Model Performance Assessment Statistics – Concordance: Steps to Calculate

Binary Decision Outcome is one of most common analytics problem being solved in the industry. Some examples where binary predictive models are used.. Some of the common Model Performance Statistics which are used assessing performance of a Binary Predictive Models are: Confusion Matrix ROC Chart Concordance % Gini KS Lift Chart Gain Table and Chart ... Read more

Tutorial on Random Forest using Python

In the previous blog, we explained  Random Forest algorithm and steps you take in building Random Forest Model using R. In this blog, we will show high level steps required to build a Machine Learning Model in Python. Random Forest algorithm is based on Classification and Regression Tree  (CART) decision tree algorithm. But it builds ... Read more

Step by Step Tutorial on Decision Tree using Python

In this blog, the aim is to show you steps of building a Decision Tree using Python Jupiter Notebook. If you are interested to learn Decision Tree algorithm, we have an excellent tutorial on "Decision Tree Algorithm - CART". We are using the same data for explaining the steps involved in building a decision tree. ... Read more

Random Forest using R - Step by Step on a Sample Data

Some of the interested candidates have asked us to show steps on building Random Forest  for a sample data and score another sample using the Random Forest Model built. Here are the steps..

     

Decision Tree CART Algorithm Part 3

In the precious blogs, we have explained on selecting Best Split for each of the independent variables. Now we need to select the best variable, again consideration is Gini Index Value. For each of the independent Variables, we have best split and its Gini Index value. Here is the table. Variable Spend in the last ... Read more

CART Algorithm: Best Split for a Categorical Variable

Similar to continuous variables, Decision Tree Algorithm - CART has to find the best split for categorical variable as well. Only difference will be to find possible cut off values. For example, we have a variable - education- it had 4 levels -"University","Graduate","High School" and "Others". We consider all possible two way splits for the ... Read more

CART Algorithm for Decision Tree

Classification and Regression Tree (CART) is one of commonly used Decision Tree algorithms. In this post, we will explained the steps of CART algorithm using an example data. Decision Tree is a recursive partitioning approach and CART split each of the input node into two child nodes, so CART decision tree is Binary Decision Tree. ... Read more

Reading CSV file and Text File in Python

Reading Comma Separated (CSV) file in Python is one of the commonly used activities before proceeding to Data Science or Data Analysis steps. We can read csv data at least two different ways.

    Reading CVS using function from Pandas Library.

   

Data Wrangling using Python- Part 1

In this blog, we will show some of the commonly used data wrangling steps using Python.  We will be using pandas data frame as our data object to show all the steps. Importing Python Packages In this part of blog, we will use pandas and numpy packages available in Python. We need to import these ... Read more

Reading Text File in Python

Reading data into an analytical tool is one of the first steps before proceeding to analytics. Some of the common challenges while reading a text file are Knowing Delimiter Presence of Missing  or null values Column values holding date values In this blog, we will read text file in which values are separated by a ... Read more

Python Learning - Finding Answers

In this blog, we are sharing some of the scenario arose while working on a project and we are providing the steps to get the work done. I am sure, there would be multiple ways to achieve the outcome but I am sharing my solutions.   Q1:  How do we extract id value from html ... Read more

Visualisation using R - Commonly used functions

We will discussing some of the commonly used Base R Graphic functions.  Some of the commonly used functions are plot: Plotting Line Chart and Scatter Plot boxplot: Box Whiskers Plot for a continuous variable  or distributions by different groups hist: Histogram Scatter  Plot We will create a sample data points and then use for the scatter ... Read more

Powerful Proc Tabulate Explained

In this blog, we will use PROC TABULATE , one of the most powerful PROCS for data summarization using SAS. For creating summary table of the information (similar to Pivot in Excel), we need to define Classification Variables, also called dimensions Measurement Variables, also called Facts Structure of the Output Summary Tables PROC FREQ, PROC ... Read more

Concatenating Datasets in SAS

Author: Mrinmoy Saikia Data preparation is one of the most significant steps in Data Science, Analytics or Reporting Projects. In this blog, we focus on learning - "Combining 2 or more SAS datasets Vertically". Combining SAS datasets vertically is also referred as Concatenating Datasets. Concatenating is combining 2 or more datasets one below another. From ... Read more

Data Science: Profile Screening Model for Mid-Management Roles

Business Context: The client was an executive search firm. It has built a candidate database with over a million candidate profiles.  The client wanted to leverage the candidate database for smart candidate selection and recruitment process. For this project, the aim was to build a predictive model which will help in identifying a list of ... Read more

SAS Control Statements

In this blog, we will discuss some of the SAS control statements with examples. IF/WHERE Statement IF or WHERE statement is applied to select observations. WHERE condition is applied while reading observations from Input dataset where as IF condition is applied at Program Data Vector (PDV). Using the Where statement may improve the efficiency of ... Read more

Chi Square Test using SAS

A chi-square test is an statistical method to test association between two categorical variables (especially between nominal variables).  Type of Variables. Correlation Analysis: When both the variables are continuous, and it can be done using Pearson Correlation Coefficient.  Correlation Analysis. ANOVA: One variable is categorical and other variable is continuous. Finding how levels of categorical variable ... Read more