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max0907github / repository
I'm building a Loan Approval Prediction Model using Jupyter Notebook. Use machine learning techniques to predict whether a loan application will be approved or not based on various factors such as applicant income, loan amount, credit history, etc.
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This repository contains a predictive model for determining loan approvals based on applicant data. The model uses various applicant features such as income, education level, marital status, and more to classify applicants as either approved or not approved for a loan.
In this project, we built a machine learning model that leverages features from applicant profiles to predict the likelihood of loan approval. The data processing, scaling, and predictive analysis have been performed in Python, and the entire workflow is available in a Jupyter notebook.
The dataset for this project includes information on loan applicants, such as income, loan amount, credit history, and demographics. You can find the dataset on my GitHub using the respiratory link. (provide the actual path or link to the data file in your repository or a public data source if available).
The dataset includes the following fields:
Loan_ID: Unique identifier for each loan applicantGender: Gender of the applicantMarried: Marital statusDependents: Number of dependentsEducation: Education level (Graduate/Not Graduate)Self_Employed: Employment statusApplicantIncome: Applicant's incomeCoapplicantIncome: Co-applicant's incomeLoanAmount: Loan amount requestedLoan_Amount_Term: Term of the loanCredit_History: Credit history meets guidelines or notProperty_Area: Location of the propertyLoan_Status: Approval status of the loan (Y/N)To run this project, you’ll need the following dependencies:
You can install these requirements using:
pip install -r requirements.txt
The project uses a Jupyter notebook (FutureInterns_DS_03_Loan_Approval_Prediction_Model.ipynb) to preprocess the data, train the model, and make predictions. Here’s a breakdown of the workflow:
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julioklau /
I'm a data analyst with solid experience across the machine learning pipeline, built through years of hands-on work with Jupyter notebooks. I’m comfortable with data preprocessing, feature engineering, model training, evaluation, and tuning. Currently, I’m building public repositories to showcase the knowledge I’ve developed over time.
34/100 healthTo run the model and make predictions, open the notebook in Jupyter:
jupyter notebook FutureInterns_DS_03_Loan_Approval_Prediction_Model.ipynb
Follow the steps in the notebook to load the data, preprocess it, and generate predictions.
The model outputs a prediction for each applicant: either "Loan Approved" or "Loan Not Approved."
This project is open-source and available under the MIT License.