SMS_Spam_Classifier.ipynb

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      "source": [
        "#SMS/Email Spam Classifier\n",
        "\n",
        "Dataset: https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection\n",
        "\n",
        "<br>\n",
        "\n",
        "**Steps:**\n",
        "1. Loading Dataset\n",
        "2. Data Cleaning\n",
        "3. Exploratory Data Analysis\n",
        "4. Text Preprocessing\n",
        "5. Model Building\n",
        "6. Model Evaluation\n",
        "7. Model Improvement\n",
        "8. Deployment \n",
        "\n",
        "<br>"
      ],
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      "source": [
        "## 1. Loading Dataset"
      ],
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.1)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install nltk\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/VirajPatidar/SMS-Email-Spam-Classifier/main/Dataset/spamCollection.csv', encoding='latin-1')\n",
        "df.sample(5)"
      ],
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              "       v1                                                 v2 Unnamed: 2  \\\n",
              "4431  ham  Dont think you need yellow card for uk travel....        NaN   \n",
              "2508  ham                                              Ok...        NaN   \n",
              "3801  ham          No da. I am happy that we sit together na        NaN   \n",
              "1002  ham  Yes..he is really great..bhaji told kallis bes...        NaN   \n",
              "4820  ham         Im good! I have been thinking about you...        NaN   \n",
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              "     Unnamed: 3 Unnamed: 4  \n",
              "4431        NaN        NaN  \n",
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              "(5572, 5)"
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        "## 2. Data Cleaning"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   v1          5572 non-null   object\n",
            " 1   v2          5572 non-null   object\n",
            " 2   Unnamed: 2  50 non-null     object\n",
            " 3   Unnamed: 3  12 non-null     object\n",
            " 4   Unnamed: 4  6 non-null      object\n",
            "dtypes: object(5)\n",
            "memory usage: 217.8+ KB\n"
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        "# dropping last 3 columns\n",
        "df.drop(columns=['Unnamed: 2','Unnamed: 3','Unnamed: 4'],inplace=True)\n",
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              "       v1                                                 v2\n",
              "2702  ham  Omw back to tampa from west palm, you hear wha...\n",
              "4460  ham  Thanks again for your reply today. When is ur ...\n",
              "1996  ham                   Lol boo I was hoping for a laugh\n",
              "2166  ham            Thank you. And by the way, I just lost.\n",
              "2081  ham           I'm aight. Wat's happening on your side."
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              "     target                                               text\n",
              "4735    ham   Not for possession, especially not first offense\n",
              "632     ham  Oh that was a forwarded message. I thought you...\n",
              "5317    ham  K, fyi I'm back in my parents' place in south ...\n",
              "1765    ham  Hi this is yijue... It's regarding the 3230 te...\n",
              "3376    ham  Yup. Wun believe wat? U really neva c e msg i ..."
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>ham</td>\n",
              "      <td>Oh that was a forwarded message. I thought you...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5317</th>\n",
              "      <td>ham</td>\n",
              "      <td>K, fyi I'm back in my parents' place in south ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1765</th>\n",
              "      <td>ham</td>\n",
              "      <td>Hi this is yijue... It's regarding the 3230 te...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3376</th>\n",
              "      <td>ham</td>\n",
              "      <td>Yup. Wun believe wat? U really neva c e msg i ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-753e8352-f3a7-4fa7-a5f0-cdc20201e60e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-753e8352-f3a7-4fa7-a5f0-cdc20201e60e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-753e8352-f3a7-4fa7-a5f0-cdc20201e60e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "df['target'] = encoder.fit_transform(df['target'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lHQOVFOxlhIb",
        "outputId": "f6f10122-e05b-4aa1-c42a-31ef1f9554ad"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text\n",
              "0       0  Go until jurong point, crazy.. Available only ...\n",
              "1       0                      Ok lar... Joking wif u oni...\n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3       0  U dun say so early hor... U c already then say...\n",
              "4       0  Nah I don't think he goes to usf, he lives aro..."
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>target</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</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>0</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>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f013819-71ff-4506-b952-9104d33a1a8d')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9f013819-71ff-4506-b952-9104d33a1a8d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f013819-71ff-4506-b952-9104d33a1a8d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# finding missing values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iscZMrFxlsK-",
        "outputId": "da11db68-d401-4674-fdaa-0747067c4093"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target    0\n",
              "text      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for duplicate values\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_o6gQOealvKd",
        "outputId": "9297a204-9b6f-4105-f059-c7a9d6678700"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "403"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# removing duplicates\n",
        "df = df.drop_duplicates(keep='first')\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xk19JaZDl0dg",
        "outputId": "01e0325c-1f89-4643-ea7b-4ee21ec6a197"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQiDzko0l5Wc",
        "outputId": "1748a1d5-a4ee-4158-91d5-59d56d3c89a1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5169, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Cleaning Conclusion:**\n",
        "- Dropped last 3 null colums of the dataset\n",
        "- Renamed colums\n",
        "- Mapped target column values to 0 (ham/not-spam) and 1 (spam) using label encoder (Label encoding converts the data in machine-readable form)\n",
        "- Removed duplicate rows\n"
      ],
      "metadata": {
        "id": "JZbZnmRerajY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "n5XGAvlAl55x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hf20ls40l8S5",
        "outputId": "00852a2f-cca1-443f-f0e6-9840e1889c99"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text\n",
              "0       0  Go until jurong point, crazy.. Available only ...\n",
              "1       0                      Ok lar... Joking wif u oni...\n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3       0  U dun say so early hor... U c already then say...\n",
              "4       0  Nah I don't think he goes to usf, he lives aro..."
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-63837a77-016c-4c5f-abee-ab375f3fe34e button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-63837a77-016c-4c5f-abee-ab375f3fe34e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXLsAfWel_tI",
        "outputId": "87c35841-4d53-442d-b353-565301275dea"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4516\n",
              "1     653\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(df['target'].value_counts(), labels=['ham','spam'],autopct=\"%0.2f\")\n",
        "plt.show()\n",
        "\n",
        "# Conclusion: Data is imbalanced"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "W2sRE9O7mBPL",
        "outputId": "dda29ffe-1937-4c10-80f2-7131eecabc67"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mhnZibzmbxp",
        "outputId": "1bbf5f1c-792c-44ff-fd87-150b576e1530"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of characters in the message\n",
        "df['num_characters'] = df['text'].apply(len)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yZQyW92xmfVL",
        "outputId": "b9df205d-0b6d-439d-99cc-fe54e0075c84"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text  num_characters\n",
              "0       0  Go until jurong point, crazy.. Available only ...             111\n",
              "1       0                      Ok lar... Joking wif u oni...              29\n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155\n",
              "3       0  U dun say so early hor... U c already then say...              49\n",
              "4       0  Nah I don't think he goes to usf, he lives aro...              61"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8ba1130-c5df-4f0e-af00-54108097073d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>target</th>\n",
              "      <th>text</th>\n",
              "      <th>num_characters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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        "# number of words in the message\n",
        "df['num_words'] = df['text'].apply(lambda x:len(nltk.word_tokenize(x)))\n",
        "df.head()"
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      "execution_count": 17,
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            "text/plain": [
              "   target                                               text  num_characters  \\\n",
              "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
              "1       0                      Ok lar... Joking wif u oni...              29   \n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
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              "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
              "\n",
              "   num_words  \n",
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              "                                                     [key], {});\n",
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              "\n",
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        "# number of sentences in the message\n",
        "df['num_sentences'] = df['text'].apply(lambda x:len(nltk.sent_tokenize(x)))\n",
        "df.head()"
      ],
      "metadata": {
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      "execution_count": 18,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   target                                               text  num_characters  \\\n",
              "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
              "1       0                      Ok lar... Joking wif u oni...              29   \n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
              "3       0  U dun say so early hor... U c already then say...              49   \n",
              "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
              "\n",
              "   num_words  num_sentences  \n",
              "0         24              2  \n",
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              "2         37              2  \n",
              "3         13              1  \n",
              "4         15              1  "
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
          "execution_count": 18
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    {
      "cell_type": "code",
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        "df[['num_characters','num_words','num_sentences']].describe()"
      ],
      "metadata": {
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          "height": 300
        },
        "id": "5WU8IMt_nDFe",
        "outputId": "b2e891a3-4d74-4c14-94c5-07ca075a8115"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       num_characters    num_words  num_sentences\n",
              "count     5169.000000  5169.000000    5169.000000\n",
              "mean        78.977945    18.453279       1.947185\n",
              "std         58.236293    13.324793       1.362406\n",
              "min          2.000000     1.000000       1.000000\n",
              "25%         36.000000     9.000000       1.000000\n",
              "50%         60.000000    15.000000       1.000000\n",
              "75%        117.000000    26.000000       2.000000\n",
              "max        910.000000   220.000000      28.000000"
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              "\n",
              "  <div id=\"df-2fcc79e5-5345-485e-904d-b9465720c0de\">\n",
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              "      <th></th>\n",
              "      <th>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "      <th>num_sentences</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5169.000000</td>\n",
              "      <td>5169.000000</td>\n",
              "      <td>5169.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>78.977945</td>\n",
              "      <td>18.453279</td>\n",
              "      <td>1.947185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>58.236293</td>\n",
              "      <td>13.324793</td>\n",
              "      <td>1.362406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>117.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>910.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fcc79e5-5345-485e-904d-b9465720c0de')\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2fcc79e5-5345-485e-904d-b9465720c0de');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 19
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for ham messages\n",
        "df[df['target'] == 0][['num_characters','num_words','num_sentences']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "s_Q-9JmbnGN_",
        "outputId": "cc54d667-a24d-4e2b-ebd4-6e1be16df50c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       num_characters    num_words  num_sentences\n",
              "count     4516.000000  4516.000000    4516.000000\n",
              "mean        70.459256    17.120903       1.799601\n",
              "std         56.358207    13.493725       1.278465\n",
              "min          2.000000     1.000000       1.000000\n",
              "25%         34.000000     8.000000       1.000000\n",
              "50%         52.000000    13.000000       1.000000\n",
              "75%         90.000000    22.000000       2.000000\n",
              "max        910.000000   220.000000      28.000000"
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              "\n",
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              "      <th></th>\n",
              "      <th>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "      <th>num_sentences</th>\n",
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              "  <tbody>\n",
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              "      <td>4516.000000</td>\n",
              "      <td>4516.000000</td>\n",
              "      <td>4516.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>70.459256</td>\n",
              "      <td>17.120903</td>\n",
              "      <td>1.799601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>56.358207</td>\n",
              "      <td>13.493725</td>\n",
              "      <td>1.278465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>52.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>910.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-183e1168-ba13-4b2b-bd6f-ddfc37f01537')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-183e1168-ba13-4b2b-bd6f-ddfc37f01537');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for spam messages\n",
        "df[df['target'] == 1][['num_characters','num_words','num_sentences']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "D4yni3iBnJ2O",
        "outputId": "ea115ec2-f5a8-4411-f172-f3b05a45351c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       num_characters   num_words  num_sentences\n",
              "count      653.000000  653.000000     653.000000\n",
              "mean       137.891271   27.667688       2.967841\n",
              "std         30.137753    7.008418       1.483201\n",
              "min         13.000000    2.000000       1.000000\n",
              "25%        132.000000   25.000000       2.000000\n",
              "50%        149.000000   29.000000       3.000000\n",
              "75%        157.000000   32.000000       4.000000\n",
              "max        224.000000   46.000000       8.000000"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num_characters</th>\n",
              "      <th>num_words</th>\n",
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              "  <tbody>\n",
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              "      <td>653.000000</td>\n",
              "      <td>653.000000</td>\n",
              "      <td>653.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>137.891271</td>\n",
              "      <td>27.667688</td>\n",
              "      <td>2.967841</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
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              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>132.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>149.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>157.000000</td>\n",
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              "      <td>4.000000</td>\n",
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              "    <tr>\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2707e200-c935-481c-bc48-1abc255097fb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2707e200-c935-481c-bc48-1abc255097fb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.histplot(df[df['target'] == 0]['num_characters'], color='green')\n",
        "sns.histplot(df[df['target'] == 1]['num_characters'], color='red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "ESV0JrkVntRf",
        "outputId": "7716c355-0fe7-4185-a54e-8c54e16f424a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff197a26e50>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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XAdfV9L+nuKvHhcCBmukgkiRJUtvpbOK+Xwu8G+iPiO8Wfb8HXAl8MSIuB3YB7yzW3QC8BdgBDAGbmlibJEmSdNyaFqYz8zYgnmP1RXW2T+B9zapHkiRJmm0+AVGSJEkqyTAtSZIklWSYliRJkkoyTEuSJEklNfNuHmojlbEK/f39U/rWr19PV1dXiyqSJEma/wzTi8TQ3iG2PLyF1btXAzA4MMi2zdvYuHFjiyuTJEmavwzTi0j3Gd2sXLey1WVIkiQtGIbpRaretA9w6ockSdJMGKYXqenTPsCpH5IkSTNlmF7EnPYhSZJ0fLw1niRJklSSI9Oa5O3zJEmSZsYwrUnePk+SJGlmDNNNMjIyQl9f32S7v7+fSlZaWFFj5vM86sOHD3PfffdN6TvvvPNYunRpiyqSJEkLnWG6Sfr6+ti0dRM9a3sA2H/3frrP6WYVq1pc2cJ133338eCll3LuCScA8ODQEFxzDRdccEGLK5MkSQuVYbqJetb2TI7yHho41OJqFodzTziBC1asaHUZkiRpkfBuHpIkSVJJhmlJkiSpJMO0JEmSVJJhWpIkSSrJMC1JkiSVZJiWJEmSSjJMS5IkSSUZpiVJkqSSDNOSJElSSYZpSZIkqSTDtCRJklSSYVqSJEkqyTAtSZIklWSYliRJkkoyTEuSJEklGaYlSZKkkgzTkiRJUkmGaUmSJKkkw7QkSZJUkmFakiRJKskwLUmSJJVkmJYkSZJKMkxLkiRJJRmmJUmSpJIM05IkSVJJhmlJkiSpJMO0JEmSVJJhWpIkSSqps9UFqH1Vxir09/cf0b9+/Xq6urpaUJEkSVJ7MUzrOQ3tHWLLw1tYvXv1ZN/gwCDbNm9j48aNLaxMkiSpPRimdVTdZ3Szct3KVpchSZLUlpwzLUmSJJVkmJYkSZJKalqYjoi/jYjHI+K+mr5TIuLGiHio+L6q6I+I+HRE7IiIeyPilc2qS5IkSZotzRyZ/jvgTdP6rgBuysx1wE1FG+DNwLriazPwmSbWJUmSJM2Kpl2AmJnfiojead0XA68vlq8GbgV+p+j/XGYmcEdEnBwRp2fmo82qTwvDyMgIfX19AHz/+9/nhYcOUenpoaPDGUySJKn55vpuHqfVBOS9wGnF8hpgd812A0WfYVpHdeedd/LuP303y09bzvD+Yf5o315efsIJrFzpHUgkSVLztezWeJmZEZEzfV1EbKY6FYSzzjpr1uvSzNSODNeaqwe77Ny5k1+54zFedOJSbh8cIxyQliRJc2iuw/RjE9M3IuJ04PGifw9wZs12a4u+I2TmVmArwIYNG2YcxjW7+vr62LR1Ez1reyb7Du46yIdf92HOP//8yb5mhuvepR2c293JIyPjcNhTQpIkzZ25DtPXA5cBVxbfr6vpf39EfB74SeCA86Xnj561PVMe7HJo4BBbbn72yYk+NVGSJC1UTQvTEfGPVC82XB0RA8AfUg3RX4yIy4FdwDuLzW8A3gLsAIaATc2qS3PDJydKkqTFoJl38/il51h1UZ1tE3hfs2qRJEmSmqFlFyBKzZAJhwYHATg4NMTOe+7hmWeembMLIiVJ0uJimNaCkmMVdj25i6UjS/nB6DhX3nYleWs6Z1uSJDWFYVoLTkdXB53LO1kScOJZJ1IZrbS6JEmStEB5V15JkiSpJEem1RZa/fAXSZKkMgzTarrKWIX+/v4j+muDcr2Hv3h/akmS1O4M02q6ob1DbHn42Ye4QP2gPP3hL/VCuCPVkiSpnRimNSemP8RlelDu7++nklMvFJwewh2pliRJ7cYwrZaYHpT3372f7nO6WcWqKdv5JEVJktTODNOakUZGlBtVG5QPDRyalfokSZLmkmFaM9LoiLIkSdJiYJjWjDmiLEmSVOVDWyRJkqSSHJnWvFHvVnkPPfQQL81sUUWSJGmxM0xr3hjaO8TH/uNj9Nz17INd9t21jz8fN0xLkqTWMExrXollwWX/bze9y6ozlL61f5hcGi2uSpIkLVaGac07vcs6OLe7euo+tMQgLUmSWscLECVJkqSSDNOSJElSSYZpSZIkqSTnTM+CkZER+vr6pvQdz2O2JUmSND8YpmdBX18fm7Zuomfts7ds8zHbsyPHk4O7DgIw/MQwo2Oj4J3wJElSmzBMz5KetT2Tj9gGH7NdVmWswuCeQaAIz4+N8t+u3UNv1xJGDx7mjsPjVHqWwnJPXUmS1HomErWVwT2DvP2vH6B3WcdkeD6rZynndncyMjzOropTZyRJUvswTKvtTNxH2vAsSZLanXfzkCRJkkoyTEuSJEklGaYlSZKkkgzTkiRJUkmGaUmSJKkkw7QkSZJUkmFakiRJKskwLUmSJJVkmJYkSZJKMkxLkiRJJfk4cS1YY5kMPTpE5XCF73//+yxfvpzzzjuPpUuXtro0SZK0QBimtWANjFa49NqdnN0ZnHrjH3J3BPd89KO85CUvYf369XR1dbW6REmSNM8ZpksYGRmhr69vst3f308lKy2sSM+ld2kHL0qoPPMYXZ0dfOy2K8lbk22bt7Fx48ZWlydJkuY5w3QJfX19bNq6iZ61PQDsv3s/3ed0s4pVLa5Mz6Wjq4MlnUs48awTqYz6wUeSJM0Ow3RJPWt7WLluJQCHBg61uJr5qzJWYXDPIMNPDMNh6BzuhGx1VZIkSY0xTGvOVcYqk+F53/Z9vPu6RzhjtAJLYHvuo9IZsNxTU5IktT8Ti+bc4J5B3vuvezm7q4PtuY+zOoMXLe2ATvjheKurkyRJapxhWrMuK8nI4yMc3HUQgOEnhuk6aeqdM87u7OCcZR2GZ0mSNK8ZpjXrRp4c4b2372Xd7U8C8OCPhvnrsedxcHU1XA89OtR286Kn36EF8PZ5kiTpmAzTmrEczymjzjmSPL3zaWJJADB6YJSzl3Rwbnf19Hoogl+7/Qle8uAgALcPjpHjFVr1AM7KWIX+/v4pff39/Xz89o+z4swVAAwODHr7PEmSdEyG6UWikakXjRp5YoS3f/kBepd1MHrwMHccHmftN/fxwhOrTxb81v5hcmlMec3ZS2IyXD8yMg6jx/HDHKehvUNseXgLq3evnuybuL3hxB1aJEmSGmGYXiSmT7146MkRPnvS80vvr3dZdeR5ZHicXZUKvUtrRqKXxDFe3XrdZ0wNztNvb1hv9Hp0tPoJYNmyZXXbE5weIknS4mGYXqBqp2LAkVMvRjsPt6q0eeG5Rq9ZAavXra7bBji46yAfft2HOf/88yf7yoRr53BLkjQ/GKbbWO39mA/uOkiOJ8P7n20D9Kzpqfva2qkYcOTUi7FMRn80Ormf6fue3u5Z00NHZ2vmOM+WsUyGHh2a8js92s9Vd/R6FVMf1lPTnujbcvOzIbzs3OvpT9ls5hxug7skSeW1VZiOiDcBnwKWAJ/NzCtbXFJL1d6PedntT3L74Birnxmj94QlLLv9SR4ZrfDlX33pc75+YioGHDn1Ys94HnFRYO2+a9u7b9nPP1zcywmnn8DwE8OMjo223d04GjEwWuHSa3eyNoElHPFzcbj6AeZ4PzRMD+HHUi/M9vf3s3zN8sn91Jt2ArMTeqcH93qj67N1LEmSFpq2CdMRsQT4K+CngQHgroi4PjPvb21lzTP9osBn9j1DjMRke+jRIc5eUr0fc1d3J4+MjHP6kphsw9iz+5p2h41GAu/0iwJr913b/uE4XHrtTl544tLJCw4rPUvn5VMKe5d28KJk8gExtT/Xzso4XzrpxFkP19NND8/T7yQCz14QuYpVQP1pJ9ND7/HM4e5Z2zNlxL12dL3RY9XrWwhTXOrVA364kCRVtVMaejWwIzN3AkTE54GLgXkVpitjFQb3DB4xlWD6eoCDjxzk1+/70eRFgd/aP8zzO+Ald1TbM7mFXL07bMxm4J24wHDigsOFYsrPNVKZEq53ZYXrz/kxVpy94ojXTZ+CA/Wn3DzXbfj+9LY/pbOr+rd56oGnWLF+xVEviIT6005qQ28jc7jrBd7+/n4qOfVvWuZY0/saGeF+rlH52g8XszVSXu9YjXwoqPdhp8y0m0aPP/3nauTDxfRtGvm5yn74arcPO+2mzO+n7Ac2/xbzix/MZ26+nOPtFKbXALtr2gPAT7aolmMaHBicXB5+fBiegQM9BxjaO8R/unYnp44lBOxbspcbX38Gw/uGoQdGHx7lp2/9Ic/v7ODeg4epLA3GR6qPAcyxJJcw2R4/PM4joxU4NEbnOOx+ZoyRmvYjhyvsv3s/w/uGOTx+mPHRccYzycMVcizZ+czY5OD0D0cqVJZA54GRuu3p+65t76Y6Bp7A2NAYPxyrEDlWtz3TY03s+3DxmeF4jlXvZ6i376Mda82y6u8/D1fITPbfvZ9DP6gG26cfehp6oONgB8P7h/mpb/6Q53cGS7oeZ+9YZcrfueNg9QPQUw88xRXfuIKeU58N2oO7B+k4qYNfePAgz+/sYM/gGDf0wIGTDtQ9p+q1J/uOzPlTjDwxwu9f8/ucdMZJ1Z9hx9NwApPtib7lL1pOR3Qc17GOdWyA4f3D/ME7/mAyGPf39/PHX/pjuld3H1HPiuKAjeynEc91rHq/j9q+6fXU7m8mGjl+vZ9r+usa2abRn2v6No38XhupZzEr8/upd274t1h4yv6dF7N65/iX/vhLbfcMiMhsj8mvEXEJ8KbMfG/Rfjfwk5n5/mnbbQY2F81zgH+f00JhNbB/jo+p9ud5oXo8L1SP54Xq8bxof2dn5qnTO9tpZHoPcGZNe23RN0VmbgW2zlVR00XE9szc0Krjqz15XqgezwvV43mhejwv5q92utfZXcC6iHhBRCwD3gVc3+KaJEmSpOfUNiPTmTkWEe8H/pnqrfH+NjO/1+KyJEmSpOfUNmEaIDNvAG5odR3H0LIpJmprnheqx/NC9XheqB7Pi3mqbS5AlCRJkuabdpozLUmSJM0rhukZiIg3RcS/R8SOiLii1fVo7kTEmRFxS0TcHxHfi4gPFP2nRMSNEfFQ8X1V0R8R8eniXLk3Il7Z2p9AzRIRSyLiOxHxtaL9goi4s/jbf6G4oJqI6CraO4r1va2sW80VESdHxLUR8WBEPBARr/H9QhHxm8W/IfdFxD9GRLfvGfOfYbpBNY87fzPwMuCXIuJlra1Kc2gM+K3MfBlwIfC+4u9/BXBTZq4DbiraUD1P1hVfm4HPzH3JmiMfAB6oaf8J8InMfDHwJHB50X858GTR/4liOy1cnwK+npnnAi+neo74frGIRcQa4DeADZl5HtWbLbwL3zPmPcN04yYfd56Zo8DE4861CGTmo5l5d7F8kOo/jGuongNXF5tdDbytWL4Y+FxW3QGcHBGnz3HZarKIWAv8DPDZoh3AG4Fri02mnxMT58q1wEXF9lpgImIl8J+BqwAyczQzn8L3C1Vv/LA8IjqBE4BH8T1j3jNMN67e487XtKgWtVDxX20XAHcCp2Xmo8WqvcBpxbLny+LwSeAjQKVoPw94KjPHinbt333ynCjWHyi218LzAmAfsK2YAvTZiOjB94tFLTP3AB8HfkA1RB8A+vA9Y94zTEszEBEnAl8GPpiZT9euy+qtcbw9ziIREW8FHs/MvlbXorbTCbwS+ExmXgAM8uyUDsD3i8WomCN/MdUPW2cAPcCbWlqUZoVhunENPe5cC1dELKUapK/JzK8U3Y9N/Hds8f3xot/zZeF7LfBzEfEI1Wlfb6Q6T/bk4r9wYerfffKcKNavBJ6Yy4I1ZwaAgcy8s2hfSzVc+36xuP0U8HBm7svMw8BXqL6P+J4xzxmmG+fjzhexYp7aVcADmfnnNauuBy4rli8Drqvpf09xlf6FwIGa/97VApCZv5uZazOzl+r7wc2ZeSlwC3BJsdn0c2LiXLmk2N6RyQUoM/cCuyPinKLrIuB+fL9Y7H4AXBgRJxT/pkycF75nzHM+tGUGIuItVOdITjzufEuLS9IciYjXAf8K9PPs/Njfozpv+ovAWcAu4J2Z+aPijfIvqf4X3hCwKTO3z3nhmhMR8Xrgw5n51oh4IdWR6lOA7wC/nJkjEdEN/D3V+fY/At6VmTtbVbOaKyJeQfXC1GXATmAT1QEs3y8WsYj4I+AXqd4h6jvAe6nOjfY9Yx4zTEuSJEklOc1DkizWznAAAAOcSURBVCRJKskwLUmSJJVkmJYkSZJKMkxLkiRJJRmmJUmSpJIM05IkSVJJhmlJWmAi4vUR8bU5PubvzeXxJKldGKYlSVPUPNp4JmYcpiNiSYnjSFJbMUxL0iyLiN6IeCAi/iYivhcR34iI5RFxa0RsKLZZHRGPFMv/NSL+T0TcGBGPRMT7I+JDEfGdiLgjIk45yrFeHBHfjIh7IuLuiHhRserEiLg2Ih6MiGuKp+wREX8QEXdFxH0RsbWm/9aI+GREbAc+EBE/GxF3FjV8MyJOK7Y7MSK2RUR/RNwbEW+PiCuB5RHx3Yi4ptjulyPi20XfX08E54g4FBF/FhH3AK+JiCsj4v5iXx9vyh9EkprIMC1JzbEO+KvM/HHgKeDtx9j+POAXgFcBW4ChzLwA+DfgPUd53TXFcV4ObAQeLfovAD4IvAx4IfDaov8vM/NVmXkesBx4a82+lmXmhsz8M+A24MKihs8DHym2+Z/Agcw8PzN/Arg5M68AnsnMV2TmpRHxUqqPTH5tZr4CGAcuLV7fA9xZ1PsA8PPAjxf7+tgxfkeS1HbK/FeeJOnYHs7M7xbLfUDvMba/JTMPAgcj4gDwT0V/P/AT9V4QESuANZn5VYDMHC76Ab6dmQNF+7vF8W8D3hARHwFOAE4BvldzrC/U7H4t8IWIOB1YBjxc9P8U8K6JjTLzyTqlXQSsB+4qalkOPF6sGwe+XCwfAIaBq4o53nM6z1uSZoMj05LUHCM1y+NUBy/GePZ9t/so21dq2hXKDXwccfyI6Ab+F3BJZp4P/M20OgZrlv+C6ij2+cCv1qn3aAK4uhipfkVmnpOZHy3WDWfmOEBmjgGvBq6lOkL+9RkcQ5LagmFakubOI1RHbAEuOd6dFSPZAxHxNoCI6IqIE47ykolAvD8iTjxGDSuBPcXyZTX9NwLvm2hExKpi8XBELC2WbwIuiYgfK7Y5JSLOnn6AooaVmXkD8JvAy49SjyS1JcO0JM2djwO/FhHfAVbP0j7fDfxGRNwL3A48/7k2zMynqI5G3wf8M3DXUfb7UeBLEdEH7K/p/xiwqriA8R7gDUX/VuDeiLgmM+8Hfh/4RlHXjcDpdY6xAvhasc1twIeO9cNKUruJzGx1DZIkSdK85Mi0JEmSVJJ385CkeSAi/opnb2834VOZua0V9UiSqpzmIUmSJJXkNA9JkiSpJMO0JEmSVJJhWpIkSSrJMC1JkiSVZJiWJEmSSvr/nISDA7L8ty4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.histplot(df[df['target'] == 0]['num_words'], color='green')\n",
        "sns.histplot(df[df['target'] == 1]['num_words'],color='red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "GuVvWlcbnv-U",
        "outputId": "616d957a-712a-4968-c4d9-f2a14712bd02"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff19787fa90>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df, hue='target')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "N-wcJafQnxcu",
        "outputId": "7cf3ad7f-6104-4e96-a11e-267f042a519a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ff197574290>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 582.375x540 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "77ZkUfBdnz0c",
        "outputId": "792865ff-d3e4-459f-8cc1-7ccff10f5bd8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1974eddd0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EDA Conclusion:**\n",
        "- Given data is imbalanced\n",
        "- There are more number of characters, words and sentences in spam messages\n",
        "- Number of sentences is not linearly propotional to number of words\n",
        "- Outliers are present in the dataset\n",
        "- There is a strong correlation between spam messages (target=1) and number of characters present in text"
      ],
      "metadata": {
        "id": "vCP0j-ZPoP2e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Text Preprocessing\n",
        "- Convert text to lower case\n",
        "- Tokenization: Split the string, text into a list of tokens \n",
        "- Remove special characters\n",
        "- Remove stop words and punctuation\n",
        "- Stemming"
      ],
      "metadata": {
        "id": "F-qa6sQGp0qK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem.porter import PorterStemmer\n",
        "ps = PorterStemmer()\n",
        "print(ps.stem('played'))\n",
        "print(ps.stem('playing'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfwauAmUtozp",
        "outputId": "fcdf5d96-536c-40e4-9eb9-6a6fcbd5947d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "play\n",
            "play\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "string.punctuation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "MSsHUDJeudwo",
        "outputId": "edef9315-542c-4c16-b599-8b99f3851c8f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "stopwords.words('english')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSkExn8puitC",
        "outputId": "ce3cdf4d-528e-4921-959d-919fbe2b7dfd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "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": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def transform_text(text):\n",
        "    text = text.lower()\n",
        "    text = nltk.word_tokenize(text)\n",
        "    \n",
        "    y = []\n",
        "    for i in text:\n",
        "        if i.isalnum():\n",
        "            y.append(i)\n",
        "    \n",
        "    text = y[:]\n",
        "    y.clear()\n",
        "    \n",
        "    for i in text:\n",
        "        if i not in stopwords.words('english') and i not in string.punctuation:\n",
        "            y.append(i)\n",
        "            \n",
        "    text = y[:]\n",
        "    y.clear()\n",
        "    \n",
        "    for i in text:\n",
        "        y.append(ps.stem(i))\n",
        "    \n",
        "            \n",
        "    return \" \".join(y)"
      ],
      "metadata": {
        "id": "mpYezhNwpdAs"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'][23]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "8z-2c79Vtc5c",
        "outputId": "8acd55f5-05af-4952-8d22-8c66bf8a27cf"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Aft i finish my lunch then i go str down lor. Ard 3 smth lor. U finish ur lunch already?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transform_text(df['text'][23])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "YgoiwWFMt0M0",
        "outputId": "85b9bfcc-d36a-4e41-c236-20bfff59e981"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'aft finish lunch go str lor ard 3 smth lor u finish ur lunch alreadi'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['transformed_text'] = df['text'].apply(transform_text)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "seUesFnmtkME",
        "outputId": "6f6fa97e-696d-49e7-b4ec-c967fc0350f4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text  num_characters  \\\n",
              "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
              "1       0                      Ok lar... Joking wif u oni...              29   \n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
              "3       0  U dun say so early hor... U c already then say...              49   \n",
              "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
              "\n",
              "   num_words  num_sentences                                   transformed_text  \n",
              "0         24              2  go jurong point crazi avail bugi n great world...  \n",
              "1          8              2                              ok lar joke wif u oni  \n",
              "2         37              2  free entri 2 wkli comp win fa cup final tkt 21...  \n",
              "3         13              1                u dun say earli hor u c alreadi say  \n",
              "4         15              1               nah think goe usf live around though  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e6751ed-f037-4dd2-84f9-d18ecb657912\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "      <th>text</th>\n",
              "      <th>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "      <th>num_sentences</th>\n",
              "      <th>transformed_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>111</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>go jurong point crazi avail bugi n great world...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>ok lar joke wif u oni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>155</td>\n",
              "      <td>37</td>\n",
              "      <td>2</td>\n",
              "      <td>free entri 2 wkli comp win fa cup final tkt 21...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>49</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>u dun say earli hor u c alreadi say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>61</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>nah think goe usf live around though</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e6751ed-f037-4dd2-84f9-d18ecb657912')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9e6751ed-f037-4dd2-84f9-d18ecb657912 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9e6751ed-f037-4dd2-84f9-d18ecb657912');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud\n",
        "wc = WordCloud(width=500,height=500,min_font_size=10,background_color='white')"
      ],
      "metadata": {
        "id": "Y3OblhJFvmoY"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spam_wc = wc.generate(df[df['target'] == 1]['transformed_text'].str.cat(sep=\" \"))\n",
        "plt.figure(figsize=(15,6))\n",
        "plt.imshow(spam_wc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "ppr0IoA1vtOz",
        "outputId": "2cc9a2b1-d225-4160-9198-c42ea142087f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff192f7ce50>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ham_wc = wc.generate(df[df['target'] == 0]['transformed_text'].str.cat(sep=\" \"))\n",
        "plt.figure(figsize=(15,6))\n",
        "plt.imshow(ham_wc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "L4vfEfdCvwb8",
        "outputId": "1a17d827-b041-46c2-f7ca-0aa83718ad0b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff192321dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spam_corpus = []\n",
        "for msg in df[df['target'] == 1]['transformed_text'].tolist():\n",
        "    for word in msg.split():\n",
        "        spam_corpus.append(word)\n",
        "len(spam_corpus)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYENps4iv5aB",
        "outputId": "d1899e68-4967-4421-ec71-4bad3a9e8bc7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9939"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "from collections import Counter\n",
        "sns.barplot(pd.DataFrame(Counter(spam_corpus).most_common(30))[0],pd.DataFrame(Counter(spam_corpus).most_common(30))[1])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "EANqfrRJwLtY",
        "outputId": "4e26ba05-1298-4981-f2d0-92eac9eae45d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ham_corpus = []\n",
        "for msg in df[df['target'] == 0]['transformed_text'].tolist():\n",
        "    for word in msg.split():\n",
        "        ham_corpus.append(word)\n",
        "len(ham_corpus)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--6Y_dDywS_4",
        "outputId": "56e32c27-a9a4-46f8-cddd-8f4fe4042548"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35394"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "sns.barplot(pd.DataFrame(Counter(ham_corpus).most_common(30))[0],pd.DataFrame(Counter(ham_corpus).most_common(30))[1])\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "n0VdBo8dwY82",
        "outputId": "20761f5d-0f4f-4301-cde9-13c29513a277"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Nqs3yiIi/VBzTyWu8JHBvRGzYpJz1Q8sFbAf8Kpfhgw2OE7A/8OaI+LqktYA3RsRtFWnPLn/W803U7IjYfpDn1zJtk/eF0ulVvi9eBxwM/DPpekV+Lcrn0HG++bi2rwFVemZI6mBFxAnlbUnHk4a31jsbuAfYJ28fAJwD7Fk6dkPSP2slSXuWjl2R0j+wlP6NwATg9ZI2r0u/bF3a/YAPA+vUfVhWAOo/oJ8AtoiIFyRNBmZJmhwRJ5PeGFXOAeYCW+ftx4ALgfLFuFzezUp59StvnQNIwXQv4DhJLwM3RkTV3W3LcuRz7tQPgSOA/wKIiLsknQ/0+0CTZsnPkvThXO4Dgfc3yljSJ0hLqKwKrEuaQPkDoHZhqdUyDs2/z8u/96/Ia2vgncD4uuC3ImnIdSM3S/oe8FPgxdrOiLijIu0mpPk8Z0pagvTe/klE/LWUZi7pwiJgLeCZ/Hhl4I9AVc237dc4Iv43r2y8VkT8scE5TQTuA84slWUKcEKD9DXfB/4BvBf4OvA8KQi+vSLtAknfj4hPSVoFuCKfR5VO3kPtpB1I7fM84AHgA6Rz259081c20FptO5+9hsZMUKiwLOnNWG/diNirtP01SXfWpdmA9A9ZGdittP950oW63geAj+a/d3xd+n+rS/tr4HHSmiQn1KW9qy7tEhHxAkBE/EHStqSL3No0DgrrRsSHcvAhIv5WUbUul7d8N/488KUG+RIRD0v6O/BK/tkO+KeBliMivtbobzWxbETcVpfVaw3K+5CkfYH/R7oAvj8iXmqS96GkhRlvzcf/TtLqpfweAZC0Q7lGBxyVayXl5d6XBpYnfcbKwe+vpGDVyKb5d+21EelC+t6K83uedNH6oaT/A5wPnCRpFvCNiJgfEevkMv8QuCQirszbOwG7NyhD269xtgpwr6Tb6BvIajWAKcBngS8DR0TEnZJeiojrm+QJae2zzSXNy/k9ozSxtZ+I+HdJ35H0A2AL4NsRcdEQnF/LtLX3RYfWi4i9JU2NiBk50Nw4BPlCe9eAhsZMUKirai0JjCdF4HovSXpXRNyUj9sG6HOhyE00syVtHRG3tPrbETEDmJGbsGp3QuTHq0raNCLuzGkfAR5hURRv5sm6Y19QahM/G3hbg2Neyc0ekc9vXeDlBuXdq8kHpx9JvyctrnU+cBbwmYj4x0DLIemUZn8vIg6r2P3nnFct32mkIFvOt77avSrpPXGrpMpmnuzliHil9vmRNK4un9Kf0DYRcXPeeCd1/XP5gne9pP/u8MN9XcW+yiar3HSzCzAdmEy6yZhJqhVdCbyllHyriChuaCLiKknfaVCGlq9xna82eY78HjlJ0oX595O0d+15NZ9jrRzjSTWHQl1N/tZcltuAkLRnRFxckW8n59fO++15GrxPSM08K1adW/79rKSNgCeA1csJ6vItX1Oa5QttfPaaGTNBgb5VrdeAJyOiKvp/EjhXqW8BUnX6oAZ5Pi3pGtpoe8y2IN0VXUr6x+1Kuvv/pKQLI+I7Hb6BDqT/XclrwIGS/qtBGY4GfgZMkjQT2IZUK6hyjaQTgffk7Yb9K9kpwLuA/YDNSBe9GyLi9wMsx9wGf6eZQ0mzNDeU9BjwMPCRujTl98IqpItkkO7Enm2S9/WSvkRqVtsB+BRwWUW6g4Gz83tIpPfQxyrSASwj6QzSRbv4vEVEvzv/7IXS46JvrEHa3wHXAsdFxK9L+2dJek9d2j9J+grwo7y9P/CnBvm28xoXIuJ6SWuwqFnntoh4qiLdAmBvSbuQakytnAJcAqwu6ZukGtZX6tLsVrc9D1gq7w+gKih0cn5Vafs0F0bEQJpBz8jNXF8hXS+Wpy64DjBf6Owa0M+Y6WhuV6l9tzaa5wXgOWBu7Y68lPZ6cntirblA0j0RsVGDvG8Adq41+Sh1dl0B7JjzH5YvDZL0BmAr0gXrNxFRucyupItI/Ssz8q4DgE0iYs+q9KXjlifdnX4RmBgR/drIlTo4VS4HsEJEPDygk+qf/3Kk5rVmnd2HkZr7Ls5l2B34YUSc2iD9EqQL/vtz+qtJK/c2ulNfCaBJEEXSb0n9EnOB/63tj4i2AqLSCJqrI2LbiueWr73X2shnVdLF4j2ki+UNpBuAfh3NpWNavsY53T7AcaRajkhB+IiImNVO2VrkvSGpT0fANRHRKEAOJO923kNbRMTcclpJu0bfARMrRsRf1aBTv/waq3pwhRYl7T+4Ih/3LtIAiHMkrUaTz9JgP3uLY1A4n+q7+cnAhRHxnVLa2yPi7SqNCpJ0Z0Rs2j9nkPQA8LaIeDVvLwP8NiI2VN3Iom7KNZrJ9L0z7XfHVHUuLc7vBFJNYXngFtKd940R8VBF2puBnWqdnpL+ifT69guouVngSNI37pVHYvS7m5a0MqkGVX9+/ZqaJN0FbB0RL+bt5YBbGjUf5aaIKyKiaVU7B4PaBRaaj2BrOFqtHflu8vaIWK/iuZYjWCqOWa72ejRJswxpMMFk+r7GVc2xtcC3Q612kP+fv4yITZr9nXbk859UV45+ne75b36iosz9Xotcq/lPYM2I2EnpGx63joizKtLWj2DbF/h8lEawSbo8InaV9DB9m49zEeLNpbRH54cbkGpWtcEmu5FqWP1qLPmYKaQRRG+RtCbps9RoaHPbn70qY6n5qF0Tgc1Ld/NHk+7m30O6myu3s3batjqT1G49O2/vBpyfL0aVQwaHmqSzgY2Be1nU/tqoGt2yf6XOLcB3IuLJNoryn8BlknYGNgTOpWKUTjaTNNpmF1Lz3kHAwgZpryTd+dxNXftyBVG6O8+Pm3W47UZq874hl+dnDZogW45gK7lM0qdIzSBFsGl0h672+8agvREstXzfSRr9szywlqRNgP8bEZ+qSD6bXHumvbboJeqai55mCOZASfoGqdnj9yx6TSo73UllvhH4JX3/51X+m/T/+nLe/h/S/7tfUKCNEWwRsWv+3XAOUynt16BoVdg8Fg13PoZ0HaqyB6m59o6cx58kNWta6uSzV1nIxeqH9CFaqrS9DPBAfjyvLu2bSW+yv5GGdd1E+saiZvnXRlp8FpgyAud3XwdpNwF+C/wh/8wDNm5xzAdJI6yOB3ZrkXZ30miru4G3NEk3N/++q7Tv9gZp7+jg/A7P53dM/rmTNE+h2TFL5XOcSRoQcGZFmjvb2Zf3P1zx81CTv7926WcCaZ5Eo7Tzyq9bLvtvGqS9lXTHPa+0754GaSv3NynHcaSmto/mn6uAY4fgvfwgsHSbaStf/wZpby+/fq2OJ3XY30dqp399k3TXtLOvdG7LlLaXAR5skPa2/PuO/Hu58melwTFtffaqfhbHmkInd/O7k+5MryXd+bwIvC83CdQPYwUgIuYAc7pS8vbcIumt0WAyU53tSf0J5f6Vt0taour8JH2LNGRzZt51mNIIrS+V0pxK3470lUh3ep9WGvlTNaKoNhLj8dwJ+SfSiKEq5ynNJ7icFnfeEXGipOtITV4A0yNiXoN8a8e8KumqfA6vJ70HPl6XrO0aVrRx91iXvpORSi1HsNTl/aj6jkxsdEf9a0lvi4i72ylERBwhaS9ShybAGRFxSTvHtnAPaVh4v07rCpdL2jnykNsWXsz9brUWgK1INaOCOhjBlpvxlgVWy81d5Xk/ExqU4VzgNkm112l3Ug2mygVKA0tWzu/9j1ExB2OAn71+Frs+BQBJU1j0Br45X8ir0rXd/9ArlMarX0q6QLzMolFNVbMqOzq/3Ea/aeRhqErDBefVfUAajeQCiuGw9eXYlVT1nwScSvowHRMR/Ub+SDoU+CZpFFHRpBCldtuBUhq7/yFgW1Kn6QXAz6OuCUnSpqRgWht99BfgoIion2dSS78R/ftLBr3Mg6SPkyZzvY10QVke+GpE9BuZpjR34UTge8A7WFST3bci7X3A+sBDtHgPdVP+nM4mBYfyDUC/GdBKo/qWy+lepcmwTaUJpqcCG+W8xwPTyv8/pblADZWDt9LyNJ8D1iS1KNTmljxPCpCnNTi/zVm0ssINjW5YJB1LarEoD4B4X0QcWZeu489e5d9bHINCu9Qjo4k6IWk+qdmkT5t71R1op+eXg8K2tbvyPMrhusFeLCTNIC3h8Wwp3+OjupPwIWDLaDCiapDl+DGpbfmqaNHZnNOvCBB9ZxDXpzmaFGTeSqp17gTcFBHNJrC1+rsdj2BRGrFyMvC+nPbnpNf86Yq0a7NoKC+kkUrP1r+HNLDx+W2TdC9pJnH9e7nVpLdW+e5NurBOInWov4MUTKtmjdeOWZ2+Qb3f7G2ltda+G2kk0leBzUmTCBvm22Z5q5ZKuatbQXpxbD7qxOr07Wh7lTRn4SWlJR560cKIaPdrTDs9v28B8yRdS/rgv4e+s3iRdEFE7FNR/QagwRt541pAyGn+orT8RpX5pD6eIRcR++WRKTvkZpbK8fb1o4+Uhi43mt8xjdR3My8ipuf8f1SRrhO1TsbKESxVB+Qg2m5nY63JrDaU9zxSc0Wfobwx8HH07fpbRDSd4Chpw4h4QH2Xlyk0uCB/NSIuzE0925H6x04nBYf6/D9ImhS4JqkZa21SZ/4/V+Q7LdIaTe8idYY3zLcdkv6FNFfmzfmGrGYF4OYmx61P+qzW107bqk07KDQ34qOJBmBebha6jL5V7qrRRx2dX0T8OLfR1yYpHRkRT9Qlq6302sm6LUtIWiUinoGiptDovfkicGcOTOXza6u9tJl8B3k8i8bbnyqparx9J6OP/h4R/5D0Wq5ZPEW6Qx2wGMAIFnWwCBxpmOtWsWgo77GkkWeV8zu66Mbcj3Upff/X5Qv94aT1qqrWUWo0UqnWl7ILad7KFZIaTUj9Bmm8/y8jYjNJ29F4olsn+bbjfFKn/bfoe/P1fFUfWsk5pJuWk0hBbzodjAZz81EL7fY/9ApJ51TsjqqmmJy+o/OTNIF0t1QeC37DAItby/NA0ppLF+ZdewPfjIjzKtJWtpu2217aohxtjbdXB/M7JH2fdG77klaufIE00mX6EJT3QVIt6+W8vQxpVMoGFWnbnoiZa3lvj4i/5+3XkUbsNFpapSty4K8X0Xg2eLv5Xk5q+9+B1MTzEqlW2G9ehaQ5ETElvzc2ywH+tw3Stp1vNynPjZF0d+1/pg7my7im0EKM/GiijnR6senk/PId44foPwfihlKajtuZI+JcSXNYdFe3ZzQYPTUUF/8m2h1v38n8jhVJQe460pDGFRt1SA9AJyNYOlkE7hxSDbKcb9UY/q6KiO06Sa80F2MyfW9Yqjr09yH1mx0fEc9KehMpYFZ5Nve13QDMlPQUfZciGWi+3fSy0uz830n6NClQLd/imIJrCmOMpImkan7t7v9GUofigiHIu8+d6XBq0VcRQ3E3Juk40sS/H+ddHyLdedeP8tiEdEHus35W1cU+Nze8O/+sS5oLckOk5c8HrYMRLFcBnyaNKttcaSLmwRGxU5N8a0N5b2yUbzfV993QfOb4eaTX904WNePEYJsVlWbxH0G6Odif9D/fJCIOHky+3STp7aR+j5VJzV8rkiad3trW8Q4KY4ukX5DaImtNLx8B9o+IHYYg76uAvaPN9XaGkqQ3RcTjki6g792XSG/4fRoc2unfKY+3vzEqxturg/WzcvolSf0w25FmbL8UTb6UphskvZm0sNs7SUHsYdL7YqDLM3edOlibS9L9wFtjiC9owz3yZyjkJuEvk5p5l8q7o90yOyiMMZ20dw8g74tII2muYYg7eTsow4h/SNXZ+lnXkMbP19aKuqlqRNMwlHkZ0kioyaSJWH8lXSgaLaEx4jrsu7kQOCwimi1D08nfro38WZc04q1mBVLfW8NVY0dartEfQRvD0qu4T2HseVrSR1jUBLIfqW18KNzCouGPNd0elggMfHhem3l32g/SyfpZd5GWVN+IVJt4VtIt0fzLfrphNmnC3x00XjK717Tsu5F0Gel/twJwn9IX/TSd6NamgY786QWdDEvvxzWFMUZp4tGppC/xCdL6J5+JiEeHIO+Ov/N4qOT25VXogQ+pBrAartICZh8lLTf+xogY1i+VbzTSqJep78xxqOi7UZrBL+BY4F/Lh5PWX+r6e7PXSNqedDNYX6OvGpbej2sKY8/XSR+c8jvkOvQAAAINSURBVJj/42n8JTCd6Og7j4dS7lx8jvRmH2ltz+/Ioz/eTaot/IE0x6HP1y4Ok47WM+oR95NqXeuSOk2fI42EKoJC5NnNkpaKupnOSt8+tjiaTloddSlar5Tcj2sKY0zVnWqju9cB5v8WFn3n8R4j0AzSE9qd3yHpi6QgMDeql+EeFkrrGa1H6mAesfWMOiHpZyxq8ip/QdEJpTRFsyJp8beanm/77xZJD1bNVWn7eAeFsSVPstm2rqZw/WAmHlUMA12ddNf2MjRcusJ6iBos8Nbjo49aNnn1UrNir8gTWI9rNNenFTcfjT0nkJbP7jM7eJB5drJkhfWgXr74N9GyyavHmhV7xVakpWAGVCt0TWEMUvp6wdrs4F8N9I7BbCSUaqbj6IElvEebwdYKHRTMrKc0uqjVjNJaz6jhoGBmZoVBf7m2mZmNHQ4KZmZWcFAwG2KSdpT0oKT5ko5qfYRZ73CfgtkQyiui/g/pi1YWALcD+3kEmI0WrimYDa0tgfkR8VBEvAL8BJg6wmUya5uDgtnQmgCUFx9ckPeZjQoOCmZmVnBQMBtajwGTStsT8z6zUcFBwWxo3Q6sL2kdSUsD+9L/i4nMepYXxDMbQhHxWv4OhauBJYGzI+LeES6WWds8JNXMzApuPjIzs4KDgpmZFRwUzMys4KBgZmYFBwUzMys4KJiZWcFBwczMCv8fk3rQdE++LtIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Model Building"
      ],
      "metadata": {
        "id": "mt7t07jvy9js"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "tfidf = TfidfVectorizer(max_features=3000) # 3000 is giving good accuracy and precision\n",
        "\n",
        "X = tfidf.fit_transform(df['transformed_text']).toarray()\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3mqHgTQzOEL",
        "outputId": "f74fe258-f09b-4d5d-c1bf-f6967645aa0e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5169, 3000)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # scaling\n",
        "# from sklearn.preprocessing import MinMaxScaler\n",
        "# scaler = MinMaxScaler()\n",
        "# X = scaler.fit_transform(X)\n",
        "\n",
        "# # appending the num_character feature to X\n",
        "# X = np.hstack((X,df['num_characters'].values.reshape(-1,1)))"
      ],
      "metadata": {
        "id": "81g-uc3CUXOH"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['target'].values\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score\n",
        "\n",
        "gnb = GaussianNB()\n",
        "mnb = MultinomialNB()\n",
        "bnb = BernoulliNB()\n",
        "gnb.fit(X_train,y_train)\n",
        "\n",
        "y_pred1 = gnb.predict(X_test)\n",
        "\n",
        "print(accuracy_score(y_test,y_pred1))\n",
        "print(confusion_matrix(y_test,y_pred1))\n",
        "print(precision_score(y_test,y_pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgqL6zOWAINA",
        "outputId": "3ccd86ad-59a1-4569-9b8e-fba5a6573ba3"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8694390715667312\n",
            "[[788 108]\n",
            " [ 27 111]]\n",
            "0.5068493150684932\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mnb.fit(X_train,y_train)\n",
        "y_pred2 = mnb.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred2))\n",
        "print(confusion_matrix(y_test,y_pred2))\n",
        "print(precision_score(y_test,y_pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sd2nm9lcAP0c",
        "outputId": "4eb0ce44-6a55-4654-dfe9-db12c93aba3d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9709864603481625\n",
            "[[896   0]\n",
            " [ 30 108]]\n",
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bnb.fit(X_train,y_train)\n",
        "y_pred3 = bnb.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred3))\n",
        "print(confusion_matrix(y_test,y_pred3))\n",
        "print(precision_score(y_test,y_pred3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-3BK7_1ARPB",
        "outputId": "605dfaee-c3a5-4d10-dcf9-090c979d9861"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9835589941972921\n",
            "[[895   1]\n",
            " [ 16 122]]\n",
            "0.991869918699187\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "\n",
        "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
        "knc = KNeighborsClassifier()\n",
        "mnb = MultinomialNB()\n",
        "dtc = DecisionTreeClassifier(max_depth=5)\n",
        "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
        "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
        "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
        "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
        "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
        "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
        "xgb = XGBClassifier(n_estimators=50,random_state=2)\n",
        "\n",
        "\n",
        "clfs = {\n",
        "    'SVC' : svc,\n",
        "    'KN' : knc, \n",
        "    'NB': mnb, \n",
        "    'DT': dtc, \n",
        "    'LR': lrc, \n",
        "    'RF': rfc, \n",
        "    'AdaBoost': abc, \n",
        "    'BgC': bc, \n",
        "    'ETC': etc,\n",
        "    'GBDT':gbdt,\n",
        "    'xgb':xgb\n",
        "}"
      ],
      "metadata": {
        "id": "CpPtZz2YAWTj"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
        "    clf.fit(X_train,y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test,y_pred)\n",
        "    precision = precision_score(y_test,y_pred)\n",
        "    \n",
        "    return accuracy,precision"
      ],
      "metadata": {
        "id": "p-OEAS9NAb7h"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_scores = []\n",
        "precision_scores = []\n",
        "\n",
        "for name,clf in clfs.items():\n",
        "    \n",
        "    current_accuracy,current_precision = train_classifier(clf, X_train,y_train,X_test,y_test)\n",
        "    \n",
        "    print(\"For \",name)\n",
        "    print(\"Accuracy - \",current_accuracy)\n",
        "    print(\"Precision - \",current_precision)\n",
        "    \n",
        "    accuracy_scores.append(current_accuracy)\n",
        "    precision_scores.append(current_precision)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttY6J4moAiGn",
        "outputId": "7688c696-7fc7-457f-cadc-9646239f48bd"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For  SVC\n",
            "Accuracy -  0.9758220502901354\n",
            "Precision -  0.9747899159663865\n",
            "For  KN\n",
            "Accuracy -  0.9052224371373307\n",
            "Precision -  1.0\n",
            "For  NB\n",
            "Accuracy -  0.9709864603481625\n",
            "Precision -  1.0\n",
            "For  DT\n",
            "Accuracy -  0.9323017408123792\n",
            "Precision -  0.8333333333333334\n",
            "For  LR\n",
            "Accuracy -  0.9584139264990329\n",
            "Precision -  0.9702970297029703\n",
            "For  RF\n",
            "Accuracy -  0.9748549323017408\n",
            "Precision -  0.9827586206896551\n",
            "For  AdaBoost\n",
            "Accuracy -  0.960348162475822\n",
            "Precision -  0.9292035398230089\n",
            "For  BgC\n",
            "Accuracy -  0.9574468085106383\n",
            "Precision -  0.8671875\n",
            "For  ETC\n",
            "Accuracy -  0.9748549323017408\n",
            "Precision -  0.9745762711864406\n",
            "For  GBDT\n",
            "Accuracy -  0.9477756286266924\n",
            "Precision -  0.92\n",
            "For  xgb\n",
            "Accuracy -  0.9439071566731141\n",
            "Precision -  0.9347826086956522\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "performance_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores}).sort_values('Precision',ascending=False)\n",
        "performance_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "niCBefuhAz4G",
        "outputId": "23861627-e4ff-42d3-a7ba-a706f22aaedd"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Algorithm  Accuracy  Precision\n",
              "1         KN  0.905222   1.000000\n",
              "2         NB  0.970986   1.000000\n",
              "5         RF  0.974855   0.982759\n",
              "0        SVC  0.975822   0.974790\n",
              "8        ETC  0.974855   0.974576\n",
              "4         LR  0.958414   0.970297\n",
              "10       xgb  0.943907   0.934783\n",
              "6   AdaBoost  0.960348   0.929204\n",
              "9       GBDT  0.947776   0.920000\n",
              "7        BgC  0.957447   0.867188\n",
              "3         DT  0.932302   0.833333"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KN</td>\n",
              "      <td>0.905222</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NB</td>\n",
              "      <td>0.970986</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RF</td>\n",
              "      <td>0.974855</td>\n",
              "      <td>0.982759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SVC</td>\n",
              "      <td>0.975822</td>\n",
              "      <td>0.974790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ETC</td>\n",
              "      <td>0.974855</td>\n",
              "      <td>0.974576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LR</td>\n",
              "      <td>0.958414</td>\n",
              "      <td>0.970297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>xgb</td>\n",
              "      <td>0.943907</td>\n",
              "      <td>0.934783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>0.960348</td>\n",
              "      <td>0.929204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GBDT</td>\n",
              "      <td>0.947776</td>\n",
              "      <td>0.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>BgC</td>\n",
              "      <td>0.957447</td>\n",
              "      <td>0.867188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DT</td>\n",
              "      <td>0.932302</td>\n",
              "      <td>0.833333</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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    {
      "cell_type": "code",
      "source": [
        "performance_df1 = pd.melt(performance_df, id_vars = \"Algorithm\")\n",
        "performance_df1"
      ],
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        "colab": {
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          "height": 739
        },
        "id": "YhkqKldCBE70",
        "outputId": "10a84c14-253c-4a1f-9fb5-4d4c6db5bc15"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Algorithm   variable     value\n",
              "0         KN   Accuracy  0.905222\n",
              "1         NB   Accuracy  0.970986\n",
              "2         RF   Accuracy  0.974855\n",
              "3        SVC   Accuracy  0.975822\n",
              "4        ETC   Accuracy  0.974855\n",
              "5         LR   Accuracy  0.958414\n",
              "6        xgb   Accuracy  0.943907\n",
              "7   AdaBoost   Accuracy  0.960348\n",
              "8       GBDT   Accuracy  0.947776\n",
              "9        BgC   Accuracy  0.957447\n",
              "10        DT   Accuracy  0.932302\n",
              "11        KN  Precision  1.000000\n",
              "12        NB  Precision  1.000000\n",
              "13        RF  Precision  0.982759\n",
              "14       SVC  Precision  0.974790\n",
              "15       ETC  Precision  0.974576\n",
              "16        LR  Precision  0.970297\n",
              "17       xgb  Precision  0.934783\n",
              "18  AdaBoost  Precision  0.929204\n",
              "19      GBDT  Precision  0.920000\n",
              "20       BgC  Precision  0.867188\n",
              "21        DT  Precision  0.833333"
            ],
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              "      <th></th>\n",
              "      <th>Algorithm</th>\n",
              "      <th>variable</th>\n",
              "      <th>value</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KN</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.905222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NB</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.970986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RF</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.974855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVC</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.975822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ETC</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.974855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LR</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.958414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>xgb</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.943907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.960348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GBDT</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.947776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>BgC</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.957447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>DT</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.932302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>KN</td>\n",
              "      <td>Precision</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>NB</td>\n",
              "      <td>Precision</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>RF</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.982759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>SVC</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.974790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>ETC</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.974576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>LR</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.970297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>xgb</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.934783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.929204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>GBDT</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>BgC</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.867188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>DT</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.833333</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(x = 'Algorithm', y='value', hue = 'variable',data=performance_df1, kind='bar',height=5)\n",
        "plt.ylim(0.5,1.0)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "E_S0059pBOb9",
        "outputId": "72710ea1-c9f5-4e99-b4a0-7f9631a50eb3"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 441.75x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_scaling':accuracy_scores,'Precision_scaling':precision_scores}).sort_values('Precision_scaling',ascending=False)\n",
        "# new_df_scaled = performance_df.merge(temp_df,on='Algorithm')\n",
        "# new_df_scaled\n",
        "\n",
        "# temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_num_chars':accuracy_scores,'Precision_num_chars':precision_scores}).sort_values('Precision_num_chars',ascending=False)\n",
        "# new_df_scaled.merge(temp_df,on='Algorithm')"
      ],
      "metadata": {
        "id": "BC07hPdhB0Mt"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Voting Classifier\n",
        "\n",
        "svc = SVC(kernel='sigmoid', gamma=1.0,probability=True)\n",
        "mnb = MultinomialNB()\n",
        "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
        "\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "voting = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('et', etc)],voting='soft')\n",
        "voting.fit(X_train,y_train)\n",
        "y_pred = voting.predict(X_test)\n",
        "\n",
        "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
        "print(\"Precision\",precision_score(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A_GF3xnkB8F1",
        "outputId": "0280e44a-3b50-4f1a-b4bb-5c36ffae4f11"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.9816247582205029\n",
            "Precision 0.9917355371900827\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying stacking\n",
        "\n",
        "estimators=[('svm', svc), ('nb', mnb), ('et', etc)]\n",
        "final_estimator=RandomForestClassifier()\n",
        "\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "\n",
        "clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)\n",
        "clf.fit(X_train,y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
        "print(\"Precision\",precision_score(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wFF6DJ7cCBFq",
        "outputId": "b9a1815d-e032-4fe8-9cc0-031b618823a5"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.9806576402321083\n",
            "Precision 0.946969696969697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "pickle.dump(tfidf,open('vectorizer.pkl','wb'))\n",
        "pickle.dump(mnb,open('model.pkl','wb'))"
      ],
      "metadata": {
        "id": "We9O6hfeCEyh"
      },
      "execution_count": 54,
      "outputs": []
    }
  ]
}