This project aims to build a machine learning model to predict loan approvals based on applicant data. The model evaluates various factors such as income, credit history, and loan amount to determine approval chances.
The dataset contains applicant details, including:
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Gender
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Marital Status
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Dependents
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Education Level
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Self-Employment Status
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Applicant and Co-applicant Income
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Loan Amount & Term
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Credit History
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Property Area
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Loan Approval Status (Target variable)
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Python
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Pandas & NumPy
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Scikit-learn
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Matplotlib & Seaborn (for data visualization)
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Jupyter Notebook
1 Data Preprocessing: Handling missing values, encoding categorical variables, and scaling numerical features.
2 Exploratory Data Analysis (EDA): Identifying key insights and correlations.
3 Feature Engineering: Transforming data to enhance model performance.
4 Model Selection & Training: Comparing machine learning models like Logistic Regression, Decision Trees, and Random Forest.
5 Model Evaluation: Using accuracy, precision, recall, and ROC-AUC score.
6 Predictions & Insights: Interpreting results for better loan approval strategies.
Install dependencies using:
pip install pandas numpy scikit-learn matplotlib seaborn
Open and run Loan_Prediction.ipynb in Jupyter Notebook.
The project identifies key factors influencing loan approvals and builds an accurate predictive model. Further improvements can be made by using deep learning models or advanced feature engineering techniques.