loan-prediction | Predict which loans will be | Machine Learning library

by dataquestio Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(7)Vulnerabilities Install Support

kandi X-RAY | loan-prediction Summary

loan-prediction is a Python library typically used in Artificial Intelligence, Machine Learning, Keras, Pandas applications. loan-prediction has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Predict whether or not loans acquired by Fannie Mae will go into foreclosure. Fannie Mae acquires loans from other lenders as a way of inducing them to lend more. Fannie Mae releases data on the loans it has acquired and their performance afterwards here.

Support

Quality

Security

License

Reuse

Support

loan-prediction has a low active ecosystem.

It has 192 star(s) with 140 fork(s). There are 21 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 2 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of loan-prediction is current.

Quality

loan-prediction has 0 bugs and 0 code smells.

Security

loan-prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

loan-prediction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

loan-prediction does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

loan-prediction releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are available. Examples and code snippets are not available.

loan-prediction saves you 68 person hours of effort in developing the same functionality from scratch.

It has 176 lines of code, 11 functions and 4 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed loan-prediction and discovered the below as its top functions. This is intended to give you an instant insight into loan-prediction implemented functionality, and help decide if they suit your requirements.

Annotate an acquisition
Get the value for a performance summary
Return a dictionary with the performance counts for each loan
Concatenate data to a single CSV file
Cross validation
Reads the acquisition txt file
Computes the false negative negatives
Compute false positives
Write the acquisition to a csv file
Compute the error between target and predictions

Get all kandi verified functions for this library.

loan-prediction Key Features

No Key Features are available at this moment for loan-prediction.

loan-prediction Examples and Code Snippets

No Code Snippets are available at this moment for loan-prediction.

Community Discussions

Trending Discussions on loan-prediction

How Can I Find Whether My Dataset is balanced or not?

Why is the probability predicted by my keras model always zero

Unable to run PCA on a dataset

Julia - describe() function display incomplete summary statistics

While using onehot library in R, I get an error in the model.matrix command

ValueError: invalid fill value with a

ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

QUESTION

How Can I Find Whether My Dataset is balanced or not?

Asked 2021-Apr-27 at 17:42

I have created loan risk prediction python machine learning model for Predict whether borrower will able to pay bank loan or not. My model is working perfectly fine with 78% accuracy. However my professor told me that first I have to check whether my dataset is balanced or not and ( Apply SMOTE from imblearn library if it's not. ). the main issue is I don't know how to check is balanced or not. Please let me know if anyone know how to check.

Dataset Link :- https://www.kaggle.com/omkar5/dataset-for-bank-loan-prediction?select=credit_train.csv ( this Dataset I used to for create model )

...

ANSWER

Answered 2021-Apr-27 at 17:14

In simple words, you need to check if there is an imbalance in the classes present in your target variable.

For example:

If you check the ratio between DEATH_EVENT=1 and DEATH_EVENT=0, it is 2:1 which means our dataset is imbalanced.

To balance, we can either oversample or undersample the data. Undersampling in this case would be to remove 100 rows where the DEATH_EVENT=0 for 1:1 ratio but we would lose a lot of data in the process.

On the other hand, SMOTE can help us generate rows for the minority class. You can do that using the following code:

Source https://stackoverflow.com/questions/67287472

QUESTION

Why is the probability predicted by my keras model always zero

Asked 2020-Dec-03 at 17:21

I've built a model to predict loan suitability on a Kaggle dataset here

...

ANSWER

Answered 2020-Dec-01 at 17:27

You're performing standardization for the training part, which is great. However, you're predicted with values that are mis-standardized. When you perform standardization for the training part, you calculate the mean and std of each column and make the operation.

However, the predicting part is not good because you calculate the mean and std of the row.

The correct training process is :

Calculate mean and std of all the columns from your training dataset
Operate the standardization with the column values :

X_standard = (X - mean_column) / std_column

Train your model

The correct predicting process is :

Select a row and standardize each element by the corresponding mean and std calculated at 1.
Predict

Source https://stackoverflow.com/questions/65094975

QUESTION

Unable to run PCA on a dataset

Asked 2019-Oct-21 at 09:18

I am trying to run PCA on the loan dataset - find test here and train.

The code snippet is as follows,

...

ANSWER

Answered 2019-Oct-21 at 09:18

Doing a PCA only require :

Source https://stackoverflow.com/questions/58483060

QUESTION

Julia - describe() function display incomplete summary statistics

Asked 2019-Jan-05 at 15:56

I'm trying basic data analysis with Julia

I'm following this tutorial with the train datasets that can be found here (the one named train_u6lujuX_CVtuZ9i.csv) with the following code:

...

ANSWER

Answered 2019-Jan-05 at 12:01

This is how it is currently (in the current release) implemented in StatsBase.jl. In short train[:LoanAmount] does not have eltype that is subtype of Real and then StatsBase.jl uses a fallback method that only prints length, eltype and number of unique values. You can write describe(collect(skipmissing(train[:LoanAmount]))) to get summary statistics (except number of missings of course).

Actually, however, I would recommend you to use another approach. If you want to get a more verbose output on a single column use:

Source https://stackoverflow.com/questions/54051240

QUESTION

While using onehot library in R, I get an error in the model.matrix command

Asked 2018-Jul-28 at 23:16

For label encoding I am using model.matrix from library onehot in R. The data set is available here.

I have renamed the file as train.csv The feature to be encoded is Education. It has got two levels, Graduate and Not Graduate. However on executing the code,

...

ANSWER

Answered 2018-Jul-28 at 23:16

Sorry it was a typo. I should have used the complete dataset for model.matrix. The fix is to replace

Source https://stackoverflow.com/questions/49770415

QUESTION

ValueError: invalid fill value with a

Asked 2017-Sep-02 at 18:00

I am practising on a loan prediction practise problem and trying to fill missing values in my data. I obtained the data from here. To complete this problem I am following this tutorial.

You can find the entire code (file name model.py) I am using and the data here on GitHub.

The DataFrame looks like this:

...

ANSWER

Answered 2017-Jun-13 at 07:04

It seems author of tutorial want replace NaN by values of table.

But need first create Series by unstack and set_index for align data.

First remove replacing to NaN by mean:

Source https://stackoverflow.com/questions/44450725

QUESTION

ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

Asked 2017-Jun-21 at 08:27

I am practising on a loan prediction practise problem and trying to fill missing values in my data. I obtained the data from here. To complete this problem I am following this tutorial.

You can find the entire code (file name model.py) I am using and the data on GitHub.

The DataFrame looks like this:

After the last line is executed (corresponds to line 122 in the model.py file)

...

ANSWER

Answered 2017-Jun-21 at 08:27

You can use fillna:

Source https://stackoverflow.com/questions/44669921

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install loan-prediction

Clone this repo to your computer.
Get into the folder using cd loan-prediction.
Run mkdir data.
Switch into the data directory using cd data.
Download the data files from Fannie Mae into the data directory. You can find the data here. You'll need to register with Fannie Mae to download the data. It's recommended to download all the data from 2012 Q1 to present.
Extract all of the .zip files you downloaded. On OSX, you can run find ./ -name \*.zip -exec unzip {} \;. At the end, you should have a bunch of text files called Acquisition_YQX.txt, and Performance_YQX.txt, where Y is a year, and X is a number from 1 to 4.
Remove all the zip files by running rm *.zip.
Switch back into the loan-prediction directory using cd ...
Install the requirements using pip install -r requirements.txt. Make sure you use Python 3. You may want to use a virtual environment for this.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: