Project Workflow~

1. Data Preprocessing:

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o Handled missing values through appropriate imputation techniques.

o Transformed categorical variables using Label Encoder.

o Scaled numerical features for better model performance.

2. Exploratory Data Analysis:

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o Visualized the relationships between features and the loan status.

o Analyzed distribution patterns of key attributes like income, loan amount, and credit history.

3. Model Building:

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o Implemented various machine learning algorithms including:

• Logistic Regression

• Random Forest

• Support Vector Machine (SVM)

• KNeighborsClassifier

o Performed hyperparameter tuning to optimize model performance.

4. Model Evaluation:

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o Evaluated models using accuracy_score

5. Final Model:

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o Selected the best-performing model based on evaluation metrics.

o Provided insights on feature importance to understand the key factors influencing loan approval.

Results~

• The final model achieved an accuracy of 78%

• The most influential features in predicting loan status included credit history, applicant’s income, and loan amount.