Binary Logistic Regression in Python – a tutorial Part 1
In this tutorial, we will learn about binary logistic regression and its application to real life data using Python. We have also covered binary logistic regression in R in another tutorial. Without a doubt, binary logistic regression remains the most widely used predictive modeling method. Logistic Regression is a classification algorithm that is used to predict the probability of a categorical dependent variable. The method is used to model a binary variable that takes two possible values, typically coded as 0 and 1
Introduction to Multiple Linear Regression – Python
Multiple Linear Regression (MLR) is the backbone of predictive modelling and machine learning and an in-depth knowledge of MLR is critical in the predictive modeling world. we previously discussed implementing multiple linear regression in R tutorial, now we’ll look at implementing multiple linear regression using Python programming.
Binary Logistic Regression – a tutorial
In this tutorial we’ll learn about binary logistic regression and its application to real life data. Without any doubt, binary logistic regression remains the most widely used predictive modeling method.
Binary Logistic Regression with R – a tutorial
In a previous tutorial, we discussed the concept and application of binary logistic regression. We’ll now learn more about binary logistic regression model building and its assessment using R.
Firstly, we’ll recap our earlier case study and then develop a binary logistic regression model in R. followed by and explanation of model sensitivity and specificity, and how to estimate these using R.
Multiple Linear Regression in R – a tutorial
Multiple Linear Regression (MLR) is the backbone of predictive modeling and machine learning and an in-depth knowledge of MLR is critical to understanding these key areas of data science. This tutorial is intended to provide an initial introduction to MLR using R. If you’d like to cover the same area using Python, you can find our tutorial here
Predictive Analytics – An introductory overview
We’ll begin with an introduction to predictive modelling. We’ll then discuss important statistical models, followed by a general approach to building predictive models and finally, we’ll cover the key steps in building predictive models. Please note that prerequisites for starting out in predictive modeling are an understanding of exploratory data analysis and statistical inference.
T Distribution , Kolmogrov Smirnov, Shapiro Wilk Tests
In a previous tutorial we looked at key concepts in statistical inference. We’ll now look at T Distribution , Kolmogrov Smirnov, Shapiro Wilk, and standard parametric tests. Parametric tests are tests that make assumptions about the parameters of the population distribution from which a sample is drawn. We’ll begin with normality assessment using the Quantile-Quantile Plot (also called the Q-Q plot), the Shapiro-Wilk test and the Kolmogrov Smirnov test. Then, we’ll cover T distribution briefly. Finally, the one sample t-test, which is a standard parametric test will be looked in detail.
What is Statistical Inference – Key concepts
In this session, we’ll learn the concept of Statistical Inference. Statistical inference is a vast area which includes many statistical methods from analyzing data to drawing inferences or conclusions in research or business problems. It plays a vital role in the application of data science across industries.
Joins in Python- Merging, Appending and Aggregating Data
In this tutorial, we’ll look at different types of joins in Python used to merge two datasets.Then we’ll study how to aggregate data using the groupby function.
Pandas Series and Pandas Dataframe – A Quick Tutorial
In this tutorial we’re going to look at pandas data structures are and how to use them. We ‘ll start with an introduction to the pandas package and look at why it is important. We will begin to understand what data structures provides – particularly Pandas Series and Pandas DataFrame – and illustrate how to perform basic tasks on these data structures.