Abalone - Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# hide warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# print all columns\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ref - https://www.kaggle.com/rodolfomendes/abalone-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>length</th>\n",
       "      <th>diameter</th>\n",
       "      <th>height</th>\n",
       "      <th>whole_weight</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>viscera_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   shell_weight  rings  \n",
       "0         0.150     15  \n",
       "1         0.070      7  \n",
       "2         0.210      9  \n",
       "3         0.155     10  \n",
       "4         0.055      7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data\n",
    "df = pd.read_csv(\"abalone.csv\", names = ['sex','length','diameter','height','whole_weight','shucked_weight','viscera_weight','shell_weight','rings'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4177, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable in this dataset is the `age` of Abalones. From source - \n",
    "> The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age.\n",
    "\n",
    "To calculate age of Abalones, the following relationship is used - \n",
    "$Age = n(Rings) + 1.5$\n",
    "\n",
    "We will now create `age` feature and drop `rings` feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>length</th>\n",
       "      <th>diameter</th>\n",
       "      <th>height</th>\n",
       "      <th>whole_weight</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>viscera_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   shell_weight   age  \n",
       "0         0.150  16.5  \n",
       "1         0.070   8.5  \n",
       "2         0.210  10.5  \n",
       "3         0.155  11.5  \n",
       "4         0.055   8.5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating age variable\n",
    "df['age'] = df['rings'] + 1.5\n",
    "df.drop('rings', axis = 1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4177.000000\n",
       "mean       11.433684\n",
       "std         3.224169\n",
       "min         2.500000\n",
       "25%         9.500000\n",
       "50%        10.500000\n",
       "75%        12.500000\n",
       "max        30.500000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "sns.histplot(df['age'], binwidth=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To treat this as a classification problem, we will convert age into categorical variable using `age_class` with below conditions -  \n",
    "- Category 0 - `Age < 11`\n",
    "- Category 1 - `Age >= 11`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def age_classify(x):\n",
    "    if x < 11:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>sex</th>\n",
       "      <th>length</th>\n",
       "      <th>diameter</th>\n",
       "      <th>height</th>\n",
       "      <th>whole_weight</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>viscera_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>age</th>\n",
       "      <th>age_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>16.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>I</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.3515</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.120</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.7775</td>\n",
       "      <td>0.2370</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.330</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.7680</td>\n",
       "      <td>0.2940</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.260</td>\n",
       "      <td>17.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5095</td>\n",
       "      <td>0.2165</td>\n",
       "      <td>0.1125</td>\n",
       "      <td>0.165</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>F</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.8945</td>\n",
       "      <td>0.3145</td>\n",
       "      <td>0.1510</td>\n",
       "      <td>0.320</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>F</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.6065</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>0.210</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.4060</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>0.135</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.5415</td>\n",
       "      <td>0.2175</td>\n",
       "      <td>0.0950</td>\n",
       "      <td>0.190</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>F</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.6845</td>\n",
       "      <td>0.2725</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>0.205</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>F</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.4755</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>0.0805</td>\n",
       "      <td>0.185</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.6645</td>\n",
       "      <td>0.2580</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>0.240</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>I</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.2905</td>\n",
       "      <td>0.0950</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.115</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>F</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.4510</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.0870</td>\n",
       "      <td>0.130</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2555</td>\n",
       "      <td>0.0970</td>\n",
       "      <td>0.0430</td>\n",
       "      <td>0.100</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.3810</td>\n",
       "      <td>0.1705</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.115</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  length  diameter  height  whole_weight  shucked_weight  \\\n",
       "0    M   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1    M   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2    F   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3    M   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4    I   0.330     0.255   0.080        0.2050          0.0895   \n",
       "5    I   0.425     0.300   0.095        0.3515          0.1410   \n",
       "6    F   0.530     0.415   0.150        0.7775          0.2370   \n",
       "7    F   0.545     0.425   0.125        0.7680          0.2940   \n",
       "8    M   0.475     0.370   0.125        0.5095          0.2165   \n",
       "9    F   0.550     0.440   0.150        0.8945          0.3145   \n",
       "10   F   0.525     0.380   0.140        0.6065          0.1940   \n",
       "11   M   0.430     0.350   0.110        0.4060          0.1675   \n",
       "12   M   0.490     0.380   0.135        0.5415          0.2175   \n",
       "13   F   0.535     0.405   0.145        0.6845          0.2725   \n",
       "14   F   0.470     0.355   0.100        0.4755          0.1675   \n",
       "15   M   0.500     0.400   0.130        0.6645          0.2580   \n",
       "16   I   0.355     0.280   0.085        0.2905          0.0950   \n",
       "17   F   0.440     0.340   0.100        0.4510          0.1880   \n",
       "18   M   0.365     0.295   0.080        0.2555          0.0970   \n",
       "19   M   0.450     0.320   0.100        0.3810          0.1705   \n",
       "\n",
       "    viscera_weight  shell_weight   age  age_class  \n",
       "0           0.1010         0.150  16.5          1  \n",
       "1           0.0485         0.070   8.5          0  \n",
       "2           0.1415         0.210  10.5          0  \n",
       "3           0.1140         0.155  11.5          1  \n",
       "4           0.0395         0.055   8.5          0  \n",
       "5           0.0775         0.120   9.5          0  \n",
       "6           0.1415         0.330  21.5          1  \n",
       "7           0.1495         0.260  17.5          1  \n",
       "8           0.1125         0.165  10.5          0  \n",
       "9           0.1510         0.320  20.5          1  \n",
       "10          0.1475         0.210  15.5          1  \n",
       "11          0.0810         0.135  11.5          1  \n",
       "12          0.0950         0.190  12.5          1  \n",
       "13          0.1710         0.205  11.5          1  \n",
       "14          0.0805         0.185  11.5          1  \n",
       "15          0.1330         0.240  13.5          1  \n",
       "16          0.0395         0.115   8.5          0  \n",
       "17          0.0870         0.130  11.5          1  \n",
       "18          0.0430         0.100   8.5          0  \n",
       "19          0.0750         0.115  10.5          0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_class'] = df['age'].map(age_classify)\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age_class', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "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": [
    "sns.countplot(df['age_class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop age\n",
    "df.drop('age', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4177, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop duplicates, if any\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>length</th>\n",
       "      <th>diameter</th>\n",
       "      <th>height</th>\n",
       "      <th>whole_weight</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>viscera_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>age_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4177</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.523992</td>\n",
       "      <td>0.407881</td>\n",
       "      <td>0.139516</td>\n",
       "      <td>0.828742</td>\n",
       "      <td>0.359367</td>\n",
       "      <td>0.180594</td>\n",
       "      <td>0.238831</td>\n",
       "      <td>0.498204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.120093</td>\n",
       "      <td>0.099240</td>\n",
       "      <td>0.041827</td>\n",
       "      <td>0.490389</td>\n",
       "      <td>0.221963</td>\n",
       "      <td>0.109614</td>\n",
       "      <td>0.139203</td>\n",
       "      <td>0.500057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.441500</td>\n",
       "      <td>0.186000</td>\n",
       "      <td>0.093500</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.545000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.799500</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.234000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.615000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>1.153000</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.253000</td>\n",
       "      <td>0.329000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>2.825500</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sex       length     diameter       height  whole_weight  \\\n",
       "count   4177  4177.000000  4177.000000  4177.000000   4177.000000   \n",
       "unique     3          NaN          NaN          NaN           NaN   \n",
       "top        M          NaN          NaN          NaN           NaN   \n",
       "freq    1528          NaN          NaN          NaN           NaN   \n",
       "mean     NaN     0.523992     0.407881     0.139516      0.828742   \n",
       "std      NaN     0.120093     0.099240     0.041827      0.490389   \n",
       "min      NaN     0.075000     0.055000     0.000000      0.002000   \n",
       "25%      NaN     0.450000     0.350000     0.115000      0.441500   \n",
       "50%      NaN     0.545000     0.425000     0.140000      0.799500   \n",
       "75%      NaN     0.615000     0.480000     0.165000      1.153000   \n",
       "max      NaN     0.815000     0.650000     1.130000      2.825500   \n",
       "\n",
       "        shucked_weight  viscera_weight  shell_weight    age_class  \n",
       "count      4177.000000     4177.000000   4177.000000  4177.000000  \n",
       "unique             NaN             NaN           NaN          NaN  \n",
       "top                NaN             NaN           NaN          NaN  \n",
       "freq               NaN             NaN           NaN          NaN  \n",
       "mean          0.359367        0.180594      0.238831     0.498204  \n",
       "std           0.221963        0.109614      0.139203     0.500057  \n",
       "min           0.001000        0.000500      0.001500     0.000000  \n",
       "25%           0.186000        0.093500      0.130000     0.000000  \n",
       "50%           0.336000        0.171000      0.234000     0.000000  \n",
       "75%           0.502000        0.253000      0.329000     1.000000  \n",
       "max           1.488000        0.760000      1.005000     1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary stats\n",
    "df.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>sex</th>\n",
       "      <th>length</th>\n",
       "      <th>diameter</th>\n",
       "      <th>height</th>\n",
       "      <th>whole_weight</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>viscera_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>age_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>I</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.2065</td>\n",
       "      <td>0.0860</td>\n",
       "      <td>0.1150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>I</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.0575</td>\n",
       "      <td>0.0285</td>\n",
       "      <td>0.3505</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sex  length  diameter  height  whole_weight  shucked_weight  \\\n",
       "1257   I   0.430      0.34     0.0         0.428          0.2065   \n",
       "3996   I   0.315      0.23     0.0         0.134          0.0575   \n",
       "\n",
       "      viscera_weight  shell_weight  age_class  \n",
       "1257          0.0860        0.1150          0  \n",
       "3996          0.0285        0.3505          0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['height'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 2 records show `height` has value = 0 which should not be possible. This is probably an error or a missed measurement. We will drop those records. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4175, 9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.height != 0]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex               0\n",
       "length            0\n",
       "diameter          0\n",
       "height            0\n",
       "whole_weight      0\n",
       "shucked_weight    0\n",
       "viscera_weight    0\n",
       "shell_weight      0\n",
       "age_class         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check na\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check for multicollinearity\n",
    "# correlation plot\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.set_context('paper', font_scale=1.5)\n",
    "\n",
    "df_corr = df.iloc[:, :-1].corr()\n",
    "\n",
    "sns.heatmap(df_corr, annot=True, cmap = 'coolwarm', vmin = -1, vmax = 1, fmt='.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Almost all of the features have some correlation with each other, which is understandable given that the data is for a living species whose features are all directly correlated with each other.\n",
    "- We will still drop variables that are highly correlated i.e. r > 0.95."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>r</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>rcum_Var_1</th>\n",
       "      <th>Var_2</th>\n",
       "      <th>rcum_Var_2</th>\n",
       "      <th>Var_to_Remove</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.99</td>\n",
       "      <td>diameter</td>\n",
       "      <td>5.45</td>\n",
       "      <td>length</td>\n",
       "      <td>5.44</td>\n",
       "      <td>diameter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.93</td>\n",
       "      <td>length</td>\n",
       "      <td>5.44</td>\n",
       "      <td>whole_weight</td>\n",
       "      <td>5.56</td>\n",
       "      <td>whole_weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.90</td>\n",
       "      <td>length</td>\n",
       "      <td>5.44</td>\n",
       "      <td>viscera_weight</td>\n",
       "      <td>5.41</td>\n",
       "      <td>length</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.93</td>\n",
       "      <td>diameter</td>\n",
       "      <td>5.45</td>\n",
       "      <td>whole_weight</td>\n",
       "      <td>5.56</td>\n",
       "      <td>whole_weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.91</td>\n",
       "      <td>diameter</td>\n",
       "      <td>5.45</td>\n",
       "      <td>shell_weight</td>\n",
       "      <td>5.37</td>\n",
       "      <td>diameter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.97</td>\n",
       "      <td>shucked_weight</td>\n",
       "      <td>5.35</td>\n",
       "      <td>whole_weight</td>\n",
       "      <td>5.56</td>\n",
       "      <td>whole_weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.97</td>\n",
       "      <td>viscera_weight</td>\n",
       "      <td>5.41</td>\n",
       "      <td>whole_weight</td>\n",
       "      <td>5.56</td>\n",
       "      <td>whole_weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.96</td>\n",
       "      <td>shell_weight</td>\n",
       "      <td>5.37</td>\n",
       "      <td>whole_weight</td>\n",
       "      <td>5.56</td>\n",
       "      <td>whole_weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.93</td>\n",
       "      <td>shucked_weight</td>\n",
       "      <td>5.35</td>\n",
       "      <td>viscera_weight</td>\n",
       "      <td>5.41</td>\n",
       "      <td>viscera_weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.91</td>\n",
       "      <td>shell_weight</td>\n",
       "      <td>5.37</td>\n",
       "      <td>viscera_weight</td>\n",
       "      <td>5.41</td>\n",
       "      <td>viscera_weight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      r           Var_1  rcum_Var_1           Var_2  rcum_Var_2  \\\n",
       "0  0.99        diameter        5.45          length        5.44   \n",
       "1  0.93          length        5.44    whole_weight        5.56   \n",
       "2  0.90          length        5.44  viscera_weight        5.41   \n",
       "3  0.93        diameter        5.45    whole_weight        5.56   \n",
       "4  0.91        diameter        5.45    shell_weight        5.37   \n",
       "5  0.97  shucked_weight        5.35    whole_weight        5.56   \n",
       "6  0.97  viscera_weight        5.41    whole_weight        5.56   \n",
       "7  0.96    shell_weight        5.37    whole_weight        5.56   \n",
       "8  0.93  shucked_weight        5.35  viscera_weight        5.41   \n",
       "9  0.91    shell_weight        5.37  viscera_weight        5.41   \n",
       "\n",
       "    Var_to_Remove  \n",
       "0        diameter  \n",
       "1    whole_weight  \n",
       "2          length  \n",
       "3    whole_weight  \n",
       "4        diameter  \n",
       "5    whole_weight  \n",
       "6    whole_weight  \n",
       "7    whole_weight  \n",
       "8  viscera_weight  \n",
       "9  viscera_weight  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding correlations\n",
    "## extract variable pairs having |correlation| > 0.9\n",
    "corr_var = []\n",
    "for c in df_corr.columns:\n",
    "    for r in df_corr.index:\n",
    "        if ((df_corr.loc[c, r] >= 0.9) | (df_corr.loc[c, r] <= -0.9)) & (c != r):\n",
    "            corr_var.append(list([c, r]))\n",
    "            \n",
    "## sort and remove duplicate pairs\n",
    "for i in range(len(corr_var)):\n",
    "    corr_var[i].sort()\n",
    "    \n",
    "temp = []\n",
    "for e in corr_var:\n",
    "    if e not in temp:\n",
    "        temp.append(e)\n",
    "\n",
    "corr_var = temp\n",
    "del(temp) # removing unnecessary variable\n",
    "\n",
    "## extracting data for dataframe\n",
    "corr_var_1, corr_var_2, corr_val = [], [], []\n",
    "for e in corr_var:\n",
    "    corr_var_1.append(e[0])\n",
    "    corr_var_2.append(e[1])\n",
    "    corr_val.append(round(df_corr.loc[e[0], e[1]], 2))\n",
    "\n",
    "## Cumulative correlation\n",
    "rcum_1, rcum_2 = [], []\n",
    "for e in corr_var_1:\n",
    "    rcum_1.append(round((df_corr[e].sum() - 1), 2))\n",
    "    \n",
    "for e in corr_var_2:\n",
    "    rcum_2.append(round((df_corr[e].sum() - 1), 2))\n",
    "\n",
    "# creating dataframe\n",
    "correlations = pd.DataFrame({'r': corr_val, 'Var_1': corr_var_1, 'rcum_Var_1':rcum_1, 'Var_2':corr_var_2, 'rcum_Var_2':rcum_2})\n",
    "correlations['Var_to_Remove'] = np.where((correlations['rcum_Var_1']) > (correlations['rcum_Var_2']), \n",
    "                                         correlations['Var_1'], correlations['Var_2'])\n",
    "correlations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['diameter', 'whole_weight', 'length', 'viscera_weight'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variables to drop\n",
    "correlations.Var_to_Remove.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>sex</th>\n",
       "      <th>height</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>age_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.155</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex  height  shucked_weight  shell_weight  age_class\n",
       "0   M   0.095          0.2245         0.150          1\n",
       "1   M   0.090          0.0995         0.070          0\n",
       "2   F   0.135          0.2565         0.210          0\n",
       "3   M   0.125          0.2155         0.155          1\n",
       "4   I   0.080          0.0895         0.055          0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping correlated variables\n",
    "df.drop(list(correlations.Var_to_Remove.unique()), axis = 1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F7aj22APeeit2CpGiynLkej3h1/wnk3WrDbdqADOrTu6PAXD3bcDJwCBgOfA34G7g2gwzZ6OuDnr1ip0ingEDwryzSAXpnNUHau0Ek7svBHo0emwecGKauaLbuDEspq/QayPysvfeYd75xMr+p5aORZe/xjZ3bmXv9JqP6motx5KKo+Ia25w54Sqljqy6WtMCUnFUXGN74w0V1x49whVqIhVExTW2117TtADAzjvDypWxU4gUjYprbHPmqLiCLiaQiqPiGltdHey6a+wU8TWsGBCpECquMW3ZEpZgdeRlWA0GDNB+WlJRVFxjWrgQ9twzdorSUF2tkatUFBXXmObN00qBBrvvDkuWxE4hUjQqrjEtWBCKioSpkaoq2LQpdhKRolBxjWnuXOjTJ3aK0tG/f1j3K1IBVFxjmj9fc6659t5by7GkYqi4xjR/vuZcc+29t1YMSMVQcY2po25K2BytGJAKouIay7ZtsROUnr331pyrVAwV11iWLoXevWOnKC1dusC770J9fewkIu2m4hrLggVhexPZXp8+sHhx7BQi7abiGsvChVrj2hQ1cJEKoeIay7x5WuPalL32UuNsqQgqrrEsWKDi2pTqai3Hkoqg4hrLokUqrk3Rli9SIVRcY9Fqgab17AkrVsROIdJuKq6xvPsu7LBD7BSlqVs3WLcudgqRdlFxjWHrVuikL32zBgwIe4uJlDH9D49h2TLYbbfYKUpXv35ajiVlT8U1hsWLtca1JQMGwKuvxk4h0i4qrjEsWqSRa0vUwEUqgIprDCquLevbN7RjFCljKq4x6NLXllVVheYtW7fGTiLSZiquMSxapOLamr32CpcIi5QpFdcYdHVW6wYM0JVaUtZUXGNYuxa6d4+dorT166eTWlLWCiquZnZkWkE6FPewlbQ0r6ZGy7GkrBU6cn3azF41swvNbNdUElW6DRtCx31pma7SkjJXaHEdAjwAXAQsNrP/NbOjix+rgi1bpoYt+ejaFd55J4zyRcpQQcXV3ee7+xVADXAa0AN4zMxmm9k3zUynwFuzdCnsqkF/XnbbLXy9RMpQm05ouXu9uz8MfBr4BjAQuBZ4y8xuNTMNzZqzbBn06hU7RXnQli9SxtpUXM1siJn9EFgEXAn8EjiQUGwPAP5QpHyVZ8mS0LNUWte/v1YMSNnqXMjBZnY6cDZwDPAi8G3gHnffkBzyipnNArT5fHMWL9aca76qq+GVV2KnEGmTQkeuvyAUzkPc/TB3vz2nsDaoJYxmpSkqrvlTAxcpYwWNXIF+7r6+pQPcfSPw3bZHqnBLl6ppS7569oS3346dQqRNCh25rjGzPRo/aGa9zWxbkTJVthUrdEKrEDvvDCtXxk4hUrBCi2tzlxV1AVptYWRmp5vZFDNba2atLmA0s/lmtsnM1ufcTi4wc2nZujV0fZL81NSox4CUpbymBczsjOSvDpxmZmtznq4CxgFz8nirVcDPgW7AbXlmPM/d78jz2NK2TYP7gvXvDzNnwlFHxU4iUpB851xzC+F1jZ7bDMwDvtnam7j7JAAzOybPj1tZNCVQOK0YkDKV17SAu+/g7jsAC4G9Gu4nt+7uvl9D4UzB1Wb2dtLT4GIza3I/ajObYGaee0spT9vV1qq4FmrgwDByFSkzhV7+OsjdV6QVpglnEvoZ7AGcA5xHM8u83H2Cu1vuLcOc+Vm+XMW1UL16ha+bSJlpdVrAzD4H3O/um5O/N8vd7ypasvB+T+XcfcbM/hu4GrikmB8nM7W1ujqrLXr0gFWr1JNByko+c67/CzwGLE/+3hwHilpcm1BP8ysWSt/SpSqubVFTo5NaUnZanRZw907uvjzn783dWl1fZGZVZtaVsHQLM+ua3D6Qw8yGmdnRDc+b2WHA94C7C/4sS8WyZRp9tcWAAWqcLWUn621evghsBBpOfm1MbkebWXWyjnVM8tyuwA1AHbAauBO4nXKdEgAV17aqqYGXX46dQqQghTZuOR7Y4O5/T+6fTTjRNBO4wN3XtfT6ZL3qHS0c0iPn2BeAUYXkK3m1tSqubTFwINx/f+wUIgUpdOR6NbA7gJkNB24EpgKHANcUN1oF0jrXtunZU5fAStlpyzYvDZNfnwQec/evEdoQlvdlqVnYskWXvrZVz55QVxc7hUje2jLn2rA4fyzw1+TviwH10WuJu/aDag/tBitlptDi+grwtWRTwvG8X1wHEE48SXPWrAnrNaVtdBmslJlCi+ulwJeBycCv3b2hk/EpwD+KGazi6NLX9hk0CKZNi51CJG8FrRZw978l/Vx3dvfVOU/dCrxTzGAVR5e+ts/AgXD77bFTiOSt0J0IcPdthHWnuY+9WaxAFUuXvrZPt27wzjtQXw+dsl6eLVK4Qte5diI0UzkO2JNG0wruPr540SrM8uWwyy6xU5S3vfaC+fNh8ODYSURaVejI9VrgfEKvgfm8v3JAWrNsmaYF2qumBmbMUHGVslBocf0c8Dl31+Uyhaqthf33j52ivNXUhJNa//IvsZOItKrQyasdAJ2ybYu6Oo1c22vwYPUYkLJRaHH9DfDpNIJUPK0WaL9+/eBNnTuV8lDotMAa4BIzOwJ4mbB/1nvc/QdFylV51q6F7t1jpyhvVVXQuTNs2AA77RQ7jUiLCi2uZwBrCd2qGnesckDFtTn19WDl2+e7ZAwcGC6DPeyw2ElEWlToRQSD0gpS0errYyeoHDU1MH26iquUvDavxjaz3mYaiuVl1SrYeefYKSrD4MHw4ouxU4i0qqDimmzT8l0zWwXUAoOSx68ys3PTCFgRtFKgeAYPDiNXkRJX6Mj1EsIVWhey/cmsacBZRcpUeerqdHVWsfToETqMqX2jlLhCi+uZwHnu/htgW87jM4DhRUtVaVRci6tv33AZrEgJK7S4VgP/bOLxrUC39sepULW1Kq7FpIsJpAwUWlzn0/SmgccBs9udplKpl2txDRwIU6fGTiHSokKL68+B683shOT+MDP7d8L61p8VNVklUXEtrqFD4aWXYqcQaVGh61xvMLPewAOEaYC/AJuAH7i7Ohk3Z/lyGD06dorKseeesGBB7BQiLWpLs+wJZnYb7/dznenu2oWgJStWqFF2MZmFdcMrV0Jv7YsppSnvaQEz62NmtyVrXOcDzwOPAD8xsz4p5asMq1frIoJiGzJEe2pJSctr5GpmOwFTgD6EzlgzAQP2I/R4PdLMDnb3jWkFLWvbtmlrkmIbNCic1DruuNhJRJqU77TAfxDmWPd39yW5T5jZD4FngH8HflTceBXAXQve0zB8ODz4YOwUIs3Kdzh1KuGk1ZLGT7j7YuAqQO3hm7J+vdrjpaF/f3j99dgpRJqVb3EdCfytheenAPu0P04F0smsdFRVQZcuoU+uSAnKt7j2BFa28PzK5BhpTMU1PUOG6EotKVn5Ftcqtu8l0Fh9cow0VlenlQJpGTxYV2pJycr3hJYBvzWzzc0836VIeSrPihUqrmkZMQIeeSR2CpEm5Vtc78zjmHntCVKxli9X05a0VFfDP5vqIyQSX17F1d2/lHaQilVbC310jUUqqqpghx3CSS39AJMSo5XtadMuBOkaNkxNXKQkqbimbflyrRZI05Ah8MILsVOIfICKa9pWrlRxTdOIEfDss7FTiHyAimvaNm6Erl1jp6hculJLSpSKa9rq62MnqGydOoXfDJYti51EZDsqrmnaujX0HpV0jRgBzz8fO4XIdlRc07RqleZbszB8OPz977FTiGxHxTVNK1Zo/WUWRo6E556LnUJkO5kWVzM73cymmNlaM2u1yamZDTKzR8xsvZnVmtmVZmX0e7aKazZ23TV8rbe11P5CJFtZj1xXEXaQ/X+tHWhmVcDDwEKgL3A08HngWynmKy71FcjOoEEwY0bsFCLvybS4uvskd78bmJvH4WOAocDF7r7e3V8DribseFAeli9Xcc3KyJGad5WSUspzrqOAOe6+OuexqcAgM/vA79pmNsHMPPeWVdBm6eqs7Oy7Lzz1VOwUIu8p5eK6C7C60WOrc57bjrtPcHfLvaWcr3UqrtkZOBBmzoydQuQ9pVxc1/LB3Q165TxX+urqVFyzUlUVGuQsXhw7iQhQ2sV1OjDUzHKr08HAPHcvj+KqLV6ytd9+8PTTsVOIANkvxaoys64kOxeYWdfk1lSOKcCbwEQz625mw4GLgZuyS9xOb7+tpVhZ2m8/eOyx2ClEgOxHrl8ENgKTkvsbk9vRZladrGcdA+Du24CTgUHAcsLus3cD12acue02b4bO+W72IO02ciT84x+xU4gA+W/zUhTufgdwRwuH9Gh0/DzgxBQjpcvjL1joUHbYAbp3D01c+vaNnUY6uFKecy1vmzaF/+ySrf3205IsKQkqrmlZuVLbu8RwwAEwaVLrx4mkTMU1LeorEMc++6j9oJQEFde01NWpuMbQuXPYbffNN2MnkQ5OxTUtKq7xjBqlqQGJTsU1LXV1atoSy8EHw1/+EjuFdHAqrmlZtkwntGIZMADmzAnrjEUiUXFNi/oKxGMWlmRNmRI7iXRgKq5pUUesuA49FP74x9gppANTcU3LihWaFojpwAPhiSdip5AOTMU1LWvXwk47xU7RcXXpEpZkzZ4dO4l0UCquaXEPc38Sz+jRcP/9sVNIB6Ximob6+tgJBODwwzXvKtGouKZh9WqtcS0Fu+wSmucsXBg7iXRAKq5p0A4EpeOII+C++2KnkA5IxTUNuvS1dIwZo+IqUai4pkHFtXT07BlWDsydGzuJdDAqrmlYsUJzrqVkzBj4v/+LnUI6GBXXNNTWas61lBx5ZFiSpW13JEMqrmlQ05bS0qMH7LknTJ8eO4l0ICquaVi+XMW11IwdC7/6VewU0oGouKahthZ23TV2Csl12GGhgfbWrbGTSAeh4pqGVat0QqvU7LAD7L8/PPZY7CTSQai4pqG+HjrpS1tyjj0WbrstdgrpIFQBis1dZ6VL1YgR8M9/wpo1sZNIB6DiWmyrV4ez01J6zHTFlmRGxbXYtFKgtI0fD3feGTuFdAAqrsWmvbNKW58+YdpGl8NKylRci017Z5W+Y46BO+6InUIqnIprsdXWqmlLqTvqKHjgAZ14lFSpuBabLn0tfd26QXU1PP987CRSwVRci03FtTwcc4wuh5VUqbgW2/LluvS1HHz4w/DUU7BlS+wkUqFUXItNJ7TKQ1UVjBqly2ElNSquxbZuHXTvHjuF5GPsWPj1r2OnkAql4lps9fXhSiApffvsE3q8btwYO4lUIBXXYnr3XejcOXYKyZcZHHIIPPJI7CRSgVRci6m2FnbbLXYKKYT215KUqLgW07JlKq7lZuhQmDULNmyInUQqjIprMS1bppUC5cYMDj0U/vzn2Emkwqi4FtPSpSqu5ejII+Gee2KnkAqj4lpMS5ZoWqAcDRsGM2fCpk2xk0gFybS4mlknM/uBmdWa2Xoze8TMalo43s1sY3Jsw23/LDMXRMW1PJmFK7YefTR2EqkgWY9cLwY+CxwN9AUWAg+ZWUs5TnL3Hjm3GVkEbROd0CpfmhqQIsu6uJ4HXO3ur7n7ekKxHQEclXGOdGhL7fI1ciS8+KJ6DUjRZFZczawnUANMbXjM3VcDc4ADW3jp3Wa20sxeMrOzW3j/Cck0wnu3IkXP38aNsOOOmX9YKYJOncLW208/HTuJVIgsR64NHaRXN3p8dc5zjR0HDAb2Ai4DJprZ15o60N0nuLvl3tofuQDa9bX8HX64Ni+UosmyuK5N/my8VqlXznPbcffH3X2ju29290eAHwNfTC9iO6xbF5owS/kaNSqMXOvrYyeRCpBZcXX3NcAC4JCGx5KpgiHAy3m+TT1Qml1RFi8Om99J+ercOSzLeuGF2EmkAmR9Qutm4CIzG25m3YGJwOvA3xofaGYfNrNDzKyLmXU2s+OBbwJ3Zxs5T4sXa6VAJfjIRzQ1IEWRdXG9GriPUEyXA4OAU9293szGJOtYq5Nj9wZ+A7wNrACuBS53959mnDk/Kq6V4ZBD4K9/1fy5tFum/fHcvR74r+TW+LkpQI+c+w8BD2WXrp0WLoTevWOnkPbacUfo1w9mzIADDoidRsqYLn8tloULNedaKbRqQIpAxbVYFi2C3XePnUKK4fDD4eGHY6eQMqfiWixqlF05dtopXGn32muxk0gZU3Etls2btcVLJTniCLj33tgppIypuBbDli3h8kmpHKNHwwMPxE4hZUwVoRiWLtV8a6Xp0QN69QpbwIi0gYprMSxerGVYlWjMGLjrrtgppEypuBbDokVqNViJRo+GP/5RFxRIm6i4FsO8ebDHHrFTSLF16wbV1fDcc7GTSBlScS2GOXOgb9/YKSQN48fD7bfHTiFlSMW1GObNg732ip1C0nDggfC3v2nzQimYimsxaHuXylVVFeZedTmsFEjFtb3cYds2rXOtZCedBDfdFDuFlBlVhPZavlyj1krXp084ufXyy7GTSBlRcW2v+fN1MqsjOPVUuOqq2CmkjKi4ttf8+Wo12BGMGhVWhcybFzuJlAkV1/Z6802tce0IzOBTn4L/+Z/YSaRMqLi2l9a4dhyjR8O0aTB7duwkUgZUXNvrtdegf//YKSQLZvClL8E3vhE7iZQBFdf2Wr0adt45dgrJyn77hbWvd5fmJsRSOlRc22P1aujePXYKydp558H3vx+6oYk0Q8W1PWbPhgEDYqeQrPXoEQrsJz8JGzbETiMlSsW1PWbPDtswS8dzwAFw7LHw6U/Du+/GTiMlSMW1PV59VSPXjuzEE+FDH4KTT4Y1a2KnkRKj4toes2aFfp/ScZ18ctixYOxYbQkj21FxbY+33tIFBAJHHw0XXginnQa33KKdCwRQcW279evDkhx1wxKAwYPhmmtg0iQ45RSoq4udSCJTZWirl1+GYcNip5BS0q1bGMEeeWQYzT76aOxEElHn2AHK1gsvwJAhsVNIKRo9GkaMgO98ByZPDmtiq6pip5KMaeTaVs8/D8OHx04hpWq33UJRXb4cjj8+/CkdioprW82aBTU1sVNIKauqgi98IexkMG6cdpHtYDQt0BZr10KXLvpVT/Jz6KFhPfTXvgZnnglf/3poAiMVTSPXtnjuuTCnJpKvvn1h4kSYMiX0hV29OnYiSZmKa1v86U9hy2WRQnTpAhdcEL53jjwSnnwydiJJkXkFL3g2M0/l8xs1KoxCunYt/ntLx1BXBz/5Sbh89pproGfP2ImkbZqd39HItVCLF4c2gyqs0h59+sCVV8Luu8Phh8Ott4Yt2qViqLgWatIkOOig2CmkEpjBCSeEkeuUKfDhD4cm3CqyFUHTAoUaNw6++lXYe+/ivq/IypXwu9/BK6+E77Evfxl22SV2KmlZs9MCWopViHnzYONGFVZJR+/ecM45oW/FI4+E6YJDDw2PHXGElm+VGY1cC3HZZdC5M4wfX7z3FGmOO0yfDo8/DnPnwsc/DqefHqalVGhLRbP/ECqu+Vq1Kowerr8+LKkRydKmTeGS62efhYULQw/ZU04J01Taxy0mFdd2O//8sFzm+OOL834ibbVlC8yYAS+9FOZnG74vTzgBDjkk/HYlWSmN4mpmnYArga8A3YG/Aee6+4Jmjj8IuBE4EFgBXOvuPy3g4xWnuD7+OFx6aVjbqv6tUmrefju0wJw+HV5/PfSWPfHEsMfXvvtqCiFdJVNcLwXOBT4KLAZ+DBwOHOju9Y2O3QV4A/g5MJFQYP8MnOPu9+f58dpfXKdMeX8r5d12a997iaTNPazFnjYt7PG2YEHoa3DooWFTxaFDw/3dd1dvjOIomeI6H5jo7jcl93sBtcDx7v50o2PPAn4A9G8ovGY2ETjU3fM6o9Tm4uoevjFvuCGMBr75Tdhzz8LfRyQ293A12Ouvh22Jli8P99esCc/ljmqrqsLFMTvuCDvtFG49erz/965dw/kGM6ivh82bw1zw+vWhmdHateHvGze+vyNup06hiXivXuH/UP/+MHBg6ChXXR3ul/eccfylWGbWE6gBpjY85u6rzWwOYVT6dKOXjAKmNRrRTgXObub9JwDfaeLxduUGwhlaEZEPcndvsshkOfPdsBp6daPHV+c81/j4fI/F3ScAE9oWrXiS0XJZTXKVW2blTZfyFkeWZ2fWJn827lDRK+e5xsfne6yISEnJrLi6+xpgAXBIw2PJVMEQ4OUmXjIdOChZYdDg4GaOFREpKVmvK7oZuMjMhptZd8IqgNcJS7Ia+z1h2uLbZrajmR1GmG+9KbO0IiJtlHVxvRq4j1BMlwODgFPdvd7MxpjZejOrBnD3tYQlWx8jzLX+Dviuu/8248yF+m7sAG1QbpmVN13KWwQVfYWWiEgsutxIRCQFKq4iIilQcRURSYGKa4HMrJOZ/cDMapMTcI+YWU0Lxx9kZs+Y2QYzW2hmF5ZqXjM73Mz+lBy71sxeMrNPlWreRq872My2mNmTGcTM/biFfj90M7MfmdkiM3vHzOaY2UklnPfzZjYj+X5YZGY/MbMdM8x7uplNST5+qyeIzGxQ8jmtTz7HK60ol2m2gbvrVsANuBSYB4wAegC/AF4BOjVx7C6E3gnfAboSmtS8DXymRPN+DDgD2J3wg/fTwCbgsFLMm/OarsAM4DHgyRL+fjDgkeQ2KHmsPzCwRPOOArYBn0my1wD/BL6fYd4Tgc8CXw7lqsVjq4CZyefUI/kc5wH/meX3xHt5YnzQcr4B84Gv5dzvBbwLHN3EsWcBS3K/cQlre58oxbzNvH4q8M1Szgv8CPgJ4fLnJ0v4++EEYCOwe5YZ25H3k0Bdo8euBR6KkPuYPIrrMcnn0ivnsa8Bc2N8rTUtUIDmms8ADc1nGmuu+UxTxxZdG/I2fn0/YB/C1XKpa0teMzsaOBm4LP2EH/jYheYdTxhJXW5my8xsvpndYGY9MojblryTgHnJr+ZVZjYEOIVwgU8pGgXMST6nBlOBQUkL00ypuBYm1eYzKSg073uS//C/J4xSHi96sqYVlDfJeDtwtrtvSDVZ0wr9+u5O+GEFMBg4GhhNGHlnoaC8ydf0NsKVle8SivCzwK9TS9g+zf1/a3guUyquhSm35jOF5gXeG+E8AiwlzMFmpdC81wJ/9ka9gDNUaN51hDnMS919g7svJEwTfSKtgI0UlNfMzgR+SBitdgH6EX5A3JlexHZp7v9bw3OZUnEtgJdZ85k25MXMegOPA28B/+rum9NPGrQh70eBM8xshZmtAC4GjkzuDyrBvC81vDT3bdLK11gb8h4CTHb3Ke5e7+5LCSeL/iWDuG0xHRiafE4NDgbmebicPlsxJnrL+UY42/omMJywD9jNtL5a4ApgR+AwYCWhaJVi3r7Aq4RftZs9O19iefvn3H5M+LW1P1BVgnl7ELY3ujb5fugHvADcWKJf39OS79fRhNUCewAPk+FJQ8IKgK6Ek4Ge/L1rM3kbVgvcnHxuw4G5wEVRvpdjfNByvhFG+z8kNJ55hzDpPzB5bgywHqjOOf6g5D/8RsJo8MJSzUtYMubJcetzbjeXYt4mXjshy//4bfx++BAwOXl8EXAdsFMJ5/06MJswpVEL3AsMyDDvWcn3ZOPbMUB1kndMzvGDks/pneRz/B+SHipZ39S4RUQkBZpzFRFJgYqriEgKVFxFRFKg4ioikgIVVxGRFKi4ioikQMVVojCzgWbmZnZUEd7LzewLOffnm9nl7X3fduQp+OOb2ZNm9su0Mkn2OscOIFKBDgWK3kgmKb5D3f2YYr+3FJ+Kq0iRuXtd7AwSn6YFJFVmdpSZ/d3M1iW36WZ2Ys4h/czs4WQbnLlmdlaj1/cws+vNbHFyzLRibT1jZkOSKYVhOY/NN7NFOfeHJceMSO7vYGYTzGyemW0ys5lmdm6j991uWsDMepvZb5NtXWrN7PtmdqeZPdZEpiuSXq9vm9mvG3q9mtkE4CvA2CSPN/5aSWlRcZXUmFln4EHgeeDDyW0C2//KfBWhP+gBwD3AL81sePJ6Ax4iNEH+N2A/4CbgHjM7tr353P1NYCGhiTVJM+g9gZ4NGZLnFrv7a8n9W4FPAecSerN+D5hoZl9p4UP9KvkcTk7erz9Ntxn8DLAb4br505PjL0meuxa4i9CnYq/kdm8hn69kS9MCkqadgV2BB939jeSxNyCc0Eru/8zd70seuwK4ABgHvA6MJXRk2tNDuzyAX5jZ4clxxWjiPRk4FriFUPieITSGHp9kGJ8cQ9LG8AxgX3efnbx+XjKqvYDQWHo7yaj4FOA4d294n3OA45rIssDdv5H8fbaZ3Zscd4W7rzezjcBmd1/W/k9b0qbiKqlx91XJSZhJZvYE8BTwQM4oEHL6iLr7NjNbThg9Qjgx1AVY3GgDzy4kRboIJgPXJKPk8YSCvQUYb2a3EEaRlybHHkJovTe1UZ7OhCbYTdk3+fO5hgfcfYuZTSX88MnVeDudJYQN+qQMqbhKqtz9bDO7ntCP83jg+2Z2PqEtHEDjZtzO+9NVnYA1hCLbWLGaeD8B9CFMS4wDricU14uA/Qk9TJ/IyQNwBB9cDdBae7l82s+19LWQMqPiKqlz91cJTbh/bGY3A+fwfnFtyVTCNh1dk/dII9tbZvYm4df6bsA/CEWtM6GX6Vx3X5Ac/mLyZ7W7P5znh5iV/DmaZBojmYs+mDDtUIjNhIbQUgb0U1FSY2ZDzWxismKgxsxGExoyz2rttYkngMeA35vZJ8xssJkdbGYXmNnZRYz6BHAm8LS7b/OwW+9ThPnVhlEr7j6HsEvDrWb2xeTzG2VmXzazS5p642Su+SHgRjMba2b7EuZ3d6HwLV7mASPN7ENmtruZ7VjoJyrZUXGVNL0DDCOsAngd+B3hhNH5+bzYQyf3Uwm70F5H6Ij/J+DjhK1KimUyYaT6RM5jTzTxGIRR93XAtwk/JB4nFOa5Lbz/lwgj978ATxK2enkU2FRgztsII+tngDrgswW+XjKknQhEMmZmVYQfFA+6+7di55F0aM5VJGVmdjThxNg0wgqBbwADgTvipZK0qbhKxTKzMYRfxZtzkrtPySBKFXA5MJSwEuFVYJy7z8jgY0skmhaQimVm3YC9WzhksbtvzCqPdCwqriIiKdBqARGRFKi4ioikQMVVRCQFKq4iIilQcRURScH/B96G48r6tP8EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking dsitributions\n",
    "for e in df.columns[1:-1]:\n",
    "    sns.displot(df[e], kind = 'kde', color='red', fill = True)\n",
    "    plt.title(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check `height` since it has a very narrow distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to check normality\n",
    "# pdf, cdf and qq plot\n",
    "def prob_plots(x):\n",
    "    from scipy.stats import probplot\n",
    "    fig, ax = plt.subplots(1, 3, figsize = (18, 6))\n",
    "\n",
    "    res = probplot(x, plot = ax[0])\n",
    "    ax[0].set_title(\"Q-Q Plot\")\n",
    "\n",
    "    ax[1] = sns.kdeplot(x, fill = True, ax = ax[1])\n",
    "    ax[1].set_title(\"KDE (PDF) Plot\")\n",
    "\n",
    "    ax[2] = sns.ecdfplot(x, ax = ax[2])\n",
    "    ax[2].set_title(\"KDE (PDF) Plot\")\n",
    "\n",
    "# checking height\n",
    "prob_plots(df['height'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`height` has two outliers which, if fixed, can make the distribution normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR = 0.05\n",
      "Upper, Lower Bound for Outliers = 0.24000000000000002 , 0.039999999999999994\n",
      "Values to replace outliers with = 0.185 , 0.09\n",
      "Number of Outliers = 27\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.05, 0.24000000000000002, 0.039999999999999994, 0.185, 0.09)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixing outliers for height\n",
    "# function to get outliers and fix them\n",
    "def outlier_fix(x):\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    \n",
    "    # we will use values > 1.5(IQR) as outliers and equate them to the 90th/10th percentile values \n",
    "    \n",
    "    iqr = np.subtract(*np.percentile(x, [75, 25]))\n",
    "    iqr_upper_bound = 1.5*iqr + np.percentile(x, 75)\n",
    "    iqr_lower_bound = np.percentile(x, 25) - 1.5*iqr\n",
    "    upper_replacement = np.percentile(x, 90)\n",
    "    lower_replacement = np.percentile(x, 10)\n",
    "    print(\"IQR =\", iqr)\n",
    "    print(\"Upper, Lower Bound for Outliers =\", iqr_upper_bound, \",\", iqr_lower_bound)\n",
    "    print(\"Values to replace outliers with =\", upper_replacement, \",\", lower_replacement)\n",
    "    print(\"Number of Outliers =\", len(x[(x > iqr_upper_bound) | (x < iqr_lower_bound)]))\n",
    "    \n",
    "    return iqr, iqr_upper_bound, iqr_lower_bound, upper_replacement, lower_replacement\n",
    "\n",
    "outlier_fix(df['height'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will equate outliers to upper limit (0.9 IQR)\n",
    "df.loc[(df['height'] > 0.25), 'height'] = 0.24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob_plots(df['height'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers in Remaining Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "height\n",
      "IQR = 0.05\n",
      "Upper, Lower Bound for Outliers = 0.24 , 0.04\n",
      "Values to replace outliers with = 0.185 , 0.09\n",
      "Number of Outliers = 25\n",
      "\n",
      "shucked_weight\n",
      "IQR = 0.316\n",
      "Upper, Lower Bound for Outliers = 0.976 , -0.287\n",
      "Values to replace outliers with = 0.647 , 0.0865\n",
      "Number of Outliers = 48\n",
      "\n",
      "shell_weight\n",
      "IQR = 0.199\n",
      "Upper, Lower Bound for Outliers = 0.627 , -0.168\n",
      "Values to replace outliers with = 0.42 , 0.065\n",
      "Number of Outliers = 35\n",
      "\n",
      "age_class\n",
      "IQR = 1.0\n",
      "Upper, Lower Bound for Outliers = 2.5 , -1.5\n",
      "Values to replace outliers with = 1.0 , 0.0\n",
      "Number of Outliers = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# fixing outliers for height\n",
    "# function to check for outliers\n",
    "def outlier_fix(x):\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    \n",
    "    # we will use values > 1.5(IQR) as outliers and equate them to the 90th/10th percentile values \n",
    "    \n",
    "    iqr = np.subtract(*np.percentile(x, [75, 25]))\n",
    "    iqr_upper_bound = 1.5*iqr + np.percentile(x, 75)\n",
    "    iqr_lower_bound = np.percentile(x, 25) - 1.5*iqr\n",
    "    upper_replacement = np.percentile(x, 90)\n",
    "    lower_replacement = np.percentile(x, 10)\n",
    "    print(\"IQR =\", format(iqr, '.3'))\n",
    "    print(\"Upper, Lower Bound for Outliers =\", format(iqr_upper_bound, '.3'), \",\", format(iqr_lower_bound, '.3'))\n",
    "    print(\"Values to replace outliers with =\", format(upper_replacement, '.3'), \",\", format(lower_replacement, '.3'))\n",
    "#     print(\"Ratio of Outliers =\", format(len(x[(x > iqr_upper_bound) | (x < iqr_lower_bound)])/len(x), '.2f'))\n",
    "    print(\"Number of Outliers =\", len(x[(x > iqr_upper_bound) | (x < iqr_lower_bound)]))\n",
    "    \n",
    "    return iqr, iqr_upper_bound, iqr_lower_bound, upper_replacement, lower_replacement\n",
    "\n",
    "for e in df.columns[1:]:\n",
    "    print(e)\n",
    "    outlier_fix(df[e])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Too many values still outside the `1.5xIQR` range so we will leave them as is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "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": [
    "# check dependent variable distribution\n",
    "sns.countplot(df['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# compare dependent variable's categories\n",
    "for e in df.columns[1:-1]:\n",
    "    sns.boxplot(x = df['sex'], y = df[e])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- All the categories are fairly equally distriburted. \n",
    "- All the parameters are low for `Infants` which is as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>height</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>age_class</th>\n",
       "      <th>sex_I</th>\n",
       "      <th>sex_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.095</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.090</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.135</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.125</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.155</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.080</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   height  shucked_weight  shell_weight  age_class  sex_I  sex_M\n",
       "0   0.095          0.2245         0.150          1      0      1\n",
       "1   0.090          0.0995         0.070          0      0      1\n",
       "2   0.135          0.2565         0.210          0      0      0\n",
       "3   0.125          0.2155         0.155          1      0      1\n",
       "4   0.080          0.0895         0.055          0      1      0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode dummies for sex\n",
    "df = pd.get_dummies(df, drop_first=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>height</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>age_class</th>\n",
       "      <th>sex_I</th>\n",
       "      <th>sex_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4175.000000</td>\n",
       "      <td>4175.000000</td>\n",
       "      <td>4175.000000</td>\n",
       "      <td>4175.000000</td>\n",
       "      <td>4175.000000</td>\n",
       "      <td>4175.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.139304</td>\n",
       "      <td>0.359476</td>\n",
       "      <td>0.238834</td>\n",
       "      <td>0.498443</td>\n",
       "      <td>0.320958</td>\n",
       "      <td>0.365988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.038432</td>\n",
       "      <td>0.221954</td>\n",
       "      <td>0.139212</td>\n",
       "      <td>0.500057</td>\n",
       "      <td>0.466901</td>\n",
       "      <td>0.481764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.186250</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>0.234000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.328750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            height  shucked_weight  shell_weight    age_class        sex_I  \\\n",
       "count  4175.000000     4175.000000   4175.000000  4175.000000  4175.000000   \n",
       "mean      0.139304        0.359476      0.238834     0.498443     0.320958   \n",
       "std       0.038432        0.221954      0.139212     0.500057     0.466901   \n",
       "min       0.010000        0.001000      0.001500     0.000000     0.000000   \n",
       "25%       0.115000        0.186250      0.130000     0.000000     0.000000   \n",
       "50%       0.140000        0.336000      0.234000     0.000000     0.000000   \n",
       "75%       0.165000        0.502000      0.328750     1.000000     1.000000   \n",
       "max       0.250000        1.488000      1.005000     1.000000     1.000000   \n",
       "\n",
       "             sex_M  \n",
       "count  4175.000000  \n",
       "mean      0.365988  \n",
       "std       0.481764  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train = (3340, 6)\n",
      "X_test = (835, 6)\n",
      "y_train = (3340,)\n",
      "y_test = (835,)\n"
     ]
    }
   ],
   "source": [
    "# train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop('age_class', axis = 1)\n",
    "y = df['age_class']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# add constants to X_train, X_test\n",
    "X_train, X_test = sm.add_constant(X_train), sm.add_constant(X_test)\n",
    "\n",
    "print('X_train =', X_train.shape)\n",
    "print('X_test =', X_test.shape)\n",
    "print('y_train =', y_train.shape)\n",
    "print('y_test =', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.451533\n",
      "         Iterations 7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>age_class</td>    <th>  No. Observations:  </th>  <td>  3340</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  3334</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 12 Apr 2022</td> <th>  Pseudo R-squ.:     </th>  <td>0.3486</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:54:52</td>     <th>  Log-Likelihood:    </th> <td> -1508.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -2315.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>          <td>   -3.7758</td> <td>    0.305</td> <td>  -12.393</td> <td> 0.000</td> <td>   -4.373</td> <td>   -3.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>height</th>         <td>   15.4284</td> <td>    3.052</td> <td>    5.055</td> <td> 0.000</td> <td>    9.447</td> <td>   21.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shucked_weight</th> <td>   -7.1630</td> <td>    0.531</td> <td>  -13.493</td> <td> 0.000</td> <td>   -8.204</td> <td>   -6.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shell_weight</th>   <td>   18.9386</td> <td>    1.230</td> <td>   15.392</td> <td> 0.000</td> <td>   16.527</td> <td>   21.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex_I</th>          <td>   -0.8297</td> <td>    0.125</td> <td>   -6.652</td> <td> 0.000</td> <td>   -1.074</td> <td>   -0.585</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex_M</th>          <td>    0.0662</td> <td>    0.106</td> <td>    0.626</td> <td> 0.531</td> <td>   -0.141</td> <td>    0.273</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:              age_class   No. Observations:                 3340\n",
       "Model:                          Logit   Df Residuals:                     3334\n",
       "Method:                           MLE   Df Model:                            5\n",
       "Date:                Tue, 12 Apr 2022   Pseudo R-squ.:                  0.3486\n",
       "Time:                        13:54:52   Log-Likelihood:                -1508.1\n",
       "converged:                       True   LL-Null:                       -2315.1\n",
       "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const             -3.7758      0.305    -12.393      0.000      -4.373      -3.179\n",
       "height            15.4284      3.052      5.055      0.000       9.447      21.410\n",
       "shucked_weight    -7.1630      0.531    -13.493      0.000      -8.204      -6.123\n",
       "shell_weight      18.9386      1.230     15.392      0.000      16.527      21.350\n",
       "sex_I             -0.8297      0.125     -6.652      0.000      -1.074      -0.585\n",
       "sex_M              0.0662      0.106      0.626      0.531      -0.141       0.273\n",
       "==================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "logistic_regressor = sm.Logit(y_train, X_train).fit()\n",
    "logistic_regressor.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Summary Statistics -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Model has a **Pseudo R-Squared of ~0.35** which is low. But, we will know more once we have the full picture with confusion matrix.\n",
    "2. **Log-Likelihood** - Also known as LL(Fit) it is an indication of how good the model fits and is usually used to compare with other models. But, it should be as low as possible compared to LL-Null (Log Likelihood of model with only intercept considered). \n",
    "3. **LLR p-value** - This is significant indicating that the model represented by LL(Fit) is significantly better than LL(Null) or LL(Observed)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bh/L4XzdbXWan9y/itPOnscOIxSxZ1JUHfz+I26/blsbG6mlJrQtfcWNF9Ru4kqPPfpMttq+jS7dGXvxnX3560XaoFo445W16brSKw0+eyZCd6ujRp4HX/9ObWy7YnvpltXxu7OsA1C+t4YrjdmHozsv49u+fY8mCLtz09R1YunAHvnLDS8UrYLk4e+xUhu+6iB9dNoy33+zJXh+cy/lXvcClS7ow6YkBbDKonu/cOpnH/7Yp1126E1sOXcY5V0wFBeOv2b7S1a8oD9ysh/TmmFcApwC9gceAL5R6b7eOYLfRC9aa33zobCY+MIAXnuzLEae8zbCRSxg2csnq/E23Ws6Up/rywlNr7ib/2O83ZfG8Lpx1/Uv06tsAwMlXvMZVJ+3MZy6YzmZbL2+TbemsunZr4IMfmc33LhzOpCeTp4/ed8dW7L7PfI47fTqTnhjAkf89k2VLavnhJTsRIWa80puB16/g5K++wq9v2oblddXT5SxXNV2WWIktOQ/4DHAAyRPPZgD3FdxZuGpEwLRJfZg6sS8j9l3YbJm3pvVk8oT+jNhnzd3kX5q4EcNGLlkdIAF2PWABqgmmTtxog9e7s+vSJaipDVYuX3u3XFFfw/DdFlHbpZEReyzk6ScGkH36wMTHBtCjVyPb77y4ravcrpTzjJv2rhLd7TOAcRExFUDSecC7wP7AoxWozwaxbFEtX9xrFKtWimgUnzrnDQ4/+Z21ypy510gWzevKqhU1fPjEdzjxktdX581/txv9Nl2xVvkuXYM+/VexYFa3ttiETq1uWRemTOrHf39hBq+82IfZb/dg5H7z+MBBc+naLejbfyUDNl3BlEn91nrd/DnJZzNgkxXNLbbT8Oj2OpLUj+Th4NnHOy6QNI3k8Y6PZsqOBS5ty/rlqUefBsbdP5nl9TW8NHEjfjNuKBsPWsFBx81aXWbs3f9heV0Nrz/fmzuuHEq/TYbw6a+9UcFaW9b3zt+Zr3x7Kj994B9EI7z5ei8euHswHz/hLcp4dlWn5NHtdVfy4x0jYiwwNpsmKeggampg0Lb1AAzdeRlLF3bhzqu2XitINh1XHLJTHTU18KOv7MjHvvgWPXo1svHmK5g7s/tay1y1UixZ0IX+m3XuVkpbmfV2Dy46bTe692ig90armDe7Oyd/7RWWLq5l4byuzJvdjY0LWowbD0zm583p3K39jtKVLkVbHwfMPt4xqz8lPt6xo4pG3nN8a638gGgUq1YkO9ewUYt56d99WLZ4zS/yc3/vRzSKnUZ17uNdbW15fS3zZnenS9dG9jtkNk8+vAkRYsqkfuyxz3yyv90j959H/bIaXnmhcx83zvNBYJXWpi3JiFgoaTrJ4x0nwuou+PaseQRkh/e77w9h+N6L2GzrehpWiRf+0Zc/3rglHzo2aUX+6eYt2GKHOgZvV4eAV5/tza++M5SRh8yjT/9koGb/T8zmnmu34vqzduS482awZEEXfnrxduzzsTke2W4je+wzjy5dgzde7cUmg5Yz5suv0b1HIz//4XYA/PnOLfjY8W9x9mVTuffnQxi8dR1jznqNP96xZace2YbqGt2uxMDNTcA3JD0MvAWMI3k27mMVqMsGsWxxLbdeuB3z3ulGtx7BZlvXc9z5MzhkTDJw07BK3HHlUGa/0R3VwCZbLufQz73DEae+vXoZPXo3cvGvn+dnl2zHJUe9n249kpPJx3zr9QptVefTq08DJ537KpsNrqduWS1PPz6A71+4M3NnJYdB5rzTg4tO25XTznuF6343kSWLu3D/77bg9uu2rXDNK6+jtBJLoYi2PcyXnurzHd57nuTrJbw25ry5xYatoOXqhF2PrHQVrEwPzLuFWI+RKUlx9ONfLKnsPfvduF7ragtt3pKMiEbgwnQysyq0qtHdbTOzFlVTd9tB0sxy5yBpZlZENZ0n6SBpZrlzS9LMrAgP3JiZFeGWpJlZEe381MeyOEiaWe48cGNmVkQ1dber5+iqmbUbESppKoWkcZKel7RI0kxJt0kaWFAmJNVJWpKZ3p/Jr5F0paR307z7JQ0tZf0OkmaWu4bGmpKmUhcHnAgMJLk59xBgfDPlDo+IPpnpuUzeOj82xt1tM8tdnt3tiPhmZnaWpOuAO8pczDo/NsYtSTPLXURpE6zuKmensa0s/mBgcjPpv5Y0V9LTkk5rSmzpsTFA02NjinJL0sxyV87odjm3SpN0LHAq8KGCrA8DT5B0zQ8C7pDUJSJupIzHxjTHLUkzy12eAzdNJB0H3Ax8PCKeXnt98VBE1EXEioi4H/gBMCbNXq/HxhRtSUr6awl1b6rkoaWWNbPqlvcpQJJOAa4GPhoRj5dSBUias+v72JjWuttvlVAZM7O1NDbmFyQlnQ18Czg0IiY2k78nSa/4WZLgeCDwVeDbmWLr/NiYokEyIj5f2maYma2R82WJ1wKrgAnSWssdEREzgC2Bq0hODVoFTAcujoibMmWvIuluP8aax8Z8PH1SQlFlD9ykzdQdgOciwg+ANrP3yPkUoKILi4j7gPtaKbPOj40peeBGUi9JPwfmA/8EtkrTb5J0cbkrNrPqVc4pQO1dOaPblwO7kQy912XS7weOzrNSZtaxbYjR7Uopp7t9NDAmIh6TlP0NmAJsl2+1zKwjq6YbXJQTJAcBbzST3rXM5ZhZlesorcRSlNPdfhHYr5n0o2j+EiEz66yixKkDKKcFOA74saQ+JCdpjpZ0BnA2cOyGqJyZdUzV1JIsOUhGxG8k9QAuBXoBt5J0v0+NiD9uoPqZWQfUUUauS1HWscSIGA+Ml7QJUBMRszZIrcysQ+uULckmkrYDRqT/Px8Rr+VeKzPr0CLHyxIrreQgKWljki72JzPJIekPwCkRMT/vyplZB1VF3e1yRrdvBt4PfATok06HAbukeWZmQOc9mfxIkmdIZG91/rf0DsB/ybdaZtahVVFLspwgOR+Y00z6XEq4caWZdR4dpZVYinK6298DrkzPkwQg/f/yNM/MLBEqbeoAyr0z+X8BMyVNSed3Tv/2IblduplZp+puF96Z/J6C+RdyrIuZVYvOEiR9Z3IzWycdpCtdCt+9x8xy12kvS5T0OeB4kgd9d8vmRYTvKWlmiSoKkuU8vuGrwPUkxyG3ITk3chowAPjFhqicmXVMalRJU0dQzilApwNnRMQ5wErgB+mztq8nCZRmZokqup9kOUFya6DpoeD1wEbp/7cDx+VZKTPr4KroPMlyguRsoH/6/5vA7un/W5I8wsHMLFFFLclyBm4eJbm5xWTgTuCHkg4DRpM8MdHMLNFBAmApygmSZwHd0//HAQ3AB4FfklyaaGaW6IxBMiIWZP4P4Op0MjNbS0cZuS6FTyY3s/x1lpakpJWUuLkR0a31UmZmHUtrLcnTaGe/CccN2bfSVbAyPDDzkUpXwcpUO3j9l6F2FTXWT2s3uBjfRvUws2rSQc6BLIWPSZpZ/horXYH8lHMyuZlZSRSlTSUtSxon6XlJiyTNlHSbpIEFZfaQ9ISkZZJmSDq7IL+npJslzUuXc6ekki6ndpA0s/zle8VNA3AiMJDkSr8hwPimTEl9SS5oeYDkPhLHAmMlHZNZxjXASOB9JHcx60NySXWr3N02s/zlOHATEd/MzM6SdB1wRybtaJJAenlENAJPSboFOBO4S1JP4HPA0RExE0DS14EpkraOiBnF1u+WpJnlLs/udjMOJrk8usluwKQ0QDaZyJr7SwwDeqRpAETEC8CyTJkWlRUkJR0o6R5Jz0raKk07RdLocpZjZlWujLsASYqCaWxLi5V0LHAq8JVMcl9gQUHRBWk6mb/FyrSonJvufhL4X5Lnbw9jzZ3JewLnlbocM6t+aixtAogIFUxjm12mdBxwM/DxiHg6k7UI6FdQvH+aTuZvsTItKqcleTHw5Yg4heSmu02eoIQmq5l1IjnfKk3SKcANwEcjovAKhcnAHpKy8Wwk8Ez6/0sk98AdlVnecKAXa3fbm1VOkBwOPNhM+nx8Z3Izy8j5FKCzSe48dmhEPN5MkXtIBqEvktRd0t4kVwveCBARdcDPgW9LGixpY5Kb8/wlIqa3tv5yguR8oLkLlnblvc/nNrPOLN+W5LUkXeUJkpZkpq0BImIRcBhwBMlxxruByyLid5llnEvSspwCTAfqgDGlrLycU4DuBr4j6ah0PiSNIInwd5axHDOrdvmeAtTqNY4RMQnYp0h+Hclzuk4vd/3ltCS/CQh4l6QvPxF4jiQqX1buis2sem3gU4DaVDk33V0KHJie7jOKJMBOjIiHN0zVzKzD6iABsBRlX3ETEROACbnXxMyqRkdpJZai5CAp6VvF8iPi2+tfHTOrCp0xSPLekaCuJI+TrQfeBhwkzSzRGYNkROxYmCZpM5Lzj27Os1Jm1rFVU3d7vW5wERGzSK7EGZdPdcysGpRzWWJ7l8et0lYCW+SwHDOrFlXUkixn4KbwCVwiCY7nkbkFkZlZpwySwGMkm1549vvjJNdJmpkB1XVMspwguW3BfCMwOyLqc6yPmVWDKgqSJQ3cSOoKfBfoEhHT0+kNB0gza1bOt0qrpJKCZESsBA6nqh4UaWYbSjWNbpdzCtCfSQKlmVlRnfIGF8BTwGWSdgf+BSzNZkbEHc29yMw6oQ4SAEvRapCU9CqwF8mNLyF5CM+pBcWCtR/xaGadWWcKksA2QG1E+PGzZlaSVu+S24HkccWNmdnaOllLEmCQpKJlI2JmDvUxsyrQUUauS1FqkJxUJE8kvxu1618dM6sKnbAleQwwb0NWxMyqR0c5vacUpQbJx9PbopmZta6TBckq2lwzawudrSVZTaP5ZtYGOtXAjc+PNLOydbKWpJlZeRwkzcxa1tmOSZqZlcdB0sysZYrqiZIOkmaWu041um1mVrbqaUg6SJpZ/qpp4MbnQJpZ/nJ+EJik4yT9XdIi6b0hWFJIqpO0JDO9P5NfI+lKSe+mefdLGlrKuh0kzSx3G+AZN/OBG4BzipQ5PCL6ZKbnMnnnAZ8BDgAGATOA+yS1GgPd3Taz3OU9cBMRDwBIGr2OizgDGBcRU9PlnAe8C+wPPFrshW5Jmln+Ikqb8vVrSXMlPS3ptKZESf2AocDENdWLBcA0YPfWFuqWZBv4+T+mMGjIyvekT5/andMPHM5Vd01jt32Xvie/flkNR+3w/vekW/4evGtj7r1lU96Z0Y0Vy2vYfKsVHH7CXI4+fTYSTJywEb/8/iDeerU7dctq2GTQSkZ/Yj4nnPsuXbut+bI/el8/7vzR5rz5Sne692zkfXsv5ZSLZ7LltisquHVtr5yudDPHGC+LiLFlrvLDwBNAA3AQcIekLhFxI9A3LbOg4DULMnktcpBsA2cfPoya2jX7Qc/ejdz40FQm/GFjAC4/dRu6dF2TL8F1f3mZf//fRm1e186q/yarOOGcd9lqh3q6dgv+84/eXH/hVtTUBp88dQ69+jTwiVNms83wenr2aeCV53rxw/O2on5ZDWdcljy55MWne/E/X9yGz573NqOPWsDiBbX85LIt+daY7bjtsRcrvIVtrIwgGRHrfaexiHgoM3u/pB8AY4AbgUVper+Cl/XP5LWozYOkpOOALwG7ARvl8Qa1dwvnrf02f+CQuXTpEtx/xwAAFi9YO3/PAxaz6RYr+fPtA9usjp3dqNGL15ofPHQFT9zfj2ef6MMnT53DiFHLGDFq2er8zbdayLNP9ebZJ/qsTpvy79707tfAZ86elS4DjjljFpeetB1LF9XQu28VnWHdinZwMnkj6W0eI2KhpOnAKNIud9oF3x54prUFVeKYZCmjVFXtiDFzeepv/Zg3q2uz+UeOmcvLz/Xkpcm92rhmBsmhshcn9eL5f/Vmt32XNFtmxsvd+dfDfdltvzX5I0YtZenCWv7vj/1pbIQlC2t58O6N2WWvJZ0qQAIb4hSgWkk9gG7pfI90qpG0p6RRkrpJ6iLpEOCrwK8zi7gJ+IakYZJ6A+OAl4DHWlt3m7ckcxil6tB23HUZw3arY/x3BzWbP2CzlXzg0IX8+KKt2rhmtnRRDcfvuQurVorGRnHiV9/hE6fOWavMCSNHsHBuF1auqOHIz87htG+teUjo8D2WcenPXuN752zNuC8PpWGVGL7nUi6//dW23pSKU2PugzJjgJ9l5uvSvwcCGwFXAUOAVcB04OKIuClT/iqS7vZjQO/078cjotVfLx+TbGNHjpnL29O7tXi88SPHzWPF8hoeubd/21bM6NmnkRv+NpXldTVMmdibn/3PYAZuvpLDjl/zDLzv3zuN+roaXvlPT277zmD6DxzEZ7/xDpC0Lq+/YCs+eepsPnDIIhYvrOUX3xvEZadsy1V3TaO2Ez1PNO8rbiJiPDC+SJH7Wnl9I3BhOpWl3QZJSWOBSytdjzz16tPA6E8s4I5rN6O5p2JIwWEnzOWRezambmkn+ka1EzU1rB6F3m5EPUsW1jJ+3OC1guSgrZP8bXaqp6YmuOqsoRz7pVn06NXIb67fnG2G13PCue+uLr/FNtMZs9cuTH68D3se0HzXvSr5ssQNLyLGRoSyU6XrtL4O+tR8unQN/vqbAc3mjzpwMYOGrOTPv2w+39pWY6NYsbzlr0hE0i1fuSLZNeuX1VBTs3Z0qE3PaqiiO4eVZANccVMx7bYlWY2OPHEuT97flwVzWh6wmTqpJ6/8xwM2be32qwfxvv9awuChK1i1Ujz3j9787sebceh/J63Iu27alCE7LGer7epB8PLkXtx6xWA+cOhCNurfAMC+hy3k++dszT0/2ZQPHLqQJQtrky77oBUM33NZsdVXnyr6VajEKUC1QFcyo1Rp1opSDqJ2VMP3XMp2u9Rz82VbNJs/cNBK9j54Edee7wGbSli2pIbrLhjC3He60q17I4O2XsHnL3ybIz+bDNw0rBS3XjGYd9/oRk0NbLbVCj520hw+eers1cv48DHzWV5Xw33jN2H8uEH06NXIznsu48o7XqX3RlW7azero7QSS6Fo44gv6STWHqVqcmBETGjltfFhHbMhqmUbyAMzn6l0FaxMtYOnrdcJ3pLigI9eVVLZR/90Xi4nk29IlTgFaDzFR6nMrKPL/xSgivExSTPLX/XESAdJM8tfNR2TdJA0s/x5dNvMrGXt4AYXuXGQNLPc+bnbZmbFuCVpZtYytyTNzIqpnhjpIGlmG4BbkmZmLdsAN92tGAdJM8udTwEyMyvG3W0zsyKqJ0Y6SJpZ/nwKkJlZMQ6SZmYtU4ODpJlZy9ySNDMrwkHSzKwInydpZtYyj26bmRXTWD1NSQdJM8ufW5JmZkVUT0PSQdLM8udjkmZmxVRRkKypdAXMrAo1RmlTiSQdJ+nvkhZJ732qt6Q9JD0haZmkGZLOLsjvKelmSfPSZdwpaUAp63aQNLP8NTaWNpVuPnADcE5hhqS+wP3AA8AA4FhgrKRjMsWuAUYC7wOGAn2A20tZsbvbZpa/nLvbEfEAgKTRzWQfDTQAl0dEI/CUpFuAM4G7JPUEPgccHREz0+V8HZgiaeuImFFs3W5Jmln+cu5ut2I3YFIaIJtMBHZP/x8G9EjTAIiIF4BlmTItcpA0s/xFY2kTICkKprFlrq0vsKAgbUGaTuZvsTItcnfbzPJXRnc7IrSea1sEbF6Q1j9NJ/O3HzCnhTItckvSzPLXtt3tycAekrLxbCTwTPr/S0A9MKopU9JwoFf62qIcJM0sfzmPbkuqldQD6JbO90inGuAekl7xRZK6S9obOA24ESAi6oCfA9+WNFjSxsDVwF8iYnpr63aQNLP8RZQ2lW4MUEdymg/p/3XAARGxCDgMOILkOOPdwGUR8bvM688laVlOAaanrx1Tyop9TNLM8pfzXYAiYjwwvkj+JGCfIvl1wOnpVBYHSTPLXxVdluggaWb5c5A0M2tZNDRUugq5cZA0s/zld3pPxTlImln+3N02MyvCz7gxMyvCLUkzs5aFW5JmZkU0OEiambUsHCTNzFoUPgXIzKwItyTNzFpWTS1JRQcaqpcUHam+Zh2RpPW6W7ik10meSFiK6RGxzbquqy10uCBZ6TqYdQY5PFKhanSoIFmt0hayd8oOxJ9Z5+E7k5uZFeEgaWZWhIOkmVkRDpLtw2WVroCVzZ9ZJ+GBGzOzItySNDMrwkHSzKwIB8kKklQj6UpJ70paIul+SaVeqWBtTNJxkv4uaZEvbOg8HCQr6zzgM8ABwCBgBnCfJH8u7dN84AbgnArXw9qQB24qKL3GdVxE3JjO9wfeBQ6JiEcrWDUrQtJo4BFfcdM5uMVSIZL6kdwEYGJTWkQsAKYBu1emVmZWyEGycvqmfxcUpC/I5JlZhTlIVs6i9G+/gvT+mTwzqzAHyQqJiIXAdGBUU1raBd8eeKZC1TKzAg6SlXUT8A1JwyT1BsYBLwGPVbZa1hxJtZJ6AN3S+R7p5O9RFfOHW1lXAb8lCYqzgG2Bj0dU0QNCqssYoA54IJ2vS6cDKlYj2+B8CpCZWRFuSZqZFeEgaWZWhIOkmVkRDpJmZkU4SJqZFeEgaWZWhINkJyZpgqRbW5pv47psIykk7V+kzFhJ08pcbi7bVMn3xirLQbIdkTQ+DRQhaZWk6ZJukjSwjapwNPDVUgtLmiZp7Iarjlnldal0Bew9/g4cS/LZjARuBYYARxYWlCSgS0SszGPFETEvj+WYVRO3JNufFRHxTkS8GRF/AH4IHCapp6ST0hbmgZImAcuBD0vqmnZFX5NUL+l5SV/ILlTS0PTxEHWS3pB0VuGKm+tSSvqSpCmSlkuaJenuprIkN+O4NNP63SbN20HS3ZIWSJov6a+S3l+w3GPTlmi9pCeAXct9oyRtK+keSTMlLZP0nKQxzRStkfRdSXPSRy/8JL0GO7ussyS9mNbnZUkXSXIjwtyS7ADqSH7Mmj6rGpIbYXyV5C5Ci4FbgD2BLwAvA3sDN0taFRG3pS3Oe4EGYDRJcL06fU2Lx/gkXQZ8DbgA+CvQBzg8zT4a+DdwN/C9NG22pM1JrkW/F/ggsAL4MjBB0vCImC1pD+DXJNeujwd2Aa5dh/emD/AwyTOwlwBHAD+T9GZEPJIpdwxwZ1qfHYDbgKXAuel2jgU+T/JYhmeAnUluPtIDuGQd6mXVJCI8tZOJJGA8mJkfAbwCPJXOnwQE8MFMmW2BRmB4wbK+BTyT/v/h9HXDMvmbkgTgWzNpE5rmgd5p/teL1HcaMLYgbWxTfTNpSrfjnHT+l8DjBWW+nNZx/yLrGwtMa+U9/ANwS8E2vQ7UZtJOB+rTbewFLAMOK1jOZ4EFzb03njrX5JZk+zNa0hKgFugOPETSQsz6V+b/USRBaGLSYFytC0nLEZJgOyciXmrKjKRFN7VIPXYhaUn9tcz67wWMTLchqyewY6Y+DxXkl317OEm9SH4MPgYMJrmFWXfgkYKi/4yIhsz842m57dO/PYG7C56AWAv0kLRpRMwut25WPRwk259/AJ8DVgEzI2JFQX5DRNRn5puOK+9L0iLKqsQtnmpIAuCXm8lbmPO6rgaOIjn0MJWkC/193nu392Ka3r9Pk9zLs5AHszo5B8n2py4iyjkX8N/p360j4k8tlJkCbCJpx4h4GUDSJsBOZB5E1sxr6oFDgWdbKLOCpMWVNZHksMCbBcG8cNn7FqTt10LZYg4AfhURv4XkOebAMJInTmbtJak205rcl+S47CskrfB6YLuI+Ms61MGqnEe3O7g0oP4UuEXSmHRkeTdJJ0s6Py32EDAZ+KWkvSXtDvwKaPHUoYhYQtIqG5uOcA9Ll3thpthrwH6Stpa0SRqkfkQSOP8g6YPpSeL7S/qOpKbAeA2wT5o2TNInSQaIyjUVOCrdphHAT4Atmik3EPixpJ0lHQlcDtwcEUvT7bwSuDLdzp0k7SLpOEnj1qFOVmUcJKvD6SSB5yKSVtpDJF32VwEiIoBPkHR3HwX+BPwFeLqV5V6SLvNs4D8kxyf3zORfSvLgsqnAbJLW7LvAPsAc4J4071ckj899O63Pv4HjgeOA50hGz89dh+0+l2SE/5F0m98C7mqm3F0kZwE8BvyGZPsvaMqMiMtJuuynkfyYPJYu+/V1qJNVGd+Z3MysCLckzcyKcJA0MyvCQdLMrAgHSTOzIhwkzcyKcJA0MyvCQdLMrAgHSTOzIhwkzcyK+H/kxWM8gA7GagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "y_true = y_test\n",
    "y_pred = np.round(logistic_regressor.predict(X_test))\n",
    "\n",
    "# cm = confusion_matrix(y_true = y_test, y_pred = logistic_regressor.predict(X_test), labels = [0, 1])\n",
    "# cm_norm = confusion_matrix(y_true = y_test, y_pred = logistic_regressor.predict(X_test), labels = [0, 1], normalize=True)\n",
    "titles_options = [\n",
    "    (\"Confusion matrix, without normalization\", None),\n",
    "    (\"Normalized confusion matrix\", \"true\"),\n",
    "]\n",
    "\n",
    "for title, normalize in titles_options:\n",
    "    cm = confusion_matrix(y_true = y_true, y_pred = y_pred, labels = [0, 1], normalize = normalize)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "    disp.plot()\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Regularized Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e+15, 1.00000000e+10, 1.00000000e+08, 1.00000000e+03,\n",
       "       1.00000000e+02, 1.00000000e+00, 2.00000000e-01, 1.00000000e-01,\n",
       "       5.00000000e-02, 3.33333333e-02, 2.85714286e-02, 2.50000000e-02,\n",
       "       2.22222222e-02, 2.00000000e-02, 1.81818182e-02, 1.00000000e-02]),\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
       "                         'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n",
       "                                    'saga']},\n",
       "             return_train_score='True', scoring='accuracy')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "cv_parameters = {'penalty' : ['l1', 'l2', 'elasticnet', 'none'],\n",
    "                 'C' : np.reciprocal([1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40,45,50,55,100]),\n",
    "                 'solver' : ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']}\n",
    "\n",
    "logistic_regressor_cv = GridSearchCV(estimator=LogisticRegression(), \n",
    "                                  param_grid=cv_parameters,\n",
    "                                  cv = 5,\n",
    "                                  scoring = 'accuracy',\n",
    "                                  return_train_score = 'True')\n",
    "logistic_regressor_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.778443113772455"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_regressor_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>param_C</th>\n",
       "      <th>param_penalty</th>\n",
       "      <th>param_solver</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>0.778443</td>\n",
       "      <td>0.779641</td>\n",
       "      <td>0.016963</td>\n",
       "      <td>0.003949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>saga</td>\n",
       "      <td>0.778443</td>\n",
       "      <td>0.779491</td>\n",
       "      <td>0.017121</td>\n",
       "      <td>0.003956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>sag</td>\n",
       "      <td>0.778443</td>\n",
       "      <td>0.779641</td>\n",
       "      <td>0.016963</td>\n",
       "      <td>0.003949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>0.778443</td>\n",
       "      <td>0.779641</td>\n",
       "      <td>0.016963</td>\n",
       "      <td>0.003949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>0.778443</td>\n",
       "      <td>0.779641</td>\n",
       "      <td>0.016963</td>\n",
       "      <td>0.003949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_C param_penalty param_solver  mean_test_score  mean_train_score  \\\n",
       "87     100            l2    liblinear         0.778443          0.779641   \n",
       "89     100            l2         saga         0.778443          0.779491   \n",
       "88     100            l2          sag         0.778443          0.779641   \n",
       "86     100            l2        lbfgs         0.778443          0.779641   \n",
       "85     100            l2    newton-cg         0.778443          0.779641   \n",
       "\n",
       "    std_test_score  std_train_score  \n",
       "87        0.016963         0.003949  \n",
       "89        0.017121         0.003956  \n",
       "88        0.016963         0.003949  \n",
       "86        0.016963         0.003949  \n",
       "85        0.016963         0.003949  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results = pd.DataFrame(logistic_regressor_cv.cv_results_).dropna()\n",
    "cv_results = cv_results.iloc[:, [4,5,6,13,21,14,22]]\n",
    "# lasso_cv_results.iloc[lasso_regressor.best_index_, :]  # for best index\n",
    "# list(enumerate(lasso_cv_results.columns))\n",
    "cv_results.sort_values(by = 'mean_test_score', ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Performance Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bh/L4XzdbXWan9y/itPOnscOIxSxZ1JUHfz+I26/blsbG6mlJrQtfcWNF9Ru4kqPPfpMttq+jS7dGXvxnX3560XaoFo445W16brSKw0+eyZCd6ujRp4HX/9ObWy7YnvpltXxu7OsA1C+t4YrjdmHozsv49u+fY8mCLtz09R1YunAHvnLDS8UrYLk4e+xUhu+6iB9dNoy33+zJXh+cy/lXvcClS7ow6YkBbDKonu/cOpnH/7Yp1126E1sOXcY5V0wFBeOv2b7S1a8oD9ysh/TmmFcApwC9gceAL5R6b7eOYLfRC9aa33zobCY+MIAXnuzLEae8zbCRSxg2csnq/E23Ws6Up/rywlNr7ib/2O83ZfG8Lpx1/Uv06tsAwMlXvMZVJ+3MZy6YzmZbL2+TbemsunZr4IMfmc33LhzOpCeTp4/ed8dW7L7PfI47fTqTnhjAkf89k2VLavnhJTsRIWa80puB16/g5K++wq9v2oblddXT5SxXNV2WWIktOQ/4DHAAyRPPZgD3FdxZuGpEwLRJfZg6sS8j9l3YbJm3pvVk8oT+jNhnzd3kX5q4EcNGLlkdIAF2PWABqgmmTtxog9e7s+vSJaipDVYuX3u3XFFfw/DdFlHbpZEReyzk6ScGkH36wMTHBtCjVyPb77y4ravcrpTzjJv2rhLd7TOAcRExFUDSecC7wP7AoxWozwaxbFEtX9xrFKtWimgUnzrnDQ4/+Z21ypy510gWzevKqhU1fPjEdzjxktdX581/txv9Nl2xVvkuXYM+/VexYFa3ttiETq1uWRemTOrHf39hBq+82IfZb/dg5H7z+MBBc+naLejbfyUDNl3BlEn91nrd/DnJZzNgkxXNLbbT8Oj2OpLUj+Th4NnHOy6QNI3k8Y6PZsqOBS5ty/rlqUefBsbdP5nl9TW8NHEjfjNuKBsPWsFBx81aXWbs3f9heV0Nrz/fmzuuHEq/TYbw6a+9UcFaW9b3zt+Zr3x7Kj994B9EI7z5ei8euHswHz/hLcp4dlWn5NHtdVfy4x0jYiwwNpsmKeggampg0Lb1AAzdeRlLF3bhzqu2XitINh1XHLJTHTU18KOv7MjHvvgWPXo1svHmK5g7s/tay1y1UixZ0IX+m3XuVkpbmfV2Dy46bTe692ig90armDe7Oyd/7RWWLq5l4byuzJvdjY0LWowbD0zm583p3K39jtKVLkVbHwfMPt4xqz8lPt6xo4pG3nN8a638gGgUq1YkO9ewUYt56d99WLZ4zS/yc3/vRzSKnUZ17uNdbW15fS3zZnenS9dG9jtkNk8+vAkRYsqkfuyxz3yyv90j959H/bIaXnmhcx83zvNBYJXWpi3JiFgoaTrJ4x0nwuou+PaseQRkh/e77w9h+N6L2GzrehpWiRf+0Zc/3rglHzo2aUX+6eYt2GKHOgZvV4eAV5/tza++M5SRh8yjT/9koGb/T8zmnmu34vqzduS482awZEEXfnrxduzzsTke2W4je+wzjy5dgzde7cUmg5Yz5suv0b1HIz//4XYA/PnOLfjY8W9x9mVTuffnQxi8dR1jznqNP96xZace2YbqGt2uxMDNTcA3JD0MvAWMI3k27mMVqMsGsWxxLbdeuB3z3ulGtx7BZlvXc9z5MzhkTDJw07BK3HHlUGa/0R3VwCZbLufQz73DEae+vXoZPXo3cvGvn+dnl2zHJUe9n249kpPJx3zr9QptVefTq08DJ537KpsNrqduWS1PPz6A71+4M3NnJYdB5rzTg4tO25XTznuF6343kSWLu3D/77bg9uu2rXDNK6+jtBJLoYi2PcyXnurzHd57nuTrJbw25ry5xYatoOXqhF2PrHQVrEwPzLuFWI+RKUlx9ONfLKnsPfvduF7ragtt3pKMiEbgwnQysyq0qtHdbTOzFlVTd9tB0sxy5yBpZlZENZ0n6SBpZrlzS9LMrAgP3JiZFeGWpJlZEe381MeyOEiaWe48cGNmVkQ1dber5+iqmbUbESppKoWkcZKel7RI0kxJt0kaWFAmJNVJWpKZ3p/Jr5F0paR307z7JQ0tZf0OkmaWu4bGmpKmUhcHnAgMJLk59xBgfDPlDo+IPpnpuUzeOj82xt1tM8tdnt3tiPhmZnaWpOuAO8pczDo/NsYtSTPLXURpE6zuKmensa0s/mBgcjPpv5Y0V9LTkk5rSmzpsTFA02NjinJL0sxyV87odjm3SpN0LHAq8KGCrA8DT5B0zQ8C7pDUJSJupIzHxjTHLUkzy12eAzdNJB0H3Ax8PCKeXnt98VBE1EXEioi4H/gBMCbNXq/HxhRtSUr6awl1b6rkoaWWNbPqlvcpQJJOAa4GPhoRj5dSBUias+v72JjWuttvlVAZM7O1NDbmFyQlnQ18Czg0IiY2k78nSa/4WZLgeCDwVeDbmWLr/NiYokEyIj5f2maYma2R82WJ1wKrgAnSWssdEREzgC2Bq0hODVoFTAcujoibMmWvIuluP8aax8Z8PH1SQlFlD9ykzdQdgOciwg+ANrP3yPkUoKILi4j7gPtaKbPOj40peeBGUi9JPwfmA/8EtkrTb5J0cbkrNrPqVc4pQO1dOaPblwO7kQy912XS7weOzrNSZtaxbYjR7Uopp7t9NDAmIh6TlP0NmAJsl2+1zKwjq6YbXJQTJAcBbzST3rXM5ZhZlesorcRSlNPdfhHYr5n0o2j+EiEz66yixKkDKKcFOA74saQ+JCdpjpZ0BnA2cOyGqJyZdUzV1JIsOUhGxG8k9QAuBXoBt5J0v0+NiD9uoPqZWQfUUUauS1HWscSIGA+Ml7QJUBMRszZIrcysQ+uULckmkrYDRqT/Px8Rr+VeKzPr0CLHyxIrreQgKWljki72JzPJIekPwCkRMT/vyplZB1VF3e1yRrdvBt4PfATok06HAbukeWZmQOc9mfxIkmdIZG91/rf0DsB/ybdaZtahVVFLspwgOR+Y00z6XEq4caWZdR4dpZVYinK6298DrkzPkwQg/f/yNM/MLBEqbeoAyr0z+X8BMyVNSed3Tv/2IblduplZp+puF96Z/J6C+RdyrIuZVYvOEiR9Z3IzWycdpCtdCt+9x8xy12kvS5T0OeB4kgd9d8vmRYTvKWlmiSoKkuU8vuGrwPUkxyG3ITk3chowAPjFhqicmXVMalRJU0dQzilApwNnRMQ5wErgB+mztq8nCZRmZokqup9kOUFya6DpoeD1wEbp/7cDx+VZKTPr4KroPMlyguRsoH/6/5vA7un/W5I8wsHMLFFFLclyBm4eJbm5xWTgTuCHkg4DRpM8MdHMLNFBAmApygmSZwHd0//HAQ3AB4FfklyaaGaW6IxBMiIWZP4P4Op0MjNbS0cZuS6FTyY3s/x1lpakpJWUuLkR0a31UmZmHUtrLcnTaGe/CccN2bfSVbAyPDDzkUpXwcpUO3j9l6F2FTXWT2s3uBjfRvUws2rSQc6BLIWPSZpZ/horXYH8lHMyuZlZSRSlTSUtSxon6XlJiyTNlHSbpIEFZfaQ9ISkZZJmSDq7IL+npJslzUuXc6ekki6ndpA0s/zle8VNA3AiMJDkSr8hwPimTEl9SS5oeYDkPhLHAmMlHZNZxjXASOB9JHcx60NySXWr3N02s/zlOHATEd/MzM6SdB1wRybtaJJAenlENAJPSboFOBO4S1JP4HPA0RExE0DS14EpkraOiBnF1u+WpJnlLs/udjMOJrk8usluwKQ0QDaZyJr7SwwDeqRpAETEC8CyTJkWlRUkJR0o6R5Jz0raKk07RdLocpZjZlWujLsASYqCaWxLi5V0LHAq8JVMcl9gQUHRBWk6mb/FyrSonJvufhL4X5Lnbw9jzZ3JewLnlbocM6t+aixtAogIFUxjm12mdBxwM/DxiHg6k7UI6FdQvH+aTuZvsTItKqcleTHw5Yg4heSmu02eoIQmq5l1IjnfKk3SKcANwEcjovAKhcnAHpKy8Wwk8Ez6/0sk98AdlVnecKAXa3fbm1VOkBwOPNhM+nx8Z3Izy8j5FKCzSe48dmhEPN5MkXtIBqEvktRd0t4kVwveCBARdcDPgW9LGixpY5Kb8/wlIqa3tv5yguR8oLkLlnblvc/nNrPOLN+W5LUkXeUJkpZkpq0BImIRcBhwBMlxxruByyLid5llnEvSspwCTAfqgDGlrLycU4DuBr4j6ah0PiSNIInwd5axHDOrdvmeAtTqNY4RMQnYp0h+Hclzuk4vd/3ltCS/CQh4l6QvPxF4jiQqX1buis2sem3gU4DaVDk33V0KHJie7jOKJMBOjIiHN0zVzKzD6iABsBRlX3ETEROACbnXxMyqRkdpJZai5CAp6VvF8iPi2+tfHTOrCp0xSPLekaCuJI+TrQfeBhwkzSzRGYNkROxYmCZpM5Lzj27Os1Jm1rFVU3d7vW5wERGzSK7EGZdPdcysGpRzWWJ7l8et0lYCW+SwHDOrFlXUkixn4KbwCVwiCY7nkbkFkZlZpwySwGMkm1549vvjJNdJmpkB1XVMspwguW3BfCMwOyLqc6yPmVWDKgqSJQ3cSOoKfBfoEhHT0+kNB0gza1bOt0qrpJKCZESsBA6nqh4UaWYbSjWNbpdzCtCfSQKlmVlRnfIGF8BTwGWSdgf+BSzNZkbEHc29yMw6oQ4SAEvRapCU9CqwF8mNLyF5CM+pBcWCtR/xaGadWWcKksA2QG1E+PGzZlaSVu+S24HkccWNmdnaOllLEmCQpKJlI2JmDvUxsyrQUUauS1FqkJxUJE8kvxu1618dM6sKnbAleQwwb0NWxMyqR0c5vacUpQbJx9PbopmZta6TBckq2lwzawudrSVZTaP5ZtYGOtXAjc+PNLOydbKWpJlZeRwkzcxa1tmOSZqZlcdB0sysZYrqiZIOkmaWu041um1mVrbqaUg6SJpZ/qpp4MbnQJpZ/nJ+EJik4yT9XdIi6b0hWFJIqpO0JDO9P5NfI+lKSe+mefdLGlrKuh0kzSx3G+AZN/OBG4BzipQ5PCL6ZKbnMnnnAZ8BDgAGATOA+yS1GgPd3Taz3OU9cBMRDwBIGr2OizgDGBcRU9PlnAe8C+wPPFrshW5Jmln+Ikqb8vVrSXMlPS3ptKZESf2AocDENdWLBcA0YPfWFuqWZBv4+T+mMGjIyvekT5/andMPHM5Vd01jt32Xvie/flkNR+3w/vekW/4evGtj7r1lU96Z0Y0Vy2vYfKsVHH7CXI4+fTYSTJywEb/8/iDeerU7dctq2GTQSkZ/Yj4nnPsuXbut+bI/el8/7vzR5rz5Sne692zkfXsv5ZSLZ7LltisquHVtr5yudDPHGC+LiLFlrvLDwBNAA3AQcIekLhFxI9A3LbOg4DULMnktcpBsA2cfPoya2jX7Qc/ejdz40FQm/GFjAC4/dRu6dF2TL8F1f3mZf//fRm1e186q/yarOOGcd9lqh3q6dgv+84/eXH/hVtTUBp88dQ69+jTwiVNms83wenr2aeCV53rxw/O2on5ZDWdcljy55MWne/E/X9yGz573NqOPWsDiBbX85LIt+daY7bjtsRcrvIVtrIwgGRHrfaexiHgoM3u/pB8AY4AbgUVper+Cl/XP5LWozYOkpOOALwG7ARvl8Qa1dwvnrf02f+CQuXTpEtx/xwAAFi9YO3/PAxaz6RYr+fPtA9usjp3dqNGL15ofPHQFT9zfj2ef6MMnT53DiFHLGDFq2er8zbdayLNP9ebZJ/qsTpvy79707tfAZ86elS4DjjljFpeetB1LF9XQu28VnWHdinZwMnkj6W0eI2KhpOnAKNIud9oF3x54prUFVeKYZCmjVFXtiDFzeepv/Zg3q2uz+UeOmcvLz/Xkpcm92rhmBsmhshcn9eL5f/Vmt32XNFtmxsvd+dfDfdltvzX5I0YtZenCWv7vj/1pbIQlC2t58O6N2WWvJZ0qQAIb4hSgWkk9gG7pfI90qpG0p6RRkrpJ6iLpEOCrwK8zi7gJ+IakYZJ6A+OAl4DHWlt3m7ckcxil6tB23HUZw3arY/x3BzWbP2CzlXzg0IX8+KKt2rhmtnRRDcfvuQurVorGRnHiV9/hE6fOWavMCSNHsHBuF1auqOHIz87htG+teUjo8D2WcenPXuN752zNuC8PpWGVGL7nUi6//dW23pSKU2PugzJjgJ9l5uvSvwcCGwFXAUOAVcB04OKIuClT/iqS7vZjQO/078cjotVfLx+TbGNHjpnL29O7tXi88SPHzWPF8hoeubd/21bM6NmnkRv+NpXldTVMmdibn/3PYAZuvpLDjl/zDLzv3zuN+roaXvlPT277zmD6DxzEZ7/xDpC0Lq+/YCs+eepsPnDIIhYvrOUX3xvEZadsy1V3TaO2Ez1PNO8rbiJiPDC+SJH7Wnl9I3BhOpWl3QZJSWOBSytdjzz16tPA6E8s4I5rN6O5p2JIwWEnzOWRezambmkn+ka1EzU1rB6F3m5EPUsW1jJ+3OC1guSgrZP8bXaqp6YmuOqsoRz7pVn06NXIb67fnG2G13PCue+uLr/FNtMZs9cuTH68D3se0HzXvSr5ssQNLyLGRoSyU6XrtL4O+tR8unQN/vqbAc3mjzpwMYOGrOTPv2w+39pWY6NYsbzlr0hE0i1fuSLZNeuX1VBTs3Z0qE3PaqiiO4eVZANccVMx7bYlWY2OPHEuT97flwVzWh6wmTqpJ6/8xwM2be32qwfxvv9awuChK1i1Ujz3j9787sebceh/J63Iu27alCE7LGer7epB8PLkXtx6xWA+cOhCNurfAMC+hy3k++dszT0/2ZQPHLqQJQtrky77oBUM33NZsdVXnyr6VajEKUC1QFcyo1Rp1opSDqJ2VMP3XMp2u9Rz82VbNJs/cNBK9j54Edee7wGbSli2pIbrLhjC3He60q17I4O2XsHnL3ybIz+bDNw0rBS3XjGYd9/oRk0NbLbVCj520hw+eers1cv48DHzWV5Xw33jN2H8uEH06NXIznsu48o7XqX3RlW7azero7QSS6Fo44gv6STWHqVqcmBETGjltfFhHbMhqmUbyAMzn6l0FaxMtYOnrdcJ3pLigI9eVVLZR/90Xi4nk29IlTgFaDzFR6nMrKPL/xSgivExSTPLX/XESAdJM8tfNR2TdJA0s/x5dNvMrGXt4AYXuXGQNLPc+bnbZmbFuCVpZtYytyTNzIqpnhjpIGlmG4BbkmZmLdsAN92tGAdJM8udTwEyMyvG3W0zsyKqJ0Y6SJpZ/nwKkJlZMQ6SZmYtU4ODpJlZy9ySNDMrwkHSzKwInydpZtYyj26bmRXTWD1NSQdJM8ufW5JmZkVUT0PSQdLM8udjkmZmxVRRkKypdAXMrAo1RmlTiSQdJ+nvkhZJ732qt6Q9JD0haZmkGZLOLsjvKelmSfPSZdwpaUAp63aQNLP8NTaWNpVuPnADcE5hhqS+wP3AA8AA4FhgrKRjMsWuAUYC7wOGAn2A20tZsbvbZpa/nLvbEfEAgKTRzWQfDTQAl0dEI/CUpFuAM4G7JPUEPgccHREz0+V8HZgiaeuImFFs3W5Jmln+cu5ut2I3YFIaIJtMBHZP/x8G9EjTAIiIF4BlmTItcpA0s/xFY2kTICkKprFlrq0vsKAgbUGaTuZvsTItcnfbzPJXRnc7IrSea1sEbF6Q1j9NJ/O3HzCnhTItckvSzPLXtt3tycAekrLxbCTwTPr/S0A9MKopU9JwoFf62qIcJM0sfzmPbkuqldQD6JbO90inGuAekl7xRZK6S9obOA24ESAi6oCfA9+WNFjSxsDVwF8iYnpr63aQNLP8RZQ2lW4MUEdymg/p/3XAARGxCDgMOILkOOPdwGUR8bvM688laVlOAaanrx1Tyop9TNLM8pfzXYAiYjwwvkj+JGCfIvl1wOnpVBYHSTPLXxVdluggaWb5c5A0M2tZNDRUugq5cZA0s/zld3pPxTlImln+3N02MyvCz7gxMyvCLUkzs5aFW5JmZkU0OEiambUsHCTNzFoUPgXIzKwItyTNzFpWTS1JRQcaqpcUHam+Zh2RpPW6W7ik10meSFiK6RGxzbquqy10uCBZ6TqYdQY5PFKhanSoIFmt0hayd8oOxJ9Z5+E7k5uZFeEgaWZWhIOkmVkRDpLtw2WVroCVzZ9ZJ+GBGzOzItySNDMrwkHSzKwIB8kKklQj6UpJ70paIul+SaVeqWBtTNJxkv4uaZEvbOg8HCQr6zzgM8ABwCBgBnCfJH8u7dN84AbgnArXw9qQB24qKL3GdVxE3JjO9wfeBQ6JiEcrWDUrQtJo4BFfcdM5uMVSIZL6kdwEYGJTWkQsAKYBu1emVmZWyEGycvqmfxcUpC/I5JlZhTlIVs6i9G+/gvT+mTwzqzAHyQqJiIXAdGBUU1raBd8eeKZC1TKzAg6SlXUT8A1JwyT1BsYBLwGPVbZa1hxJtZJ6AN3S+R7p5O9RFfOHW1lXAb8lCYqzgG2Bj0dU0QNCqssYoA54IJ2vS6cDKlYj2+B8CpCZWRFuSZqZFeEgaWZWhIOkmVkRDpJmZkU4SJqZFeEgaWZWhINkJyZpgqRbW5pv47psIykk7V+kzFhJ08pcbi7bVMn3xirLQbIdkTQ+DRQhaZWk6ZJukjSwjapwNPDVUgtLmiZp7Iarjlnldal0Bew9/g4cS/LZjARuBYYARxYWlCSgS0SszGPFETEvj+WYVRO3JNufFRHxTkS8GRF/AH4IHCapp6ST0hbmgZImAcuBD0vqmnZFX5NUL+l5SV/ILlTS0PTxEHWS3pB0VuGKm+tSSvqSpCmSlkuaJenuprIkN+O4NNP63SbN20HS3ZIWSJov6a+S3l+w3GPTlmi9pCeAXct9oyRtK+keSTMlLZP0nKQxzRStkfRdSXPSRy/8JL0GO7ussyS9mNbnZUkXSXIjwtyS7ADqSH7Mmj6rGpIbYXyV5C5Ci4FbgD2BLwAvA3sDN0taFRG3pS3Oe4EGYDRJcL06fU2Lx/gkXQZ8DbgA+CvQBzg8zT4a+DdwN/C9NG22pM1JrkW/F/ggsAL4MjBB0vCImC1pD+DXJNeujwd2Aa5dh/emD/AwyTOwlwBHAD+T9GZEPJIpdwxwZ1qfHYDbgKXAuel2jgU+T/JYhmeAnUluPtIDuGQd6mXVJCI8tZOJJGA8mJkfAbwCPJXOnwQE8MFMmW2BRmB4wbK+BTyT/v/h9HXDMvmbkgTgWzNpE5rmgd5p/teL1HcaMLYgbWxTfTNpSrfjnHT+l8DjBWW+nNZx/yLrGwtMa+U9/ANwS8E2vQ7UZtJOB+rTbewFLAMOK1jOZ4EFzb03njrX5JZk+zNa0hKgFugOPETSQsz6V+b/USRBaGLSYFytC0nLEZJgOyciXmrKjKRFN7VIPXYhaUn9tcz67wWMTLchqyewY6Y+DxXkl317OEm9SH4MPgYMJrmFWXfgkYKi/4yIhsz842m57dO/PYG7C56AWAv0kLRpRMwut25WPRwk259/AJ8DVgEzI2JFQX5DRNRn5puOK+9L0iLKqsQtnmpIAuCXm8lbmPO6rgaOIjn0MJWkC/193nu392Ka3r9Pk9zLs5AHszo5B8n2py4iyjkX8N/p360j4k8tlJkCbCJpx4h4GUDSJsBOZB5E1sxr6oFDgWdbKLOCpMWVNZHksMCbBcG8cNn7FqTt10LZYg4AfhURv4XkOebAMJInTmbtJak205rcl+S47CskrfB6YLuI+Ms61MGqnEe3O7g0oP4UuEXSmHRkeTdJJ0s6Py32EDAZ+KWkvSXtDvwKaPHUoYhYQtIqG5uOcA9Ll3thpthrwH6Stpa0SRqkfkQSOP8g6YPpSeL7S/qOpKbAeA2wT5o2TNInSQaIyjUVOCrdphHAT4Atmik3EPixpJ0lHQlcDtwcEUvT7bwSuDLdzp0k7SLpOEnj1qFOVmUcJKvD6SSB5yKSVtpDJF32VwEiIoBPkHR3HwX+BPwFeLqV5V6SLvNs4D8kxyf3zORfSvLgsqnAbJLW7LvAPsAc4J4071ckj899O63Pv4HjgeOA50hGz89dh+0+l2SE/5F0m98C7mqm3F0kZwE8BvyGZPsvaMqMiMtJuuynkfyYPJYu+/V1qJNVGd+Z3MysCLckzcyKcJA0MyvCQdLMrAgHSTOzIhwkzcyKcJA0MyvCQdLMrAgHSTOzIhwkzcyK+H/kxWM8gA7GagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "y_true = y_test\n",
    "y_pred = logistic_regressor_cv.predict(X_test)\n",
    "\n",
    "# cm = confusion_matrix(y_true = y_test, y_pred = logistic_regressor.predict(X_test), labels = [0, 1])\n",
    "# cm_norm = confusion_matrix(y_true = y_test, y_pred = logistic_regressor.predict(X_test), labels = [0, 1], normalize=True)\n",
    "titles_options = [\n",
    "    (\"Confusion matrix, without normalization\", None),\n",
    "    (\"Normalized confusion matrix\", \"true\"),\n",
    "]\n",
    "\n",
    "for title, normalize in titles_options:\n",
    "    cm = confusion_matrix(y_true = y_true, y_pred = y_pred, labels = [0, 1], normalize = normalize)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=logistic_regressor_cv.classes_)\n",
    "    disp.plot()\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy, Precision and Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, cohen_kappa_score, precision_score\n",
    "accuracy_score(y_true = y_true, y_pred = y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.79      0.80       420\n",
      "           1       0.79      0.81      0.80       415\n",
      "\n",
      "    accuracy                           0.80       835\n",
      "   macro avg       0.80      0.80      0.80       835\n",
      "weighted avg       0.80      0.80      0.80       835\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true = y_true, y_pred = y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive Class = 1\n",
      "Recall (Sensitivity, True Positive Rate) = 0.81\n",
      "Specificity = 0.79\n",
      "Precision (Positive Predicted Value) = 0.79\n",
      "Negative Predicted Value = 0.81\n"
     ]
    }
   ],
   "source": [
    "tn, fp, fn, tp = cm.ravel()\n",
    "recall_sensitivity_tpr = tp/(tp+fn)\n",
    "specificity_tnr = tn/(tn+fp)\n",
    "precision_ppv = tp/(tp+fp)\n",
    "npv = tn/(tn+fn)\n",
    "print(\"Positive Class =\", 1)\n",
    "print(\"Recall (Sensitivity, True Positive Rate) =\", format(recall_sensitivity_tpr, '.2f'))\n",
    "print(\"Specificity =\", format(specificity_tnr, '.2f'))\n",
    "print(\"Precision (Positive Predicted Value) =\", format(precision_ppv, '.2f'))\n",
    "print(\"Negative Predicted Value =\", format(npv, '.2f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": [
    "from sklearn.metrics import RocCurveDisplay\n",
    "logistic_regressor_disp = RocCurveDisplay.from_estimator(logistic_regressor_cv, X_test, y_test, name = \"Logistic Regressor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Final Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>height</th>\n",
       "      <th>shucked_weight</th>\n",
       "      <th>shell_weight</th>\n",
       "      <th>sex</th>\n",
       "      <th>age_class</th>\n",
       "      <th>predicted_ageClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>0.155</td>\n",
       "      <td>0.3270</td>\n",
       "      <td>0.3350</td>\n",
       "      <td>M</td>\n",
       "      <td>11+</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>0.095</td>\n",
       "      <td>0.0525</td>\n",
       "      <td>0.0480</td>\n",
       "      <td>M</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>0.090</td>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>I</td>\n",
       "      <td>11+</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.3225</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>I</td>\n",
       "      <td>11+</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3572</th>\n",
       "      <td>0.165</td>\n",
       "      <td>0.4860</td>\n",
       "      <td>0.2940</td>\n",
       "      <td>F</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>0.095</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>M</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>0.195</td>\n",
       "      <td>0.8300</td>\n",
       "      <td>0.4400</td>\n",
       "      <td>F</td>\n",
       "      <td>11+</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.2945</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>I</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2179</th>\n",
       "      <td>0.250</td>\n",
       "      <td>0.4620</td>\n",
       "      <td>0.4450</td>\n",
       "      <td>F</td>\n",
       "      <td>11+</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2698</th>\n",
       "      <td>0.165</td>\n",
       "      <td>0.5985</td>\n",
       "      <td>0.3600</td>\n",
       "      <td>F</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2743</th>\n",
       "      <td>0.115</td>\n",
       "      <td>0.2790</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>I</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>0.145</td>\n",
       "      <td>0.2815</td>\n",
       "      <td>0.3550</td>\n",
       "      <td>M</td>\n",
       "      <td>11+</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.2215</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>M</td>\n",
       "      <td>11+</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>0.180</td>\n",
       "      <td>0.6715</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>M</td>\n",
       "      <td>11+</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>0.185</td>\n",
       "      <td>0.6935</td>\n",
       "      <td>0.4700</td>\n",
       "      <td>F</td>\n",
       "      <td>11+</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0.215</td>\n",
       "      <td>0.4535</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>F</td>\n",
       "      <td>11+</td>\n",
       "      <td>11+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3361</th>\n",
       "      <td>0.095</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>M</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>0.120</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>0.0950</td>\n",
       "      <td>M</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>0.100</td>\n",
       "      <td>0.1175</td>\n",
       "      <td>0.0850</td>\n",
       "      <td>M</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3404</th>\n",
       "      <td>0.105</td>\n",
       "      <td>0.0815</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>M</td>\n",
       "      <td>0 - 10</td>\n",
       "      <td>0 - 10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      height  shucked_weight  shell_weight sex age_class predicted_ageClass\n",
       "2331   0.155          0.3270        0.3350   M       11+                11+\n",
       "2412   0.095          0.0525        0.0480   M    0 - 10             0 - 10\n",
       "2196   0.090          0.0905        0.0700   I       11+             0 - 10\n",
       "2382   0.150          0.3225        0.2150   I       11+             0 - 10\n",
       "3572   0.165          0.4860        0.2940   F    0 - 10                11+\n",
       "2429   0.095          0.0810        0.0700   M    0 - 10             0 - 10\n",
       "1984   0.195          0.8300        0.4400   F       11+                11+\n",
       "3650   0.140          0.2945        0.1710   I    0 - 10             0 - 10\n",
       "2179   0.250          0.4620        0.4450   F       11+                11+\n",
       "2698   0.165          0.5985        0.3600   F    0 - 10                11+\n",
       "2743   0.115          0.2790        0.1325   I    0 - 10             0 - 10\n",
       "2499   0.145          0.2815        0.3550   M       11+                11+\n",
       "298    0.140          0.2215        0.1500   M       11+             0 - 10\n",
       "1968   0.180          0.6715        0.4000   M       11+                11+\n",
       "3239   0.185          0.6935        0.4700   F       11+                11+\n",
       "270    0.215          0.4535        0.5500   F       11+                11+\n",
       "3361   0.095          0.1210        0.0900   M    0 - 10             0 - 10\n",
       "805    0.120          0.1235        0.0950   M    0 - 10             0 - 10\n",
       "2103   0.100          0.1175        0.0850   M    0 - 10             0 - 10\n",
       "3404   0.105          0.0815        0.0750   M    0 - 10             0 - 10"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predictions vs actuals\n",
    "predictions = y_pred\n",
    "\n",
    "# comparing \n",
    "## generate DF\n",
    "df_test_output = pd.concat([X_test, y_test], axis = 1)\n",
    "df_test_output['predicted_ageClass'] = predictions\n",
    "\n",
    "## restore dummies tocategorical\n",
    "df_test_output['sex'] = df_test_output.loc[:, ['sex_I', 'sex_M']].idxmax(axis=1)\n",
    "df_test_output['sex'] = df_test_output['sex'].apply(lambda e : e.replace('sex_', ''))\n",
    "df_test_output.loc[(df_test_output['sex_I'] == 0) & (df_test_output['sex_M'] == 0), 'sex'] = 'F'\n",
    "df_test_output.drop(['sex_I', 'sex_M'], axis=1, inplace = True)\n",
    "\n",
    "df_test_output.loc[(df_test_output['age_class'] == 0), 'age_class'] = '0 - 10'\n",
    "df_test_output.loc[(df_test_output['age_class'] == 1), 'age_class'] = '11+'\n",
    "df_test_output.loc[(df_test_output['predicted_ageClass'] == 0), 'predicted_ageClass'] = '0 - 10'\n",
    "df_test_output.loc[(df_test_output['predicted_ageClass'] == 1), 'predicted_ageClass'] = '11+'\n",
    "\n",
    "# rearranging columns\n",
    "df_test_output = df_test_output.iloc[:, [1,2,3,6,4,5]]\n",
    "\n",
    "df_test_output.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(logistic_regressor_cv.best_estimator_, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# THE END"
   ]
  }
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