$$ SMS Spam Classification: Detecting Unwanted Messages $$

Life Cycle of the Project

Steps to be Performed

Introduction

Problem Statement $$

Data Checks to Perform $$

Data Cleaning $$

EDA $$

Text Preprocessing $$

Model Training $$

Evaluation $$

Conclusion $$

Author Message $$

$1.$ Introduction

This Kaggle notebook presents a step$-$by$-$step guide to building an efficient SMS spam classification model using the SMS Spam Collection dataset. By the end of this notebook, you'll have a powerful tool to help you filter out unwanted messages and ensure that your text messaging experience is smoother and safer.

$2.$ Problem Statement

The primary goal of this notebook is to develop a predictive model that accurately classifies incoming SMS messages as either ham or spam. We will use the SMS Spam Collection dataset, which consists of $5,574$ SMS messages tagged with their respective labels.

$3.$ Data Checks to Perform $$

$3\mathrm{.}1$ Import Necessary Libraries

# Importing necessary libraries

import numpy as np # For numerical operations

import pandas as pd # For data manipulation and analysis

import matplotlib.pyplot as plt # For data visualization

$\%$matplotlib inline

# Importing WordCloud for text visualization

from wordcloud import WordCloud

# Importing NLTK for natural language processing

import nltk

from nltk$.$corpus import stopwords # For stopwords

# Downloading NLTK data

nltk$.$download$(\text{'}$stopwords$\text{'})$ # Downloading stopwords data

nltk$.$download$(\text{'}$punkt$\text{'})$ # Downloading tokenizer data

$/$opt$/$conda$/$lib$/$python$3\mathrm{.}10/$site$-$packages$/$scipy$/\_\_$init$\_\_.$py:$146$: UserWarning: A NumPy version $>=1\mathrm{.}16\mathrm{.}5$ and $<1\mathrm{.}23\mathrm{.}0$ is required for this version of SciPy $($detected version $1\mathrm{.}23\mathrm{.}5$

warnings.warn$($f$"$A NumPy version $>=\{$np$\_$minversion$\}$ and $<\{$np$\_$maxversion$\}"$

$[$nltk$\_$data$]$ Downloading package stopwords to $/$usr$/$share$/$nltk$\_$data...

$[$nltk$\_$data$]$ Package stopwords is already up$-$to$-$date!

$[$nltk$\_$data$]$ Downloading package punkt to $/$usr$/$share$/$nltk$\_$data...

$[$nltk$\_$data$]$ Package punkt is already up$-$to$-$date!

True

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$3\mathrm{.}2$ Load the Data

df $=$ pd$.$read$\_$csv$(\text{'}/$kaggle$/$input$/$sms$-$spam$-$collection$-$dataset$/$spam$.$csv$\text{'},$ encoding$=$'latin$1\text{'})$

styled$\_$df $=$ df$.$head$()$

styled$\_$df $=$ styled$\_$df$.$style.set$\_$table$\_$styles$([$

$\{"$selector$"$: $"$th$",$ "props": $[("$color$",$ 'black'$),("$background$-$color", $"$#FF$00$CC$")]\}$

$])$

styled$\_$df

v$1$ v$2$ Unnamed: $2$ Unnamed: $3$ Unnamed: $4$

$0$ ham Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat... nan nan nan

$1$ ham Ok lar... Joking wif u oni... nan nan nan

$2$ spam Free entry in $2$ a wkly comp to win FA Cup final tkts $21$st May $2005.$ Text FA to $87121$ to receive entry question$($std txt rate$)$T&C$\text{'}$s apply $08452810075$over$18\text{'}$s nan nan nan

$3$ ham U dun say so early hor... U c already then say... nan nan nan

$4$ ham Nah I don't think he goes to usf, he lives around here though nan nan nan

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$4.$ Data Cleaning

$4\mathrm{.}1|$ Data Info

df$.$info$()$

RangeIndex: $5572$ entries, $0$ to $5571$

Data columns $($total $5$ columns$)$:

# Column Non$-$Null Count Dtype

$----------------------------$

$0$ v$15572$ non$-$null object

$1$ v$25572$ non$-$null object

$2$ Unnamed: $250$ non$-$null object

$3$ Unnamed: $312$ non$-$null object

$4$ Unnamed: $46$ non$-$null object

dtypes: object$(5)$

memory usage: $217\mathrm{.}8+$ KB

$4\mathrm{.}2|$ Drop the Columns

df$.$drop$($columns $=[\text{'}$Unnamed: $2\text{'},$ 'Unnamed: $3\text{'},$ 'Unnamed: $4\text{'}],$ inplace $=$ True$)$

styled$\_$df $=$ df$.$head$(5).$style

# Modify the color and background color of the table headers $($th$)$

styled$\_$df$.$set$\_$table$\_$styles$([$

$\{"$selector$"$: $"$th$",$ "props": $[("$color$",$ 'Black'$),("$background$-$color", $"$#FF$00$CC$"),(\text{'}$font$-$weight', 'bold'$)]\}$

$])$

v$1$ v$2$

$0$ ham Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...

$1$ ham Ok lar... Joking wif u oni...

$2$ spam Free entry in $2$ a wkly comp to win FA Cup final tkts $21$st May $2005.$ Text FA to $87121$ to receive entry question$($std txt rate$)$T&C$\text{'}$s apply $08452810075$over$18\text{'}$s

$3$ ham U dun say so early hor... U c already then say...

$4$ ham Nah I don't think he goes to usf, he lives around here though

$4\mathrm{.}3|$ Rename the Column

# Rename the columns name

df$.$rename$($columns $=\{\text{'}$v$1\text{'}$: 'target', $\text{'}$v$2\text{'}$: 'text'$\},$ inplace $=$ True$)$

$4\mathrm{.}4|$ Convert the target variable

from sklearn.preprocessing import LabelEncoder

encoder $=$ LabelEncoder$()$

df$[\text{'}$target$\text{'}]=$ encoder.fit$\_$transform$($df$[\text{'}$target$\text{'}])$

styled$\_$df $=$ df$.$head$().$style

# Modify the color and background color of the table headers $($th$)$

styled$\_$df$.$set$\_$table$\_$styles$([$

$\{"$selector$"$: $"$th$",$ "props": $[("$color$",$ 'Black'$),("$background$-$color", $"$#FF$00$CC$"),(\text{'}$font$-$weight', 'bold'$)]\}$

$])$

target text

$00$ Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...

$10$ Ok lar... Joking wif u oni...

$21$ Free entry in $2$ a wkly comp to win FA Cup final t

Can you explain this code