SMS Spam Detection Analysis using Python (NLP) | Machine Learning Project Tutorial

Combat SMS spam using Python! This tutorial delves into NLP techniques and machine learning algorithms for accurate spam detection. Learn to preprocess text data, extract meaningful features, and build models that can distinguish between legitimate and spam messages. Enhance your skills in natural language processing, machine learning, and contribute to a safer communication environment. Join this comprehensive project tutorial to unravel the world of SMS spam detection with Python. #SMSSpamDetection #Python #NLP #MachineLearning #TextClassification #SpamDetection

In this project tutorial we are going to analyze and classify the text messages from the dataset using a classifying model with pipelines.

You can watch the step by step explanation video tutorial down below

The "spam" concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography, etc.,

The SMS Spam Collection is a set of SMS tagged messages that have been collected for SMS Spam research. It contains one set of SMS messages in English of 5,574 messages, tagged according being ham (legitimate) or spam.

Download the dataset here

import pandas as pd
 import numpy as np
 import nltk
 import re
 from nltk.corpus import stopwords

Now we load the dataset for preprocessing

df = pd.read_csv('spam.csv')
 df.head()

Let us extract the relevant data for preprocessing

# get necessary columns for processing
 df = df[['v2', 'v1']]
 # df.rename(columns={'v2': 'messages', 'v1': 'label'}, inplace=True)
 df = df.rename(columns={'v2': 'messages', 'v1': 'label'})
 df.head()

# check for null values
 df.isnull().sum()

STOPWORDS = set(stopwords.words('english'))
 
 def clean_text(text):
  # convert to lowercase
  text = text.lower()
  # remove special characters
  text = re.sub(r'[^0-9a-zA-Z]', ' ', text)
  # remove extra spaces
  text = re.sub(r'\s+', ' ', text)
  # remove stopwords
  text = " ".join(word for word in text.split() if word not in STOPWORDS)
  return text

Now let us clean the text messages

# clean the messages
 df['clean_text'] = df['messages'].apply(clean_text)
 df.head()

Let us split the data for training

X = df['clean_text']
 y = df['label']

from sklearn.pipeline import Pipeline
 from sklearn.model_selection import train_test_split, cross_val_score
 from sklearn.metrics import classification_report
 from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer
 
 def classify(model, X, y):
  # train test split
  x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, shuffle=True, stratify=y)
  # model training
  pipeline_model = Pipeline([('vect', CountVectorizer()),
  ('tfidf',TfidfTransformer()),
  ('clf', model)])
  pipeline_model.fit(x_train, y_train)
 
  print('Accuracy:', pipeline_model.score(x_test, y_test)*100)
 
 # cv_score = cross_val_score(model, X, y, cv=5)
 # print("CV Score:", np.mean(cv_score)*100)
  y_pred = pipeline_model.predict(x_test)
  print(classification_report(y_test, y_pred))

from sklearn.linear_model import LogisticRegression
 model = LogisticRegression()
 classify(model, X, y)

from sklearn.naive_bayes import MultinomialNB
 model = MultinomialNB()
 classify(model, X, y)

from sklearn.svm import SVC
 model = SVC(C=3)
 classify(model, X, y)

from sklearn.ensemble import RandomForestClassifier
 model = RandomForestClassifier()
 classify(model, X, y)

In this project tutorial, we have explored the SMS Spam Detection Analysis dataset as a classification machine learning project in NLP. The data has been preprocessed with custom cleaning functions and processed using pipelines.

 Get the project notebook from here
 
 Thanks for reading the article!!!
 
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