and target
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=0.3, random_state=42
)
Step 9. Build model
Logistic Regression.
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)
Step 10. Predictions
y_pred = model.predict(X_test)
Step 11. Evaluation
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
confusion_matrix(y_test, y_pred)
Classification report.python
print(classification_report(y_test, y_pred))
Typical result
- Accuracy around 80 percent
- Strong precision for approved loans
- Recall needs focus for rejected loans
Step 12. Model improvement ideas
- Use Random Forest
- Tune hyperparameters
- Handle class imbalance
- Track recall for rejected cases
Resume bullet example
- Built loan approval prediction model using Logistic Regression
- Achieved ~80 percent accuracy
- Identified credit history as top approval driver
Interview explanation flow
- Start with bank risk problem
- Explain feature impact
- Justify Logistic Regression
- Discuss recall vs accuracy
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