Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python
DOWNLOAD
Download Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python
DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-13
Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-13 with Computers categories.
In this project, we aim to predict the risk of defaulting on a loan based on customer behavior using machine learning and deep learning techniques. We start by exploring the dataset and understanding its structure and contents. The dataset contains various features related to customer behavior, such as credit history, income, employment status, loan amount, and more. We analyze the distribution of these features to gain insights into their characteristics and potential impact on loan default. Next, we preprocess the data by handling missing values, encoding categorical variables, and normalizing numerical features. This ensures that the data is in a suitable format for training machine learning models. To predict the risk flag for loan default, we apply various machine learning models. We start with logistic regression, which models the relationship between the input features and the probability of loan default. We evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score. Next, we employ decision tree-based algorithms, such as random forest and gradient boosting, which can capture non-linear relationships and interactions among features. These models provide better predictive power and help identify important features that contribute to loan default. Additionally, we explore support vector machines (SVM), which aim to find an optimal hyperplane that separates the loan default and non-default instances in a high-dimensional feature space. SVMs can handle complex data distributions and can be tuned to optimize the classification performance. After evaluating the performance of these machine learning models, we turn our attention to deep learning techniques. We design and train an Artificial Neural Network (ANN) to predict the risk flag for loan default. The ANN consists of multiple layers of interconnected neurons that learn hierarchical representations of the input features. We configure the ANN with several hidden layers, each containing a varying number of neurons. We use the ReLU activation function to introduce non-linearity and ensure the model's ability to capture complex relationships. Dropout layers are incorporated to prevent overfitting and improve generalization. We compile the ANN using the Adam optimizer and the binary cross-entropy loss function. We train the model using the preprocessed dataset, splitting it into training and validation sets. The model is trained for a specific number of epochs, with a defined batch size. Throughout the training process, we monitor the model's performance using metrics such as loss and accuracy on both the training and validation sets. We make use of early stopping to prevent overfitting and save the best model based on the validation performance. Once the ANN is trained, we evaluate its performance on a separate test set. We calculate metrics such as accuracy, precision, recall, and F1-score to assess the model's predictive capabilities in identifying loan default risk. In conclusion, this project involves the exploration of a loan dataset, preprocessing of the data, and the application of various machine learning models and a deep learning ANN to predict the risk flag for loan default. The machine learning models, including logistic regression, decision trees, SVM, and ensemble methods, provide insights into feature importance and achieve reasonable predictive performance. The deep learning ANN, with its ability to capture complex relationships, offers the potential for improved accuracy in predicting loan default risk. By combining these approaches, we can assist financial institutions in making informed decisions and managing loan default risks more effectively.
Analysis And Prediction Projects Using Machine Learning And Deep Learning With Python
DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-02-17
Analysis And Prediction Projects Using Machine Learning And Deep Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-17 with Computers categories.
PROJECT 1: DEFAULT LOAN PREDICTION BASED ON CUSTOMER BEHAVIOR Using Machine Learning and Deep Learning with Python In finance, default is failure to meet the legal obligations (or conditions) of a loan, for example when a home buyer fails to make a mortgage payment, or when a corporation or government fails to pay a bond which has reached maturity. A national or sovereign default is the failure or refusal of a government to repay its national debt. The dataset used in this project belongs to a Hackathon organized by "Univ.AI". All values were provided at the time of the loan application. Following are the features in the dataset: Income, Age, Experience, Married/Single, House_Ownership, Car_Ownership, Profession, CITY, STATE, CURRENT_JOB_YRS, CURRENT_HOUSE_YRS, and Risk_Flag. The Risk_Flag indicates whether there has been a default in the past or not. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: AIRLINE PASSENGER SATISFACTION Analysis and Prediction Using Machine Learning and Deep Learning with Python The dataset used in this project contains an airline passenger satisfaction survey. In this case, you will determine what factors are highly correlated to a satisfied (or dissatisfied) passenger and predict passenger satisfaction. Below are the features in the dataset: Gender: Gender of the passengers (Female, Male); Customer Type: The customer type (Loyal customer, disloyal customer); Age: The actual age of the passengers; Type of Travel: Purpose of the flight of the passengers (Personal Travel, Business Travel); Class: Travel class in the plane of the passengers (Business, Eco, Eco Plus); Flight distance: The flight distance of this journey; Inflight wifi service: Satisfaction level of the inflight wifi service (0:Not Applicable;1-5); Departure/Arrival time convenient: Satisfaction level of Departure/Arrival time convenient; Ease of Online booking: Satisfaction level of online booking; Gate location: Satisfaction level of Gate location; Food and drink: Satisfaction level of Food and drink; Online boarding: Satisfaction level of online boarding; Seat comfort: Satisfaction level of Seat comfort; Inflight entertainment: Satisfaction level of inflight entertainment; On-board service: Satisfaction level of On-board service; Leg room service: Satisfaction level of Leg room service; Baggage handling: Satisfaction level of baggage handling; Check-in service: Satisfaction level of Check-in service; Inflight service: Satisfaction level of inflight service; Cleanliness: Satisfaction level of Cleanliness; Departure Delay in Minutes: Minutes delayed when departure; Arrival Delay in Minutes: Minutes delayed when Arrival; and Satisfaction: Airline satisfaction level (Satisfaction, neutral or dissatisfaction) The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: CREDIT CARD CHURNING CUSTOMER ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON The dataset used in this project consists of more than 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc. There are 20 features in the dataset. In the dataset, there are only 16.07% of customers who have churned. Thus, it's a bit difficult to train our model to predict churning customers. Following are the features in the dataset: 'Attrition_Flag', 'Customer_Age', 'Gender', 'Dependent_count', 'Education_Level', 'Marital_Status', 'Income_Category', 'Card_Category', 'Months_on_book', 'Total_Relationship_Count', 'Months_Inactive_12_mon', 'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal', 'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', and 'Avg_Utilization_Ratio',. The target variable is 'Attrition_Flag'. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: MARKETING ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON This data set was provided to students for their final project in order to test their statistical analysis skills as part of a MSc. in Business Analytics. It can be utilized for EDA, Statistical Analysis, and Visualizations. Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; Complain = 1 if customer complained in the last 2 years, 0 otherwise; and Country = Customer's location. The machine and deep learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 5: METEOROLOGICAL DATA ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON Meteorological phenomena are described and quantified by the variables of Earth's atmosphere: temperature, air pressure, water vapour, mass flow, and the variations and interactions of these variables, and how they change over time. Different spatial scales are used to describe and predict weather on local, regional, and global levels. The dataset used in this project consists of meteorological data with 96453 total number of data points and with 11 attributes/columns. Following are the columns in the dataset: Formatted Date; Summary; Precip Type; Temperature (C); Apparent Temperature (C); Humidity; Wind Speed (km/h); Wind Bearing (degrees); Visibility (km); Pressure (millibars); and Daily Summary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Business Analytics And Business Intelligence Machine Learning Model To Predict Bank Loan Defaults
DOWNLOAD
Author : dr. V.V.L.N. Sastry
language : en
Publisher: Idea Publishing
Release Date : 2020-05-29
Business Analytics And Business Intelligence Machine Learning Model To Predict Bank Loan Defaults written by dr. V.V.L.N. Sastry and has been published by Idea Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-29 with Computers categories.
Predictive Analytics offers a unique opportunity to identify future trends and allows organizations to act upon them. In this book we are dealing with ‘loan default’ which is always a threat to banks and financial institutions and should be predicted in advance based on various features of the borrowers or applicants. In this book we aim at applying machine learning models to classify the borrowers with and without loan default from a group of predicting variables and evaluate their performance. As a part of building a model to predict loan default, we have submitted in detail the introduction of the problem, exploratory data analysis (EDA), data cleaning and pre-processing, model building, interpretation, model tuning, model validation, and final interpretation & recommendations. Under the current project of loan default forming part of predictive analytics of business analytics and intelligence, we have studied research-based review parameters in detail which have also been annexed for ready reference as Annexure I. Data dictionary has been annexed as Annexure-2. R. Code for the same is provided at the URL which can be downloaded from www.drvvlnsastry.com/businessanalytics/data The study finds out that logistic regression is the best model to classify those applicants with loan default.
Predicting Loan Defaults Using Machine Learning Techniques
DOWNLOAD
Author : Abhishek Bhagat
language : en
Publisher:
Release Date : 2018
Predicting Loan Defaults Using Machine Learning Techniques written by Abhishek Bhagat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
In today’s world, obtaining loans from financial institutions has become a very common phenomenon. Every day people apply for loans, for a variety of purposes. But not all the applicants are reliable, and not everyone can be approved. Every year, there are cases where people do not repay the bulk of the loan amount to the bank which results in huge financial loss. The risk associated with making a decision on a loan approval is immense. Hence, the idea of the project is to gather loan data from the lending club website and use machine learning techniques on this data to extract important information and predict if a customer would be able to repay the loan or not. In other words, the goal is to predict if the customer would be a defaulter or not.
Bank Loan Status Classification And Prediction Using Machine Learning With Python Gui
DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-02
Bank Loan Status Classification And Prediction Using Machine Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-02 with Computers categories.
The project "Bank Loan Status Classification and Prediction Using Machine Learning with Python GUI" begins with data exploration, where the dataset containing information about bank loan applicants is analyzed. The data is examined to understand its structure, check for missing values, and gain insights into the distribution of features. Exploratory data analysis techniques are used to visualize the distribution of loan statuses, such as approved and rejected loans, and the distribution of various features like credit score, number of open accounts, and annual income. After data exploration, the preprocessing stage begins, where data cleaning and feature engineering techniques are applied. Missing values are imputed or removed, and categorical variables are encoded to numerical form for model compatibility. The dataset is split into training and testing sets to prepare for the machine learning model's training and evaluation process. Three preprocessing methods are used: raw data, normalization, and standardization. The machine learning process involves training several classifiers on the preprocessed data. Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Naive Bayes, Adaboost, XGBoost, and LightGBM classifiers are considered. Each classifier is trained using the training data and evaluated using performance metrics such as accuracy, precision, recall, and F1-score on the testing data. To enhance model performance, hyperparameter tuning is performed using Grid Search with cross-validation. Grid Search explores different combinations of hyperparameters for each model, seeking the optimal configuration that yields the best performance. This step helps to find the most suitable hyperparameters for each classifier, improving their predictive capabilities. The implementation of a graphical user interface (GUI) using PyQt comes next. The GUI allows users to interact with the trained machine learning models easily. Users can select their preferred preprocessing method and classifier from the available options. The GUI provides visualizations of the models' performance, including confusion matrices, real vs. predicted value plots, learning curves, scalability curves, and performance curves. Users can examine the decision boundaries of the classifiers for different features to gain insights into their behavior. The application of the GUI is intuitive and user-friendly. Users can visualize the results of different models, compare their performance, and choose the most suitable classifier based on their preferences and requirements. The GUI allows users to assess the performance of each classifier on the test dataset, providing a clear understanding of their strengths and weaknesses. The project fosters transparency and reproducibility by saving the trained machine learning models using joblib's pickle functionality. This enables users to load and use pre-trained models in the future without retraining, saving time and resources. Throughout the project, the team pays close attention to data handling and model evaluation, ensuring that no data leakage occurs and the models are well-evaluated using appropriate evaluation metrics. The GUI is designed to present results in a visually appealing and informative manner, making it accessible to both technical and non-technical users. The project's effectiveness is validated by its ability to accurately predict the loan status of bank applicants based on various features. It demonstrates how machine learning techniques can aid in decision-making processes, such as loan approval or rejection, in financial institutions. Overall, the "Bank Loan Status Classification and Prediction Using Machine Learning with Python GUI" project combines data exploration, feature preprocessing, model training, hyperparameter tuning, and GUI implementation to create a user-friendly application for loan status prediction. The project empowers users with valuable insights into the loan application process, supporting banks and financial institutions in making informed decisions and improving customer experience.
Machine Learning Application To Loan Default Comparison And Prediction A Case Study In Usa And China
DOWNLOAD
Author : 詹鳳華
language : en
Publisher:
Release Date : 2017
Machine Learning Application To Loan Default Comparison And Prediction A Case Study In Usa And China written by 詹鳳華 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Novel Financial Applications Of Machine Learning And Deep Learning
DOWNLOAD
Author : Mohammad Zoynul Abedin
language : en
Publisher: Springer Nature
Release Date : 2023-03-01
Novel Financial Applications Of Machine Learning And Deep Learning written by Mohammad Zoynul Abedin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-01 with Business & Economics categories.
This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
P2p Loan Acceptance And Default Prediction With Artificial Intelligence
DOWNLOAD
Author : Jeremy Turiel
language : en
Publisher:
Release Date : 2019
P2p Loan Acceptance And Default Prediction With Artificial Intelligence written by Jeremy Turiel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection, while the second one predicts default risk for approved loans. Logistic Regression was found to be the best performer for the first phase, with test set recall macro score of 77.4%. Deep Neural Networks were applied to the second phase only, were they achieved best performance, with validation set recall score of 72%, for defaults. This shows that AI can improve current credit risk models reducing the default risk of issued loans by as much as 70%. The models were also applied to loans taken for small businesses alone. The first phase of the model performs significantly better when trained on the whole dataset. Instead, the second phase performs significantly better when trained on the small business subset. This suggests a potential discrepancy between how these loans are screened and how they should be analysed in terms of default prediction.
Machine Learning For Financial Risk Management With Python
DOWNLOAD
Author : Abdullah Karasan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-07
Machine Learning For Financial Risk Management With Python written by Abdullah Karasan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
A Machine Learning Approach To Predict Loan Default
DOWNLOAD
Author : Hailey Markay Owen
language : en
Publisher:
Release Date : 2019
A Machine Learning Approach To Predict Loan Default written by Hailey Markay Owen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.