Articles
Building Credit Risk Model
The loan data typically have a higher proportion of good loans. We can achieve high accuracy just by labeling all loans...
Create a Function and Prepare Test Data in R
When we build the model, we will need the same set of columns in the test data also and will also need to apply all the...
Remove Dimensions By Fitting Logistic Regression
We will use the preProcess function from the caret package to center and scale (Normalize) the data...
Data Cleaning in R - Part 5
Numeric Features Let’s look at all numeric features we have left. > str(data_train[getNumericColumns(data_tra...
5 Factors That Influence the Stock Market – Explained
While the success of a trader relies mostly on their abilities to anticipate market changes and act upon them, the stoc...
Advanced Concept of Risk-reward Ratio in Trading
Naïve traders always thinking by following the simple concept of risk-reward ratio, they can make huge money. Things do...
Data Cleaning in R - Part 3
Default by States We take a look at default rate for each state. We filter out states that have too small number of ...
Data Cleaning in R - Part 2
Attributes with Zero Variance Datasets can sometimes contain attributes (predictors) that have near-zero variance, o...
Data Cleaning in R - Part 1
Discarding Attributes LendingClub also provides a data dictionary that contains details of all attributes of out dat...
Loan Data - Training and Test Data Sets
For building the model, we will divide our data into two different data sets, namely training and testing datasets. The...
Credit Risk Modelling - Required R Packages
During our analysis, we will make use of various R packages. So, let’s look at what these packages are and let’s instal...
Explore Loan Data in R - Loan Grade and Interest Rate
There is no set path to how one would go about analyzing a data set. Typically, a data scientist would spend quite some...
Explore Financial Data in R
Now that we have the data file in our working directory, we can load it in our R session and start exploring it. Use...
Credit Risk Modelling - Case Study- Lending Club Data
To build a good model, it is important to use high quality data. For the purpose of this course, we will use the loan d...
Create a Confusion Matrix in R
A confusion matrix is a tabular representation of Actual vs Predicted values. As you can see, the confusion matr...
Measure Model Performance in R Using ROCR Package
R’s ROCR package can be used for evaluating and visualizing the performance of classifiers / fitted models. It is helpf...
Logistic Regression Model in R
Logistic regression aims to model the probability of an event occurring depending on the values of independent variable...
Build the Predictive Model
We have now gathered our data and cleansed/transformed it to suit our modeling needs. The next step is to actually buil...
Credit Modelling: Training and Test Data Sets
For building the model, we will divide our data into two different data sets, namely training and testing datasets. The...
German Credit Data : Data Preprocessing and Feature Selectio...
The purpose of preprocessing is to make your raw data suitable for the data science algorithms. For example, we may wan...
Data Science in Finance: 9-Book Bundle
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- Machine Learning in Finance using Python
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