click here to download project abstract/base paper of Machine Learning Algorithm
At datapro, we provide final year projects with source code in python for computer science students in Hyderabad, Visakhapatnam.
ABSTRACT
We provide machine learning Algorithm in this paper. In our banking system, banks have many products to sell but main source of income of any banks is on its credit line. So they can earn from interest of those loans which they credits. A bank’s profit or a loss depends to a large extent on loans i.e. whether the customers are paying back the loan or defaulting. By predicting the loan defaulters, the bank can reduce its Non-Performing Assets. This makes the study of this phenomenon very important. Previous research in this era has shown that there are so many methods to study the problem of controlling loan default. But as the right predictions are very important for the maximization of profits, it is essential to study the nature of the different methods and their comparison. A very important approach in predictive analytics is used to study the problem of predicting loan defaulters: The Logistic regression model. The data is collected from the Kaggle for studying and prediction. Logistic Regression models have been performed and the different measures of performances are computed. The models are compared on the basis of the performance measures such as sensitivity and specificity. The final results have shown that the model produce different results. Model is marginally better because it includes variables (personal attributes of customer like age, purpose, credit history, credit amount, credit duration, etc.) other than checking account information (which shows wealth of a customer) that should be taken into account to calculate the probability of default on loan correctly. Therefore, by using a logistic regression approach, the right customers to be targeted for granting loan can be easily detected by evaluating their likelihood of default on loan. The model concludes that a bank should not only target the rich customers for granting loan but it should assess the other attributes of a customer as well which play a very important part in credit granting decisions and predicting the loan defaulters.
Bank Loan Approval using Machine Learning
Abstract:
This project aims to streamline the bank loan approval process through the integration of machine learning algorithms. Leveraging Python and web technologies, the proposed system utilizes historical data to train a model that can predict the likelihood of loan approval. The web-based interface enhances user experience, providing a transparent and efficient platform for both applicants and bank officials.
Existing System:
The current bank loan approval process often involves manual assessment of applications, leading to potential delays and inconsistencies. Decision-making is subjective, and there’s a need for a more data-driven and automated approach to improve efficiency and reduce bias.
Proposed System:
The proposed system introduces a machine learning-driven approach to bank loan approval. By analyzing historical data, the system can predict the likelihood of loan approval based on various factors. The web interface facilitates seamless communication between applicants and bank officials, providing transparency and improving the overall loan approval process.
Problem Statement:
Traditional loan approval processes are often time-consuming, subject to human bias, and lack a systematic approach for evaluating applicants’ creditworthiness. The need for a more efficient, transparent, and data-driven system is crucial to address these challenges.
Motivation:
The motivation behind this project is to enhance the loan approval process by incorporating machine learning. This not only improves the speed of decision-making but also ensures a fair and objective evaluation, minimizing the risk of human bias in the approval process.
Modules Explanation:
- Data Preprocessing:
- Cleans and prepares historical loan data for training the machine learning model.
- Machine Learning Model:
- Utilizes algorithms such as logistic regression or decision trees to predict the likelihood of loan approval based on applicant information.
- Web Interface:
- Provides a user-friendly platform for loan applicants to submit information and track the status of their loan application. Bank officials can use the interface for reviewing applications and making informed decisions.
System Requirements:
- Hardware:
- Standard computer systems for running machine learning models.
- Internet-connected devices for accessing the web interface.
- Software:
- Python for implementing machine learning algorithms.
- Web development framework (e.g., Flask or Django).
Algorithms:
- Logistic Regression or Decision Trees:
- Employed for the machine learning model to predict loan approval based on historical data.
Hardware and Software Requirements:
- Hardware:
- Standard computer systems for running machine learning models.
- Internet-connected devices for accessing the web interface.
- Software:
- Python 3.x
- Web development framework (Flask or Django).
Architecture:
- Data Preprocessing:
- Cleans and prepares historical loan data.
- Machine Learning Model:
- Trains and deploys a machine learning model for predicting loan approval.
- Web Interface:
- Connects users (applicants and bank officials) to the machine learning model, providing a platform for loan application and review.
Technologies Used:
- Python, scikit-learn for machine learning implementation.
- Web development frameworks (Flask/Django) for creating the web interface.
Web User Interface:
The web interface allows loan applicants to submit their information, check the status of their applications, and receive transparent feedback on the likelihood of loan approval. Bank officials can use the interface to review applications, access predictions from the machine learning model, and make informed decisions. The user-friendly design promotes efficiency and enhances the overall experience of both applicants and bank personnel in the loan approval process.
UML DIAGRAMS
Collaboration Diagram
Architecture diagram
class diagram
sequence diagram
use case diagram
activity diagram
component diagram
Deployment Diagram
Flow chart Diagram
