The Loan Amount Prediction project aims to predict whether a loan will be approved based on various features related to the applicant's personal and financial information, by using various Machine Learning algorithms we can analyze and model the data to make accurate predictions.
DataSet used: Dataset.csv
| Variable Name | Description of the variable |
|---|---|
| Loan ID | Unique Loan ID |
| Gender | Male / Female |
| Married | Applicant married (Y/N) |
| Dependents | Number of Dependents |
| Education | Graduate/ Under Graduate |
| Self Employed | Self Employed (Y/N) |
| Applicant Income | Applicant Income |
| Coapplicantincome | Co applicant income |
| Loan Amount | Loan amount in thousands |
| Loan Amount Term | Term of loan in months |
| Credit History | Credit History meets guidelines |
| Property Area | Urban/ Semi Urban/ Rural |
| Loan Status | Loan Approved (Y/N) |
Libraries Used :
- Scikit-Learn
- Numpy
- Matplotlib
- Seaborn
- Scikit-learn
Algorithms Implemented :
- Random Forest
- Naive Bayes (NB)
- Decision Tree
- KNeighbors
- Support Vector Classifier (SVC)
The project includes:
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA) using visualizations
- Implementation of multiple machine learning algorithms
- Evaluation and comparison of model performances
- Final model selection and prediction
Conclusion :
This project successfully implemented machine learning techniques to predict loan approvals based on applicant data. The SVC model provided the best results, highlighting its potential for use in real-world loan approval processes.