Loan Approval Prediction using Machine Learning

[flexycode]

Issue 1: Data Collection

• Title: Collect additional loan application data

• Description: The current dataset for loan approval prediction may not include all relevant variables or customer attributes. We need to explore additional data sources or techniques to collect more comprehensive data that captures important factors influencing loan approval decisions, such as income, credit history, employment status, and loan details.

Issue 2: Data Preprocessing

• Title: Implement data cleaning and preprocessing techniques

• Description: The loan application data may contain missing values, outliers, or categorical variables that need to be handled appropriately. We need to implement data cleaning techniques, handle missing values, normalize numerical features, and encode categorical variables to prepare the data for machine learning algorithms.

Issue 3: Model Selection

• Title: Evaluate different machine learning algorithms for loan approval prediction

• Description: We need to compare the performance of different machine learning algorithms, such as logistic regression, random forests, support vector machines, or neural networks, for loan approval prediction. This will help us identify the most suitable algorithm that provides accurate predictions and meets the requirements of the lending institution.

Issue 4: Model Evaluation

• Title: Implement evaluation metrics for loan approval prediction models

• Description: We need to define and implement appropriate evaluation metrics, such as accuracy, precision, recall, or F1-score, to assess the performance of our loan approval prediction models. This will help us measure the effectiveness of our models and make informed decisions based on their performance.

Issue 5: Documentation

• Title: Improve project documentation and README file

• Description: The project documentation, including the README file, needs to be updated and expanded to provide comprehensive information about the project, its objectives, and the steps involved in loan approval prediction. This will help new contributors understand the project and contribute effectively.

[0CottonBuds]

Issue 1 comment

will find a more suitable data set

Issue 2 comment

as far as I know the data is reported to be clean by kaggle but I will look into it.