Deep_Learning_NLP.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# example text for model training (SMS messages)\n",
        "simple_train = ['call you tonight', 'Call me a cab', 'please call me... PLEASE!']\n",
        "\n",
        "# example response vector\n",
        "y = [0, 0, 1]\n",
        "\n",
        "\n",
        "# example text for model testing\n",
        "simple_test = [\"please don't call me\"]"
      ],
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        "id": "c2OyvD-E_2ot"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "vect = CountVectorizer()"
      ],
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# learn the 'vocabulary' of the training data\n",
        "vect.fit(simple_train)"
      ],
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              "CountVectorizer()"
            ]
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        "vect.get_feature_names_out()"
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            "text/plain": [
              "array(['cab', 'call', 'me', 'please', 'tonight', 'you'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
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      "source": [
        "simple_train_dtm = vect.transform(simple_train)"
      ],
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      "source": [
        "print(simple_train_dtm)"
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            "  (0, 1)\t1\n",
            "  (0, 4)\t1\n",
            "  (0, 5)\t1\n",
            "  (1, 0)\t1\n",
            "  (1, 1)\t1\n",
            "  (1, 2)\t1\n",
            "  (2, 1)\t1\n",
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            "  (2, 3)\t2\n"
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      "source": [
        "# convert sparse matrix to a dense matrix\n",
        "simple_train_dtm.toarray()"
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              "array([[0, 1, 0, 0, 1, 1],\n",
              "       [1, 1, 1, 0, 0, 0],\n",
              "       [0, 1, 1, 2, 0, 0]])"
            ]
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        "pd.DataFrame(simple_train_dtm.toarray(),columns=vect.get_feature_names_out())"
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            "text/plain": [
              "   cab  call  me  please  tonight  you\n",
              "0    0     1   0       0        1    1\n",
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              "2    0     1   1       2        0    0"
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      "source": [
        "# Build a model to predict the target\n",
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "knn.fit(simple_train_dtm, y)"
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              "KNeighborsClassifier(n_neighbors=1)"
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      "source": [
        "# transform testing data into a document-term matrix (using existing vocabulary)\n",
        "simple_test_dtm = vect.fit_transform(simple_test)\n",
        "simple_test_dtm.toarray()"
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              "array([[1, 1, 1, 1]])"
            ]
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        "pd.DataFrame(simple_test_dtm.toarray(), columns=vect.get_feature_names())"
      ],
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
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            "text/plain": [
              "   call  don  me  please\n",
              "0     1    1   1       1"
            ],
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              "      <th>call</th>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "**Summary:**\n",
        "\n",
        "- `vect.fit(train)` **learns the vocabulary** of the training data\n",
        "- `vect.transform(train)` uses the **fitted vocabulary** to build a document-term matrix from the training data\n",
        "- `vect.transform(test)` uses the **fitted vocabulary** to build a document-term matrix from the testing data and **ignores tokens** it hasn't seen before"
      ],
      "metadata": {
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    {
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      "source": [
        "sms = pd.read_table('sms.tsv',header=None,names = ['label', 'message'])"
      ],
      "metadata": {
        "id": "BAQ02piNE_qA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# examine the shape\n",
        "sms.shape"
      ],
      "metadata": {
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        },
        "id": "NIjD5Rd5G2aa",
        "outputId": "5638ff2d-04ec-4c15-b433-9ef93926b697"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# examine the first 10 rows\n",
        "sms.head(10)"
      ],
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          "data": {
            "text/plain": [
              "  label                                            message\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...\n",
              "5  spam  FreeMsg Hey there darling it's been 3 week's n...\n",
              "6   ham  Even my brother is not like to speak with me. ...\n",
              "7   ham  As per your request 'Melle Melle (Oru Minnamin...\n",
              "8  spam  WINNER!! As a valued network customer you have...\n",
              "9  spam  Had your mobile 11 months or more? U R entitle..."
            ],
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              "    <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",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
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              "    <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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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>spam</td>\n",
              "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ham</td>\n",
              "      <td>Even my brother is not like to speak with me. ...</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ham</td>\n",
              "      <td>As per your request 'Melle Melle (Oru Minnamin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>spam</td>\n",
              "      <td>WINNER!! As a valued network customer you have...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>spam</td>\n",
              "      <td>Had your mobile 11 months or more? U R entitle...</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# examine the class distribution\n",
        "sms.label.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KtIGAa_G-AQ",
        "outputId": "aa9851b4-dae3-4fb8-e3f2-980e711f0d19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ham     4825\n",
              "spam     747\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sms.label.value_counts(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEfah_LYHCn-",
        "outputId": "b2c27555-c7b3-48f2-d8c2-7f29175b6526"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ham     0.865937\n",
              "spam    0.134063\n",
              "Name: label, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert label to a numerical variable\n",
        "sms['label_num'] = sms.label.map({'ham':0, 'spam':1})"
      ],
      "metadata": {
        "id": "tK2Bssw_HI8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sms.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gIjBjQI3HRAL",
        "outputId": "429f0412-8e44-441a-9c35-0a97fe6d356f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  label                                            message  label_num\n",
              "0   ham  Go until jurong point, crazy.. Available only ...          0\n",
              "1   ham                      Ok lar... Joking wif u oni...          0\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...          1\n",
              "3   ham  U dun say so early hor... U c already then say...          0\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...          0"
            ],
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              "\n",
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              "      <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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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9a29f54e-512e-4218-9286-63498b0fb5df');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# how to define X and y (from the SMS data) for use with COUNTVECTORIZER\n",
        "X = sms.message\n",
        "y = sms.label_num\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GNso0Qq5HUvO",
        "outputId": "9f39cee0-b1d3-46ba-97a3-f39f66b0c5c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5572,)\n",
            "(5572,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split X and y into training and testing sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1LTATb0HhlO",
        "outputId": "4e986e5b-7c49-4b71-8b91-d74fcf8e0b31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4179,)\n",
            "(1393,)\n",
            "(4179,)\n",
            "(1393,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# instantiate the vectorizer\n",
        "vect = CountVectorizer()"
      ],
      "metadata": {
        "id": "Qpd88RbdHj5i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# equivalently: combine fit and transform into a single step\n",
        "X_train_dtm = vect.fit_transform(X_train)"
      ],
      "metadata": {
        "id": "8oQ5tWX_KqqN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# examine the document-term matrix\n",
        "X_train_dtm"
      ],
      "metadata": {
        "id": "iDAXoYYyKyU-",
        "outputId": "7c6cc938-08b6-4a0e-e22b-76b298ff7f2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<4179x7450 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 55393 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# transform testing data (using fitted vocabulary) into a document-term matrix\n",
        "X_test_dtm = vect.transform(X_test)\n",
        "X_test_dtm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMt2oSnJKuMA",
        "outputId": "0c561a1f-1103-4d87-c475-ca0757d23200"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<1393x7450 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 17416 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "P-hGEXV_KwHw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ss = StandardScaler(with_mean=False)"
      ],
      "metadata": {
        "id": "b_symBBjycol"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled = ss.fit_transform(X_train_dtm)"
      ],
      "metadata": {
        "id": "UgDQw2_zyd1A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJ3QDP5JyiTo",
        "outputId": "327e46ed-f2bb-4579-eff6-af8a02dd6f2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<4179x7450 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 55393 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaled = ss.transform(X_test_dtm)"
      ],
      "metadata": {
        "id": "umBSODd_zQEH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7beUdVCGzTZn",
        "outputId": "8a664802-ae4f-416d-ab0a-aa77c67afaf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<1393x7450 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 17416 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier"
      ],
      "metadata": {
        "id": "HCx6MAvozkKL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nncl = MLPClassifier(hidden_layer_sizes=(100,),\n",
        "                    max_iter = 5000,\n",
        "                    solver = 'sgd',\n",
        "                    activation = 'logistic',\n",
        "                    random_state = 1,\n",
        "                    verbose=True,\n",
        "                    tol = 0.01,\n",
        "                    batch_size=20,\n",
        "                    learning_rate = 'adaptive',\n",
        "                    learning_rate_init= 0.01)"
      ],
      "metadata": {
        "id": "lZnVHn52z8hM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "nncl.fit(X_train_scaled,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXNwrkgA0Bwo",
        "outputId": "19aaf904-3288-4380-a363-b69ce51bbf92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration 1, loss = 0.20610888\n",
            "Iteration 2, loss = 0.01635206\n",
            "Iteration 3, loss = 0.00640968\n",
            "Iteration 4, loss = 0.00412298\n",
            "Iteration 5, loss = 0.00305457\n",
            "Iteration 6, loss = 0.00243861\n",
            "Iteration 7, loss = 0.00203682\n",
            "Iteration 8, loss = 0.00175528\n",
            "Iteration 9, loss = 0.00154784\n",
            "Iteration 10, loss = 0.00138848\n",
            "Iteration 11, loss = 0.00126190\n",
            "Iteration 12, loss = 0.00115978\n",
            "Iteration 13, loss = 0.00107561\n",
            "Training loss did not improve more than tol=0.010000 for 10 consecutive epochs. Setting learning rate to 0.002000\n",
            "Iteration 14, loss = 0.00102373\n",
            "Iteration 15, loss = 0.00101040\n",
            "Iteration 16, loss = 0.00099749\n",
            "Iteration 17, loss = 0.00098513\n",
            "Iteration 18, loss = 0.00097306\n",
            "Iteration 19, loss = 0.00096136\n",
            "Iteration 20, loss = 0.00095003\n",
            "Iteration 21, loss = 0.00093903\n",
            "Iteration 22, loss = 0.00092835\n",
            "Iteration 23, loss = 0.00091796\n",
            "Iteration 24, loss = 0.00090788\n",
            "Training loss did not improve more than tol=0.010000 for 10 consecutive epochs. Setting learning rate to 0.000400\n",
            "Iteration 25, loss = 0.00090089\n",
            "Iteration 26, loss = 0.00089893\n",
            "Iteration 27, loss = 0.00089701\n",
            "Iteration 28, loss = 0.00089509\n",
            "Iteration 29, loss = 0.00089318\n",
            "Iteration 30, loss = 0.00089128\n",
            "Iteration 31, loss = 0.00088939\n",
            "Iteration 32, loss = 0.00088751\n",
            "Iteration 33, loss = 0.00088565\n",
            "Iteration 34, loss = 0.00088379\n",
            "Iteration 35, loss = 0.00088195\n",
            "Training loss did not improve more than tol=0.010000 for 10 consecutive epochs. Setting learning rate to 0.000080\n",
            "Iteration 36, loss = 0.00088064\n",
            "Iteration 37, loss = 0.00088027\n",
            "Iteration 38, loss = 0.00087991\n",
            "Iteration 39, loss = 0.00087954\n",
            "Iteration 40, loss = 0.00087918\n",
            "Iteration 41, loss = 0.00087881\n",
            "Iteration 42, loss = 0.00087845\n",
            "Iteration 43, loss = 0.00087808\n",
            "Iteration 44, loss = 0.00087772\n",
            "Iteration 45, loss = 0.00087736\n",
            "Iteration 46, loss = 0.00087699\n",
            "Training loss did not improve more than tol=0.010000 for 10 consecutive epochs. Setting learning rate to 0.000016\n",
            "Iteration 47, loss = 0.00087674\n",
            "Iteration 48, loss = 0.00087666\n",
            "Iteration 49, loss = 0.00087659\n",
            "Iteration 50, loss = 0.00087652\n",
            "Iteration 51, loss = 0.00087645\n",
            "Iteration 52, loss = 0.00087637\n",
            "Iteration 53, loss = 0.00087630\n",
            "Iteration 54, loss = 0.00087623\n",
            "Iteration 55, loss = 0.00087616\n",
            "Iteration 56, loss = 0.00087608\n",
            "Iteration 57, loss = 0.00087601\n",
            "Training loss did not improve more than tol=0.010000 for 10 consecutive epochs. Setting learning rate to 0.000003\n",
            "Iteration 58, loss = 0.00087596\n",
            "Iteration 59, loss = 0.00087595\n",
            "Iteration 60, loss = 0.00087593\n",
            "Iteration 61, loss = 0.00087592\n",
            "Iteration 62, loss = 0.00087590\n",
            "Iteration 63, loss = 0.00087589\n",
            "Iteration 64, loss = 0.00087587\n",
            "Iteration 65, loss = 0.00087586\n",
            "Iteration 66, loss = 0.00087584\n",
            "Iteration 67, loss = 0.00087583\n",
            "Iteration 68, loss = 0.00087582\n",
            "Training loss did not improve more than tol=0.010000 for 10 consecutive epochs. Setting learning rate to 0.000001\n",
            "Iteration 69, loss = 0.00087581\n",
            "Iteration 70, loss = 0.00087580\n",
            "Iteration 71, loss = 0.00087580\n",
            "Iteration 72, loss = 0.00087580\n",
            "Iteration 73, loss = 0.00087579\n",
            "Iteration 74, loss = 0.00087579\n",
            "Iteration 75, loss = 0.00087579\n",
            "Iteration 76, loss = 0.00087579\n",
            "Iteration 77, loss = 0.00087578\n",
            "Iteration 78, loss = 0.00087578\n",
            "Iteration 79, loss = 0.00087578\n",
            "Training loss did not improve more than tol=0.010000 for 10 consecutive epochs. Learning rate too small. Stopping.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(activation='logistic', batch_size=20, learning_rate='adaptive',\n",
              "              learning_rate_init=0.01, max_iter=5000, random_state=1,\n",
              "              solver='sgd', tol=0.01, verbose=True)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nncl.score(X_train_scaled,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-FMhljAQ0M-E",
        "outputId": "316d72c4-aef6-47d7-aaa3-10539106baa1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nncl.score(X_test_scaled,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vncrA62G02xB",
        "outputId": "203b8d56-ac23-4a07-e34d-8803941720df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9777458722182341"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-_lQZsGQ07Eu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Thank You ! Rudhra"
      ],
      "metadata": {
        "id": "xTQXkrvg8dZx"
      }
    }
  ]
}