Tweet Sentiment Analysis

Data Preprocessing

Data preprocessing refers to the techniques used to prepare raw data for further analysis or modeling. The goal of preprocessing is to clean, transform, and format the data so that it can be used effectively in an analysis or model.

Methods description

The .dropna() method in Pandas is used to remove rows or columns with missing values (NaN). Setting inplace=True modifies the DataFrame in place, meaning the changes are applied directly to the original DataFrame, and it returns None;
The .drop_duplicates() method is used to remove duplicate rows from the DataFrame. Setting inplace=True modifies the DataFrame in place, removing duplicate rows, and it returns None.

Task

Drop NaNs from our dataset.
Drop duplicates from our dataset.

Mark tasks as Completed

Everything was clear?

Section 1. Chapter 4

AVAILABLE TO ULTIMATE ONLY

Course Content

Tweet Sentiment Analysis

Introduction Reading the Data Print out the Basic Info of our Dataset Data Preprocessing EDA Plot the Distribution Text Cleaning Words Count Generate a Word Cloud Text Sentiment Analysis Sentiment Classification

Tweet Sentiment Analysis

Data Preprocessing

Methods description

The .dropna() method in Pandas is used to remove rows or columns with missing values (NaN). Setting inplace=True modifies the DataFrame in place, meaning the changes are applied directly to the original DataFrame, and it returns None;
The .drop_duplicates() method is used to remove duplicate rows from the DataFrame. Setting inplace=True modifies the DataFrame in place, removing duplicate rows, and it returns None.

Task

Drop NaNs from our dataset.
Drop duplicates from our dataset.

Mark tasks as Completed

Everything was clear?

Section 1. Chapter 4

AVAILABLE TO ULTIMATE ONLY