main.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4784RTObTp56"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.feature_extraction.text import  TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mj19Faa7O6UK",
        "outputId": "ae00242d-687e-4e78-ad77-31e2a4ebdc4d"
      },
      "outputs": [],
      "source": [
        "# get dataset dari public link drive ku (ga perlu mount/akun)\n",
        "!pip install gdown\n",
        "\n",
        "file_id = '1cXASsh88T1kqOfXAPRKhC2CxqxjUnUvI'\n",
        "url = f'https://drive.google.com/uc?id={file_id}'\n",
        "output_filename = 'SpamEmailClassificationDataset.csv'\n",
        "\n",
        "!gdown --id {file_id} --output {output_filename}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "WKFayTTncgnP",
        "outputId": "e84e2b6a-0038-4432-ba6c-9ceb3f5cf84f"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>ounce feather bowl hummingbird opec moment ala...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>wulvob get your medircations online qnb ikud v...</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>computer connection from cnn com wednesday es...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>university degree obtain a prosperous future m...</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>thanks for all your answers guys i know i shou...</td>\n",
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              "      <td>hi given a date how do i get the last date of ...</td>\n",
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              "      <th>83444</th>\n",
              "      <td>1</td>\n",
              "      <td>now you can order software on cd or download i...</td>\n",
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              "    <tr>\n",
              "      <th>83445</th>\n",
              "      <td>1</td>\n",
              "      <td>dear valued member canadianpharmacy provides a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83446</th>\n",
              "      <td>0</td>\n",
              "      <td>subscribe change profile contact us long term ...</td>\n",
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              "    <tr>\n",
              "      <th>83447</th>\n",
              "      <td>1</td>\n",
              "      <td>get the most out of life ! viagra has helped m...</td>\n",
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              "</table>\n",
              "<p>83448 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       label                                               text\n",
              "0          1  ounce feather bowl hummingbird opec moment ala...\n",
              "1          1  wulvob get your medircations online qnb ikud v...\n",
              "2          0   computer connection from cnn com wednesday es...\n",
              "3          1  university degree obtain a prosperous future m...\n",
              "4          0  thanks for all your answers guys i know i shou...\n",
              "...      ...                                                ...\n",
              "83443      0  hi given a date how do i get the last date of ...\n",
              "83444      1  now you can order software on cd or download i...\n",
              "83445      1  dear valued member canadianpharmacy provides a...\n",
              "83446      0  subscribe change profile contact us long term ...\n",
              "83447      1  get the most out of life ! viagra has helped m...\n",
              "\n",
              "[83448 rows x 2 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('SpamEmailClassificationDataset.csv')\n",
        "df"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "uYOOjmeaYO6x"
      },
      "source": [
        "# **Data Pre-Processing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9Kt4EbrTxPu",
        "outputId": "e28a0869-cda1-48c1-e40a-1f618e2b3b55"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "label    0\n",
              "text     0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJcXmx44T24v",
        "outputId": "3429283f-7f35-45eb-a5e5-39019071e035"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "label     int64\n",
              "text     object\n",
              "dtype: object"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "wBQ1cwbDUAZH"
      },
      "outputs": [],
      "source": [
        "df.loc[df['label'] == 'spam', 'label',] = 0\n",
        "df.loc[df['label'] == 'ham', 'label',] = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hYIJ8WzyUSx_",
        "outputId": "201e1fb0-230f-41b5-9750-7b7b8dc8c3a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "label\n",
              "1    43910\n",
              "0    39538\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "aa195BabUc5v"
      },
      "outputs": [],
      "source": [
        "X = df['text']\n",
        "\n",
        "Y = df['label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izl_aqeVUjLC",
        "outputId": "8516d17e-77a0-48aa-e07a-a5fca27b4508"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        ounce feather bowl hummingbird opec moment ala...\n",
              "1        wulvob get your medircations online qnb ikud v...\n",
              "2         computer connection from cnn com wednesday es...\n",
              "3        university degree obtain a prosperous future m...\n",
              "4        thanks for all your answers guys i know i shou...\n",
              "                               ...                        \n",
              "83443    hi given a date how do i get the last date of ...\n",
              "83444    now you can order software on cd or download i...\n",
              "83445    dear valued member canadianpharmacy provides a...\n",
              "83446    subscribe change profile contact us long term ...\n",
              "83447    get the most out of life ! viagra has helped m...\n",
              "Name: text, Length: 83448, dtype: object"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGZjIja8UlOe",
        "outputId": "6f1e14ee-63ca-473a-9bde-522af0870c19"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        1\n",
              "1        1\n",
              "2        0\n",
              "3        1\n",
              "4        0\n",
              "        ..\n",
              "83443    0\n",
              "83444    1\n",
              "83445    1\n",
              "83446    0\n",
              "83447    1\n",
              "Name: label, Length: 83448, dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "BF5xrFbSUpDA"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RDtF2W2bUvme",
        "outputId": "2c227fe6-2584-4b6b-abb6-40f852676478"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(83448,)\n",
            "(66758,)\n",
            "(16690,)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "VBdC-7DZV2gY"
      },
      "outputs": [],
      "source": [
        "feature_extraction = TfidfVectorizer(min_df=1, stop_words='english', lowercase=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Q-lJpyPuWs1w"
      },
      "outputs": [],
      "source": [
        "X_train_features = feature_extraction.fit_transform(X_train)\n",
        "X_test_features = feature_extraction.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Sl6NNnhUW2np"
      },
      "outputs": [],
      "source": [
        "Y_train = Y_train.astype('int')\n",
        "Y_test = Y_test.astype('int')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "tN3aBtL7Ye__"
      },
      "source": [
        "# **Model Training, Testing & Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "IkWBViXaaLro"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "WHscfGO9aXEu"
      },
      "source": [
        "**DECISION TREE**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_WlUcB4qaJPO",
        "outputId": "250cfbdb-83c7-4080-8949-03669a98ebdc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Decision Tress:\n",
            "\n",
            "Training Data Accuracy: 0.9998651846969652\n",
            "Testing Data Accuracy : 0.9645296584781307\n",
            "Precision             : 0.9653068143293028\n",
            "Recall                : 0.9677126996714626\n",
            "F1 Score              : 0.9665082597872822\n"
          ]
        }
      ],
      "source": [
        "dtrees = DecisionTreeClassifier()\n",
        "dtrees.fit(X_train_features, Y_train)\n",
        "\n",
        "dt_train = dtrees.predict(X_train_features)\n",
        "dt_test = dtrees.predict(X_test_features)\n",
        "\n",
        "dt_train_acc = accuracy_score(Y_train, dt_train)\n",
        "dt_test_acc = accuracy_score(Y_test, dt_test)\n",
        "\n",
        "dt_precision = precision_score(Y_test, dt_test)\n",
        "dt_recall = recall_score(Y_test, dt_test)\n",
        "dt_f1 = f1_score(Y_test, dt_test)\n",
        "\n",
        "\n",
        "print(\"Decision Tress:\\n\")\n",
        "print(\"Training Data Accuracy:\", dt_train_acc)\n",
        "print(\"Testing Data Accuracy :\", dt_test_acc)\n",
        "\n",
        "print(\"Precision             :\", dt_precision)\n",
        "print(\"Recall                :\", dt_recall)\n",
        "print(\"F1 Score              :\", dt_f1)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "pmKcgU_PdDjE"
      },
      "source": [
        "**Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqWl8nt0dHRQ",
        "outputId": "43606303-6ecb-47a1-9298-ea6c020e01b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic Regression:\n",
            "\n",
            "Training Data Accuracy: 0.9895443242757422\n",
            "Testing Data Accuracy : 0.984661473936489\n",
            "Precision             : 0.9775994650618522\n",
            "Recall                : 0.9937691174804577\n",
            "F1 Score              : 0.9856179775280899\n"
          ]
        }
      ],
      "source": [
        "lr = LogisticRegression()\n",
        "lr.fit(X_train_features, Y_train)\n",
        "\n",
        "lr_train = lr.predict(X_train_features)\n",
        "lr_test = lr.predict(X_test_features)\n",
        "\n",
        "lr_train_acc = accuracy_score(Y_train, lr_train)\n",
        "lr_test_acc = accuracy_score(Y_test, lr_test)\n",
        "\n",
        "lr_precision = precision_score(Y_test, lr_test)\n",
        "lr_recall = recall_score(Y_test, lr_test)\n",
        "lr_f1 = f1_score(Y_test, lr_test)\n",
        "\n",
        "\n",
        "print(\"Logistic Regression:\\n\")\n",
        "print(\"Training Data Accuracy:\", lr_train_acc)\n",
        "print(\"Testing Data Accuracy :\", lr_test_acc)\n",
        "\n",
        "print(\"Precision             :\", lr_precision)\n",
        "print(\"Recall                :\", lr_recall)\n",
        "print(\"F1 Score              :\", lr_f1)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "py9ZPboxfsYy"
      },
      "source": [
        "**KNN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "lKIb9i3ZRHdm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "K Nearest Neighbors:\n",
            "\n",
            "Training Data Accuracy: 0.8629827136822553\n",
            "Testing Data Accuracy : 0.8104254044337927\n",
            "Precision             : 0.7405079419009598\n",
            "Recall                : 0.987651523733998\n",
            "F1 Score              : 0.8464077669902913\n"
          ]
        }
      ],
      "source": [
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_train_features, Y_train)\n",
        "\n",
        "knn_train = knn.predict(X_train_features)\n",
        "knn_test = knn.predict(X_test_features)\n",
        "\n",
        "knn_train_acc = accuracy_score(Y_train, knn_train)\n",
        "knn_test_acc = accuracy_score(Y_test, knn_test)\n",
        "\n",
        "knn_precision = precision_score(Y_test, knn_test)\n",
        "knn_recall = recall_score(Y_test, knn_test)\n",
        "knn_f1 = f1_score(Y_test, knn_test)\n",
        "\n",
        "\n",
        "print(\"K Nearest Neighbors:\\n\")\n",
        "print(\"Training Data Accuracy:\", knn_train_acc)\n",
        "print(\"Testing Data Accuracy :\", knn_test_acc)\n",
        "\n",
        "print(\"Precision             :\", knn_precision)\n",
        "print(\"Recall                :\", knn_recall)\n",
        "print(\"F1 Score              :\", knn_f1)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "zxz6RZ5bf2AC"
      },
      "source": [
        "**RANDOM FOREST**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "rP5c8kjZf0qE"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest:\n",
            "\n",
            "Training Data Accuracy: 0.9998651846969652\n",
            "Testing Data Accuracy : 0.9837028160575195\n",
            "Precision             : 0.987575515787074\n",
            "Recall                : 0.9815339299875382\n",
            "F1 Score              : 0.9845454545454545\n"
          ]
        }
      ],
      "source": [
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train_features, Y_train)\n",
        "\n",
        "rf_train = rf.predict(X_train_features)\n",
        "rf_test = rf.predict(X_test_features)\n",
        "\n",
        "rf_train_acc = accuracy_score(Y_train, rf_train)\n",
        "rf_test_acc = accuracy_score(Y_test, rf_test)\n",
        "\n",
        "rf_precision = precision_score(Y_test, rf_test)\n",
        "rf_recall = recall_score(Y_test, rf_test)\n",
        "rf_f1 = f1_score(Y_test, rf_test)\n",
        "\n",
        "\n",
        "print(\"Random Forest:\\n\")\n",
        "print(\"Training Data Accuracy:\", rf_train_acc)\n",
        "print(\"Testing Data Accuracy :\", rf_test_acc)\n",
        "\n",
        "print(\"Precision             :\", rf_precision)\n",
        "print(\"Recall                :\", rf_recall)\n",
        "print(\"F1 Score              :\", rf_f1)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "9YW3R8sL5A46"
      },
      "source": [
        "# **Stacking Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "fWOHH25i5mGh"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import StackingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "JHTqALzZ5NA9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Stacking Classifier:\n",
            "\n",
            "Training Data Accuracy: 0.9988016417508014\n",
            "Testing Data Accuracy : 0.9881366087477531\n",
            "Precision             : 0.9866892272983644\n",
            "Recall                : 0.990936898153393\n",
            "F1 Score              : 0.988808501017409\n"
          ]
        }
      ],
      "source": [
        "estimators = [ ('dtree', dtrees),('lr', lr), ('rf', rf) ] #('knn', knn),\n",
        "stack = StackingClassifier(estimators, final_estimator = SVC(kernel='linear'))\n",
        "stack.fit(X_train_features, Y_train)\n",
        "\n",
        "stack_train = stack.predict(X_train_features)\n",
        "stack_test = stack.predict(X_test_features)\n",
        "\n",
        "stack_train_acc = accuracy_score(Y_train, stack_train)\n",
        "stack_test_acc = accuracy_score(Y_test, stack_test)\n",
        "\n",
        "stack_precision = precision_score(Y_test, stack_test)\n",
        "stack_recall = recall_score(Y_test, stack_test)\n",
        "stack_f1 = f1_score(Y_test, stack_test)\n",
        "\n",
        "\n",
        "print(\"Stacking Classifier:\\n\")\n",
        "print(\"Training Data Accuracy:\", stack_train_acc)\n",
        "print(\"Testing Data Accuracy :\", stack_test_acc)\n",
        "\n",
        "print(\"Precision             :\", stack_precision)\n",
        "print(\"Recall                :\", stack_recall)\n",
        "print(\"F1 Score              :\", stack_f1)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "pdgUhnxrZtIO"
      },
      "source": [
        "# **Metrics Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "qtPqSnlIkm2m"
      },
      "outputs": [],
      "source": [
        "train_acc_list = {\"LR\":lr_train_acc,\n",
        "                  \"DT\":dt_train_acc,\n",
        "                  \"KNN\":knn_train_acc,\n",
        "                  \"RF\":rf_train_acc}\n",
        "\n",
        "test_acc_list = {\"LR\":lr_test_acc,\n",
        "                  \"DT\":dt_test_acc,\n",
        "                  \"KNN\":knn_test_acc,\n",
        "                  \"RF\":rf_test_acc}\n",
        "\n",
        "precision_list = {\"LR\":lr_precision,\n",
        "                  \"DT\":dt_precision,\n",
        "                  \"KNN\":knn_precision,\n",
        "                  \"RF\":rf_precision}\n",
        "\n",
        "recall_list = {\"LR\":lr_recall,\n",
        "               \"DT\":dt_recall,\n",
        "               \"KNN\":knn_recall,\n",
        "               \"RF\":rf_recall}\n",
        "\n",
        "f1_list = {\"LR\":lr_f1,\n",
        "               \"DT\":dt_f1,\n",
        "               \"KNN\":knn_f1,\n",
        "               \"RF\":rf_f1}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "CSEqqwFlktU0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Traning Accuracy</th>\n",
              "      <th>Testing Accuracy</th>\n",
              "      <th>Precision Score</th>\n",
              "      <th>Recall Score</th>\n",
              "      <th>F1 Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LR</th>\n",
              "      <td>0.989544</td>\n",
              "      <td>0.984661</td>\n",
              "      <td>0.977599</td>\n",
              "      <td>0.993769</td>\n",
              "      <td>0.985618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DT</th>\n",
              "      <td>0.999865</td>\n",
              "      <td>0.964530</td>\n",
              "      <td>0.965307</td>\n",
              "      <td>0.967713</td>\n",
              "      <td>0.966508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNN</th>\n",
              "      <td>0.862983</td>\n",
              "      <td>0.810425</td>\n",
              "      <td>0.740508</td>\n",
              "      <td>0.987652</td>\n",
              "      <td>0.846408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RF</th>\n",
              "      <td>0.999865</td>\n",
              "      <td>0.983703</td>\n",
              "      <td>0.987576</td>\n",
              "      <td>0.981534</td>\n",
              "      <td>0.984545</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Traning Accuracy  Testing Accuracy  Precision Score  Recall Score  \\\n",
              "LR           0.989544          0.984661         0.977599      0.993769   \n",
              "DT           0.999865          0.964530         0.965307      0.967713   \n",
              "KNN          0.862983          0.810425         0.740508      0.987652   \n",
              "RF           0.999865          0.983703         0.987576      0.981534   \n",
              "\n",
              "     F1 Score  \n",
              "LR   0.985618  \n",
              "DT   0.966508  \n",
              "KNN  0.846408  \n",
              "RF   0.984545  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a1 =  pd.DataFrame.from_dict(train_acc_list, orient = 'index', columns = [\"Traning Accuracy\"])\n",
        "a2 =  pd.DataFrame.from_dict(test_acc_list, orient = 'index', columns = [\"Testing Accuracy\"])\n",
        "a3 =  pd.DataFrame.from_dict(precision_list, orient = 'index', columns = [\"Precision Score\"])\n",
        "a4 =  pd.DataFrame.from_dict(recall_list, orient = 'index', columns = [\"Recall Score\"])\n",
        "a5 =  pd.DataFrame.from_dict(f1_list, orient = 'index', columns = [\"F1 Score\"])\n",
        "\n",
        "org = pd.concat([a1, a2, a3, a4, a5], axis = 1)\n",
        "org"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "GZGoX6R4k3B4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "alg = ['LR','DT','KNN','RF']\n",
        "plt.plot(alg,a1)\n",
        "plt.plot(alg,a2)\n",
        "plt.plot(alg,a3)\n",
        "plt.plot(alg,a4)\n",
        "plt.plot(alg,a5)\n",
        "legend = ['Traning Accuracy', 'Testing Accuracy', 'Precision Score', 'Recall Score', 'F1 Score']\n",
        "plt.title(\"METRICS COMPARISION\")\n",
        "plt.legend(legend)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "lf-NvZwxk9zE"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HAM MAIL\n"
          ]
        }
      ],
      "source": [
        "input_mail = [\"if you want to going out, please hit me up\"]\n",
        "\n",
        "input_mail_features = feature_extraction.transform(input_mail)\n",
        "\n",
        "prediction = stack.predict(input_mail_features)\n",
        "\n",
        "if(prediction == 0):\n",
        "    print(\"SPAM MAIL\")\n",
        "else:\n",
        "    print(\"HAM MAIL\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}