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Welcome to HCL GUVI

Hey there! Welcome to HCL GUVI—Grab Your Vernacular Imprint—where tech learning is easy, fun, and curated specially for you. Incubated by IIT Madras & IIM Ahmedabad in 2014 and now part of HCL Group, we're making quality tech education accessible to all.

Join 3M+ learners breaking barriers and upskilling for a brighter future. We're here to guide you every step of the way! 🚀

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Practice Platforms

Enhance your coding skills with HCL GUVI's Practice Platforms—interactive, structured, and designed to help you master programming effortlessly.

CodeKata:

A structured coding practice platform with 1500+ coding problems designed by industry experts. Ideal for beginners and professionals preparing for tech interviews with real-world coding challenges.

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WebKata:

An interactive platform to master HTML, CSS, JavaScript, and Bootstrap with a live coding environment. Perfect for hands-on web development practice without any setup.

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SQLKata:

A practice ground for mastering SQL queries used in real-world applications. Write, optimize, and refine your queries to build strong database skills.

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FixTheCode:

Hone your bug-fixing skills with real-world debugging challenges in Python, C++, JavaScript, and Golang. More languages coming soon!

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IDE:

A free online compiler supporting 20+ programming languages with auto-complete, debugging, and AI-powered code generation—all in the cloud!

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Home
Python 3
Cleaning Review Text

Cleaning Review Text

Cleaning Review Text
Code
Apply the Function
View Results

Cleaning Review Text

Raw reviews often contain punctuation, numbers, symbols, extra spaces, and uppercase letters. These elements usually do not contribute meaningful information for text classification.

Cleaning the text helps the model focus on the actual words.

We will perform the following preprocessing steps:

Convert text to lowercase
Remove punctuation
Remove numbers
Remove special characters
Remove extra spaces

Code

import re
def clean_text(text):

Convert to lowercase

text = text.lower()

Remove punctuation and special characters

text = re.sub(r'[^a-zA-Z\s]', '', text)

Remove extra spaces

text = re.sub(r'\s+', ' ', text)
return text.strip()

Apply the Function

df['clean_review'] = df['review'].apply(clean_text)

View Results

df[['review','clean_review']].head()

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Cleaning Review Text

Contents

Cleaning Review Text

Code

Convert to lowercase

Remove punctuation and special characters

Remove extra spaces

Apply the Function

View Results

Fake Review Detection System using Machine Learning

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