Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables

Overview

Mortgage-Application-Analysis

Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables: age, income level, occupancy type, accepted, and debt-income ratio, Eliminating all the demographic bias except for age We picked 5 attributes from the Mortgage data set provided and created a separate *.csv file to avoid extra data loss from the null values of the attributes which we neglect in our model. We preprocessed the data to drop any null values of the applicants which might skew our datasets using the pandas library For the processing part, we had some classification data with controlled intervals. We used Ordinal encoding to convert those into numeric discrete data for training and testing our model. We also had one, unique string data attribute, which was encoded using One-hot encoding to extract numeric values for processing. With this clean data, we divided the data into two groups, 80% for validation and 20%, and trained our model to establish a correlation between mortgage application acceptance.

Using Matlab plot, we carried out data/representation/ visualization and found out, other than debt-to-income ratio, there isn’t any significant correlation between acceptance and other non-demographic factors After this visualization to establish our hypothesis, we trained our model using the data set we created., and evaluate the model we created we applied 4 types of algorithms to test it out: We used the Logistic Regression model to create a line the best fit for log-odds values to calculate the acceptance rate for the mortgage application. The F1 score, precision score, and recall score for this testing were very high, which suggested that the non-demographic factor which we accounted for didn’t have many roles in the application being accepted or rejected. Similarly, we carried out a random forest model, Decision Tree, and Support Vector machine algorithm and each of those evaluations had really high precision, recall, and F1 score supporting the evidence from data visualization.

You might also like...
Easy to start. Use deep nerual network to predict the sentiment of movie review.

Easy to start. Use deep nerual network to predict the sentiment of movie review. Various methods, word2vec, tf-idf and df to generate text vectors. Various models including lstm and cov1d. Achieve f1 score 92.

Predict an emoji that is associated with a text
Predict an emoji that is associated with a text

Sentiment Analysis Sentiment analysis in computational linguistics is a general term for techniques that quantify sentiment or mood in a text. Can you

RoNER is a Named Entity Recognition model based on a pre-trained BERT transformer model trained on RONECv2

RoNER RoNER is a Named Entity Recognition model based on a pre-trained BERT transformer model trained on RONECv2. It is meant to be an easy to use, hi

A machine learning model for analyzing text for user sentiment and determine whether its a positive, neutral, or negative review.
A machine learning model for analyzing text for user sentiment and determine whether its a positive, neutral, or negative review.

Sentiment Analysis on Yelp's Dataset Author: Roberto Sanchez, Talent Path: D1 Group Docker Deployment: Deployment of this application can be found her

Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.

keytotext Idea is to build a model which will take keywords as inputs and generate sentences as outputs. Potential use case can include: Marketing Sea

Code for ACL 2021 main conference paper
Code for ACL 2021 main conference paper "Conversations are not Flat: Modeling the Intrinsic Information Flow between Dialogue Utterances".

Conversations are not Flat: Modeling the Intrinsic Information Flow between Dialogue Utterances This repository contains the code and pre-trained mode

Tool to check whether a GCP bucket is public or not.
Tool to check whether a GCP bucket is public or not.

Tool to check publicly accessible GCP bucket. Blog https://justm0rph3u5.medium.com/gcp-inspector-auditing-publicly-exposed-gcp-bucket-ac6cad55618c Wha

apple's universal binaries BUT MUCH WORSE (PRACTICAL SHITPOST) (NOT PRODUCTION READY)

hyperuniversality investment opportunity: what if we could run multiple architectures in a single file, again apple universal binaries, but worse how

A python project made to generate code using either OpenAI's codex or GPT-J (Although not as good as codex)

CodeJ A python project made to generate code using either OpenAI's codex or GPT-J (Although not as good as codex) Install requirements pip install -r

Owner
null
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Rasa Open Source Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual

Rasa 15.3k Jan 3, 2023
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Rasa Open Source Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual

Rasa 10.8k Feb 18, 2021
The model is designed to train a single and large neural network in order to predict correct translation by reading the given sentence.

Neural Machine Translation communication system The model is basically direct to convert one source language to another targeted language using encode

Nishant Banjade 7 Sep 22, 2022
This repository serves as a place to document a toy attempt on how to create a generative text model in Catalan, based on GPT-2

GPT-2 Catalan playground and scripts to train a GPT-2 model either from scrath or from another pretrained model.

Laura 1 Jan 28, 2022
The aim of this task is to predict someone's English proficiency based on a text input.

English_proficiency_prediction_NLP The aim of this task is to predict someone's English proficiency based on a text input. Using the The NICT JLE Corp

null 1 Dec 13, 2021
Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles

Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles (TASLP 2022)

Zhuosheng Zhang 3 Apr 14, 2022
A CRM department in a local bank works on classify their lost customers with their past datas. So they want predict with these method that average loss balance and passive duration for future.

Rule-Based-Classification-in-a-Banking-Case. A CRM department in a local bank works on classify their lost customers with their past datas. So they wa

ÖMER YILDIZ 4 Mar 20, 2022
A flask application to predict the speech emotion of any .wav file.

This is a speech emotion recognition app. It will allow you to train a modular MLP model with the RAVDESS dataset, and then use that model with a flask application to predict the speech emotion of any .wav file.

Aryan Vijaywargia 2 Dec 15, 2021