LogisticRegression_Data_Processing.ipynb

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      "name": "LogisticRegression_Data Processing.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score"
      ],
      "metadata": {
        "id": "54t_OtFd2_Gk"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhTbV2gc2qke",
        "outputId": "7ed93389-a871-4a3c-8e86-3be5dfe5532e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/DAC_datasets/adult_incom_pred.csv\")"
      ],
      "metadata": {
        "id": "a4qBc0jB29gV"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA EXPLORATION"
      ],
      "metadata": {
        "id": "l2_zqLrG3yUs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = data.copy()\n",
        "df.info()"
      ],
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        },
        "id": "TlVDWfl63OJt",
        "outputId": "dd9e93b9-8a32-4896-81a7-2257aea14dca"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             32561 non-null  int64 \n",
            " 1   workclass       32561 non-null  object\n",
            " 2   fnlwgt          32561 non-null  int64 \n",
            " 3   education       32561 non-null  object\n",
            " 4   education.num   32561 non-null  int64 \n",
            " 5   marital.status  32561 non-null  object\n",
            " 6   occupation      32561 non-null  object\n",
            " 7   relationship    32561 non-null  object\n",
            " 8   race            32561 non-null  object\n",
            " 9   sex             32561 non-null  object\n",
            " 10  capital.gain    32561 non-null  int64 \n",
            " 11  capital.loss    32561 non-null  int64 \n",
            " 12  hours.per.week  32561 non-null  int64 \n",
            " 13  native.country  32561 non-null  object\n",
            " 14  income          32561 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "id": "RNW_xfnn3408",
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      "execution_count": 5,
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        {
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>140359</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>4</td>\n",
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              "      <td>3900</td>\n",
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              "      <th>4</th>\n",
              "      <td>41</td>\n",
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              "      <td>264663</td>\n",
              "      <td>Some-college</td>\n",
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            "text/plain": [
              "   age workclass  fnlwgt  ... hours.per.week  native.country income\n",
              "0   90         ?   77053  ...             40   United-States  <=50K\n",
              "1   82   Private  132870  ...             18   United-States  <=50K\n",
              "2   66         ?  186061  ...             40   United-States  <=50K\n",
              "3   54   Private  140359  ...             40   United-States  <=50K\n",
              "4   41   Private  264663  ...             40   United-States  <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNLuC--H4kUV",
        "outputId": "48681d0c-8e9b-49c1-c1f4-40ff1b77cd88"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "workclass         0\n",
              "fnlwgt            0\n",
              "education         0\n",
              "education.num     0\n",
              "marital.status    0\n",
              "occupation        0\n",
              "relationship      0\n",
              "race              0\n",
              "sex               0\n",
              "capital.gain      0\n",
              "capital.loss      0\n",
              "hours.per.week    0\n",
              "native.country    0\n",
              "income            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Summary of Numerical Data\n",
        "summary = df.describe()\n",
        "print(summary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pt8HCmz8wli1",
        "outputId": "781d7a92-d79a-4747-ace7-624b75290ea1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                age        fnlwgt  ...  capital.loss  hours.per.week\n",
            "count  32561.000000  3.256100e+04  ...  32561.000000    32561.000000\n",
            "mean      38.581647  1.897784e+05  ...     87.303830       40.437456\n",
            "std       13.640433  1.055500e+05  ...    402.960219       12.347429\n",
            "min       17.000000  1.228500e+04  ...      0.000000        1.000000\n",
            "25%       28.000000  1.178270e+05  ...      0.000000       40.000000\n",
            "50%       37.000000  1.783560e+05  ...      0.000000       40.000000\n",
            "75%       48.000000  2.370510e+05  ...      0.000000       45.000000\n",
            "max       90.000000  1.484705e+06  ...   4356.000000       99.000000\n",
            "\n",
            "[8 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Summary of Categorical Data\n",
        "summary_cat = df.describe(include = \"O\")\n",
        "print(summary_cat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOfmbvYW2mEy",
        "outputId": "ecde119b-42e5-4dfb-e908-fa77439e738e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       workclass education      marital.status  ...    sex native.country income\n",
            "count      32561     32561               32561  ...  32561          32561  32561\n",
            "unique         9        16                   7  ...      2             42      2\n",
            "top      Private   HS-grad  Married-civ-spouse  ...   Male  United-States  <=50K\n",
            "freq       22696     10501               14976  ...  21790          29170  24720\n",
            "\n",
            "[4 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['workclass'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5g563wK3jox",
        "outputId": "e491367d-73df-4739-ca89-1aab0ab4c08d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Private             22696\n",
              "Self-emp-not-inc     2541\n",
              "Local-gov            2093\n",
              "?                    1836\n",
              "State-gov            1298\n",
              "Self-emp-inc         1116\n",
              "Federal-gov           960\n",
              "Without-pay            14\n",
              "Never-worked            7\n",
              "Name: workclass, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['occupation'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZFSRfrni37Ow",
        "outputId": "1ed4f535-e979-461c-a2da-a0420624e716"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prof-specialty       4140\n",
              "Craft-repair         4099\n",
              "Exec-managerial      4066\n",
              "Adm-clerical         3770\n",
              "Sales                3650\n",
              "Other-service        3295\n",
              "Machine-op-inspct    2002\n",
              "?                    1843\n",
              "Transport-moving     1597\n",
              "Handlers-cleaners    1370\n",
              "Farming-fishing       994\n",
              "Tech-support          928\n",
              "Protective-serv       649\n",
              "Priv-house-serv       149\n",
              "Armed-Forces            9\n",
              "Name: occupation, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping null values\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/DAC_datasets/adult_incom_pred.csv\", na_values=['?'])"
      ],
      "metadata": {
        "id": "50loV8O0Aod3"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gwwkF7PCGFn",
        "outputId": "8359c179-fd13-4a7e-c54d-70f28365ed9a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  0\n",
              "workclass         1836\n",
              "fnlwgt               0\n",
              "education            0\n",
              "education.num        0\n",
              "marital.status       0\n",
              "occupation        1843\n",
              "relationship         0\n",
              "race                 0\n",
              "sex                  0\n",
              "capital.gain         0\n",
              "capital.loss         0\n",
              "hours.per.week       0\n",
              "native.country     583\n",
              "income               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Analyszing missing values\n",
        "missing = df[df.isnull().any(axis=1)]\n",
        "missing.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-7oB6eZCScL",
        "outputId": "59df8c60-3447-44e4-e28f-58de6b3a5a05"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2399, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing.head()"
      ],
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          "height": 250
        },
        "id": "cTQEBA51CuzU",
        "outputId": "dc3a3a73-d4da-4f78-d0ff-b708006365e0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>age</th>\n",
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              "      <th>education</th>\n",
              "      <th>education.num</th>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>41</td>\n",
              "      <td>Private</td>\n",
              "      <td>70037</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>3004</td>\n",
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              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>51</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Doctorate</td>\n",
              "      <td>16</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
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              "      <td>0</td>\n",
              "      <td>2824</td>\n",
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              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>22</td>\n",
              "      <td>Private</td>\n",
              "      <td>119592</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>2824</td>\n",
              "      <td>40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>&gt;50K</td>\n",
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              "  "
            ],
            "text/plain": [
              "    age workclass  fnlwgt  ... hours.per.week  native.country income\n",
              "0    90       NaN   77053  ...             40   United-States  <=50K\n",
              "2    66       NaN  186061  ...             40   United-States  <=50K\n",
              "9    41   Private   70037  ...             60             NaN   >50K\n",
              "14   51       NaN  172175  ...             40   United-States   >50K\n",
              "18   22   Private  119592  ...             40             NaN   >50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Handling missing values"
      ],
      "metadata": {
        "id": "t2y7QHMPlKEO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Relationship between independent variables"
      ],
      "metadata": {
        "id": "iDxst4IbsPsq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = df2.corr()\n",
        "print(correlation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YCahXqwpgAZ",
        "outputId": "abe83ad1-4c44-43e2-87da-f9918bde24ed"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     age    fnlwgt  ...  capital.loss  hours.per.week\n",
            "age             1.000000 -0.076511  ...      0.060165        0.101599\n",
            "fnlwgt         -0.076511  1.000000  ...     -0.009750       -0.022886\n",
            "education.num   0.043526 -0.044992  ...      0.079646        0.152522\n",
            "capital.gain    0.080154  0.000422  ...     -0.032229        0.080432\n",
            "capital.loss    0.060165 -0.009750  ...      1.000000        0.052417\n",
            "hours.per.week  0.101599 -0.022886  ...      0.052417        1.000000\n",
            "\n",
            "[6 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is little correlation between variables"
      ],
      "metadata": {
        "id": "_oH9jCfFsw87"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping missing values\n",
        "df2 = df.dropna(axis=0)\n",
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxuGSLytlIeB",
        "outputId": "e34f3009-f735-456e-f2b5-6485059f13c1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30162, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Income distribution\n",
        "incom = sns.countplot(df2['income'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "-5tlErnXsyeD",
        "outputId": "32e86ab7-b17e-439c-9d8d-d636846c99c3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Histogram of age\n",
        "sns.displot(df2['age'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "eV61eHMJt9cD",
        "outputId": "93f7d4aa-4338-443a-f963-6e75eddaf05a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7efe92ccfa90>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['income'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNnOzI9FwcB0",
        "outputId": "32d2d30b-3e3f-42a4-de90-0c043109d0c9"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['<=50K', '>50K'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.to_csv(\"/content/drive/MyDrive/DAC_datasets/adult_income_pred.csv\")"
      ],
      "metadata": {
        "id": "zuBhZrdmU1Cg"
      },
      "execution_count": 22,
      "outputs": []
    }
  ]
}