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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Loan Approval Prediction Machine Learning

V
Vedansh Last Updated : 04 Feb, 2022
6 min read

Introduction

In this article, we are going to solve the Loan Approval Prediction Hackathon hosted by Analytics Vidhya. This is a classification problem in which we need to classify whether the loan will be approved or not. classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. A few examples of classification problems are Spam Email detection, Cancer detection, Sentiment Analysis, etc.

To understand more about classification problems you can go through this link.

Table of Content

  1. Understanding the problem statement

  2. About the dataset

  3. Load essential Python Libraries

  4. Load Training/Test datasets

  5. Data Preprocessing

  6. Exploratory data analysis (EDA).

  7. Feature Engineering.

  8. Build Machine Learning Model

  9. Make predictions on the test dataset

  10. Prepare submission file

  11. Conclusion

Understanding the Problem Statement 

Dream Housing Finance company deals in all kinds of home loans. They have a presence across all urban, semi-urban and rural areas. The customer first applies for a home loan and after that, the company validates the customer eligibility for the loan.

The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling out online application forms. These details are Gender, Marital Status, Education, number of Dependents, Income, Loan Amount, Credit History, and others.

To automate this process, they have provided a dataset to identify the customer segments that are eligible for loan amounts so that they can specifically target these customers.

You can find the complete details about the problem statement here and also download the training and test data.

As mentioned above this is a Binary Classification problem in which we need to predict our Target label which is “Loan Status”.

Loan status can have two values: Yes or NO.

Yes: if the loan is approved

NO: if the loan is not approved

So using the training dataset we will train our model and try to predict our target column that is “Loan Status” on the test dataset.

About the dataset 

So train and test dataset would have the same columns except for the target column that is “Loan Status”.

Train dataset:

Load Essential Python Libraries


Load Training/ Test Dataset


Size of Train/Test Data


So we have 614 rows and 13 columns in our training dataset.

In test data, we have 367 rows and 12 columns because the target column is not included in the test data.

First look at the Dataset


Categorical Columns: Gender (Male/Female), Married (Yes/No), Number of dependents (Possible values:0,1,2,3+), Education (Graduate / Not Graduate), Self-Employed (No/Yes), credit history(Yes/No), Property Area (Rural/Semi-Urban/Urban) and Loan Status (Y/N)(i. e. Target variable)

Numerical Columns: Loan ID, Applicant Income, Co-applicant Income, Loan Amount, and Loan amount term

Data Preprocessing

Concatenating the train and test data for data preprocessing:

dropping the unwanted column :

Identify missing values :

Imputing the missing values:


  • Fill null values with mode

Next, we will be using Iterative imputer for filling missing values of LoanAmount and Loan_Amount_Term

So now as we have imputed all the missing values we go on to mapping the categorical variables with the integers.

We map the values so that we can input the train data into the model as the model does not accept any string values.

Exploratory Data Analysis (EDA) 

Splitting the data to new_train and new_test so that we can perform EDA.

Mapping ‘N’ to 0 and ‘Y’ to 1

Univariate Analysis:

Output:

Univariate Analysis Observations

  1. More Loans are approved Vs Rejected

  2. Count of Male applicants is more than Female

  3. Count of Married applicant is more than Non-married

  4. Count of graduate is more than non-Graduate

  5. Count of self-employed is less than that of Non-Self-employed

  6. Maximum properties are located in Semiurban areas

  7. Credit History is present for many applicants

  8. The count of applicants with several dependents=0 is maximum.

Bivariate Analysis


Mean ApplicantIncome of 0 and 1 are almost the same (o: no,1: Yes)


Mean Co- ApplicantIncome of 1 is slightly more than 0 (o: no,1 Yes)

 


The mean value of Loan Amount applied by males (0) is slightly higher than Females(1).

 


If you are married then the loan amount requested is slightly higher than non-married

 


Male have higher Co-applicant income than females in all three property areas

Correlation matrix


Output:


 

Feature Engineering

 

Total Income :


 

EMI :

Lets assume that interest rate=10.0 # hence r = ((10/12)/100) = 0.00833


 

Additional Features :


 

Bin Information :


 

Drop Unwanted Column :


 

Size after feature engineering :


We have added 8 new features

 

Building Machine Learning Model:

Creating X (input variables) and Y (Target Variable) from the new_train data.

Using train test split on the training data for validation

We have a (70:30) split on the training data.

Using ML algorithm for training

We have used multiple algorithms for training purposes like Decision Tree, Random Forest, SVC, Logistic Regression, XGB Regressor, etc.

Among all the algorithms logistic regression performs best on the validation data with an accuracy score of 82.7%.

After getting an accuracy of 82.7% I tried fine-tuning it to improve my accuracy score using GridSearchCV.

The best parameters I got after fine-tuning were:

After fine-tuning the logistic regression model the accuracy score improved from 82.7% to 83.24%.

 

Predicting on test data


 

Prepare Sumbisson file:


 

Conclusion

After the Final Submission of test data, my accuracy score was 78%.

Feature engineering helped me increase my accuracy.

Amazingly Logistic Regression worked better than all other Ensemble models.

loan approvalLoan Predictionmachine learning
V
Vedansh

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Hello sir, I am learning coding in Python. I found your blog amazing and full of resources. Bravo

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