Outlier Detection.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('headbrain.csv')\n",
        "df"
      ],
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      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "     Gender  Age Range  Head Size(cm^3)  Brain Weight(grams)\n",
              "0         1          1             4512                 1530\n",
              "1         1          1             3738                 1297\n",
              "2         1          1             4261                 1335\n",
              "3         1          1             3777                 1282\n",
              "4         1          1             4177                 1590\n",
              "..      ...        ...              ...                  ...\n",
              "232       2          2             3214                 1110\n",
              "233       2          2             3394                 1215\n",
              "234       2          2             3233                 1104\n",
              "235       2          2             3352                 1170\n",
              "236       2          2             3391                 1120\n",
              "\n",
              "[237 rows x 4 columns]"
            ],
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              "      <th>Brain Weight(grams)</th>\n",
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              "      <td>1110</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3394</td>\n",
              "      <td>1215</td>\n",
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              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3233</td>\n",
              "      <td>1104</td>\n",
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              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>1170</td>\n",
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              "      <th>236</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3391</td>\n",
              "      <td>1120</td>\n",
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              "<p>237 rows × 4 columns</p>\n",
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      "source": [
        "X = df['Head Size(cm^3)'].tolist()\n",
        "Y = df['Brain Weight(grams)'].tolist()\n",
        "plt.xlabel('Head size (cm^3)')\n",
        "plt.ylabel('Brain weight (grams)')\n",
        "plt.scatter(X, Y)\n",
        "plt.show()"
      ],
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          "height": 279
        },
        "outputId": "a155fa73-55ae-49e0-b5c9-c17da31dc970"
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.hist(df['Head Size(cm^3)'])\n"
      ],
      "metadata": {
        "id": "f4wgw8pqF6lZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "334c380d-c507-49b6-8798-60e4eb4ca0be"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 4., 13., 31., 50., 50., 43., 23., 14.,  8.,  1.]),\n",
              " array([2720. , 2922.7, 3125.4, 3328.1, 3530.8, 3733.5, 3936.2, 4138.9,\n",
              "        4341.6, 4544.3, 4747. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "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": "5FM3p3wYak2h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Head Size(cm^3)'], bins=[1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000])"
      ],
      "metadata": {
        "id": "u4LMlDLdIY4p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "fe40493b-88e2-4865-d6ab-955ff63dc82a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  0.,   0.,   0.,   7.,  85., 105.,  38.,   2.]),\n",
              " array([1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000]),\n",
              " <a list of 8 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "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": "rVB8gPfJal1a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Head Size(cm^3)'], bins=9, align='right', color='purple', edgecolor='black')"
      ],
      "metadata": {
        "id": "eWQFl2TUKt0G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "af4e9c49-667e-46ed-f1ec-e10da0cb8eab"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 6., 18., 42., 55., 49., 41., 15., 10.,  1.]),\n",
              " array([2720.        , 2945.22222222, 3170.44444444, 3395.66666667,\n",
              "        3620.88888889, 3846.11111111, 4071.33333333, 4296.55555556,\n",
              "        4521.77777778, 4747.        ]),\n",
              " <a list of 9 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": [
        "# Using Box plot for Outlier Detection\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.boxplot(data=df, x=df[\"Head Size(cm^3)\"])\n",
        "plt.title(\"Boxplot of HeadBrain Head Size\")"
      ],
      "metadata": {
        "id": "NoAa9D3BG_nW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "6da5fea2-299b-4941-caca-87c494c0f25e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot of HeadBrain Head Size')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "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": [
        "def boxplot(column):\n",
        "    sns.boxplot(data=df,x=df[f\"{column}\"])\n",
        "    plt.title(f\"Boxplot of Swiss Banknote {column}\")\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "boxplot('Head Size(cm^3)')\n",
        "boxplot('Brain Weight(grams)')\n",
        "boxplot('Age Range')\n",
        "boxplot('Gender')"
      ],
      "metadata": {
        "id": "tuE-gIgDQLA6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8c253478-97b0-4570-a583-f995d3044fd5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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": [
        "#Position of Outlier\n",
        "print(np.where(df['Head Size(cm^3)'] >4000))"
      ],
      "metadata": {
        "id": "9vqiJSueRbIG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5114113a-f264-4613-c219-3a0e68f8e140"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(array([  0,   2,   4,  13,  19,  21,  24,  25,  26,  27,  29,  32,  33,\n",
            "        34,  42,  45,  46,  52,  53,  54,  57,  62,  63,  68,  69,  70,\n",
            "        72,  73,  90, 106, 109, 118, 121, 122, 124, 128, 132, 158, 182,\n",
            "       227]),)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.where(df['Brain Weight(grams)'] >1500))"
      ],
      "metadata": {
        "id": "njVzBse0SP4n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d063011f-8826-4f9d-9903-9ed31b4a657b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(array([  0,   4,  13,  24,  25,  42,  43,  46,  57,  68, 102, 109, 161]),)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KA4ZFSqSUazJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}