Loan Prediction Model Overview This repository contains a Jupyter Notebook (LoanPM.ipynb) that implements a loan prediction model using machine learning techniques. The goal of this model is to predict whether a loan will be approved or not based on several factors about the applicant, such as their income, marital status, education, number of dependents, and credit history.
Dataset The dataset used in this project includes various attributes about the loan applicants and their loan approval status. The attributes include:
Loan_ID: Unique loan ID Gender: Male/Female Married: Applicant married (Yes/No) Dependents: Number of dependents Education: Applicant Education (Graduate/Undergraduate) Self_Employed: Self-employed (Yes/No) ApplicantIncome: Income of the applicant CoapplicantIncome: Income of the co-applicant LoanAmount: Loan amount in thousands Loan_Amount_Term: Term of the loan in months Credit_History: Credit history meets guidelines Property_Area: Urban/Semi Urban/Rural Loan_Status: Loan approved (Y/N) Prerequisites Before running the notebook, ensure you have the following installed:
Python 3.8 or above Jupyter Notebook Required Python libraries: pandas, numpy, matplotlib, seaborn, scikit-learn You can install the necessary Python libraries using pip:
bash Copy code pip install pandas numpy matplotlib seaborn scikit-learn Running the Notebook To run the notebook:
Clone this repository to your local machine. bash Copy code git clone https://github.com/minashoukrala/Loan-Prediction-Model.git Navigate to the cloned directory. bash Copy code cd Loan-Prediction-Model Start the Jupyter Notebook. bash Copy code jupyter notebook Open LoanPM.ipynb and run the cells sequentially. Notebook Content The notebook includes the following steps:
Data Loading: Load the dataset into a pandas DataFrame. Data Preprocessing: Clean the data and prepare it for modeling. This includes handling missing values, converting categorical variables to numeric ones, and normalizing the data. Exploratory Data Analysis (EDA): Analyze the data to understand the distribution and relationship between different variables. Model Building: Train various machine learning models and evaluate their performance. Model Evaluation: Compare the performance of different models using metrics such as accuracy, precision, and recall. Conclusion: Draw conclusions based on the model evaluations. Contributions Contributions to this project are welcome! You can contribute in the following ways:
By suggesting or adding new features By working on existing issues/improvements By improving the documentation Please feel free to fork the repository and submit a pull request.
License This project is licensed under the MIT License - see the LICENSE.md file for details.
Contact If you have any questions, please feel free to contact Mina Shoukrala.