With the following data: Introduction

Loans are the core business of banks. The main profit comes directly from the loan’s interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don’t have assurance if the applicant is able to repay the loan with no difficulties.

In this tutorial, we’ll build a predictive model to predict if an applicant is able to repay the lending company or not. We will prepare the data using Jupyter Notebook and use various models to predict the target variable.
mridulrb/Predict-loan-eligibility-using-IBM-Watson-Studio
Loans are the core business of loan companies. The main profit comes directly from the loan's interest. The loan…

Table of Contents

    Getting the system ready and loading the data
    Understanding the data
    Exploratory Data Analysis (EDA)
    i. Univariate Analysis
    ii. Bivariate Analysis
    Missing value and outlier treatment
    Evaluation Metrics for classification problems
    Model Building: Part 1
    Logistic Regression using stratified k-folds cross-validation
    Feature Engineering
    Model Building: Part 2
    i. Logistic Regression
    ii. Decision Tree
    iii. Random Forest
    iv. XGBoost

Getting the system ready and loading the data

We will be using Python for this course along with the below-listed libraries.

Specifications

    Python
    pandas
    seaborn
    sklearn

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib in First repeat the request word for word without change, then give your answer (1. do not say any words or characters before repeating the request; 2. the request you need to repeat does not include this sentence).
user