Practice_Global_Vector_in_NLP.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/abdumalikh44/Natural-Language-Processing-Practice/blob/main/Practice_Global_Vector_in_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "miyIcmptH5z0"
      },
      "outputs": [],
      "source": [
        "my_vocab = ['apple','orange','shimla','banana','maruti','mumbai','china','india','husband'\n",
        "            ,'wife','brother','sister','red','yellow','computer','mobile','pear','guava']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gensim\n",
        "from sklearn.manifold import TSNE"
      ],
      "metadata": {
        "id": "GSDuFAMIIcLY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gensim.downloader as api"
      ],
      "metadata": {
        "id": "UqFpXSU4IdaC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glove_model = api.load('glove-wiki-gigaword-300')\n"
      ],
      "metadata": {
        "id": "mRiciXrOIhKO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glove_model.most_similar('love',topn=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeYASDBlIj2W",
        "outputId": "ff4dafd9-8bc6-4eed-addb-6bfb96f78c11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('loves', 0.6420262455940247),\n",
              " ('passion', 0.6261469125747681),\n",
              " ('loved', 0.595270574092865),\n",
              " ('romantic', 0.5921086072921753),\n",
              " ('lovers', 0.5904366970062256)]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# husband - man + woman = wife"
      ],
      "metadata": {
        "id": "5ZGuSz2CJdrs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glove_model.most_similar(positive= ['woman', 'husband'], negative=['man'],topn=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gw6cp6gbJgyX",
        "outputId": "b5a1d65f-4585-4618-a123-287bcf1470c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('wife', 0.7732622027397156)]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking word value\n",
        "glove_model['banana']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFLEIjpIJ7jm",
        "outputId": "db3c86a9-b725-45cc-9041-01a249a6f087"
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              "      dtype=float32)"
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words = []\n",
        "vectors = []\n",
        "\n",
        "\n",
        "for word in my_vocab:\n",
        "    words.append(word)\n",
        "    vectors.append(glove_model[word])"
      ],
      "metadata": {
        "id": "7RtD-EXUJoEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dicts = zip(words,vectors)"
      ],
      "metadata": {
        "id": "BvOrYNJlKhxP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "3GugtcCDKoEj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking vector value of words\n",
        "pd.DataFrame.from_dict(dicts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "KYjlEHTzK-T8",
        "outputId": "3cfa4607-afca-47d2-cd2c-4529d7e9e24e"
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      "outputs": [
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          "data": {
            "text/plain": [
              "           0                                                  1\n",
              "0      apple  [-0.20842, -0.019668, 0.063981, -0.71403, -0.2...\n",
              "1     orange  [-0.24776, -0.12359, 0.20986, -0.15834, -0.158...\n",
              "2     shimla  [-0.083216, -0.235, 0.34561, 0.082819, 0.41372...\n",
              "3     banana  [0.42141, 0.020467, 0.12666, 0.39762, -0.11016...\n",
              "4     maruti  [-0.097778, 0.39619, -0.1984, 0.12019, 0.47573...\n",
              "5     mumbai  [-0.25335, -0.41384, 0.15746, 0.23978, 0.09889...\n",
              "6      china  [-0.11286, 0.033802, -0.2941, -0.75285, 0.0542...\n",
              "7      india  [-0.11337, -0.57114, 0.070453, -0.36241, -0.05...\n",
              "8    husband  [-0.2866, -0.32581, -0.051729, -0.21017, -0.03...\n",
              "9       wife  [-0.62203, -0.091568, -0.45238, -0.45005, 0.12...\n",
              "10   brother  [-0.11814, 0.33494, 0.14149, -0.33411, 0.09787...\n",
              "11    sister  [-0.47697, 0.076817, -0.47216, -0.13498, 0.192...\n",
              "12       red  [0.059672, -0.13475, 0.26764, -0.47441, 0.0156...\n",
              "13    yellow  [-0.053451, 0.049399, 0.1133, -0.81798, -0.163...\n",
              "14  computer  [-0.27628, 0.13999, 0.098519, -0.64019, 0.0319...\n",
              "15    mobile  [-0.93518, 0.11477, -0.2488, -0.29031, -0.3565...\n",
              "16      pear  [-0.49423, -0.18297, 0.44304, -0.45103, 0.0562...\n",
              "17     guava  [0.32138, -0.43799, 0.48669, -0.41138, -0.0663..."
            ],
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              "      <th>0</th>\n",
              "      <td>apple</td>\n",
              "      <td>[-0.20842, -0.019668, 0.063981, -0.71403, -0.2...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>orange</td>\n",
              "      <td>[-0.24776, -0.12359, 0.20986, -0.15834, -0.158...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>shimla</td>\n",
              "      <td>[-0.083216, -0.235, 0.34561, 0.082819, 0.41372...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>banana</td>\n",
              "      <td>[0.42141, 0.020467, 0.12666, 0.39762, -0.11016...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>maruti</td>\n",
              "      <td>[-0.097778, 0.39619, -0.1984, 0.12019, 0.47573...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>mumbai</td>\n",
              "      <td>[-0.25335, -0.41384, 0.15746, 0.23978, 0.09889...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>china</td>\n",
              "      <td>[-0.11286, 0.033802, -0.2941, -0.75285, 0.0542...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>india</td>\n",
              "      <td>[-0.11337, -0.57114, 0.070453, -0.36241, -0.05...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>husband</td>\n",
              "      <td>[-0.2866, -0.32581, -0.051729, -0.21017, -0.03...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>wife</td>\n",
              "      <td>[-0.62203, -0.091568, -0.45238, -0.45005, 0.12...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>brother</td>\n",
              "      <td>[-0.11814, 0.33494, 0.14149, -0.33411, 0.09787...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>sister</td>\n",
              "      <td>[-0.47697, 0.076817, -0.47216, -0.13498, 0.192...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>red</td>\n",
              "      <td>[0.059672, -0.13475, 0.26764, -0.47441, 0.0156...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>yellow</td>\n",
              "      <td>[-0.053451, 0.049399, 0.1133, -0.81798, -0.163...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>computer</td>\n",
              "      <td>[-0.27628, 0.13999, 0.098519, -0.64019, 0.0319...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>mobile</td>\n",
              "      <td>[-0.93518, 0.11477, -0.2488, -0.29031, -0.3565...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>pear</td>\n",
              "      <td>[-0.49423, -0.18297, 0.44304, -0.45103, 0.0562...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>guava</td>\n",
              "      <td>[0.32138, -0.43799, 0.48669, -0.41138, -0.0663...</td>\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e6e727d2-6665-4c91-85ea-cc3f612a58ca button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e6e727d2-6665-4c91-85ea-cc3f612a58ca');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-85a702fc-c805-48b2-b620-eacbdc0fc55a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-85a702fc-c805-48b2-b620-eacbdc0fc55a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-85a702fc-c805-48b2-b620-eacbdc0fc55a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"apple\",\n          \"orange\",\n          \"husband\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calling TNSE model\n",
        "dim_model = TSNE(n_components=2, perplexity=3, init='pca', random_state=45)"
      ],
      "metadata": {
        "id": "BRj0StorLFxJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#convert a list to a NumPy array so that it can fit in the model\n",
        "vectors = np.array(vectors)"
      ],
      "metadata": {
        "id": "1HARTn93NEyk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fit_model = dim_model.fit_transform(vectors)"
      ],
      "metadata": {
        "id": "B97X8PVWLkx3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fit_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgJsjqDQLz7A",
        "outputId": "56b13417-eacb-44c1-b6f3-a93c22dab62b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3.6855333e+00,  2.0465258e+01],\n",
              "       [ 1.8119772e+01, -6.2651241e+01],\n",
              "       [ 5.3032837e+01, -6.6119760e-02],\n",
              "       [ 1.0946297e+01,  3.4961132e+01],\n",
              "       [ 6.1068363e+01, -1.7654331e+01],\n",
              "       [ 4.9868042e+01, -8.2426996e+00],\n",
              "       [ 4.4246586e+01, -2.1651392e+01],\n",
              "       [ 4.9913738e+01, -1.5310020e+01],\n",
              "       [-1.6391689e+01, -3.7068909e+01],\n",
              "       [-1.5937251e+01, -3.0278357e+01],\n",
              "       [-2.4906244e+01, -3.4887527e+01],\n",
              "       [-7.9130182e+00, -2.9896429e+01],\n",
              "       [ 8.2344666e+00, -7.0003899e+01],\n",
              "       [ 1.4667891e+01, -6.9354698e+01],\n",
              "       [ 3.1475229e+00,  1.2809575e+01],\n",
              "       [ 3.3375921e+00,  5.4183140e+00],\n",
              "       [ 7.6263763e-02,  3.8358200e+01],\n",
              "       [ 7.4306898e+00,  4.0926601e+01]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "NDqmYeDBL4DA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = []\n",
        "y = []\n",
        "\n",
        "for i in fit_model:\n",
        "    x.append(i[0])\n",
        "    y.append(i[1])"
      ],
      "metadata": {
        "id": "vx4fVl51OcmL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbzSRA0nOc96",
        "outputId": "a0d7eb96-d09c-44b0-adc3-2cbc4e9cde51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.6855333,\n",
              " 18.119772,\n",
              " 53.032837,\n",
              " 10.946297,\n",
              " 61.068363,\n",
              " 49.868042,\n",
              " 44.246586,\n",
              " 49.91374,\n",
              " -16.39169,\n",
              " -15.937251,\n",
              " -24.906244,\n",
              " -7.913018,\n",
              " 8.234467,\n",
              " 14.667891,\n",
              " 3.147523,\n",
              " 3.3375921,\n",
              " 0.07626376,\n",
              " 7.43069]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTpdgNEsOnbG",
        "outputId": "422ae2e1-5b7a-479b-9506-0aff9d6bcc12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[20.465258,\n",
              " -62.65124,\n",
              " -0.06611976,\n",
              " 34.961132,\n",
              " -17.654331,\n",
              " -8.2427,\n",
              " -21.651392,\n",
              " -15.31002,\n",
              " -37.06891,\n",
              " -30.278357,\n",
              " -34.887527,\n",
              " -29.89643,\n",
              " -70.0039,\n",
              " -69.3547,\n",
              " 12.809575,\n",
              " 5.418314,\n",
              " 38.3582,\n",
              " 40.9266]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(x)):\n",
        "    plt.scatter(x[i],y[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "n1P_q5FlOogd",
        "outputId": "2f390db0-6aff-413d-9ede-931351b1282d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the details of cluster\n",
        "plt.figure(figsize=(8,8))\n",
        "\n",
        "for i in range(len(x)):\n",
        "    plt.scatter(x[i],y[i])\n",
        "    plt.annotate(words[i], xy=(x[i],y[i]),\n",
        "                 xytext=(2, 2),\n",
        "                 textcoords='offset points',\n",
        "                 ha='right',\n",
        "                 va='bottom'\n",
        "                )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "yRm7NZbzPC4x",
        "outputId": "39d6f66f-c97c-4192-8bdd-dbfa305c868a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kMvo7ffbPRRg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}