Loan prediction dataset python Our dataset includes various financial and demographic variables, such as credit score, annual income, employment status, debt-to-income ratio, and previous payment history, alongside loan-specific metrics like loan amount Learn how to predict if a person will be able to pay the loan with logistic regression algorithm using sklearn library for machine learning. Uses Python, Pand Apr 6, 2023 · The dataset Loan Prediction: Machine Learning is indispensable for the beginner in Data Science, this dataset allows you to work on supervised learning, more preciously a classification problem. python machine-learning bank ml python3 xgboost hackerearth loan risk-assessment credit-scoring loan-data loan-default-prediction hackerexperience Updated on Sep 4, 2022 Jupyter Notebook Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. This Welcome to the Loan Prediction Project repository! This project focuses on predicting loan approval using machine learning techniques, Big Data, AI, and Android development. The dataset contains 13 features: Jan 7, 2025 · Welcome to this article on Loan Prediction Problem. The purpose of this project is design a machine learning algorithm using Artificial Neural Network that would predict the likelihood of a customer getting approved for a bank loan. Loan Approval Prediction: Overview and Dataset Loan approval prediction involves the analysis of various factors, such as the applicant’s financial history, income, credit rating, employment status, and other relevant attributes. Learn to preprocess data, handle missing values, select meaningful features, and build models that can accurately predict loan outcomes. I leverage a dataset of historical loan data and employ various classification algorithms, including Logistic Regression and Decision Trees, to build a predictive model. Key features included income, education, mortgage, and credit card usage. The Objective of the Article This article is designed for people who want to solve binary classification problems using Python. Practice and apply your data skills with curated datasets in DataLab Loan_Prediction Python code using a sample dataset to predict loan approval. It includes data preprocessing, EDA, model training (Logistic Regression, XGBoos Explore this free Loan Data dataset. It leverages a Random Forest Classifier, a machine learning model, to make informed predictions using a dataset of past loan applications. The dataset is prepared and processed to ensure high-quality predictions using machine learning models. Aug 21, 2024 · The dataset I used contains information about previous loan applications, including details like gender, marital status, number of dependents, education level, employment status, income, loan amount, and credit history. 12 input variables were registered for each applicant. Understanding the patterns and correlations in the data Mar 3, 2025 · Conclusion In this tutorial, we walked through the step-by-step process of building a Loan Approval Prediction model using Logistic Regression in Python. Data Exploration: In-depth analysis of the Oct 28, 2024 · This article will walk you through how one can start by exploring a loan prediction system as a data science and machine learning problem and build a system/application for loan prediction using your own machine learning project. It can be used for classification and regression. A Decision Tree Classifier was implemented to predict personal loan acceptance using a dataset of 5,000 customers. loan_approval_submission_optimized. Given with the data set consisting of details of applicants loan and status whether the loan application is approved or not. 0. 'Y': If Loan is approved 2. Loan Approval Prediction Problem Type Binary Classification Training Accuracy 84% Loan approval prediction is classic problem to learn and apply lots of data analysis techniques to create best classification model. Identifies key risk factors and applies classification models. Loan is a financial agreement in which a lender provides money or other assets to a borrower …. The objective is to build a predictive model that can accurately predict whether a loan application will be approved or not based on the other features in the dataset. So, if you want to learn how to use Machine Learning for Loan Approval Training dataset : It consists of details of the customer (like Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. 0, $59. Step 1: Dataset Creation Let's create a sample dataset. Unlock the power of loan prediction with Python! This tutorial explores classification techniques and machine learning algorithms to analysis and predict loan approvals. Apr 20, 2025 · This project, Loan Eligibility Predictor, is an AI-powered application built using Python and Streamlit, designed to predict the eligibility of loan applicants based on their financial and personal data. Jun 20, 2025 · The idea is simple: Can we train a model to predict whether a loan will be approved based on applicant attributes like income, credit history, marital status, and property type? 📂 Dataset Overview I used a cleaned version of a public dataset available on Kaggle under the name: 📌 “Loan Prediction Problem Dataset” Key features: Source to avail the dataset:- Loan prediction dataset Now, let us understand the implementation of K-Nearest Neighbors (KNN) in Python Import the Libraries We will start by importing the necessary libraries required to implement the KNN Algorithm in Python. csv: The submission file containing loan approval probabilities for each applicant in the test set. Enroll free. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Mar 15, 2025 · About Predicting loan default risk using machine learning models (Random Forest, SVM, Logistic Regression). Built with: Python (Pandas, NumPy, Scikit-learn) Streamlit (for interactive web app) Matplotlib & Seaborn (for EDA visualizations) Jul 23, 2025 · Have you ever thought about the apps that can predict whether you will get your loan approved or not? In this article, we are going to develop one such model that can predict whether a person will get his/her loan approved or not by using some of the background information of the applicant like the applicant's gender, marital status, income, etc. e. Feb 4, 2022 · In this article, we will learn how to predict loan approval by using machine learning concepts. Example 1: Loan Approval Prediction using Logistic Regression Explore and run machine learning code with Kaggle Notebooks | Using data from Analytics Vidhya Loan Prediction Loan-prediction-using-Machine-Learning-and-Python Aim Our aim from the project is to make use of pandas, matplotlib, & seaborn libraries from python to extract insights from the data and xgboost, & scikit-learn libraries for machine learning. May 2, 2025 · Loan prediction using machine learning involves thorough exploratory data analysis (EDA) to understand dataset characteristics. Below is a brief introduction to this topic to get you acquainted with what you will be learning. Problem Welcome to the Loan Repayment Prediction Project! This repository hosts a comprehensive machine learning project designed to predict the likelihood of successful loan repayment by borrowers. ipynb: The complete Python script used for data preprocessing, model training, and generating predictions for submission, running on Google Colab. The distribution of loan amounts in the dataset can be summarized as follows: The most common loan amount is $120. 0, $214. The Home Equity dataset (HMEQ) contains baseline and loan performance information for 5,960 recent home equity loans. Ideal for beginners in Data Science and Machine Learning. Welcome to the Loan Prediction repository! This project uses machine learning to predict loan approval based on applicant data. Classification models are developed leveraging artificial intelligence algorithms, Utilizing Python libraries and techniques like boosting, random forest classifiers, and logistic regression. Jul 18, 2023 · The popular Python modules Pandas and Scikit-Learn will be used in the examples that follow to develop loan approval prediction. We will import the numpy libraries for scientific calculation. By the end of this article, you will have the necessary skills and techniques required to solve such problems. Below is an overview of the dataset, Python files, and expected output. ) along with the target column which is Loan_Status with values : 1. The project aims to assist banks and financial institutions in automating and improving their loan approval process by providing an accurate predictive model. We have explored various concepts like EDA This project investigates the relationship between applicant financial profiles and loan approval decisions through exploratory data analysis (EDA) and predictive modeling. Supervised ML project for predicting loan defaults using a dataset provided by 10 Alytics (a financial institution). May 13, 2020 · The goal of this project is that from the data collected on the loan’s applicants, preprocess the data and predict based on the information who will be able to receive the loan or not. 0, occurring 15 times. It explores data preprocessing, feature engineering, and model training techniques to build a pr In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. Dec 25, 2024 · This project uses a dataset containing information about loan applicants to predict loan approval. This project aims to predict loan defaults using historical data from the Lending Club platform. 2. Are you excited? Introduction The Loan Approval Prediction project aims to leverage machine learning techniques to predict whether a loan application should be approved or denied based on various features and historical data. 🏧 Loan Eligibility Prediction 💰 using Machine Learning Models 🤖 Introduction In this notebook kernal, I'm going to predictions customers are eligible for the loan and check whether what are the missing criteria to know why customer not getting loan to make there own house. Data Description The dataset for this competition (both train and test) was generated from a deep learning model trained on the Loan Approval Prediction dataset. 🚀 ⭐️ Content Description ⭐️In this video, I have explained about loan prediction dataset and its analysis in python. Aug 10, 2025 · About Dataset (Recommended): Loan-Approval-Prediction-Dataset (Kaggle Build a model to predict whether a loan application will be approved Handle missing values and encode categorical features Train a classification model and evaluate performance on imbalanced data Focus on precision, recall, and F1-score Tools & Libraries: Python Pandas In this capstone project, I address the challenge of predicting whether a loan applicant is likely to default on their loan. Jul 18, 2023 · Conclusion One typical use case in the banking and finance sector is loan eligibility prediction. 0, which appears 17 times, and $100. May 16, 2024 · Loan Approval Prediction - Machine Learning. Step 1: Data Exploration and Preprocessing Before training the model, I first explored the data to understand its This project, part of the Coursera Data Science Coding Challenge, aims to predict loan defaults based on various borrower-specific features. The target (BAD) is a binary variable that indicates whether an applicant has ultimately defaulted or has been severely delinquent. 🏦 Loan Approval Prediction App A complete End-to-End Machine Learning Project using the Loan Prediction Dataset (Kaggle) to predict whether a loan application will be approved or rejected. The predictive model is built using machine learning algorithms, with an emphasis on data exploration, cleaning, and interactive user input. 0, occurring 20 times in the data. The distribution shows a range of loan values, with some values occurring only once, such as $240. Basis o… Oct 30, 2023 · The dataset is a lending data available online which shows the varying profile of people that applied for loan and if they paid back or not. Loan Prediction using Logistic Regression This repository contains a machine learning project that utilizes Logistic Regression to predict loan approval decisions. We covered data preprocessing, feature This Edureka video on "Loan Eligibility Prediction Tutorial” will provide you with comprehensive and detailed knowledge of Data Science concepts with a hands-on project where you will learn to Mar 24, 2023 · Solve a real-life loan prediction problem using Python. 0, $166. Loan approval prediction means using credit history data of the loan applicants and algorithms to build an intelligent system that can determine loan approvals. This project has significant real-world implications for financial institutions, enabling them to make more informed and efficient lending decisions while minimizing risks. In this article, we looked at how to forecast loan eligibility using Python and machine learning models. python machine-learning bank ml python3 xgboost hackerearth loan risk-assessment credit-scoring loan-data loan-default-prediction hackerexperience Updated on Sep 4, 2022 Jupyter Notebook The Loan Prediction dataset from Kaggle contains 614 loan applications with 13 features, including gender, marital status, income, loan amount, credit history, and loan status. We have data of some predicted loans from history. I am going to use XGBoost Aug 14, 2024 · Data Analysis on a Loan Application Dataset using Python An Exploratory Data Analysis and a Linear Regression model on Loan Applications at a Bank. This is followed by $110. BAD: 1 = Client defaulted on Oct 24, 2024 · Support Vector Machine falls under the "supervised machine learning algorithms" category. Loan Approval Dataset from Kaggle using Logistic Regression. Overall, there are 203 Nov 29, 2021 · Predict the potential loan defaulters using the Machine Learning model. The major aim of this notebook is to predict which of the customers will have their loan approved. We put the Logistic Regression, Decision Tree, and Random Forest models into practise and assessed how well they worked. , pred_test respectively. Sep 9, 2024 · What is Loan Approval Prediction? Loan approval prediction involves using machine learning algorithms to determine whether a loan application should be approved or rejected based on the applicant’s profile and financial data. The analysis is conducted using Python, with the main work done in a Jupyter Notebook. The Dataset This project focuses on predicting loan approval outcomes through an extensive analysis of a curated dataset. Loan sanctioning and credit scoring forms a multi-billion dollar industry -- in the US alone. May 22, 2022 · Import the dataset This analysis will be using the Loan Prediction dataset, from the Analytics Vidhya competition (link on the header image caption). Dataset - https:/ Loan Default Prediction project using machine learning to predict the likelihood of a borrower defaulting on a loan. The target column is called 'Personal This repository contains a Python-based project that uses machine learning to predict loan defaults. This dataset will include common features that influence loan approval decisions such as gender, marital status, applicant income, loan amount, and credit history. Includes data preprocessing, EDA, feature scaling, model evaluation, hyperparameter tuning, and default probability prediction. 0, and $253. Sep 14, 2020 · We only need the Loan_ID and the corresponding Loan_Status for the final submission. Through comprehensive data preprocessing, exploratory data analysis (EDA), feature engineering, and the application of deep learning models, we seek to uncover patterns that predict loan repayment behaviors. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model. - Git Loan_Predication problemSomething went wrong and this page crashed! If the issue persists, it's likely a problem on our side. This is the reason why I would like to introduce you to an analysis of this one. The target variable is whether the loan was approved or not. Enhance your skills in data preprocessing, feature engineering, and contribute to May 15, 2023 · In this article, I’ll take you through the task of Loan Approval Prediction with Machine Learning using Python. Feb 24, 2024 · Learn how to predict loan default risk with Python! Explore the essential steps of building a machine-learning model with this guide. Built with Python, Pandas, Scikit-Learn, Matplotlib, and Seaborn. The model achieved 97% accuracy, with 92% precision and 76% recall for positive loan predictions, validated using a classification report and confusion matrix. Here are what the columns of the dataset represent: Jan 1, 2025 · Image by Author Loan Approval Prediction is one of the problems that Machine Learning has solved in fintech businesses like banks and financial institutions. This adverse outcome occurred in 1,189 cases (20 percent). We will learning about, Data Analysis Preprocess such as, Jul 23, 2025 · Loan Approval Prediction using Machine Learning You can download the used data by visiting this link. Aug 19, 2020 · Photo by Sean Pollock on Unsplash Table of Content · Introduction · About the Dataset · Import Dataset into the Database · Connect Python to MySQL Database · Feature Extraction · Feature Transformation · Modeling · Conclusion and Future Directions · About Me Note: If you are interested in the details beyond this post, the Berka Dataset, all the code, and notebooks can be found on my Files Included loan_approval_prediction. The dataset for this project consists of labeled data with following features. A loan approval prediction system using machine learning, featuring a web-based interface with Python (Flask) backend and HTML frontend for classifying loan approval based on applicant data. Learn binary classification. we will fill these columns with the Loan_ID of the test dataset and the predictions that we made, i. hgcf apd daatxr smtvtp tro fmqpxw exg fgniswr hjql wpekf wclbl ophiuc zygu qabvbp midkfr