K_means_iris.ipynb

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      "metadata": {
        "id": "Ixl3vSm1EiYa"
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      "source": [
        "from sklearn.cluster import KMeans\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris"
      ],
      "metadata": {
        "id": "802jBXS3EyDl"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "iris=load_iris()"
      ],
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        "id": "oheaFMqbE2o0"
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      "execution_count": 11,
      "outputs": []
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              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                  5.1               3.5                1.4               0.2\n",
              "1                  4.9               3.0                1.4               0.2\n",
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              "3                  4.6               3.1                1.5               0.2\n",
              "4                  5.0               3.6                1.4               0.2\n",
              "..                 ...               ...                ...               ...\n",
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              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8\n",
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            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
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        "df['flower'] = iris.target\n",
        "df.head()"
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              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                5.1               3.5                1.4               0.2   \n",
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              "2                4.7               3.2                1.3               0.2   \n",
              "3                4.6               3.1                1.5               0.2   \n",
              "4                5.0               3.6                1.4               0.2   \n",
              "\n",
              "   flower  \n",
              "0       0  \n",
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              "2       0  \n",
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              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34dc4e03-74d4-449b-9a7d-54422c0f75b2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-34dc4e03-74d4-449b-9a7d-54422c0f75b2');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['sepal length (cm)', 'sepal width (cm)', 'flower'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "bEuZTiDrFG8H"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "km = KMeans(n_clusters=3)\n",
        "yp = km.fit_predict(df)\n",
        "yp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgeoNyEvFg-K",
        "outputId": "174ad28c-eca1-4125-d691-f4e9f02e8900"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cluster'] = yp\n",
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "w5lTAYSlFjil",
        "outputId": "9f8af4c1-5c7e-40de-bd0d-842d95ef7624"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   petal length (cm)  petal width (cm)  cluster\n",
              "0                1.4               0.2        0\n",
              "1                1.4               0.2        0"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d5dcea7-8c8e-4439-8d30-7e20e252676d')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-1d5dcea7-8c8e-4439-8d30-7e20e252676d 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-1d5dcea7-8c8e-4439-8d30-7e20e252676d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df[df.cluster==0]\n",
        "df2 = df[df.cluster==1]\n",
        "df3 = df[df.cluster==2]"
      ],
      "metadata": {
        "id": "6NRNjKnwFnLy"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='blue')\n",
        "plt.scatter(df2['petal length (cm)'],df2['petal width (cm)'],color='green')\n",
        "plt.scatter(df3['petal length (cm)'],df3['petal width (cm)'],color='yellow')\n",
        "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='Aqua',marker='*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0s85uSQSFxzj",
        "outputId": "fa98eb8a-496f-4dc7-a1a7-caf38a8781a7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f714c7bfc50>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "km.cluster_centers_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsy8T-6PFz0q",
        "outputId": "017e5f52-7273-4712-eef8-16d8f424624e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.46200000e+00, 2.46000000e-01, 1.00000000e+00],\n",
              "       [5.57346939e+00, 2.03265306e+00, 2.00000000e+00],\n",
              "       [4.26470588e+00, 1.33333333e+00, 4.44089210e-16]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sse = []\n",
        "k_rng = range(1,10)\n",
        "for k in k_rng:\n",
        "    km = KMeans(n_clusters=k)\n",
        "    km.fit(df)\n",
        "    sse.append(km.inertia_)"
      ],
      "metadata": {
        "id": "IPX974cPF7Bs"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('K')\n",
        "plt.ylabel('Sum of squared error')\n",
        "plt.plot(k_rng,sse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "I06UZ8AMGHbj",
        "outputId": "3d878768-6dff-45e8-ed32-1979c06c6d7c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f714c759810>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "km = KMeans(n_clusters=2)\n",
        "yp = km.fit_predict(df)\n",
        "yp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1zge6lopGJ87",
        "outputId": "35b13727-75ea-4539-9c71-a9177d4cc61f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cluster'] = yp\n",
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "z41KfjvhGP05",
        "outputId": "87eea357-3530-48f1-e909-682b2d2bb9d6"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   petal length (cm)  petal width (cm)  cluster\n",
              "0                1.4               0.2        1\n",
              "1                1.4               0.2        1"
            ],
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              "          document.querySelector('#df-2c102bb9-ea8a-48a6-9d85-8a3d9fbd48eb 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-2c102bb9-ea8a-48a6-9d85-8a3d9fbd48eb');\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df[df.cluster==0]\n",
        "df2 = df[df.cluster==1]"
      ],
      "metadata": {
        "id": "EZsuUNMBGSqu"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='blue')\n",
        "plt.scatter(df2['petal length (cm)'],df2['petal width (cm)'],color='pink')\n",
        "km.cluster_centers_\n",
        "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='Aqua',marker='^')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "f8j_MQgUGV1a",
        "outputId": "41e397bd-f32d-44b4-f9aa-9da32a3c0bf7"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7149c3a050>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WTTopRXtGjF2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}