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A confusion matrix is a N x N matrix that is used to interpret model evaluation performance systematically. Here N is the number of...

Logistic Regression is a classification algorithm where we only have two choices either true or false. A categorical target variable is...

We will be building a House Price prediction model using linear regression, I will try to make everything simple for you. You can access...

It is an optimization algorithm that works iteratively and aims to find the minimum value of a convex function with respect to a set of...

Linear regression is a statistical method that is used for predictive analysis. It is used to predict sales, salary, prices, age, etc....

They are used to calculate the errors in machine learning models, we will have a detailed look at various model evaluation techniques. 1....

Categorical variables are the non-numeric data present in the dataset. We can use it to well to enhance the accuracy of our machine...

Data exploration and preprocessing are the key aspects of machine learning. With proper exploration and preprocessing we can enhance the...

Descriptive statistics are calculated only for numerical variables. It gives us the detailed results of our dataset which includes mean,...

Graphs are the most useful thing used in data exploration and feature engineering part of machine learning. With the help of graphs, we...

EDA is a process of summarizing data, identifying patterns and relationships, and detecting outliers. This type of data analysis in...

A variable represents one specific characteristic of the data or tells specific information about the data. The above table represents...

Machine learning contains a set of algorithms that work on a huge amount of data. Data is fed to these algorithms to train them, and on...

Machine learning is the ability of machines to learn from past experiences and predict future outcomes. Therefore it is an art to teach...