House Loan Data Analysis
- kaggle.com
zipUpdated May 25, 2021
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EmailClick to copy linkLink copiedCiteVikas Chellaboina (2021). House Loan Data Analysis [Dataset]. https://www.kaggle.com/urstrulyvikas/house-loan-data-analysiszip(37847513 bytes)Available download formatsDataset updatedMay 25, 2021AuthorsVikas ChellaboinaDescriptionDESCRIPTION
For safe and secure lending experience, it's important to analyze the past data. In this project, you have to build a deep learning model to predict the chance of default for future loans using the historical data. As you will see, this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.
Objective: Create a model that predicts whether or not an applicant will be able to repay a loan using historical data.
Domain: Finance
Analysis to be done: Perform data preprocessing and build a deep learning prediction model.
Steps to be done:
⦁ Load the dataset that is given to you ⦁ Check for null values in the dataset ⦁ Print percentage of default to payer of the dataset for the TARGET column ⦁ Balance the dataset if the data is imbalanced ⦁ Plot the balanced data or imbalanced data ⦁ Encode the columns that is required for the model ⦁ Calculate Sensitivity as a metrice ⦁ Calculate area under receiver operating characteristics curve
House Loan Data Analysis-Deep Learning
- kaggle.com
zipUpdated Aug 6, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteDeependra Verma (2023). House Loan Data Analysis-Deep Learning [Dataset]. https://www.kaggle.com/datasets/deependraverma13/house-loan-data-analysis-deep-learningzip(37847521 bytes)Available download formatsDataset updatedAug 6, 2023AuthorsDeependra VermaDescriptionDESCRIPTION
For safe and secure lending experience, it's important to analyze the past data. In this project, you have to build a deep learning model to predict the chance of default for future loans using the historical data. As you will see, this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.
Objective: Create a model that predicts whether or not an applicant will be able to repay a loan using historical data.
Domain: Finance
Analysis to be done: Perform data preprocessing and build a deep learning prediction model.
Steps to be done:
⦁ Load the dataset that is given to you ⦁ Check for null values in the dataset ⦁ Print percentage of default to payer of the dataset for the TARGET column ⦁ Balance the dataset if the data is imbalanced ⦁ Plot the balanced data or imbalanced data ⦁ Encode the columns that is required for the model ⦁ Calculate Sensitivity as a metrice ⦁ Calculate area under receiver operating characteristics curve
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FacebookTwitterEmailDESCRIPTION
For safe and secure lending experience, it's important to analyze the past data. In this project, you have to build a deep learning model to predict the chance of default for future loans using the historical data. As you will see, this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.
Objective: Create a model that predicts whether or not an applicant will be able to repay a loan using historical data.
Domain: Finance
Analysis to be done: Perform data preprocessing and build a deep learning prediction model.
Steps to be done:
⦁ Load the dataset that is given to you ⦁ Check for null values in the dataset ⦁ Print percentage of default to payer of the dataset for the TARGET column ⦁ Balance the dataset if the data is imbalanced ⦁ Plot the balanced data or imbalanced data ⦁ Encode the columns that is required for the model ⦁ Calculate Sensitivity as a metrice ⦁ Calculate area under receiver operating characteristics curve