🚀 Data Cleaning/Data Preprocessing Before Building a Model - A Comprehensive Guide

welcome to the new video in this video we are going to see how to do the pre-processing of data before building machine learning model so before we fit the data to the machine learning model we need to pre-process the data in order to see whether there is any missing value whether there's any outliers

whether there is any duplicates value present in the data or either there is any garbage values are present in the data so we need to understand the data first before building any machine learning model we need to check for the relationship between the data how the data is related to each other then only

we can build a best machine learning model so for that purpose we need to do the pre-processing of data so in this video we're going to see step by step how to pre-process the data before fitting the data into machine learning model so in this video we are going to cover the topics like the steps of

pre-processing of data so we start from importing necessary libraries and then read the data set then we do the sanity check of the data and after that we do the exploratory data analysis to understand the data better after that we will do the missing value treatments of data and after that we'll do outli

treatments then check for the duplicates and garbage value then do the treatment of the duplicates and garbage value after that let's see what's the normalization how to do normalization of data and why needed then after that at last encoding of categorical data to fit in the model so then let's see how to do

pre-processing of data step by step first I will import all the necessary Library so I will import pandas as PD then import numpy Library as in P after that visualization libraries like cbor and math plot Li [Music] input math plot CP plts sorry math plotly P plot and as PLT so first I will import all the