Standardization and Normalization in Machine Learning

In this post you will learn about Normalization and Standardization in machine learning.You will also learn why it is important and why and how to use Normalization and Standardization in Machine Learning in Python. Why to Normalize or Standardize? Sometimes,features of our dataset may have different scales.For example,One feature may… Continue reading

Evaluating Regression models with python scikit-learn

In this guide,you will learn how to evaluate Regression models with various metrics like Root Mean Square Error(RMSE), Mean Absolute Error(MAE) ,Mean Square Error(MSE),R-Squared Score and Adjusted R-squared Score. Let’s first implement our regression model then,we will evaluate it using rmse ,mse,mae,r-square and adjusted r-squared metrics.If you want to know… Continue reading

Logistic Regression–A detailed explanation with scikit-learn Implementation

This guide will give you a brief and detailed explanation of Logistic Regression and Multinobial logistic regression.You will also learn how to implement Logistic Regression and Multinobial Logistic Regression with scikit-learn. What is Logistic Regression? Logistic Regression is a statistical method for predicting for predicting a dependent variable given a… Continue reading

6 Different ways to create DataFrame in Pandas.

Data Science or data Analytics is a process of analysing large set of data points to get answers on questions related to that data set. Now,if you have been following data science or analytics or practising it,you might have heard of or have used pandas. PANDAS-Introduction Pandas is a high-level… Continue reading