Adult(FullProject).ipynb

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        "id": "OicYaSrXoqK1"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder , StandardScaler\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score , mean_squared_error , r2_score , confusion_matrix , classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data Exploration , Preprocessing , and Visualization"
      ],
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        "id": "rptjiilTKeQG"
      }
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      "source": [
        "df = pd.read_csv(\"/content/Copy of adult.csv\")\n",
        "df"
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      "execution_count": null,
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            "text/plain": [
              "       age     workclass  fnlwgt     education  educational-num  \\\n",
              "0       25       Private  226802          11th                7   \n",
              "1       38       Private   89814       HS-grad                9   \n",
              "2       28     Local-gov  336951    Assoc-acdm               12   \n",
              "3       44       Private  160323  Some-college               10   \n",
              "4       18             ?  103497  Some-college               10   \n",
              "...    ...           ...     ...           ...              ...   \n",
              "48837   27       Private  257302    Assoc-acdm               12   \n",
              "48838   40       Private  154374       HS-grad                9   \n",
              "48839   58       Private  151910       HS-grad                9   \n",
              "48840   22       Private  201490       HS-grad                9   \n",
              "48841   52  Self-emp-inc  287927       HS-grad                9   \n",
              "\n",
              "           marital-status         occupation relationship   race  gender  \\\n",
              "0           Never-married  Machine-op-inspct    Own-child  Black    Male   \n",
              "1      Married-civ-spouse    Farming-fishing      Husband  White    Male   \n",
              "2      Married-civ-spouse    Protective-serv      Husband  White    Male   \n",
              "3      Married-civ-spouse  Machine-op-inspct      Husband  Black    Male   \n",
              "4           Never-married                  ?    Own-child  White  Female   \n",
              "...                   ...                ...          ...    ...     ...   \n",
              "48837  Married-civ-spouse       Tech-support         Wife  White  Female   \n",
              "48838  Married-civ-spouse  Machine-op-inspct      Husband  White    Male   \n",
              "48839             Widowed       Adm-clerical    Unmarried  White  Female   \n",
              "48840       Never-married       Adm-clerical    Own-child  White    Male   \n",
              "48841  Married-civ-spouse    Exec-managerial         Wife  White  Female   \n",
              "\n",
              "       capital-gain  capital-loss  hours-per-week native-country income  \n",
              "0                 0             0              40  United-States  <=50K  \n",
              "1                 0             0              50  United-States  <=50K  \n",
              "2                 0             0              40  United-States   >50K  \n",
              "3              7688             0              40  United-States   >50K  \n",
              "4                 0             0              30  United-States  <=50K  \n",
              "...             ...           ...             ...            ...    ...  \n",
              "48837             0             0              38  United-States  <=50K  \n",
              "48838             0             0              40  United-States   >50K  \n",
              "48839             0             0              40  United-States  <=50K  \n",
              "48840             0             0              20  United-States  <=50K  \n",
              "48841         15024             0              40  United-States   >50K  \n",
              "\n",
              "[48842 rows x 15 columns]"
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-59aea8a5-1a23-490f-affe-7ff4a45d6707 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_16c40cc2-7472-4aea-a3b0-be652f39bb90\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_16c40cc2-7472-4aea-a3b0-be652f39bb90 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48842,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 17,\n        \"max\": 90,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          18,\n          74,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Without-pay\",\n          \"Local-gov\",\n          \"State-gov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105604,\n        \"min\": 12285,\n        \"max\": 1490400,\n        \"num_unique_values\": 28523,\n        \"samples\": [\n          171041,\n          20296,\n          263896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"11th\",\n          \"HS-grad\",\n          \"Prof-school\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"educational-num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 16,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          7,\n          9,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital-status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Never-married\",\n          \"Married-civ-spouse\",\n          \"Married-spouse-absent\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Tech-support\",\n          \"Priv-house-serv\",\n          \"Machine-op-inspct\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relationship\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Own-child\",\n          \"Husband\",\n          \"Other-relative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"White\",\n          \"Amer-Indian-Eskimo\",\n          \"Asian-Pac-Islander\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7452,\n        \"min\": 0,\n        \"max\": 99999,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          4064,\n          4787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 403,\n        \"min\": 0,\n        \"max\": 4356,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2238,\n          1564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours-per-week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          9,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"native-country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 42,\n        \"samples\": [\n          \"Canada\",\n          \"Vietnam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \">50K\",\n          \"<=50K\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKOhyOZrptbq",
        "outputId": "8147fec8-2ad0-4522-b366-5964a0d27c42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 48842 entries, 0 to 48841\n",
            "Data columns (total 15 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   age              48842 non-null  int64 \n",
            " 1   workclass        48842 non-null  object\n",
            " 2   fnlwgt           48842 non-null  int64 \n",
            " 3   education        48842 non-null  object\n",
            " 4   educational-num  48842 non-null  int64 \n",
            " 5   marital-status   48842 non-null  object\n",
            " 6   occupation       48842 non-null  object\n",
            " 7   relationship     48842 non-null  object\n",
            " 8   race             48842 non-null  object\n",
            " 9   gender           48842 non-null  object\n",
            " 10  capital-gain     48842 non-null  int64 \n",
            " 11  capital-loss     48842 non-null  int64 \n",
            " 12  hours-per-week   48842 non-null  int64 \n",
            " 13  native-country   48842 non-null  object\n",
            " 14  income           48842 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 5.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "8rfo7PtcqXjT",
        "outputId": "5262e45e-816c-4329-f8c5-a956160b3ffe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                0\n",
              "workclass          0\n",
              "fnlwgt             0\n",
              "education          0\n",
              "educational-num    0\n",
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              "gender             0\n",
              "capital-gain       0\n",
              "capital-loss       0\n",
              "hours-per-week     0\n",
              "native-country     0\n",
              "income             0\n",
              "dtype: int64"
            ],
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              "    .dataframe tbody tr th {\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>capital-loss</th>\n",
              "      <td>0</td>\n",
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              "      <th>hours-per-week</th>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    print(column ,\":\",df[column].nunique() ,\"\\n\", \"unique values\",df[column].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFs4oX0Os0LA",
        "outputId": "34c95b72-b30c-489f-9406-09368beea149"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age : 74 \n",
            " unique values [25 38 28 44 18 34 29 63 24 55 65 36 26 58 48 43 20 37 40 72 45 22 23 54\n",
            " 32 46 56 17 39 52 21 42 33 30 47 41 19 69 50 31 59 49 51 27 57 61 64 79\n",
            " 73 53 77 80 62 35 68 66 75 60 67 71 70 90 81 74 78 82 83 85 76 84 89 88\n",
            " 87 86]\n",
            "workclass : 9 \n",
            " unique values ['Private' 'Local-gov' '?' 'Self-emp-not-inc' 'Federal-gov' 'State-gov'\n",
            " 'Self-emp-inc' 'Without-pay' 'Never-worked']\n",
            "fnlwgt : 28523 \n",
            " unique values [226802  89814 336951 ... 129912 255835 257302]\n",
            "education : 16 \n",
            " unique values ['11th' 'HS-grad' 'Assoc-acdm' 'Some-college' '10th' 'Prof-school'\n",
            " '7th-8th' 'Bachelors' 'Masters' 'Doctorate' '5th-6th' 'Assoc-voc' '9th'\n",
            " '12th' '1st-4th' 'Preschool']\n",
            "educational-num : 16 \n",
            " unique values [ 7  9 12 10  6 15  4 13 14 16  3 11  5  8  2  1]\n",
            "marital-status : 7 \n",
            " unique values ['Never-married' 'Married-civ-spouse' 'Widowed' 'Divorced' 'Separated'\n",
            " 'Married-spouse-absent' 'Married-AF-spouse']\n",
            "occupation : 15 \n",
            " unique values ['Machine-op-inspct' 'Farming-fishing' 'Protective-serv' '?'\n",
            " 'Other-service' 'Prof-specialty' 'Craft-repair' 'Adm-clerical'\n",
            " 'Exec-managerial' 'Tech-support' 'Sales' 'Priv-house-serv'\n",
            " 'Transport-moving' 'Handlers-cleaners' 'Armed-Forces']\n",
            "relationship : 6 \n",
            " unique values ['Own-child' 'Husband' 'Not-in-family' 'Unmarried' 'Wife' 'Other-relative']\n",
            "race : 5 \n",
            " unique values ['Black' 'White' 'Asian-Pac-Islander' 'Other' 'Amer-Indian-Eskimo']\n",
            "gender : 2 \n",
            " unique values ['Male' 'Female']\n",
            "capital-gain : 123 \n",
            " unique values [    0  7688  3103  6418  7298  3908 14084  5178 15024 99999  2597  2907\n",
            "  4650  6497  1055  5013 27828  4934  4064  3674  2174 10605  3418   114\n",
            "  2580  3411  4508  4386  8614 13550  6849  2463  3137  2885  2964  1471\n",
            " 10566  2354  1424  1455  3325  4416 25236   594  2105  4787  2829   401\n",
            "  4865  1264  1506 10520  3464  2653 20051  4101  1797  2407  3471  1086\n",
            "  1848 14344  1151  2993  2290 15020  9386  2202  3818  2176  5455 11678\n",
            "  7978  7262  6514 41310  3456  7430  2414  2062 34095  1831  6723  5060\n",
            " 15831  2977  2346  3273  2329  9562  2635  4931  1731  6097   914  7896\n",
            "  5556  1409  3781  3942  2538  3887 25124  7443  5721  1173  4687  6612\n",
            "  6767  2961   991  2036  2936  2050  1111  2228 22040  3432  6360  2009\n",
            "  1639 18481  2387]\n",
            "capital-loss : 99 \n",
            " unique values [   0 1721 1876 2415 1887  625 1977 2057 1429 1590 1485 2051 2377 1672\n",
            " 1628 1902 1602 1741 2444 1408 2001 2042 1740 1825 1848 1719 3004 2179\n",
            " 1573 2205 1258 2339 1726 2258 1340 1504 2559 1668 1974 1980 1564 2547\n",
            " 2002 1669 1617  323 3175 2472 2174 1579 2129 1510 1735 2282 1870 1411\n",
            " 1911 1651 1092 1762 2457 2231 2238  653 1138 2246 2603 2392 1944 1380\n",
            " 2465 1421 3770 1594  213 2149 2824 1844 2467 2163 1816 1648 2206 2352\n",
            "  419 1539  880  810  974 4356 2489 1755 3683 2267 2080  155 3900 2201\n",
            " 2754]\n",
            "hours-per-week : 96 \n",
            " unique values [40 50 30 32 10 39 35 48 25 20 45 47  6 43 90 54 60 38 36 18 24 44 56 28\n",
            " 16 41 22 55 14 33 37  8 12 70 15 75 52 84 42 80 68 99 65  5 17 72 53 29\n",
            " 96 21 46  3  1 23 49 67 76  7  2 58 26 34  4 51 78 63 31 92 77 27 85 13\n",
            " 19 98 62 66 57 11 86 59  9 64 73 61 88 79 89 74 69 87 97 94 82 91 81 95]\n",
            "native-country : 42 \n",
            " unique values ['United-States' '?' 'Peru' 'Guatemala' 'Mexico' 'Dominican-Republic'\n",
            " 'Ireland' 'Germany' 'Philippines' 'Thailand' 'Haiti' 'El-Salvador'\n",
            " 'Puerto-Rico' 'Vietnam' 'South' 'Columbia' 'Japan' 'India' 'Cambodia'\n",
            " 'Poland' 'Laos' 'England' 'Cuba' 'Taiwan' 'Italy' 'Canada' 'Portugal'\n",
            " 'China' 'Nicaragua' 'Honduras' 'Iran' 'Scotland' 'Jamaica' 'Ecuador'\n",
            " 'Yugoslavia' 'Hungary' 'Hong' 'Greece' 'Trinadad&Tobago'\n",
            " 'Outlying-US(Guam-USVI-etc)' 'France' 'Holand-Netherlands']\n",
            "income : 2 \n",
            " unique values ['<=50K' '>50K']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace(\"?\",np.nan,inplace=True)"
      ],
      "metadata": {
        "id": "Q_FU-uqbqfQe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "FDHuPSfCrMOs",
        "outputId": "f0336e7a-af52-45a0-c038-5be44ed70c74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                   0\n",
              "workclass          2799\n",
              "fnlwgt                0\n",
              "education             0\n",
              "educational-num       0\n",
              "marital-status        0\n",
              "occupation         2809\n",
              "relationship          0\n",
              "race                  0\n",
              "gender                0\n",
              "capital-gain          0\n",
              "capital-loss          0\n",
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              "    <tr>\n",
              "      <th>fnlwgt</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>educational-num</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital-status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation</th>\n",
              "      <td>2809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relationship</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>race</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital-gain</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital-loss</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hours-per-week</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>native-country</th>\n",
              "      <td>857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>income</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in [\"workclass\",\"occupation\",\"native-country\"]:\n",
        "  df[col].fillna(df[col].mode()[0],inplace=True)"
      ],
      "metadata": {
        "id": "a3ptC_WMrvTP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKEgb4Ob1fBn",
        "outputId": "4555326e-57b9-4729-e2db-ad0c68e26f66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "FORaM7C11lY1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "for col in df.select_dtypes(include=[\"object\"]).columns:\n",
        "  df[col] = le.fit_transform(df[col])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_3c2-Oq7sYOk",
        "outputId": "22e3c45c-158f-46e5-bf5d-02d9c8e9c63f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  workclass  fnlwgt  education  educational-num  marital-status  \\\n",
              "0   25          3  226802          1                7               4   \n",
              "1   38          3   89814         11                9               2   \n",
              "2   28          1  336951          7               12               2   \n",
              "3   44          3  160323         15               10               2   \n",
              "4   18          3  103497         15               10               4   \n",
              "\n",
              "   occupation  relationship  race  gender  capital-gain  capital-loss  \\\n",
              "0           6             3     2       1             0             0   \n",
              "1           4             0     4       1             0             0   \n",
              "2          10             0     4       1             0             0   \n",
              "3           6             0     2       1          7688             0   \n",
              "4           9             3     4       0             0             0   \n",
              "\n",
              "   hours-per-week  native-country  income  \n",
              "0              40              38       0  \n",
              "1              50              38       0  \n",
              "2              40              38       1  \n",
              "3              40              38       1  \n",
              "4              30              38       0  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48789,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 17,\n        \"max\": 90,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          18,\n          74,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105618,\n        \"min\": 12285,\n        \"max\": 1490400,\n        \"num_unique_values\": 28523,\n        \"samples\": [\n          171041,\n          20296,\n          263896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 15,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          1,\n          11,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"educational-num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 16,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          7,\n          9,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital-status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          11,\n          13,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relationship\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7455,\n        \"min\": 0,\n        \"max\": 99999,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          4064,\n          4787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 403,\n        \"min\": 0,\n        \"max\": 4356,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2238,\n          1564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours-per-week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          9,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"native-country\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 40,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          1,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(df.corr() , annot=True , cmap=\"coolwarm\", vmin=-1 , vmax=1 , center=0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "egz5Hhq018oy",
        "outputId": "4470bdfc-01ab-4821-877d-99f4ccc01a57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  print(col ,\"min\", df[col].min() ,\"max\", df[col].max())\n",
        "  print(df[col].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHjCaLwy2Cgd",
        "outputId": "fdaa8c85-246e-475a-cad9-a1f0cd47b40d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age min 17 max 90\n",
            "age\n",
            "36    1348\n",
            "35    1336\n",
            "33    1335\n",
            "23    1325\n",
            "31    1324\n",
            "      ... \n",
            "88       6\n",
            "85       5\n",
            "87       3\n",
            "89       2\n",
            "86       1\n",
            "Name: count, Length: 74, dtype: int64\n",
            "workclass min 0 max 7\n",
            "workclass\n",
            "3    36654\n",
            "5     3861\n",
            "1     3136\n",
            "6     1981\n",
            "4     1694\n",
            "0     1432\n",
            "7       21\n",
            "2       10\n",
            "Name: count, dtype: int64\n",
            "fnlwgt min 12285 max 1490400\n",
            "fnlwgt\n",
            "203488    21\n",
            "120277    19\n",
            "190290    19\n",
            "125892    18\n",
            "126569    18\n",
            "          ..\n",
            "160467     1\n",
            "114648     1\n",
            "301229     1\n",
            "155382     1\n",
            "268083     1\n",
            "Name: count, Length: 28523, dtype: int64\n",
            "education min 0 max 15\n",
            "education\n",
            "11    15770\n",
            "15    10862\n",
            "9      8013\n",
            "12     2656\n",
            "8      2060\n",
            "1      1812\n",
            "7      1601\n",
            "0      1389\n",
            "5       954\n",
            "14      834\n",
            "6       756\n",
            "2       655\n",
            "10      594\n",
            "4       507\n",
            "3       245\n",
            "13       81\n",
            "Name: count, dtype: int64\n",
            "educational-num min 1 max 16\n",
            "educational-num\n",
            "9     15770\n",
            "10    10862\n",
            "13     8013\n",
            "14     2656\n",
            "11     2060\n",
            "7      1812\n",
            "12     1601\n",
            "6      1389\n",
            "4       954\n",
            "15      834\n",
            "5       756\n",
            "8       655\n",
            "16      594\n",
            "3       507\n",
            "2       245\n",
            "1        81\n",
            "Name: count, dtype: int64\n",
            "marital-status min 0 max 6\n",
            "marital-status\n",
            "2    22366\n",
            "4    16081\n",
            "0     6630\n",
            "5     1530\n",
            "6     1518\n",
            "3      627\n",
            "1       37\n",
            "Name: count, dtype: int64\n",
            "occupation min 0 max 13\n",
            "occupation\n",
            "9     8969\n",
            "2     6102\n",
            "3     6082\n",
            "0     5606\n",
            "11    5501\n",
            "7     4919\n",
            "6     3017\n",
            "13    2355\n",
            "5     2071\n",
            "4     1485\n",
            "12    1445\n",
            "10     982\n",
            "8      240\n",
            "1       15\n",
            "Name: count, dtype: int64\n",
            "relationship min 0 max 5\n",
            "relationship\n",
            "0    19703\n",
            "1    12557\n",
            "3     7568\n",
            "4     5124\n",
            "5     2331\n",
            "2     1506\n",
            "Name: count, dtype: int64\n",
            "race min 0 max 4\n",
            "race\n",
            "4    41713\n",
            "2     4683\n",
            "1     1517\n",
            "0      470\n",
            "3      406\n",
            "Name: count, dtype: int64\n",
            "gender min 0 max 1\n",
            "gender\n",
            "1    32614\n",
            "0    16175\n",
            "Name: count, dtype: int64\n",
            "capital-gain min 0 max 99999\n",
            "capital-gain\n",
            "0        44754\n",
            "15024      513\n",
            "7688       410\n",
            "7298       364\n",
            "99999      244\n",
            "         ...  \n",
            "1111         1\n",
            "1639         1\n",
            "1731         1\n",
            "7262         1\n",
            "2387         1\n",
            "Name: count, Length: 123, dtype: int64\n",
            "capital-loss min 0 max 4356\n",
            "capital-loss\n",
            "0       46507\n",
            "1902      304\n",
            "1977      253\n",
            "1887      233\n",
            "2415       72\n",
            "        ...  \n",
            "2080        1\n",
            "1421        1\n",
            "2465        1\n",
            "1539        1\n",
            "2201        1\n",
            "Name: count, Length: 99, dtype: int64\n",
            "hours-per-week min 1 max 99\n",
            "hours-per-week\n",
            "40    22772\n",
            "50     4242\n",
            "45     2715\n",
            "60     2177\n",
            "35     1934\n",
            "      ...  \n",
            "82        1\n",
            "69        1\n",
            "87        1\n",
            "94        1\n",
            "79        1\n",
            "Name: count, Length: 96, dtype: int64\n",
            "native-country min 0 max 40\n",
            "native-country\n",
            "38    44647\n",
            "25      943\n",
            "29      294\n",
            "10      206\n",
            "32      184\n",
            "1       182\n",
            "7       155\n",
            "18      151\n",
            "4       138\n",
            "8       127\n",
            "2       122\n",
            "34      115\n",
            "22      106\n",
            "21      105\n",
            "5       103\n",
            "23       92\n",
            "30       87\n",
            "39       86\n",
            "12       86\n",
            "3        85\n",
            "13       75\n",
            "31       67\n",
            "35       65\n",
            "19       59\n",
            "26       49\n",
            "11       49\n",
            "28       46\n",
            "6        45\n",
            "9        38\n",
            "20       37\n",
            "36       30\n",
            "16       30\n",
            "0        28\n",
            "37       27\n",
            "24       23\n",
            "27       23\n",
            "40       23\n",
            "33       21\n",
            "15       20\n",
            "17       19\n",
            "14        1\n",
            "Name: count, dtype: int64\n",
            "income min 0 max 1\n",
            "income\n",
            "0    37108\n",
            "1    11681\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"capital-gain\"]=np.log1p(df[\"capital-gain\"])\n",
        "df[\"capital-loss\"]=np.log1p(df[\"capital-loss\"])\n",
        "df[\"native-country\"]=np.log1p(df[\"native-country\"])\n",
        "scaler = StandardScaler()\n",
        "df[\"fnlwgt\"] = scaler.fit_transform(df[[\"fnlwgt\"]])"
      ],
      "metadata": {
        "id": "MVFFHigq--k8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6KaCHFFY_AQe",
        "outputId": "df39eea7-8d53-4bde-d675-009942bca6b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  workclass    fnlwgt  education  educational-num  marital-status  \\\n",
              "0       25          3  0.351575          1                7               4   \n",
              "1       38          3 -0.945449         11                9               2   \n",
              "2       28          1  1.394483          7               12               2   \n",
              "3       44          3 -0.277859         15               10               2   \n",
              "4       18          3 -0.815897         15               10               4   \n",
              "...    ...        ...       ...        ...              ...             ...   \n",
              "48837   27          3  0.640354          7               12               2   \n",
              "48838   40          3 -0.334185         11                9               2   \n",
              "48839   58          3 -0.357515         11                9               6   \n",
              "48840   22          3  0.111917         11                9               4   \n",
              "48841   52          4  0.930316         11                9               2   \n",
              "\n",
              "       occupation  relationship  race  gender  capital-gain  capital-loss  \\\n",
              "0               6             3     2       1      0.000000           0.0   \n",
              "1               4             0     4       1      0.000000           0.0   \n",
              "2              10             0     4       1      0.000000           0.0   \n",
              "3               6             0     2       1      8.947546           0.0   \n",
              "4               9             3     4       0      0.000000           0.0   \n",
              "...           ...           ...   ...     ...           ...           ...   \n",
              "48837          12             5     4       0      0.000000           0.0   \n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_852170aa-27c1-4e52-b498-a0b10923b6eb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48789,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 17,\n        \"max\": 90,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          18,\n          74,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000102483692286,\n        \"min\": -1.6795065587458562,\n        \"max\": 12.315524391997021,\n        \"num_unique_values\": 28523,\n        \"samples\": [\n          -0.1763791744651092,\n          -1.603657120138981,\n          0.702786924878467\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 15,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          1,\n          11,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"educational-num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 16,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          7,\n          9,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital-status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          11,\n          13,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relationship\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4466896406329,\n        \"min\": 0.0,\n        \"max\": 11.512925464970229,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          8.310169021981912,\n          8.473868066677863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5870515682197543,\n        \"min\": 0.0,\n        \"max\": 8.379539026117442,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          7.713784616598755,\n          7.355641102974253\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours-per-week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          9,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"native-country\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3448042744600912,\n        \"min\": 0.0,\n        \"max\": 3.7135720667043075,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          0.6931471805599453,\n          3.5553480614894135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  sns.countplot(x=df[col])\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NZpqLIZz6m5k",
        "outputId": "886a895e-29d6-4146-fb72-d97e8cd42381"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-a67d6aa665cf>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, fill, hue_norm, stat, width, dodge, gap, log_scale, native_scale, formatter, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2672\u001b[0m     \u001b[0maggregator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEstimateAggregator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sum\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrorbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2674\u001b[0;31m     p.plot_bars(\n\u001b[0m\u001b[1;32m   2675\u001b[0m         \u001b[0maggregator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2676\u001b[0m         \u001b[0mdodge\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdodge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mplot_bars\u001b[0;34m(self, aggregator, dodge, gap, width, fill, color, capsize, err_kws, plot_kws)\u001b[0m\n\u001b[1;32m   1312\u001b[0m                 \u001b[0mkws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmain_color\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmain_color\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"none\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m             \u001b[0mbar_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kws\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0maggregator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_method\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2492\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# horizontal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2493\u001b[0m                 \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msticky_edges\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2494\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_patch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2495\u001b[0m             \u001b[0mpatches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_patch\u001b[0;34m(self, p)\u001b[0m\n\u001b[1;32m   2376\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2378\u001b[0;31m             \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2379\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_patch_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2380\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_children\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mset_clip_path\u001b[0;34m(self, path, transform)\u001b[0m\n\u001b[1;32m    807\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRectangle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    808\u001b[0m                 self.clipbox = TransformedBbox(Bbox.unit(),\n\u001b[0;32m--> 809\u001b[0;31m                                                path.get_transform())\n\u001b[0m\u001b[1;32m    810\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clippath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    811\u001b[0m                 \u001b[0msuccess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mget_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;34m\"\"\"Return the `~.transforms.Transform` applied to the `Patch`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_patch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArtist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_data_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mget_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    745\u001b[0m         \u001b[0;31m# important to call the accessor method and not directly access the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m         \u001b[0;31m# transformation member variable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 747\u001b[0;31m         \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    748\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrotation_point\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'center'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m             \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mget_bbox\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    877\u001b[0m         \u001b[0;34m\"\"\"Return the `.Bbox`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    878\u001b[0m         \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 879\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_extents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m     \u001b[0mxy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproperty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_xy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset_xy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mfrom_extents\u001b[0;34m(minpos, *args)\u001b[0m\n\u001b[1;32m    824\u001b[0m            \u001b[0mscales\u001b[0m \u001b[0mwhere\u001b[0m \u001b[0mnegative\u001b[0m \u001b[0mbounds\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfloating\u001b[0m \u001b[0mpoint\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    825\u001b[0m         \"\"\"\n\u001b[0;32m--> 826\u001b[0;31m         \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    827\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mminpos\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    828\u001b[0m             \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mminpos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    283\u001b[0m            [5, 6]])\n\u001b[1;32m    284\u001b[0m     \"\"\"\n\u001b[0;32m--> 285\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mwrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in callback <function _draw_all_if_interactive at 0x7d06f2682b00> (for post_execute):\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_draw_all_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m                 \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2082\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2084\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    399\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3139\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3140\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3141\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   1549\u001b[0m                 \"_tick_class or reimplement _get_tick()\")\n\u001b[1;32m   1550\u001b[0m         \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_major_tick_kw\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1551\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tick_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1553\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_tick_label_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 417\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    418\u001b[0m         \u001b[0;31m# x in data coords, y in axes coords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    419\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0mgrid_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m         self.tick1line = mlines.Line2D(\n\u001b[0m\u001b[1;32m    157\u001b[0m             \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"none\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvisible\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtick1On\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    452\u001b[0m                 \u001b[0;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 454\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;31m# Don't modify *func*'s signature, as boilerplate.py needs it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, gapcolor, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m         \"\"\"\n\u001b[0;32m--> 308\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;31m# Convert sequences to NumPy arrays.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0;31m# only if they override get_cursor_data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mouseover\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cursor_data\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mArtist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cursor_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCallbackRegistry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pchanged\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, exception_handler, signals)\u001b[0m\n\u001b[1;32m    188\u001b[0m     \u001b[0;31m#   _func_cid_map: signal -> {weakref-to-callback -> cid}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexception_handler\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_exception_printer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    191\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_signals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msignals\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignals\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Copy it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexception_handler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in callback <function flush_figures at 0x7d06f2681d80> (for post_execute):\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m                 \u001b[0;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m             display(\n\u001b[0m\u001b[1;32m     91\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextras\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2345\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"tight\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2346\u001b[0;31m                     bbox_inches = self.figure.get_tightbbox(\n\u001b[0m\u001b[1;32m   2347\u001b[0m                         renderer, bbox_extra_artists=bbox_extra_artists)\n\u001b[1;32m   2348\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mpad_inches\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, bbox_extra_artists)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1743\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1744\u001b[0;31m             \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1745\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 \u001b[0mbb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001b[0m\n\u001b[1;32m   4383\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axis_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4384\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxison\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4385\u001b[0;31m                 \u001b[0mba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmartist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tightbbox_for_layout_only\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4386\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mba\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4387\u001b[0m                     \u001b[0mbb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mba\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_get_tightbbox_for_layout_only\u001b[0;34m(obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1413\u001b[0m     \"\"\"\n\u001b[1;32m   1414\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1415\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"for_layout_only\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1416\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m   1326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1327\u001b[0m         \u001b[0;31m# go back to just this axis's tick labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1328\u001b[0;31m         \u001b[0mtlb1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtlb2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ticklabel_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks_to_draw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1330\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_offset_text_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtlb1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtlb2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_ticklabel_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m   1302\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1304\u001b[0;31m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m   1305\u001b[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m   1306\u001b[0m                 [tick.label2.get_window_extent(renderer)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1302\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1304\u001b[0;31m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m   1305\u001b[0m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m   1306\u001b[0m                 [tick.label2.get_window_extent(renderer)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 959\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    960\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preprocess_math\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m                 w, h, d = _get_text_metrics_with_cache(\n\u001b[0m\u001b[1;32m    387\u001b[0m                     \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m                     ismath=ismath, dpi=self.figure.dpi)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;31m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;31m# the passed-in argument do not mess up the cache.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m     return _get_text_metrics_with_cache_impl(\n\u001b[0m\u001b[1;32m     98\u001b[0m         weakref.ref(renderer), text, fontprop.copy(), ismath, dpi)\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m    103\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m    104\u001b[0m     \u001b[0;31m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrenderer_ref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_width_height_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0mfont\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_font\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m         \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mget_hinting_flag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m         \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_width_height\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# width and height of unrotated string\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data Splitting"
      ],
      "metadata": {
        "id": "cGWM_IH6Kulm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df[\"income\"]\n",
        "x = df.drop(\"income\",axis=1)"
      ],
      "metadata": {
        "id": "sywV_JMS-M8S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "8bs53_0F-YVn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Models Building"
      ],
      "metadata": {
        "id": "y-gmlranKzhR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1-Linear Regression"
      ],
      "metadata": {
        "id": "a1_go2SzCyua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear_reg = LinearRegression()\n",
        "linear_reg.fit(x_train, y_train)\n",
        "y_pred_lr = linear_reg.predict(x_test)"
      ],
      "metadata": {
        "id": "hfCjxpPsBYW-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse_lr = mean_squared_error(y_test, y_pred_lr)\n",
        "print(f\"Linear Regression MSE: {mse_lr}\")\n",
        "print(f\"Linear Regression R2 Score: {r2_score(y_test, y_pred_lr)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FPEPxZ5BX9D",
        "outputId": "78bbb2aa-7049-4cab-faa5-2f8ac5d59047"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression MSE: 0.13305719742730485\n",
            "Linear Regression R2 Score: 0.28215115467075935\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2-Logistic Regression"
      ],
      "metadata": {
        "id": "vbbh14WiDygQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(x_train, y_train)\n",
        "y_pred_log = log_reg.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6sedZk3DdKZ",
        "outputId": "00c9ca23-5446-4a02-888d-56e00ed2b359"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Logistic Regression Accuracy: {accuracy_score(y_test, y_pred_log)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_log)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBDDlZ4YEiFA",
        "outputId": "b6587903-27ca-45bb-a702-22229efd07ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 0.8022135683541709\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.93      0.88      7360\n",
            "           1       0.65      0.42      0.51      2398\n",
            "\n",
            "    accuracy                           0.80      9758\n",
            "   macro avg       0.74      0.67      0.69      9758\n",
            "weighted avg       0.79      0.80      0.79      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Logistic Regression with GridSearchCV"
      ],
      "metadata": {
        "id": "KmpxB6wIFFYN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid_log_reg = {\n",
        "    'C': [0.01, 0.1, 1, 10],\n",
        "    'solver': ['lbfgs', 'liblinear']\n",
        "}\n",
        "grid_search_log_reg = GridSearchCV(LogisticRegression(max_iter=200), param_grid_log_reg, cv=5, scoring='accuracy',n_jobs=-1, verbose=1)\n",
        "grid_search_log_reg.fit(x_train, y_train)\n",
        "best_log_reg = grid_search_log_reg.best_estimator_\n",
        "y_pred_best_log_reg = best_log_reg.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nSK_Z-YZFA9J",
        "outputId": "1b2af761-138b-4940-f565-7f150110fbee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best Logistic Regression Accuracy: {accuracy_score(y_test, y_pred_best_log_reg)}\")\n",
        "print(f\"Best Logistic Regression Classification Report:\\n{classification_report(y_test, y_pred_best_log_reg)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILzO3POVFVd4",
        "outputId": "333439e3-bef2-4df3-f675-21d3e4e213af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Logistic Regression Accuracy: 0.8133838901414224\n",
            "Best Logistic Regression Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.94      0.88      7360\n",
            "           1       0.69      0.44      0.53      2398\n",
            "\n",
            "    accuracy                           0.81      9758\n",
            "   macro avg       0.76      0.69      0.71      9758\n",
            "weighted avg       0.80      0.81      0.80      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3-K-Nearest Neighbors (KNN)"
      ],
      "metadata": {
        "id": "3xg-J1_VFtDJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier()\n",
        "knn.fit(x_train, y_train)\n",
        "y_pred_knn = knn.predict(x_test)"
      ],
      "metadata": {
        "id": "K5zfj7CTFicq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"KNN Accuracy: {accuracy_score(y_test, y_pred_knn)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_knn)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdoN3eTsF2gL",
        "outputId": "0b85c8cf-c279-463a-e868-4eb9715e54cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Accuracy: 0.8078499692559951\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.88      0.87      7360\n",
            "           1       0.62      0.58      0.60      2398\n",
            "\n",
            "    accuracy                           0.81      9758\n",
            "   macro avg       0.74      0.73      0.73      9758\n",
            "weighted avg       0.80      0.81      0.81      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##KNN with GridSearchCV"
      ],
      "metadata": {
        "id": "dqJ-YroMF9Zo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid_knn = {\n",
        "    'n_neighbors': [3, 5, 7, 9],\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'metric': ['euclidean', 'manhattan']\n",
        "}\n",
        "grid_search_knn = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=3,n_jobs=-1, scoring='accuracy', verbose=1)\n",
        "grid_search_knn.fit(x_train, y_train)\n",
        "best_knn = grid_search_knn.best_estimator_\n",
        "y_pred_best_knn = best_knn.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZCd0A3gF_s9",
        "outputId": "8f4b4061-317f-4b99-ca93-65a7b292b038"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best KNN Accuracy: {accuracy_score(y_test, y_pred_best_knn)}\")\n",
        "print(f\"Best KNN Classification Report:\\n{classification_report(y_test, y_pred_best_knn)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKuu-fvPGIXV",
        "outputId": "31a10ffb-9182-4fce-eb90-e78c734c38f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best KNN Accuracy: 0.8242467718794835\n",
            "Best KNN Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.90      0.89      7360\n",
            "           1       0.66      0.59      0.62      2398\n",
            "\n",
            "    accuracy                           0.82      9758\n",
            "   macro avg       0.76      0.75      0.75      9758\n",
            "weighted avg       0.82      0.82      0.82      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4-Support Vector Machine (SVM)"
      ],
      "metadata": {
        "id": "0Tl7oLxbGs6t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm = SVC()\n",
        "svm.fit(x_train, y_train)\n",
        "y_pred_svm = svm.predict(x_test)"
      ],
      "metadata": {
        "id": "Df7Yk101Gjnw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(f\"SVM Accuracy: {accuracy_score(y_test, y_pred_svm)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_svm)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HuHPC-WG1zI",
        "outputId": "295518d2-7043-4069-9686-d7e72421ca59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Accuracy: 0.8167657306825169\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.95      0.89      7360\n",
            "           1       0.74      0.39      0.51      2398\n",
            "\n",
            "    accuracy                           0.82      9758\n",
            "   macro avg       0.78      0.67      0.70      9758\n",
            "weighted avg       0.81      0.82      0.80      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##5-Decision Tree"
      ],
      "metadata": {
        "id": "rc0NMFQGIyeU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "decision_tree = DecisionTreeClassifier()\n",
        "decision_tree.fit(x_train, y_train)\n",
        "y_pred_tree = decision_tree.predict(x_test)"
      ],
      "metadata": {
        "id": "3bmH5JXKIuTv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Decision Tree Accuracy: {accuracy_score(y_test, y_pred_tree)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_tree)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TPghCb9UI62b",
        "outputId": "14ddda8b-6f8d-40a5-ff3c-b6780815c993"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy: 0.8152285304365648\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.88      0.88      7360\n",
            "           1       0.63      0.62      0.62      2398\n",
            "\n",
            "    accuracy                           0.82      9758\n",
            "   macro avg       0.75      0.75      0.75      9758\n",
            "weighted avg       0.81      0.82      0.81      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Decision Tree with GridSearchCV"
      ],
      "metadata": {
        "id": "8ADG1ypCI79E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid_tree = {\n",
        "    'criterion': ['gini'],\n",
        "    'max_depth': [10, 20],\n",
        "    'min_samples_split': [2, 5]\n",
        "}\n",
        "grid_search_tree = GridSearchCV(DecisionTreeClassifier(), param_grid_tree, cv=3, scoring='accuracy', n_jobs=-1, verbose=1)\n",
        "grid_search_tree.fit(x_train, y_train)\n",
        "best_tree = grid_search_tree.best_estimator_\n",
        "y_pred_best_tree = best_tree.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPHJL-FCJCO8",
        "outputId": "63218e42-8ca2-46db-9154-ec168c682d3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best Decision Tree Accuracy: {accuracy_score(y_test, y_pred_best_tree)}\")\n",
        "print(f\"Best Decision Tree Classification Report:\\n{classification_report(y_test, y_pred_best_tree)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3bVGPD5JQyz",
        "outputId": "afe56c13-8529-4c95-8fe9-592c46d24095"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Decision Tree Accuracy: 0.8521213363394138\n",
            "Best Decision Tree Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.92      0.90      7360\n",
            "           1       0.73      0.64      0.68      2398\n",
            "\n",
            "    accuracy                           0.85      9758\n",
            "   macro avg       0.81      0.78      0.79      9758\n",
            "weighted avg       0.85      0.85      0.85      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##6-Random Forest"
      ],
      "metadata": {
        "id": "IyNwbhj5NbUQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest = RandomForestClassifier()\n",
        "random_forest.fit(x_train, y_train)\n",
        "y_pred_rf = random_forest.predict(x_test)"
      ],
      "metadata": {
        "id": "xHs3iAINNc5R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Random Forest Accuracy: {accuracy_score(y_test, y_pred_rf)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_rf)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HlCJNTkNiLA",
        "outputId": "08dd093f-53c1-466c-cbfb-35c0263773e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 0.8565279770444764\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.93      0.91      7360\n",
            "           1       0.75      0.62      0.68      2398\n",
            "\n",
            "    accuracy                           0.86      9758\n",
            "   macro avg       0.82      0.78      0.79      9758\n",
            "weighted avg       0.85      0.86      0.85      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Random Forest with GridSearchCV"
      ],
      "metadata": {
        "id": "bPRg1jeRNlcK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest = RandomForestClassifier()\n",
        "random_forest.fit(x_train, y_train)\n",
        "y_pred_rf = random_forest.predict(x_test)"
      ],
      "metadata": {
        "id": "MOylzPE1Ns-S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Random Forest Accuracy: {accuracy_score(y_test, y_pred_rf)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_rf)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYPqAxLvXKXh",
        "outputId": "0a75bef5-fad1-42bf-9996-58d0180d6a37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 0.8571428571428571\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.94      0.91      7360\n",
            "           1       0.76      0.62      0.68      2398\n",
            "\n",
            "    accuracy                           0.86      9758\n",
            "   macro avg       0.82      0.78      0.79      9758\n",
            "weighted avg       0.85      0.86      0.85      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Random Forest with GridSearchCV"
      ],
      "metadata": {
        "id": "rj_RhespXlEE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid_rf = {\n",
        "    'n_estimators': [50, 100],\n",
        "    'max_depth': [10, 20],\n",
        "    'min_samples_split': [2, 5]\n",
        "}\n",
        "grid_search_rf = GridSearchCV(RandomForestClassifier(), param_grid_rf, cv=3, scoring='accuracy', n_jobs=-1, verbose=1)\n",
        "grid_search_rf.fit(x_train, y_train)\n",
        "best_rf = grid_search_rf.best_estimator_\n",
        "y_pred_best_rf = best_rf.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rt8FH9QBXjV7",
        "outputId": "843c122c-acea-4b19-ab1d-5cbccf4e0ac1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best Random Forest Accuracy: {accuracy_score(y_test, y_pred_best_rf)}\")\n",
        "print(f\"Best Random Forest Classification Report:\\n{classification_report(y_test, y_pred_best_rf)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtGM4Q5TXX1u",
        "outputId": "7b350c73-4f1b-4cdc-cbca-b912ce90a249"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Random Forest Accuracy: 0.8615494978479197\n",
            "Best Random Forest Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.94      0.91      7360\n",
            "           1       0.78      0.61      0.68      2398\n",
            "\n",
            "    accuracy                           0.86      9758\n",
            "   macro avg       0.83      0.78      0.80      9758\n",
            "weighted avg       0.86      0.86      0.86      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Bagging"
      ],
      "metadata": {
        "id": "BXiSx1WKYDO1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bagging = BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=10, random_state=42)\n",
        "bagging.fit(x_train, y_train)\n",
        "y_pred_bagging = bagging.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFf9hkpNYAPa",
        "outputId": "d6a9db8b-4eb3-4d23-9803-dba9ecf8bdf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Bagging Accuracy: {accuracy_score(y_test, y_pred_bagging)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_bagging)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5KD-SIuYK6i",
        "outputId": "15301bf3-e995-4df1-d60a-1799c4eb9b8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bagging Accuracy: 0.8505841360934617\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.94      0.90      7360\n",
            "           1       0.75      0.59      0.66      2398\n",
            "\n",
            "    accuracy                           0.85      9758\n",
            "   macro avg       0.81      0.76      0.78      9758\n",
            "weighted avg       0.84      0.85      0.84      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Bagging with GridSearchCV"
      ],
      "metadata": {
        "id": "tUV_gnNqYQ7Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid_bagging = {\n",
        "    'n_estimators': [10, 20],\n",
        "    'max_samples': [0.5, 0.7]\n",
        "}\n",
        "grid_search_bagging = GridSearchCV(BaggingClassifier(base_estimator=DecisionTreeClassifier()), param_grid_bagging, cv=3, scoring='accuracy', n_jobs=-1, verbose=1)\n",
        "grid_search_bagging.fit(x_train, y_train)\n",
        "best_bagging = grid_search_bagging.best_estimator_\n",
        "y_pred_best_bagging = best_bagging.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33ZTqZIdYM6P",
        "outputId": "7d9ac202-5c12-4b47-fdfd-73eb4b9f1bb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best Bagging Accuracy: {accuracy_score(y_test, y_pred_best_bagging)}\")\n",
        "print(f\"Best Bagging Classification Report:\\n{classification_report(y_test, y_pred_best_bagging)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P699lC2WYgMA",
        "outputId": "9b8aab10-e939-4215-c9d7-9a6a6ef5da1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Bagging Accuracy: 0.8546833367493338\n",
            "Best Bagging Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.94      0.91      7360\n",
            "           1       0.76      0.60      0.67      2398\n",
            "\n",
            "    accuracy                           0.85      9758\n",
            "   macro avg       0.82      0.77      0.79      9758\n",
            "weighted avg       0.85      0.85      0.85      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##AdaBoost (Boosting)"
      ],
      "metadata": {
        "id": "srNFeeDuYp3b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "boosting = AdaBoostClassifier(n_estimators=50, random_state=42)\n",
        "boosting.fit(x_train, y_train)\n",
        "y_pred_boost = boosting.predict(x_test)"
      ],
      "metadata": {
        "id": "gqh9U8AzYiu8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Boosting (AdaBoost) Accuracy: {accuracy_score(y_test, y_pred_boost)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_boost)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k4fLH4r6Y4wq",
        "outputId": "61473e0c-0230-43c7-8bfd-3ee66f586f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Boosting (AdaBoost) Accuracy: 0.8544783767165403\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.94      0.91      7360\n",
            "           1       0.77      0.58      0.66      2398\n",
            "\n",
            "    accuracy                           0.85      9758\n",
            "   macro avg       0.82      0.76      0.79      9758\n",
            "weighted avg       0.85      0.85      0.85      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Boosting (AdaBoost) with GridSearchCV"
      ],
      "metadata": {
        "id": "NdWTzogsY9b7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid_boost = {\n",
        "    'n_estimators': [50, 100],\n",
        "    'learning_rate': [0.01, 0.1]\n",
        "}\n",
        "grid_search_boost = GridSearchCV(AdaBoostClassifier(), param_grid_boost, cv=3, scoring='accuracy', n_jobs=-1, verbose=1)\n",
        "grid_search_boost.fit(x_train, y_train)\n",
        "best_boost = grid_search_boost.best_estimator_\n",
        "y_pred_best_boost = best_boost.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzFdiBJbY6fA",
        "outputId": "b9b7ca51-7be9-43b9-ca50-7e1d5b58ac06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best Boosting (AdaBoost) Accuracy: {accuracy_score(y_test, y_pred_best_boost)}\")\n",
        "print(f\"Best Boosting (AdaBoost) Classification Report:\\n{classification_report(y_test, y_pred_best_boost)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YjIoUIrGZLMs",
        "outputId": "2251f55a-28cc-4ec4-98ec-30cd7973f2eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Boosting (AdaBoost) Accuracy: 0.8441278950604633\n",
            "Best Boosting (AdaBoost) Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.96      0.90      7360\n",
            "           1       0.79      0.50      0.61      2398\n",
            "\n",
            "    accuracy                           0.84      9758\n",
            "   macro avg       0.82      0.73      0.76      9758\n",
            "weighted avg       0.84      0.84      0.83      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##XGBoost"
      ],
      "metadata": {
        "id": "FJiyrCetZQ7o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb = XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
        "xgb.fit(x_train, y_train)\n",
        "y_pred_xgb = xgb.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_5A0qUlZN_V",
        "outputId": "f4c260e1-4278-4ec8-c920-f6d074b84c11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [14:31:48] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"XGBoost Accuracy: {accuracy_score(y_test, y_pred_xgb)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_xgb)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnEoX2sTZYHA",
        "outputId": "8055e7aa-ee32-471b-e0a2-93d8347f21ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Accuracy: 0.8682106989137118\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.94      0.92      7360\n",
            "           1       0.79      0.64      0.70      2398\n",
            "\n",
            "    accuracy                           0.87      9758\n",
            "   macro avg       0.84      0.79      0.81      9758\n",
            "weighted avg       0.86      0.87      0.86      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid_xgb = {\n",
        "    'n_estimators': [50, 100],\n",
        "    'learning_rate': [0.1],\n",
        "    'max_depth': [3, 5]\n",
        "}\n",
        "grid_search_xgb = GridSearchCV(XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'), param_grid_xgb, cv=3, scoring='accuracy', n_jobs=-1, verbose=1)\n",
        "grid_search_xgb.fit(x_train, y_train)\n",
        "best_xgb = grid_search_xgb.best_estimator_\n",
        "y_pred_best_xgb = best_xgb.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2OgXj-MZamH",
        "outputId": "14959a74-0765-41c3-e79d-2e39f4a2a6eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [14:33:08] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best XGBoost Accuracy: {accuracy_score(y_test, y_pred_best_xgb)}\")\n",
        "print(f\"Best XGBoost Classification Report:\\n{classification_report(y_test, y_pred_best_xgb)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8CDksQ1_ZqtV",
        "outputId": "0d45c682-5b18-4051-97ce-1e8956eef168"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best XGBoost Accuracy: 0.8672883787661406\n",
            "Best XGBoost Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.95      0.91      7360\n",
            "           1       0.79      0.63      0.70      2398\n",
            "\n",
            "    accuracy                           0.87      9758\n",
            "   macro avg       0.84      0.79      0.81      9758\n",
            "weighted avg       0.86      0.87      0.86      9758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "INDMM2RaZu4Z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}