SMS_Spam_Collection.ipynb

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  "metadata": {
    "colab": {
      "name": "SMS_Spam_Collection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOeJDNkRFuCwaAl+sg9qqGI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/memetics19/sms_spam_collection/blob/master/SMS_Spam_Collection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-pLpC9gQvSO",
        "colab_type": "text"
      },
      "source": [
        "# Importing all the libraries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5cTFB4zxyyT",
        "colab_type": "code",
        "outputId": "1ca620b7-fcdf-47ad-8077-6b4495fba33a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import string \n",
        "import re\n",
        "import nltk \n",
        "from matplotlib import pyplot \n",
        "from nltk.stem import PorterStemmer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import HashingVectorizer\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "%matplotlib inline"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkKjW0ZcePOs",
        "colab_type": "code",
        "outputId": "8579fedf-85e6-4cd7-a222-75027bea44f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "doc = open(\"SMSSpamCollection\").read()\n",
        "doc[0:500]"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"ham\\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\nham\\tOk lar... Joking wif u oni...\\nspam\\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\\nham\\tU dun say so early hor... U c already then say...\\nham\\tNah I don't think he goes to usf, he lives around here though\\nspam\\tFreeMsg Hey there darling it's been 3 week's now and no word bac\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKarzIXFpxt_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "parsed_data=doc.replace('\\t','\\n').split('\\n')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "duMABu5EqS-y",
        "colab_type": "code",
        "outputId": "37be6919-5e60-42aa-b425-9e237f159ae1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "label_list=parsed_data[0::2]\n",
        "msg_list=parsed_data[1::2]\n",
        "print(label_list[0:5])\n",
        "print(msg_list[0:5])"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ham', 'ham', 'spam', 'ham', 'ham']\n",
            "['Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...', 'Ok lar... Joking wif u oni...', \"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\", 'U dun say so early hor... U c already then say...', \"Nah I don't think he goes to usf, he lives around here though\"]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4AmRp8dzLSQ",
        "colab_type": "text"
      },
      "source": [
        "# Read the Dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Md1-5Eyudca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.DataFrame({\n",
        "    'label':label_list[:-1],\n",
        "    'sms':msg_list\n",
        "    \n",
        "})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iivTodYuv49",
        "colab_type": "code",
        "outputId": "c8284655-97aa-433d-d13d-ee5ca9893583",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label                                                sms\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5Uvii3Av2Fp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=pd.read_csv(\"SMSSpamCollection\",sep=\"\\t\",header=None)\n",
        "data.columns=['label','sms']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZCaj7dc3qA3",
        "colab_type": "code",
        "outputId": "1a77f27e-68d2-4dbf-9622-14bc4bc6e17e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label                                                sms\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWjw2JlOzXdp",
        "colab_type": "text"
      },
      "source": [
        "# Shape of the Datasets \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpLpeKUhx3II",
        "colab_type": "code",
        "outputId": "4cff30bf-d72c-408c-98de-a9a4caab8f26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(f\"input data as {len(data)} rows and {len(data.columns)} columns\")"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input data as 5572 rows and 2 columns\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jYxvNvszvTn",
        "colab_type": "text"
      },
      "source": [
        "# Length of the ham and spam "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8QD0O9ozuXA",
        "colab_type": "code",
        "outputId": "9c32e439-5bb8-4f2f-cffc-0ac3b4c6a90f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(f'ham= {len(data[data[\"label\"]==\"ham\"])}')"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ham= 4825\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4e7IrVK3yCJ",
        "colab_type": "code",
        "outputId": "667e9803-027b-4bfb-c1c7-7cf1d69ad88f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(f'spam= {len(data[data[\"label\"]==\"spam\"])}')"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "spam= 747\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vL_TKiKl4H8q",
        "colab_type": "text"
      },
      "source": [
        "Let's check for missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdExRnQd33j7",
        "colab_type": "code",
        "outputId": "2fa3d11b-5a21-4acb-de3e-bdb4888e2833",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(f\"number of missing label = {data['label'].isnull().sum()}\")\n",
        "print(f\"number of missing messages  = {data['sms'].isnull().sum()}\")"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of missing label = 0\n",
            "number of missing messages  = 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NcgC4DpCG_VW",
        "colab_type": "text"
      },
      "source": [
        "# Removing the Punctuation \n",
        "Punctuation is present in the string library. Let's see the punctuations present in it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08d0accMHHsZ",
        "colab_type": "code",
        "outputId": "4fdbfb9c-c1ec-4fd7-dab5-d2b714ab1f94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "string.punctuation"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OpvgL3J9KGxs",
        "colab_type": "text"
      },
      "source": [
        "using list comprehension feature removing the punctuation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLlSbcMRJ4Rf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def rem_punctate(txt):\n",
        "  txt_nopunt=\"\".join([ c for c in txt if c not in string.punctuation])\n",
        "  return txt_nopunt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwjKggbeKr67",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['sms_clean']=data['sms'].apply(lambda x: rem_punctate(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjC7QXrxLgoL",
        "colab_type": "code",
        "outputId": "de1724ca-ba0a-4613-a966-cf4dc190bdae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "      <th>sms_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>Go until jurong point crazy Available only in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>U dun say so early hor U c already then say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label  ...                                          sms_clean\n",
              "0   ham  ...  Go until jurong point crazy Available only in ...\n",
              "1   ham  ...                            Ok lar Joking wif u oni\n",
              "2  spam  ...  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  ...        U dun say so early hor U c already then say\n",
              "4   ham  ...  Nah I dont think he goes to usf he lives aroun...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5v7uAHBNGZP",
        "colab_type": "text"
      },
      "source": [
        "# Tokenization \n",
        "splitting text into list of words \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wR8UAlNjNE2Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tokenize(txt):\n",
        "  tokens=re.split(\"\\W+\",txt)# \"W+\" is represented for non words and \"+\" represents one or more words\n",
        "  return tokens"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cjq7Gm0NOe5k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data[\"sms_clean_tokenized\"]=data[\"sms_clean\"].apply(lambda x:tokenize(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fy2YemZfO0i-",
        "colab_type": "code",
        "outputId": "32662433-d67f-4387-fcea-d76645847b70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "      <th>sms_clean</th>\n",
              "      <th>sms_clean_tokenized</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>Go until jurong point crazy Available only in ...</td>\n",
              "      <td>[Go, until, jurong, point, crazy, Available, o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>[Free, entry, in, 2, a, wkly, comp, to, win, F...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>U dun say so early hor U c already then say</td>\n",
              "      <td>[U, dun, say, so, early, hor, U, c, already, t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
              "      <td>[Nah, I, dont, think, he, goes, to, usf, he, l...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label  ...                                sms_clean_tokenized\n",
              "0   ham  ...  [Go, until, jurong, point, crazy, Available, o...\n",
              "1   ham  ...                     [Ok, lar, Joking, wif, u, oni]\n",
              "2  spam  ...  [Free, entry, in, 2, a, wkly, comp, to, win, F...\n",
              "3   ham  ...  [U, dun, say, so, early, hor, U, c, already, t...\n",
              "4   ham  ...  [Nah, I, dont, think, he, goes, to, usf, he, l...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ytxb3RWeP9dc",
        "colab_type": "text"
      },
      "source": [
        "# Removing the stop words\n",
        "\n",
        "stop words are the words are \"is\",\"the\",\"am\".. etc...\n",
        "\n",
        "It should be removed because it will make the data less which is feasible for machine learning model \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4OfYX5YP8d0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stop_words=nltk.corpus.stopwords.words('english')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMgCZ1pxR7SH",
        "colab_type": "text"
      },
      "source": [
        "let's see 10 stop words "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJmft7icRk0S",
        "colab_type": "code",
        "outputId": "c49517fc-fe7d-4906-9568-34fd76b89850",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "stop_words[0:10]"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkhadSP1SFKz",
        "colab_type": "text"
      },
      "source": [
        "let's see all the stopwords "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdOHsFomSERD",
        "colab_type": "code",
        "outputId": "b4c19cb3-8aff-48ff-9d6a-ea045feb5c69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "stop_words"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i',\n",
              " 'me',\n",
              " 'my',\n",
              " 'myself',\n",
              " 'we',\n",
              " 'our',\n",
              " 'ours',\n",
              " 'ourselves',\n",
              " 'you',\n",
              " \"you're\",\n",
              " \"you've\",\n",
              " \"you'll\",\n",
              " \"you'd\",\n",
              " 'your',\n",
              " 'yours',\n",
              " 'yourself',\n",
              " 'yourselves',\n",
              " 'he',\n",
              " 'him',\n",
              " 'his',\n",
              " 'himself',\n",
              " 'she',\n",
              " \"she's\",\n",
              " 'her',\n",
              " 'hers',\n",
              " 'herself',\n",
              " 'it',\n",
              " \"it's\",\n",
              " 'its',\n",
              " 'itself',\n",
              " 'they',\n",
              " 'them',\n",
              " 'their',\n",
              " 'theirs',\n",
              " 'themselves',\n",
              " 'what',\n",
              " 'which',\n",
              " 'who',\n",
              " 'whom',\n",
              " 'this',\n",
              " 'that',\n",
              " \"that'll\",\n",
              " 'these',\n",
              " 'those',\n",
              " 'am',\n",
              " 'is',\n",
              " 'are',\n",
              " 'was',\n",
              " 'were',\n",
              " 'be',\n",
              " 'been',\n",
              " 'being',\n",
              " 'have',\n",
              " 'has',\n",
              " 'had',\n",
              " 'having',\n",
              " 'do',\n",
              " 'does',\n",
              " 'did',\n",
              " 'doing',\n",
              " 'a',\n",
              " 'an',\n",
              " 'the',\n",
              " 'and',\n",
              " 'but',\n",
              " 'if',\n",
              " 'or',\n",
              " 'because',\n",
              " 'as',\n",
              " 'until',\n",
              " 'while',\n",
              " 'of',\n",
              " 'at',\n",
              " 'by',\n",
              " 'for',\n",
              " 'with',\n",
              " 'about',\n",
              " 'against',\n",
              " 'between',\n",
              " 'into',\n",
              " 'through',\n",
              " 'during',\n",
              " 'before',\n",
              " 'after',\n",
              " 'above',\n",
              " 'below',\n",
              " 'to',\n",
              " 'from',\n",
              " 'up',\n",
              " 'down',\n",
              " 'in',\n",
              " 'out',\n",
              " 'on',\n",
              " 'off',\n",
              " 'over',\n",
              " 'under',\n",
              " 'again',\n",
              " 'further',\n",
              " 'then',\n",
              " 'once',\n",
              " 'here',\n",
              " 'there',\n",
              " 'when',\n",
              " 'where',\n",
              " 'why',\n",
              " 'how',\n",
              " 'all',\n",
              " 'any',\n",
              " 'both',\n",
              " 'each',\n",
              " 'few',\n",
              " 'more',\n",
              " 'most',\n",
              " 'other',\n",
              " 'some',\n",
              " 'such',\n",
              " 'no',\n",
              " 'nor',\n",
              " 'not',\n",
              " 'only',\n",
              " 'own',\n",
              " 'same',\n",
              " 'so',\n",
              " 'than',\n",
              " 'too',\n",
              " 'very',\n",
              " 's',\n",
              " 't',\n",
              " 'can',\n",
              " 'will',\n",
              " 'just',\n",
              " 'don',\n",
              " \"don't\",\n",
              " 'should',\n",
              " \"should've\",\n",
              " 'now',\n",
              " 'd',\n",
              " 'll',\n",
              " 'm',\n",
              " 'o',\n",
              " 're',\n",
              " 've',\n",
              " 'y',\n",
              " 'ain',\n",
              " 'aren',\n",
              " \"aren't\",\n",
              " 'couldn',\n",
              " \"couldn't\",\n",
              " 'didn',\n",
              " \"didn't\",\n",
              " 'doesn',\n",
              " \"doesn't\",\n",
              " 'hadn',\n",
              " \"hadn't\",\n",
              " 'hasn',\n",
              " \"hasn't\",\n",
              " 'haven',\n",
              " \"haven't\",\n",
              " 'isn',\n",
              " \"isn't\",\n",
              " 'ma',\n",
              " 'mightn',\n",
              " \"mightn't\",\n",
              " 'mustn',\n",
              " \"mustn't\",\n",
              " 'needn',\n",
              " \"needn't\",\n",
              " 'shan',\n",
              " \"shan't\",\n",
              " 'shouldn',\n",
              " \"shouldn't\",\n",
              " 'wasn',\n",
              " \"wasn't\",\n",
              " 'weren',\n",
              " \"weren't\",\n",
              " 'won',\n",
              " \"won't\",\n",
              " 'wouldn',\n",
              " \"wouldn't\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huXZ5FnmSO9p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def rem_stopwords(txt_tokenized):\n",
        "  txt_clean=[word for word in txt_tokenized if word not in stop_words]\n",
        "  return txt_clean\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyJ6LLlsWDyS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data[\"sms_no_sw\"]=data[\"sms_clean_tokenized\"].apply(lambda x: rem_stopwords(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa7T15VzaNym",
        "colab_type": "code",
        "outputId": "b7cf4fcc-74c9-4eae-b9db-ddb5be9a8ae4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "      <th>sms_clean</th>\n",
              "      <th>sms_clean_tokenized</th>\n",
              "      <th>sms_no_sw</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>Go until jurong point crazy Available only in ...</td>\n",
              "      <td>[Go, until, jurong, point, crazy, Available, o...</td>\n",
              "      <td>[Go, jurong, point, crazy, Available, bugis, n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>[Free, entry, in, 2, a, wkly, comp, to, win, F...</td>\n",
              "      <td>[Free, entry, 2, wkly, comp, win, FA, Cup, fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>U dun say so early hor U c already then say</td>\n",
              "      <td>[U, dun, say, so, early, hor, U, c, already, t...</td>\n",
              "      <td>[U, dun, say, early, hor, U, c, already, say]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
              "      <td>[Nah, I, dont, think, he, goes, to, usf, he, l...</td>\n",
              "      <td>[Nah, I, dont, think, goes, usf, lives, around...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label  ...                                          sms_no_sw\n",
              "0   ham  ...  [Go, jurong, point, crazy, Available, bugis, n...\n",
              "1   ham  ...                     [Ok, lar, Joking, wif, u, oni]\n",
              "2  spam  ...  [Free, entry, 2, wkly, comp, win, FA, Cup, fin...\n",
              "3   ham  ...      [U, dun, say, early, hor, U, c, already, say]\n",
              "4   ham  ...  [Nah, I, dont, think, goes, usf, lives, around...\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSWsEWaoh4Rx",
        "colab_type": "text"
      },
      "source": [
        "#Stemming\n",
        "processing into root word by using porter stemmization algorithm "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoQq4D4Watp4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ps=PorterStemmer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFCGhx9Bns5Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def stemming(tokenized_text):\n",
        "  text=[ps.stem(word) for word in tokenized_text]\n",
        "  return text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jrjlYT5sOx5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data[\"stemmed_data\"]=data[\"sms_no_sw\"].apply(lambda x: stemming(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8Q9p6qqsmgK",
        "colab_type": "code",
        "outputId": "6006d6e6-875b-4e46-b78c-54fc6ff60fbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "      <th>sms_clean</th>\n",
              "      <th>sms_clean_tokenized</th>\n",
              "      <th>sms_no_sw</th>\n",
              "      <th>stemmed_data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>Go until jurong point crazy Available only in ...</td>\n",
              "      <td>[Go, until, jurong, point, crazy, Available, o...</td>\n",
              "      <td>[Go, jurong, point, crazy, Available, bugis, n...</td>\n",
              "      <td>[Go, jurong, point, crazi, avail, bugi, n, gre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, joke, wif, u, oni]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>[Free, entry, in, 2, a, wkly, comp, to, win, F...</td>\n",
              "      <td>[Free, entry, 2, wkly, comp, win, FA, Cup, fin...</td>\n",
              "      <td>[free, entri, 2, wkli, comp, win, FA, cup, fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>U dun say so early hor U c already then say</td>\n",
              "      <td>[U, dun, say, so, early, hor, U, c, already, t...</td>\n",
              "      <td>[U, dun, say, early, hor, U, c, already, say]</td>\n",
              "      <td>[U, dun, say, earli, hor, U, c, alreadi, say]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
              "      <td>[Nah, I, dont, think, he, goes, to, usf, he, l...</td>\n",
              "      <td>[Nah, I, dont, think, goes, usf, lives, around...</td>\n",
              "      <td>[nah, I, dont, think, goe, usf, live, around, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label  ...                                       stemmed_data\n",
              "0   ham  ...  [Go, jurong, point, crazi, avail, bugi, n, gre...\n",
              "1   ham  ...                       [Ok, lar, joke, wif, u, oni]\n",
              "2  spam  ...  [free, entri, 2, wkli, comp, win, FA, cup, fin...\n",
              "3   ham  ...      [U, dun, say, earli, hor, U, c, alreadi, say]\n",
              "4   ham  ...  [nah, I, dont, think, goe, usf, live, around, ...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KfOnnNmCk2aC",
        "colab_type": "text"
      },
      "source": [
        "# Lemmatization\n",
        " using WordNet Lemmatization algorithm \n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Vu8b8L9k05v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wn=nltk.WordNetLemmatizer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0r53Ue45sLsT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def lemmatization(token_txt):\n",
        "  text=[wn.lemmatize(word) for word in token_txt]\n",
        "  return text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vO71Q91puXwd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['sms_tokenized']=data['sms_no_sw'].apply(lambda x:lemmatization(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8KOwyA7unzx",
        "colab_type": "code",
        "outputId": "3e34018d-220f-4445-8e13-754f93addb96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "      <th>sms_clean</th>\n",
              "      <th>sms_clean_tokenized</th>\n",
              "      <th>sms_no_sw</th>\n",
              "      <th>stemmed_data</th>\n",
              "      <th>sms_tokenized</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>Go until jurong point crazy Available only in ...</td>\n",
              "      <td>[Go, until, jurong, point, crazy, Available, o...</td>\n",
              "      <td>[Go, jurong, point, crazy, Available, bugis, n...</td>\n",
              "      <td>[Go, jurong, point, crazi, avail, bugi, n, gre...</td>\n",
              "      <td>[Go, jurong, point, crazy, Available, bugis, n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, joke, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>[Free, entry, in, 2, a, wkly, comp, to, win, F...</td>\n",
              "      <td>[Free, entry, 2, wkly, comp, win, FA, Cup, fin...</td>\n",
              "      <td>[free, entri, 2, wkli, comp, win, FA, cup, fin...</td>\n",
              "      <td>[Free, entry, 2, wkly, comp, win, FA, Cup, fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>U dun say so early hor U c already then say</td>\n",
              "      <td>[U, dun, say, so, early, hor, U, c, already, t...</td>\n",
              "      <td>[U, dun, say, early, hor, U, c, already, say]</td>\n",
              "      <td>[U, dun, say, earli, hor, U, c, alreadi, say]</td>\n",
              "      <td>[U, dun, say, early, hor, U, c, already, say]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
              "      <td>[Nah, I, dont, think, he, goes, to, usf, he, l...</td>\n",
              "      <td>[Nah, I, dont, think, goes, usf, lives, around...</td>\n",
              "      <td>[nah, I, dont, think, goe, usf, live, around, ...</td>\n",
              "      <td>[Nah, I, dont, think, go, usf, life, around, t...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label  ...                                      sms_tokenized\n",
              "0   ham  ...  [Go, jurong, point, crazy, Available, bugis, n...\n",
              "1   ham  ...                     [Ok, lar, Joking, wif, u, oni]\n",
              "2  spam  ...  [Free, entry, 2, wkly, comp, win, FA, Cup, fin...\n",
              "3   ham  ...      [U, dun, say, early, hor, U, c, already, say]\n",
              "4   ham  ...  [Nah, I, dont, think, go, usf, life, around, t...\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xdt4_VG7v678",
        "colab_type": "text"
      },
      "source": [
        "#Vectorization\n",
        "using CountVectorization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVjhLYvRv6H1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv=CountVectorizer(analyzer=lemmatization)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLUMzMriEDOz",
        "colab_type": "code",
        "outputId": "99db5f7b-c36f-4cc7-8365-f30c6efeab85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X = cv.fit_transform(data[\"sms_tokenized\"])\n",
        "print(X.shape)"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5572, 11043)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwkCFVj_KCuu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_sample=data[0:100]\n",
        "cv1=CountVectorizer(analyzer=lemmatization)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lu-GWLHpKPmh",
        "colab_type": "code",
        "outputId": "eeed3edc-32ae-4120-80f4-2be89e47d36f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X=cv1.fit_transform(data_sample[\"sms_tokenized\"])\n",
        "print(X.shape)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 745)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGQ5QeJVKwsU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.DataFrame(X.toarray(), columns=cv1.get_feature_names())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqLXLhuFK_Jw",
        "colab_type": "code",
        "outputId": "d083ede5-390f-4ae4-8e17-470851a8baf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>valued</th>\n",
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              "      <th>want</th>\n",
              "      <th>wanted</th>\n",
              "      <th>wat</th>\n",
              "      <th>watching</th>\n",
              "      <th>watt</th>\n",
              "      <th>way</th>\n",
              "      <th>weak</th>\n",
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              "      <th>wet</th>\n",
              "      <th>whole</th>\n",
              "      <th>wif</th>\n",
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              "      <th>wkly</th>\n",
              "      <th>wonderful</th>\n",
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              "      <th>year</th>\n",
              "      <th>yesterday</th>\n",
              "      <th>yo</th>\n",
              "      <th>youd</th>\n",
              "      <th>youhow</th>\n",
              "      <th>ü</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 745 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      07732584351  0800  08000930705  ...  yo  youd  youhow  ü\n",
              "0  0            0     0            0  ...   0     0       0  0\n",
              "1  0            0     0            0  ...   0     0       0  0\n",
              "2  0            0     0            0  ...   0     0       0  0\n",
              "3  0            0     0            0  ...   0     0       0  0\n",
              "4  0            0     0            0  ...   0     0       0  0\n",
              "\n",
              "[5 rows x 745 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyVClKGALwVB",
        "colab_type": "text"
      },
      "source": [
        "using  TF-IDF  Vectorization\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xpn8_KFtzH9_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "9daf3239-2ef5-4ca8-a9ff-46613818b804"
      },
      "source": [
        "tf = TfidfVectorizer(analyzer = lemmatization)"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-210-e9aaaa890ce2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTfidfVectorizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manalyzer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlemmatization\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_features'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AK_I8jj90lUw",
        "colab_type": "code",
        "outputId": "fbff7e4e-9bdb-4fe3-c335-30c36a69193a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data_sample=data[0:10]\n",
        "X=tf.fit_transform(data_sample[\"sms_tokenized\"])\n",
        "print(X.shape)"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10, 136)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfvITc921YLB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.DataFrame(X.toarray(), columns=tf.get_feature_names())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5A1jv2T1cEz",
        "colab_type": "code",
        "outputId": "4978780d-9872-4534-fa4f-e4f9e5f903b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.00</td>\n",
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              "    <tr>\n",
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              "      <td>0.193446</td>\n",
              "      <td>0.193446</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.193446</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.386892</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 136 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   08002986030  08452810075over18s  09061701461  ...      wkly  word  world\n",
              "0          0.0            0.000000          0.0  ...  0.000000   0.0   0.25\n",
              "1          0.0            0.000000          0.0  ...  0.000000   0.0   0.00\n",
              "2          0.0            0.193446          0.0  ...  0.193446   0.0   0.00\n",
              "3          0.0            0.000000          0.0  ...  0.000000   0.0   0.00\n",
              "4          0.0            0.000000          0.0  ...  0.000000   0.0   0.00\n",
              "\n",
              "[5 rows x 136 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhnSAUCICxD5",
        "colab_type": "text"
      },
      "source": [
        "Using Hashing Vectorizer \n",
        "\n",
        "\n",
        "\n",
        "> Didn't Observe a good result \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDwEnSh3_Fdr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hv = HashingVectorizer(analyzer = lemmatization, n_features=2**4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xCwpa5vKAI2I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c30c0e62-9db4-4b00-cb46-e67df04421b8"
      },
      "source": [
        "X = hv.fit_transform(data[\"sms_tokenized\"])\n",
        "print(X.shape)"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5572, 16)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mWIcmMYAQ0f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_sample=data[0:100]\n",
        "hv1=HashingVectorizer(analyzer=lemmatization)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHrBqSesAZxg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "40272703-1bec-4ba0-dc14-dd19fa8a87a7"
      },
      "source": [
        "X=hv1.fit_transform(data_sample[\"sms_tokenized\"])\n",
        "print(X.shape)"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 1048576)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n87izI1uAc8n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.DataFrame(X.toarray())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7u9hMdrAlmX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "outputId": "ceedfaa4-8165-40b7-da94-06dc48455cce"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "    <tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1048576 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   0        1        2        3        ...  1048572  1048573  1048574  1048575\n",
              "0      0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1      0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "2      0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "3      0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "4      0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "\n",
              "[5 rows x 1048576 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITD1E-uEdj5K",
        "colab_type": "text"
      },
      "source": [
        "# Feature Engineering \n",
        "\n",
        "Feature Creation:  Message Length "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3V1yJoYhhqv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "53fde673-9bf0-43bc-8ee5-610410ad5485"
      },
      "source": [
        "data['sms_length'] = data['sms'].apply(lambda x:len(x))\n",
        "data.head()"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "      <th>sms_clean</th>\n",
              "      <th>sms_clean_tokenized</th>\n",
              "      <th>sms_no_sw</th>\n",
              "      <th>stemmed_data</th>\n",
              "      <th>sms_tokenized</th>\n",
              "      <th>sms_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>Go until jurong point crazy Available only in ...</td>\n",
              "      <td>[Go, until, jurong, point, crazy, Available, o...</td>\n",
              "      <td>[Go, jurong, point, crazy, Available, bugis, n...</td>\n",
              "      <td>[Go, jurong, point, crazi, avail, bugi, n, gre...</td>\n",
              "      <td>[Go, jurong, point, crazy, Available, bugis, n...</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, joke, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>[Free, entry, in, 2, a, wkly, comp, to, win, F...</td>\n",
              "      <td>[Free, entry, 2, wkly, comp, win, FA, Cup, fin...</td>\n",
              "      <td>[free, entri, 2, wkli, comp, win, FA, cup, fin...</td>\n",
              "      <td>[Free, entry, 2, wkly, comp, win, FA, Cup, fin...</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>U dun say so early hor U c already then say</td>\n",
              "      <td>[U, dun, say, so, early, hor, U, c, already, t...</td>\n",
              "      <td>[U, dun, say, early, hor, U, c, already, say]</td>\n",
              "      <td>[U, dun, say, earli, hor, U, c, alreadi, say]</td>\n",
              "      <td>[U, dun, say, early, hor, U, c, already, say]</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
              "      <td>[Nah, I, dont, think, he, goes, to, usf, he, l...</td>\n",
              "      <td>[Nah, I, dont, think, goes, usf, lives, around...</td>\n",
              "      <td>[nah, I, dont, think, goe, usf, live, around, ...</td>\n",
              "      <td>[Nah, I, dont, think, go, usf, life, around, t...</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label  ... sms_length\n",
              "0   ham  ...        111\n",
              "1   ham  ...         29\n",
              "2  spam  ...        155\n",
              "3   ham  ...         49\n",
              "4   ham  ...         61\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VG_h9lqgjZI0",
        "colab_type": "text"
      },
      "source": [
        "Punctuation Usage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35g_TG0bdiYc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def punctuation_count(txt):\n",
        "  count = sum([1 for c in txt if c in string.punctuation])\n",
        "  return 100*count/len(txt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEkWInCrjghA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "5524067b-24fe-4291-bfc5-fd7f625e9a5f"
      },
      "source": [
        "data['punctuation_usage'] = data['sms'].apply(lambda x: punctuation_count(x))\n",
        "data.head()"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sms</th>\n",
              "      <th>sms_clean</th>\n",
              "      <th>sms_clean_tokenized</th>\n",
              "      <th>sms_no_sw</th>\n",
              "      <th>stemmed_data</th>\n",
              "      <th>sms_tokenized</th>\n",
              "      <th>sms_length</th>\n",
              "      <th>punctuation_usage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>Go until jurong point crazy Available only in ...</td>\n",
              "      <td>[Go, until, jurong, point, crazy, Available, o...</td>\n",
              "      <td>[Go, jurong, point, crazy, Available, bugis, n...</td>\n",
              "      <td>[Go, jurong, point, crazi, avail, bugi, n, gre...</td>\n",
              "      <td>[Go, jurong, point, crazy, Available, bugis, n...</td>\n",
              "      <td>111</td>\n",
              "      <td>8.108108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, joke, wif, u, oni]</td>\n",
              "      <td>[Ok, lar, Joking, wif, u, oni]</td>\n",
              "      <td>29</td>\n",
              "      <td>20.689655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>[Free, entry, in, 2, a, wkly, comp, to, win, F...</td>\n",
              "      <td>[Free, entry, 2, wkly, comp, win, FA, Cup, fin...</td>\n",
              "      <td>[free, entri, 2, wkli, comp, win, FA, cup, fin...</td>\n",
              "      <td>[Free, entry, 2, wkly, comp, win, FA, Cup, fin...</td>\n",
              "      <td>155</td>\n",
              "      <td>3.870968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>U dun say so early hor U c already then say</td>\n",
              "      <td>[U, dun, say, so, early, hor, U, c, already, t...</td>\n",
              "      <td>[U, dun, say, early, hor, U, c, already, say]</td>\n",
              "      <td>[U, dun, say, earli, hor, U, c, alreadi, say]</td>\n",
              "      <td>[U, dun, say, early, hor, U, c, already, say]</td>\n",
              "      <td>49</td>\n",
              "      <td>12.244898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
              "      <td>[Nah, I, dont, think, he, goes, to, usf, he, l...</td>\n",
              "      <td>[Nah, I, dont, think, goes, usf, lives, around...</td>\n",
              "      <td>[nah, I, dont, think, goe, usf, live, around, ...</td>\n",
              "      <td>[Nah, I, dont, think, go, usf, life, around, t...</td>\n",
              "      <td>61</td>\n",
              "      <td>3.278689</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label  ... punctuation_usage\n",
              "0   ham  ...          8.108108\n",
              "1   ham  ...         20.689655\n",
              "2  spam  ...          3.870968\n",
              "3   ham  ...         12.244898\n",
              "4   ham  ...          3.278689\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGhNUM-GnT3Q",
        "colab_type": "text"
      },
      "source": [
        "# Feature Evaluation\n",
        "\n",
        "Plotting the Sms length of spam \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZAj-VZcoZjS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "144e2550-afe8-45b5-b65f-6dc5edf16616"
      },
      "source": [
        "bin = np.linspace(0,250,50)\n",
        "pyplot.hist(data[data['label'] =='spam']['sms_length'],bin,label = 'spam',density = True)\n",
        "pyplot.hist(data[data['label'] =='ham']['sms_length'],bin,label = 'ham',density = True)\n",
        "pyplot.legend(loc = 'upper right')\n",
        "pyplot.show()"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcB3o_FJq83Z",
        "colab_type": "text"
      },
      "source": [
        "Finding the correlation using heatmap "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MemozTbmDMu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "b1a7fd70-b4c4-49da-a4f9-fdf45bedf26d"
      },
      "source": [
        "corr = data.corr()\n",
        "sns.heatmap(corr)"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb243c3080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrWEi0nQrG4s",
        "colab_type": "text"
      },
      "source": [
        "Evaulate Punctuation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NP_qlF0KrDvo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "1a869287-2c89-4500-c2ec-83f4f1abc19f"
      },
      "source": [
        "bin = np.linspace(0,40,50)\n",
        "pyplot.hist(data[data['label'] =='spam']['punctuation_usage'],bin,label = 'spam',density = True)\n",
        "pyplot.hist(data[data['label'] =='ham']['punctuation_usage'],bin,label = 'ham', density = True)\n",
        "pyplot.legend(loc = 'upper right')\n",
        "pyplot.show()"
      ],
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsIgZjjIt3Kx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "bfe01685-ae4b-40db-cf3d-b1f6a7de6074"
      },
      "source": [
        "count_Class = pd.value_counts(data[\"label\"], sort = True)   \n",
        "\n",
        "labels = 'Ham', 'Spam'\n",
        "sizes = [count_Class[0], count_Class[1]]\n",
        "colors = ['red', 'yellow']\n",
        "explode = (0.1, 0.1)\n",
        "\n",
        "\n",
        "pyplot.pie(sizes, explode = explode, labels = labels, colors = colors,\n",
        "        autopct = '%1.1f%%', shadow = True, startangle = 90)\n",
        "pyplot.axis('equal')\n",
        "pyplot.show()"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awr4PEJ-FtHU",
        "colab_type": "text"
      },
      "source": [
        "# Power Transformation \n",
        "\n",
        "Message length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LvRGS376FLS6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "ddefcbec-be77-4271-e84c-80b9a4e92c2a"
      },
      "source": [
        "bin = np.linspace(0,250,50)\n",
        "pyplot.hist(data['sms_length'],bin,density = True)\n",
        "pyplot.title('Sms Length')\n",
        "pyplot.show() # Bi modal graph "
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2PURjMvcOSSPAkzRCgqreXTS+3MeAa2yfqHb934BPSVoEHAT+wzT3LSIiTqLWrSBs7wR2tpRd17T8HHBFh21vAG5oU/4gMDCRxkZ3TXTUkF+WEXNbrgSOiChUAiAiolAJgIiIQiUAIiIKlQCIiChUAiAiolAJgIiIQiUAIiIKlQCIiChUHgofsy73pYmYGzICiIgoVAIgIqJQCYCIiEJlDiBiDstzG2ImZQQQEVGojABixuTXa8TclhFAREShEgAREYVKAEREFKpWAEhaK+mApBFJm9qsXyzpzmr9bkn9TeuurcoPSLq4ZbsFkr4m6c+n2pGIiJiYcQNA0gJgC3AJsBq4StLqlmobgKdsnwXcBGyutl0NDALnAGuBW6r9veDdwP6pdiIiIiauzllAa4AR2wcBJG0H1gH7muqsAz5YLe8Abpakqny77eeBRySNVPv7iqTlwNuBG4Dfnoa+RMxpuQdSzDV1DgEtAw43vT5SlbWtY3sMOAYsHWfbjwD/A/jxyd5c0tWShiUNj46O1mhuRETU0ZVJYEm/DDxm+/7x6treanvA9kBfX98stC4iogx1AuAosKLp9fKqrG0dSQuBJcATJ9n2F4F/I+kQsB3415L+eBLtj4iISaoTAHuAVZJWSlpEY1J3qKXOELC+Wr4c2GXbVflgdZbQSmAVcJ/ta20vt91f7W+X7XdOQ38iIqKmcSeBbY9J2gjcDSwAttneK+l6YNj2EHArcEc1yfskjS91qnp30ZgwHgOusX1ihvoSPaDTRGmvT5LmthnRDbXuBWR7J7Czpey6puXngCs6bHsDjTN9Ou37y8CX67QjIiKmT24GF/NCqSODiJmUW0FERBQqARARUagcAooYRw4/Ra/KCCAiolAZAURPyq/2iPElACImKSET810CIOa1XEAVMXmZA4iIKFQCICKiUDkEFEWZjYey5LBUzBcZAUREFCoBEBFRqBwCiqjk0E2UJiOAiIhCJQAiIgqVAIiIKFQCICKiUAmAiIhC1QoASWslHZA0ImlTm/WLJd1Zrd8tqb9p3bVV+QFJF1dlKyTdI2mfpL2S3j1dHYqIiHrGPQ1U0gJgC3AhcATYI2nI9r6mahuAp2yfJWkQ2AxcKWk1MAicA5wJfEnS2cAY8F7bD0h6JXC/pC+27HPOyumCEdEL6owA1gAjtg/aPg5sB9a11FkH3F4t7wAukKSqfLvt520/AowAa2w/avsBANvfB/YDy6benYiIqKtOACwDDje9PsJPflm/WMf2GHAMWFpn2+pw0RuA3e3eXNLVkoYlDY+OjtZobkRE1NHVSWBJpwCfAd5j++l2dWxvtT1ge6Cvr292GxgR0cPqBMBRYEXT6+VVWds6khYCS4AnTratpJfR+PL/lO3PTqbxERExeXUCYA+wStJKSYtoTOoOtdQZAtZXy5cDu2y7Kh+szhJaCawC7qvmB24F9tv+8HR0JCIiJmbcs4Bsj0naCNwNLAC22d4r6Xpg2PYQjS/zOySNAE/SCAmqencB+2ic+XON7ROSzgfeBXxd0oPVW/2O7Z3T3cGIiGhPjR/q88PAwICHh4dn5b1yqmeUIg+x732S7rc90FqeK4EjIgqVAIiIKFQCICKiUAmAiIhCJQAiIgqVZwJHFK7TGW85O6j3FR8AOd0zIkpVTADkiz4i4qUyBxARUagEQEREoRIAERGFSgBERBSqmEngiJhZJzvRIqeUzk0JgIhoK9cH9L4cAoqIKFRGABExIbmmpnckACKia3KYqbsSABExbyQwplfmACIiClXrmcCS1gIfpfFQ+E/YvrFl/WLgk8AbgSeAK20fqtZdC2wATgC/ZfvuOvtsZyrPBM5xy4gyTXR0MJ2jjIl+78zUSKbTM4HHPQQkaQGwBbgQOALskTRke19TtQ3AU7bPkjQIbAaulLQaGATOAc4EviTp7Gqb8fYZETFl0/WFPhs/Imf7EFedOYA1wIjtgwCStgPrgOYv63XAB6vlHcDNklSVb7f9PPCIpJFqf9TYZ0TEjMlRgXoBsAw43PT6CPDmTnVsj0k6Biytyr/asu2yanm8fQIg6Wrg6urlM5IO1GhzO2cAj09y2/kqfS5DaX0urb9o85T7/HPtCuf8WUC2twJbp7ofScPtjoH1svS5DKX1ubT+wsz1uc5ZQEeBFU2vl1dlbetIWggsoTEZ3GnbOvuMiIgZVCcA9gCrJK2UtIjGpO5QS50hYH21fDmwy43Ti4aAQUmLJa0EVgH31dxnRETMoHEPAVXH9DcCd9M4ZXOb7b2SrgeGbQ8BtwJ3VJO8T9L4QqeqdxeNyd0x4BrbJwDa7XP6u/cSUz6MNA+lz2Uorc+l9RdmqM+1rgOIiIjekyuBIyIKlQCIiChUzweApLWSDkgakbSp2+2ZKZIOSfq6pAclDVdlPyvpi5K+Vf17erfbORWStkl6TNLDTWVt+6iGj1Wf+0OSzu1eyyevQ58/KOlo9Vk/KOnSpnXXVn0+IOni7rR6aiStkHSPpH2S9kp6d1Xes5/1Sfo8s5+17Z79ozHB/G3g54FFwN8Bq7vdrhnq6yHgjJay3wM2VcubgM3dbucU+/g24Fzg4fH6CFwK/AUg4C3A7m63fxr7/EHgfW3qrq7+H18MrKz+31/Q7T5Mos+vBs6tll8JfLPqW89+1ifp84x+1r0+AnjxNha2jwMv3HKiFOuA26vl24HLutiWKbP9NzTOMmvWqY/rgE+64avAaZJePTstnT4d+tzJi7desf0I0HzrlXnD9qO2H6iWvw/sp3EHgZ79rE/S506m5bPu9QBodxuLk/1Hnc8MfEHS/dXtMwBeZfvRavk7wKu607QZ1amPvf7Zb6wOd2xrOrTXc32W1A+8AdhNIZ91S59hBj/rXg+Akpxv+1zgEuAaSW9rXunGuLGnz/ktoY+VjwOvAV4PPAr8QXebMzMknQJ8BniP7aeb1/XqZ92mzzP6Wfd6ABRzywnbR6t/HwM+R2M4+N0XhsLVv491r4UzplMfe/azt/1d2yds/xj4Q/5x6N8zfZb0MhpfhJ+y/dmquKc/63Z9nunPutcDoIhbTkh6haRXvrAMXAQ8zEtv0bEe+LPutHBGderjEPDr1RkibwGONR0+mNdajm//Ko3PGjrfemVekSQadxfYb/vDTat69rPu1OcZ/6y7Pfs9C7Prl9KYUf828P5ut2eG+vjzNM4I+Dtg7wv9pHFL7r8CvgV8CfjZbrd1iv38NI1h8I9oHPPc0KmPNM4I2VJ97l8HBrrd/mns8x1Vnx6qvghe3VT//VWfDwCXdLv9k+zz+TQO7zwEPFj9XdrLn/VJ+jyjn3VuBRERUahePwQUEREdJAAiIgqVAIiIKFQCICKiUAmAiIhCJQAiIgqVAIiIKNT/B2hfc1z+C4h1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7gwhXv5LF23X",
        "colab_type": "text"
      },
      "source": [
        "Punctuation Usage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3YdJtaEF2C9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "2b82e498-d53a-4ac5-cf5d-96a2eb8cb5fb"
      },
      "source": [
        "bin = np.linspace(0,50,50)\n",
        "pyplot.hist(data['punctuation_usage'],bin,density = True)\n",
        "pyplot.title(\" Punctation Usage \")\n",
        "pyplot.show()# Long Right tail not a complete U "
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PiwI2VJVnJiVpFvX6wlRVbWAw7TJcdu7Q8kPAqbvZ90oGl1hKkuaAt0CQpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxPnhkP7Snu136UBJJe8sRvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuN6BX2S1UnuSLI5ydoJth+a5Opu+81Jlo1sPzrJg0nePj3NliT1NWnQJ5kHXAycCKwATk+yYqTaWcCOqjoGuBC4YGT7+4D/M/XmSpL2Vp8R/Spgc1XdWVUPA1cBa0bqrAEu75avBV6RJABJXgv8C7BpeposSdobfYJ+MbB1aH1bVzZhnaraCTwALExyOPBfgL/c0wskOTvJWJKx8fHxvm2XJPUw0ydjzwMurKoH91Spqi6pqpVVtXLRokUz3CRJOrj0eZTgdmDp0PqSrmyiOtuSzAeOAO4FXgSckuSvgCOBR5I8VFUXTbnlkqRe+gT9RuDYJMsZBPppwBtG6qwHzgBuAk4BbqyqAl66q0KS84AHDXlJml2TBn1V7UxyDnA9MA+4rKo2JVkHjFXVeuBS4Iokm4H7GHwYSJL2A31G9FTVBmDDSNm5Q8sPAadOcozz9qF9kqQp8puxktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjet190rtP5atvW7C8i3nnzTLLZF0oHBEL0mNM+glqXEGvSQ1zqCXpMZ5MrYRnqSVtDuO6CWpcQa9JDWuV9AnWZ3kjiSbk6ydYPuhSa7utt+cZFlX/jtJbklyW/f75dPbfEnSZCYN+iTzgIuBE4EVwOlJVoxUOwvYUVXHABcCF3Tl9wCvqarnAWcAV0xXwyVJ/fQZ0a8CNlfVnVX1MHAVsGakzhrg8m75WuAVSVJVX6qq73Tlm4AnJDl0OhouSeqnT9AvBrYOrW/ryiasU1U7gQeAhSN1fg+4tap+OvoCSc5OMpZkbHx8vG/bJUk9zMrJ2CTPZTCd8+aJtlfVJVW1sqpWLlq0aDaaJEkHjT7X0W8Hlg6tL+nKJqqzLcl84AjgXoAkS4BPAm+qqm9NucXaK15fL6nPiH4jcGyS5UkWAKcB60fqrGdwshXgFODGqqokRwLXAWur6vPT1WhJUn+TBn03534OcD1wO3BNVW1Ksi7JyV21S4GFSTYDfwrsugTzHOAY4NwkX+5+njbtvZAk7VavWyBU1QZgw0jZuUPLDwGnTrDfe4D3TLGNkqQp8JuxktQ4g16SGufdKw9S+3I1jlfwSAcmR/SS1DiDXpIaZ9BLUuOco9dj7G4eXtKByxG9JDXOoJekxhn0ktQ4g16SGmfQS1LjvOpGU+Y3ZqX9myN6SWqcQS9JjXPqRrPOqR5pdjmil6TGOaLXfmNPt19wtC/tO4NeBwSne6R9Z9BrxszGDdL8AJAm5xy9JDWu14g+yWrgfwDzgA9W1fkj2w8FPgK8ALgXeH1Vbem2vRM4C/g58J+q6vppa720G7Px18Tu/mrYH//K8PzHwW3SoE8yD7gY+B1gG7Axyfqq+tpQtbOAHVV1TJLTgAuA1ydZAZwGPBd4JvB3SZ5TVT+f7o5IB7L98cPhQOIH2Z71GdGvAjZX1Z0ASa4C1gDDQb8GOK9bvha4KEm68quq6qfAvyTZ3B3vpulpvjR39vavhn35K2N/fBDM3rbJoP1ls/3B3ifoFwNbh9a3AS/aXZ2q2pnkAWBhV/6FkX0Xj75AkrOBs7vVB5Pc0av1E3sqcM8U9j8QHWx9Ptj6CzPY51wwE0edluNPS59nun/TKRdMqc/P2t2G/eKqm6q6BLhkOo6VZKyqVk7HsQ4UB1ufD7b+gn0+WMxUn/tcdbMdWDq0vqQrm7BOkvnAEQxOyvbZV5I0g/oE/Ubg2CTLkyxgcHJ1/Uid9cAZ3fIpwI1VVV35aUkOTbIcOBb44vQ0XZLUx6RTN92c+znA9Qwur7ysqjYlWQeMVdV64FLgiu5k630MPgzo6l3D4MTtTuCPZ+GKm2mZAjrAHGx9Ptj6C/b5YDEjfc5g4C1JapXfjJWkxhn0ktS4ZoI+yeokdyTZnGTtXLdnJiS5LMndSf55qOyoJDck+Wb3+ylz2cbplmRpks8m+VqSTUn+pCtvtt9JHp/ki0m+0vX5L7vy5Ulu7t7jV3cXRzQjybwkX0ry6W696f4CJNmS5LYkX04y1pVN+3u7iaAfuk3DicAK4PTu9gut+TCweqRsLfD3VXUs8Pfdekt2Am+rqhXAi4E/7v7bttzvnwIvr6rnA8cBq5O8mMGtRS6sqmOAHQxuPdKSPwFuH1pvvb+7/PuqOm7o+vlpf283EfQM3aahqh4Gdt2moSlV9TkGVzUNWwNc3i1fDrx2Vhs1w6rqu1V1a7f8QwZBsJiG+10DD3arh3Q/BbycwS1GoLE+J1kCnAR8sFsPDfd3EtP+3m4l6Ce6TcMv3WqhUU+vqu92y98Dnj6XjZlJSZYB/w64mcb73U1jfBm4G7gB+BZwf1Xt7Kq09h5/P/AO4JFufSFt93eXAj6T5JbuVjAwA+/t/eIWCJoeVVVJmrxeNsnhwMeB/1xVPxgM+AZa7Hf3fZPjkhwJfBL4N3PcpBmT5NXA3VV1S5IT5ro9s+wlVbU9ydOAG5J8fXjjdL23WxnRH8y3Wvh+kmcAdL/vnuP2TLskhzAI+b+pqk90xc33G6Cq7gc+CxwPHNndYgTaeo//JnByki0Mpl1fzuD5F6329xeqanv3+24GH+irmIH3ditB3+c2Da0avv3EGcD/msO2TLturvZS4Paqet/Qpmb7nWRRN5InyRMYPAvidgaBf0pXrZk+V9U7q2pJVS1j8P/ujVX1Rhrt7y5JDkvypF3LwCuBf2YG3tvNfDM2yasYzPPtuk3De+e4SdMuyceAExjcvvX7wLuATwHXAEcDdwGvq6rRE7YHrCQvAf4RuI1H52//nME8fZP9TvJrDE7CzWMwGLumqtYleTaDEe9RwJeA3++e9dCMburm7VX16tb72/Xvk93qfOCjVfXeJAuZ5vd2M0EvSZpYK1M3kqTdMOglqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4/4/kwMk36qlbBIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5saj_gq7G3oJ",
        "colab_type": "text"
      },
      "source": [
        "applying Transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRz8OWfxG22z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "62a9890c-6586-4b01-e7bc-129b497bb7d7"
      },
      "source": [
        "for i in [1, 2, 3, 4, 5]:\n",
        "  pyplot.hist(data['punctuation_usage']**(1/i), bins = 50)\n",
        "  pyplot.title(f'Transform = 1/{i}')\n",
        "  pyplot.show()"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uktShGcM9ySeS7E/ytaG2o5LcmOSb7eeRrT1JLkmyJ8ldSU5azOIlSVMbZeb+R8Bpk9p2ADdV1fHATW0f4HTg+HbZDly6MGVKkmZjxnCvqi8B35nUvBXY2bZ3AmcNtV9RA7cCa5Mcu1DFSpJGM9c19/VV9VDbfhhY37Y3AA8O9dvb2iRJYzTvF1SrqoCa7fWSbE+yO8nuiYmJ+ZYhSRoy1/e5P5Lk2Kp6qC277G/t+4BNQ/02trYXqarLgMsAtmzZMutfDpIWx3SfEbj/ojPHXInmY64z913Atra9DbhmqP289q6ZU4AnhpZvJEljMuPMPclngLcAxyTZC7wfuAi4KskFwAPAO1r364EzgD3A08D5i1Cz9EN+ElWa2ozhXlU/P82hU6foW8CF8y1KkjQ/fkJVkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchvYpI0koN9psBPry4/ztwlqUOGuyR1yHCXpA4Z7pLUIcNdkjrku2UkzZvngF9+nLlLUocMd0nqkOEuSR0y3CWpQ76gqhXBr9NbmXyhdekY7lpWDHFpYRjuksbOGf3ic81dkjpkuEtShwx3SeqQa+6Slj3X6GfPcNeS8F0xmspy+3exkn+pLEq4JzkN+ChwCPDxqrpoMe5H0uq2ksN3sS14uCc5BPhPwE8Be4Hbk+yqqnsW+r4kaSqz/Qugx18GizFzPxnYU1X3ASS5EtgKGO6SujCXvxjG/VdGqmphbzA5Gzitqt7d9s8FfqKqfnFSv+3A9rb7WuAv53iXxwDfnuN1V7rVOvbVOm5YvWNfreOGg4/9R6tq3VQHluwF1aq6DLhsvreTZHdVbVmAklac1Tr21TpuWL1jX63jhrmPfTHe574P2DS0v7G1SZLGZDHC/Xbg+CSvTnIYcA6waxHuR5I0jQVflqmq55L8IvAFBm+F/ERVfX2h72fIvJd2VrDVOvbVOm5YvWNfreOGOY59wV9QlSQtPc8tI0kdMtwlqUMrJtyTnJbkL5PsSbJjiuOHJ/lsO35bks3jr3LhjTDudyWZSHJnu7x7KepcaEk+kWR/kq9NczxJLmmPy11JThp3jYtlhLG/JckTQ8/5b467xsWQZFOSW5Lck+TrSd4zRZ/unvcRxz3757yqlv2FwQuzfw38Y+Aw4KvA6yf1+bfAH7Ttc4DPLnXdYxr3u4CPLXWtizD2NwMnAV+b5vgZwA1AgFOA25a65jGO/S3AtUtd5yKM+1jgpLb9cuCvpvj33t3zPuK4Z/2cr5SZ+w9PaVBVfwccOKXBsK3AzrZ9NXBqkoyxxsUwyri7VFVfAr5zkC5bgStq4FZgbZJjx1Pd4hph7F2qqoeq6stt+yngXmDDpG7dPe8jjnvWVkq4bwAeHNrfy4sH/8M+VfUc8ARw9FiqWzyjjBvgZ9ufqFcn2TTF8R6N+tj06o1JvprkhiT/dKmLWWhtWfVE4LZJh7p+3g8ybpjlc75Swl3T+6/A5qr6MeBG/v6vF/XrywzOKXIC8PvAf1niehZUkpcBnwN+uaqeXOp6xmWGcc/6OV8p4T7KKQ1+2CfJGuAI4NGxVLd4Zhx3VT1aVd9vux8HfnxMtS21VXuai6p6sqq+27avBw5NcswSl7UgkhzKIOA+XVV/NkWXLp/3mcY9l+d8pYT7KKc02AVsa9tnAzdXeyViBZtx3JPWG9/OYL1uNdgFnNfePXEK8ERVPbTURY1DklcdeD0pyckM/h+v9IkMbUyXA/dW1Yen6dbd8z7KuOfynK+Ir9mraU5pkORDwO6q2sXgwflUkj0MXow6Z+kqXhgjjvuXkrwdeI7BuN+1ZAUvoCSfYfAOgWOS7AXeDxwKUFV/AFzP4J0Te4CngfOXptKFN8LYzwb+TZLngGeAczqYyAC8CTgXuDvJna3t14HjoOvnfZRxz/o59/QDktShlbIsI0maBcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdej/Ax5o5i5l99xwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}