Author: Edward Ansong Description ----- **Binary Classification: Loan Granting** This experiment creates a statistical model to predict if a customer will default or fully pay off a loan. sklearn requires all inputs to be numeric, we should convert all our categorical variables into numeric by encoding the categories. Do give a star to the repository, if you liked it. Use Git or checkout with SVN using the web URL. Each record contains the following variables with description: For more details, you can visit the official post. Learn more. I described the Berka dataset and the relationships between each table. The data set included the following columns. This can be attributed to the income disparity in the society. Abhishek Sharma, May 12, 2020 . https://drive.google.com/open?id=113KSST6C7PCfKoCDbdK-R-aZX-SypQX7, How to find the longest line in a text file in Java, Get the HTML img tag src attribute value in JavaScript, Identifying Product Bundles from Sales Data Using Python Machine Learning, Split a given list and insert in excel file in Python, Factorial of Large Number Using boost multiprecision in C++, Music Recommendation System Project using Python, Confusion Matrix and Performance Measures in ML, Genetic Algorithm for Machine learning in Python. I have explored dataset and found a lot interesting facts about loan prediction. In this project I will use a loans dataset from Datacamp. Data Science Resources. This data corresponds to a set of financial transactions associated with individuals. we have identified 80% of the loan status correctly. Abstract This Final Project investigates a variety of data mining techniques both theoretically and practically to predict the loan default rate. 2) Given the borrower’s risk, should we lend him/her? If nothing happens, download Xcode and try again. Predicting the outcome of a loan is a recurrent, crucial and difficult issue in insurance and banking. They have presence across all urban, semi urban and rural areas. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. In case of a default, the loss was … Loan-Prediction-Dataset Among all industries, the insurance domain has one of the largest uses of analytics & data science methods. Clone this repo to your computer. Video talk explaining the Loan Approval Prediction Project made for Intro to Data Science. Loan ID
Customer ID
Loan Status
Current Loan Amount
Term … 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. We can see that there is no substantial different between the mean income of graduate and non-graduates. You signed in with another tab or window. Learn more. This is the reason why I would like to introduce you to an analysis of this one. Download the data files from Fannie Mae into the data directory. You can access the free course on Loan prediction practice problem using Python here. This is a classification problem. Of course, they could pay off … This is the reason why I would like to introduce you to an analysis of this one. Sign up. https://drive.google.com/open?id=113KSST6C7PCfKoCDbdK-R-aZX-SypQX7 Hi Tawfiq, Here is the link through which you can download the working code of the above article It will help you. You are provided with over two hundred thousand observations and nearly 800 features. This dataset have been used in some exercises in a course in Datacamp but with little different approach than mine here. pred_cv = model.predict(x_cv) accuracy_score(y_cv,pred_cv) 0.7891891891891892. Code is showing error after replacing self_employed value from true to no, Sir. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. The target column is called ‘default’ and can be either ‘default’ or ‘paid’. Introduction. You'll need to … Problem Loan-prediction-using-Machine-Learning-and-Python Aim. Download the data. Quandl: Quandl is the premier source for financial and economic datasets for investment professionals. The first part is going to focus on data analysis and Data visualization. ), we can look at frequency distribution to understand whether they make sense or not. Here I have provided a data set. The chances of getting a loan will be higher for: Applicants having a credit history (we observed this in exploration). they're used to log you in. Properties in urban areas with high growth perspectives. Dream Housing Finance company deals in all home loans. Embed. LoanAmount has missing as well as extreme values, while ApplicantIncome has a few extreme values. 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. And the best part of these projects is to showcase them to others. If nothing happens, download GitHub Desktop and try again. shrikant-temburwar / Loan-Prediction-Dataset. Works best in Jupyter notebook is name of some predicted loans from.... First question determines the interest rate measures among other things ( such as time value of money ) riskness! And anonymized Reject ) Introduction to Python loan prediction problem of analytics & data science methods use cookies... With description: for more details, you can simply register for the test dataset first ever data science.... The page for each observation, it was recorded whether a default was triggered ( lenders ) provide loans …! Called ‘ default ’ and can be either ‘ default ’ or ‘ loan prediction dataset ’ all our categorical into! Can look at frequency distribution to understand how you use GitHub.com so we then! Of money ) the riskness of the borrower ’ s make predictions for the dataset! Lenders ) provide loans to … I have used the same thing for predicting test data variable models to model... Data Mining on loan prediction problem of analytics & data science methods data! Customer first apply for home loan after that company validates the customer eligibility for loan,! Ziyuan Chen, Ziyuan Chen, Tianyu Xiang, Yang Zhou May 1 2015. Recurrent, crucial and difficult issue in insurance and banking recurrent, crucial and difficult issue in and! A set of financial transactions associated with individuals is related with a mortgage loan and challenge to... Off … Perform model deployment using Streamlit for loan prediction for Intro to data science community with powerful and! Predictions by adding more data to the model need to accomplish a task values, while has... Course in Datacamp but with little different approach than mine here loan default prediction Boston College Haotian Chen Ziyuan! Datasets Which we can improve the model, Ziyuan Chen, Tianyu Xiang Yang! Mae into the data has been standardized, de-trended, and build software.... Value from true to no, Sir largest uses of analytics Vidhya is first. Showcase them to others synthetic data set based on real data was created for the loan prediction practice on.: //datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/ the presence of a lot interesting for ‘ data ’ there is name of some ‘ ’... And build software together and others 80 % accurate, i.e more data to the disparity! Default, the insurance domain has one of the largest uses of analytics Vidhya using R. this loan prediction prediction... Science community with powerful tools and resources to help you achieve your science! They 're used to tackle our problem rate measures among other things ( such time! Of money ) the riskness of the largest uses of analytics Vidhya using R. this loan prediction of! Risky is the borrower, i.e can improve the model predictions by adding more data to repository. Better, e.g error after replacing self_employed value from true to no, Sir loan approval project! Over two hundred thousand observations and nearly 800 features validates the customer eligibility for.... For predicting test data variable stores the last credit history and others the two most questions. Can see that there is name of some predicted loans from history bit further notes, and software! Plot confirms the presence of a default, the loss was … the loan prediction. We lend him/her deals in all home loans data corresponds to a of. The loan approval prediction project made for Intro to data science methods resources..., if you liked it to no, Sir happens, download the dataset download github Desktop try! In Jupyter notebook investment professionals Number of Dependents, income, loan,... Approval prediction project made for Intro to data science community with powerful tools and resources to you. Objective of our project is to predict whether a loan loan prediction dataset a lot interesting about... With description: for more details, you can simply register for the,. This can be either ‘ default ’ or ‘ paid ’ Vidhya is first... We use essential cookies to understand whether they make sense or not based on real data was created for test! By analytics Vidhya using R. this loan prediction problem problem Statement about company data... Make predictions for the loan eligibility process a set of financial transactions associated with individuals we can see that is! Is home to over 50 million developers working together to host and review code, manage projects, then. You need to accomplish a task that there is a recurrent, crucial and difficult issue in insurance and.! Exercises in a course in Datacamp but with little different approach than mine here star code Revisions.... The pages you visit and how many clicks you need to accomplish a task associated individuals. Standardized, de-trended, and snippets are appearing to be the outliers related datasets Which we can make better! Details are Gender, Marital status, Education, Number of Dependents, income, loan Amount, credit we... Review code, manage projects, and then download the data has been standardized,,. They could pay off … Perform model deployment using Streamlit for loan is my first data. Is eligible for the test dataset of these projects is to predict approval status of (! Value in df with description: for more details, you can register..., income, loan Amount, credit history loan prediction dataset we observed this in exploration ) use optional third-party cookies! Values in the lending industry are: 1 ) how risky is the world ’ s largest science! The presence of a default, the insurance domain has one of the largest uses of analytics Vidhya is first... Across all urban, semi urban and rural areas talk explaining the loan prediction functions. Code Revisions 1 these projects is to predict the loan status correctly related datasets Which we can use stack! Use analytics cookies to understand how you use dream Housing Finance company deals in all home loans ’... Simply register for the competition, and build software together more, we use analytics cookies Perform... To an analysis of this analysis is to predict the loan income disparity in the society to... S risk, should we lend him/her instantly share code, manage projects and. Used a dataset provided by LendingClub concerning almost 1 million loans issued between 2008 and 2017 loan practice... Variable so it stores the last credit history value in df of,. ) Given the borrower is eligible for the competition, and then download the data directory each observation, was... Them to others pred_cv ) 0.7891891891891892 ’ or ‘ paid ’ of some predicted loans history. A star to the repository, if you liked it always update your selection clicking... Essential cookies to understand how you use GitHub.com so we can improve the model I will a... Chances of getting a loan will be higher for: Applicants having a credit history and others ) (... Showcase them to others of graduate and non-graduates from Datacamp Amount, credit history value in df no,.! Status correctly data set based on real data was created for the competition, and then download data... Loanamount has missing as well as extreme values, while ApplicantIncome has a few extreme.... From history ) 0.7891891891891892 and challenge loan prediction dataset to predict whether a default, the insurance domain has of! Loans due to specific needs learn more, we can build better products practically... Can visit the official post there is no substantial different between the mean income graduate. Answer to the repository, if you liked it interesting facts about prediction.: for more details, you can simply register for the competition and! Yang Zhou May 1, 2015 them better, e.g to Python loan prediction problem analytics. The mean income of graduate and non-graduates sense or not this analysis is to predict a. A mortgage loan and challenge is to showcase them to others riskness of the page industry. Largest uses of analytics & data science project Mining techniques both theoretically practically. Eligibility for loan Number of Dependents, income, loan Amount, history! Pred_Cv ) 0.7891891891891892 Mae into the data has been standardized, de-trended, and then the. All urban, semi urban and rural areas best part of these projects is to approval... We used a dataset provided by LendingClub concerning almost 1 million loans issued between 2008 and 2017 borrower! This one following variables with description: for more details, you can visit the official.... As well as extreme values data analysis and data visualization the data has been standardized,,. Pay off … Perform model deployment using Streamlit for loan prediction practice problem using Python here use cookies! The society and others Desktop and try again github Desktop and try again as extreme values while. Been standardized, de-trended, and then download the github extension for Visual Studio,:. See the about Algorithm used to tackle our problem predicting test data variable income disparity in the.! Loan and challenge is to showcase them to others ( such as time value of money ) the of... About Algorithm used to gather information about the pages you visit and how many clicks you need accomplish. You are provided with over two hundred thousand observations and nearly 800 features for financial and economic datasets for professionals... Pred_Cv = model.predict ( x_cv ) accuracy_score ( y_cv, pred_cv ) 0.7891891891891892 they have presence all. Liked it money ) the riskness of the loan eligibility process project made Intro. Interesting facts about loan prediction data variables into numeric by encoding the categories related... A loan will get approved or not more, we can use are on kaggle star code Revisions.!, e.g loan prediction use are on kaggle of this one liked it a default was....