{"id":15131137,"url":"https://github.com/manish506/loan-approval-prediction","last_synced_at":"2026-01-19T22:00:41.947Z","repository":{"id":256935715,"uuid":"856872361","full_name":"manish506/Loan-Approval-Prediction","owner":"manish506","description":"Explore predictive modeling in this project by applying classification techniques to a loan approval dataset. Analyze and preprocess the data, then use models like K-Nearest Neighbors, Random Forest, SVC, and Logistic Regression to predict loan outcomes. 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The workflow includes data preprocessing, exploratory data analysis (EDA), and model evaluation.\n\n## Steps\n\n1. **Import Libraries**: Essential libraries for data manipulation, visualization, and machine learning are imported.\n2. **Load Data**: The dataset is loaded and initial exploration is conducted.\n3. **Identify Categorical Variables**: Categorical variables are detected and visualized.\n4. **Encode Categorical Variables**: Categorical variables are converted to numerical values.\n5. **Visualize Correlations**: Correlations between features are visualized using a heatmap.\n6. **Handle Missing Values**: Missing values are imputed with column means.\n7. **Split Data**: The dataset is split into training and testing sets.\n8. **Train and Evaluate Models**: Several machine learning models are trained and evaluated for accuracy.\n\n## Dataset\n\n- **File**: `LoanApprovalPrediction.csv`\n- **Description**: Contains loan application data with features and loan status.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanish506%2Floan-approval-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmanish506%2Floan-approval-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanish506%2Floan-approval-prediction/lists"}