GeneralML/Others/Ship_Crew_Size_ML_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Model for Predicting a Ship's Crew Size \n",
    "We build a simple model using the cruise_ship_info.csv data set for predicting a ship's crew size. This project is organized as follows: (a) data proprocessing and variable selection; (b) basic regression model; (c) hyper-parameters tuning; and (d) techniques for dimensionality reduction.\n",
    "\n",
    "Author: Benjamin O. Tayo\n",
    "Date: 4/8/2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Read dataset and display columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"cruise_ship_info.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ship_name</th>\n",
       "      <th>Cruise_line</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tonnage</th>\n",
       "      <th>passengers</th>\n",
       "      <th>length</th>\n",
       "      <th>cabins</th>\n",
       "      <th>passenger_density</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Journey</td>\n",
       "      <td>Azamara</td>\n",
       "      <td>6</td>\n",
       "      <td>30.277</td>\n",
       "      <td>6.94</td>\n",
       "      <td>5.94</td>\n",
       "      <td>3.55</td>\n",
       "      <td>42.64</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Quest</td>\n",
       "      <td>Azamara</td>\n",
       "      <td>6</td>\n",
       "      <td>30.277</td>\n",
       "      <td>6.94</td>\n",
       "      <td>5.94</td>\n",
       "      <td>3.55</td>\n",
       "      <td>42.64</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Celebration</td>\n",
       "      <td>Carnival</td>\n",
       "      <td>26</td>\n",
       "      <td>47.262</td>\n",
       "      <td>14.86</td>\n",
       "      <td>7.22</td>\n",
       "      <td>7.43</td>\n",
       "      <td>31.80</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conquest</td>\n",
       "      <td>Carnival</td>\n",
       "      <td>11</td>\n",
       "      <td>110.000</td>\n",
       "      <td>29.74</td>\n",
       "      <td>9.53</td>\n",
       "      <td>14.88</td>\n",
       "      <td>36.99</td>\n",
       "      <td>19.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Destiny</td>\n",
       "      <td>Carnival</td>\n",
       "      <td>17</td>\n",
       "      <td>101.353</td>\n",
       "      <td>26.42</td>\n",
       "      <td>8.92</td>\n",
       "      <td>13.21</td>\n",
       "      <td>38.36</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ship_name Cruise_line  Age  Tonnage  passengers  length  cabins  \\\n",
       "0      Journey     Azamara    6   30.277        6.94    5.94    3.55   \n",
       "1        Quest     Azamara    6   30.277        6.94    5.94    3.55   \n",
       "2  Celebration    Carnival   26   47.262       14.86    7.22    7.43   \n",
       "3     Conquest    Carnival   11  110.000       29.74    9.53   14.88   \n",
       "4      Destiny    Carnival   17  101.353       26.42    8.92   13.21   \n",
       "\n",
       "   passenger_density   crew  \n",
       "0              42.64   3.55  \n",
       "1              42.64   3.55  \n",
       "2              31.80   6.70  \n",
       "3              36.99  19.10  \n",
       "4              38.36  10.00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Calculate basic statistics of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Tonnage</th>\n",
       "      <th>passengers</th>\n",
       "      <th>length</th>\n",
       "      <th>cabins</th>\n",
       "      <th>passenger_density</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.689873</td>\n",
       "      <td>71.284671</td>\n",
       "      <td>18.457405</td>\n",
       "      <td>8.130633</td>\n",
       "      <td>8.830000</td>\n",
       "      <td>39.900949</td>\n",
       "      <td>7.794177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.615691</td>\n",
       "      <td>37.229540</td>\n",
       "      <td>9.677095</td>\n",
       "      <td>1.793474</td>\n",
       "      <td>4.471417</td>\n",
       "      <td>8.639217</td>\n",
       "      <td>3.503487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.329000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>2.790000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>17.700000</td>\n",
       "      <td>0.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>46.013000</td>\n",
       "      <td>12.535000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>6.132500</td>\n",
       "      <td>34.570000</td>\n",
       "      <td>5.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>71.899000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>8.555000</td>\n",
       "      <td>9.570000</td>\n",
       "      <td>39.085000</td>\n",
       "      <td>8.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>90.772500</td>\n",
       "      <td>24.845000</td>\n",
       "      <td>9.510000</td>\n",
       "      <td>10.885000</td>\n",
       "      <td>44.185000</td>\n",
       "      <td>9.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>11.820000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>71.430000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age     Tonnage  passengers      length      cabins  \\\n",
       "count  158.000000  158.000000  158.000000  158.000000  158.000000   \n",
       "mean    15.689873   71.284671   18.457405    8.130633    8.830000   \n",
       "std      7.615691   37.229540    9.677095    1.793474    4.471417   \n",
       "min      4.000000    2.329000    0.660000    2.790000    0.330000   \n",
       "25%     10.000000   46.013000   12.535000    7.100000    6.132500   \n",
       "50%     14.000000   71.899000   19.500000    8.555000    9.570000   \n",
       "75%     20.000000   90.772500   24.845000    9.510000   10.885000   \n",
       "max     48.000000  220.000000   54.000000   11.820000   27.000000   \n",
       "\n",
       "       passenger_density        crew  \n",
       "count         158.000000  158.000000  \n",
       "mean           39.900949    7.794177  \n",
       "std             8.639217    3.503487  \n",
       "min            17.700000    0.590000  \n",
       "25%            34.570000    5.480000  \n",
       "50%            39.085000    8.150000  \n",
       "75%            44.185000    9.990000  \n",
       "max            71.430000   21.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Age', 'Tonnage', 'passengers', 'length', 'cabins','passenger_density','crew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xaf75c50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b8e5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[cols], size=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations from part 2\n",
    "1) We observe that variables are on different scales, for sample the Age variable ranges from about 16 years to 48 years, while the Tonnage variable ranges from 2 to 220, see probability density plots below. It is therefore important that when a regression model is built using these variables, variables be brought to same scale either by standardizing or normalizing the data.\n",
    "\n",
    "2) We also observe that the target variable 'crew' correlates well with 4 predictor variables, namely, 'Tonnage', 'passengers', 'length', and 'cabins'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3532b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Age'],bins=20)\n",
    "plt.title('probability distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc58b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Tonnage'],bins=20)\n",
    "plt.title('probability distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Variable selection for predicting \"crew\" size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 (a) Calculation of covariance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Age', 'Tonnage', 'passengers', 'length', 'cabins','passenger_density','crew']\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "stdsc = StandardScaler()\n",
    "X_std = stdsc.fit_transform(df[cols].iloc[:,range(0,7)].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf2ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cov_mat =np.cov(X_std.T)\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.set(font_scale=1.5)\n",
    "hm = sns.heatmap(cov_mat,\n",
    "                 cbar=True,\n",
    "                 annot=True,\n",
    "                 square=True,\n",
    "                 fmt='.2f',\n",
    "                 annot_kws={'size': 12},\n",
    "                 yticklabels=cols,\n",
    "                 xticklabels=cols)\n",
    "plt.title('Covariance matrix showing correlation coefficients')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 (b) Selecting important variables (columns)\n",
    "From the covariance matrix plot above, we see that the \"crew\" variable correlates strongly with 4 predictor variables: \"Tonnage\", \"passengers\", \"length, and \"cabins\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_selected = ['Tonnage', 'passengers', 'length', 'cabins','crew']"
   ]
  },
  {
   "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>Tonnage</th>\n",
       "      <th>passengers</th>\n",
       "      <th>length</th>\n",
       "      <th>cabins</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.277</td>\n",
       "      <td>6.94</td>\n",
       "      <td>5.94</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.277</td>\n",
       "      <td>6.94</td>\n",
       "      <td>5.94</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.262</td>\n",
       "      <td>14.86</td>\n",
       "      <td>7.22</td>\n",
       "      <td>7.43</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110.000</td>\n",
       "      <td>29.74</td>\n",
       "      <td>9.53</td>\n",
       "      <td>14.88</td>\n",
       "      <td>19.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101.353</td>\n",
       "      <td>26.42</td>\n",
       "      <td>8.92</td>\n",
       "      <td>13.21</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tonnage  passengers  length  cabins   crew\n",
       "0   30.277        6.94    5.94    3.55   3.55\n",
       "1   30.277        6.94    5.94    3.55   3.55\n",
       "2   47.262       14.86    7.22    7.43   6.70\n",
       "3  110.000       29.74    9.53   14.88  19.10\n",
       "4  101.353       26.42    8.92   13.21  10.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cols_selected].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[cols_selected].iloc[:,0:4].values    # features matrix \n",
    "y = df[cols_selected]['crew'].values        # target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. One-hot encoding for categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Tonnage</th>\n",
       "      <th>passengers</th>\n",
       "      <th>length</th>\n",
       "      <th>cabins</th>\n",
       "      <th>passenger_density</th>\n",
       "      <th>crew</th>\n",
       "      <th>Ship_name_Adventure</th>\n",
       "      <th>Ship_name_Allegra</th>\n",
       "      <th>Ship_name_Amsterdam</th>\n",
       "      <th>...</th>\n",
       "      <th>Cruise_line_Oceania</th>\n",
       "      <th>Cruise_line_Orient</th>\n",
       "      <th>Cruise_line_P&amp;O</th>\n",
       "      <th>Cruise_line_Princess</th>\n",
       "      <th>Cruise_line_Regent_Seven_Seas</th>\n",
       "      <th>Cruise_line_Royal_Caribbean</th>\n",
       "      <th>Cruise_line_Seabourn</th>\n",
       "      <th>Cruise_line_Silversea</th>\n",
       "      <th>Cruise_line_Star</th>\n",
       "      <th>Cruise_line_Windstar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>30.277</td>\n",
       "      <td>6.94</td>\n",
       "      <td>5.94</td>\n",
       "      <td>3.55</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>30.277</td>\n",
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       "      <td>7.22</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>110.000</td>\n",
       "      <td>29.74</td>\n",
       "      <td>9.53</td>\n",
       "      <td>14.88</td>\n",
       "      <td>36.99</td>\n",
       "      <td>19.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>101.353</td>\n",
       "      <td>26.42</td>\n",
       "      <td>8.92</td>\n",
       "      <td>13.21</td>\n",
       "      <td>38.36</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>70.367</td>\n",
       "      <td>20.52</td>\n",
       "      <td>8.55</td>\n",
       "      <td>10.20</td>\n",
       "      <td>34.29</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>70.367</td>\n",
       "      <td>20.52</td>\n",
       "      <td>8.55</td>\n",
       "      <td>10.20</td>\n",
       "      <td>34.29</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23</td>\n",
       "      <td>70.367</td>\n",
       "      <td>20.56</td>\n",
       "      <td>8.55</td>\n",
       "      <td>10.22</td>\n",
       "      <td>34.23</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19</td>\n",
       "      <td>70.367</td>\n",
       "      <td>20.52</td>\n",
       "      <td>8.55</td>\n",
       "      <td>10.20</td>\n",
       "      <td>34.29</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>110.239</td>\n",
       "      <td>37.00</td>\n",
       "      <td>9.51</td>\n",
       "      <td>14.87</td>\n",
       "      <td>29.79</td>\n",
       "      <td>11.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>110.000</td>\n",
       "      <td>29.74</td>\n",
       "      <td>9.51</td>\n",
       "      <td>14.87</td>\n",
       "      <td>36.99</td>\n",
       "      <td>11.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>46.052</td>\n",
       "      <td>14.52</td>\n",
       "      <td>7.27</td>\n",
       "      <td>7.26</td>\n",
       "      <td>31.72</td>\n",
       "      <td>6.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>70.367</td>\n",
       "      <td>20.52</td>\n",
       "      <td>8.55</td>\n",
       "      <td>10.20</td>\n",
       "      <td>34.29</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17</td>\n",
       "      <td>70.367</td>\n",
       "      <td>20.52</td>\n",
       "      <td>8.55</td>\n",
       "      <td>10.20</td>\n",
       "      <td>34.29</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11</td>\n",
       "      <td>86.000</td>\n",
       "      <td>21.24</td>\n",
       "      <td>9.63</td>\n",
       "      <td>10.62</td>\n",
       "      <td>40.49</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>110.000</td>\n",
       "      <td>29.74</td>\n",
       "      <td>9.51</td>\n",
       "      <td>14.87</td>\n",
       "      <td>36.99</td>\n",
       "      <td>11.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>88.500</td>\n",
       "      <td>21.24</td>\n",
       "      <td>9.63</td>\n",
       "      <td>10.62</td>\n",
       "      <td>41.67</td>\n",
       "      <td>10.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15</td>\n",
       "      <td>70.367</td>\n",
       "      <td>20.52</td>\n",
       "      <td>8.55</td>\n",
       "      <td>10.20</td>\n",
       "      <td>34.29</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>88.500</td>\n",
       "      <td>21.24</td>\n",
       "      <td>9.63</td>\n",
       "      <td>11.62</td>\n",
       "      <td>41.67</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>70.367</td>\n",
       "      <td>20.52</td>\n",
       "      <td>8.55</td>\n",
       "      <td>10.20</td>\n",
       "      <td>34.29</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12</td>\n",
       "      <td>88.500</td>\n",
       "      <td>21.24</td>\n",
       "      <td>9.63</td>\n",
       "      <td>10.56</td>\n",
       "      <td>41.67</td>\n",
       "      <td>10.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14</td>\n",
       "      <td>101.509</td>\n",
       "      <td>27.58</td>\n",
       "      <td>8.93</td>\n",
       "      <td>13.21</td>\n",
       "      <td>36.81</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9</td>\n",
       "      <td>110.000</td>\n",
       "      <td>29.74</td>\n",
       "      <td>9.52</td>\n",
       "      <td>14.87</td>\n",
       "      <td>36.99</td>\n",
       "      <td>11.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13</td>\n",
       "      <td>101.509</td>\n",
       "      <td>27.58</td>\n",
       "      <td>8.93</td>\n",
       "      <td>13.79</td>\n",
       "      <td>36.81</td>\n",
       "      <td>11.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>18</td>\n",
       "      <td>70.606</td>\n",
       "      <td>17.70</td>\n",
       "      <td>8.15</td>\n",
       "      <td>8.75</td>\n",
       "      <td>39.89</td>\n",
       "      <td>8.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11</td>\n",
       "      <td>91.000</td>\n",
       "      <td>20.32</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.75</td>\n",
       "      <td>44.78</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17</td>\n",
       "      <td>77.713</td>\n",
       "      <td>18.90</td>\n",
       "      <td>8.66</td>\n",
       "      <td>9.35</td>\n",
       "      <td>41.12</td>\n",
       "      <td>9.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12</td>\n",
       "      <td>91.000</td>\n",
       "      <td>20.32</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.75</td>\n",
       "      <td>44.78</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>77.713</td>\n",
       "      <td>18.82</td>\n",
       "      <td>8.66</td>\n",
       "      <td>9.35</td>\n",
       "      <td>41.29</td>\n",
       "      <td>9.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>13</td>\n",
       "      <td>91.000</td>\n",
       "      <td>20.32</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.75</td>\n",
       "      <td>44.78</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>18</td>\n",
       "      <td>70.000</td>\n",
       "      <td>18.00</td>\n",
       "      <td>8.67</td>\n",
       "      <td>9.00</td>\n",
       "      <td>38.89</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>6</td>\n",
       "      <td>158.000</td>\n",
       "      <td>43.70</td>\n",
       "      <td>11.25</td>\n",
       "      <td>18.00</td>\n",
       "      <td>36.16</td>\n",
       "      <td>13.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>21</td>\n",
       "      <td>73.941</td>\n",
       "      <td>27.44</td>\n",
       "      <td>8.80</td>\n",
       "      <td>11.75</td>\n",
       "      <td>26.95</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>10</td>\n",
       "      <td>138.000</td>\n",
       "      <td>31.14</td>\n",
       "      <td>10.20</td>\n",
       "      <td>15.57</td>\n",
       "      <td>44.32</td>\n",
       "      <td>11.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>22</td>\n",
       "      <td>73.941</td>\n",
       "      <td>27.44</td>\n",
       "      <td>8.80</td>\n",
       "      <td>11.77</td>\n",
       "      <td>30.94</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>11</td>\n",
       "      <td>138.000</td>\n",
       "      <td>31.14</td>\n",
       "      <td>10.20</td>\n",
       "      <td>15.57</td>\n",
       "      <td>44.32</td>\n",
       "      <td>11.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>4</td>\n",
       "      <td>220.000</td>\n",
       "      <td>54.00</td>\n",
       "      <td>11.82</td>\n",
       "      <td>27.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>12</td>\n",
       "      <td>90.090</td>\n",
       "      <td>25.01</td>\n",
       "      <td>9.62</td>\n",
       "      <td>10.50</td>\n",
       "      <td>36.02</td>\n",
       "      <td>8.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>16</td>\n",
       "      <td>78.491</td>\n",
       "      <td>24.35</td>\n",
       "      <td>9.15</td>\n",
       "      <td>10.00</td>\n",
       "      <td>32.23</td>\n",
       "      <td>7.65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>10</td>\n",
       "      <td>90.090</td>\n",
       "      <td>25.01</td>\n",
       "      <td>9.62</td>\n",
       "      <td>10.50</td>\n",
       "      <td>36.02</td>\n",
       "      <td>8.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>25</td>\n",
       "      <td>73.192</td>\n",
       "      <td>28.52</td>\n",
       "      <td>8.80</td>\n",
       "      <td>11.38</td>\n",
       "      <td>25.66</td>\n",
       "      <td>8.08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>17</td>\n",
       "      <td>70.000</td>\n",
       "      <td>20.76</td>\n",
       "      <td>8.67</td>\n",
       "      <td>9.02</td>\n",
       "      <td>33.72</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>15</td>\n",
       "      <td>78.491</td>\n",
       "      <td>24.35</td>\n",
       "      <td>9.15</td>\n",
       "      <td>10.00</td>\n",
       "      <td>32.23</td>\n",
       "      <td>6.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>14</td>\n",
       "      <td>138.000</td>\n",
       "      <td>31.14</td>\n",
       "      <td>10.20</td>\n",
       "      <td>15.57</td>\n",
       "      <td>44.32</td>\n",
       "      <td>11.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>21</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2.08</td>\n",
       "      <td>4.40</td>\n",
       "      <td>1.04</td>\n",
       "      <td>48.08</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>27</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2.08</td>\n",
       "      <td>4.40</td>\n",
       "      <td>1.04</td>\n",
       "      <td>48.08</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>24</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2.08</td>\n",
       "      <td>4.40</td>\n",
       "      <td>1.04</td>\n",
       "      <td>48.08</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>19</td>\n",
       "      <td>16.800</td>\n",
       "      <td>2.96</td>\n",
       "      <td>5.14</td>\n",
       "      <td>1.48</td>\n",
       "      <td>56.76</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>13</td>\n",
       "      <td>25.000</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.97</td>\n",
       "      <td>1.94</td>\n",
       "      <td>65.45</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>12</td>\n",
       "      <td>25.000</td>\n",
       "      <td>3.88</td>\n",
       "      <td>5.97</td>\n",
       "      <td>1.94</td>\n",
       "      <td>64.43</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>19</td>\n",
       "      <td>16.800</td>\n",
       "      <td>2.96</td>\n",
       "      <td>5.14</td>\n",
       "      <td>1.48</td>\n",
       "      <td>56.76</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>22</td>\n",
       "      <td>3.341</td>\n",
       "      <td>0.66</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.33</td>\n",
       "      <td>50.62</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>21</td>\n",
       "      <td>19.093</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.37</td>\n",
       "      <td>4.00</td>\n",
       "      <td>23.87</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12</td>\n",
       "      <td>42.000</td>\n",
       "      <td>14.80</td>\n",
       "      <td>7.13</td>\n",
       "      <td>7.40</td>\n",
       "      <td>28.38</td>\n",
       "      <td>6.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>24</td>\n",
       "      <td>40.053</td>\n",
       "      <td>12.87</td>\n",
       "      <td>5.79</td>\n",
       "      <td>7.76</td>\n",
       "      <td>31.12</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>22</td>\n",
       "      <td>3.341</td>\n",
       "      <td>0.66</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.33</td>\n",
       "      <td>50.62</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>14</td>\n",
       "      <td>76.800</td>\n",
       "      <td>19.60</td>\n",
       "      <td>8.79</td>\n",
       "      <td>9.67</td>\n",
       "      <td>39.18</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>25</td>\n",
       "      <td>5.350</td>\n",
       "      <td>1.58</td>\n",
       "      <td>4.40</td>\n",
       "      <td>0.74</td>\n",
       "      <td>33.86</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>27</td>\n",
       "      <td>5.350</td>\n",
       "      <td>1.67</td>\n",
       "      <td>4.40</td>\n",
       "      <td>0.74</td>\n",
       "      <td>32.04</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>23</td>\n",
       "      <td>14.745</td>\n",
       "      <td>3.08</td>\n",
       "      <td>6.17</td>\n",
       "      <td>1.56</td>\n",
       "      <td>47.87</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Tonnage  passengers  length  cabins  passenger_density   crew  \\\n",
       "0      6   30.277        6.94    5.94    3.55              42.64   3.55   \n",
       "1      6   30.277        6.94    5.94    3.55              42.64   3.55   \n",
       "2     26   47.262       14.86    7.22    7.43              31.80   6.70   \n",
       "3     11  110.000       29.74    9.53   14.88              36.99  19.10   \n",
       "4     17  101.353       26.42    8.92   13.21              38.36  10.00   \n",
       "5     22   70.367       20.52    8.55   10.20              34.29   9.20   \n",
       "6     15   70.367       20.52    8.55   10.20              34.29   9.20   \n",
       "7     23   70.367       20.56    8.55   10.22              34.23   9.20   \n",
       "8     19   70.367       20.52    8.55   10.20              34.29   9.20   \n",
       "9      6  110.239       37.00    9.51   14.87              29.79  11.50   \n",
       "10    10  110.000       29.74    9.51   14.87              36.99  11.60   \n",
       "11    28   46.052       14.52    7.27    7.26              31.72   6.60   \n",
       "12    18   70.367       20.52    8.55   10.20              34.29   9.20   \n",
       "13    17   70.367       20.52    8.55   10.20              34.29   9.20   \n",
       "14    11   86.000       21.24    9.63   10.62              40.49   9.30   \n",
       "15     8  110.000       29.74    9.51   14.87              36.99  11.60   \n",
       "16     9   88.500       21.24    9.63   10.62              41.67  10.30   \n",
       "17    15   70.367       20.52    8.55   10.20              34.29   9.20   \n",
       "18    12   88.500       21.24    9.63   11.62              41.67   9.30   \n",
       "19    20   70.367       20.52    8.55   10.20              34.29   9.20   \n",
       "20    12   88.500       21.24    9.63   10.56              41.67  10.29   \n",
       "21    14  101.509       27.58    8.93   13.21              36.81  10.00   \n",
       "22     9  110.000       29.74    9.52   14.87              36.99  11.60   \n",
       "23    13  101.509       27.58    8.93   13.79              36.81  11.50   \n",
       "24    18   70.606       17.70    8.15    8.75              39.89   8.58   \n",
       "25    11   91.000       20.32    9.65    9.75              44.78   9.99   \n",
       "26    17   77.713       18.90    8.66    9.35              41.12   9.09   \n",
       "27    12   91.000       20.32    9.65    9.75              44.78   9.99   \n",
       "28    16   77.713       18.82    8.66    9.35              41.29   9.09   \n",
       "29    13   91.000       20.32    9.65    9.75              44.78   9.99   \n",
       "..   ...      ...         ...     ...     ...                ...    ...   \n",
       "128   18   70.000       18.00    8.67    9.00              38.89   7.20   \n",
       "129    6  158.000       43.70   11.25   18.00              36.16  13.60   \n",
       "130   21   73.941       27.44    8.80   11.75              26.95   8.22   \n",
       "131   10  138.000       31.14   10.20   15.57              44.32  11.85   \n",
       "132   22   73.941       27.44    8.80   11.77              30.94   8.22   \n",
       "133   11  138.000       31.14   10.20   15.57              44.32  11.85   \n",
       "134    4  220.000       54.00   11.82   27.00              40.74  21.00   \n",
       "135   12   90.090       25.01    9.62   10.50              36.02   8.68   \n",
       "136   16   78.491       24.35    9.15   10.00              32.23   7.65   \n",
       "137   10   90.090       25.01    9.62   10.50              36.02   8.58   \n",
       "138   25   73.192       28.52    8.80   11.38              25.66   8.08   \n",
       "139   17   70.000       20.76    8.67    9.02              33.72   7.20   \n",
       "140   15   78.491       24.35    9.15   10.00              32.23   6.60   \n",
       "141   14  138.000       31.14   10.20   15.57              44.32  11.76   \n",
       "142   21   10.000        2.08    4.40    1.04              48.08   1.60   \n",
       "143   27   10.000        2.08    4.40    1.04              48.08   1.60   \n",
       "144   24   10.000        2.08    4.40    1.04              48.08   1.60   \n",
       "145   19   16.800        2.96    5.14    1.48              56.76   2.10   \n",
       "146   13   25.000        3.82    5.97    1.94              65.45   2.95   \n",
       "147   12   25.000        3.88    5.97    1.94              64.43   2.87   \n",
       "148   19   16.800        2.96    5.14    1.48              56.76   1.97   \n",
       "149   22    3.341        0.66    2.80    0.33              50.62   0.59   \n",
       "150   21   19.093        8.00    5.37    4.00              23.87   4.70   \n",
       "151   12   42.000       14.80    7.13    7.40              28.38   6.80   \n",
       "152   24   40.053       12.87    5.79    7.76              31.12   7.50   \n",
       "153   22    3.341        0.66    2.79    0.33              50.62   0.59   \n",
       "154   14   76.800       19.60    8.79    9.67              39.18  12.00   \n",
       "155   25    5.350        1.58    4.40    0.74              33.86   0.88   \n",
       "156   27    5.350        1.67    4.40    0.74              32.04   0.88   \n",
       "157   23   14.745        3.08    6.17    1.56              47.87   1.80   \n",
       "\n",
       "     Ship_name_Adventure  Ship_name_Allegra  Ship_name_Amsterdam  \\\n",
       "0                      0                  0                    0   \n",
       "1                      0                  0                    0   \n",
       "2                      0                  0                    0   \n",
       "3                      0                  0                    0   \n",
       "4                      0                  0                    0   \n",
       "5                      0                  0                    0   \n",
       "6                      0                  0                    0   \n",
       "7                      0                  0                    0   \n",
       "8                      0                  0                    0   \n",
       "9                      0                  0                    0   \n",
       "10                     0                  0                    0   \n",
       "11                     0                  0                    0   \n",
       "12                     0                  0                    0   \n",
       "13                     0                  0                    0   \n",
       "14                     0                  0                    0   \n",
       "15                     0                  0                    0   \n",
       "16                     0                  0                    0   \n",
       "17                     0                  0                    0   \n",
       "18                     0                  0                    0   \n",
       "19                     0                  0                    0   \n",
       "20                     0                  0                    0   \n",
       "21                     0                  0                    0   \n",
       "22                     0                  0                    0   \n",
       "23                     0                  0                    0   \n",
       "24                     0                  0                    0   \n",
       "25                     0                  0                    0   \n",
       "26                     0                  0                    0   \n",
       "27                     0                  0                    0   \n",
       "28                     0                  0                    0   \n",
       "29                     0                  0                    0   \n",
       "..                   ...                ...                  ...   \n",
       "128                    0                  0                    0   \n",
       "129                    0                  0                    0   \n",
       "130                    0                  0                    0   \n",
       "131                    0                  0                    0   \n",
       "132                    0                  0                    0   \n",
       "133                    0                  0                    0   \n",
       "134                    0                  0                    0   \n",
       "135                    0                  0                    0   \n",
       "136                    0                  0                    0   \n",
       "137                    0                  0                    0   \n",
       "138                    0                  0                    0   \n",
       "139                    0                  0                    0   \n",
       "140                    0                  0                    0   \n",
       "141                    0                  0                    0   \n",
       "142                    0                  0                    0   \n",
       "143                    0                  0                    0   \n",
       "144                    0                  0                    0   \n",
       "145                    0                  0                    0   \n",
       "146                    0                  0                    0   \n",
       "147                    0                  0                    0   \n",
       "148                    0                  0                    0   \n",
       "149                    0                  0                    0   \n",
       "150                    0                  0                    0   \n",
       "151                    0                  0                    0   \n",
       "152                    0                  0                    0   \n",
       "153                    0                  0                    0   \n",
       "154                    0                  0                    0   \n",
       "155                    0                  0                    0   \n",
       "156                    0                  0                    0   \n",
       "157                    0                  0                    0   \n",
       "\n",
       "             ...           Cruise_line_Oceania  Cruise_line_Orient  \\\n",
       "0            ...                             0                   0   \n",
       "1            ...                             0                   0   \n",
       "2            ...                             0                   0   \n",
       "3            ...                             0                   0   \n",
       "4            ...                             0                   0   \n",
       "5            ...                             0                   0   \n",
       "6            ...                             0                   0   \n",
       "7            ...                             0                   0   \n",
       "8            ...                             0                   0   \n",
       "9            ...                             0                   0   \n",
       "10           ...                             0                   0   \n",
       "11           ...                             0                   0   \n",
       "12           ...                             0                   0   \n",
       "13           ...                             0                   0   \n",
       "14           ...                             0                   0   \n",
       "15           ...                             0                   0   \n",
       "16           ...                             0                   0   \n",
       "17           ...                             0                   0   \n",
       "18           ...                             0                   0   \n",
       "19           ...                             0                   0   \n",
       "20           ...                             0                   0   \n",
       "21           ...                             0                   0   \n",
       "22           ...                             0                   0   \n",
       "23           ...                             0                   0   \n",
       "24           ...                             0                   0   \n",
       "25           ...                             0                   0   \n",
       "26           ...                             0                   0   \n",
       "27           ...                             0                   0   \n",
       "28           ...                             0                   0   \n",
       "29           ...                             0                   0   \n",
       "..           ...                           ...                 ...   \n",
       "128          ...                             0                   0   \n",
       "129          ...                             0                   0   \n",
       "130          ...                             0                   0   \n",
       "131          ...                             0                   0   \n",
       "132          ...                             0                   0   \n",
       "133          ...                             0                   0   \n",
       "134          ...                             0                   0   \n",
       "135          ...                             0                   0   \n",
       "136          ...                             0                   0   \n",
       "137          ...                             0                   0   \n",
       "138          ...                             0                   0   \n",
       "139          ...                             0                   0   \n",
       "140          ...                             0                   0   \n",
       "141          ...                             0                   0   \n",
       "142          ...                             0                   0   \n",
       "143          ...                             0                   0   \n",
       "144          ...                             0                   0   \n",
       "145          ...                             0                   0   \n",
       "146          ...                             0                   0   \n",
       "147          ...                             0                   0   \n",
       "148          ...                             0                   0   \n",
       "149          ...                             0                   0   \n",
       "150          ...                             0                   0   \n",
       "151          ...                             0                   0   \n",
       "152          ...                             0                   0   \n",
       "153          ...                             0                   0   \n",
       "154          ...                             0                   0   \n",
       "155          ...                             0                   0   \n",
       "156          ...                             0                   0   \n",
       "157          ...                             0                   0   \n",
       "\n",
       "     Cruise_line_P&O  Cruise_line_Princess  Cruise_line_Regent_Seven_Seas  \\\n",
       "0                  0                     0                              0   \n",
       "1                  0                     0                              0   \n",
       "2                  0                     0                              0   \n",
       "3                  0                     0                              0   \n",
       "4                  0                     0                              0   \n",
       "5                  0                     0                              0   \n",
       "6                  0                     0                              0   \n",
       "7                  0                     0                              0   \n",
       "8                  0                     0                              0   \n",
       "9                  0                     0                              0   \n",
       "10                 0                     0                              0   \n",
       "11                 0                     0                              0   \n",
       "12                 0                     0                              0   \n",
       "13                 0                     0                              0   \n",
       "14                 0                     0                              0   \n",
       "15                 0                     0                              0   \n",
       "16                 0                     0                              0   \n",
       "17                 0                     0                              0   \n",
       "18                 0                     0                              0   \n",
       "19                 0                     0                              0   \n",
       "20                 0                     0                              0   \n",
       "21                 0                     0                              0   \n",
       "22                 0                     0                              0   \n",
       "23                 0                     0                              0   \n",
       "24                 0                     0                              0   \n",
       "25                 0                     0                              0   \n",
       "26                 0                     0                              0   \n",
       "27                 0                     0                              0   \n",
       "28                 0                     0                              0   \n",
       "29                 0                     0                              0   \n",
       "..               ...                   ...                            ...   \n",
       "128                0                     0                              0   \n",
       "129                0                     0                              0   \n",
       "130                0                     0                              0   \n",
       "131                0                     0                              0   \n",
       "132                0                     0                              0   \n",
       "133                0                     0                              0   \n",
       "134                0                     0                              0   \n",
       "135                0                     0                              0   \n",
       "136                0                     0                              0   \n",
       "137                0                     0                              0   \n",
       "138                0                     0                              0   \n",
       "139                0                     0                              0   \n",
       "140                0                     0                              0   \n",
       "141                0                     0                              0   \n",
       "142                0                     0                              0   \n",
       "143                0                     0                              0   \n",
       "144                0                     0                              0   \n",
       "145                0                     0                              0   \n",
       "146                0                     0                              0   \n",
       "147                0                     0                              0   \n",
       "148                0                     0                              0   \n",
       "149                0                     0                              0   \n",
       "150                0                     0                              0   \n",
       "151                0                     0                              0   \n",
       "152                0                     0                              0   \n",
       "153                0                     0                              0   \n",
       "154                0                     0                              0   \n",
       "155                0                     0                              0   \n",
       "156                0                     0                              0   \n",
       "157                0                     0                              0   \n",
       "\n",
       "     Cruise_line_Royal_Caribbean  Cruise_line_Seabourn  Cruise_line_Silversea  \\\n",
       "0                              0                     0                      0   \n",
       "1                              0                     0                      0   \n",
       "2                              0                     0                      0   \n",
       "3                              0                     0                      0   \n",
       "4                              0                     0                      0   \n",
       "5                              0                     0                      0   \n",
       "6                              0                     0                      0   \n",
       "7                              0                     0                      0   \n",
       "8                              0                     0                      0   \n",
       "9                              0                     0                      0   \n",
       "10                             0                     0                      0   \n",
       "11                             0                     0                      0   \n",
       "12                             0                     0                      0   \n",
       "13                             0                     0                      0   \n",
       "14                             0                     0                      0   \n",
       "15                             0                     0                      0   \n",
       "16                             0                     0                      0   \n",
       "17                             0                     0                      0   \n",
       "18                             0                     0                      0   \n",
       "19                             0                     0                      0   \n",
       "20                             0                     0                      0   \n",
       "21                             0                     0                      0   \n",
       "22                             0                     0                      0   \n",
       "23                             0                     0                      0   \n",
       "24                             0                     0                      0   \n",
       "25                             0                     0                      0   \n",
       "26                             0                     0                      0   \n",
       "27                             0                     0                      0   \n",
       "28                             0                     0                      0   \n",
       "29                             0                     0                      0   \n",
       "..                           ...                   ...                    ...   \n",
       "128                            1                     0                      0   \n",
       "129                            1                     0                      0   \n",
       "130                            1                     0                      0   \n",
       "131                            1                     0                      0   \n",
       "132                            1                     0                      0   \n",
       "133                            1                     0                      0   \n",
       "134                            1                     0                      0   \n",
       "135                            1                     0                      0   \n",
       "136                            1                     0                      0   \n",
       "137                            1                     0                      0   \n",
       "138                            1                     0                      0   \n",
       "139                            1                     0                      0   \n",
       "140                            1                     0                      0   \n",
       "141                            1                     0                      0   \n",
       "142                            0                     1                      0   \n",
       "143                            0                     1                      0   \n",
       "144                            0                     1                      0   \n",
       "145                            0                     0                      1   \n",
       "146                            0                     0                      1   \n",
       "147                            0                     0                      1   \n",
       "148                            0                     0                      1   \n",
       "149                            0                     0                      0   \n",
       "150                            0                     0                      0   \n",
       "151                            0                     0                      0   \n",
       "152                            0                     0                      0   \n",
       "153                            0                     0                      0   \n",
       "154                            0                     0                      0   \n",
       "155                            0                     0                      0   \n",
       "156                            0                     0                      0   \n",
       "157                            0                     0                      0   \n",
       "\n",
       "     Cruise_line_Star  Cruise_line_Windstar  \n",
       "0                   0                     0  \n",
       "1                   0                     0  \n",
       "2                   0                     0  \n",
       "3                   0                     0  \n",
       "4                   0                     0  \n",
       "5                   0                     0  \n",
       "6                   0                     0  \n",
       "7                   0                     0  \n",
       "8                   0                     0  \n",
       "9                   0                     0  \n",
       "10                  0                     0  \n",
       "11                  0                     0  \n",
       "12                  0                     0  \n",
       "13                  0                     0  \n",
       "14                  0                     0  \n",
       "15                  0                     0  \n",
       "16                  0                     0  \n",
       "17                  0                     0  \n",
       "18                  0                     0  \n",
       "19                  0                     0  \n",
       "20                  0                     0  \n",
       "21                  0                     0  \n",
       "22                  0                     0  \n",
       "23                  0                     0  \n",
       "24                  0                     0  \n",
       "25                  0                     0  \n",
       "26                  0                     0  \n",
       "27                  0                     0  \n",
       "28                  0                     0  \n",
       "29                  0                     0  \n",
       "..                ...                   ...  \n",
       "128                 0                     0  \n",
       "129                 0                     0  \n",
       "130                 0                     0  \n",
       "131                 0                     0  \n",
       "132                 0                     0  \n",
       "133                 0                     0  \n",
       "134                 0                     0  \n",
       "135                 0                     0  \n",
       "136                 0                     0  \n",
       "137                 0                     0  \n",
       "138                 0                     0  \n",
       "139                 0                     0  \n",
       "140                 0                     0  \n",
       "141                 0                     0  \n",
       "142                 0                     0  \n",
       "143                 0                     0  \n",
       "144                 0                     0  \n",
       "145                 0                     0  \n",
       "146                 0                     0  \n",
       "147                 0                     0  \n",
       "148                 0                     0  \n",
       "149                 1                     0  \n",
       "150                 1                     0  \n",
       "151                 1                     0  \n",
       "152                 1                     0  \n",
       "153                 1                     0  \n",
       "154                 1                     0  \n",
       "155                 0                     1  \n",
       "156                 0                     1  \n",
       "157                 0                     1  \n",
       "\n",
       "[158 rows x 165 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe = OneHotEncoder(categorical_features=[0 ,1])\n",
    "pd.get_dummies(df[['Ship_name', 'Cruise_line','Age', 'Tonnage', 'passengers', 'length', 'cabins','passenger_density','crew']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.get_dummies(df[['Ship_name', 'Cruise_line','Age', 'Tonnage', 'passengers', 'length', 'cabins','passenger_density','crew']])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Tonnage</th>\n",
       "      <th>passengers</th>\n",
       "      <th>length</th>\n",
       "      <th>cabins</th>\n",
       "      <th>passenger_density</th>\n",
       "      <th>crew</th>\n",
       "      <th>Ship_name_Adventure</th>\n",
       "      <th>Ship_name_Allegra</th>\n",
       "      <th>Ship_name_Amsterdam</th>\n",
       "      <th>...</th>\n",
       "      <th>Cruise_line_Oceania</th>\n",
       "      <th>Cruise_line_Orient</th>\n",
       "      <th>Cruise_line_P&amp;O</th>\n",
       "      <th>Cruise_line_Princess</th>\n",
       "      <th>Cruise_line_Regent_Seven_Seas</th>\n",
       "      <th>Cruise_line_Royal_Caribbean</th>\n",
       "      <th>Cruise_line_Seabourn</th>\n",
       "      <th>Cruise_line_Silversea</th>\n",
       "      <th>Cruise_line_Star</th>\n",
       "      <th>Cruise_line_Windstar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>6.94</td>\n",
       "      <td>5.94</td>\n",
       "      <td>3.55</td>\n",
       "      <td>42.64</td>\n",
       "      <td>3.55</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>30.277</td>\n",
       "      <td>6.94</td>\n",
       "      <td>5.94</td>\n",
       "      <td>3.55</td>\n",
       "      <td>42.64</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>47.262</td>\n",
       "      <td>14.86</td>\n",
       "      <td>7.22</td>\n",
       "      <td>7.43</td>\n",
       "      <td>31.80</td>\n",
       "      <td>6.70</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>110.000</td>\n",
       "      <td>29.74</td>\n",
       "      <td>9.53</td>\n",
       "      <td>14.88</td>\n",
       "      <td>36.99</td>\n",
       "      <td>19.10</td>\n",
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       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>101.353</td>\n",
       "      <td>26.42</td>\n",
       "      <td>8.92</td>\n",
       "      <td>13.21</td>\n",
       "      <td>38.36</td>\n",
       "      <td>10.00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Tonnage  passengers  length  cabins  passenger_density   crew  \\\n",
       "0    6   30.277        6.94    5.94    3.55              42.64   3.55   \n",
       "1    6   30.277        6.94    5.94    3.55              42.64   3.55   \n",
       "2   26   47.262       14.86    7.22    7.43              31.80   6.70   \n",
       "3   11  110.000       29.74    9.53   14.88              36.99  19.10   \n",
       "4   17  101.353       26.42    8.92   13.21              38.36  10.00   \n",
       "\n",
       "   Ship_name_Adventure  Ship_name_Allegra  Ship_name_Amsterdam  \\\n",
       "0                    0                  0                    0   \n",
       "1                    0                  0                    0   \n",
       "2                    0                  0                    0   \n",
       "3                    0                  0                    0   \n",
       "4                    0                  0                    0   \n",
       "\n",
       "           ...           Cruise_line_Oceania  Cruise_line_Orient  \\\n",
       "0          ...                             0                   0   \n",
       "1          ...                             0                   0   \n",
       "2          ...                             0                   0   \n",
       "3          ...                             0                   0   \n",
       "4          ...                             0                   0   \n",
       "\n",
       "   Cruise_line_P&O  Cruise_line_Princess  Cruise_line_Regent_Seven_Seas  \\\n",
       "0                0                     0                              0   \n",
       "1                0                     0                              0   \n",
       "2                0                     0                              0   \n",
       "3                0                     0                              0   \n",
       "4                0                     0                              0   \n",
       "\n",
       "   Cruise_line_Royal_Caribbean  Cruise_line_Seabourn  Cruise_line_Silversea  \\\n",
       "0                            0                     0                      0   \n",
       "1                            0                     0                      0   \n",
       "2                            0                     0                      0   \n",
       "3                            0                     0                      0   \n",
       "4                            0                     0                      0   \n",
       "\n",
       "   Cruise_line_Star  Cruise_line_Windstar  \n",
       "0                 0                     0  \n",
       "1                 0                     0  \n",
       "2                 0                     0  \n",
       "3                 0                     0  \n",
       "4                 0                     0  \n",
       "\n",
       "[5 rows x 165 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf16e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df2['Ship_name_Adventure'],df2['crew'])\n",
    "plt.xlabel('Ship_name_Adventure')\n",
    "plt.ylabel('crew')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations from part 4\n",
    "\n",
    "In order to build a simplified regression model, we shall focus only on ordinal features. The categorical features \"Ship_name\"\tand \"Cruise_line\" will not be used. A simple model built using only the 4 ordinal features \"Tonnage\", \"passengers\", \"length, and \"cabins\" will be simple to interpret."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Data partitioning into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df[cols_selected].iloc[:,0:4].values     \n",
    "y = df[cols_selected]['crew']                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Building a muilt-regression model\n",
    "\n",
    "Our machine learning regression model for predicting a ship's \"crew\" size can be expressed as:\n",
    "\n",
    "$$ \\hat{y}_{i} = w_0 + \\sum_{j=1}^{4} X_{ij} w_j $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "slr = LinearRegression()\n",
    "\n",
    "slr.fit(X_train, y_train)\n",
    "y_train_pred = slr.predict(X_train)\n",
    "y_test_pred = slr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xefb4470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train_pred,  y_train_pred - y_train,\n",
    "            c='steelblue', marker='o', edgecolor='white',\n",
    "            label='Training data')\n",
    "plt.scatter(y_test_pred,  y_test_pred - y_test,\n",
    "            c='limegreen', marker='s', edgecolor='white',\n",
    "            label='Test data')\n",
    "plt.xlabel('Predicted values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.legend(loc='upper left')\n",
    "plt.hlines(y=0, xmin=-10, xmax=50, color='black', lw=2)\n",
    "plt.xlim([-10, 50])\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE train: 0.955, test: 0.889\n",
      "R^2 train: 0.920, test: 0.928\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "print('MSE train: %.3f, test: %.3f' % (\n",
    "        mean_squared_error(y_train, y_train_pred),\n",
    "        mean_squared_error(y_test, y_test_pred)))\n",
    "print('R^2 train: %.3f, test: %.3f' % (\n",
    "        r2_score(y_train, y_train_pred),\n",
    "        r2_score(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7525074496158393"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr.fit(X_train, y_train).intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01902703, -0.15001099,  0.37876395,  0.77613801])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr.fit(X_train, y_train).coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Feature Standardization, Cross Validation, and Hyper-parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = df[cols_selected].iloc[:,0:4].values     \n",
    "y = df[cols_selected]['crew']  \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_y = StandardScaler()\n",
    "sc_x = StandardScaler()\n",
    "y_std = sc_y.fit_transform(y_train[:, np.newaxis]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = []\n",
    "test_score = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=i)\n",
    "    y_train_std = sc_y.fit_transform(y_train[:, np.newaxis]).flatten()\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    from sklearn.decomposition import PCA\n",
    "    from sklearn.linear_model import LinearRegression\n",
    "    from sklearn.pipeline import Pipeline\n",
    "    pipe_lr = Pipeline([('scl', StandardScaler()),('pca', PCA(n_components=4)),('slr', LinearRegression())])\n",
    "    pipe_lr.fit(X_train, y_train_std)\n",
    "    y_train_pred_std=pipe_lr.predict(X_train)\n",
    "    y_test_pred_std=pipe_lr.predict(X_test)\n",
    "    y_train_pred=sc_y.inverse_transform(y_train_pred_std)\n",
    "    y_test_pred=sc_y.inverse_transform(y_test_pred_std)\n",
    "    train_score = np.append(train_score, r2_score(y_train, y_train_pred))\n",
    "    test_score = np.append(test_score, r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92028261, 0.91733937, 0.94839385, 0.93899476, 0.90621451,\n",
       "       0.91156903, 0.92726066, 0.94000795, 0.93922948, 0.93629554])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92827978, 0.93807946, 0.8741834 , 0.89901199, 0.94781315,\n",
       "       0.91880183, 0.91437408, 0.89660876, 0.90427477, 0.90139208])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 train: 0.929 +/- 0.013\n"
     ]
    }
   ],
   "source": [
    "print('R2 train: %.3f +/- %.3f' % (np.mean(train_score),np.std(train_score)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 test: 0.912 +/- 0.021\n"
     ]
    }
   ],
   "source": [
    "print('R2 test: %.3f +/- %.3f' % (np.mean(test_score),np.std(test_score)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Techniques of Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 (a) Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = []\n",
    "test_score = []\n",
    "cum_variance = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1,5):\n",
    "    X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=0)\n",
    "    y_train_std = sc_y.fit_transform(y_train[:, np.newaxis]).flatten()\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    from sklearn.decomposition import PCA\n",
    "    from sklearn.linear_model import LinearRegression\n",
    "    from sklearn.pipeline import Pipeline\n",
    "    pipe_lr = Pipeline([('scl', StandardScaler()),('pca', PCA(n_components=i)),('slr', LinearRegression())])\n",
    "    pipe_lr.fit(X_train, y_train_std)\n",
    "    y_train_pred_std=pipe_lr.predict(X_train)\n",
    "    y_test_pred_std=pipe_lr.predict(X_test)\n",
    "    y_train_pred=sc_y.inverse_transform(y_train_pred_std)\n",
    "    y_test_pred=sc_y.inverse_transform(y_test_pred_std)\n",
    "    train_score = np.append(train_score, r2_score(y_train, y_train_pred))\n",
    "    test_score = np.append(test_score, r2_score(y_test, y_test_pred))\n",
    "    cum_variance = np.append(cum_variance, np.sum(pipe_lr.fit(X_train, y_train).named_steps['pca'].explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90411898, 0.9041488 , 0.90416405, 0.92028261])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.89217843, 0.89174896, 0.89159266, 0.92827978])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.949817  , 0.98322819, 0.99587366, 1.        ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cum_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xefb4198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cum_variance,train_score, label = 'train_score')\n",
    "plt.plot(cum_variance, train_score)\n",
    "plt.scatter(cum_variance,test_score, label = 'test_score')\n",
    "plt.plot(cum_variance, test_score)\n",
    "plt.xlabel('cumulative variance')\n",
    "plt.ylabel('R2_score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations: PCA\n",
    "\n",
    "We observe that by increasing the number of principal components from 1 to 4, the train and test scores improve. This is because with less components, there is high bias error in the model, since model is overly simplified. As we increase the number of principal components, the bias error will reduce, but complexity in the model increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 (b) Regularized Regression: Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=0)\n",
    "y_train_std = sc_y.fit_transform(y_train[:, np.newaxis]).flatten()\n",
    "X_train_std = sc_x.fit_transform(X_train)\n",
    "X_test_std = sc_x.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = np.linspace(0.01,0.4,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso = Lasso(alpha=0.7)\n",
    "\n",
    "r2_train=[]\n",
    "r2_test=[]\n",
    "norm = []\n",
    "for i in range(10):\n",
    "    lasso = Lasso(alpha=alpha[i])\n",
    "    lasso.fit(X_train_std,y_train_std)\n",
    "    y_train_std=lasso.predict(X_train_std)\n",
    "    y_test_std=lasso.predict(X_test_std)\n",
    "    r2_train=np.append(r2_train,r2_score(y_train,sc_y.inverse_transform(y_train_std)))\n",
    "    r2_test=np.append(r2_test,r2_score(y_test,sc_y.inverse_transform(y_test_std)))\n",
    "    norm= np.append(norm,np.linalg.norm(lasso.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf323358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(alpha,r2_train,label='r2_train')\n",
    "plt.plot(alpha,r2_train)\n",
    "plt.scatter(alpha,r2_test,label='r2_test')\n",
    "plt.plot(alpha,r2_test)\n",
    "plt.scatter(alpha,norm,label = 'norm')\n",
    "plt.plot(alpha,norm)\n",
    "plt.ylim(-0.1,1)\n",
    "plt.xlim(0,.43)\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('R2_score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # Observations: Lasso\n",
    "\n",
    "We observe that as the regularization parameter $\\alpha$ increases, the norm of the regression coefficients become smaller and smaller. This means more regression coefficients are forced to zero, which intend increases bias error (over simplification). The best value to balance bias-variance tradeoff is when $\\alpha$ is kept low, say $\\alpha = 0.1$ or less."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}