If we had a crystal ball, we would only loan money to someone if we knew they would pay us back. A lending institution can make use of predictive analytics to reduce the number of loans it offers to those borrowers most likely to default, increasing the profitability of its loan portfolio.
This is commonly called credit risk scoring or loan default optimization. A model is created to predict whether a borrower will default. This model is used to help decide whether or not to grant a loan for each borrower. The solution is used to reduce the risk of borrowers defaulting on their loan and not being able to pay part of their loan to the lender. Loan Credit Risk. Are you unable to connect to your Virtual Machine? See this important information for how to resolve.Pytorch jacobian
On the VM created for you using the 'Deploy to Azure' button on the Quick start page, the SQL Server database Loans contains all the data and results of the end-to-end modeling process. A Windows PowerShell script to invoke the SQL scripts that execute the end-to-end modeling process is provided for convenience. This solution shows how to pre-process data cleaning and feature engineeringtrain prediction models, and perform scoring on the HDInsight Spark cluster with Microsoft ML Server deployed from using the 'Deploy to Azure' button on the Quick start page.
HDInsight Spark cluster billing starts once a cluster is created and stops when the cluster is deleted. See these instructions for important information about deleting a cluster and re-using your files on a new cluster. View On GitHub.Applying machine learning to predict loan charge-offs on LendingClub.
Classification problem to predict loan defaulters using Lending Club Dataset. The objective of this project is to predict the probability of borrower defaulting on a vehicle loan in the first EMI Equated Monthly Installments on the due date. Minimization of risk and maximization of profit on behalf of the bank. A simple sample program that will determine an applicant's eligibility for a loan.
Uni-variate and Bi-variate analysis to understand the driving factor behind loan default. Add a description, image, and links to the loan-default-prediction topic page so that developers can more easily learn about it.
Curate this topic. To associate your repository with the loan-default-prediction topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 48 public repositories matching this topic Language: All Filter by language.
Sort options. Star Code Issues Pull requests. Lending Club Loan data analysis. Updated Jul 8, Jupyter Notebook.
Updated May 11, Jupyter Notebook. Updated Mar 29, Jupyter Notebook. Updated Jun 15, Jupyter Notebook. Updated Jul 18, Jupyter Notebook. Updated Jun 19, Jupyter Notebook. Star 7.
Predicting Loan Defaults in the Fannie Mae Data Set
Updated Apr 23, Jupyter Notebook. Star 6. Capstone Project: Predicting default in P2P lending. Updated Feb 27, Jupyter Notebook. Updated Jan 26, Jupyter Notebook. Star 4. Updated Apr 8, C. Predicting the default customers. Updated Mar 8, Jupyter Notebook. Star 3. Updated Dec 6, Jupyter Notebook. Updated Jun 23, Jupyter Notebook. Star 2.We have the dataset with the loan applicants data and whether the application was approved or not.
In this tutorial we will build a machine learning model to predict the loan approval probabilty. This would be last project in this course. We have the loan application information like the applicant's name, personal details, financial information and requested loan amount and related details and the outcome whether the application was approved or rejected.
Based on this we are going to train a model and predict if a loan will get approved or not. Our dataset has records. Though our dataset has lot of columns, we are only going to use the Income fields, loan amount, loan duration and credit history fields to train our model. We are going to apply the below four algorithms to this problem and evaluate its effectiveness. And finally choose the best algorithm and train it. So, Regression algorithm works fine for our use case. We built an end-to-end project and tested different algorithms in this tutorial.
This concludes this mini course on machine learning. Hope the course gave you a good primer to the machine learning concepts and boosted your overall confidence with machine learning.
Its simple and takes less than 5 seconds. For the above comment, the type of the values is Long, and I guess that's why it's not accepting it. Thanks so much for sharing these three projects! I enjoyed working on them and learnt the basics. Log in.
Sign up. Post Comment. I was looking for the hyperlinks for the texts in Next steps. Are those tasks for us? Glad it was useful. Yes, The next steps are action items.
Jyotir d Thanks so much for sharing these three projects! Yes, The next steps are action items 0 Reply Report. Jyotir d Thanks again!Applying machine learning to predict loan charge-offs on LendingClub.
Classification problem to predict loan defaulters using Lending Club Dataset. The objective of this project is to predict the probability of borrower defaulting on a vehicle loan in the first EMI Equated Monthly Installments on the due date. Minimization of risk and maximization of profit on behalf of the bank.
A simple sample program that will determine an applicant's eligibility for a loan. Uni-variate and Bi-variate analysis to understand the driving factor behind loan default. Add a description, image, and links to the loan-default-prediction topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the loan-default-prediction topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content.
Here are 48 public repositories matching this topic Language: All Filter by language. Sort options. Star Code Issues Pull requests. Lending Club Loan data analysis.Generac generator wifi setup
Updated Jul 8, Jupyter Notebook. Updated May 11, Jupyter Notebook. Updated Mar 29, Jupyter Notebook. Updated Jun 15, Jupyter Notebook. Updated Jul 18, Jupyter Notebook. Updated Jun 19, Jupyter Notebook. Star 7. Updated Apr 23, Jupyter Notebook. Star 6. Capstone Project: Predicting default in P2P lending. Updated Feb 27, Jupyter Notebook. Updated Jan 26, Jupyter Notebook.Machine learning engineer sharing interesting finds in tech and in life.
ResNet is proposed in the paper Deep Residual Learning for Image Recognition to solve the problem of the increasing difficulty to optimize parameters in It is an algorithm for joint text and image classification. The F-RankClass project began as a class proje After building the Goose Dataset and using it to run object detection experiments, I thought of another application with the dataset: generating artificial g I did a similar project at the AI Bootcamp for Machin The Goose Dataset is released on GitHub.
In addition to the images and annotations, I also implemented some APIs that allow people to load the data similar t This is an exploratory project for me to apply different Machine Learning ML models and techniques and have a better understanding of how each of them work This part covers Sections Christine Ying-Yu Chen Machine learning engineer sharing interesting finds in tech and in life.
Posts by Tag machine-learning 7 project 4 computer-vision 3 github 2 paper-study 2 deep-learning 2 blog 1 notes 1 probability 1 dataset 1 object-detection 1 yolo 1 gan 1 data-mining 1 machine-learning Personal Recommendation Using Deep Recurrent Neural Networks in NetEase ICDE Paper less than 1 minute read The paper Personal Recommendation Using Deep Recurrent Neural Networks in NetEase proposes a session-based recommender system for e-commerce based on a The GANder Project 6 minute read After building the Goose Dataset and using it to run object detection experiments, I thought of another application with the dataset: generating artificial g Loan Default Prediction Machine Learning Project 6 minute read This is an exploratory project for me to apply different Machine Learning ML models and techniques and have a better understanding of how each of them work Hello world!Posted by Kyle DeGrave on May 16, Posted by Kyle DeGrave on April 28, After these mortgages are acquired, Fannie Mae sells them as securities in the bond market.
In fact, between andmany hundreds of thousands of people had defaulted, causing these securities to decreases significantly in value, thereby strongly impacting the global economy. On its website, Fannie Mae has made a subset of its single family loan performance SFLP data available to anyone interested in looking at it.
The SFLP data cover the yearsand can be downloaded here. The goal of this project it so see if we can predict from this data, with some accuracy, those borrowers who are most at risk of defaulting on their mortgage loans.
The perfomance data contains information regarding loan payment history, and whether or not a borrower ended up defaulting on their loan. Additional information regarding the contents of these two files can be found in the Layout and Glossary of Terms files.
In the performance data, we are really only interested in the LoanID and ForeclosureDate columns, as this will give us the borrower identifiaction number and whether or not they ended up defaulting. After reading in the two datasets, we can perform an inner join on the acquisition and performance dataframes using the LoanID column.
The resulting dataframe, df, will contain the ForeclosureDate column, and will be our target variable. For clarity, we will also rename this column as Default. In the Default column, a 1 is placed next to any borrower that was found to have defaulted, and a 0 is placed next to any borrower that has not defaulted. The dataframe hasrows and 26 columns, and contains information regarding loan interest rate, payment dates, property state, and the last few digits of each property ZIP code, among several other things.
Many of the columns contain missing values, and these will have to be filled in before we start making our predictions. There appears to be eight data columns that contain at least one missing value. These can be handled in a number of ways; depending on the distribution of data in each column, we can fill in missing values with the column median or mean, or we could sample randomly from a distribution defined by the present values.
We could also fit for the missing values using a machine learning algorithm applied to the complete columns, or we could drop the missing data altogether. We can start with our target variable, Default. For very imbalanced data sets, it is often the case that machine learning algorithms will have a tendency to always predict the more dominant class when presented with new, unseen test data. To avoid an overabundance of false negatives, we can eventually balance the classes so that the dataframe contains equal numbers of defaulters and non-defaulters.
The figures above show boxplots for several columns in our dataset. The green boxes and whiskers show the distribution of values spanned by the default class, while the blue boxes show the values spanned by the non-default class. The median value of the data is represented by the horizontal line in the middle of each box. The figures show that on average, defaulters have a higher debt-to-income ratio than do non-defaulters, lower credit scores, and higher interest rates.
The figure below shows the fraction of people that have defaulted from the ten most common ZIP codes having more than borrowers. Comparing certain locations for example, ZIP code vs. We will see shortly that the values represented in these figures are some of the most discriptive features in terms of identifying which class a borrower belongs to.
We can perform a potentially important pre-processing step and split up any date columns into their month and year components, just in case they might have some predictive power later.
Rather than simply using the column mean, median, etc. We can define a function to loop over columns with missing values. We then initialize a random forest classifier composed of random decision trees, fit it to the training data, and then predict the test set classes. The confusion matrix is a table which shows the percentage of correct true positives or true negatives and incorrect false positives or false negatives classifications for each positive default or negative non-default class.
In the table below, the true class is given along the x-axis, while the predicted class is given along the y-axis. Graphically, this looks like:. In terms of profitability to Fannie Mae, false negatives are the most important metric here. This is because Fannie Mae loses money when we incorrectly label a defaulter as being a non-defaulter. The fact that we incorrectly classify some of our non-defaulters is of little consequence, though, because there are so many of them present in the full data set i.
One may point out that both our training and test sets have been balanced before analysis, and wonder if this predictive capability holds up when the algorithm is presented with new, very imbalanced data.Machine learning engineer sharing interesting finds in tech and in life. This is an exploratory project for me to apply different Machine Learning ML models and techniques and have a better understanding of how each of them work and interact with the data:. The data is from a Kaggle competition Loan Default Prediction.Conflict management in ethiopia pdf
This is originally a regression problem, predicting the percentage of loan not paid for, but I performed most of the experiments by making it a binary classification problem, i. I started with data cleaning and data preprocessing, applied each of the ML model to the data, performed hyperparameter tuning to explore the potential of the models, and then analyzed the accuracy, Receiver Operating Characteristic ROC curves, and Precision-Recall PR curves and their respective Areas Under Curve AUC to evaluate the quality of the resulting models.
I also applied different feature engineering techniques and compared the model performance. The detailed experiments and reports in the form of Jupyter notebooks are available on GitHub.
I will also present some highlights here in this post. The idea of using a hybrid model of decision tree ensembles and logistic regression comes from the paper Practical Lessons from Predicting Clicks on Ads at Facebook. In the paper, they trained gradient-boosted decision tree model and used it to perform feature discretization. The output leaf node of each tree is used as a categorical feature and an input to the logistic regression model.
It is observed in the paper that this greatly helps the logistic regression model to take account of the nonlinearity and improve the accuracy metrics. For both methods, I limited the tree depth to 6 and the number of trees to I used the best hyperparameters I found to train the models.
Loan Default Prediction Machine Learning Project
I also tried using only the discretized features as the input to the logistic regression model, or using the discretized features concatenated with the original features. For Random ForestsI extracted the decision path for each example from each tree. The decision paths are expressed as a vector of boolean values, indicating which subtree the decision path takes at each branch. These are used as the discretized features.
Using concatenated features, I got 0. I used the XGBoost implementation of gradient-boosted decision trees. The leaf nodes are expressed as integers, which can be treated as categorical features.
I then performed one-hot encoding on these categorical features and used them as the discretized features. Using concatenated features, the best I got is also 0. Unfortunately, these techniques do not bring significant improvement on the model performance on this dataset as demonstrated in the Facebook paper. One hypothesis is that not many features have nonlinear relationships with the label. The dataset comes with features andentries. After performing one-hot encoding on categorical features, the number of features increased to 1, even more with feature expansion.
For some ML models, it takes at least a few minutes or even up to hours to train on a personal computer. Therefore, I wanted to explore feature reduction techniques to reduce the number of features while retaining a high model performance.
The techniques I tried are:. For each technique, I set the target number of features k to be [,50, 20], selected the features, trained a Logistic Regression model with these features, and plotted the model accuracy, ROCAUC, and PRAUC to observe the trend of model quality as k decreases.
The first feature reduction technique I tried is feature hashingalso known as the hashing trick. Given a target number of features kfeature hashing uses a hash function to combine various original features into one column, resulting in a total of k columns.Arshi ff completed mature wattpad
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