Course/Module 6/Topic 1 of 4Beginner

Text Preprocessing

Learn how to prepare raw text data for NLP tasks.

45 min•By Priygop Team•Last updated: Feb 2026

Why Preprocess Text?

Text preprocessing helps clean and standardize text, improving model accuracy.

Common Steps

Lowercasing
Removing punctuation
Tokenization
Stopword removal
Stemming & Lemmatization

Implementation

Example

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string

nltk.download('punkt')
nltk.download('stopwords')

text = "Natural Language Processing is fun and powerful!"
tokens = word_tokenize(text.lower())
tokens = [t for t in tokens if t not in string.punctuation]
tokens = [t for t in tokens if t not in stopwords.words('english')]

print(tokens)

Try It Yourself — Text Preprocessing

Try It Yourself — Text PreprocessingJavaScript

JavaScript Editor

✓ ValidTab = 2 spaces

// AI & Machine Learning concepts in JavaScript
// Simple linear regression simulation

// Training data: [hours studied] -> [score]
const data = [
  [1, 50], [2, 55], [3, 65], [4, 70],
  [5, 75], [6, 80], [7, 85], [8, 90],
];

// Calculate linear regression (y = mx + b)
function linearRegression(data) {
  const n = data.length;
  const sumX = data.reduce((s, [x]) => s + x, 0);
  const sumY = data.reduce((s, [, y]) => s + y, 0);
  const sumXY = data.reduce((s, [x, y]) => s + x * y, 0);
  const sumX2 = data.reduce((s, [x]) => s + x * x, 0);
  const m = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
  const b = (sumY - m * sumX) / n;
  return { m, b };
}

const { m, b } = linearRegression(data);
console.log(`Model: score = ${m.toFixed(2)} * hours + ${b.toFixed(2)}`);

// Predict
function predict(hours) {
  return Math.round(m * hours + b);
}

console.log("Predictions:");
[3, 5, 7, 10].forEach(h => {
  console.log(`  ${h} hours → predicted score: ${predict(h)}`);
});