/Loan-Prediction-Model

Primary LanguageJupyter NotebookMIT LicenseMIT

Loan Prediction System

Table of Contents

  1. Overview
  2. Features
  3. Dataset
  4. Installation
  5. Usage
  6. Modeling
  7. Results
  8. Future Work
  9. License
  10. Contact

Overview

The Loan Prediction System is a machine learning project designed to predict whether a loan applicant is likely to be approved for a loan based on their financial and personal data. This system aims to assist financial institutions in making more informed lending decisions by leveraging historical loan data and advanced predictive modeling techniques.

Features

  1. Data Preprocessing: Cleans and preprocesses loan application data.
  2. Feature Engineering: Creates meaningful features to improve model performance.
  3. Model Training: Trains various machine learning models to predict loan approval.
  4. Model Evaluation: Evaluates models using accuracy, precision, recall, and F1 score.
  5. Prediction: Provides a reliable prediction of loan approval status.

##Dataset The project utilizes a dataset containing the following features:

  • Loan_ID: Unique Loan ID
  • Gender: Male/Female
  • Married: Applicant married (Y/N)
  • Dependents: Number of dependents
  • Education: Applicant Education (Graduate/Undergraduate)
  • Self_Employed: Self-employed (Y/N)
  • ApplicantIncome: Applicant income
  • CoapplicantIncome: Coapplicant income
  • LoanAmount: Loan amount in thousands
  • Loan_Amount_Term: Term of the loan in months
  • Credit_History: Credit history meets guidelines (1/0)
  • Property_Area: Urban/Semi-Urban/Rural
  • Loan_Status: Loan approved (Y/N)

Files

  • train_u6lujuX_CVtuZ9i.csv: Training dataset
  • test_Y3wMUE5_7gLdaTN.csv: Test dataset

##Installation To run this project, follow these steps:

  • Clone the repository: git clone https://github.com/yourusername/loan-prediction-system.git

  • Navigate to the project directory: Installand import these required packages:

  • Data manipulation and analysis import numpy as np import pandas as pd

  • Data visualization import matplotlib.pyplot as plt import seaborn as sns

  • Machine Learning from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier from sklearn.svm import SVC from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV

  • Handling imbalanced datasets from imblearn.over_sampling import SMOTE from imblearn.pipeline import Pipeline as ImbPipeline

  • Others import warnings warnings.filterwarnings('ignore')

Usage

  1. Data Exploration: Analyze the training data using the notebook.
  2. Data Cleaning: Handle missing values and outliers.
  3. Feature Engineering: Create new features or transform existing ones.
  4. Model Training: Train machine learning models using the processed data.
  5. Model Evaluation: Evaluate the models and choose the best one.
  6. Make Predictions: Use the trained model to make predictions on new data.

Modeling

  • Preprocessing
  1. Missing Value Imputation
  2. Encoding Categorical Variables
  3. Scaling Numerical Variables
  • Models Used
  1. Logistic Regression
  2. Decision Trees
  3. Random Forests
  4. Gradient Boosting
  5. Support Vector Machine (SVM)
  • Evaluation Metrics
  1. Accuracy
  2. Precision
  3. Recall
  4. F1 Score
  5. ROC-AUC Curve

Results

  • The best model achieved an accuracy of XX%.
  • Detailed results and performance metrics are available in the notebook.

Future Work

  • Hyperparameter Tuning: Further refine model parameters to improve performance.
  • Advanced Features: Incorporate additional features such as employment history and spending patterns.
  • Model Deployment: Develop a web application to deploy the model for real-time loan prediction.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or suggestions, please feel free to contact:

Your Name - dhruvsharma4054@gmail.com GitHub: testgithubritttttt