Task_7_Assignment_10_Euclidean and Manhattan Distance using KNN.ipynb

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        "<a href=\"https://colab.research.google.com/github/shreehere/Machine-Learning--Task-7/blob/main/Task_7_Assignment_10_Euclidean%20and%20Manhattan%20Distance%20using%20KNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "##K- Nearest Neighbour (KNN) Classifier"
      ],
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      "cell_type": "code",
      "source": [
        "# importing all the necessary Libraries\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from scipy.spatial import distance"
      ],
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        "id": "5AYkz69chb-U"
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      "execution_count": 1,
      "outputs": []
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        "train_df=pd.read_csv(\"/content/trainKNN.txt\",header=None)\n",
        "train_df"
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        "Changing the column names to the original data column names"
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        "train_df.columns=['Id','RI','Na','Mg','Al','Si','K','Ca','Ba','Fe','Type of glass']\n",
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              "      Id       RI     Na    Mg    Al     Si     K    Ca    Ba   Fe  \\\n",
              "0      1  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0   \n",
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              "4      5  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0   \n",
              "..   ...      ...    ...   ...   ...    ...   ...   ...   ...  ...   \n",
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              "195  211  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0   \n",
              "\n",
              "     Type of glass  \n",
              "0                1  \n",
              "1                1  \n",
              "2                1  \n",
              "3                1  \n",
              "4                1  \n",
              "..             ...  \n",
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              "      <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",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>207</td>\n",
              "      <td>1.51645</td>\n",
              "      <td>14.94</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.87</td>\n",
              "      <td>73.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.67</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>208</td>\n",
              "      <td>1.51831</td>\n",
              "      <td>14.39</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.82</td>\n",
              "      <td>72.86</td>\n",
              "      <td>1.41</td>\n",
              "      <td>6.47</td>\n",
              "      <td>2.88</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>209</td>\n",
              "      <td>1.51640</td>\n",
              "      <td>14.37</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.74</td>\n",
              "      <td>72.85</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.45</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>210</td>\n",
              "      <td>1.51623</td>\n",
              "      <td>14.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.88</td>\n",
              "      <td>72.61</td>\n",
              "      <td>0.08</td>\n",
              "      <td>9.18</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>211</td>\n",
              "      <td>1.51685</td>\n",
              "      <td>14.92</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.99</td>\n",
              "      <td>73.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.40</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>196 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fbdcb88-2052-44d9-b899-fd5775ca91bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </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",
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              "      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-7fbdcb88-2052-44d9-b899-fd5775ca91bb 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-7fbdcb88-2052-44d9-b899-fd5775ca91bb');\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
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Droping the 'Id' column since it is not required"
      ],
      "metadata": {
        "id": "YUmh1hBv5hCx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df=train_df.drop('Id',axis=1)\n",
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5QEmglhet-pz",
        "outputId": "8284425b-90b1-4f51-adb9-66d960c372cd"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          RI     Na    Mg    Al     Si     K    Ca    Ba   Fe  Type of glass\n",
              "0    1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0              1\n",
              "1    1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.00  0.0              1\n",
              "2    1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.00  0.0              1\n",
              "3    1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.00  0.0              1\n",
              "4    1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0              1\n",
              "..       ...    ...   ...   ...    ...   ...   ...   ...  ...            ...\n",
              "191  1.51645  14.94  0.00  1.87  73.11  0.00  8.67  1.38  0.0              7\n",
              "192  1.51831  14.39  0.00  1.82  72.86  1.41  6.47  2.88  0.0              7\n",
              "193  1.51640  14.37  0.00  2.74  72.85  0.00  9.45  0.54  0.0              7\n",
              "194  1.51623  14.14  0.00  2.88  72.61  0.08  9.18  1.06  0.0              7\n",
              "195  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0              7\n",
              "\n",
              "[196 rows x 10 columns]"
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              "      <th></th>\n",
              "      <th>RI</th>\n",
              "      <th>Na</th>\n",
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              "      <th>Al</th>\n",
              "      <th>Si</th>\n",
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              "      <th>Fe</th>\n",
              "      <th>Type of glass</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1.52101</td>\n",
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              "      <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",
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              "      <th>2</th>\n",
              "      <td>1.51618</td>\n",
              "      <td>13.53</td>\n",
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              "      <th>3</th>\n",
              "      <td>1.51766</td>\n",
              "      <td>13.21</td>\n",
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              "      <th>4</th>\n",
              "      <td>1.51742</td>\n",
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              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>1.51645</td>\n",
              "      <td>14.94</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.87</td>\n",
              "      <td>73.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.67</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>1.51831</td>\n",
              "      <td>14.39</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.82</td>\n",
              "      <td>72.86</td>\n",
              "      <td>1.41</td>\n",
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              "      <td>2.88</td>\n",
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              "      <th>193</th>\n",
              "      <td>1.51640</td>\n",
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              "      <td>72.85</td>\n",
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              "      <td>9.45</td>\n",
              "      <td>0.54</td>\n",
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              "      <td>72.61</td>\n",
              "      <td>0.08</td>\n",
              "      <td>9.18</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
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              "      <th>195</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>196 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1c6afc5-4d28-4e40-90f2-9e2c560821a0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-b1c6afc5-4d28-4e40-90f2-9e2c560821a0 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-b1c6afc5-4d28-4e40-90f2-9e2c560821a0');\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
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Doing the same for the test data"
      ],
      "metadata": {
        "id": "7VdiVY206IpS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_df=pd.read_csv(\"/content/testKNN.txt\",header=None)\n",
        "test_df.columns=['Id','RI','Na','Mg','Al','Si','K','Ca','Ba','Fe','Type of glass']\n",
        "test_df=test_df.drop('Id',axis=1)\n",
        "test_df"
      ],
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        "id": "LXgph8HauUrK",
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      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         RI     Na    Mg    Al     Si     K     Ca    Ba    Fe  Type of glass\n",
              "0   1.52152  13.05  3.65  0.87  72.32  0.19   9.85  0.00  0.17              1\n",
              "1   1.52152  13.12  3.58  0.90  72.20  0.23   9.82  0.00  0.16              1\n",
              "2   1.52300  13.31  3.58  0.82  71.99  0.12  10.17  0.00  0.03              1\n",
              "3   1.51709  13.00  3.47  1.79  72.72  0.66   8.18  0.00  0.00              2\n",
              "4   1.51660  12.99  3.18  1.23  72.97  0.58   8.81  0.00  0.24              2\n",
              "5   1.51839  12.85  3.67  1.24  72.57  0.62   8.68  0.00  0.35              2\n",
              "6   1.51832  13.33  3.34  1.54  72.14  0.56   8.99  0.00  0.00              3\n",
              "7   1.51934  13.64  3.54  0.75  72.65  0.16   8.89  0.15  0.24              3\n",
              "8   1.52211  14.19  3.78  0.91  71.36  0.23   9.14  0.00  0.37              3\n",
              "9   1.52043  13.38  0.00  1.40  72.25  0.33  12.50  0.00  0.00              5\n",
              "10  1.52058  12.85  1.61  2.17  72.18  0.76   9.70  0.24  0.51              5\n",
              "11  1.52119  12.97  0.33  1.51  73.39  0.13  11.27  0.00  0.28              5\n",
              "12  1.51916  14.15  0.00  2.09  72.74  0.00  10.88  0.00  0.00              6\n",
              "13  1.51969  14.56  0.00  0.56  73.48  0.00  11.22  0.00  0.00              6\n",
              "14  1.51115  17.38  0.00  0.34  75.41  0.00   6.65  0.00  0.00              6\n",
              "15  1.52065  14.36  0.00  2.02  73.42  0.00   8.44  1.64  0.00              7\n",
              "16  1.51651  14.38  0.00  1.94  73.61  0.00   8.48  1.57  0.00              7\n",
              "17  1.51711  14.23  0.00  2.08  73.36  0.00   8.62  1.67  0.00              7"
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              "      <th></th>\n",
              "      <th>RI</th>\n",
              "      <th>Na</th>\n",
              "      <th>Mg</th>\n",
              "      <th>Al</th>\n",
              "      <th>Si</th>\n",
              "      <th>K</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Ba</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.52152</td>\n",
              "      <td>13.05</td>\n",
              "      <td>3.65</td>\n",
              "      <td>0.87</td>\n",
              "      <td>72.32</td>\n",
              "      <td>0.19</td>\n",
              "      <td>9.85</td>\n",
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              "      <th>1</th>\n",
              "      <td>1.52152</td>\n",
              "      <td>13.12</td>\n",
              "      <td>3.58</td>\n",
              "      <td>0.90</td>\n",
              "      <td>72.20</td>\n",
              "      <td>0.23</td>\n",
              "      <td>9.82</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.52300</td>\n",
              "      <td>13.31</td>\n",
              "      <td>3.58</td>\n",
              "      <td>0.82</td>\n",
              "      <td>71.99</td>\n",
              "      <td>0.12</td>\n",
              "      <td>10.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.51709</td>\n",
              "      <td>13.00</td>\n",
              "      <td>3.47</td>\n",
              "      <td>1.79</td>\n",
              "      <td>72.72</td>\n",
              "      <td>0.66</td>\n",
              "      <td>8.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.51660</td>\n",
              "      <td>12.99</td>\n",
              "      <td>3.18</td>\n",
              "      <td>1.23</td>\n",
              "      <td>72.97</td>\n",
              "      <td>0.58</td>\n",
              "      <td>8.81</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.51839</td>\n",
              "      <td>12.85</td>\n",
              "      <td>3.67</td>\n",
              "      <td>1.24</td>\n",
              "      <td>72.57</td>\n",
              "      <td>0.62</td>\n",
              "      <td>8.68</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.35</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.51832</td>\n",
              "      <td>13.33</td>\n",
              "      <td>3.34</td>\n",
              "      <td>1.54</td>\n",
              "      <td>72.14</td>\n",
              "      <td>0.56</td>\n",
              "      <td>8.99</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.51934</td>\n",
              "      <td>13.64</td>\n",
              "      <td>3.54</td>\n",
              "      <td>0.75</td>\n",
              "      <td>72.65</td>\n",
              "      <td>0.16</td>\n",
              "      <td>8.89</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.24</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.52211</td>\n",
              "      <td>14.19</td>\n",
              "      <td>3.78</td>\n",
              "      <td>0.91</td>\n",
              "      <td>71.36</td>\n",
              "      <td>0.23</td>\n",
              "      <td>9.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.37</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.52043</td>\n",
              "      <td>13.38</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.40</td>\n",
              "      <td>72.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>12.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.52058</td>\n",
              "      <td>12.85</td>\n",
              "      <td>1.61</td>\n",
              "      <td>2.17</td>\n",
              "      <td>72.18</td>\n",
              "      <td>0.76</td>\n",
              "      <td>9.70</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.51</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.52119</td>\n",
              "      <td>12.97</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.51</td>\n",
              "      <td>73.39</td>\n",
              "      <td>0.13</td>\n",
              "      <td>11.27</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.28</td>\n",
              "      <td>5</td>\n",
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              "      <th>12</th>\n",
              "      <td>1.51916</td>\n",
              "      <td>14.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.09</td>\n",
              "      <td>72.74</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.51969</td>\n",
              "      <td>14.56</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.56</td>\n",
              "      <td>73.48</td>\n",
              "      <td>0.00</td>\n",
              "      <td>11.22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.51115</td>\n",
              "      <td>17.38</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.34</td>\n",
              "      <td>75.41</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.65</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.52065</td>\n",
              "      <td>14.36</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.02</td>\n",
              "      <td>73.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.44</td>\n",
              "      <td>1.64</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
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              "      <th>16</th>\n",
              "      <td>1.51651</td>\n",
              "      <td>14.38</td>\n",
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              "      <td>1.94</td>\n",
              "      <td>73.61</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.48</td>\n",
              "      <td>1.57</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.51711</td>\n",
              "      <td>14.23</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.08</td>\n",
              "      <td>73.36</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.62</td>\n",
              "      <td>1.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
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          },
          "metadata": {},
          "execution_count": 9
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.describe()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
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        "id": "YPnn8VFcv2_E",
        "outputId": "ec01d7ea-f863-4940-d92e-da22dc406ef4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               RI          Na          Mg          Al          Si           K  \\\n",
              "count  196.000000  196.000000  196.000000  196.000000  196.000000  196.000000   \n",
              "mean     1.518295   13.375204    2.758980    1.454337   72.635408    0.519388   \n",
              "std      0.003055    0.783145    1.392641    0.491688    0.763578    0.672703   \n",
              "min      1.511310   10.730000    0.000000    0.290000   69.810000    0.000000   \n",
              "25%      1.516458   12.877500    2.362500    1.190000   72.317500    0.140000   \n",
              "50%      1.517630   13.280000    3.480000    1.360000   72.810000    0.560000   \n",
              "75%      1.518985   13.792500    3.610000    1.622500   73.080000    0.610000   \n",
              "max      1.533930   15.790000    4.490000    3.500000   75.180000    6.210000   \n",
              "\n",
              "               Ca          Ba          Fe  Type of glass  \n",
              "count  196.000000  196.000000  196.000000     196.000000  \n",
              "mean     8.910714    0.164235    0.050255       2.668367  \n",
              "std      1.421490    0.485198    0.086359       2.062416  \n",
              "min      5.430000    0.000000    0.000000       1.000000  \n",
              "25%      8.220000    0.000000    0.000000       1.000000  \n",
              "50%      8.575000    0.000000    0.000000       2.000000  \n",
              "75%      9.092500    0.000000    0.090000       3.000000  \n",
              "max     16.190000    3.150000    0.340000       7.000000  "
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              "      <th></th>\n",
              "      <th>RI</th>\n",
              "      <th>Na</th>\n",
              "      <th>Mg</th>\n",
              "      <th>Al</th>\n",
              "      <th>Si</th>\n",
              "      <th>K</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Ba</th>\n",
              "      <th>Fe</th>\n",
              "      <th>Type of glass</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>196.000000</td>\n",
              "      <td>196.000000</td>\n",
              "      <td>196.000000</td>\n",
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              "      <td>196.000000</td>\n",
              "      <td>196.000000</td>\n",
              "      <td>196.000000</td>\n",
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              "      <td>196.000000</td>\n",
              "      <td>196.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.518295</td>\n",
              "      <td>13.375204</td>\n",
              "      <td>2.758980</td>\n",
              "      <td>1.454337</td>\n",
              "      <td>72.635408</td>\n",
              "      <td>0.519388</td>\n",
              "      <td>8.910714</td>\n",
              "      <td>0.164235</td>\n",
              "      <td>0.050255</td>\n",
              "      <td>2.668367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.003055</td>\n",
              "      <td>0.783145</td>\n",
              "      <td>1.392641</td>\n",
              "      <td>0.491688</td>\n",
              "      <td>0.763578</td>\n",
              "      <td>0.672703</td>\n",
              "      <td>1.421490</td>\n",
              "      <td>0.485198</td>\n",
              "      <td>0.086359</td>\n",
              "      <td>2.062416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.511310</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",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.516458</td>\n",
              "      <td>12.877500</td>\n",
              "      <td>2.362500</td>\n",
              "      <td>1.190000</td>\n",
              "      <td>72.317500</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>8.220000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.517630</td>\n",
              "      <td>13.280000</td>\n",
              "      <td>3.480000</td>\n",
              "      <td>1.360000</td>\n",
              "      <td>72.810000</td>\n",
              "      <td>0.560000</td>\n",
              "      <td>8.575000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.518985</td>\n",
              "      <td>13.792500</td>\n",
              "      <td>3.610000</td>\n",
              "      <td>1.622500</td>\n",
              "      <td>73.080000</td>\n",
              "      <td>0.610000</td>\n",
              "      <td>9.092500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.533930</td>\n",
              "      <td>15.790000</td>\n",
              "      <td>4.490000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>75.180000</td>\n",
              "      <td>6.210000</td>\n",
              "      <td>16.190000</td>\n",
              "      <td>3.150000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cacb92d-b899-4b7c-b2c0-d56613c494e6')\"\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-6cacb92d-b899-4b7c-b2c0-d56613c494e6 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-6cacb92d-b899-4b7c-b2c0-d56613c494e6');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "B9fUWKANwLTQ",
        "outputId": "cf84dbbc-af0a-4319-de04-68f4028d54fd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              RI         Na         Mg         Al         Si          K  \\\n",
              "count  18.000000  18.000000  18.000000  18.000000  18.000000  18.000000   \n",
              "mean    1.519131  13.763333   1.873889   1.342222  72.820000   0.253889   \n",
              "std     0.002791   1.083491   1.749753   0.581312   0.892004   0.265133   \n",
              "min     1.511150  12.850000   0.000000   0.340000  71.360000   0.000000   \n",
              "25%     1.517412  13.012500   0.000000   0.877500  72.212500   0.000000   \n",
              "50%     1.519515  13.355000   2.395000   1.320000  72.685000   0.175000   \n",
              "75%     1.521055  14.220000   3.570000   1.902500  73.382500   0.502500   \n",
              "max     1.523000  17.380000   3.780000   2.170000  75.410000   0.760000   \n",
              "\n",
              "              Ca         Ba         Fe  Type of glass  \n",
              "count  18.000000  18.000000  18.000000      18.000000  \n",
              "mean    9.460556   0.292778   0.130556       4.000000  \n",
              "std     1.380432   0.617422   0.164798       2.222876  \n",
              "min     6.650000   0.000000   0.000000       1.000000  \n",
              "25%     8.635000   0.000000   0.000000       2.000000  \n",
              "50%     9.065000   0.000000   0.015000       4.000000  \n",
              "75%    10.090000   0.112500   0.240000       6.000000  \n",
              "max    12.500000   1.670000   0.510000       7.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6513933d-0f18-4d60-b833-e69f0c15afa3\">\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>RI</th>\n",
              "      <th>Na</th>\n",
              "      <th>Mg</th>\n",
              "      <th>Al</th>\n",
              "      <th>Si</th>\n",
              "      <th>K</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Ba</th>\n",
              "      <th>Fe</th>\n",
              "      <th>Type of glass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.519131</td>\n",
              "      <td>13.763333</td>\n",
              "      <td>1.873889</td>\n",
              "      <td>1.342222</td>\n",
              "      <td>72.820000</td>\n",
              "      <td>0.253889</td>\n",
              "      <td>9.460556</td>\n",
              "      <td>0.292778</td>\n",
              "      <td>0.130556</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.002791</td>\n",
              "      <td>1.083491</td>\n",
              "      <td>1.749753</td>\n",
              "      <td>0.581312</td>\n",
              "      <td>0.892004</td>\n",
              "      <td>0.265133</td>\n",
              "      <td>1.380432</td>\n",
              "      <td>0.617422</td>\n",
              "      <td>0.164798</td>\n",
              "      <td>2.222876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.511150</td>\n",
              "      <td>12.850000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>71.360000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.650000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.517412</td>\n",
              "      <td>13.012500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.877500</td>\n",
              "      <td>72.212500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.635000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.519515</td>\n",
              "      <td>13.355000</td>\n",
              "      <td>2.395000</td>\n",
              "      <td>1.320000</td>\n",
              "      <td>72.685000</td>\n",
              "      <td>0.175000</td>\n",
              "      <td>9.065000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.521055</td>\n",
              "      <td>14.220000</td>\n",
              "      <td>3.570000</td>\n",
              "      <td>1.902500</td>\n",
              "      <td>73.382500</td>\n",
              "      <td>0.502500</td>\n",
              "      <td>10.090000</td>\n",
              "      <td>0.112500</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.523000</td>\n",
              "      <td>17.380000</td>\n",
              "      <td>3.780000</td>\n",
              "      <td>2.170000</td>\n",
              "      <td>75.410000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>1.670000</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6513933d-0f18-4d60-b833-e69f0c15afa3')\"\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",
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              "      border-radius: 50%;\n",
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              "      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-6513933d-0f18-4d60-b833-e69f0c15afa3 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-6513933d-0f18-4d60-b833-e69f0c15afa3');\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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding unique values for train and test data"
      ],
      "metadata": {
        "id": "1wPmrWYI6U7e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['Type of glass'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJE-AVrVwVKy",
        "outputId": "22ae30c1-c4f0-496d-d115-95621f2ce4e7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 5, 6, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df['Type of glass'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLUwrl_pwfuh",
        "outputId": "b7719278-142b-4078-842a-c45e8587255c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 5, 6, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "Ra8SP0OxxK1d"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the test and the train Data**"
      ],
      "metadata": {
        "id": "gzGKkR6W6gRl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=train_df.iloc[:,1:-1]\n",
        "y_train=train_df['Type of glass']\n",
        "x_test=test_df.iloc[:,1:-1]\n",
        "y_test=test_df['Type of glass']"
      ],
      "metadata": {
        "id": "Pd7EI49px_eY"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Scaling**"
      ],
      "metadata": {
        "id": "ePmlovX36raA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "st=StandardScaler()\n",
        "x_train=st.fit_transform(x_train)"
      ],
      "metadata": {
        "id": "IaVep3Z8yoKZ"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test=st.fit_transform(x_test)"
      ],
      "metadata": {
        "id": "Cf6_SZ51yvQt"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hptlE1Hpy2j_",
        "outputId": "f4101d8f-1067-45af-bbc3-ddecb15e4a14"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.3389847 ,  1.24616006, -0.72249922, ..., -0.11334995,\n",
              "        -0.33935667, -0.5834224 ],\n",
              "       [ 0.65902807,  0.60544985, -0.19235437, ..., -0.76221546,\n",
              "        -0.33935667, -0.5834224 ],\n",
              "       [ 0.19816562,  0.56945489,  0.174669  , ..., -0.79747989,\n",
              "        -0.33935667, -0.5834224 ],\n",
              "       ...,\n",
              "       [ 1.27351132, -1.98618697,  2.62149143, ...,  0.38035207,\n",
              "         0.77644048, -0.5834224 ],\n",
              "       [ 0.97907143, -1.98618697,  2.90695404, ...,  0.18992414,\n",
              "         1.8509118 , -0.5834224 ],\n",
              "       [ 1.97760672, -1.98618697,  1.09222741, ..., -0.36020096,\n",
              "         2.94604604, -0.5834224 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5QK-vurzQsj",
        "outputId": "d70f9615-c470-4074-a410-156a0f39e0f3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.67745268,  1.04449191, -0.83588893, -0.57678658, -0.24795484,\n",
              "         0.29029673, -0.48794184,  0.24628886],\n",
              "       [-0.61097368,  1.00332645, -0.7827854 , -0.71521535, -0.09271355,\n",
              "         0.26793435, -0.48794184,  0.18384943],\n",
              "       [-0.43053068,  1.00332645, -0.92439482, -0.95746572, -0.5196271 ,\n",
              "         0.5288287 , -0.48794184, -0.62786315],\n",
              "       [-0.72493768,  0.93863786,  0.79261939, -0.11535732,  1.57613032,\n",
              "        -0.95454202, -0.48794184, -0.81518144],\n",
              "       [-0.73443468,  0.76809524, -0.19864655,  0.17303597,  1.26564774,\n",
              "        -0.48493219, -0.48794184,  0.68336487],\n",
              "       [-0.86739269,  1.05625347, -0.18094537, -0.28839329,  1.42088903,\n",
              "        -0.58183581, -0.48794184,  1.3701986 ],\n",
              "       [-0.41153668,  0.86218772,  0.35008995, -0.78442974,  1.1880271 ,\n",
              "        -0.35075796, -0.48794184, -0.81518144],\n",
              "       [-0.11712967,  0.97980332, -1.04830306, -0.19610744, -0.36438581,\n",
              "        -0.4252992 , -0.23795266,  0.68336487],\n",
              "       [ 0.40520534,  1.12094205, -0.76508423, -1.6842168 , -0.09271355,\n",
              "        -0.23894609, -0.48794184,  1.49507746],\n",
              "       [-0.36405167, -1.10199287,  0.10227347, -0.6575367 ,  0.29538968,\n",
              "         2.26563965, -0.48794184, -0.81518144],\n",
              "       [-0.86739269, -0.15518726,  1.46526413, -0.73828682,  1.96423355,\n",
              "         0.17848486, -0.08795915,  2.36922947],\n",
              "       [-0.75342868, -0.90792712,  0.29698642,  0.6575367 , -0.48081677,\n",
              "         1.34878237, -0.48794184,  0.93312259],\n",
              "       [ 0.36721734, -1.10199287,  1.32365471, -0.09228585, -0.98535097,\n",
              "         1.05807152, -0.48794184, -0.81518144],\n",
              "       [ 0.75659435, -1.10199287, -1.38462544,  0.76135828, -0.98535097,\n",
              "         1.31151175, -0.48794184, -0.81518144],\n",
              "       [ 3.43474841, -1.10199287, -1.77405134,  2.98775446, -0.98535097,\n",
              "        -2.09502303, -0.48794184, -0.81518144],\n",
              "       [ 0.56665435, -1.10199287,  1.19974647,  0.69214389, -0.98535097,\n",
              "        -0.76073479,  2.24527316, -0.81518144],\n",
              "       [ 0.58564835, -1.10199287,  1.05813705,  0.91132279, -0.98535097,\n",
              "        -0.73091829,  2.12861155, -0.81518144],\n",
              "       [ 0.44319334, -1.10199287,  1.30595354,  0.6229295 , -0.98535097,\n",
              "        -0.62656055,  2.295271  , -0.81518144]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Euclidean Distance"
      ],
      "metadata": {
        "id": "LznYg8mk4HZY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "euc_knn=KNeighborsClassifier(metric=distance.sqeuclidean)"
      ],
      "metadata": {
        "id": "U_TKSAQwzd02"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model created for Euclidean distance\n",
        "\n",
        "euc_knn.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSYQQJu_zuRF",
        "outputId": "cc89bf88-6e31-4afd-8d3c-b418f45ace61"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric=<function sqeuclidean at 0x7f2a1fef8ca0>)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction_euc=euc_knn.predict(x_test)"
      ],
      "metadata": {
        "id": "9sbF85Ah0Ai8"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction_euc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUyc2RpY0WJl",
        "outputId": "d3f3f379-e6c9-419f-f72f-8175d9102e54"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 2, 1, 2, 2, 2, 2, 5, 2, 5, 6, 2, 1, 7, 7, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "EUgQ4VQi0bB1"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Finding accuracy level of the Euclidean prediction\n",
        "\n",
        "accuracy_euc=accuracy_score(y_test,y_prediction_euc)*100\n",
        "accuracy_euc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCDHsrCt0uR6",
        "outputId": "4f805f05-235a-4661-d7a8-2867354804c6"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "61.111111111111114"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Manhattan distance"
      ],
      "metadata": {
        "id": "scm_0hKg2YEQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#model created for Manhattan distance\n",
        "\n",
        "manhattan_knn=KNeighborsClassifier(metric='manhattan')"
      ],
      "metadata": {
        "id": "yHVgnQHM2bBs"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manhattan_knn.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5clsyuRa29Zf",
        "outputId": "fe2157ef-37fa-4909-b16a-4d4ceb42679a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='manhattan')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction_manhattan=manhattan_knn.predict(x_test)"
      ],
      "metadata": {
        "id": "kZUbZRP03D-X"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Finding accuracy level of the Manhattan prediction\n",
        "\n",
        "accuracy_manhattan=accuracy_score(y_test,y_prediction_manhattan)*100\n",
        "accuracy_manhattan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmbTfeOM3TXd",
        "outputId": "3402edc4-985b-4fff-ee9d-bca718c7c285"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66.66666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ignoring warnings \n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "0OYcm7W3322i"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Report for Euclidean and Manhattan**"
      ],
      "metadata": {
        "id": "uZJSQaQd7r4H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_prediction_euc))\n",
        "print(\"-----------------------------------------------------------------------\")\n",
        "print(\"The accuracy score of Euclidean Distance for the test Data :\", accuracy_euc  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqUi1kW63sDX",
        "outputId": "f8545d4c-31e4-458f-d15e-df686efde0aa"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.60      1.00      0.75         3\n",
            "           2       0.29      0.67      0.40         3\n",
            "           3       0.00      0.00      0.00         3\n",
            "           5       1.00      0.67      0.80         3\n",
            "           6       1.00      0.33      0.50         3\n",
            "           7       1.00      1.00      1.00         3\n",
            "\n",
            "    accuracy                           0.61        18\n",
            "   macro avg       0.65      0.61      0.58        18\n",
            "weighted avg       0.65      0.61      0.57        18\n",
            "\n",
            "----------------------------------------\n",
            "The accuracy score of Euclidean Distance for the test Data : 61.111111111111114\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_prediction_manhattan))\n",
        "print(\"-----------------------------------------------------------------------\")\n",
        "print(\"The accuracy score of Manhattan Distance for the test Data :\", accuracy_manhattan)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdCVs-GP4RRH",
        "outputId": "bad0173d-92f0-4e48-98ff-b9698808c7e5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.75      1.00      0.86         3\n",
            "           2       0.29      0.67      0.40         3\n",
            "           3       0.00      0.00      0.00         3\n",
            "           5       1.00      0.67      0.80         3\n",
            "           6       1.00      0.67      0.80         3\n",
            "           7       1.00      1.00      1.00         3\n",
            "\n",
            "    accuracy                           0.67        18\n",
            "   macro avg       0.67      0.67      0.64        18\n",
            "weighted avg       0.67      0.67      0.64        18\n",
            "\n",
            "-----------------------------------------------------------------------\n",
            "The accuracy score of Manhattan Distance for the test Data : 66.66666666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CONCLUSION\n",
        "\n",
        "The accuracy score of Manhattan Distance is 66.66 and the accuracy for Euclidean score is 61.11. This shows the model created for Manhattan Distance fits more in the given data"
      ],
      "metadata": {
        "id": "5nUw_Nq37yuW"
      }
    }
  ]
}