├── README.md
└── E_mail_Spam_Clasifier.ipynb


/README.md:
--------------------------------------------------------------------------------
 1 | # Spam-E-mail-detector
 2 | 
 3 | 
 4 | ## Overview
 5 | This is a machine learning-based project designed to classify emails as spam or not spam (ham) using natural language processing and predictive modeling. The detector analyzes email content to identify spam patterns, providing a practical tool for email filtering and security. It leverages a trained model to deliver reliable predictions based on text data.
 6 | 
 7 | ## Features
 8 | - **Spam Detection**: Classifies emails as spam or ham with high accuracy.
 9 | - **Text Analysis**: Processes email content using NLP techniques.
10 | - **Customizable**: Can be trained on custom datasets for improved performance.
11 | - **User-Friendly**: Simple interface for inputting email text and receiving results.
12 | 
13 | ## Technologies Used
14 | 
15 | - Python: Core programming language.
16 |   
17 | - Scikit-learn: For machine learning model development and evaluation.
18 | - NLTK/Spacy: For natural language processing and text preprocessing.
19 | - Pandas: For data manipulation and analysis.
20 | - NumPy: For numerical computations.
21 | - Matplotlib/Seaborn: For visualization (optional, if included).
22 | - Jupyter Notebook (optional): For development and testing.
23 | 
24 | 
25 | 
26 | ## Usage
27 | - Input Data: Provide email text through the script or notebook interface.
28 | - Prediction: The model outputs a classification (e.g., "Spam" or "Ham").
29 | - Accuracy: 95%
30 | 
31 | ## Folder Structure
32 | ```
33 | spam-email-detector/
34 | ├── data/              # Dataset files (e.g., spam_ham_dataset.csv)
35 | ├── src/               # Source code (e.g., detector.py)
36 | ├── notebooks/         # Jupyter notebooks (e.g., spam_detector.ipynb)
37 | ├── requirements.txt   # Python dependencies
38 | └── README.md          # Project documentation
39 | ```
40 | 
41 | 
42 | ## License
43 | This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
44 | 
45 | 
46 | 
47 | 
48 | 


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/E_mail_Spam_Clasifier.ipynb:
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   1 | {
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   4 |   "metadata": {
   5 |     "colab": {
   6 |       "provenance": []
   7 |     },
   8 |     "kernelspec": {
   9 |       "name": "python3",
  10 |       "display_name": "Python 3"
  11 |     },
  12 |     "language_info": {
  13 |       "name": "python"
  14 |     }
  15 |   },
  16 |   "cells": [
  17 |     {
  18 |       "cell_type": "code",
  19 |       "execution_count": 1,
  20 |       "metadata": {
  21 |         "id": "8EOKja88UzSy"
  22 |       },
  23 |       "outputs": [],
  24 |       "source": [
  25 |         "import numpy as np\n",
  26 |         "import pandas as pd\n",
  27 |         "from sklearn.model_selection import train_test_split\n",
  28 |         "from sklearn.feature_extraction.text import TfidfVectorizer\n",
  29 |         "from sklearn.linear_model import LogisticRegression\n",
  30 |         "from sklearn.metrics import accuracy_score"
  31 |       ]
  32 |     },
  33 |     {
  34 |       "cell_type": "code",
  35 |       "source": [
  36 |         "df = pd.read_csv('mail_data.csv')"
  37 |       ],
  38 |       "metadata": {
  39 |         "id": "eSDmkIuCVP3s"
  40 |       },
  41 |       "execution_count": 18,
  42 |       "outputs": []
  43 |     },
  44 |     {
  45 |       "cell_type": "code",
  46 |       "source": [
  47 |         "print(df)"
  48 |       ],
  49 |       "metadata": {
  50 |         "colab": {
  51 |           "base_uri": "https://localhost:8080/"
  52 |         },
  53 |         "id": "8J9UzNMLVnBk",
  54 |         "outputId": "f1b00088-f71f-4f95-b1cc-0ceb036097ac"
  55 |       },
  56 |       "execution_count": 19,
  57 |       "outputs": [
  58 |         {
  59 |           "output_type": "stream",
  60 |           "name": "stdout",
  61 |           "text": [
  62 |             "     Category                                            Message\n",
  63 |             "0         ham  Go until jurong point, crazy.. Available only ...\n",
  64 |             "1         ham                      Ok lar... Joking wif u oni...\n",
  65 |             "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
  66 |             "3         ham  U dun say so early hor... U c already then say...\n",
  67 |             "4         ham  Nah I don't think he goes to usf, he lives aro...\n",
  68 |             "...       ...                                                ...\n",
  69 |             "5567     spam  This is the 2nd time we have tried 2 contact u...\n",
  70 |             "5568      ham               Will ü b going to esplanade fr home?\n",
  71 |             "5569      ham  Pity, * was in mood for that. So...any other s...\n",
  72 |             "5570      ham  The guy did some bitching but I acted like i'd...\n",
  73 |             "5571      ham                         Rofl. Its true to its name\n",
  74 |             "\n",
  75 |             "[5572 rows x 2 columns]\n"
  76 |           ]
  77 |         }
  78 |       ]
  79 |     },
  80 |     {
  81 |       "cell_type": "code",
  82 |       "source": [
  83 |         "data = df.where((pd.notnull(df)), '')"
  84 |       ],
  85 |       "metadata": {
  86 |         "id": "pX6xnDr5cYMj"
  87 |       },
  88 |       "execution_count": 21,
  89 |       "outputs": []
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  93 |       "source": [
  94 |         "data.head(15)"
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 110 |               "   Category                                            Message\n",
 111 |               "0       ham  Go until jurong point, crazy.. Available only ...\n",
 112 |               "1       ham                      Ok lar... Joking wif u oni...\n",
 113 |               "2      spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
 114 |               "3       ham  U dun say so early hor... U c already then say...\n",
 115 |               "4       ham  Nah I don't think he goes to usf, he lives aro...\n",
 116 |               "5      spam  FreeMsg Hey there darling it's been 3 week's n...\n",
 117 |               "6       ham  Even my brother is not like to speak with me. ...\n",
 118 |               "7       ham  As per your request 'Melle Melle (Oru Minnamin...\n",
 119 |               "8      spam  WINNER!! As a valued network customer you have...\n",
 120 |               "9      spam  Had your mobile 11 months or more? U R entitle...\n",
 121 |               "10      ham  I'm gonna be home soon and i don't want to tal...\n",
 122 |               "11     spam  SIX chances to win CASH! From 100 to 20,000 po...\n",
 123 |               "12     spam  URGENT! You have won a 1 week FREE membership ...\n",
 124 |               "13      ham  I've been searching for the right words to tha...\n",
 125 |               "14      ham                I HAVE A DATE ON SUNDAY WITH WILL!!"
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 161 |               "      <td>Ok lar... Joking wif u oni...</td>\n",
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 176 |               "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
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 178 |               "    <tr>\n",
 179 |               "      <th>5</th>\n",
 180 |               "      <td>spam</td>\n",
 181 |               "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
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 183 |               "    <tr>\n",
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 185 |               "      <td>ham</td>\n",
 186 |               "      <td>Even my brother is not like to speak with me. ...</td>\n",
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 190 |               "      <td>ham</td>\n",
 191 |               "      <td>As per your request 'Melle Melle (Oru Minnamin...</td>\n",
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 195 |               "      <td>spam</td>\n",
 196 |               "      <td>WINNER!! As a valued network customer you have...</td>\n",
 197 |               "    </tr>\n",
 198 |               "    <tr>\n",
 199 |               "      <th>9</th>\n",
 200 |               "      <td>spam</td>\n",
 201 |               "      <td>Had your mobile 11 months or more? U R entitle...</td>\n",
 202 |               "    </tr>\n",
 203 |               "    <tr>\n",
 204 |               "      <th>10</th>\n",
 205 |               "      <td>ham</td>\n",
 206 |               "      <td>I'm gonna be home soon and i don't want to tal...</td>\n",
 207 |               "    </tr>\n",
 208 |               "    <tr>\n",
 209 |               "      <th>11</th>\n",
 210 |               "      <td>spam</td>\n",
 211 |               "      <td>SIX chances to win CASH! From 100 to 20,000 po...</td>\n",
 212 |               "    </tr>\n",
 213 |               "    <tr>\n",
 214 |               "      <th>12</th>\n",
 215 |               "      <td>spam</td>\n",
 216 |               "      <td>URGENT! You have won a 1 week FREE membership ...</td>\n",
 217 |               "    </tr>\n",
 218 |               "    <tr>\n",
 219 |               "      <th>13</th>\n",
 220 |               "      <td>ham</td>\n",
 221 |               "      <td>I've been searching for the right words to tha...</td>\n",
 222 |               "    </tr>\n",
 223 |               "    <tr>\n",
 224 |               "      <th>14</th>\n",
 225 |               "      <td>ham</td>\n",
 226 |               "      <td>I HAVE A DATE ON SUNDAY WITH WILL!!</td>\n",
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 452 |               "<h4 class=\"colab-quickchart-section-title\">Categorical distributions</h4>\n",
 453 |               "<style>\n",
 454 |               "  .colab-quickchart-section-title {\n",
 455 |               "      clear: both;\n",
 456 |               "  }\n",
 457 |               "</style>"
 458 |             ]
 459 |           },
 460 |           "metadata": {}
 461 |         },
 462 |         {
 463 |           "output_type": "display_data",
 464 |           "data": {
 465 |             "text/plain": [
 466 |               "from matplotlib import pyplot as plt\n",
 467 |               "import seaborn as sns\n",
 468 |               "_df_0.groupby('Category').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
 469 |               "plt.gca().spines[['top', 'right',]].set_visible(False)"
 470 |             ],
 471 |             "text/html": [
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 560 |               "jSYKPQAAAABJRU5ErkJggg==\n",
 561 |               "\">\n",
 562 |               "      </div>\n",
 563 |               "      <script type=\"text/javascript\">\n",
 564 |               "        (() => {\n",
 565 |               "          const chartElement = document.getElementById(\"chart-8782f8af-5f3b-4b47-a94b-5222c96c6637\");\n",
 566 |               "          async function getCodeForChartHandler(event) {\n",
 567 |               "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
 568 |               "                'getCodeForChart', [\"chart-8782f8af-5f3b-4b47-a94b-5222c96c6637\"], {});\n",
 569 |               "            const responseJson = chartCodeResponse.data['application/json'];\n",
 570 |               "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
 571 |               "          }\n",
 572 |               "          chartElement.onclick = getCodeForChartHandler;\n",
 573 |               "        })();\n",
 574 |               "      </script>\n",
 575 |               "      <style>\n",
 576 |               "        .colab-quickchart-chart-with-code  {\n",
 577 |               "            display: block;\n",
 578 |               "            float: left;\n",
 579 |               "            border: 1px solid transparent;\n",
 580 |               "        }\n",
 581 |               "\n",
 582 |               "        .colab-quickchart-chart-with-code:hover {\n",
 583 |               "            cursor: pointer;\n",
 584 |               "            border: 1px solid #aaa;\n",
 585 |               "        }\n",
 586 |               "      </style>"
 587 |             ]
 588 |           },
 589 |           "metadata": {}
 590 |         },
 591 |         {
 592 |           "output_type": "display_data",
 593 |           "data": {
 594 |             "text/plain": [
 595 |               "<google.colab._quickchart_helpers.SectionTitle at 0x7c3d3faa6e90>"
 596 |             ],
 597 |             "text/html": [
 598 |               "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
 599 |               "<style>\n",
 600 |               "  .colab-quickchart-section-title {\n",
 601 |               "      clear: both;\n",
 602 |               "  }\n",
 603 |               "</style>"
 604 |             ]
 605 |           },
 606 |           "metadata": {}
 607 |         },
 608 |         {
 609 |           "output_type": "display_data",
 610 |           "data": {
 611 |             "text/plain": [
 612 |               "from matplotlib import pyplot as plt\n",
 613 |               "_df_1['index'].plot(kind='hist', bins=20, title='index')\n",
 614 |               "plt.gca().spines[['top', 'right',]].set_visible(False)"
 615 |             ],
 616 |             "text/html": [
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 729 |               "Z+rUqRkyZEgqlUouuuiifPe7393jfnfu3Jn29vZuj87OzlodBgBQR5VqtVqtxY4ffPDBXHjhhXni\n",
 730 |               "iSe6tk2cODGLFy/O6aef3rXtL//yL3PYYYdlwYIFSZKNGzfmrLPOSmtr61vud+HChbnyyiu7bfvw\n",
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 755 |               "nlo99thjq8cff3x1+fLl9R5pr/rOd77TdezHHnts9d/+7d/qPVLNzZ07t3r44YdX+/XrVx08eHD1\n",
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 757 |               "AIolVACAYgkVAKBYQgUAKJZQAQCK9f8Ak8euaOSMVWEAAAAASUVORK5CYII=\n",
 758 |               "\">\n",
 759 |               "      </div>\n",
 760 |               "      <script type=\"text/javascript\">\n",
 761 |               "        (() => {\n",
 762 |               "          const chartElement = document.getElementById(\"chart-1ce8bcfd-fc93-4b24-b5cd-dbd4d4155ac4\");\n",
 763 |               "          async function getCodeForChartHandler(event) {\n",
 764 |               "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
 765 |               "                'getCodeForChart', [\"chart-1ce8bcfd-fc93-4b24-b5cd-dbd4d4155ac4\"], {});\n",
 766 |               "            const responseJson = chartCodeResponse.data['application/json'];\n",
 767 |               "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
 768 |               "          }\n",
 769 |               "          chartElement.onclick = getCodeForChartHandler;\n",
 770 |               "        })();\n",
 771 |               "      </script>\n",
 772 |               "      <style>\n",
 773 |               "        .colab-quickchart-chart-with-code  {\n",
 774 |               "            display: block;\n",
 775 |               "            float: left;\n",
 776 |               "            border: 1px solid transparent;\n",
 777 |               "        }\n",
 778 |               "\n",
 779 |               "        .colab-quickchart-chart-with-code:hover {\n",
 780 |               "            cursor: pointer;\n",
 781 |               "            border: 1px solid #aaa;\n",
 782 |               "        }\n",
 783 |               "      </style>"
 784 |             ]
 785 |           },
 786 |           "metadata": {}
 787 |         },
 788 |         {
 789 |           "output_type": "display_data",
 790 |           "data": {
 791 |             "text/plain": [
 792 |               "<google.colab._quickchart_helpers.SectionTitle at 0x7c3d3faa7790>"
 793 |             ],
 794 |             "text/html": [
 795 |               "<h4 class=\"colab-quickchart-section-title\">Categorical distributions</h4>\n",
 796 |               "<style>\n",
 797 |               "  .colab-quickchart-section-title {\n",
 798 |               "      clear: both;\n",
 799 |               "  }\n",
 800 |               "</style>"
 801 |             ]
 802 |           },
 803 |           "metadata": {}
 804 |         },
 805 |         {
 806 |           "output_type": "display_data",
 807 |           "data": {
 808 |             "text/plain": [
 809 |               "from matplotlib import pyplot as plt\n",
 810 |               "import seaborn as sns\n",
 811 |               "_df_2.groupby('Category').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
 812 |               "plt.gca().spines[['top', 'right',]].set_visible(False)"
 813 |             ],
 814 |             "text/html": [
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 904 |               "\">\n",
 905 |               "      </div>\n",
 906 |               "      <script type=\"text/javascript\">\n",
 907 |               "        (() => {\n",
 908 |               "          const chartElement = document.getElementById(\"chart-b3dacb74-1e1a-4e34-aa2f-368d2477450e\");\n",
 909 |               "          async function getCodeForChartHandler(event) {\n",
 910 |               "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
 911 |               "                'getCodeForChart', [\"chart-b3dacb74-1e1a-4e34-aa2f-368d2477450e\"], {});\n",
 912 |               "            const responseJson = chartCodeResponse.data['application/json'];\n",
 913 |               "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
 914 |               "          }\n",
 915 |               "          chartElement.onclick = getCodeForChartHandler;\n",
 916 |               "        })();\n",
 917 |               "      </script>\n",
 918 |               "      <style>\n",
 919 |               "        .colab-quickchart-chart-with-code  {\n",
 920 |               "            display: block;\n",
 921 |               "            float: left;\n",
 922 |               "            border: 1px solid transparent;\n",
 923 |               "        }\n",
 924 |               "\n",
 925 |               "        .colab-quickchart-chart-with-code:hover {\n",
 926 |               "            cursor: pointer;\n",
 927 |               "            border: 1px solid #aaa;\n",
 928 |               "        }\n",
 929 |               "      </style>"
 930 |             ]
 931 |           },
 932 |           "metadata": {}
 933 |         },
 934 |         {
 935 |           "output_type": "display_data",
 936 |           "data": {
 937 |             "text/plain": [
 938 |               "<google.colab._quickchart_helpers.SectionTitle at 0x7c3d4436f430>"
 939 |             ],
 940 |             "text/html": [
 941 |               "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
 942 |               "<style>\n",
 943 |               "  .colab-quickchart-section-title {\n",
 944 |               "      clear: both;\n",
 945 |               "  }\n",
 946 |               "</style>"
 947 |             ]
 948 |           },
 949 |           "metadata": {}
 950 |         },
 951 |         {
 952 |           "output_type": "display_data",
 953 |           "data": {
 954 |             "text/plain": [
 955 |               "from matplotlib import pyplot as plt\n",
 956 |               "import seaborn as sns\n",
 957 |               "def _plot_series(series, series_name, series_index=0):\n",
 958 |               "  from matplotlib import pyplot as plt\n",
 959 |               "  import seaborn as sns\n",
 960 |               "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
 961 |               "  counted = (series['index']\n",
 962 |               "                .value_counts()\n",
 963 |               "              .reset_index(name='counts')\n",
 964 |               "              .rename({'index': 'index'}, axis=1)\n",
 965 |               "              .sort_values('index', ascending=True))\n",
 966 |               "  xs = counted['index']\n",
 967 |               "  ys = counted['counts']\n",
 968 |               "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
 969 |               "\n",
 970 |               "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
 971 |               "df_sorted = _df_3.sort_values('index', ascending=True)\n",
 972 |               "for i, (series_name, series) in enumerate(df_sorted.groupby('Category')):\n",
 973 |               "  _plot_series(series, series_name, i)\n",
 974 |               "  fig.legend(title='Category', bbox_to_anchor=(1, 1), loc='upper left')\n",
 975 |               "sns.despine(fig=fig, ax=ax)\n",
 976 |               "plt.xlabel('index')\n",
 977 |               "_ = plt.ylabel('count()')"
 978 |             ],
 979 |             "text/html": [
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1034 |               "uOGGPPnkk6lWq7noootyzDHHJElmz56dRx99NB0dHbnlllsyZMiQdHR0vGzgAAAAADZuNY0cbW1t\n",
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1121 |               "AAAAoAgiBwAAAFAEkQMAAAAogsgBAAAAFEHkAAAAAIogcgAAAABFqGnkeOihh3LggQdmzJgx2X//\n",
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1178 |               "AABQBJEDAAAAKILIAQAAABTh/wMzGXKeqIau5wAAAABJRU5ErkJggg==\n",
1179 |               "\">\n",
1180 |               "      </div>\n",
1181 |               "      <script type=\"text/javascript\">\n",
1182 |               "        (() => {\n",
1183 |               "          const chartElement = document.getElementById(\"chart-95144052-ecef-423a-ac87-e789c3403935\");\n",
1184 |               "          async function getCodeForChartHandler(event) {\n",
1185 |               "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
1186 |               "                'getCodeForChart', [\"chart-95144052-ecef-423a-ac87-e789c3403935\"], {});\n",
1187 |               "            const responseJson = chartCodeResponse.data['application/json'];\n",
1188 |               "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
1189 |               "          }\n",
1190 |               "          chartElement.onclick = getCodeForChartHandler;\n",
1191 |               "        })();\n",
1192 |               "      </script>\n",
1193 |               "      <style>\n",
1194 |               "        .colab-quickchart-chart-with-code  {\n",
1195 |               "            display: block;\n",
1196 |               "            float: left;\n",
1197 |               "            border: 1px solid transparent;\n",
1198 |               "        }\n",
1199 |               "\n",
1200 |               "        .colab-quickchart-chart-with-code:hover {\n",
1201 |               "            cursor: pointer;\n",
1202 |               "            border: 1px solid #aaa;\n",
1203 |               "        }\n",
1204 |               "      </style>"
1205 |             ]
1206 |           },
1207 |           "metadata": {}
1208 |         },
1209 |         {
1210 |           "output_type": "display_data",
1211 |           "data": {
1212 |             "text/plain": [
1213 |               "<google.colab._quickchart_helpers.SectionTitle at 0x7c3d4436efb0>"
1214 |             ],
1215 |             "text/html": [
1216 |               "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
1217 |               "<style>\n",
1218 |               "  .colab-quickchart-section-title {\n",
1219 |               "      clear: both;\n",
1220 |               "  }\n",
1221 |               "</style>"
1222 |             ]
1223 |           },
1224 |           "metadata": {}
1225 |         },
1226 |         {
1227 |           "output_type": "display_data",
1228 |           "data": {
1229 |             "text/plain": [
1230 |               "from matplotlib import pyplot as plt\n",
1231 |               "_df_4['index'].plot(kind='line', figsize=(8, 4), title='index')\n",
1232 |               "plt.gca().spines[['top', 'right']].set_visible(False)"
1233 |             ],
1234 |             "text/html": [
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1488 |               "TkSuQmCC\n",
1489 |               "\">\n",
1490 |               "      </div>\n",
1491 |               "      <script type=\"text/javascript\">\n",
1492 |               "        (() => {\n",
1493 |               "          const chartElement = document.getElementById(\"chart-f01513ac-3109-4584-98ea-b12c71b634a1\");\n",
1494 |               "          async function getCodeForChartHandler(event) {\n",
1495 |               "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
1496 |               "                'getCodeForChart', [\"chart-f01513ac-3109-4584-98ea-b12c71b634a1\"], {});\n",
1497 |               "            const responseJson = chartCodeResponse.data['application/json'];\n",
1498 |               "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
1499 |               "          }\n",
1500 |               "          chartElement.onclick = getCodeForChartHandler;\n",
1501 |               "        })();\n",
1502 |               "      </script>\n",
1503 |               "      <style>\n",
1504 |               "        .colab-quickchart-chart-with-code  {\n",
1505 |               "            display: block;\n",
1506 |               "            float: left;\n",
1507 |               "            border: 1px solid transparent;\n",
1508 |               "        }\n",
1509 |               "\n",
1510 |               "        .colab-quickchart-chart-with-code:hover {\n",
1511 |               "            cursor: pointer;\n",
1512 |               "            border: 1px solid #aaa;\n",
1513 |               "        }\n",
1514 |               "      </style>"
1515 |             ]
1516 |           },
1517 |           "metadata": {}
1518 |         },
1519 |         {
1520 |           "output_type": "display_data",
1521 |           "data": {
1522 |             "text/plain": [
1523 |               "<google.colab._quickchart_helpers.SectionTitle at 0x7c3d4436fb80>"
1524 |             ],
1525 |             "text/html": [
1526 |               "<h4 class=\"colab-quickchart-section-title\">Faceted distributions</h4>\n",
1527 |               "<style>\n",
1528 |               "  .colab-quickchart-section-title {\n",
1529 |               "      clear: both;\n",
1530 |               "  }\n",
1531 |               "</style>"
1532 |             ]
1533 |           },
1534 |           "metadata": {}
1535 |         },
1536 |         {
1537 |           "output_type": "display_data",
1538 |           "data": {
1539 |             "text/plain": [
1540 |               "from matplotlib import pyplot as plt\n",
1541 |               "import seaborn as sns\n",
1542 |               "figsize = (12, 1.2 * len(_df_5['Category'].unique()))\n",
1543 |               "plt.figure(figsize=figsize)\n",
1544 |               "sns.violinplot(_df_5, x='index', y='Category', inner='stick', palette='Dark2')\n",
1545 |               "sns.despine(top=True, right=True, bottom=True, left=True)"
1546 |             ],
1547 |             "text/html": [
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1664 |               "2+NCSEeYt9kb87aer6vP25yhLgDoTlatWqWKigpbTcg3btwoSdqwYYPuuece7dy5M8QVmXXkyBFt\n",
1665 |               "3bpVe/fuDXUpQbd371653W41Nzdr5cqVWrhwYY8/3j6fT5988onGjBmjRx99VAcOHND06dNVVlam\n",
1666 |               "IUOGhLq8oFi/fr0WLFjgDyM9mc/n08MPP6xt27Zp6tSpKikpUU5Ojg4fPqyBAweGujxjIiMjVVRU\n",
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1670 |               "zp3Tyy+/rJKSklCXEhQHDx7U8ePHNXXqVElSRkaGYmJi/P/Q0pNlZWUpKytL0jcLBUZFRWnMmDEh\n",
1671 |               "rio4mLfZE/M25m1dZd7GLeUXYPDgwUpPT9fmzZslSVu3blVMTIzi4+NDXBlMWbt2rV588UW98cYb\n",
1672 |               "rd4f05OdOXNGx48f9z/evn27BgwYoP79+4ewKvNuv/121dbWqrq6WtXV1Ro/fryeeeaZLvNH25TP\n",
1673 |               "P//c/75WSXrxxRc1duzY0BUUJAMHDlR2drZef/11SVJVVZWqqqo0evToEFcWHFu2bFFqaqquvPLK\n",
1674 |               "UJcSFN8Grw8++ECSVFFRocrKSo0aNSrElZlXW1vr//qhhx7StGnTbDNvYd5mP8zbmLd1qXlbqFdt\n",
1675 |               "664+/PBDa/z48dbIkSOtq666ynr//fdDXVJQLF682Bo6dKgVFhZmDR482BoxYkSoSzKupqbGkmQN\n",
1676 |               "Hz7cSk1NtVJTU62rr7461GUZV11dbWVkZFhJSUlWSkqKlZ2d3SNXOP0h11xzTZdb7dKEyspKKy0t\n",
1677 |               "zUpOTraSkpKsnJwcq6qqKtRlBUVlZaWVmZnpP9eLiopCXVLQTJgwwfrrX/8a6jKC6oUXXvAf66Sk\n",
1678 |               "JOv5558PdUlBkZeXZ40aNcoaMWKENX/+/FarGfckHc1Tevq8raO+e/q8rb3+7DBva69vO8zbzud8\n",
1679 |               "7orzNodlWVaoQz8AAAAAAD0Nt5QDAAAAAGAAgRsAAAAAAAMI3AAA9ABpaWk6e/ZswM+76aab9Nxz\n",
1680 |               "z3V+QQAAgI8FAwCgJzh48GCoSwAAAN/BFW4AAHoAh8Ph/2i3uLg4/fa3v9WECRN0xRVX6OGHH/aP\n",
1681 |               "+/DDDzVx4kQlJiZqzpw5amxs9H/v7NmzWrRoka6++mqlpKRo8eLF+uqrr1ReXq6YmBh9/PHHkr75\n",
1682 |               "fNuZM2eqpaUlqD0CANDdELgBAOiBzpw5o3feeUclJSVas2aNPvvsM0nSLbfcottuu01lZWV66KGH\n",
1683 |               "tGfPHv9zfvWrX2nKlCl67733dOjQIbW0tOiJJ57QqFGjtGbNGt1888365z//qaeeekqbNm1Sr15M\n",
1684 |               "IwAA+D7cUg4AQA/0s5/9TJI0cOBADR8+XFVVVerbt68OHjyo3NxcSVJycrImT57sf8727dv1zjvv\n",
1685 |               "aO3atZKkL7/8UmFhYZKkn/70p9q9e7dmzJihXbt2adCgQcFtCACAbojADQBADxQeHu7/OiwsTD6f\n",
1686 |               "r91xDofD/7VlWdq6dasSEhLajPP5fDpy5Ij69+/vv1oOAAC+H/eCAQBgExERERo7dqw2btwoSSor\n",
1687 |               "K1NxcbH/+3PmzNHq1av94fz06dOqqKiQJN17770aNWqU3n77bf3617/27wcAAB0jcAMAYCMbN27U\n",
1688 |               "M888o6SkJK1cuVJTp071f+/xxx9Xnz59lJaWppSUFGVnZ6u6ulqvvPKKXnvtNT311FOKj4/X2rVr\n",
1689 |               "dfPNN6upqSmEnQAA0PU5LMuyQl0EAAAAAAA9DVe4AQAAAAAwgMANAAAAAIABBG4AAAAAAAwgcAMA\n",
1690 |               "AAAAYACBGwAAAAAAAwjcAAAAAAAYQOAGAAAAAMAAAjcAAAAAAAYQuAEAAAAAMIDADQAAAACAAf8P\n",
1691 |               "FCuO3kk0DWUAAAAASUVORK5CYII=\n",
1692 |               "\">\n",
1693 |               "      </div>\n",
1694 |               "      <script type=\"text/javascript\">\n",
1695 |               "        (() => {\n",
1696 |               "          const chartElement = document.getElementById(\"chart-dc232ef0-761e-4e88-af52-55faee3b17f1\");\n",
1697 |               "          async function getCodeForChartHandler(event) {\n",
1698 |               "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
1699 |               "                'getCodeForChart', [\"chart-dc232ef0-761e-4e88-af52-55faee3b17f1\"], {});\n",
1700 |               "            const responseJson = chartCodeResponse.data['application/json'];\n",
1701 |               "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
1702 |               "          }\n",
1703 |               "          chartElement.onclick = getCodeForChartHandler;\n",
1704 |               "        })();\n",
1705 |               "      </script>\n",
1706 |               "      <style>\n",
1707 |               "        .colab-quickchart-chart-with-code  {\n",
1708 |               "            display: block;\n",
1709 |               "            float: left;\n",
1710 |               "            border: 1px solid transparent;\n",
1711 |               "        }\n",
1712 |               "\n",
1713 |               "        .colab-quickchart-chart-with-code:hover {\n",
1714 |               "            cursor: pointer;\n",
1715 |               "            border: 1px solid #aaa;\n",
1716 |               "        }\n",
1717 |               "      </style>"
1718 |             ]
1719 |           },
1720 |           "metadata": {}
1721 |         }
1722 |       ]
1723 |     },
1724 |     {
1725 |       "cell_type": "code",
1726 |       "source": [
1727 |         "data.info()"
1728 |       ],
1729 |       "metadata": {
1730 |         "colab": {
1731 |           "base_uri": "https://localhost:8080/"
1732 |         },
1733 |         "id": "HNMBxZJgfY8i",
1734 |         "outputId": "33d4fd43-19f5-49b7-a174-7ea37c16ca18"
1735 |       },
1736 |       "execution_count": 23,
1737 |       "outputs": [
1738 |         {
1739 |           "output_type": "stream",
1740 |           "name": "stdout",
1741 |           "text": [
1742 |             "<class 'pandas.core.frame.DataFrame'>\n",
1743 |             "RangeIndex: 5572 entries, 0 to 5571\n",
1744 |             "Data columns (total 2 columns):\n",
1745 |             " #   Column    Non-Null Count  Dtype \n",
1746 |             "---  ------    --------------  ----- \n",
1747 |             " 0   Category  5572 non-null   object\n",
1748 |             " 1   Message   5572 non-null   object\n",
1749 |             "dtypes: object(2)\n",
1750 |             "memory usage: 87.2+ KB\n"
1751 |           ]
1752 |         }
1753 |       ]
1754 |     },
1755 |     {
1756 |       "cell_type": "code",
1757 |       "source": [
1758 |         "data.shape"
1759 |       ],
1760 |       "metadata": {
1761 |         "colab": {
1762 |           "base_uri": "https://localhost:8080/"
1763 |         },
1764 |         "id": "J-alKBCtfnzQ",
1765 |         "outputId": "cf4b6e87-166b-497c-bcce-7b7129e033de"
1766 |       },
1767 |       "execution_count": 24,
1768 |       "outputs": [
1769 |         {
1770 |           "output_type": "execute_result",
1771 |           "data": {
1772 |             "text/plain": [
1773 |               "(5572, 2)"
1774 |             ]
1775 |           },
1776 |           "metadata": {},
1777 |           "execution_count": 24
1778 |         }
1779 |       ]
1780 |     },
1781 |     {
1782 |       "cell_type": "code",
1783 |       "source": [
1784 |         "data.loc[data['Category'] == 'spam' , 'Category',] = 0\n",
1785 |         "data.loc[data['Category'] == 'ham' , 'Category',] = 1"
1786 |       ],
1787 |       "metadata": {
1788 |         "id": "mYElEEbegCVX"
1789 |       },
1790 |       "execution_count": 25,
1791 |       "outputs": []
1792 |     },
1793 |     {
1794 |       "cell_type": "code",
1795 |       "source": [
1796 |         "X = data['Message']\n",
1797 |         "\n",
1798 |         "Y = data['Category']"
1799 |       ],
1800 |       "metadata": {
1801 |         "id": "t30D9nQFgaUo"
1802 |       },
1803 |       "execution_count": 26,
1804 |       "outputs": []
1805 |     },
1806 |     {
1807 |       "cell_type": "code",
1808 |       "source": [
1809 |         "print(X)"
1810 |       ],
1811 |       "metadata": {
1812 |         "colab": {
1813 |           "base_uri": "https://localhost:8080/"
1814 |         },
1815 |         "id": "WvlJSiUogxt4",
1816 |         "outputId": "2f02a8f7-162d-451d-c364-1c92595483e5"
1817 |       },
1818 |       "execution_count": 27,
1819 |       "outputs": [
1820 |         {
1821 |           "output_type": "stream",
1822 |           "name": "stdout",
1823 |           "text": [
1824 |             "0       Go until jurong point, crazy.. Available only ...\n",
1825 |             "1                           Ok lar... Joking wif u oni...\n",
1826 |             "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
1827 |             "3       U dun say so early hor... U c already then say...\n",
1828 |             "4       Nah I don't think he goes to usf, he lives aro...\n",
1829 |             "                              ...                        \n",
1830 |             "5567    This is the 2nd time we have tried 2 contact u...\n",
1831 |             "5568                 Will ü b going to esplanade fr home?\n",
1832 |             "5569    Pity, * was in mood for that. So...any other s...\n",
1833 |             "5570    The guy did some bitching but I acted like i'd...\n",
1834 |             "5571                           Rofl. Its true to its name\n",
1835 |             "Name: Message, Length: 5572, dtype: object\n"
1836 |           ]
1837 |         }
1838 |       ]
1839 |     },
1840 |     {
1841 |       "cell_type": "code",
1842 |       "source": [
1843 |         "print(Y)"
1844 |       ],
1845 |       "metadata": {
1846 |         "colab": {
1847 |           "base_uri": "https://localhost:8080/"
1848 |         },
1849 |         "id": "FQLdsGZy20Ml",
1850 |         "outputId": "68496bfe-ca75-4445-991b-01903df87203"
1851 |       },
1852 |       "execution_count": 28,
1853 |       "outputs": [
1854 |         {
1855 |           "output_type": "stream",
1856 |           "name": "stdout",
1857 |           "text": [
1858 |             "0       1\n",
1859 |             "1       1\n",
1860 |             "2       0\n",
1861 |             "3       1\n",
1862 |             "4       1\n",
1863 |             "       ..\n",
1864 |             "5567    0\n",
1865 |             "5568    1\n",
1866 |             "5569    1\n",
1867 |             "5570    1\n",
1868 |             "5571    1\n",
1869 |             "Name: Category, Length: 5572, dtype: object\n"
1870 |           ]
1871 |         }
1872 |       ]
1873 |     },
1874 |     {
1875 |       "cell_type": "code",
1876 |       "source": [
1877 |         "X_train ,X_test , Y_train , Y_test = train_test_split(X , Y , test_size = 0.2 , random_state = 3)"
1878 |       ],
1879 |       "metadata": {
1880 |         "id": "2lmBU2PR264l"
1881 |       },
1882 |       "execution_count": 62,
1883 |       "outputs": []
1884 |     },
1885 |     {
1886 |       "cell_type": "code",
1887 |       "source": [
1888 |         "print(X.shape)\n",
1889 |         "print(X_train.shape)\n",
1890 |         "print(X_test.shape)"
1891 |       ],
1892 |       "metadata": {
1893 |         "colab": {
1894 |           "base_uri": "https://localhost:8080/"
1895 |         },
1896 |         "id": "jxU7Ds7c4Aq5",
1897 |         "outputId": "6f1960e8-21bc-4c67-fa8f-059577e9184e"
1898 |       },
1899 |       "execution_count": 63,
1900 |       "outputs": [
1901 |         {
1902 |           "output_type": "stream",
1903 |           "name": "stdout",
1904 |           "text": [
1905 |             "(5572,)\n",
1906 |             "(4457,)\n",
1907 |             "(1115,)\n"
1908 |           ]
1909 |         }
1910 |       ]
1911 |     },
1912 |     {
1913 |       "cell_type": "code",
1914 |       "source": [
1915 |         "print(Y.shape)\n",
1916 |         "print(Y_train.shape)\n",
1917 |         "print(Y_test.shape)"
1918 |       ],
1919 |       "metadata": {
1920 |         "colab": {
1921 |           "base_uri": "https://localhost:8080/"
1922 |         },
1923 |         "id": "Eiu6bkkE4pRK",
1924 |         "outputId": "0ee21223-2a13-4080-ba28-a80e9c440a22"
1925 |       },
1926 |       "execution_count": 64,
1927 |       "outputs": [
1928 |         {
1929 |           "output_type": "stream",
1930 |           "name": "stdout",
1931 |           "text": [
1932 |             "(5572,)\n",
1933 |             "(4457,)\n",
1934 |             "(1115,)\n"
1935 |           ]
1936 |         }
1937 |       ]
1938 |     },
1939 |     {
1940 |       "cell_type": "code",
1941 |       "source": [
1942 |         "feature_extraction = TfidfVectorizer(min_df = 1 , stop_words = 'english' , lowercase=True)\n",
1943 |         "\n",
1944 |         "\n",
1945 |         "X_train_features  = feature_extraction.fit_transform(X_train)\n",
1946 |         "X_test_features = feature_extraction.transform(X_test)\n",
1947 |         "\n",
1948 |         "Y_train = Y_train.astype('int')\n",
1949 |         "Y_test = Y_test.astype('int')\n"
1950 |       ],
1951 |       "metadata": {
1952 |         "id": "ouN-SWcp2RST"
1953 |       },
1954 |       "execution_count": 65,
1955 |       "outputs": []
1956 |     },
1957 |     {
1958 |       "cell_type": "code",
1959 |       "source": [
1960 |         "print(X_train)"
1961 |       ],
1962 |       "metadata": {
1963 |         "colab": {
1964 |           "base_uri": "https://localhost:8080/"
1965 |         },
1966 |         "id": "ZezxctswwA46",
1967 |         "outputId": "8aa89089-130a-4b19-891a-f2846566a498"
1968 |       },
1969 |       "execution_count": 66,
1970 |       "outputs": [
1971 |         {
1972 |           "output_type": "stream",
1973 |           "name": "stdout",
1974 |           "text": [
1975 |             "3075                  Don know. I did't msg him recently.\n",
1976 |             "1787    Do you know why god created gap between your f...\n",
1977 |             "1614                         Thnx dude. u guys out 2nite?\n",
1978 |             "4304                                      Yup i'm free...\n",
1979 |             "3266    44 7732584351, Do you want a New Nokia 3510i c...\n",
1980 |             "                              ...                        \n",
1981 |             "789     5 Free Top Polyphonic Tones call 087018728737,...\n",
1982 |             "968     What do u want when i come back?.a beautiful n...\n",
1983 |             "1667    Guess who spent all last night phasing in and ...\n",
1984 |             "3321    Eh sorry leh... I din c ur msg. Not sad alread...\n",
1985 |             "1688    Free Top ringtone -sub to weekly ringtone-get ...\n",
1986 |             "Name: Message, Length: 4457, dtype: object\n"
1987 |           ]
1988 |         }
1989 |       ]
1990 |     },
1991 |     {
1992 |       "cell_type": "code",
1993 |       "source": [
1994 |         "print(X_train_features)"
1995 |       ],
1996 |       "metadata": {
1997 |         "colab": {
1998 |           "base_uri": "https://localhost:8080/"
1999 |         },
2000 |         "id": "rlA8CQbswOMl",
2001 |         "outputId": "b19c8254-7638-4491-e48c-b251a2d6ae16"
2002 |       },
2003 |       "execution_count": 67,
2004 |       "outputs": [
2005 |         {
2006 |           "output_type": "stream",
2007 |           "name": "stdout",
2008 |           "text": [
2009 |             "  (0, 5413)\t0.6198254967574347\n",
2010 |             "  (0, 4456)\t0.4168658090846482\n",
2011 |             "  (0, 2224)\t0.413103377943378\n",
2012 |             "  (0, 3811)\t0.34780165336891333\n",
2013 |             "  (0, 2329)\t0.38783870336935383\n",
2014 |             "  (1, 4080)\t0.18880584110891163\n",
2015 |             "  (1, 3185)\t0.29694482957694585\n",
2016 |             "  (1, 3325)\t0.31610586766078863\n",
2017 |             "  (1, 2957)\t0.3398297002864083\n",
2018 |             "  (1, 2746)\t0.3398297002864083\n",
2019 |             "  (1, 918)\t0.22871581159877646\n",
2020 |             "  (1, 1839)\t0.2784903590561455\n",
2021 |             "  (1, 2758)\t0.3226407885943799\n",
2022 |             "  (1, 2956)\t0.33036995955537024\n",
2023 |             "  (1, 1991)\t0.33036995955537024\n",
2024 |             "  (1, 3046)\t0.2503712792613518\n",
2025 |             "  (1, 3811)\t0.17419952275504033\n",
2026 |             "  (2, 407)\t0.509272536051008\n",
2027 |             "  (2, 3156)\t0.4107239318312698\n",
2028 |             "  (2, 2404)\t0.45287711070606745\n",
2029 |             "  (2, 6601)\t0.6056811524587518\n",
2030 |             "  (3, 2870)\t0.5864269879324768\n",
2031 |             "  (3, 7414)\t0.8100020912469564\n",
2032 |             "  (4, 50)\t0.23633754072626942\n",
2033 |             "  (4, 5497)\t0.15743785051118356\n",
2034 |             "  :\t:\n",
2035 |             "  (4454, 4602)\t0.2669765732445391\n",
2036 |             "  (4454, 3142)\t0.32014451677763156\n",
2037 |             "  (4455, 2247)\t0.37052851863170466\n",
2038 |             "  (4455, 2469)\t0.35441545511837946\n",
2039 |             "  (4455, 5646)\t0.33545678464631296\n",
2040 |             "  (4455, 6810)\t0.29731757715898277\n",
2041 |             "  (4455, 6091)\t0.23103841516927642\n",
2042 |             "  (4455, 7113)\t0.30536590342067704\n",
2043 |             "  (4455, 3872)\t0.3108911491788658\n",
2044 |             "  (4455, 4715)\t0.30714144758811196\n",
2045 |             "  (4455, 6916)\t0.19636985317119715\n",
2046 |             "  (4455, 3922)\t0.31287563163368587\n",
2047 |             "  (4455, 4456)\t0.24920025316220423\n",
2048 |             "  (4456, 141)\t0.292943737785358\n",
2049 |             "  (4456, 647)\t0.30133182431707617\n",
2050 |             "  (4456, 6311)\t0.30133182431707617\n",
2051 |             "  (4456, 5569)\t0.4619395404299172\n",
2052 |             "  (4456, 6028)\t0.21034888000987115\n",
2053 |             "  (4456, 7154)\t0.24083218452280053\n",
2054 |             "  (4456, 7150)\t0.3677554681447669\n",
2055 |             "  (4456, 6249)\t0.17573831794959716\n",
2056 |             "  (4456, 6307)\t0.2752760476857975\n",
2057 |             "  (4456, 334)\t0.2220077711654938\n",
2058 |             "  (4456, 5778)\t0.16243064490100795\n",
2059 |             "  (4456, 2870)\t0.31523196273113385\n"
2060 |           ]
2061 |         }
2062 |       ]
2063 |     },
2064 |     {
2065 |       "cell_type": "code",
2066 |       "source": [
2067 |         "model = LogisticRegression()"
2068 |       ],
2069 |       "metadata": {
2070 |         "id": "KxY32fZlwX_9"
2071 |       },
2072 |       "execution_count": 68,
2073 |       "outputs": []
2074 |     },
2075 |     {
2076 |       "cell_type": "code",
2077 |       "source": [
2078 |         "model.fit(X_train_features,Y_train)"
2079 |       ],
2080 |       "metadata": {
2081 |         "colab": {
2082 |           "base_uri": "https://localhost:8080/",
2083 |           "height": 52
2084 |         },
2085 |         "id": "3lWPU-uMwm-Y",
2086 |         "outputId": "86351b57-0c22-4be5-b6cd-ac6361abf9b1"
2087 |       },
2088 |       "execution_count": 69,
2089 |       "outputs": [
2090 |         {
2091 |           "output_type": "execute_result",
2092 |           "data": {
2093 |             "text/plain": [
2094 |               "LogisticRegression()"
2095 |             ],
2096 |             "text/html": [
2097 |               "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
2098 |             ]
2099 |           },
2100 |           "metadata": {},
2101 |           "execution_count": 69
2102 |         }
2103 |       ]
2104 |     },
2105 |     {
2106 |       "cell_type": "code",
2107 |       "source": [
2108 |         "prediction_on_training_data = model.predict(X_train_features)\n",
2109 |         "accuracy_on_training_data = accuracy_score(Y_train,prediction_on_training_data)"
2110 |       ],
2111 |       "metadata": {
2112 |         "id": "Ng-7kYRjxLBA"
2113 |       },
2114 |       "execution_count": 70,
2115 |       "outputs": []
2116 |     },
2117 |     {
2118 |       "cell_type": "code",
2119 |       "source": [
2120 |         "print('Accuracy on training data : ',accuracy_on_training_data)"
2121 |       ],
2122 |       "metadata": {
2123 |         "colab": {
2124 |           "base_uri": "https://localhost:8080/"
2125 |         },
2126 |         "id": "8gWcjBvcyDZ6",
2127 |         "outputId": "7fc92db4-bcab-474d-ee0c-0c6bfb091c0a"
2128 |       },
2129 |       "execution_count": 71,
2130 |       "outputs": [
2131 |         {
2132 |           "output_type": "stream",
2133 |           "name": "stdout",
2134 |           "text": [
2135 |             "Accuracy on training data :  0.9670181736594121\n"
2136 |           ]
2137 |         }
2138 |       ]
2139 |     },
2140 |     {
2141 |       "cell_type": "code",
2142 |       "source": [
2143 |         "prediction_on_testing_data = model.predict(X_test_features)\n",
2144 |         "accuracy_on_testing_data = accuracy_score(Y_test,prediction_on_testing_data)"
2145 |       ],
2146 |       "metadata": {
2147 |         "id": "vbsG32t0yb_R"
2148 |       },
2149 |       "execution_count": 72,
2150 |       "outputs": []
2151 |     },
2152 |     {
2153 |       "cell_type": "code",
2154 |       "source": [
2155 |         "print('Accuracy on testing data : ',accuracy_on_testing_data)"
2156 |       ],
2157 |       "metadata": {
2158 |         "colab": {
2159 |           "base_uri": "https://localhost:8080/"
2160 |         },
2161 |         "id": "0e7TYC5vzAYI",
2162 |         "outputId": "3e9ed356-4f78-45c0-832f-a87d63e0b7ab"
2163 |       },
2164 |       "execution_count": 73,
2165 |       "outputs": [
2166 |         {
2167 |           "output_type": "stream",
2168 |           "name": "stdout",
2169 |           "text": [
2170 |             "Accuracy on testing data :  0.9659192825112107\n"
2171 |           ]
2172 |         }
2173 |       ]
2174 |     },
2175 |     {
2176 |       "cell_type": "code",
2177 |       "source": [
2178 |         "input_mail = [\"Had your mobile 11 months or more? U R entitled to Update to the latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on 08002986030\"]\n",
2179 |         "\n",
2180 |         "input_data_features = feature_extraction.transform(input_mail)\n",
2181 |         "\n",
2182 |         "prediction = model.predict(input_data_features)\n",
2183 |         "\n",
2184 |         "print(prediction)\n",
2185 |         "\n",
2186 |         "if(prediction[0]==1):\n",
2187 |         "  print(\"Ham Mail\")\n",
2188 |         "else:\n",
2189 |         "  print(\"Spam Mail\")\n"
2190 |       ],
2191 |       "metadata": {
2192 |         "colab": {
2193 |           "base_uri": "https://localhost:8080/"
2194 |         },
2195 |         "id": "5c_X82pbzmbC",
2196 |         "outputId": "57ee2a8b-5b10-474b-f6f9-9c369d492747"
2197 |       },
2198 |       "execution_count": 76,
2199 |       "outputs": [
2200 |         {
2201 |           "output_type": "stream",
2202 |           "name": "stdout",
2203 |           "text": [
2204 |             "[0]\n",
2205 |             "Spam Mail\n"
2206 |           ]
2207 |         }
2208 |       ]
2209 |     },
2210 |     {
2211 |       "cell_type": "code",
2212 |       "source": [],
2213 |       "metadata": {
2214 |         "id": "VbDBuuxX0cEF"
2215 |       },
2216 |       "execution_count": null,
2217 |       "outputs": []
2218 |     }
2219 |   ]
2220 | }
2221 | 


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