Bank Loan Approval Prediction project aims to develop a predictive model using Logistic Regression to estimate the likelihood of a loan applicant getting approved for a bank loan. The financial industry is continually evolving, and with the assistance of data science techniques, we can now make more informed predictions about loan approval outcomes. By employing the Logistic Regression algorithm, which analyzes the relationship between variables and the probability of loan approval, we can effectively capture intricate patterns and critical factors that influence loan approval decisions.
The model takes into account various factors such as Gender, Marital Status, Credit Score, Property Area, Loan duration, and other significant attributes to Estimate the likelihood of obtaining a bank loan.. It has been trained on a comprehensive dataset, Ensuring that it provides accurate predictions in line with market trends.
Data Collection: Analytics Vidhya Data link: https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/#ProblemStatement
Data Preprocessing: Cleaning and preprocessing the dataset to handle missing values, outliers, and inconsistencies. This step also involves transforming categorical variables into numerical representations, normalizing numeric features, and splitting the dataset into training and testing subsets.
Model Training:Implementing logistic regression using appropriate libraries or frameworks. The training process involves fitting the model to the training data, estimating the coefficients (slope and intercept), and optimizing the model's performance by minimizing the log-likelihood or cross-entropy loss between the predicted and actual binary outcomes.
Model Evaluation: Assessing the performance of the trained logistic regression model for bank loan prediction using evaluation metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). These metrics provide insights into how well the model predicts loan approval outcomes and indicate its overall effectiveness.
Prediction and Deployment: In this phase, I've used Streamlit for deploying the app to predict the likelihood of loan approval for applicants not included in the training phase.
Once the Streamlit application is running, you will be presented with a user interface containing input fields for various features. Enter the relevant details, such as Gender, Marital Status, Credit Score, Property Area, Loan duration, etc., and click on the "Predict" button. The application will utilize the trained logistic regression model to generate a predicted likelihood of loan approval for the applicant based on the provided information.
Contributions to this project are welcome. If you would like to contribute, please follow these steps:
- 1)Create a new branch from the
mainbranch to work on your changes. - 2)Make your modifications and commit your changes.
- 3)Push your branch to your forked repository.
- 4)Open a pull request to the original repository, describing the changes you made.
This project is licensed under the GPU License.
- The dataset used in this project is sourced from: https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/#About
- The Logistic Regression algorithm is implemented using the scikit-learn library.
- The Streamlit framework is used for creating the web application.
If you have any questions or suggestions regarding this project, please feel free to contact me at [email protected].