This Project is Dataquest's Monthly Challenge for the month of October, 2016.. #earliest_cr_line ⇒ String? readonly the percentage of borrowers who are correctly identified as paying the loan back). LendingClub must be aware that low graded loans, undeniably, have a higher chance of default probability. Name: Alex Husted; Project Start Date: Tuesday October 1, 2019; Project Finish Date: Tuesday October 8, 2019; Project Overview. The solid red line represents the mean installment value for loans that have been defaulted. While conducting preliminary exploratory data analysis I found that there were a large quantity of missing values at approximately 108.5 Million missing values. The data is Lending Club Loan Data and can be found at here. If nothing happens, download the GitHub extension for Visual Studio and try again. Let's try some models on the train dataset With 3 fold cross validation. It's used to modify the distributional shape of a dataset for the purpose of normally distributing so that tests and confidence limits that require normality can be appropriately used. For example, credit card fraud detection, disease classification, network intrusion and so on, are classification problem with … Each of the models had their merits, but the overwhelming success came entirely from XGBoost with results that vastly outperformed in comparison to Logistic Regression and Random Forest. It's clear to see there will be class imbalances that need to be dealt with. The cross-validation scores and ROC curves suggest the Logistic Regression is the best model, though the MNB and LDA models are pretty close behind. Unfortunately, the data on their site is fragmented into many smaller files. Embed Embed this gist in your website. GitHub - jreynolds999/LendingClub-Loan-Data: Determining the likelihood of borrowers to default on a variety of loans through data analysis and machine learning models. In this lab, you will use data from LendingClub, a well-known peer-to-peer lending platform based in San Francisco, California, to build statistical models that use debtor attributes to predict the grades given to loan applications by the Lending Club. By: Saleem Khan. Analyzing these relationships will provide intuition about how to interpret the results of the proceeding models. There is a certain methodology that needs to be followed in order to properly load effective predictors - data cleaning, exploration, and feature engineering. Work fast with our official CLI. The data was normalized to the range 0 to 1. It might involve scraping a web page, loading data put into a database, or loading customer information for a customer viewing your web page. All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. Use Git or checkout with SVN using the web URL. Borrowers can apply through an online platform for personal loans, often unsecured, that are financed by one or more peer investors. The data covers the 9,578 loans funded by the platform between May 2007 and February 2010. on the contrary, borrowers who own grade 'G' loans have the highest average DTI. When looking at our confusion matrix, our true positive rate is 67% and our true negative rate is 64%. This resulted in a massive range of text values for the following three categories: emp_title, title, and desc. Binary Logistic Regression --> Used to describe data and to explain the relationship between one dependent binary variable. The original data set contains 887383 rows and 75 columns. Oleh_Dubno Lending Club Loan Data_Draft. Learn more. GitHub Gist: instantly share code, notes, and snippets. Before training, I would first need to transform the data to account for any skewness in the variable distribution. GitHub doesn't render iframes at the moment, so plotly graphs do not show up on the page. The goal is to analyze Lending Club's issued loans and to create prediction model using Machine Learning algorithms to predict clients who might default. Looking into the distribution plot from data exploration, borrowers who end up defaulting on their loan are continuously paying higher interest rates and larger installments. Building a machine learning algorithm for the purpose of correctly identifying whether a person, given certain characteristics, has a high likelihood to default on a loan. GitHub Gist: instantly share code, notes, and snippets. This dataset consists of 75 variables, including numerical variables and many categorical variables with a range of unique cardinality from 3 to 30. preprocess_lending_club_data. I am going to use the following 4 machine learning algorithms: Linear Discriminant Analysis --> Projecting a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting. Here our true positive rate is 67% and our true negative rate is 64%. Contribute to dosei1/Lending-Club-Loan-Data development by creating an account on GitHub. Below you can identify some (not all) columns within the dataset. This dataset consists of 75 variables, including numerical variables and many categorical variables with a range of unique cardinality from 3 to 30. Loan Data (2007-2011) from Lending Club. Learn more. The optimal threshold above is where the the two graphs meet. Our preliminary EDA revealed that Lending Club grades loans based on only a small set of variables from the borrower and the prospective loan. There is a clear 3.95% increase in interest rate between defaulted loans vs. non-defaulted. It makes our work easier because the data is rich and we won’t be limited by the data … Specificity (also called the true negative rate) measures the proportion of actual negatives that are correctly identified as such (e.g. This plot identified the mean and density distribution for loan amounts per home-ownership type. Approved loans are listed on the Lending Club website, where qualified investors can browse recently approved loans, the borrower's credit score, the purpose for the loan, and other information from the application. Lending Club - Loan Default Modeling¶ Out[1]: 0. Consolidated dataset of Lending Club loan data for loans issued since 2007. It's nice to see this represented visually in this box plot format. Model accuracy might not be the sole metric to identify - the F1 score and confusion matrix should be viable metrics to analyze as well. Not surprisingly, when the company went public in 2014 they were forced to remove this feature as well as no longer state that borrowers were less likely to default on these loans. download the GitHub extension for Visual Studio, Predicting Success or Failure of Loans with Lending Club Data, https://www.kaggle.com/wordsforthewise/lending-club. Today, it is increasingly easy to store large quantities of data. If we are able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Lending-Club-Loan-Data-Analysis Introduction. The Lending Club data has 3 main classifications for home ownership: mortgage (outstanding mortgage payment), own (home is owned outright), and rent. LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. Determining the likelihood of borrowers to default on a variety of loans through data analysis and machine learning models. Python lending club data. There appeared to be a very strong correlation between grade and a combination of FICO, amount of the loan, term, borrower’s income and income to debt ratio. Today I’ve looked at their 2014 loan statistics. Using Machine Learning, is it possible to predict which loans are at risk of defaulting or incomplete payback? If you play with their data without using my code, make sure to carefully clean it to avoid data leakage. The details of how this marketplace works are available on LendingClub’s website [1]. The model can predict who are going to pay off the loan with a good accuracy of 99% but cannot predict who are going to default. Club website as a CSV and used all available loan data from 2007 to 2011. #service_fee_rate ⇒ Number readonly. Lending Club Loan Analysis 16 Feb 2020 Maximizing Investment Returns using Machine Learning . It (1) shows how I obtained the data used in the map above and (2) includes relevant exploratory plots drawn using … Random Forest --> Consists of a large number of individual decision trees that operate as an ensemble. I wanted an easy way to share all the lending club data with others. Exploratory Data Analysis, or EDA, is an integral part of understanding the LendingClub dataset. Each row is divided by an individual loan id and member id, of course, for the interest of privacy each member id has been removed from the dataset. The Receiver Operating Characteristic (ROC) curves and AUC values are often used to score binary classification models AUC is good for classification problems with a class imbalance - the cost of a false positive is different from the cost of a false negative. Correlation between installment values and loan amount for analytical categories DTI results in a massive of... 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