adult_ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt     education  educational-num      marital-status  \\\n",
       "0   25    Private  226802          11th                7       Never-married   \n",
       "1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n",
       "2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n",
       "3   44    Private  160323  Some-college               10  Married-civ-spouse   \n",
       "4   18          ?  103497  Some-college               10       Never-married   \n",
       "\n",
       "          occupation relationship   race  gender  capital-gain  capital-loss  \\\n",
       "0  Machine-op-inspct    Own-child  Black    Male             0             0   \n",
       "1    Farming-fishing      Husband  White    Male             0             0   \n",
       "2    Protective-serv      Husband  White    Male             0             0   \n",
       "3  Machine-op-inspct      Husband  Black    Male          7688             0   \n",
       "4                  ?    Own-child  White  Female             0             0   \n",
       "\n",
       "   hours-per-week native-country income  \n",
       "0              40  United-States  <=50K  \n",
       "1              50  United-States  <=50K  \n",
       "2              40  United-States   >50K  \n",
       "3              40  United-States   >50K  \n",
       "4              30  United-States  <=50K  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('adult.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0\n",
       "workclass          0\n",
       "fnlwgt             0\n",
       "education          0\n",
       "educational-num    0\n",
       "marital-status     0\n",
       "occupation         0\n",
       "relationship       0\n",
       "race               0\n",
       "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"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if there the nan values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                               0\n",
       "fnlwgt                            0\n",
       "educational-num                   0\n",
       "capital-gain                      0\n",
       "capital-loss                      0\n",
       "                                 ..\n",
       "native-country_Thailand           0\n",
       "native-country_Trinadad&Tobago    0\n",
       "native-country_United-States      0\n",
       "native-country_Vietnam            0\n",
       "native-country_Yugoslavia         0\n",
       "Length: 109, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.isin(['?']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace '?' values with 'nan' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['workclass']=df['workclass'].replace({'?':np.NaN})\n",
    "df['occupation']=df['occupation'].replace({'?':np.NaN})\n",
    "df['native-country']=df['native-country'].replace({'?':np.NaN})\n",
    "df.isin(['?']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop duplication Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop_duplicates()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 15)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace values in income col if \">50\" with 1 , else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['income'] = [1 if i.strip() == \" >50\" else 0 for i in df.income]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "convert the data to catogrical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>workclass_Federal-gov</th>\n",
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       "      <th>native-country_Portugal</th>\n",
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       "      <th>3</th>\n",
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       "      <td>160323</td>\n",
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       "      <td>7688</td>\n",
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       "      <td>0</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>52</td>\n",
       "      <td>287927</td>\n",
       "      <td>9</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  fnlwgt  educational-num  capital-gain  capital-loss  \\\n",
       "0       25  226802                7             0             0   \n",
       "1       38   89814                9             0             0   \n",
       "2       28  336951               12             0             0   \n",
       "3       44  160323               10          7688             0   \n",
       "4       18  103497               10             0             0   \n",
       "...    ...     ...              ...           ...           ...   \n",
       "48837   27  257302               12             0             0   \n",
       "48838   40  154374                9             0             0   \n",
       "48839   58  151910                9             0             0   \n",
       "48840   22  201490                9             0             0   \n",
       "48841   52  287927                9         15024             0   \n",
       "\n",
       "       hours-per-week  income  workclass_?  workclass_Federal-gov  \\\n",
       "0                  40       0        False                  False   \n",
       "1                  50       0        False                  False   \n",
       "2                  40       0        False                  False   \n",
       "3                  40       0        False                  False   \n",
       "4                  30       0         True                  False   \n",
       "...               ...     ...          ...                    ...   \n",
       "48837              38       0        False                  False   \n",
       "48838              40       0        False                  False   \n",
       "48839              40       0        False                  False   \n",
       "48840              20       0        False                  False   \n",
       "48841              40       0        False                  False   \n",
       "\n",
       "       workclass_Local-gov  ...  native-country_Portugal  \\\n",
       "0                    False  ...                    False   \n",
       "1                    False  ...                    False   \n",
       "2                     True  ...                    False   \n",
       "3                    False  ...                    False   \n",
       "4                    False  ...                    False   \n",
       "...                    ...  ...                      ...   \n",
       "48837                False  ...                    False   \n",
       "48838                False  ...                    False   \n",
       "48839                False  ...                    False   \n",
       "48840                False  ...                    False   \n",
       "48841                False  ...                    False   \n",
       "\n",
       "       native-country_Puerto-Rico  native-country_Scotland  \\\n",
       "0                           False                    False   \n",
       "1                           False                    False   \n",
       "2                           False                    False   \n",
       "3                           False                    False   \n",
       "4                           False                    False   \n",
       "...                           ...                      ...   \n",
       "48837                       False                    False   \n",
       "48838                       False                    False   \n",
       "48839                       False                    False   \n",
       "48840                       False                    False   \n",
       "48841                       False                    False   \n",
       "\n",
       "       native-country_South  native-country_Taiwan  native-country_Thailand  \\\n",
       "0                     False                  False                    False   \n",
       "1                     False                  False                    False   \n",
       "2                     False                  False                    False   \n",
       "3                     False                  False                    False   \n",
       "4                     False                  False                    False   \n",
       "...                     ...                    ...                      ...   \n",
       "48837                 False                  False                    False   \n",
       "48838                 False                  False                    False   \n",
       "48839                 False                  False                    False   \n",
       "48840                 False                  False                    False   \n",
       "48841                 False                  False                    False   \n",
       "\n",
       "       native-country_Trinadad&Tobago  native-country_United-States  \\\n",
       "0                               False                          True   \n",
       "1                               False                          True   \n",
       "2                               False                          True   \n",
       "3                               False                          True   \n",
       "4                               False                          True   \n",
       "...                               ...                           ...   \n",
       "48837                           False                          True   \n",
       "48838                           False                          True   \n",
       "48839                           False                          True   \n",
       "48840                           False                          True   \n",
       "48841                           False                          True   \n",
       "\n",
       "       native-country_Vietnam  native-country_Yugoslavia  \n",
       "0                       False                      False  \n",
       "1                       False                      False  \n",
       "2                       False                      False  \n",
       "3                       False                      False  \n",
       "4                       False                      False  \n",
       "...                       ...                        ...  \n",
       "48837                   False                      False  \n",
       "48838                   False                      False  \n",
       "48839                   False                      False  \n",
       "48840                   False                      False  \n",
       "48841                   False                      False  \n",
       "\n",
       "[48842 rows x 109 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
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       "      <th>hours-per-week</th>\n",
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       "      <th>workclass_Federal-gov</th>\n",
       "      <th>workclass_Local-gov</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_Portugal</th>\n",
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       "      <th>native-country_Thailand</th>\n",
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       "      <th>native-country_United-States</th>\n",
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       "    <tr>\n",
       "      <th>48840</th>\n",
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       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>52</td>\n",
       "      <td>287927</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  fnlwgt  educational-num  capital-gain  capital-loss  \\\n",
       "0       25  226802                7             0             0   \n",
       "1       38   89814                9             0             0   \n",
       "2       28  336951               12             0             0   \n",
       "3       44  160323               10          7688             0   \n",
       "4       18  103497               10             0             0   \n",
       "...    ...     ...              ...           ...           ...   \n",
       "48837   27  257302               12             0             0   \n",
       "48838   40  154374                9             0             0   \n",
       "48839   58  151910                9             0             0   \n",
       "48840   22  201490                9             0             0   \n",
       "48841   52  287927                9         15024             0   \n",
       "\n",
       "       hours-per-week  income  workclass_?  workclass_Federal-gov  \\\n",
       "0                  40       0            0                      0   \n",
       "1                  50       0            0                      0   \n",
       "2                  40       0            0                      0   \n",
       "3                  40       0            0                      0   \n",
       "4                  30       0            1                      0   \n",
       "...               ...     ...          ...                    ...   \n",
       "48837              38       0            0                      0   \n",
       "48838              40       0            0                      0   \n",
       "48839              40       0            0                      0   \n",
       "48840              20       0            0                      0   \n",
       "48841              40       0            0                      0   \n",
       "\n",
       "       workclass_Local-gov  ...  native-country_Portugal  \\\n",
       "0                        0  ...                        0   \n",
       "1                        0  ...                        0   \n",
       "2                        1  ...                        0   \n",
       "3                        0  ...                        0   \n",
       "4                        0  ...                        0   \n",
       "...                    ...  ...                      ...   \n",
       "48837                    0  ...                        0   \n",
       "48838                    0  ...                        0   \n",
       "48839                    0  ...                        0   \n",
       "48840                    0  ...                        0   \n",
       "48841                    0  ...                        0   \n",
       "\n",
       "       native-country_Puerto-Rico  native-country_Scotland  \\\n",
       "0                               0                        0   \n",
       "1                               0                        0   \n",
       "2                               0                        0   \n",
       "3                               0                        0   \n",
       "4                               0                        0   \n",
       "...                           ...                      ...   \n",
       "48837                           0                        0   \n",
       "48838                           0                        0   \n",
       "48839                           0                        0   \n",
       "48840                           0                        0   \n",
       "48841                           0                        0   \n",
       "\n",
       "       native-country_South  native-country_Taiwan  native-country_Thailand  \\\n",
       "0                         0                      0                        0   \n",
       "1                         0                      0                        0   \n",
       "2                         0                      0                        0   \n",
       "3                         0                      0                        0   \n",
       "4                         0                      0                        0   \n",
       "...                     ...                    ...                      ...   \n",
       "48837                     0                      0                        0   \n",
       "48838                     0                      0                        0   \n",
       "48839                     0                      0                        0   \n",
       "48840                     0                      0                        0   \n",
       "48841                     0                      0                        0   \n",
       "\n",
       "       native-country_Trinadad&Tobago  native-country_United-States  \\\n",
       "0                                   0                             1   \n",
       "1                                   0                             1   \n",
       "2                                   0                             1   \n",
       "3                                   0                             1   \n",
       "4                                   0                             1   \n",
       "...                               ...                           ...   \n",
       "48837                               0                             1   \n",
       "48838                               0                             1   \n",
       "48839                               0                             1   \n",
       "48840                               0                             1   \n",
       "48841                               0                             1   \n",
       "\n",
       "       native-country_Vietnam  native-country_Yugoslavia  \n",
       "0                           0                          0  \n",
       "1                           0                          0  \n",
       "2                           0                          0  \n",
       "3                           0                          0  \n",
       "4                           0                          0  \n",
       "...                       ...                        ...  \n",
       "48837                       0                          0  \n",
       "48838                       0                          0  \n",
       "48839                       0                          0  \n",
       "48840                       0                          0  \n",
       "48841                       0                          0  \n",
       "\n",
       "[48842 rows x 109 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.astype(\"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "divided the data to train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "x  = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, -1].values\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply The KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9998976353772137\n"
     ]
    }
   ],
   "source": [
    "# KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "knn_model = KNeighborsClassifier().fit(x_train, y_train)\n",
    "y_pred = knn_model.predict(x_test)\n",
    "\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  }
 ],
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