Skip to content

Latest commit

 

History

History
72 lines (61 loc) · 2.22 KB

README.md

File metadata and controls

72 lines (61 loc) · 2.22 KB

Loan Approval Prediction Project

Overview

This project focuses on predicting loan approvals using a dataset with various applicant details. The process includes data preprocessing, feature engineering, model training, and evaluation using Decision Tree and Naive Bayes classifiers.

Project Structure

  • loan-train.csv: Training dataset
  • loan-test.csv: Test dataset
  • loan_approval_prediction.ipynb: Jupyter notebook with detailed analysis and code

Skills and Technologies Used

  • Data Preprocessing
    • Handling missing values
    • Data normalization (log transformation)
  • Feature Engineering
    • Categorical data encoding
    • Feature scaling
  • Model Training
    • Decision Tree Classifier
    • Naive Bayes Classifier
  • Model Evaluation
    • Accuracy calculation
    • Performance metrics analysis
  • Data Manipulation
    • pandas for data manipulation
    • numpy for numerical operations
  • Machine Learning
    • sklearn for model training, evaluation, and preprocessing
  • Data Visualization
    • Matplotlib for histogram plotting of transformed features

Installation

  1. Clone the repository: 
    git clone https://github.com/crazyNerrd/Loan-Approval.git
  2. Install the required packages: 
    pip install -r requirements.txt

Usage

  1. Navigate to the project directory: 
    cd loan-approval-prediction
  2. Open the Jupyter notebook: 
    jupyter notebook loan_approval_prediction.ipynb

Data Preprocessing

  • Handle missing values using mode and mean imputation.
  • Apply log transformation to LoanAmount and TotalIncome for normalization.
  • Encode categorical variables using LabelEncoder.
  • Scale features using StandardScaler.

Model Training and Evaluation

  • Split the dataset into training and testing sets using train_test_split.
  • Train Decision Tree and Naive Bayes classifiers.
  • Evaluate models' accuracy using metrics from sklearn.

Test Data Preparation

  • Apply the same preprocessing steps to the test dataset.
  • Make predictions using the trained Naive Bayes classifier.

Results

  • Display accuracy scores for both Decision Tree and Naive Bayes classifiers.
  • Provide insights on model performance and potential improvements.