Spam_Detection_using_Machine_Learning.ipynb

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      "source": [
        "# **📚Table of Content**"
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      "source": [
        "\n",
        "\n",
        "1.   Import Libraries\n",
        "2.   Data Loading\n",
        "3.   Data Preprocessing\n",
        "4.   Data Spliting\n",
        "5.   Model Training and Testing\n",
        "6.   References\n",
        "\n",
        "\n",
        "\n"
      ],
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      "source": [
        "# **📚Import Libraries**"
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    {
      "cell_type": "code",
      "execution_count": 2,
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        "id": "WLA09_O402Wc"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.model_selection import train_test_split , GridSearchCV , KFold\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score , classification_report , confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import nltk\n",
        "from nltk.stem import PorterStemmer\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Data Loading**"
      ],
      "metadata": {
        "id": "eYMyV6Z611uu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#URL is change, data with df\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/Sanjay-dev-ds/spam_ham_email_detector/master/spam.csv\", encoding= 'latin-1')\n",
        "df.head()\n"
      ],
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            "text/plain": [
              "  Label                                          EmailText\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ],
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              "      <th>Label</th>\n",
              "      <th>EmailText</th>\n",
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              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5572,\n  \"fields\": [\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"spam\",\n          \"ham\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EmailText\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5169,\n        \"samples\": [\n          \"Did u download the fring app?\",\n          \"Pass dis to all ur contacts n see wat u get! Red;i'm in luv wid u. Blue;u put a smile on my face. Purple;u r realy hot. Pink;u r so swt. Orange;i thnk i lyk u. Green;i realy wana go out wid u. Yelow;i wnt u bck. Black;i'm jealous of u. Brown;i miss you Nw plz giv me one color\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Data Preprocessing**"
      ],
      "metadata": {
        "id": "xBEWs7pt2gFn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Remove duplicate values(New)**"
      ],
      "metadata": {
        "id": "uT1toq2sWn1h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop_duplicates(keep='first')"
      ],
      "metadata": {
        "id": "qicD6RFXW9_I"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Split into Independent and dependent variable(New)**"
      ],
      "metadata": {
        "id": "q7D82wbT3GEH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "From this dataset, class and message are the only features we need to train a machine learning model for spam detection, so let’s select these two columns as the new dataset (old):"
      ],
      "metadata": {
        "id": "JZDOywAT3O_H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df['EmailText'].values\n",
        "y = df['Label'].values"
      ],
      "metadata": {
        "id": "wPW3ad4j3S80"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Text Pre-Processing(New)**"
      ],
      "metadata": {
        "id": "j2lnkbFTewYV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "creating a function to lowercase the text, remove special characters, normalize certain words, use stems of words instead of the original form using porter stemmer algorithm(New)"
      ],
      "metadata": {
        "id": "5KoyGClte7XO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# New\n",
        "porter_stemmer=PorterStemmer()\n",
        "def preprocessor(text):\n",
        "\n",
        "    text=text.lower()\n",
        "    text=re.sub(\"\\\\W\",\" \",text)\n",
        "    text=re.sub(\"\\\\s+(in|the|all|for|and|on)\\\\s+\",\" _connector_ \",text)\n",
        "    words=re.split(\"\\\\s+\",text)\n",
        "    stemmed_words=[porter_stemmer.stem(word=word) for word in words]\n",
        "    return ' '.join(stemmed_words)"
      ],
      "metadata": {
        "id": "5OnrUWL2fCDi"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "creating tokenizer function to\n",
        "\n",
        "Create a space between special characters\n",
        "\n",
        "Split based on whitespace (New)"
      ],
      "metadata": {
        "id": "Vx-NOpaafLSk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# new\n",
        "def tokenizer(text):\n",
        "    text=re.sub(\"(\\\\W)\",\" \\\\1 \",text)\n",
        "    return re.split(\"\\\\s+\",text)"
      ],
      "metadata": {
        "id": "qtNqSqa-fRq0"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature extraction**"
      ],
      "metadata": {
        "id": "wQsn-7Ka346q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to use textual data for predictive modeling, the text must be parsed to remove certain words – this process is called tokenization. These words need to then be encoded as integers, or floating-point values, for use as inputs in machine learning algorithms. This process is called feature extraction (or vectorization).\n",
        "\n",
        "Scikit-learn’s CountVectorizer is used to convert a collection of text documents to a vector of term/token counts. It also enables the ​pre-processing of text data prior to generating the vector representation. This functionality makes it a highly flexible feature representation module for text."
      ],
      "metadata": {
        "id": "G2v0KplD3zAo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Count Vectorizer is used to transform a corpus of text to a vector of term.\n",
        "\n",
        "min_df = 0.06 ( taking 0.6% of Minimum Document Frequency )\n",
        "\n",
        "ngram_range=(1,2) ( word level Unigrams and bigrams) (NEW)"
      ],
      "metadata": {
        "id": "OY1exQCMfgVS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# new\n",
        "vectorizer = CountVectorizer(tokenizer=tokenizer,ngram_range=(1,2),min_df=0.006,preprocessor=preprocessor)\n",
        "x  = vectorizer.fit_transform(x)"
      ],
      "metadata": {
        "id": "ize_cMkx4N8Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "332c92db-e609-4564-84b0-b77a31ad34b0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwHhOXko4QIY",
        "outputId": "156a3391-759b-4f8f-b84a-47353bc05f48"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 149)\t1\n",
            "  (0, 331)\t1\n",
            "  (0, 313)\t1\n",
            "  (0, 17)\t1\n",
            "  (0, 285)\t1\n",
            "  (0, 155)\t1\n",
            "  (0, 128)\t1\n",
            "  (0, 403)\t1\n",
            "  (0, 154)\t1\n",
            "  (0, 453)\t1\n",
            "  (0, 0)\t1\n",
            "  (1, 0)\t1\n",
            "  (1, 308)\t1\n",
            "  (1, 230)\t1\n",
            "  (1, 433)\t1\n",
            "  (2, 17)\t1\n",
            "  (2, 141)\t1\n",
            "  (2, 8)\t1\n",
            "  (2, 29)\t1\n",
            "  (2, 412)\t3\n",
            "  (2, 474)\t1\n",
            "  (2, 260)\t1\n",
            "  (2, 392)\t1\n",
            "  (2, 345)\t1\n",
            "  (2, 432)\t1\n",
            "  :\t:\n",
            "  (5166, 46)\t1\n",
            "  (5166, 315)\t1\n",
            "  (5167, 17)\t3\n",
            "  (5167, 141)\t1\n",
            "  (5167, 412)\t1\n",
            "  (5167, 190)\t2\n",
            "  (5167, 172)\t1\n",
            "  (5167, 217)\t1\n",
            "  (5167, 459)\t1\n",
            "  (5167, 105)\t1\n",
            "  (5167, 239)\t1\n",
            "  (5167, 372)\t1\n",
            "  (5167, 62)\t1\n",
            "  (5167, 400)\t1\n",
            "  (5167, 292)\t1\n",
            "  (5167, 112)\t1\n",
            "  (5167, 441)\t1\n",
            "  (5167, 73)\t1\n",
            "  (5167, 374)\t1\n",
            "  (5167, 75)\t1\n",
            "  (5167, 74)\t1\n",
            "  (5167, 161)\t1\n",
            "  (5168, 412)\t1\n",
            "  (5168, 217)\t2\n",
            "  (5168, 286)\t1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Check Data is imbalanced (New)**"
      ],
      "metadata": {
        "id": "DR65PdGIf06D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['Label'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "AfZxjHh0f5qU",
        "outputId": "3a9512e9-cd60-4df8-a553-5791be1d1b49"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='count', ylabel='Label'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "target class has an uneven distribution of observations, So we are using random over sampling method to balance the target variable’s observation\n",
        "\n",
        "Random Oversampling: randomly duplicate examples in the minority class (Spam) (NEW)"
      ],
      "metadata": {
        "id": "9rcz1iUzgFBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# NEW\n",
        "from imblearn.under_sampling import NearMiss\n",
        "from collections import Counter\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "ros = RandomOverSampler(random_state=42)\n",
        "\n",
        "print('Original dataset shape', Counter(y))\n",
        "\n",
        "# fit predictor and target\n",
        "x,y = ros.fit_resample(x, y)\n",
        "\n",
        "print('Modified dataset shape', Counter(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1Z9BJL4gLxh",
        "outputId": "e20976e4-6201-4d92-8c9c-b4f310c91613"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original dataset shape Counter({'ham': 4516, 'spam': 653})\n",
            "Modified dataset shape Counter({'ham': 4516, 'spam': 4516})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Data Spliting**"
      ],
      "metadata": {
        "id": "brmCurbI4kGv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# New\n",
        "x_train , x_test , y_train , y_test   = train_test_split(x, y, test_size =0.2,random_state = 0)"
      ],
      "metadata": {
        "id": "wdXTn6ss408R"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**📚Model Training and Testing**"
      ],
      "metadata": {
        "id": "okgMRd9e44aH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NB Model**"
      ],
      "metadata": {
        "id": "SumBtb4qhwlS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = MultinomialNB()\n",
        "clf.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "e0dZklLw5Yao",
        "outputId": "fc4e1674-1492-48de-bef6-aebea5d40260"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Accuracy**"
      ],
      "metadata": {
        "id": "UHnTFQs_7pjh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# New\n",
        "y_pred_NB = clf.predict(x_test)\n",
        "NB_Acc=clf.score(x_test, y_test)\n",
        "print('Accuracy score= {:.4f}'.format(clf.score(x_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtdPANHm7owy",
        "outputId": "c672494e-7efd-46fc-f586-01b0a457c153"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score= 0.9590\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let’s test this model by taking a user input as a message to detect whether it is spam or not:\n",
        "\n"
      ],
      "metadata": {
        "id": "pfxWGV0k5jS5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# New\n",
        "sample = input('Enter a message:')\n",
        "data = vectorizer.transform([sample]).toarray()\n",
        "print(clf.predict(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0FE_XHvP5id4",
        "outputId": "9842a2e3-a12b-467d-fe03-885294649914"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a message:gfdgdfg\n",
            "['ham']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVM(NEW)**"
      ],
      "metadata": {
        "id": "IOfFC85Kh1xg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model   = SVC(C =1,kernel = \"linear\" )\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "lFNvbr_ih49Z",
        "outputId": "ffe77bc0-0b53-4157-89a4-11e6f2b1ef74"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, kernel='linear')"
            ],
            "text/html": [
              "<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>SVC(C=1, kernel=&#x27;linear&#x27;)</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = metrics.accuracy_score(y_test, model.predict(x_test))\n",
        "accuracy_percentage = 100 * accuracy\n",
        "accuracy_percentage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "707RIQi4iHKP",
        "outputId": "a7742a81-b8e4-4f2d-e239-01a5835f6020"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98.83785279468734"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Hyperparameter Optimization using Grid Search CV(New)**"
      ],
      "metadata": {
        "id": "jkjhrYPjhk23"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVM**"
      ],
      "metadata": {
        "id": "5njGbYFQiLsQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "params  = {\"C\":[0.2,0.5] , \"kernel\" : ['linear', 'sigmoid'] }\n"
      ],
      "metadata": {
        "id": "2KN2nH0-iQ4R"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cval = KFold(n_splits = 2)\n",
        "model =  SVC();\n",
        "TunedModel = GridSearchCV(model,params,cv= cval)\n",
        "TunedModel.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "rF8wHZKtiUTS",
        "outputId": "2888585a-4dd1-4ba7-9852-8f5584767fb3"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=KFold(n_splits=2, random_state=None, shuffle=False),\n",
              "             estimator=SVC(),\n",
              "             param_grid={'C': [0.2, 0.5], 'kernel': ['linear', 'sigmoid']})"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=2, random_state=None, shuffle=False),\n",
              "             estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.2, 0.5], &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;sigmoid&#x27;]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=KFold(n_splits=2, random_state=None, shuffle=False),\n",
              "             estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.2, 0.5], &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;sigmoid&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = metrics.accuracy_score(y_test, TunedModel.predict(x_test))\n",
        "accuracy_percentage = 100 * accuracy\n",
        "accuracy_percentage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNSbSZtuiYj3",
        "outputId": "d4a29aeb-2e2f-4855-dada-cfaf436ac019"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "99.0038738240177"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NB**"
      ],
      "metadata": {
        "id": "XCysyBArjHTo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold, GridSearchCV\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "params = {\n",
        "    'alpha': [0.1, 0.5, 1.0],  # Different values for alpha\n",
        "    'fit_prior': [True, False]  # Whether to fit class prior probabilities\n",
        "}\n",
        "\n",
        "cval = KFold(n_splits=2)\n",
        "model = MultinomialNB()  # Using Multinomial Naive Bayes\n",
        "TunedModel1 = GridSearchCV(model, params, cv=cval)\n",
        "TunedModel1.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "w5lS5MY_jKMo",
        "outputId": "181ccf1f-e654-4b56-d737-93eeda842599"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=KFold(n_splits=2, random_state=None, shuffle=False),\n",
              "             estimator=MultinomialNB(),\n",
              "             param_grid={'alpha': [0.1, 0.5, 1.0], 'fit_prior': [True, False]})"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=2, random_state=None, shuffle=False),\n",
              "             estimator=MultinomialNB(),\n",
              "             param_grid={&#x27;alpha&#x27;: [0.1, 0.5, 1.0], &#x27;fit_prior&#x27;: [True, False]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=KFold(n_splits=2, random_state=None, shuffle=False),\n",
              "             estimator=MultinomialNB(),\n",
              "             param_grid={&#x27;alpha&#x27;: [0.1, 0.5, 1.0], &#x27;fit_prior&#x27;: [True, False]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = metrics.accuracy_score(y_test, TunedModel1.predict(x_test))\n",
        "accuracy_percentage = 100 * accuracy\n",
        "accuracy_percentage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vyftMF9jYPQ",
        "outputId": "cf1e5087-3da3-4edc-bc7a-1add804b3308"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.40287769784173"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Evaluation**"
      ],
      "metadata": {
        "id": "48dACDEmjmz9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion-svm**"
      ],
      "metadata": {
        "id": "5nAUDCQ0jwq3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_test,TunedModel.predict(x_test)),annot = True , fmt =\"g\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.show(\"Actual\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Q2UcIVeBjtUw",
        "outputId": "6275b2f7-a367-4982-b2fa-dfe70c0da97e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**classification report-SVM**"
      ],
      "metadata": {
        "id": "dASrVKORj_lH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,TunedModel.predict(x_test)))"
      ],
      "metadata": {
        "id": "KVk2ALtLkFIy",
        "outputId": "f8544ae0-d947-4a5b-ab97-edd2b5c389b2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.95      0.98      0.96       895\n",
            "        spam       0.98      0.95      0.96       912\n",
            "\n",
            "    accuracy                           0.96      1807\n",
            "   macro avg       0.96      0.96      0.96      1807\n",
            "weighted avg       0.96      0.96      0.96      1807\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the trained model, predict whether the following five emails are spam or ham\n",
        "\n"
      ],
      "metadata": {
        "id": "lyZjJuPPkKRe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mails = [\"Hey, you have won a car !!!!. Conrgratzz\"\n",
        "              ,\"Dear applicant, Your CV has been recieved. Best regards\"\n",
        "              ,\"You have received $1000000 to your account\"\n",
        "              ,\"Join with our whatsapp group\"\n",
        "              ,\"Kindly check the previous email. Kind Regard\"]\n",
        "for mail in mails:\n",
        "  is_spam = TunedModel.predict(vectorizer.transform([mail]).toarray())\n",
        "  print(mail + \" : \" + str(is_spam))"
      ],
      "metadata": {
        "id": "5an4IG2OkPHI",
        "outputId": "e555fb70-9978-4b95-a59e-3fbfbb5c5cae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hey, you have won a car !!!!. Conrgratzz : ['spam']\n",
            "Dear applicant, Your CV has been recieved. Best regards : ['spam']\n",
            "You have received $1000000 to your account : ['spam']\n",
            "Join with our whatsapp group : ['spam']\n",
            "Kindly check the previous email. Kind Regard : ['ham']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚References**"
      ],
      "metadata": {
        "id": "stu4tDcu6VdH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1.   [How to Save a Machine Learning Model](https://thecleverprogrammer.com/2021/05/13/how-to-save-a-machine-learning-model/)?\n",
        "2.   [What's the difference between fit and fit_transform in scikit-learn models?](https://datascience.stackexchange.com/questions/12321/whats-the-difference-between-fit-and-fit-transform-in-scikit-learn-models)\n",
        "\n"
      ],
      "metadata": {
        "id": "L5FAsOVM6gZd"
      }
    }
  ]
}