penguins (1).ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U50z_xu4Qe0c"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"penguins\")"
      ],
      "metadata": {
        "id": "zrpz_F7VQjqS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 206
        },
        "id": "803_AVmYQnPI",
        "outputId": "f237e93b-2f35-43ca-df33-52f3bc990f37"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
              "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
              "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
              "3  Adelie  Torgersen             NaN            NaN                NaN   \n",
              "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
              "\n",
              "   body_mass_g     sex  \n",
              "0       3750.0    Male  \n",
              "1       3800.0  Female  \n",
              "2       3250.0  Female  \n",
              "3          NaN     NaN  \n",
              "4       3450.0  Female  "
            ],
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              "      <th>island</th>\n",
              "      <th>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
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              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
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              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>Male</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>Female</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      "source": [
        "sns.histplot(data=df,x = \"bill_length_mm\",hue = \"species\" )"
      ],
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        {
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            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff6528edee0>"
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          "metadata": {},
          "execution_count": 489
        },
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data =df,x = \"bill_depth_mm\",y = \"sex\",hue = \"species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "rDy2i8sBRAWF",
        "outputId": "71dcd4c6-24ac-48bf-a061-75ff4d6d76fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff652720d30>"
            ]
          },
          "metadata": {},
          "execution_count": 490
        },
        {
          "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": [
        "sns.scatterplot(data =df,x = \"bill_length_mm\",y = \"species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "BwphMfwwRWBe",
        "outputId": "a8815c4f-f5f0-41cc-9837-21c3e4a97fdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff6523f9f70>"
            ]
          },
          "metadata": {},
          "execution_count": 491
        },
        {
          "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": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YfvAaCtwR09Q",
        "outputId": "2a67a80c-1fab-49c6-cb49-5e42bb33dd12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
              "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
              "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
              "3  Adelie  Torgersen             NaN            NaN                NaN   \n",
              "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
              "\n",
              "   body_mass_g     sex  \n",
              "0       3750.0    Male  \n",
              "1       3800.0  Female  \n",
              "2       3250.0  Female  \n",
              "3          NaN     NaN  \n",
              "4       3450.0  Female  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62767901-91e5-4d80-ba38-f65538b0094f\">\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>species</th>\n",
              "      <th>island</th>\n",
              "      <th>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62767901-91e5-4d80-ba38-f65538b0094f')\"\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-62767901-91e5-4d80-ba38-f65538b0094f 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-62767901-91e5-4d80-ba38-f65538b0094f');\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": 492
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df,x = \"species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "UmXYfJZHTsVa",
        "outputId": "cf78ad63-127e-40d3-8e5d-620f1eb36fe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff652355610>"
            ]
          },
          "metadata": {},
          "execution_count": 493
        },
        {
          "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": [
        "sns.histplot(data = df,x = \"island\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "SG8qUPd5Tyq_",
        "outputId": "af162d7e-f9d0-49a8-f3d7-3575507ac390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff652315c10>"
            ]
          },
          "metadata": {},
          "execution_count": 494
        },
        {
          "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": [
        "sns.kdeplot(data =df,x=\"bill_length_mm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "hcp-v4RiT8sb",
        "outputId": "e41e8f82-889f-43fd-d2eb-e96c15f28ab3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff6522f6ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 495
        },
        {
          "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": [
        "sns.kdeplot(data=df,x= \"bill_depth_mm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "KFO_dH4PUPjB",
        "outputId": "5ed8e284-c49f-42d4-8674-585588d8d325"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff6522f6040>"
            ]
          },
          "metadata": {},
          "execution_count": 496
        },
        {
          "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": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ujgSY9tfUfhU",
        "outputId": "c1ca0881-7721-400b-e3b8-0672b348c558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
              "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
              "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
              "3  Adelie  Torgersen             NaN            NaN                NaN   \n",
              "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
              "\n",
              "   body_mass_g     sex  \n",
              "0       3750.0    Male  \n",
              "1       3800.0  Female  \n",
              "2       3250.0  Female  \n",
              "3          NaN     NaN  \n",
              "4       3450.0  Female  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8781c00f-a44f-443e-be31-15e1906bfdf0\">\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>species</th>\n",
              "      <th>island</th>\n",
              "      <th>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8781c00f-a44f-443e-be31-15e1906bfdf0')\"\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-8781c00f-a44f-443e-be31-15e1906bfdf0 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-8781c00f-a44f-443e-be31-15e1906bfdf0');\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": 497
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(data =df,x = \"body_mass_g\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "sjv15N2cU1Qm",
        "outputId": "f1756e25-6347-4570-ca4b-d6d044011281"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff652222a00>"
            ]
          },
          "metadata": {},
          "execution_count": 498
        },
        {
          "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": [
        "sns.kdeplot(data =df,x = \"flipper_length_mm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "LuVxhkghU7Cy",
        "outputId": "68de9c09-4f36-4fdb-be1b-34a6e370fddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff6521f85e0>"
            ]
          },
          "metadata": {},
          "execution_count": 499
        },
        {
          "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": [
        "sns.histplot(data =df,x = \"sex\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "WpmNwTjRVCWm",
        "outputId": "b66feaa8-4900-4366-94af-8aba990049bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff6521c4a60>"
            ]
          },
          "metadata": {},
          "execution_count": 500
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          "metadata": {
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8ExvSKxHVS4c",
        "outputId": "31e8f574-3b93-4efe-d669-bfbdd7a0f132"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0      False   False           False          False              False   \n",
              "1      False   False           False          False              False   \n",
              "2      False   False           False          False              False   \n",
              "3      False   False            True           True               True   \n",
              "4      False   False           False          False              False   \n",
              "..       ...     ...             ...            ...                ...   \n",
              "339    False   False            True           True               True   \n",
              "340    False   False           False          False              False   \n",
              "341    False   False           False          False              False   \n",
              "342    False   False           False          False              False   \n",
              "343    False   False           False          False              False   \n",
              "\n",
              "     body_mass_g    sex  \n",
              "0          False  False  \n",
              "1          False  False  \n",
              "2          False  False  \n",
              "3           True   True  \n",
              "4          False  False  \n",
              "..           ...    ...  \n",
              "339         True   True  \n",
              "340        False  False  \n",
              "341        False  False  \n",
              "342        False  False  \n",
              "343        False  False  \n",
              "\n",
              "[344 rows x 7 columns]"
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              "      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-2a50c960-8af2-489f-ae3f-80017cc9fd63 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-2a50c960-8af2-489f-ae3f-80017cc9fd63');\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": 501
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ej07swKYVcEH",
        "outputId": "3c029e63-d0e7-4c1c-e2fb-a6a3ebc23baa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "species               0\n",
              "island                0\n",
              "bill_length_mm        2\n",
              "bill_depth_mm         2\n",
              "flipper_length_mm     2\n",
              "body_mass_g           2\n",
              "sex                  11\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 502
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newdf = df.dropna()"
      ],
      "metadata": {
        "id": "pOnk5RPRVqCd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rUNSh8qWWGy8",
        "outputId": "b19c22a9-644c-470b-ffa1-50e079388e07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "species               0\n",
              "island                0\n",
              "bill_length_mm        2\n",
              "bill_depth_mm         2\n",
              "flipper_length_mm     2\n",
              "body_mass_g           2\n",
              "sex                  11\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 504
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtMXuM36WNW-",
        "outputId": "32a8d3fd-2b17-4e8b-fc46-9088956ad338"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "species               0\n",
              "island                0\n",
              "bill_length_mm        2\n",
              "bill_depth_mm         2\n",
              "flipper_length_mm     2\n",
              "body_mass_g           2\n",
              "sex                  11\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 505
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "HpTr7Mt9Woc_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJqtndWRW8aU",
        "outputId": "8e640d3b-f9dc-4a24-a255-98bafa6e9915"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "species              0\n",
              "island               0\n",
              "bill_length_mm       0\n",
              "bill_depth_mm        0\n",
              "flipper_length_mm    0\n",
              "body_mass_g          0\n",
              "sex                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 507
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(data =df,x =\"bill_length_mm\",hue = \"species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ZxdMrqRiW-0U",
        "outputId": "43e6de2d-fc39-4e91-e639-482220eba97e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff652183a00>"
            ]
          },
          "metadata": {},
          "execution_count": 508
        },
        {
          "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": [
        "sns.kdeplot(data =df,x =\"bill_depth_mm\",hue = \"species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "iVB-oZqdXvm8",
        "outputId": "fec9496e-1e65-46e3-c2a9-ae33c0ed5f78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff652406040>"
            ]
          },
          "metadata": {},
          "execution_count": 509
        },
        {
          "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": [
        "sns.kdeplot(data =df, x = \"flipper_length_mm\",hue = \"species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "9xhQ5325YC-q",
        "outputId": "e8f31122-4edd-4bda-ad41-cb6b89418654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff652070ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 510
        },
        {
          "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": [
        "sns.kdeplot(data =df,x = \"body_mass_g\", hue = \"species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "YIb0qmAAYsX8",
        "outputId": "a8105082-8679-45c3-aab9-87df9b5fe49b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff652037e50>"
            ]
          },
          "metadata": {},
          "execution_count": 511
        },
        {
          "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": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "je7M_uywZAJE",
        "outputId": "ff47146c-9946-4a32-d8d1-366caa2ee0ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
              "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
              "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
              "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
              "5  Adelie  Torgersen            39.3           20.6              190.0   \n",
              "\n",
              "   body_mass_g     sex  \n",
              "0       3750.0    Male  \n",
              "1       3800.0  Female  \n",
              "2       3250.0  Female  \n",
              "4       3450.0  Female  \n",
              "5       3650.0    Male  "
            ],
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              "      <td>3750.0</td>\n",
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              "    <tr>\n",
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              "      <td>Adelie</td>\n",
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              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
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              "      <td>Adelie</td>\n",
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              "      <td>39.3</td>\n",
              "      <td>20.6</td>\n",
              "      <td>190.0</td>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3666002b-30af-4a1d-88d8-f9b41f0dd4dd 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-3666002b-30af-4a1d-88d8-f9b41f0dd4dd');\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": 512
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df.iloc[:,0].values"
      ],
      "metadata": {
        "id": "NRQfmadOZWC7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YX6E-u0taoZg",
        "outputId": "004a108c-b79b-4633-bb72-3c36d055d891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Adelie', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap', 'Chinstrap',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 514
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:,2:6].values"
      ],
      "metadata": {
        "id": "QhZRx6eSapb5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rE7__Bw0bFuW",
        "outputId": "2d4ff1b6-4d88-4d69-d3e6-6abdf1c1a0bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  39.1,   18.7,  181. , 3750. ],\n",
              "       [  39.5,   17.4,  186. , 3800. ],\n",
              "       [  40.3,   18. ,  195. , 3250. ],\n",
              "       ...,\n",
              "       [  50.4,   15.7,  222. , 5750. ],\n",
              "       [  45.2,   14.8,  212. , 5200. ],\n",
              "       [  49.9,   16.1,  213. , 5400. ]])"
            ]
          },
          "metadata": {},
          "execution_count": 516
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "CflzT8u0bG4u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state = 22410)"
      ],
      "metadata": {
        "id": "UTVpoDT_bf1Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "NEri7GGTbzvg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier()"
      ],
      "metadata": {
        "id": "2q_WSB0pcEcl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKxxjG1hch1_",
        "outputId": "3c9998ad-a437-4609-8776-bd049c8c14ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 521
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict = knn.predict(x_test)"
      ],
      "metadata": {
        "id": "xrLps0HYcrtL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1GYx-9rcyh1",
        "outputId": "e8a8b2dd-ec35-4163-961c-15eeb00d0b9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Gentoo', 'Gentoo', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Gentoo', 'Adelie', 'Gentoo', 'Adelie', 'Gentoo', 'Gentoo',\n",
              "       'Chinstrap', 'Adelie', 'Gentoo', 'Adelie', 'Adelie', 'Gentoo',\n",
              "       'Gentoo', 'Adelie', 'Adelie', 'Chinstrap', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Chinstrap', 'Gentoo', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo', 'Adelie', 'Adelie',\n",
              "       'Gentoo', 'Chinstrap', 'Chinstrap', 'Gentoo', 'Adelie', 'Gentoo',\n",
              "       'Adelie', 'Adelie', 'Gentoo', 'Chinstrap', 'Gentoo', 'Adelie',\n",
              "       'Adelie', 'Gentoo', 'Adelie', 'Gentoo', 'Adelie', 'Chinstrap',\n",
              "       'Gentoo', 'Gentoo', 'Adelie', 'Gentoo', 'Adelie', 'Adelie',\n",
              "       'Gentoo', 'Gentoo', 'Gentoo', 'Adelie', 'Gentoo', 'Gentoo',\n",
              "       'Adelie', 'Adelie', 'Gentoo', 'Gentoo', 'Gentoo', 'Gentoo',\n",
              "       'Adelie', 'Adelie', 'Adelie', 'Chinstrap', 'Adelie', 'Adelie',\n",
              "       'Adelie', 'Gentoo', 'Chinstrap', 'Adelie', 'Gentoo', 'Gentoo',\n",
              "       'Gentoo', 'Adelie', 'Adelie', 'Adelie', 'Adelie', 'Adelie',\n",
              "       'Gentoo', 'Adelie', 'Adelie', 'Adelie', 'Gentoo', 'Adelie',\n",
              "       'Adelie', 'Gentoo', 'Gentoo', 'Adelie'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 523
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "M_W5NK9Ic0fr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_predict,y_test)"
      ],
      "metadata": {
        "id": "b-zcvuHidBTP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exHSOVcpdckg",
        "outputId": "7f999805-e9be-4bf7-b7f7-1d69055e692c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.94"
            ]
          },
          "metadata": {},
          "execution_count": 526
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "randomState = 22410\n",
        "while True:\n",
        "  x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state = randomState)\n",
        "  knn = KNeighborsClassifier()\n",
        "  knn.fit(x_train,y_train)\n",
        "  y_predict = knn.predict(x_test)\n",
        "  accuracy = accuracy_score(y_predict,y_test)\n",
        "  if accuracy > 0.95:\n",
        "    print(randomState)\n",
        "    break\n",
        "  randomState += 1"
      ],
      "metadata": {
        "id": "K3JfqK37dd2u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H7IZRZS4eoeV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}