Hertha  Walsh

Hertha Walsh


ML Basics : Loan Prediction

The problem:

Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan.

The Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.

It’s a classification problem , given information about the application we have to predict whether the they’ll be to pay the loan or not.

We’ll start by exploratory data analysis , then preprocessing , and finally we’ll be testing different models such as Logistic regression and decision trees.

The data consists of the following rows:

Loan_ID : Unique Loan ID

Gender : Male/ Female
Married : Applicant married (Y/N)
Dependents : Number of dependents 
Education : Applicant Education (Graduate/ Under Graduate)
Self_Employed : Self employed (Y/N)
ApplicantIncome : Applicant income
CoapplicantIncome : Coapplicant income
LoanAmount : Loan amount in thousands of dollars
Loan_Amount_Term : Term of loan in months
Credit_History : credit history meets guidelines yes or no
Property_Area : Urban/ Semi Urban/ Rural
Loan_Status : Loan approved (Y/N) this is the target variable

#data-science #data-visualization #computer-science #machine-learning

What is GEEK

Buddha Community

ML Basics : Loan Prediction

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Ian  Robinson

Ian Robinson


Predictive Modeling in Data Science

Predictive modeling is an integral tool used in the data science world — learn the five primary predictive models and how to use them properly.

Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

  1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.
  2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
  3. **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
  4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
  5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models

Easy Loan

Easy Loan


Easy Loan - Fast Cash Loan in Singapore

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ML and Trading - Impact of Machine Learning in Trading | Mobinius


#machine learning #ml-development-company #ml-development-solutions #ml-development-services #hire-ml-developers

Otho  Hagenes

Otho Hagenes


Top Five Artificial Intelligence Predictions For 2021

As AI becomes more ubiquitous, it’s also become more autonomous — able to act on its own without human supervision. This demonstrates progress, but it also introduces concerns around control over AI. The AI Arms Race has driven organizations everywhere to deliver the most sophisticated algorithms around, but this can come at a price, ignoring cultural and ethical values that are critical to responsible AI. Here are five predictions on what we should expect to see in AI in 2021:

  1. Something’s going to give around AI governance
  2. Most consumers will continue to be sceptical of AI
  3. Digital transformation (DX) finds its moment
  4. Organizations will increasingly push AI to the edge
  5. ModelOps will become the “go-to” approach for AI deployment.

#opinions #2021 ai predictions #ai predictions for 2021 #artificial intelligence predictions #five artificial intelligence predictions for 2021