Atharv_Patawar_Compute_A7_glass_kmeans.ipynb

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        "<a href=\"https://colab.research.google.com/github/savioratharv/Compute_tasks/blob/main/Atharv_Patawar_Compute_A7_glass_kmeans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# About dataset and the task\n",
        "This is a Glass Identification Data Set from UCI. It contains the following attributes:\n",
        "1. RI: refractive index\n",
        "2. Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10)\n",
        "3. Mg: Magnesium\n",
        "4. Al: Aluminum\n",
        "5. Si: Silicon\n",
        "6. K: Potassium\n",
        "7. Ca: Calcium\n",
        "8. Ba: Barium\n",
        "9. Fe: Iron\n",
        "\n",
        "The 10th attribute which is the target label has been dropped as it contained the label for different glass types. Your task is to apply K-Means on the givn dataset to find the number of glass types present in the dataset. \n",
        "\n",
        "The error of **`+/-1`** in the calculated number of glass is allowed"
      ]
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      "source": [
        "# Importing Libraries & Data"
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        "id": "I6iXMk-UNaaR"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
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              "        RI     Na    Mg    Al     Si     K    Ca   Ba   Fe\n",
              "0  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0\n",
              "1  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0\n",
              "2  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0\n",
              "3  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0\n",
              "4  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0"
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              "      <th>0</th>\n",
              "      <td>1.52101</td>\n",
              "      <td>13.64</td>\n",
              "      <td>4.49</td>\n",
              "      <td>1.10</td>\n",
              "      <td>71.78</td>\n",
              "      <td>0.06</td>\n",
              "      <td>8.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.51761</td>\n",
              "      <td>13.89</td>\n",
              "      <td>3.60</td>\n",
              "      <td>1.36</td>\n",
              "      <td>72.73</td>\n",
              "      <td>0.48</td>\n",
              "      <td>7.83</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.51618</td>\n",
              "      <td>13.53</td>\n",
              "      <td>3.55</td>\n",
              "      <td>1.54</td>\n",
              "      <td>72.99</td>\n",
              "      <td>0.39</td>\n",
              "      <td>7.78</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.51766</td>\n",
              "      <td>13.21</td>\n",
              "      <td>3.69</td>\n",
              "      <td>1.29</td>\n",
              "      <td>72.61</td>\n",
              "      <td>0.57</td>\n",
              "      <td>8.22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.51742</td>\n",
              "      <td>13.27</td>\n",
              "      <td>3.62</td>\n",
              "      <td>1.24</td>\n",
              "      <td>73.08</td>\n",
              "      <td>0.55</td>\n",
              "      <td>8.07</td>\n",
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        "data=pd.read_csv(\"glass.csv\")\n",
        "data.head()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 214 entries, 0 to 213\n",
            "Data columns (total 9 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   RI      214 non-null    float64\n",
            " 1   Na      214 non-null    float64\n",
            " 2   Mg      214 non-null    float64\n",
            " 3   Al      214 non-null    float64\n",
            " 4   Si      214 non-null    float64\n",
            " 5   K       214 non-null    float64\n",
            " 6   Ca      214 non-null    float64\n",
            " 7   Ba      214 non-null    float64\n",
            " 8   Fe      214 non-null    float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 15.2 KB\n"
          ]
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      "source": [
        "data.info()"
      ]
    },
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      "source": [
        "Dataset has no null values"
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            "text/plain": [
              "               RI          Na          Mg          Al          Si           K  \\\n",
              "count  214.000000  214.000000  214.000000  214.000000  214.000000  214.000000   \n",
              "mean     1.518365   13.407850    2.684533    1.444907   72.650935    0.497056   \n",
              "std      0.003037    0.816604    1.442408    0.499270    0.774546    0.652192   \n",
              "min      1.511150   10.730000    0.000000    0.290000   69.810000    0.000000   \n",
              "25%      1.516522   12.907500    2.115000    1.190000   72.280000    0.122500   \n",
              "50%      1.517680   13.300000    3.480000    1.360000   72.790000    0.555000   \n",
              "75%      1.519157   13.825000    3.600000    1.630000   73.087500    0.610000   \n",
              "max      1.533930   17.380000    4.490000    3.500000   75.410000    6.210000   \n",
              "\n",
              "               Ca          Ba          Fe  \n",
              "count  214.000000  214.000000  214.000000  \n",
              "mean     8.956963    0.175047    0.057009  \n",
              "std      1.423153    0.497219    0.097439  \n",
              "min      5.430000    0.000000    0.000000  \n",
              "25%      8.240000    0.000000    0.000000  \n",
              "50%      8.600000    0.000000    0.000000  \n",
              "75%      9.172500    0.000000    0.100000  \n",
              "max     16.190000    3.150000    0.510000  "
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              "      <th>Si</th>\n",
              "      <th>K</th>\n",
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              "      <th>count</th>\n",
              "      <td>214.000000</td>\n",
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              "      <td>214.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>214.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>1.518365</td>\n",
              "      <td>13.407850</td>\n",
              "      <td>2.684533</td>\n",
              "      <td>1.444907</td>\n",
              "      <td>72.650935</td>\n",
              "      <td>0.497056</td>\n",
              "      <td>8.956963</td>\n",
              "      <td>0.175047</td>\n",
              "      <td>0.057009</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.003037</td>\n",
              "      <td>0.816604</td>\n",
              "      <td>1.442408</td>\n",
              "      <td>0.499270</td>\n",
              "      <td>0.774546</td>\n",
              "      <td>0.652192</td>\n",
              "      <td>1.423153</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.511150</td>\n",
              "      <td>10.730000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.290000</td>\n",
              "      <td>69.810000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.430000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.516522</td>\n",
              "      <td>12.907500</td>\n",
              "      <td>2.115000</td>\n",
              "      <td>1.190000</td>\n",
              "      <td>72.280000</td>\n",
              "      <td>0.122500</td>\n",
              "      <td>8.240000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.517680</td>\n",
              "      <td>13.300000</td>\n",
              "      <td>3.480000</td>\n",
              "      <td>1.360000</td>\n",
              "      <td>72.790000</td>\n",
              "      <td>0.555000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.519157</td>\n",
              "      <td>13.825000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>1.630000</td>\n",
              "      <td>73.087500</td>\n",
              "      <td>0.610000</td>\n",
              "      <td>9.172500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100000</td>\n",
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              "      <th>max</th>\n",
              "      <td>1.533930</td>\n",
              "      <td>17.380000</td>\n",
              "      <td>4.490000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>75.410000</td>\n",
              "      <td>6.210000</td>\n",
              "      <td>16.190000</td>\n",
              "      <td>3.150000</td>\n",
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              "      fill: #174EA6;\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-b6c69be3-dcb6-485c-ab55-68a76a800283 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-b6c69be3-dcb6-485c-ab55-68a76a800283');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pesf1oucNaac"
      },
      "source": [
        "**There is variation in the magnitude of the data, which can be seen by the mean values of the elements**\n",
        "**Hence we will scale the data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39HO-9ydNaae"
      },
      "source": [
        "# Pre-processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fcNiIGQNaah"
      },
      "source": [
        "* Create object of StandardScaler\n",
        "* apply `fit_transform` on the dataset and store the data in the new variable `data_scaled`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "7omJr1W8Naai"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "data_scaled = scaler.fit_transform(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "njnKx6HXNaak",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "aebe6d03-a39f-41da-d37d-1cb0a08150bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  0             1             2             3             4  \\\n",
              "count  2.140000e+02  2.140000e+02  2.140000e+02  2.140000e+02  2.140000e+02   \n",
              "mean  -2.877034e-14  2.191393e-15 -1.328117e-16 -2.988264e-16  9.525091e-16   \n",
              "std    1.002345e+00  1.002345e+00  1.002345e+00  1.002345e+00  1.002345e+00   \n",
              "min   -2.381516e+00 -3.286943e+00 -1.865511e+00 -2.318616e+00 -3.676472e+00   \n",
              "25%   -6.082728e-01 -6.141580e-01 -3.957744e-01 -5.117560e-01 -4.800288e-01   \n",
              "50%   -2.262293e-01 -1.323817e-01  5.527787e-01 -1.704602e-01  1.799655e-01   \n",
              "75%    2.614331e-01  5.120326e-01  6.361680e-01  3.715977e-01  5.649621e-01   \n",
              "max    5.137232e+00  4.875637e+00  1.254639e+00  4.125851e+00  3.570524e+00   \n",
              "\n",
              "                  5             6             7             8  \n",
              "count  2.140000e+02  2.140000e+02  2.140000e+02  2.140000e+02  \n",
              "mean   3.527811e-17 -3.154278e-16 -6.640586e-17 -3.735330e-17  \n",
              "std    1.002345e+00  1.002345e+00  1.002345e+00  1.002345e+00  \n",
              "min   -7.639186e-01 -2.484084e+00 -3.528768e-01 -5.864509e-01  \n",
              "25%   -5.756501e-01 -5.049657e-01 -3.528768e-01 -5.864509e-01  \n",
              "50%    8.905322e-02 -2.514132e-01 -3.528768e-01 -5.864509e-01  \n",
              "75%    1.735820e-01  1.518057e-01 -3.528768e-01  4.422417e-01  \n",
              "max    8.780145e+00  5.094318e+00  5.997210e+00  4.659881e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-98722387-4dd6-464b-ba66-a78b08919865\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.140000e+02</td>\n",
              "      <td>2.140000e+02</td>\n",
              "      <td>2.140000e+02</td>\n",
              "      <td>2.140000e+02</td>\n",
              "      <td>2.140000e+02</td>\n",
              "      <td>2.140000e+02</td>\n",
              "      <td>2.140000e+02</td>\n",
              "      <td>2.140000e+02</td>\n",
              "      <td>2.140000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-2.877034e-14</td>\n",
              "      <td>2.191393e-15</td>\n",
              "      <td>-1.328117e-16</td>\n",
              "      <td>-2.988264e-16</td>\n",
              "      <td>9.525091e-16</td>\n",
              "      <td>3.527811e-17</td>\n",
              "      <td>-3.154278e-16</td>\n",
              "      <td>-6.640586e-17</td>\n",
              "      <td>-3.735330e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.002345e+00</td>\n",
              "      <td>1.002345e+00</td>\n",
              "      <td>1.002345e+00</td>\n",
              "      <td>1.002345e+00</td>\n",
              "      <td>1.002345e+00</td>\n",
              "      <td>1.002345e+00</td>\n",
              "      <td>1.002345e+00</td>\n",
              "      <td>1.002345e+00</td>\n",
              "      <td>1.002345e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.381516e+00</td>\n",
              "      <td>-3.286943e+00</td>\n",
              "      <td>-1.865511e+00</td>\n",
              "      <td>-2.318616e+00</td>\n",
              "      <td>-3.676472e+00</td>\n",
              "      <td>-7.639186e-01</td>\n",
              "      <td>-2.484084e+00</td>\n",
              "      <td>-3.528768e-01</td>\n",
              "      <td>-5.864509e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-6.082728e-01</td>\n",
              "      <td>-6.141580e-01</td>\n",
              "      <td>-3.957744e-01</td>\n",
              "      <td>-5.117560e-01</td>\n",
              "      <td>-4.800288e-01</td>\n",
              "      <td>-5.756501e-01</td>\n",
              "      <td>-5.049657e-01</td>\n",
              "      <td>-3.528768e-01</td>\n",
              "      <td>-5.864509e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-2.262293e-01</td>\n",
              "      <td>-1.323817e-01</td>\n",
              "      <td>5.527787e-01</td>\n",
              "      <td>-1.704602e-01</td>\n",
              "      <td>1.799655e-01</td>\n",
              "      <td>8.905322e-02</td>\n",
              "      <td>-2.514132e-01</td>\n",
              "      <td>-3.528768e-01</td>\n",
              "      <td>-5.864509e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.614331e-01</td>\n",
              "      <td>5.120326e-01</td>\n",
              "      <td>6.361680e-01</td>\n",
              "      <td>3.715977e-01</td>\n",
              "      <td>5.649621e-01</td>\n",
              "      <td>1.735820e-01</td>\n",
              "      <td>1.518057e-01</td>\n",
              "      <td>-3.528768e-01</td>\n",
              "      <td>4.422417e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.137232e+00</td>\n",
              "      <td>4.875637e+00</td>\n",
              "      <td>1.254639e+00</td>\n",
              "      <td>4.125851e+00</td>\n",
              "      <td>3.570524e+00</td>\n",
              "      <td>8.780145e+00</td>\n",
              "      <td>5.094318e+00</td>\n",
              "      <td>5.997210e+00</td>\n",
              "      <td>4.659881e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98722387-4dd6-464b-ba66-a78b08919865')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-98722387-4dd6-464b-ba66-a78b08919865 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-98722387-4dd6-464b-ba66-a78b08919865');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# statistics of scaled data\n",
        "pd.DataFrame(data_scaled).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgZq4bQuNaal"
      },
      "source": [
        "Now the data of of same scale and we can apply K-Means on this scaled data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kmPhGynXNaam"
      },
      "source": [
        "# Applying K-Means"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hz4XieHuNaan"
      },
      "source": [
        "* Set the value of k (`n_clusters`) as 2\n",
        "* Create object of kmeans function with initialization (`init`) as `k-means++`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "MbKdo5YZNaao",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ddf59e0-020c-4653-ff5d-57d4f41a5860"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=2, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# kmeans = KMeans(write appropriate params here) \n",
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters = 2, init = 'k-means++', random_state = 42)\n",
        "\n",
        "kmeans.fit(data_scaled)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nO0cdRCwNaap"
      },
      "source": [
        "Print the inertia of the two clusters (Hint: use the attribute `inertia_` of kmeans object )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "oD11UEVxNaaq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0572799c-fb70-4d5a-d679-0b5e2f77f982"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1536.3894681408005"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# find inertia here\n",
        "kmeans.inertia_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "uz_RhkpYNaar"
      },
      "outputs": [],
      "source": [
        "# fitting multiple k-means algorithms and storing the values in an empty list\n",
        "inertia_list = []\n",
        "for cluster in range(1, 16): # 1...15 clusters\n",
        "    kmeans = KMeans(n_clusters = cluster, init = 'k-means++', random_state = 42)# Create object of KMeans() with n_clusters as clusters and init as k-means++\n",
        "    \n",
        "    kmeans.fit(data_scaled)\n",
        "    \n",
        "    temp_inertia = kmeans.inertia_# find inertia and assign it to this variable\n",
        "\n",
        "    inertia_list.append(temp_inertia)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "aJrhBT33Naat",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "f110cf11-db44-447b-e7a4-c612631f1529"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Inertia')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plotting the inerta values for clusters of various sizes\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(range(1, 16), inertia_list, marker='o')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('Inertia')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWunW2SANaav"
      },
      "source": [
        "**What is the appropriate value for the number of clusters according to the above plot?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OhGTzvuoNaaw"
      },
      "source": [
        "**Ans:** *Number of clusters (No. of glass types)* = 13"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qT8WVhOwNaaw"
      },
      "source": [
        "* Create kmeans object with the identified number of clusters and init as `k-means++`\n",
        "* fit the model\n",
        "* use predict method on scaled data to find the number of points belonging to the specific clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "N7hAf6MeNaax"
      },
      "outputs": [],
      "source": [
        "kmeans = KMeans(n_clusters = 13, init = 'k-means++', random_state = 42)# KMeans(write appropriate params here) \n",
        "\n",
        "kmeans.fit(data_scaled)\n",
        "\n",
        "pred = kmeans.predict(data_scaled) # use predict() method of kmeans on scaled data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "1HNT54NxNaax"
      },
      "outputs": [],
      "source": [
        "pred = pd.Series(pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIX_Vo32Naay"
      },
      "source": [
        "**Find the number of pontints in each cluster using `value_counts()` on `pred`**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "rPmiB14jNaaz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c83de36-d14f-4d12-9d76-39ac72759113"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6     82\n",
              "2     32\n",
              "3     25\n",
              "11    21\n",
              "7     14\n",
              "4     14\n",
              "12    11\n",
              "9      5\n",
              "0      3\n",
              "1      3\n",
              "5      2\n",
              "8      1\n",
              "10     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "\"\"\"\n",
        "Find number of pontints in each cluster here\n",
        "\"\"\"\n",
        "\n",
        "pred.value_counts()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.1"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "07d623e3832b78ce69fa2c095939a54ed4f3e8fe6909ed4ae68f2da80f027625"
      }
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}