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SridharGuggilla / Loan-Predication-Using-Machine-Learning

This project predicts loan eligibility using machine learning. It preprocesses a dataset with features like income, loan amount, and credit history. After exploratory data analysis, various models (Logistic Regression, Decision Trees) are evaluated for accuracy. The final model aids financial institutions in making informed lending decisions.
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Add Model Evaluations #1

Closed SridharGuggilla closed 2 weeks ago

SridharGuggilla commented 2 weeks ago

We need to implement model evaluation techniques to assess the performance of our machine learning models. Train-Test Split or Cross-Validation: Use an appropriate method to split the data into training and testing sets or implement k-fold cross-validation for more robust evaluation.

SridharGuggilla commented 2 weeks ago

Model evaluation is added with results of **Model: LogisticRegression Accuracy: 0.8226 Recall: 0.9583 Precision: 0.8364

Model: SVC Accuracy: 0.7742 Recall: 1.0000 Precision: 0.7742

Model: RandomForestClassifier Accuracy: 0.8065 Recall: 0.9167 Precision: 0.8462**