Loan Prediction / 30_Days_of_Python

[Bharat00007]

### Title: Model Evaluation and Comparison for Loan Prediction

Description:

This issue pertains to the implementation and evaluation of various machine learning models for predicting loan approvals. The project is designed to compare multiple classification algorithms to determine the best model for loan prediction.

Functionalities and Features

Data Preprocessing:

• Dataset Handling: The project begins by loading and preprocessing the dataset. This involves normalizing the data and splitting it into training and testing sets.
• Feature Extraction: Key features related to loan approval are extracted and prepared for model training.

Model Training:

• Logistic Regression: A foundational model used for binary classification tasks.
• Support Vector Machine (SVM): A powerful classifier that works well for high-dimensional data.
• Decision Tree: A tree-based model that makes decisions based on feature values.
• Random Forest Classifier: An ensemble method that combines multiple decision trees to improve prediction accuracy.
• Naive Bayes (MultinomialNB): A probabilistic model based on Bayes' theorem, effective for categorical data.
• K-Nearest Neighbors (KNN): A non-parametric method that classifies based on the majority vote from neighboring data points.

Model Evaluation:

• Performance Metrics: The models are evaluated using various metrics including accuracy, F1 score, log loss, precision, and recall.
• Comparison Table: A summary table of model performance metrics is generated to facilitate comparison.

Model Saving:

• Best Model Selection: Based on performance metrics, the best-performing model (SVM) is saved using pickle for future use.

  Expected Outcomes

• Comparative Analysis: Provides a detailed comparison of different classification algorithms to identify the most effective model for loan prediction.

• Performance Metrics: Insights into the strengths and weaknesses of each model based on key evaluation metrics.

• Model Deployment: Availability of the best-performing model (model_svm.pkl) for integration into applications or further analysis.

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