LogisticR_H1N1_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f546f4a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "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 import metrics\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7801fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r'C:\\Users\\Bushra Ansari\\Desktop\\LMS DATASET\\LogisticR Test\\Vaccine Usage Prediction LMS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5cf63387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['h1n1_vaccine_prediction.csv',\n",
       " 'Problem Statement_LogisticReg.pdf',\n",
       " 'test_profile.xlsx',\n",
       " 'train_profile.xlsx',\n",
       " '~$test_profile.xlsx']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bab41221",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>h1n1_worry</th>\n",
       "      <th>h1n1_awareness</th>\n",
       "      <th>antiviral_medication</th>\n",
       "      <th>contact_avoidance</th>\n",
       "      <th>bought_face_mask</th>\n",
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       "      <th>...</th>\n",
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       "      <th>housing_status</th>\n",
       "      <th>employment</th>\n",
       "      <th>census_msa</th>\n",
       "      <th>no_of_adults</th>\n",
       "      <th>no_of_children</th>\n",
       "      <th>h1n1_vaccine</th>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   unique_id  h1n1_worry  h1n1_awareness  antiviral_medication  \\\n",
       "0          0         1.0             0.0                   0.0   \n",
       "1          1         3.0             2.0                   0.0   \n",
       "2          2         1.0             1.0                   0.0   \n",
       "3          3         1.0             1.0                   0.0   \n",
       "4          4         2.0             1.0                   0.0   \n",
       "\n",
       "   contact_avoidance  bought_face_mask  wash_hands_frequently  \\\n",
       "0                0.0               0.0                    0.0   \n",
       "1                1.0               0.0                    1.0   \n",
       "2                1.0               0.0                    0.0   \n",
       "3                1.0               0.0                    1.0   \n",
       "4                1.0               0.0                    1.0   \n",
       "\n",
       "   avoid_large_gatherings  reduced_outside_home_cont  avoid_touch_face  ...  \\\n",
       "0                     0.0                        1.0               1.0  ...   \n",
       "1                     0.0                        1.0               1.0  ...   \n",
       "2                     0.0                        0.0               0.0  ...   \n",
       "3                     1.0                        0.0               0.0  ...   \n",
       "4                     1.0                        0.0               1.0  ...   \n",
       "\n",
       "    race     sex               income_level  marital_status  housing_status  \\\n",
       "0  White  Female              Below Poverty     Not Married             Own   \n",
       "1  White    Male              Below Poverty     Not Married            Rent   \n",
       "2  White    Male  <= $75,000, Above Poverty     Not Married             Own   \n",
       "3  White  Female              Below Poverty     Not Married            Rent   \n",
       "4  White  Female  <= $75,000, Above Poverty         Married             Own   \n",
       "\n",
       "           employment                census_msa  no_of_adults  no_of_children  \\\n",
       "0  Not in Labor Force                   Non-MSA           0.0             0.0   \n",
       "1            Employed  MSA, Not Principle  City           0.0             0.0   \n",
       "2            Employed  MSA, Not Principle  City           2.0             0.0   \n",
       "3  Not in Labor Force       MSA, Principle City           0.0             0.0   \n",
       "4            Employed  MSA, Not Principle  City           1.0             0.0   \n",
       "\n",
       "   h1n1_vaccine  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine = pd.read_csv('h1n1_vaccine_prediction.csv')\n",
    "vaccine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d5fd2d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26707 entries, 0 to 26706\n",
      "Data columns (total 34 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   unique_id                  26707 non-null  int64  \n",
      " 1   h1n1_worry                 26615 non-null  float64\n",
      " 2   h1n1_awareness             26591 non-null  float64\n",
      " 3   antiviral_medication       26636 non-null  float64\n",
      " 4   contact_avoidance          26499 non-null  float64\n",
      " 5   bought_face_mask           26688 non-null  float64\n",
      " 6   wash_hands_frequently      26665 non-null  float64\n",
      " 7   avoid_large_gatherings     26620 non-null  float64\n",
      " 8   reduced_outside_home_cont  26625 non-null  float64\n",
      " 9   avoid_touch_face           26579 non-null  float64\n",
      " 10  dr_recc_h1n1_vacc          24547 non-null  float64\n",
      " 11  dr_recc_seasonal_vacc      24547 non-null  float64\n",
      " 12  chronic_medic_condition    25736 non-null  float64\n",
      " 13  cont_child_undr_6_mnths    25887 non-null  float64\n",
      " 14  is_health_worker           25903 non-null  float64\n",
      " 15  has_health_insur           14433 non-null  float64\n",
      " 16  is_h1n1_vacc_effective     26316 non-null  float64\n",
      " 17  is_h1n1_risky              26319 non-null  float64\n",
      " 18  sick_from_h1n1_vacc        26312 non-null  float64\n",
      " 19  is_seas_vacc_effective     26245 non-null  float64\n",
      " 20  is_seas_risky              26193 non-null  float64\n",
      " 21  sick_from_seas_vacc        26170 non-null  float64\n",
      " 22  age_bracket                26707 non-null  object \n",
      " 23  qualification              25300 non-null  object \n",
      " 24  race                       26707 non-null  object \n",
      " 25  sex                        26707 non-null  object \n",
      " 26  income_level               22284 non-null  object \n",
      " 27  marital_status             25299 non-null  object \n",
      " 28  housing_status             24665 non-null  object \n",
      " 29  employment                 25244 non-null  object \n",
      " 30  census_msa                 26707 non-null  object \n",
      " 31  no_of_adults               26458 non-null  float64\n",
      " 32  no_of_children             26458 non-null  float64\n",
      " 33  h1n1_vaccine               26707 non-null  int64  \n",
      "dtypes: float64(23), int64(2), object(9)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "vaccine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03e45e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26707, 34)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62bd1d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    23\n",
       "object      9\n",
       "int64       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b171530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['unique_id', 'h1n1_worry', 'h1n1_awareness', 'antiviral_medication',\n",
       "       'contact_avoidance', 'bought_face_mask', 'wash_hands_frequently',\n",
       "       'avoid_large_gatherings', 'reduced_outside_home_cont',\n",
       "       'avoid_touch_face', 'dr_recc_h1n1_vacc', 'dr_recc_seasonal_vacc',\n",
       "       'chronic_medic_condition', 'cont_child_undr_6_mnths',\n",
       "       'is_health_worker', 'has_health_insur', 'is_h1n1_vacc_effective',\n",
       "       'is_h1n1_risky', 'sick_from_h1n1_vacc', 'is_seas_vacc_effective',\n",
       "       'is_seas_risky', 'sick_from_seas_vacc', 'age_bracket', 'qualification',\n",
       "       'race', 'sex', 'income_level', 'marital_status', 'housing_status',\n",
       "       'employment', 'census_msa', 'no_of_adults', 'no_of_children',\n",
       "       'h1n1_vaccine'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a462378",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccine.drop(['unique_id'], inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8a1ea9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h1n1_worry                      92\n",
       "h1n1_awareness                 116\n",
       "antiviral_medication            71\n",
       "contact_avoidance              208\n",
       "bought_face_mask                19\n",
       "wash_hands_frequently           42\n",
       "avoid_large_gatherings          87\n",
       "reduced_outside_home_cont       82\n",
       "avoid_touch_face               128\n",
       "dr_recc_h1n1_vacc             2160\n",
       "dr_recc_seasonal_vacc         2160\n",
       "chronic_medic_condition        971\n",
       "cont_child_undr_6_mnths        820\n",
       "is_health_worker               804\n",
       "has_health_insur             12274\n",
       "is_h1n1_vacc_effective         391\n",
       "is_h1n1_risky                  388\n",
       "sick_from_h1n1_vacc            395\n",
       "is_seas_vacc_effective         462\n",
       "is_seas_risky                  514\n",
       "sick_from_seas_vacc            537\n",
       "age_bracket                      0\n",
       "qualification                 1407\n",
       "race                             0\n",
       "sex                              0\n",
       "income_level                  4423\n",
       "marital_status                1408\n",
       "housing_status                2042\n",
       "employment                    1463\n",
       "census_msa                       0\n",
       "no_of_adults                   249\n",
       "no_of_children                 249\n",
       "h1n1_vaccine                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "347be11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  3.,  2.,  0., nan])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['h1n1_worry'].unique()\n",
    "# 0=Not worried at all, 1=Not very worried, 2=Somewhat worried, 3=Very worried"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ce36371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['h1n1_worry'].value_counts().plot(kind = 'bar')\n",
    "plt.xlabel('H1N1 Worry')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e6c2ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vaccine['h1n1_worry'].mode(), vaccine['h1n1_worry'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cae224e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling 92 missing values by mode\n",
    "vaccine['h1n1_worry'].fillna(vaccine['h1n1_worry'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0889d3c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  1., nan])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['h1n1_awareness'].unique()\n",
    "# 0=No knowledge, 1=little knowledge, 2=good knowledge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a6ee5bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'h1n1_awareness', data = vaccine, hue = 'h1n1_vaccine')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fffbbe15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['h1n1_awareness'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "38abbf65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling 192 missing values by mode\n",
    "vaccine['h1n1_awareness'].fillna(vaccine['h1n1_awareness'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8df2c9e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['antiviral_medication'].unique()\n",
    "# 0=no, 1=yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1727c0be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['antiviral_medication'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', figsize = [5,5], explode = [0,0.2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "68e8d7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vaccine['antiviral_medication'].mode())\n",
    "# missing 71 missing values by mode\n",
    "vaccine['antiviral_medication'].fillna(vaccine['antiviral_medication'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "00dd606a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['contact_avoidance'].unique()\n",
    "# 0=no, 1=yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec526643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    19228\n",
       "0.0     7271\n",
       "Name: contact_avoidance, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['contact_avoidance'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ebcd5bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = 'contact_avoidance', data = vaccine, color = 'green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9cfcd664",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 208 missing values by mode\n",
    "vaccine['contact_avoidance'].fillna(vaccine['contact_avoidance'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "35bac754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['bought_face_mask'].unique()\n",
    "# 0=no, 1=yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1c93f9b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['bought_face_mask'].value_counts().plot(kind = 'bar', color = 'orange')\n",
    "plt.xlabel('bought_face_mask')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db53a08d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 19 missing values by mode\n",
    "vaccine['bought_face_mask'].fillna(vaccine['bought_face_mask'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d322475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['wash_hands_frequently'].unique()\n",
    "# 0-Washes hands frequently, 1=uses hand sanitizer -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "801ea6ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'wash_hands_frequently', data = vaccine)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2ba6b86f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 42 missing values by mode\n",
    "vaccine['wash_hands_frequently'].fillna(vaccine['wash_hands_frequently'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "40f7d638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['avoid_large_gatherings'].unique()\n",
    "# 0=no, 1=yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eb3fac04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    17073\n",
       "1.0     9547\n",
       "Name: avoid_large_gatherings, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['avoid_large_gatherings'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0aa68391",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 87 missing values by mode\n",
    "vaccine['avoid_large_gatherings'].fillna(vaccine['avoid_large_gatherings'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8791e18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0., nan])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['reduced_outside_home_cont'].unique()\n",
    "# 0=no, 1=yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fb268db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    17644\n",
       "1.0     8981\n",
       "Name: reduced_outside_home_cont, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['reduced_outside_home_cont'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e4569795",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 82 missing values by mode\n",
    "vaccine['reduced_outside_home_cont'].fillna(vaccine['reduced_outside_home_cont'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7b46e340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0., nan])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['avoid_touch_face'].unique()\n",
    "# 0=no, 1=yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c4d388ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    18001\n",
       "0.0     8578\n",
       "Name: avoid_touch_face, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['avoid_touch_face'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4b80096e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 128 missing values by mode\n",
    "vaccine['avoid_touch_face'].fillna(vaccine['avoid_touch_face'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "08a4f629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0., nan,  1.])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 0., nan,  1.])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vaccine['dr_recc_h1n1_vacc'].unique(), vaccine['dr_recc_seasonal_vacc'].unique())\n",
    "# 0=no, 1=yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "65163e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize = [7,6], sharey = True )\n",
    "sns.countplot(x = 'dr_recc_h1n1_vacc', data = vaccine, color = 'maroon', ax=ax[0])\n",
    "sns.countplot(x = 'dr_recc_seasonal_vacc', data = vaccine, color = 'navy', ax=ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "be61a5fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 2160 missing values by mode\n",
    "vaccine['dr_recc_h1n1_vacc'].fillna(vaccine['dr_recc_h1n1_vacc'].mode()[0], inplace = True)\n",
    "vaccine['dr_recc_seasonal_vacc'].fillna(vaccine['dr_recc_seasonal_vacc'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ae700e03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    18446\n",
       "1.0     7290\n",
       "Name: chronic_medic_condition, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['chronic_medic_condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6df408b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 971 missing values by mode\n",
    "vaccine['chronic_medic_condition'].fillna(vaccine['chronic_medic_condition'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bcc08920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['cont_child_undr_6_mnths'].value_counts().plot(kind = 'barh', cmap = 'rainbow', edgecolor = 'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f5fd3f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 820 missing values by mode\n",
    "vaccine['cont_child_undr_6_mnths'].fillna(vaccine['cont_child_undr_6_mnths'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "66511435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    23004\n",
       "1.0     2899\n",
       "Name: is_health_worker, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['is_health_worker'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "74cf7371",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 804 missing values by mode\n",
    "vaccine['is_health_worker'].fillna(vaccine['is_health_worker'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d76ca4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1., nan,  0.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['has_health_insur'].unique()\n",
    "# 0=no, 1=yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3f0c2f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['has_health_insur'].value_counts().plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5a0580",
   "metadata": {},
   "source": [
    "As no of missing values is 12274 which is almost equal to no of persons who have health insurance. Therefore, it is not a good\n",
    "option to replace missing values by mode as there is no surity whether they have insurance. Hence, putting them in\n",
    "another category named '2.0' which means 'data unknown'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1211f7c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccine['has_health_insur'].fillna(2.0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1409e451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['has_health_insur'].value_counts().plot(kind = 'barh', color = 'b', edgecolor = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5aea99fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.,  5.,  4.,  2.,  1., nan])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 2.,  4.,  5.,  3.,  1., nan])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vaccine['is_h1n1_vacc_effective'].unique(), vaccine['is_seas_vacc_effective'].unique())\n",
    "# 1=Thinks not effective at all, 2=Thinks it is not very effective, 3=Doesn't know if it is effective or not, \n",
    "# 4=Thinks it is somewhat effective, 5=Thinks it is highly effective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bab719bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#CAFF70', '#FF1493', '#00BFFF', '#FFD700', '#836FFF']\n",
    "colors1 = ['#FF7F24', '#FFB90F', '#A2CD5A', '#BF3EFF', '#EEAEEE']\n",
    "fig, (ax1,ax2) = plt.subplots(1,2, figsize = [10,10])\n",
    "\n",
    "ax1.pie(vaccine['is_h1n1_vacc_effective'].value_counts(), labels = vaccine['is_h1n1_vacc_effective'].value_counts().index , \n",
    "        autopct = '%0.2f%%', explode= [0.1,0,0,0,0], colors = colors, shadow = True)\n",
    "\n",
    "ax2.pie(vaccine['is_seas_vacc_effective'].value_counts(), labels = vaccine['is_seas_vacc_effective'].value_counts().index , \n",
    "        autopct = '%0.2f%%', explode= [0.1,0,0,0,0], colors = colors1, shadow = True)\n",
    "\n",
    "ax1.set_title('is_h1n1_vacc_effective')\n",
    "ax2.set_title('is_seas_vacc_effective')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2f348752",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 391 and 462 missing values respectively by mode\n",
    "vaccine['is_h1n1_vacc_effective'].fillna(vaccine['is_h1n1_vacc_effective'].mode()[0], inplace = True)\n",
    "vaccine['is_seas_vacc_effective'].fillna(vaccine['is_seas_vacc_effective'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d33e6d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  4.,  3.,  2.,  5., nan])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  4.,  3.,  5., nan])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vaccine['is_h1n1_risky'].unique(), vaccine['is_seas_risky'].unique())\n",
    "# 1=Thinks it is not very low risk, 2=Thinks it is somewhat low risk, 3=don’t know if it is risky or not, \n",
    "# 4=Thinks it is a somewhat high risk, 5=Thinks it is very highly risky"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9a0c6e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize = [10,10])\n",
    "vaccine['is_h1n1_risky'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', explode = [0.05,0,0,0,0], cmap = 'RdYlGn', ax = ax[0])\n",
    "vaccine['is_seas_risky'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', explode = [0.05,0,0,0,0], cmap = 'Paired', ax = ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9bd6a064",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 388 and 514 missing values respectively by mode\n",
    "vaccine['is_h1n1_risky'].fillna(vaccine['is_h1n1_risky'].mode()[0], inplace = True)\n",
    "vaccine['is_seas_risky'].fillna(vaccine['is_seas_risky'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ab732e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  4.,  1.,  5.,  3., nan])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 2.,  4.,  1.,  5., nan,  3.])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vaccine['sick_from_h1n1_vacc'].unique(), vaccine['sick_from_seas_vacc'].unique())\n",
    "# 1=Respondent not worried at all, 2=Respondent is not very worried, 3=Doesn't know, 4=Respondent is somewhat worried, \n",
    "# 5Respondent is very worried"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "07f4ec01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize = [10,10])\n",
    "vaccine['sick_from_h1n1_vacc'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', explode = [0.05,0,0,0,0], cmap = 'Spectral', ax = ax[0])\n",
    "vaccine['sick_from_seas_vacc'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', explode = [0.05,0,0,0,0], cmap = 'twilight', ax = ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "27b8378f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 395 and 537 missing values respectively by mode\n",
    "vaccine['sick_from_h1n1_vacc'].fillna(vaccine['sick_from_h1n1_vacc'].mode()[0], inplace = True)\n",
    "vaccine['sick_from_seas_vacc'].fillna(vaccine['sick_from_seas_vacc'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1e246084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oXbs2zzzzDA0bNuSSSy4hKioq+J9zwIABbNq0CeccLVq0ICYmJts+d+zYkYceeognnvjjeD3yyCN0796dkSNHpjvzvueee9i4cSPR0dGUKlWKe++9l759+5KQkMADDzzAwYMHKVOmDPPnz6ds2bKZtnXPPfewdetW6tSpg3OO8PBwZs2aleMxDQ8PZ+LEidx6663Bj/RDhw6lVq1aWW63efPmPP/888TGxmaZJG+55RY6deoUPPuHwJeYvXv3ZujQoRw9epQuXboQExPDli1bshySyO44TJgwgU6dOnHs2DHq169Pr16BC5YGDx7M3XffzbPPPkvDhg1z3F+ASpUqcc011xAZGcnf/vY3XnzxRT799FNuuOGGE66bG6pHW4hUjzZreUm0q1evZuTIkUyePDnf+yEF7/bbb+ell14iPDy8sLtCkyZNeP/996lQoUKmeXmtR6tnhkmxFhcXR/PmzUlNTSUsLKywuyOn6K233irsLgCBGxb+7//+L8skezKUaKXYu+uuu064zO7du2nRokWm+IIFC7L80upUffzxxzz66KPpYhEREcycOTPft1UY2zvdhYeH065du3xrT4lWzgiVKlXK07fcpyo+Pj5P3/QXt+1J3qiojIiIz5RoRUR8pkQrIuIzJVoREZ/pyzDJN7+uvj1f2/tTyxPf0VPc69Fu27Ytxz5v3bqVNm3asG7dugLuWd5krEk7Z84cVqxYwVNPPVXIPSsadEYrxVpxrkd77NixYtfn7GSsSXvDDTcwe/bsYBnJM50SrRRrxa0e7cSJE+nUqRM33ngjrVq1ylOfv/vuO+Li4li2bBl16tQJxjdt2kTdunXJyY4dO2jfvj0xMTHExMTwxRdfADBy5EgiIyOJjIzkX//6V6bjCDBixAiGDBkCBOrnPvroozRo0IBatWqxePFijhw5kqkmrZnRrFkz5syZk2O/zhQaOpBiqzjWowVYunQpa9asoWLFimzdujXHPqeV8NuwYQNdunRhwoQJxMbGUr58eZKSkoiNjWXChAn06NEjx20++OCDNG3alJkzZ5Kamsr+/ftJTExkwoQJLFu2DOccDRs2pGnTpie8G+rYsWMsX76cuXPn8tRTTzF//vxMNWkhUM918eLFdO7cOcf2zgQ6o5Vi60T1aJ999lmGDx/ODz/8kG25u6ioKObPn8+jjz7K4sWLKV++PBs2bAjWo42NjWXo0KEkJweKza1bt47GjRsTFRXFlClTglW90urRvv7669km9TQtW7akYsWKue7zzp07adu2LW+99RaxsbFAoMjKhAkTSE1NJSEhgdtuuy3HbX7yySf07t0bCJQGLF++PEuWLKF9+/ace+65lC1blg4dOrB48eIc2wGC9Xbr1q2b7g9FRvlZz7W4U6KVYutE9Whnz55NmTJliI+Pz7Z6f1o92qioKAYNGsTTTz+Nc47atWuTlJREUlISa9euZd68eUCgHu2rr77K2rVrGTx4cHD7Y8aMYejQofz000/Exsaye/fubPt97rnn5qnP5cuX56KLLkr3/Kqbb76ZDz/8kDlz5lC3bt2Tuo04u4JSobVcIft6rjnVck1bL7/quRZ3SrRSbBXXerTZya7PZ511FrNmzWLSpEm8/fbbAJx99tnEx8fTu3dv7rzzzmAbgwYNyrK+QYsWLRg9ejQQeGLE3r17adKkCbNmzeLAgQP8/vvvzJw5k8aNG3PBBRfwyy+/sHv3bg4fPpyrcVa/67kWdxqjlXxTIS5/Ki+d7vVos5NVn/fu3QsEzoLnzJlDy5YtOffcc2nbti1du3ZlxowZtGrVKt3+3HTTTZnafvnll+nZsyfjxo0jLCyM0aNHc/XVV9OjRw8aNGgABIYj4uLiAHjyySdp2LAhERERXHHFFSfse8aatLfccguffvopzz33XJ6Pw+lI9WgLkerRZk31aHNnxIgRpKSk8MwzzwRj8fHxfPzxx4XYq4AdO3Zw2223sWDBgsLuii9Uj1bOKGdqPdr27duzZcuWTGPPRSHJAvz444/885//LOxuFBlKtFLsnYn1aIt6ndm0J+VKgBKtnBFO93q0UrTpqgMREZ8p0YqI+EyJVkTEZ0q0IiI+05dhkm+2/XdrvrYXcedfTrhMaD3a7C7vuv7663n77bezrYvQo0cP2rRpQ8eOHU+4vcWLF9OrVy9KlSrF0qVLdYtpMbBw4ULOOussGjVqBMCrr77Kueeem+6OOr/pjFaKtdB6tNmZO3dutkk2r6ZMmUL//v1JSkpKl2RPVEhGCs/ChQuDZSEhcDngqFGjCrQPviVaMxtvZr+Y2bqQ2BAz+9nMkrzX9SHzBpnZZjPbYGbxIfG6ZrbWmzfKzMyLlzazBC++zMxq+LUvUnSF1qPdvn07TZo0ITY2lsjIyGAlqho1arBr1y4AJk2aRHR0NDExMdxxxx2Z2nviiSfo0aNHuqIqad544w2mTp3K008/TdeuXVm4cCHNmzfntttuIyoqikOHDnHnnXcSFRVFXFwcn376KRCoQduuXTtuvPFGIiIiePXVVxk5ciRxcXFcddVV7NmzJ9v9GzVqFFdeeSXR0dF06dIFCNTQveuuu6hfvz5xcXG8//77QKCObOPGjalTpw516tQJJpfsjktGo0eP5pFHHgm+nzhxIg888AAA7dq1o27dutSuXZuxY8cGl/noo4+oU6cOMTExweuU9+/fHzwO0dHRTJ+e85MyNm/ezHXXXUdMTAx16tRhy5YtOOcYMGAAkZGRREVFBesEL1y4kDZt2gTX7du3LxMnTgQC/86DBw+mTp06REVFsX79erZu3cqYMWN46aWXiI2NZfHixZxzzjnUqFGD5cuX59iv/OTn0MFE4FVgUob4S865EaEBM7sS6ALUBi4E5ptZLedcKjAa6Al8CcwFWgMfAncDvzrnLjOzLsBw4Bb/dkeKmoz1aN9++23i4+N57LHHSE1NzVTd/+uvv2bYsGF8/vnnVK5cOVOCe+SRR0hJSWHChAl4f8/Tueeee1iyZElwmGHhwoUsX76cdevWEREREbwTau3ataxfv55WrVqxceNGIFBecfXq1Rw6dIjLLruM4cOHs3r1ah5++GEmTZpEv379stzH559/nu+//57SpUsH69wOGzaMa6+9lvHjx/Pbb7/RoEEDrrvuOqpUqcL//vc/zj77bDZt2sStt97KypUrT3hc0nTs2JGrr76aF154ASBYhxcCnxwqVqzIwYMHqV+/PjfffDPHjx/n3nvvZdGiRURERASP5zPPPEP58uVZu3YtAL/++mtO/4x07dqVgQMH0r59ew4dOsTx48eZMWMGSUlJfPXVV+zatYv69evnWFA9TeXKlVm1ahWvvfYaI0aM4I033qBXr16ULVuW/v37B5dLq5WbVufBb74lWufcojycZbYF3nXOHQa+N7PNQAMz2wqUc84tBTCzSUA7Aom2LTDEW/894FUzM3emFW84g2WsR1u/fn3uuusujh49Srt27YK1W9N88skndOzYkcqVKwOkqwmbViwm9GwtNxo0aEBERAQAS5YsCZ4BXnHFFVxyySXBRNu8eXPOO+88zjvvPMqXL8+NN94IBOrhZldZDCA6OpquXbvSrl072rVrB8C8efOYPXs2I0YEzlcOHTrEjz/+yIUXXkjfvn1JSkoiLCwsuO0THZc04eHhXHrppXz55ZfUrFmTDRs2cM011wCBM+u0u9F++uknNm3axM6dO2nSpElw/9OO5/z583n33XeD7eZUSHzfvn38/PPPtG/fHghUJUs7lrfeeithYWFccMEFNG3alBUrVlCuXLls24L0tXJnzJiR7XJVqlRh/fr1ObaVnwpjjLavma3xhhbS/gWqAaF15ZK9WDVvOmM83TrOuWNACpD/91JKkZWxHm2TJk1YtGgR1apV44477mDSpPQfppxzWZ6pQiAZJSYm5vgxPiuhtWVz+hufVsMVoESJEsH3JUqUyLGm6wcffECfPn1ITEykbt26HDt2DOcc06dPD9bL/fHHH/nLX/7CSy+9xAUXXMBXX33FypUrOXLkCHDi4xLqlltuYerUqUyfPp327dtjZixcuJD58+ezdOlSvvrqK+Li4jh06FC2xzOn45zVsnmJF9dauQWdaEcDfwZige1AWtWJrP5VXA7xnNbJxMx6mtlKM1u5c+fOPHVYiq6M9Wh/+OEHqlSpwr333svdd9/NqlWr0i3fokULpk6dGizKHZpUW7duzcCBA7nhhhsy1VXNrSZNmgRr1G7cuJEff/yRyy+//KTaAjh+/Dg//fQTzZs354UXXuC3335j//79xMfH88orrwST0erVq4HAY3iqVq1KiRIlmDx5cvALuhMdl1AdOnRg1qxZvPPOO9xyyy3BditUqMA555zD+vXr+fLLL4HAEyE+++wzvv/+e+CP49mqVat0j7RJGzro1q1bpnHRcuXKUb16dWbNmgXA4cOHOXDgAE2aNCEhIYHU1FR27tzJokWLaNCgAZdccgnffPMNhw8fJiUlJVfVwYpCrdwCvbzLObcjbdrMXgfSKgonAxeFLFod2ObFq2cRD10n2cxKAuWBLE9HnHNjgbEQKJN4yjsiWbrwxhr50s7J1qNduHAhL774IqVKlaJs2bKZztxq167NY489RtOmTQkLCyMuLi74RQpAp06d2LdvHzfddBNz587N8xnP/fffT69evYiKiqJkyZJMnDgx3ZlsXqWmpnL77beTkpKCc46HH36Y888/nyeeeIJ+/foRHR2Nc44aNWowZ84c7r//fm6++WamTZtG8+bNg2fbJzouoSpUqMCVV17JN998Exy/bN26NWPGjCE6OprLL7+cq666CggMNYwdO5YOHTpw/Pjx4Bjx448/Tp8+fYiMjCQsLIzBgwfToUMH1qxZQ9WqVTNtc/Lkydx33308+eSTlCpVimnTptG+fXuWLl1KTEwMZsYLL7zAn/70JwA6d+5MdHQ0NWvWDNbPzcmNN95Ix44def/993nllVdo3Lgxn3/+OYMHD87zv8nJ8rUerTdGO8c5F+m9r+qc2+5NPww0dM51MbPawNtAAwJfhi0AajrnUs1sBfAAsIzAl2GvOOfmmlkfIMo518v7MqyDc+6ET4FTPdpTp3q0kld79+7l7rvvZtq0aYXdlXz5nSky9WjN7B2gGVDZzJKBwUAzM4sl8BF/K3AfgHPuazObCnwDHAP6eFccAPQmcAVDGQJfgn3oxccBk70vzvYQuGpBzjBnaj3a4qZcuXJFIslC4EvU0GLpBcHPqw5uzSI8LoflhwHDsoivBDINpjjnDgGdTqWPcnrITT3avGrfvn1w7DHN8OHDfSt92KdPn3QPXwR46KGHfLt7qWHDhhw+fDhdbPLkyURFRfmyvaKkZcuWBb5N3YIrkoWCLqz973//u0C3t2zZsgLd3plOt+DKKdFly3KmOZnfeSVaOWklDhq/7vtNyVbOGM45du/eHbyxIrc0dCAn7eytYexhN7vK7MrXdkvuLJWv7Ynkp7PPPpvq1aufeMEQSrRy0kqkGudsyf9fodyURxQpTjR0ICLiMyVaERGfKdGKiPhMiVZExGdKtCIiPlOiFRHxmRKtiIjPlGhFRHymRCsi4jMlWhERnynRioj4TIlWRMRnSrQiIj5TohUR8ZkSrYiIz5RoRUR8pkQrIuIzJVoREZ8p0YqI+EyJVkTEZ0q0IiI+8y3Rmtl4M/vFzNaFxF40s/VmtsbMZprZ+V68hpkdNLMk7zUmZJ26ZrbWzDab2SgzMy9e2swSvPgyM6vh176IiJwKP89oJwKtM8T+B0Q656KBjcCgkHlbnHOx3qtXSHw00BOo6b3S2rwb+NU5dxnwEjA8/3dBROTU+ZZonXOLgD0ZYvOcc8e8t18C1XNqw8yqAuWcc0udcw6YBLTzZrcF3vSm3wNapJ3tiogUJYU5RnsX8GHI+wgzW21mn5lZYy9WDUgOWSbZi6XN+wnAS94pQCV/uywiknclC2OjZvYYcAyY4oW2Axc753abWV1glpnVBrI6Q3VpzeQwL+P2ehIYfuDiiy8+la6LiORZgZ/Rmll3oA3Q1RsOwDl32Dm325tOBLYAtQicwYYOL1QHtnnTycBFXpslgfJkGKpI45wb65yr55yrFx4env87JSKSgwJNtGbWGngUuMk5dyAkHm5mYd70pQS+9PrOObcd2GdmV3njr92A973VZgPdvemOwCdpiVtEpCjxbejAzN4BmgGVzSwZGEzgKoPSwP+8762+9K4waAI8bWbHgFSgl3Mu7ey0N4ErGMoQGNNNG9cdB0w2s80EzmS7+LUvIiKnwrdE65y7NYvwuGyWnQ5Mz2beSiAyi/ghoNOp9FFEpCDozjAREZ8p0YqI+EyJVkTEZ0q0IiI+U6IVEfGZEq2IiM+UaEVEfKZEKyLiMyVaERGfKdGKiPhMiVZExGdKtCIiPlOiFRHxmRKtiIjPlGhFRHymRCsi4jMlWhERnynRioj4TIlWRMRnSrQiIj5TohUR8VmuEq2ZLchNTEREMsvxceNmdjZwDlDZzCoA5s0qB1zoc99ERE4LOSZa4D6gH4GkmsgfiXYv8G//uiUicvrIMdE6514GXjazB5xzrxRQn0RETisnOqMFwDn3ipk1AmqEruOcm+RTv0REThu5/TJsMjAC+CtQ33vVO8E6483sFzNbFxKraGb/M7NN3s8KIfMGmdlmM9tgZvEh8bpmttabN8rMzIuXNrMEL77MzGrkZcdFRApKbi/vqgdc45y73zn3gPd68ATrTARaZ4gNBBY452oCC7z3mNmVQBegtrfOa2YW5q0zGugJ1PReaW3eDfzqnLsMeAkYnst9EREpULlNtOuAP+WlYefcImBPhnBb4E1v+k2gXUj8XefcYefc98BmoIGZVQXKOeeWOuccMCnDOmltvQe0SDvbFREpSnI1RgtUBr4xs+XA4bSgc+6mPG7vAufcdm/d7WZWxYtXA74MWS7Zix31pjPG09b5yWvrmJmlAJWAXXnsk4iIr3KbaIf42Qn+uGwslMshntM6mRs360lg+IGLL774ZPonInLScnvVwWf5tL0dZlbVO5utCvzixZOBi0KWqw5s8+LVs4iHrpNsZiWB8mQeqkjr/1hgLEC9evWyTMYiIn7J7VUH+8xsr/c6ZGapZrb3JLY3G+juTXcH3g+Jd/GuJIgg8KXXcm+YYZ+ZXeWNv3bLsE5aWx2BT7xxXBGRIiW3Z7Tnhb43s3ZAg5zWMbN3gGYEbt9NBgYDzwNTzexu4Eegk9f+12Y2FfgGOAb0cc6lek31JnAFQxngQ+8FMA6YbGabCZzJdsnNvoiIFLTcjtGm45ybZWYDT7DMrdnMapHN8sOAYVnEVwKRWcQP4SVqEZGiLFeJ1sw6hLwtQeC6Wn1MFxHJhdye0d4YMn0M2ErgOlYRETmB3I7R3ul3R0RETle5veqgupnN9GoX7DCz6WZW/cRriohIbm/BnUDgcqoLCdyR9V8vJiIiJ5DbRBvunJvgnDvmvSYC4T72S0TktJHbRLvLzG43szDvdTuw28+OiYicLnKbaO8COgP/D9hO4E4sfUEmIpILub286xmgu3PuVwgU8CZQCPwuvzomInK6yO0ZbXRakgVwzu0B4vzpkojI6SW3ibZEhsfOVOQkb98VETnT5DZZ/hP4wszeI3DrbWeyqEsgIiKZ5fbOsElmthK4lkDB7Q7OuW987ZmIyGki1x//vcSq5Coikke5HaMVEZGTpEQrIuIzJVoREZ8p0YqI+EyJVkTEZ0q0IiI+U6IVEfGZbqMVKQYa/fUfvrY/Y/AG39o+mDzUt7Yj7vyLb23nJ53Rioj4TIlWRMRnSrQiIj5TohUR8VmBJ1ozu9zMkkJee82sn5kNMbOfQ+LXh6wzyMw2m9kGM4sPidc1s7XevFFmZgW9PyIiJ1LgidY5t8E5F+uciwXqAgeAmd7sl9LmOefmApjZlUAXoDbQGnjNzMK85UcDPYGa3qt1we2JiEjuFPbQQQtgi3PuhxyWaQu865w77Jz7HtgMNDCzqkA559xS55wDJgHtfO+xiEgeFXai7QK8E/K+r5mtMbPxIY/OqQb8FLJMsher5k1njIuIFCmFlmjN7CzgJmCaFxoN/BmIJfBI83+mLZrF6i6HeFbb6mlmK81s5c6dO0+l2yIieVaYZ7R/A1Y553YAOOd2OOdSnXPHgdeBBt5yycBFIetVB7Z58epZxDNxzo11ztVzztULDw/P590QEclZYSbaWwkZNvDGXNO0B9Z507OBLmZW2swiCHzptdw5tx3YZ2ZXeVcbdAPeL5iui4jkXqHUOjCzc4CWwH0h4RfMLJbAx/+tafOcc1+b2VQCzys7BvRxzqV66/QGJgJlgA+9l4hIkVIoidY5dwColCF2Rw7LDyOLx5s751YCkfneQRGRfFTYVx2IiJz2lGhFRHymRCsi4jMlWhERnynRioj4TIlWRMRnSrQiIj5TohUR8ZkSrYiIz5RoRUR8pkQrIuIzJVoREZ8p0YqI+EyJVkTEZ0q0IiI+U6IVEfGZEq2IiM+UaEVEfKZEKyLiMyVaERGfKdGKiPhMiVZExGdKtCIiPlOiFRHxmRKtiIjPCiXRmtlWM1trZklmttKLVTSz/5nZJu9nhZDlB5nZZjPbYGbxIfG6XjubzWyUmVlh7I+ISE4K84y2uXMu1jlXz3s/EFjgnKsJLPDeY2ZXAl2A2kBr4DUzC/PWGQ30BGp6r9YF2H8RkVwpSkMHbYE3vek3gXYh8Xedc4edc98Dm4EGZlYVKOecW+qcc8CkkHVERIqMwkq0DphnZolm1tOLXeCc2w7g/azixasBP4Wsm+zFqnnTGeMiIkVKyULa7jXOuW1mVgX4n5mtz2HZrMZdXQ7xzA0EknlPgIsvvjivfRUROSWFckbrnNvm/fwFmAk0AHZ4wwF4P3/xFk8GLgpZvTqwzYtXzyKe1fbGOufqOefqhYeH5+euiIicUIEnWjM718zOS5sGWgHrgNlAd2+x7sD73vRsoIuZlTazCAJfei33hhf2mdlV3tUG3ULWEREpMgpj6OACYKZ3JVZJ4G3n3EdmtgKYamZ3Az8CnQCcc1+b2VTgG+AY0Mc5l+q11RuYCJQBPvReIiJFSoEnWufcd0BMFvHdQIts1hkGDMsivhKIzO8+iojkp6J0eZeIyGlJiVZExGdKtCIiPlOiFRHxmRKtiIjPlGhFRHymRCsi4jMlWhERnynRioj4TIlWRMRnSrQiIj5TohUR8ZkSrYiIz5RoRUR8pkQrIuIzJVoREZ8p0YqI+EyJVkTEZ0q0IiI+U6IVEfGZEq2IiM+UaEVEfKZEKyLiMyVaERGfKdGKiPhMiVZExGcFnmjN7CIz+9TMvjWzr83sIS8+xMx+NrMk73V9yDqDzGyzmW0ws/iQeF0zW+vNG2VmVtD7IyJyIiULYZvHgL8751aZ2XlAopn9z5v3knNuROjCZnYl0AWoDVwIzDezWs65VGA00BP4EpgLtAY+LKD9EBHJlQI/o3XObXfOrfKm9wHfAtVyWKUt8K5z7rBz7ntgM9DAzKoC5ZxzS51zDpgEtPO39yIieVeoY7RmVgOIA5Z5ob5mtsbMxptZBS9WDfgpZLVkL1bNm84YFxEpUgot0ZpZWWA60M85t5fAMMCfgVhgO/DPtEWzWN3lEM9qWz3NbKWZrdy5c+epdl1EJE8KJdGaWSkCSXaKc24GgHNuh3Mu1Tl3HHgdaOAtngxcFLJ6dWCbF6+eRTwT59xY51w951y98PDw/N0ZEZETKIyrDgwYB3zrnBsZEq8aslh7YJ03PRvoYmalzSwCqAksd85tB/aZ2VVem92A9wtkJ0RE8qAwrjq4BrgDWGtmSV7sH8CtZhZL4OP/VuA+AOfc12Y2FfiGwBULfbwrDgB6AxOBMgSuNtAVByJS5BR4onXOLSHr8dW5OawzDBiWRXwlEJl/vRMRyX+6M0xExGdKtCIiPlOiFRHxmRKtiIjPlGhFRHymRCsi4jMlWhERnynRioj4TIlWRMRnSrQiIj5TohUR8ZkSrYiIz5RoRUR8pkQrIuIzJVoREZ8p0YqI+EyJVkTEZ0q0IiI+U6IVEfGZEq2IiM+UaEVEfKZEKyLiMyVaERGfKdGKiPhMiVZExGdKtCIiPiv2idbMWpvZBjPbbGYDC7s/IiIZFetEa2ZhwL+BvwFXArea2ZWF2ysRkfSKdaIFGgCbnXPfOeeOAO8CbQu5TyIi6RT3RFsN+CnkfbIXExEpMkoWdgdOkWURc5kWMusJ9PTe7jezDb72qgio2irPq1QGduVu0Rl5bjxP7vK3ecksj78vefhdAV9/X4rW78ol2c0o7ok2Gbgo5H11YFvGhZxzY4GxBdWp4sjMVjrn6hV2P6To0+9K3hX3oYMVQE0zizCzs4AuwOxC7pOISDrF+ozWOXfMzPoCHwNhwHjn3NeF3C0RkXSKdaIFcM7NBeYWdj9OAxpakdzS70oemXOZvjsSEZF8VNzHaEVEijwlWhERnynRioj4rNh/GSYnx8wuIHAXnQO2Oed2FHKXRE5b+jLsDGNmscAYoDzwsxeuDvwG3O+cW1U4PZOiTH+YT40S7RnGzJKA+5xzyzLErwL+45yLKZSOSZGkP8z5Q4n2DGNmm5xzNbOZt9k5d1lB90mKLv1hzh8aoz3zfGhmHwCT+KPy2UVAN+CjQuuVFFXnZkyyAM65L83s3MLoUHGkM9ozkJn9jUDd3moEKqAlA7O9u+xEgsxsFPBnsv7D/L1zrm9h9a04UaIVkRzpD/OpU6KVIDPr6ZWUFJF8pBsWJFRWhdRFsuQV1Jdc0JdhZyAzu4I/Pgo6AsXSZzvn/lOoHZPiRn+Yc0lntGcYM3uUwEMsDVhOoHi6Ae/oce2SR0cKuwPFhcZozzBmthGo7Zw7miF+FvB1dtfYimRkZj865y4u7H4UBxo6OPMcBy4EfsgQr+rNEwkyszXZzQIuKMi+FGdKtGeefsACM9vEH9dFXgxcBuiaSMnoAiAe+DVD3IAvCr47xZMS7RnGOfeRmdUCGpD+usgVzrnUQu2cFEVzgLLOuaSMM8xsYYH3ppjSGK2IiM901YGIiM+UaEVEfKZEKyLiMyVayTdmNsTM+hd2P4obM2tmZnMKux/iHyVa8ZWZnfDKltwsI1KcKdHKKTGzx8xsg5nNBy73YgvN7Fkz+wx4KJv1JprZSDP7FBhuZn82s4/MLNHMFnv1GDCzC8xsppl95b0aefFuZrbGi03OoX+dzGydt9wiLxZmZi+a2Qqvjfu8eFkzW2Bmq8xsrZm19eLnmtkHXhvrzOwWL97CzFZ7y443s9JefKuZPRXSTtq+NDCzL7x1vjCzy3NxfEt47Z0fEtvsHZcbzWyZ195877leafsxwdv2GjO72Yu39vr0lZktONG2JR855/TS66ReQF1gLXAOUA7YDPQHFgKvnWDdiQSu0Qzz3i8AanrTDYFPvOkEoJ83HUbg2VW1gQ1AZS9eMYftrAWqedPnez97Ao9706WBlUAEgevKy3nxyt7+GHAz8HpIm+WBswnc8FHLi00K6edW4AFv+n7gDW+6HFDSm74OmO5NNwPm5LAPLwN3hhyb+d50Bf64RPMe4J/e9HDgXyHrVwDCvf5GnOiY6ZX/L31kk1PRGJjpnDsAYGazQ+Yl5GL9ac65VDMrCzQCppkFC0KV9n5eS6CaPy5wQ0WKmXUD3nPO7fLie3LYxufARDObCszwYq2AaDPr6L0vD9QkcOPGs2bWhMDtyNUI3Bm1FhhhZsMJJMTFZhZD4AkDG7023gT6AP/y3qdtKxHoELKdN82sJoGqaaVycYwgcCyfBCYAXfjj2FYHEsysKnAW8L0Xv85bDgDn3K9mdiOwyDn3vRfL6ZhJPlOilVOV3R0vv+di3bRlSgC/Oedic7lNy2G76TjneplZQ+AGIMl7qqsROOP8OF2jZj0InPnVdc4dNbOtwNnOuY1mVhe4HnjOzOYBoX9UsnLY+5nKH//PngE+dc61N7MaBM78c2MpcJmZhQPtgKFe/BVgpHNutpk1A4ak7QqZj0+uj5nkP43RyqlYBLQ3szJmdh5w48k04pzbC3xvZp0ALCDt6aoLgN5ePMzMynmxzmZWyYtXzK5tM/uzc26Zc+5JYBeB5119DPQ2s1LeMrUs8KDB8sAvXpJtDlzizb8QOOCcewsYAdQB1gM1zCztqcF3AJ+dYFdDH9ndIxeHBgAX+Kw/ExgJfOuc251Fe91DVplHSN0KM6tAIFk3NbMIL5btMZP8p0QrJ805t4rAx9gkYDqw+BSa6wrcbWZfAV8TKEwOgS/TmpvZWgIfw2s7574GhgGfecuPzKHdF70vhdYR+MPwFfAG8A2wyov/h8BZ5xSgnpmt9Pqz3msjClhugUdvPwYMdc4dAu4kMNyxlsBQw5gT7OMLBM6IPycw3pwXCcDtpB+SGeJtfzGBPyJphgIV0r4EBJo753YSGJue4cVyM7Qj+US1DkREfKYzWhERn+nLMPGVmT0GdMoQnuacG1Yct+MnM7uTzNcdf+6c61MY/ZH8o6EDERGfaehARMRnSrQiIj5TohUR8ZkSrYiIz5RoRUR89v8BEu8H+L8qMVAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine.groupby(['dr_recc_seasonal_vacc']).agg({'is_seas_vacc_effective' : ['count'],\n",
    "                                      'is_seas_risky' :['count'],\n",
    "                                      'sick_from_seas_vacc' : ['count']}).plot(kind = 'bar', cmap = 'tab20b', figsize = [5,5])\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "67b1599b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine.groupby(['dr_recc_h1n1_vacc']).agg({'is_h1n1_vacc_effective' : ['count'],\n",
    "                                      'is_h1n1_risky' :['count'],\n",
    "                                      'sick_from_h1n1_vacc' : ['count']}).plot(kind = 'bar', cmap = 'tab20c', figsize = [5,5])\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6466d5ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['< 12 Years', '12 Years', 'College Graduate', 'Some College', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['qualification'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "800e072f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College Graduate    10097\n",
       "Some College         7043\n",
       "12 Years             5797\n",
       "< 12 Years           2363\n",
       "Name: qualification, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['qualification'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "358a1c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 1407 missing values by mode\n",
    "vaccine['qualification'].fillna(vaccine['qualification'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f130b596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['sex'].value_counts().plot(kind = 'barh', color = 'c', edgecolor = 'r')\n",
    "# no nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f7fe098d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Below Poverty', '<= $75,000, Above Poverty', '> $75,000', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['income_level'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f9763371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='income_level'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['income_level'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', cmap = 'magma',\n",
    "                                            explode = [0,0.1,0], figsize = [5,5], shadow = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f01dcd37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# no of missing values is 4423, better to create a new category as 'Unknown' as this will not skew the data and info will not be lost\n",
    "vaccine['income_level'].fillna('Unknown', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1a713477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Not Married', 'Married', nan], dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['marital_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "05e9cf6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married        13555\n",
       "Not Married    11744\n",
       "Name: marital_status, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['marital_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "92c100bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 1408 missing values by mode\n",
    "vaccine['marital_status'].fillna(vaccine['marital_status'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ed2438e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Own', 'Rent', nan], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['housing_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "285a3b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['housing_status'].value_counts().plot(kind = 'bar', color = 'b', edgecolor = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c8a89985",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 2402 missing values by mode\n",
    "vaccine['housing_status'].fillna(vaccine['housing_status'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "addb4077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Not in Labor Force', 'Employed', 'Unemployed', nan], dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['employment'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f025805a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='employment'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccine['employment'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', figsize = [5,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "749a31af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 1463 missing values by mode\n",
    "vaccine['employment'].fillna(vaccine['employment'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "674ec1b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  1.,  3., nan])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.,  3.,  2.,  1., nan])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vaccine['no_of_adults'].unique(), vaccine['no_of_children'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6ddcdf68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAckAAAE+CAYAAADmqOvQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbC0lEQVR4nO3df7RlZX3f8fdHRhGrKMLVjDNMBxVjgaRjZjolNSoJqRLbBuISHVYiqKSjLDVxtc2KpGtFYhcrWqNWomKwEBhrUQJRiIFEogmsGn5kUOSXkgyKMmEKiFQxRtoZv/3jPBcOd+5z587MPffcH+/XWnvdfZ797H2++zIPn7t/nH1SVUiSpN09YdwFSJK0UBmSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHWsGHcB8+2www6rtWvXjrsMab/cdNNN366qiXHXAY4pLQ29MbXsQnLt2rVs3bp13GVI+yXJN8ddwyTHlJaC3pjydKskSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHcvu2a2SRm/9b2wZdwmPuum9p467BC1iHklKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1DGykExyQZL7k9w21PapJDe36e4kN7f2tUn+cWjZR4fWWZ/k1iTbkpyTJK39wLa9bUluSLJ2VPsiSVqeRnkkeSFwwnBDVb22qtZV1TrgMuCPhxbfNbmsqt481H4usBk4sk2T2zwdeKiqng98AHjPSPZCkrRsjSwkq+pa4DvTLWtHg68BLp5pG0lWAgdX1XVVVcAW4KS2+ETgojZ/KXD85FGmJElzYVzXJF8C3FdVfzfUdkSSLye5JslLWtsqYPtQn+2tbXLZPQBVtRP4LnDoaMuWJC0n4/o+yVN4/FHkDmBNVT2YZD3wmSRHA9MdGVb7OdOyx0mymcEpW9asWbPPRUsacExpuZj3I8kkK4BXAZ+abKuqR6rqwTZ/E3AX8AIGR46rh1ZfDdzb5rcDhw9t8+l0Tu9W1XlVtaGqNkxMTMztDknLkGNKy8U4Trf+PPC1qnr0NGqSiSQHtPnnMrhB5+tVtQN4OMmx7XrjqcDlbbUrgNPa/KuBL7TrlpIkzYlRfgTkYuA64MeTbE9yelu0id1v2HkpcEuSrzC4CefNVTV5VHgG8N+BbQyOMK9q7ecDhybZBvwH4B2j2hdJ0vI0smuSVXVKp/3107RdxuAjIdP13wocM037D4GT969KSZL6fOKOJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR2GpCRJHYakJEkdhqQkSR0jC8kkFyS5P8ltQ21nJfn7JDe36ZVDy85Msi3JnUleMdS+Psmtbdk5SdLaD0zyqdZ+Q5K1o9oXSdLyNMojyQuBE6Zp/0BVrWvTlQBJjgI2AUe3dT6S5IDW/1xgM3Bkmya3eTrwUFU9H/gA8J5R7YgkaXkaWUhW1bXAd2bZ/UTgk1X1SFV9A9gGbEyyEji4qq6rqgK2ACcNrXNRm78UOH7yKFOSpLkwjmuSb01ySzsde0hrWwXcM9Rne2tb1eantj9unaraCXwXOHSUhUuSlpf5DslzgecB64AdwPta+3RHgDVD+0zr7CbJ5iRbk2x94IEH9qpgSbtzTGm5mNeQrKr7qmpXVf0I+BiwsS3aDhw+1HU1cG9rXz1N++PWSbICeDqd07tVdV5VbaiqDRMTE3O1O9Ky5ZjScjGvIdmuMU76JWDyztcrgE3tjtUjGNygc2NV7QAeTnJsu954KnD50DqntflXA19o1y0lSZoTK0a14SQXA8cBhyXZDrwTOC7JOganRe8G3gRQVbcnuQS4A9gJvKWqdrVNncHgTtmDgKvaBHA+8PEk2xgcQW4a1b5IkpankYVkVZ0yTfP5M/Q/Gzh7mvatwDHTtP8QOHl/apQkaSY+cUeSpA5DUpKkjpGdbtXofOtdPzHuEh615rdvHXcJkjQyHklKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1DGykExyQZL7k9w21PbeJF9LckuSTyd5Rmtfm+Qfk9zcpo8OrbM+ya1JtiU5J0la+4FJPtXab0iydlT7IklankZ5JHkhcMKUtquBY6rqJ4G/Bc4cWnZXVa1r05uH2s8FNgNHtmlym6cDD1XV84EPAO+Z+12QJC1nIwvJqroW+M6Uts9V1c728npg9UzbSLISOLiqrquqArYAJ7XFJwIXtflLgeMnjzIlSZoL47wm+UbgqqHXRyT5cpJrkrykta0Ctg/12d7aJpfdA9CC97vAoaMtWZK0nKwYx5sm+c/ATuATrWkHsKaqHkyyHvhMkqOB6Y4Ma3IzMyyb+n6bGZyyZc2aNftTuiQcU1o+5v1IMslpwL8FfrmdQqWqHqmqB9v8TcBdwAsYHDkOn5JdDdzb5rcDh7dtrgCezpTTu5Oq6ryq2lBVGyYmJuZ+p6RlxjGl5WJeQzLJCcBvAr9YVT8Yap9IckCbfy6DG3S+XlU7gIeTHNuuN54KXN5WuwI4rc2/GvjCZOhKkjQXRna6NcnFwHHAYUm2A+9kcDfrgcDV7R6b69udrC8F3pVkJ7ALeHNVTR4VnsHgTtmDGFzDnLyOeT7w8STbGBxBbhrVvkiSlqeRhWRVnTJN8/mdvpcBl3WWbQWOmab9h8DJ+1OjJEkz8Yk7kiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHXMKiSTfH42bZIWP8e79JgVMy1M8mTgKcBhSQ4B0hYdDDxnxLVJmkeOd2l3M4Yk8Cbg7QwGyE08Nmi+B3x4dGVJGgPHuzTFjCFZVR8EPpjkbVX1+/NUk6QxcLxLu9vTkSQAVfX7Sf4VsHZ4naraMqK6JI2J4116zGxv3Pk48HvAzwD/ok0b9rDOBUnuT3LbUNszk1yd5O/az0OGlp2ZZFuSO5O8Yqh9fZJb27JzkqS1H5jkU639hiRr92bHJU1vX8a7tFTN6kiSwQA5qqpqL7Z9IfAhYPivz3cAn6+qdyd5R3v9m0mOAjYBRzO4HvIXSV5QVbuAc4HNwPXAlcAJwFXA6cBDVfX8JJuA9wCv3Yv6JE1vX8a7tCTN9nOStwE/tjcbrqprge9MaT4RuKjNXwScNNT+yap6pKq+AWwDNiZZCRxcVde1AbtlyjqT27oUOH7yKFPSftnr8S4tVbM9kjwMuCPJjcAjk41V9Yt7+X7Prqodbd0dSZ7V2lcxOFKctL21/b82P7V9cp172rZ2JvkucCjw7b2sSdLjzdV4lxa92YbkWaMsgsduNR9WM7TPtM7uG082Mzhly5o1a/alPmk5OWtPHRxTWi5me3frNXP0fvclWdmOIlcC97f27cDhQ/1WA/e29tXTtA+vsz3JCuDp7H56d7L+84DzADZs2OB1FmkGsxnvjiktF7O9u/XhJN9r0w+T7EryvX14vyuA09r8acDlQ+2b2h2rRwBHAje2U7MPJzm2XW88dco6k9t6NfAFbzSQ9t8cjndp0ZvtkeTThl8nOQnYONM6SS4GjmPwiKvtwDuBdwOXJDkd+BZwctv+7UkuAe4AdgJvaXe2ApzB4E7Zgxjc1XpVaz8f+HiSbQyOIDfNZl8kzWxfxru0VM32muTjVNVn2kc4ZupzSmfR8Z3+ZwNnT9O+FThmmvYf0kJW0ujMZrxLS9WsQjLJq4ZePoHB56g8tSktQY536TGzPZL8d0PzO4G7GXxOUdLS43iXmtlek3zDqAuRtDA43qXHzPbu1tVJPt2exXpfksuSrN7zmpIWG8e79JjZPpbuDxl85OI5DJ508yetTdLS43iXmtmG5ERV/WFV7WzThcDECOuSND6Od6mZbUh+O8mvJDmgTb8CPDjKwiSNjeNdamYbkm8EXgP8b2AHgyfceHFfWpoc71Iz24+A/BfgtKp6CAZfnszgS1nfOKrCJI2N411qZnsk+ZOTAwagqr4DvGg0JUkaM8e71Mw2JJ+Q5JDJF+0vy316pJ2kBc/xLjWz/Yf/PuCvk1zK4PFUr2Ga56xKWhIc71Iz2yfubEmyFfg5Bl92/KqqumOklUkaC8e79JhZn0Jpg8SBIi0DjndpYLbXJCVJWnYMSUmSOgxJSZI6DElJkjoMSUmSOgxJSZI6DElJkjoMSUmSOnweI7D+N7aMu4RH3fTeU8ddgiSp8UhSkqQOQ1KSpA5DUpKkjnkPySQ/nuTmoel7Sd6e5Kwkfz/U/sqhdc5Msi3JnUleMdS+Psmtbdk5STLf+yNJWrrmPSSr6s6qWldV64D1wA+AT7fFH5hcVlVXAiQ5CtgEHA2cAHwkyQGt/7nAZuDINp0wf3siSVrqxn269Xjgrqr65gx9TgQ+WVWPVNU3gG3AxiQrgYOr6rqqKmALcNLIK5YkLRvjDslNwMVDr9+a5JYkFyQ5pLWtAu4Z6rO9ta1q81PbJUmaE2MLySRPAn4R+KPWdC7wPGAdsAN432TXaVavGdqne6/NSbYm2frAAw/sT9mScExp+RjnkeQvAF+qqvsAquq+qtpVVT8CPgZsbP22A4cPrbcauLe1r56mfTdVdV5VbaiqDRMTE3O8G9Ly45jScjHOkDyFoVOt7RrjpF8CbmvzVwCbkhyY5AgGN+jcWFU7gIeTHNvuaj0VuHx+SpckLQdjeSxdkqcA/xp401Dzf02yjsEp07snl1XV7UkuAe4AdgJvqapdbZ0zgAuBg4Cr2iRJ0pwYS0hW1Q+AQ6e0vW6G/mcDZ0/TvhU4Zs4LlCSJ8d/dKknSgmVISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUMZaQTHJ3kluT3Jxka2t7ZpKrk/xd+3nIUP8zk2xLcmeSVwy1r2/b2ZbknCQZx/5IkpamcR5J/mxVrauqDe31O4DPV9WRwOfba5IcBWwCjgZOAD6S5IC2zrnAZuDINp0wj/VLkpa4hXS69UTgojZ/EXDSUPsnq+qRqvoGsA3YmGQlcHBVXVdVBWwZWkeSpP02rpAs4HNJbkqyubU9u6p2ALSfz2rtq4B7htbd3tpWtfmp7ZIkzYkVY3rfF1fVvUmeBVyd5Gsz9J3uOmPN0L77BgZBvBlgzZo1e1urpCkcU1ouxnIkWVX3tp/3A58GNgL3tVOotJ/3t+7bgcOHVl8N3NvaV0/TPt37nVdVG6pqw8TExFzuirQsOaa0XMx7SCb5J0meNjkPvBy4DbgCOK11Ow24vM1fAWxKcmCSIxjcoHNjOyX7cJJj212tpw6tI0nSfhvH6dZnA59un9ZYAfzPqvqzJH8DXJLkdOBbwMkAVXV7kkuAO4CdwFuqalfb1hnAhcBBwFVtkiRpTsx7SFbV14F/Pk37g8DxnXXOBs6epn0rcMxc1yhJEiysj4BIkrSgGJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHUYkpIkdRiSkiR1GJKSJHWsGHcBkjRu33rXT4y7hEet+e1bx12ChngkKUlShyEpSVKHISlJUofXJCVpkXnx77943CU8zhff9sVxlzAy8x6SSQ4HtgA/BvwIOK+qPpjkLODfAw+0rr9VVVe2dc4ETgd2Ab9WVX/e2tcDFwIHAVcCv15VNX97o9lwQEtarMZxJLkT+I9V9aUkTwNuSnJ1W/aBqvq94c5JjgI2AUcDzwH+IskLqmoXcC6wGbieQUieAFw1T/shSVri5v2aZFXtqKovtfmHga8Cq2ZY5UTgk1X1SFV9A9gGbEyyEji4qq5rR49bgJNGW70kaTkZ6407SdYCLwJuaE1vTXJLkguSHNLaVgH3DK22vbWtavNT2yVJmhNjC8kkTwUuA95eVd9jcOr0ecA6YAfwvsmu06xeM7RP916bk2xNsvWBBx6YroukveCY0nIxlpBM8kQGAfmJqvpjgKq6r6p2VdWPgI8BG1v37cDhQ6uvBu5t7aunad9NVZ1XVRuqasPExMTc7oy0DDmmtFzMe0gmCXA+8NWqev9Q+8qhbr8E3NbmrwA2JTkwyRHAkcCNVbUDeDjJsW2bpwKXz8tOSJKWhXHc3fpi4HXArUlubm2/BZySZB2DU6Z3A28CqKrbk1wC3MHgzti3tDtbAc7gsY+AXIV3tkqS5tC8h2RV/S+mv5545QzrnA2cPU37VuCYuatOkqTH+Fg6SZI6DElJkjoMSUmSOgxJSZI6DElJkjoMSUmSOvw+SUnSyF3z0peNu4RHvezaa2bd1yNJSZI6DElJkjoMSUmSOgxJSZI6DElJkjoMSUmSOgxJSZI6/JykNI3F+pkuSXPLI0lJkjoMSUmSOgxJSZI6DElJkjoMSUmSOgxJSZI6DElJkjoMSUmSOgxJSZI6DElJkjoMSUmSOhZ9SCY5IcmdSbYlece465EkLR2LOiSTHAB8GPgF4CjglCRHjbcqSdJSsahDEtgIbKuqr1fV/wU+CZw45pokSUvEYg/JVcA9Q6+3tzZJkvZbqmrcNeyzJCcDr6iqX22vXwdsrKq3Tem3GdjcXv44cOcIyjkM+PYItjtKi61m633MP62qiRFte48cU12LrWbrfcy0Y2qxh+RPA2dV1Sva6zMBqup3x1DL1qraMN/vuz8WW83Wu7wsxt/fYqvZevdssZ9u/RvgyCRHJHkSsAm4Ysw1SZKWiBXjLmB/VNXOJG8F/hw4ALigqm4fc1mSpCViUYckQFVdCVw57jqA88ZdwD5YbDVb7/KyGH9/i61m692DRX1NUpKkUVrs1yQlSRoZQ3Iv7ekxeBk4py2/JclPjaPOoXouSHJ/kts6yxdavYcn+cskX01ye5Jfn6bPgqk5yZOT3JjkK63e35mmz4KpdyFyTI2WY2o/VZXTLCcGNwfdBTwXeBLwFeCoKX1eCVwFBDgWuGHMNb8U+Cngts7yhVbvSuCn2vzTgL9dyL/jVsNT2/wTgRuAYxdqvQttckzNS72Oqf2YPJLcO7N5DN6JwJYauB54RpKV813opKq6FvjODF0WWr07qupLbf5h4Kvs/hSlBVNzq+H77eUT2zT1Qv+CqXcBckyNmGNq/xiSe2c2j8FbbI/KW7D1JlkLvIjBX5LDFlTNSQ5IcjNwP3B1VS3oehcYx9Q8ckztPUNy72Satql/4cymz0KyIOtN8lTgMuDtVfW9qYunWWVsNVfVrqpaB6wGNiY5ZkqXBVXvAuOYmieOqX1jSO6d7cDhQ69XA/fuQ5+FZMHVm+SJDAbzJ6rqj6fpsuBqBqiq/wP8FXDClEULst4FwjE1DxxT+86Q3DuzeQzeFcCp7e6rY4HvVtWO+S50LyyoepMEOB/4alW9v9NtwdScZCLJM9r8QcDPA1+b0m3B1LsAOaZGzDG1fxb9E3fmU3Ueg5fkzW35Rxk8/eeVwDbgB8AbxlUvQJKLgeOAw5JsB97J4EL4gqwXeDHwOuDWdk0C4LeANbAga14JXJTBF4A/Abikqj67kP9NLCSOqXnhmNoPPnFHkqQOT7dKktRhSEqS1GFISpLUYUhKktRhSEqS1GFISpLUYUhqryV5YZKbk3w5yfP2YzuvT/KhPfRZm/aVREnWJXnlvr6ftFDs6xhKcmGSV0/T/pwkl7b545J8trP+3UkO2/fKlx9DUvviJODyqnpRVd01j++7jsEHiKXF7iTmcAxV1b1VtVt4zkZ7ao1Z0OEvZolqR2BfTfKxDL649HNJDmpHY9dn8EWln05yyAzb2K1vO5J7O/CrSf5yhnU/k+Sm9t6bh9rfkORvk1zD4Ekgk+2P+ws5yfenbO9JwLuA17a/wF+b5GVtfvIv8qfty+9Kms4CGEOntvW+kuTjQ4temuSvk3x9cswMn3GZso1DW91fTvIHtAeDD+3bR4AvAYcn+Y0kf9Pe83dm+h3sw69z8RrVF1U6jXcC1gI7gXXt9SXArwC3AC9rbe8C/tsM25i2L3AW8J/28P7PbD8PAm4DDmXwuKlvARMMvmD3i8CHWr8LgVcPrf/9of24rc2/frJ/e/0nwIvb/FOBFeP+vTstnWmcYwg4GrgTOKy9nhxPFwJ/xOAA5ygG38U5dZwcB3y2zZ8D/Hab/zcMvinjsNb/R7QvMwZeDpzHIESfAHyWwZdLT/s7GPd/m/mcPJJc2r5RVTe3+ZuA5wHPqKprWttFDAbCbpI8fbZ9O34tyVeA6xk8rf9I4F8Cf1VVD9TgC3Y/tTc7M40vAu9P8mut1p37uT1pqnGNoZ8DLq2qbwNU1fCXPH+mqn5UVXcAz97Ddl4K/I+2jT8FHhpa9s0afGExDELy5cCXGRxZvpDBmIXdfwdrZ7kPS4IPOF/aHhma3wU8Yz7eNMlxDJ7c/9NV9YMkfwU8uS3uPSx4J+30f5IwONKcUVW9O8mfMrhOeX2Sn6+qqd8WIO2PsYwhBkd0vbHyyJR+e9Lbzj9M2c7vVtUfPK6IwZc0T/0dLKvTrR5JLi/fBR5K8pL2+nXANdN1rKpZ953G04GHWkC+EDi2td8AHNeukzwROHlonbuB9W3+RNq3KkzxMPDodcckz6uqW6vqPcBWBn/9SqM0X2Po88BrkhwKkOSZ+1jvtcAvt238AtC7fvrnwBsz+GJmkqxK8qx9fM8lxSPJ5ec04KNJngJ8nZm/YmZv+g77M+DNSW5hcF3leoCq2pHkLOA6YAeD0zoHtHU+Blye5EYG/4P4h6kbBf4SeEcGX/fzu8DPJPlZBn/d3gFcNcv6pP0x8jFUg68LOxu4JskuBqdBX78Ptf4OcHGSLzEI6G913u9zSf4ZcN3gRA7fZ3D9ddc+vOeS4ldlSZLU4elWSZI6PN0qknyYoc8sNh+sqj/cw3qHMjg1OtXxVfXgXNUnLXSOoaXL062SJHV4ulWSpA5DUpKkDkNSkqQOQ1KSpA5DUpKkjv8PNkRmAL5wfAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize = [7,5], sharey = True)\n",
    "sns.countplot( x = 'no_of_adults', data = vaccine, ax = ax[0])\n",
    "sns.countplot( x = 'no_of_children', data = vaccine, ax = ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a194f6eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling 249 nan values by mode\n",
    "vaccine['no_of_adults'].fillna(vaccine['no_of_adults'].mode()[0], inplace = True)\n",
    "vaccine['no_of_children'].fillna(vaccine['no_of_children'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "25bdf9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65+ Years        6843\n",
       "55 - 64 Years    5563\n",
       "45 - 54 Years    5238\n",
       "18 - 34 Years    5215\n",
       "35 - 44 Years    3848\n",
       "Name: age_bracket, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['age_bracket'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "25bcc689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h1n1_worry                   0\n",
       "h1n1_awareness               0\n",
       "antiviral_medication         0\n",
       "contact_avoidance            0\n",
       "bought_face_mask             0\n",
       "wash_hands_frequently        0\n",
       "avoid_large_gatherings       0\n",
       "reduced_outside_home_cont    0\n",
       "avoid_touch_face             0\n",
       "dr_recc_h1n1_vacc            0\n",
       "dr_recc_seasonal_vacc        0\n",
       "chronic_medic_condition      0\n",
       "cont_child_undr_6_mnths      0\n",
       "is_health_worker             0\n",
       "has_health_insur             0\n",
       "is_h1n1_vacc_effective       0\n",
       "is_h1n1_risky                0\n",
       "sick_from_h1n1_vacc          0\n",
       "is_seas_vacc_effective       0\n",
       "is_seas_risky                0\n",
       "sick_from_seas_vacc          0\n",
       "age_bracket                  0\n",
       "qualification                0\n",
       "race                         0\n",
       "sex                          0\n",
       "income_level                 0\n",
       "marital_status               0\n",
       "housing_status               0\n",
       "employment                   0\n",
       "census_msa                   0\n",
       "no_of_adults                 0\n",
       "no_of_children               0\n",
       "h1n1_vaccine                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40339771",
   "metadata": {},
   "source": [
    "No outliers as data is categorical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3a250238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object    33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine1 = vaccine.astype({'h1n1_worry' : str, 'h1n1_awareness': str, 'antiviral_medication': str,\n",
    "       'contact_avoidance': str, 'bought_face_mask': str, 'wash_hands_frequently': str,\n",
    "       'avoid_large_gatherings': str, 'reduced_outside_home_cont': str,\n",
    "       'avoid_touch_face': str, 'dr_recc_h1n1_vacc': str, 'dr_recc_seasonal_vacc': str,\n",
    "       'chronic_medic_condition': str, 'cont_child_undr_6_mnths': str,\n",
    "       'is_health_worker': str, 'has_health_insur': str, 'is_h1n1_vacc_effective': str,\n",
    "       'is_h1n1_risky': str, 'sick_from_h1n1_vacc': str, 'is_seas_vacc_effective': str, 'age_bracket' : str,\n",
    "       'is_seas_risky': str, 'sick_from_seas_vacc': str, 'no_of_adults' : str, 'no_of_children' : str,\n",
    "        'h1n1_vaccine' : str})\n",
    "vaccine1.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ae0f57cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccine_dummy = pd.get_dummies(vaccine1, drop_first= True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8368e279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26707, 70)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine_dummy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8210a422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.787546\n",
       "1    0.212454\n",
       "Name: h1n1_vaccine_1, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine_dummy['h1n1_vaccine_1'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "eb2aa3d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=vaccine_dummy['h1n1_vaccine_1']\n",
    "x=vaccine_dummy.drop(['h1n1_vaccine_1'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "47168faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data['Var'] = x.columns\n",
    "vif_data['VIF'] = [variance_inflation_factor(x.values, i) for i in range(len(x.columns))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "bf784173",
   "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>Var</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sick_from_h1n1_vacc_3.0</td>\n",
       "      <td>1.094738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>sick_from_seas_vacc_3.0</td>\n",
       "      <td>1.087666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>is_h1n1_vacc_effective_5.0</td>\n",
       "      <td>0.379078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>is_seas_vacc_effective_3.0</td>\n",
       "      <td>0.376005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>is_seas_risky_3.0</td>\n",
       "      <td>0.351710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>h1n1_awareness_2.0</td>\n",
       "      <td>0.008856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>is_h1n1_risky_4.0</td>\n",
       "      <td>0.008779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>census_msa_Non-MSA</td>\n",
       "      <td>0.006878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>reduced_outside_home_cont_1.0</td>\n",
       "      <td>0.005734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>age_bracket_35 - 44 Years</td>\n",
       "      <td>0.003837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Var       VIF\n",
       "28        sick_from_h1n1_vacc_3.0  1.094738\n",
       "40        sick_from_seas_vacc_3.0  1.087666\n",
       "22     is_h1n1_vacc_effective_5.0  0.379078\n",
       "32     is_seas_vacc_effective_3.0  0.376005\n",
       "36              is_seas_risky_3.0  0.351710\n",
       "..                            ...       ...\n",
       "4              h1n1_awareness_2.0  0.008856\n",
       "25              is_h1n1_risky_4.0  0.008779\n",
       "62             census_msa_Non-MSA  0.006878\n",
       "10  reduced_outside_home_cont_1.0  0.005734\n",
       "43      age_bracket_35 - 44 Years  0.003837\n",
       "\n",
       "[69 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif_data.sort_values(by = ['VIF'], ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0d66fc",
   "metadata": {},
   "source": [
    "All variables are important as VIF values are less than 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c9c335d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train , y_test=train_test_split(x,y, test_size=.25, random_state=88)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaca4537",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d89628ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score : 0.8558162755866201\n",
      "Testing Score : 0.8442414257900255\n"
     ]
    }
   ],
   "source": [
    "log = LogisticRegression()\n",
    "\n",
    "log.fit(x_train,y_train)\n",
    "\n",
    "print(f\"Training Score : {log.score(x_train, y_train)}\")\n",
    "print(f\"Testing Score : {log.score(x_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674b93c6",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1c5dfee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train=log.predict(x_train)\n",
    "pred_test=log.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ee5347dc",
   "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>Pred_0</th>\n",
       "      <th>Pred_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Act_0</th>\n",
       "      <td>15031</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Act_1</th>\n",
       "      <td>2115</td>\n",
       "      <td>2111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pred_0  Pred_1\n",
       "Act_0   15031     773\n",
       "Act_1    2115    2111"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnfTrain=pd.DataFrame(metrics.confusion_matrix(y_train , pred_train), columns=[\"Pred_0\", \"Pred_1\"],\n",
    "                index=[\"Act_0\", \"Act_1\"])\n",
    "cnfTrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e47b7a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.95      0.91     15804\n",
      "           1       0.73      0.50      0.59      4226\n",
      "\n",
      "    accuracy                           0.86     20030\n",
      "   macro avg       0.80      0.73      0.75     20030\n",
      "weighted avg       0.85      0.86      0.85     20030\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_train, pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b9b712d9",
   "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>Pred_0</th>\n",
       "      <th>Pred_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Act_0</th>\n",
       "      <td>4985</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Act_1</th>\n",
       "      <td>796</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pred_0  Pred_1\n",
       "Act_0    4985     244\n",
       "Act_1     796     652"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnfTest=pd.DataFrame(metrics.confusion_matrix(y_test , pred_test), columns=[\"Pred_0\", \"Pred_1\"],\n",
    "                index=[\"Act_0\", \"Act_1\"])\n",
    "cnfTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2f1f0e56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.95      0.91      5229\n",
      "           1       0.73      0.45      0.56      1448\n",
      "\n",
      "    accuracy                           0.84      6677\n",
      "   macro avg       0.79      0.70      0.73      6677\n",
      "weighted avg       0.83      0.84      0.83      6677\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "02bf1556",
   "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>Prob_0</th>\n",
       "      <th>Prob_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.945305</td>\n",
       "      <td>0.054695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.965775</td>\n",
       "      <td>0.034225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.404756</td>\n",
       "      <td>0.595244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.988857</td>\n",
       "      <td>0.011143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.799952</td>\n",
       "      <td>0.200048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20025</th>\n",
       "      <td>0.963749</td>\n",
       "      <td>0.036251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20026</th>\n",
       "      <td>0.935796</td>\n",
       "      <td>0.064204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20027</th>\n",
       "      <td>0.735832</td>\n",
       "      <td>0.264168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20028</th>\n",
       "      <td>0.775515</td>\n",
       "      <td>0.224485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20029</th>\n",
       "      <td>0.962829</td>\n",
       "      <td>0.037171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20030 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Prob_0    Prob_1\n",
       "0      0.945305  0.054695\n",
       "1      0.965775  0.034225\n",
       "2      0.404756  0.595244\n",
       "3      0.988857  0.011143\n",
       "4      0.799952  0.200048\n",
       "...         ...       ...\n",
       "20025  0.963749  0.036251\n",
       "20026  0.935796  0.064204\n",
       "20027  0.735832  0.264168\n",
       "20028  0.775515  0.224485\n",
       "20029  0.962829  0.037171\n",
       "\n",
       "[20030 rows x 2 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(log.predict_proba(x_train), columns=[\"Prob_0\", \"Prob_1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c83e278e",
   "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>h1n1_worry_1.0</th>\n",
       "      <th>h1n1_worry_2.0</th>\n",
       "      <th>h1n1_worry_3.0</th>\n",
       "      <th>h1n1_awareness_1.0</th>\n",
       "      <th>h1n1_awareness_2.0</th>\n",
       "      <th>antiviral_medication_1.0</th>\n",
       "      <th>contact_avoidance_1.0</th>\n",
       "      <th>bought_face_mask_1.0</th>\n",
       "      <th>wash_hands_frequently_1.0</th>\n",
       "      <th>avoid_large_gatherings_1.0</th>\n",
       "      <th>...</th>\n",
       "      <th>census_msa_MSA, Principle City</th>\n",
       "      <th>census_msa_Non-MSA</th>\n",
       "      <th>no_of_adults_1.0</th>\n",
       "      <th>no_of_adults_2.0</th>\n",
       "      <th>no_of_adults_3.0</th>\n",
       "      <th>no_of_children_1.0</th>\n",
       "      <th>no_of_children_2.0</th>\n",
       "      <th>no_of_children_3.0</th>\n",
       "      <th>Actual_Default</th>\n",
       "      <th>Prob_Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9353</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6317</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.034225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13542</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.595244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23986</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16025</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24938</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2481</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.064204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4047</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.264168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.224485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26584</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.037171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20030 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       h1n1_worry_1.0  h1n1_worry_2.0  h1n1_worry_3.0  h1n1_awareness_1.0  \\\n",
       "9353                1               0               0                   1   \n",
       "6317                0               1               0                   1   \n",
       "13542               0               1               0                   1   \n",
       "23986               0               1               0                   0   \n",
       "16025               1               0               0                   0   \n",
       "...               ...             ...             ...                 ...   \n",
       "24938               0               0               0                   0   \n",
       "2481                0               0               1                   1   \n",
       "4047                0               0               1                   1   \n",
       "6432                1               0               0                   1   \n",
       "26584               0               1               0                   1   \n",
       "\n",
       "       h1n1_awareness_2.0  antiviral_medication_1.0  contact_avoidance_1.0  \\\n",
       "9353                    0                         0                      1   \n",
       "6317                    0                         0                      1   \n",
       "13542                   0                         0                      0   \n",
       "23986                   1                         0                      1   \n",
       "16025                   1                         1                      1   \n",
       "...                   ...                       ...                    ...   \n",
       "24938                   1                         0                      1   \n",
       "2481                    0                         0                      1   \n",
       "4047                    0                         0                      1   \n",
       "6432                    0                         0                      1   \n",
       "26584                   0                         0                      1   \n",
       "\n",
       "       bought_face_mask_1.0  wash_hands_frequently_1.0  \\\n",
       "9353                      0                          1   \n",
       "6317                      0                          1   \n",
       "13542                     0                          1   \n",
       "23986                     0                          1   \n",
       "16025                     0                          1   \n",
       "...                     ...                        ...   \n",
       "24938                     0                          1   \n",
       "2481                      0                          1   \n",
       "4047                      0                          1   \n",
       "6432                      0                          1   \n",
       "26584                     0                          1   \n",
       "\n",
       "       avoid_large_gatherings_1.0  ...  census_msa_MSA, Principle City  \\\n",
       "9353                            1  ...                               0   \n",
       "6317                            0  ...                               1   \n",
       "13542                           1  ...                               0   \n",
       "23986                           1  ...                               0   \n",
       "16025                           1  ...                               1   \n",
       "...                           ...  ...                             ...   \n",
       "24938                           0  ...                               0   \n",
       "2481                            1  ...                               0   \n",
       "4047                            1  ...                               0   \n",
       "6432                            0  ...                               1   \n",
       "26584                           1  ...                               1   \n",
       "\n",
       "       census_msa_Non-MSA  no_of_adults_1.0  no_of_adults_2.0  \\\n",
       "9353                    0                 1                 0   \n",
       "6317                    0                 1                 0   \n",
       "13542                   1                 0                 0   \n",
       "23986                   0                 0                 0   \n",
       "16025                   0                 1                 0   \n",
       "...                   ...               ...               ...   \n",
       "24938                   1                 0                 0   \n",
       "2481                    0                 0                 0   \n",
       "4047                    1                 1                 0   \n",
       "6432                    0                 1                 0   \n",
       "26584                   0                 1                 0   \n",
       "\n",
       "       no_of_adults_3.0  no_of_children_1.0  no_of_children_2.0  \\\n",
       "9353                  0                   0                   0   \n",
       "6317                  0                   0                   0   \n",
       "13542                 0                   0                   0   \n",
       "23986                 0                   0                   0   \n",
       "16025                 0                   0                   0   \n",
       "...                 ...                 ...                 ...   \n",
       "24938                 1                   1                   0   \n",
       "2481                  0                   0                   0   \n",
       "4047                  0                   0                   1   \n",
       "6432                  0                   1                   0   \n",
       "26584                 0                   0                   0   \n",
       "\n",
       "       no_of_children_3.0  Actual_Default  Prob_Default  \n",
       "9353                    0               0      0.054695  \n",
       "6317                    0               0      0.034225  \n",
       "13542                   0               0      0.595244  \n",
       "23986                   0               0      0.011143  \n",
       "16025                   0               1      0.200048  \n",
       "...                   ...             ...           ...  \n",
       "24938                   0               0      0.036251  \n",
       "2481                    0               0      0.064204  \n",
       "4047                    0               0      0.264168  \n",
       "6432                    0               1      0.224485  \n",
       "26584                   0               0      0.037171  \n",
       "\n",
       "[20030 rows x 71 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train1=x_train.copy()\n",
    "\n",
    "x_train1[\"Actual_Default\"]=y_train\n",
    "x_train1[\"Prob_Default\"]=log.predict_proba(x_train)[:, 1]\n",
    "\n",
    "x_train1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3661eb9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decile Analysis\n",
    "def profile_decile(X,y,trained_model):\n",
    "    X_1=X.copy()\n",
    "    y_1=y.copy()\n",
    "    y_pred1=trained_model.predict(X_1)\n",
    "    X_1[\"Prob_Event\"]=trained_model.predict_proba(X_1)[:,1]\n",
    "    X_1[\"Y_actual\"]=y_1\n",
    "    X_1[\"Y_pred\"]=y_pred1\n",
    "    X_1[\"Rank\"]=pd.qcut(X_1[\"Prob_Event\"], 10, labels=np.arange(0,10,1))\n",
    "    X_1[\"numb\"]=10\n",
    "    X_1[\"Decile\"]=X_1[\"numb\"]-X_1[\"Rank\"].astype(\"int\")\n",
    "    \n",
    "    profile=pd.DataFrame(X_1.groupby(\"Decile\") \\\n",
    "                        .apply(lambda x: pd.Series({\n",
    "        'min_score'   : x[\"Prob_Event\"].min(),\n",
    "        'max_score'   : x[\"Prob_Event\"].max(),\n",
    "        'Event'       : x[\"Y_actual\"].sum(),\n",
    "        'Non_event'   : x[\"Y_actual\"].count()-x[\"Y_actual\"].sum(),\n",
    "        'Total'       : x[\"Y_actual\"].count() })))\n",
    "    return profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7d85f2b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_profile=profile_decile(x_train, y_train, log)\n",
    "# train_profile.to_excel(\"train_profile.xlsx\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9e72a81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_profile=profile_decile(x_test, y_test, log)\n",
    "# test_profile.to_excel(\"test_profile.xlsx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0667c5e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain_pred=np.where(log.predict_proba(x_train)[:,1] > 0.232280104, 1, 0) # based on KS value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6f4f1abc",
   "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>Pred_0</th>\n",
       "      <th>Pred_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Act_0</th>\n",
       "      <td>12965</td>\n",
       "      <td>2839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Act_1</th>\n",
       "      <td>1056</td>\n",
       "      <td>3170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pred_0  Pred_1\n",
       "Act_0   12965    2839\n",
       "Act_1    1056    3170"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnfNewTrain=pd.DataFrame(metrics.confusion_matrix(y_train , newtrain_pred), columns=[\"Pred_0\", \"Pred_1\"],\n",
    "                index=[\"Act_0\", \"Act_1\"])\n",
    "cnfNewTrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e711405f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.82      0.87     15804\n",
      "           1       0.53      0.75      0.62      4226\n",
      "\n",
      "    accuracy                           0.81     20030\n",
      "   macro avg       0.73      0.79      0.74     20030\n",
      "weighted avg       0.84      0.81      0.82     20030\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_train , newtrain_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "12e19c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest_pred=np.where(log.predict_proba(x_test)[:,1] > 0.229209326, 1, 0) # based on KS value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d759d573",
   "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>Pred_0</th>\n",
       "      <th>Pred_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Act_0</th>\n",
       "      <td>4272</td>\n",
       "      <td>957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Act_1</th>\n",
       "      <td>402</td>\n",
       "      <td>1046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pred_0  Pred_1\n",
       "Act_0    4272     957\n",
       "Act_1     402    1046"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnfNewTest=pd.DataFrame(metrics.confusion_matrix(y_test , newtest_pred), columns=[\"Pred_0\", \"Pred_1\"],\n",
    "                index=[\"Act_0\", \"Act_1\"])\n",
    "cnfNewTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4c50835f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.82      0.86      5229\n",
      "           1       0.52      0.72      0.61      1448\n",
      "\n",
      "    accuracy                           0.80      6677\n",
      "   macro avg       0.72      0.77      0.73      6677\n",
      "weighted avg       0.83      0.80      0.81      6677\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test , newtest_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "63ef8b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Receiver Operating Characterestics \n",
    "\n",
    "probs=log.predict_proba(x_train)[:,1]\n",
    "\n",
    "fpr, tpr, threshold=metrics.roc_curve(y_train,probs )\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "plt.plot(fpr,tpr, label='logistic')\n",
    "plt.xlabel('fpr')\n",
    "plt.ylabel('tpr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1627b3bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8646604470787018"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.roc_auc_score(y_train,probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "5d9103cf",
   "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>Model</th>\n",
       "      <th>ROC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logical Regression</td>\n",
       "      <td>0.769679</td>\n",
       "      <td>0.522217</td>\n",
       "      <td>0.722376</td>\n",
       "      <td>0.606201</td>\n",
       "      <td>0.796465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model  ROC Score  Precision Score  Recall Score  F1 Score  \\\n",
       "0   Logical Regression    0.769679         0.522217      0.722376  0.606201   \n",
       "\n",
       "   Accuracy Score  \n",
       "0        0.796465  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_report = pd.DataFrame()\n",
    "\n",
    "tmp = pd.Series({'Model': \" Logical Regression \",\n",
    "                 'ROC Score' : metrics.roc_auc_score(y_test, newtest_pred),\n",
    "                 'Precision Score': metrics.precision_score(y_test, newtest_pred),\n",
    "                 'Recall Score': metrics.recall_score(y_test, newtest_pred),\n",
    "                 'F1 Score' : metrics.f1_score(y_test, newtest_pred),\n",
    "                 'Accuracy Score': metrics.accuracy_score(y_test, newtest_pred)})\n",
    "\n",
    "model_logR_report = model_report.append(tmp, ignore_index = True)\n",
    "model_logR_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e7436d",
   "metadata": {},
   "source": [
    "In this case, Recall Score is important as we dont want to miss out on people who actually need a h1n1 vaccine shot."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
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 "nbformat_minor": 5
}