Tutorial_for_lagou.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ![lagou_logo](http://pstatic.lagou.com/www/static/common/widgets/header_c/modules/img/logo@2x_712cdaa.png)\n",
    "# ** Lagou Talks on Toolsets **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking your installation\n",
    "You can run the following code to check the versions of the packages on your system:\n",
    "\n",
    "(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython: 4.0.1\n",
      "numpy: 1.10.1\n",
      "scipy: 0.16.0\n",
      "matplotlib: 1.5.0\n",
      "scikit-learn: 0.17\n",
      "pandas 0.17.1\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import IPython\n",
    "print('IPython:', IPython.__version__)\n",
    "\n",
    "import numpy as np\n",
    "print('numpy:', np.__version__)\n",
    "\n",
    "import scipy\n",
    "print('scipy:', scipy.__version__)\n",
    "\n",
    "import matplotlib\n",
    "print('matplotlib:', matplotlib.__version__)\n",
    "\n",
    "import sklearn\n",
    "print('scikit-learn:', sklearn.__version__)\n",
    "\n",
    "import pandas as pd\n",
    "print('pandas', pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('en_US', 'UTF-8')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import locale\n",
    "locale.getdefaultlocale()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Quick start**\n",
    "#### Here are a few commends to get started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Importing and parsing data\n",
    "url = 'https://www.kaggle.com/c/titanic/download/train.csv'\n",
    "addr = os.path.join(os.getcwd(), 'train.csv')\n",
    "titanic = pd.read_csv(addr)\n",
    "#titanic = pd.read_csv(addr, dtype={'Age': int})\n",
    "\n",
    "# Computation on columns\n",
    "titanic.Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploration of dataframe\n",
    "- `dataframe.dtypes` gives a list of data type info of all the attributes\n",
    "- `dataframe.head(n)` shows the top `n` rows/samples of all the dataset, `dataframe.tail(n)` is similar but shows in descendend order from the last rows.\n",
    "- `pandas` also provides lots of functionality on columns transformation. like `astype(type)`, `groupby(columns)`, `dropna()`, `fillna()`, `as_matrix()` and `tolist()` ,etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datatype\n",
    "titanic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizations\n",
    "titanic.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        PassengerId  Survived        Age     SibSp     Parch       Fare  \\\n",
      "Pclass                                                                    \n",
      "1        461.597222  0.629630  38.233441  0.416667  0.356481  84.154687   \n",
      "2        445.956522  0.472826  29.877630  0.402174  0.380435  20.662183   \n",
      "3        439.154786  0.242363  25.140620  0.615071  0.393075  13.675550   \n",
      "\n",
      "          Gender  \n",
      "Pclass            \n",
      "1       0.564815  \n",
      "2       0.586957  \n",
      "3       0.706721  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,\n",
       "       penalty='l2', power_t=0.5, random_state=None, shuffle=True,\n",
       "       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming columns\n",
    "titanic['Gender'] = titanic['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "\n",
    "# Aggregations\n",
    "print (titanic.groupby('Pclass').aggregate(np.mean))\n",
    "\n",
    "# Create models\n",
    "X = titanic.as_matrix(columns = ['Pclass','Gender','Fare'])\n",
    "y = titanic['Survived'].tolist()\n",
    "\n",
    "from sklearn import linear_model\n",
    "clf = linear_model.SGDClassifier()\n",
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.647587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.477990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare      Gender  \n",
       "count  891.000000  891.000000  891.000000  \n",
       "mean     0.381594   32.204208    0.647587  \n",
       "std      0.806057   49.693429    0.477990  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    7.910400    0.000000  \n",
       "50%      0.000000   14.454200    1.000000  \n",
       "75%      0.000000   31.000000    1.000000  \n",
       "max      6.000000  512.329200    1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "Gender         891 non-null int64\n",
      "dtypes: float64(2), int64(6), object(5)\n",
      "memory usage: 97.5+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Er9z9u33bDwG/aqFHREQ6ZK9rEmb26vrLc4FlwI1UE75/BGx190tbL5y5S2sS02hNoksN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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10722f3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "titanic['Age'].hist()\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seaborn 0.6.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1092ed650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "print('seaborn', seaborn.__version__)\n",
    "titanic['Age'].dropna().hist(bins=16, range=(0,80), alpha = .8)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        PassengerId  Survived    Pclass        Age     SibSp     Parch  \\\n",
      "Gender                                                                   \n",
      "0        431.028662  0.742038  2.159236  27.915709  0.694268  0.649682   \n",
      "1        454.147314  0.188908  2.389948  30.726645  0.429809  0.235702   \n",
      "\n",
      "             Fare  \n",
      "Gender             \n",
      "0       44.479818  \n",
      "1       25.523893  \n"
     ]
    }
   ],
   "source": [
    "print (titanic.groupby('Gender').aggregate(np.mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 35. ,  28. ,  21.5],\n",
       "       [ 40. ,  30. ,  25. ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_ages = np.zeros((2,3))\n",
    "\n",
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        median_ages[i,j] = titanic[(titanic['Gender'] == i) & \\\n",
    "                              (titanic['Pclass'] == j+1)]['Age'].dropna().median()\n",
    "\n",
    "median_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeFill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>32</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Pclass  Age  AgeFill\n",
       "5        1       3  NaN      NaN\n",
       "17       1       2  NaN      NaN\n",
       "19       0       3  NaN      NaN\n",
       "26       1       3  NaN      NaN\n",
       "28       0       3  NaN      NaN\n",
       "29       1       3  NaN      NaN\n",
       "31       0       1  NaN      NaN\n",
       "32       0       3  NaN      NaN\n",
       "36       1       3  NaN      NaN\n",
       "42       1       3  NaN      NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['AgeFill'] = titanic['Age']\n",
    "\n",
    "titanic[ titanic['Age'].isnull() ][['Gender','Pclass','Age', 'AgeFill']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>Pclass</th>\n",
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       "      <th>AgeFill</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Pclass  Age  AgeFill\n",
       "5        1       3  NaN     25.0\n",
       "17       1       2  NaN     30.0\n",
       "19       0       3  NaN     21.5\n",
       "26       1       3  NaN     25.0\n",
       "28       0       3  NaN     21.5\n",
       "29       1       3  NaN     25.0\n",
       "31       0       1  NaN     35.0\n",
       "32       0       3  NaN     21.5\n",
       "36       1       3  NaN     25.0\n",
       "42       1       3  NaN     25.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        titanic.loc[ (titanic.Age.isnull()) & (titanic.Gender == i) & (titanic.Pclass == j+1),\\\n",
    "                'AgeFill'] = median_ages[i,j]\n",
    "\n",
    "titanic[ titanic['Age'].isnull() ][['Gender','Pclass','Age', 'AgeFill']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Engineering\n",
    "Let's create a feature that records whether the `Age` was originally missing. This is relatively simple by allowing `pandas` to use the integer conversion of the `True/False` evaluation of its built-in function, `pandas.isnull()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic['AgeIsNull'] = pd.isnull(titanic.Age).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also create a couple of other features, this time using simple math on existing columns. \n",
    "- Since we know that `Parch` is the number of parents or children onboard, and `SibSp` is the number of siblings or spouses, we could collect those together as a `FamilySize`\n",
    "- We can also create artificial features if we think it may be advantageous to a machine learning algorithm. For example, we know `Pclass` had a large effect on survival, and it's possible `Age` will too. One artificial feature could incorporate whatever predictive power might be available from both `Age` and `Pclass` by multiplying them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch']\n",
    "titanic['Age*Class'] = titanic.AgeFill * titanic.Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Gender</th>\n",
       "      <th>AgeFill</th>\n",
       "      <th>AgeIsNull</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Age*Class</th>\n",
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       "  </thead>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>Moran, Mr. James</td>\n",
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       "      <td>0</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
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       "      <td>Palsson, Master. Gosta Leonard</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex  Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male   22      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                               Heikkinen, Miss. Laina  female   26      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                             Allen, Mr. William Henry    male   35      0   \n",
       "5                                     Moran, Mr. James    male  NaN      0   \n",
       "6                              McCarthy, Mr. Timothy J    male   54      0   \n",
       "7                       Palsson, Master. Gosta Leonard    male    2      3   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female    4      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female   58      0   \n",
       "12                      Saundercock, Mr. William Henry    male   20      0   \n",
       "13                         Andersson, Mr. Anders Johan    male   39      1   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female   14      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female   55      0   \n",
       "16                                Rice, Master. Eugene    male    2      4   \n",
       "17                        Williams, Mr. Charles Eugene    male  NaN      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female   31      1   \n",
       "19                             Masselmani, Mrs. Fatima  female  NaN      0   \n",
       "20                                Fynney, Mr. Joseph J    male   35      0   \n",
       "21                               Beesley, Mr. Lawrence    male   34      0   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female   15      0   \n",
       "23                        Sloper, Mr. William Thompson    male   28      0   \n",
       "24                       Palsson, Miss. Torborg Danira  female    8      3   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female   38      1   \n",
       "26                             Emir, Mr. Farred Chehab    male  NaN      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male   19      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0   \n",
       "29                                 Todoroff, Mr. Lalio    male  NaN      0   \n",
       "..                                                 ...     ...  ...    ...   \n",
       "861                        Giles, Mr. Frederick Edward    male   21      1   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female   48      0   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female  NaN      8   \n",
       "864                             Gill, Mr. John William    male   24      0   \n",
       "865                           Bystrom, Mrs. (Karolina)  female   42      0   \n",
       "866                       Duran y More, Miss. Asuncion  female   27      1   \n",
       "867               Roebling, Mr. Washington Augustus II    male   31      0   \n",
       "868                        van Melkebeke, Mr. Philemon    male  NaN      0   \n",
       "869                    Johnson, Master. Harold Theodor    male    4      1   \n",
       "870                                  Balkic, Mr. Cerin    male   26      0   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female   47      1   \n",
       "872                           Carlsson, Mr. Frans Olof    male   33      0   \n",
       "873                        Vander Cruyssen, Mr. Victor    male   47      0   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female   28      1   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female   15      0   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male   20      0   \n",
       "877                               Petroff, Mr. Nedelio    male   19      0   \n",
       "878                                 Laleff, Mr. Kristo    male  NaN      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female   56      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female   25      0   \n",
       "881                                 Markun, Mr. Johann    male   33      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female   22      0   \n",
       "883                      Banfield, Mr. Frederick James    male   28      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male   25      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female   39      0   \n",
       "886                              Montvila, Rev. Juozas    male   27      0   \n",
       "887                       Graham, Miss. Margaret Edith  female   19      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male   26      0   \n",
       "890                                Dooley, Mr. Patrick    male   32      0   \n",
       "\n",
       "     Parch            Ticket      Fare        Cabin Embarked  Gender  AgeFill  \\\n",
       "0        0         A/5 21171    7.2500          NaN        S       1     22.0   \n",
       "1        0          PC 17599   71.2833          C85        C       0     38.0   \n",
       "2        0  STON/O2. 3101282    7.9250          NaN        S       0     26.0   \n",
       "3        0            113803   53.1000         C123        S       0     35.0   \n",
       "4        0            373450    8.0500          NaN        S       1     35.0   \n",
       "5        0            330877    8.4583          NaN        Q       1     25.0   \n",
       "6        0             17463   51.8625          E46        S       1     54.0   \n",
       "7        1            349909   21.0750          NaN        S       1      2.0   \n",
       "8        2            347742   11.1333          NaN        S       0     27.0   \n",
       "9        0            237736   30.0708          NaN        C       0     14.0   \n",
       "10       1           PP 9549   16.7000           G6        S       0      4.0   \n",
       "11       0            113783   26.5500         C103        S       0     58.0   \n",
       "12       0         A/5. 2151    8.0500          NaN        S       1     20.0   \n",
       "13       5            347082   31.2750          NaN        S       1     39.0   \n",
       "14       0            350406    7.8542          NaN        S       0     14.0   \n",
       "15       0            248706   16.0000          NaN        S       0     55.0   \n",
       "16       1            382652   29.1250          NaN        Q       1      2.0   \n",
       "17       0            244373   13.0000          NaN        S       1     30.0   \n",
       "18       0            345763   18.0000          NaN        S       0     31.0   \n",
       "19       0              2649    7.2250          NaN        C       0     21.5   \n",
       "20       0            239865   26.0000          NaN        S       1     35.0   \n",
       "21       0            248698   13.0000          D56        S       1     34.0   \n",
       "22       0            330923    8.0292          NaN        Q       0     15.0   \n",
       "23       0            113788   35.5000           A6        S       1     28.0   \n",
       "24       1            349909   21.0750          NaN        S       0      8.0   \n",
       "25       5            347077   31.3875          NaN        S       0     38.0   \n",
       "26       0              2631    7.2250          NaN        C       1     25.0   \n",
       "27       2             19950  263.0000  C23 C25 C27        S       1     19.0   \n",
       "28       0            330959    7.8792          NaN        Q       0     21.5   \n",
       "29       0            349216    7.8958          NaN        S       1     25.0   \n",
       "..     ...               ...       ...          ...      ...     ...      ...   \n",
       "861      0             28134   11.5000          NaN        S       1     21.0   \n",
       "862      0             17466   25.9292          D17        S       0     48.0   \n",
       "863      2          CA. 2343   69.5500          NaN        S       0     21.5   \n",
       "864      0            233866   13.0000          NaN        S       1     24.0   \n",
       "865      0            236852   13.0000          NaN        S       0     42.0   \n",
       "866      0     SC/PARIS 2149   13.8583          NaN        C       0     27.0   \n",
       "867      0          PC 17590   50.4958          A24        S       1     31.0   \n",
       "868      0            345777    9.5000          NaN        S       1     25.0   \n",
       "869      1            347742   11.1333          NaN        S       1      4.0   \n",
       "870      0            349248    7.8958          NaN        S       1     26.0   \n",
       "871      1             11751   52.5542          D35        S       0     47.0   \n",
       "872      0               695    5.0000  B51 B53 B55        S       1     33.0   \n",
       "873      0            345765    9.0000          NaN        S       1     47.0   \n",
       "874      0         P/PP 3381   24.0000          NaN        C       0     28.0   \n",
       "875      0              2667    7.2250          NaN        C       0     15.0   \n",
       "876      0              7534    9.8458          NaN        S       1     20.0   \n",
       "877      0            349212    7.8958          NaN        S       1     19.0   \n",
       "878      0            349217    7.8958          NaN        S       1     25.0   \n",
       "879      1             11767   83.1583          C50        C       0     56.0   \n",
       "880      1            230433   26.0000          NaN        S       0     25.0   \n",
       "881      0            349257    7.8958          NaN        S       1     33.0   \n",
       "882      0              7552   10.5167          NaN        S       0     22.0   \n",
       "883      0  C.A./SOTON 34068   10.5000          NaN        S       1     28.0   \n",
       "884      0   SOTON/OQ 392076    7.0500          NaN        S       1     25.0   \n",
       "885      5            382652   29.1250          NaN        Q       0     39.0   \n",
       "886      0            211536   13.0000          NaN        S       1     27.0   \n",
       "887      0            112053   30.0000          B42        S       0     19.0   \n",
       "888      2        W./C. 6607   23.4500          NaN        S       0     21.5   \n",
       "889      0            111369   30.0000         C148        C       1     26.0   \n",
       "890      0            370376    7.7500          NaN        Q       1     32.0   \n",
       "\n",
       "     AgeIsNull  FamilySize  Age*Class  \n",
       "0            0           1       66.0  \n",
       "1            0           1       38.0  \n",
       "2            0           0       78.0  \n",
       "3            0           1       35.0  \n",
       "4            0           0      105.0  \n",
       "5            1           0       75.0  \n",
       "6            0           0       54.0  \n",
       "7            0           4        6.0  \n",
       "8            0           2       81.0  \n",
       "9            0           1       28.0  \n",
       "10           0           2       12.0  \n",
       "11           0           0       58.0  \n",
       "12           0           0       60.0  \n",
       "13           0           6      117.0  \n",
       "14           0           0       42.0  \n",
       "15           0           0      110.0  \n",
       "16           0           5        6.0  \n",
       "17           1           0       60.0  \n",
       "18           0           1       93.0  \n",
       "19           1           0       64.5  \n",
       "20           0           0       70.0  \n",
       "21           0           0       68.0  \n",
       "22           0           0       45.0  \n",
       "23           0           0       28.0  \n",
       "24           0           4       24.0  \n",
       "25           0           6      114.0  \n",
       "26           1           0       75.0  \n",
       "27           0           5       19.0  \n",
       "28           1           0       64.5  \n",
       "29           1           0       75.0  \n",
       "..         ...         ...        ...  \n",
       "861          0           1       42.0  \n",
       "862          0           0       48.0  \n",
       "863          1          10       64.5  \n",
       "864          0           0       48.0  \n",
       "865          0           0       84.0  \n",
       "866          0           1       54.0  \n",
       "867          0           0       31.0  \n",
       "868          1           0       75.0  \n",
       "869          0           2       12.0  \n",
       "870          0           0       78.0  \n",
       "871          0           2       47.0  \n",
       "872          0           0       33.0  \n",
       "873          0           0      141.0  \n",
       "874          0           1       56.0  \n",
       "875          0           0       45.0  \n",
       "876          0           0       60.0  \n",
       "877          0           0       57.0  \n",
       "878          1           0       75.0  \n",
       "879          0           1       56.0  \n",
       "880          0           1       50.0  \n",
       "881          0           0       99.0  \n",
       "882          0           0       66.0  \n",
       "883          0           0       56.0  \n",
       "884          0           0       75.0  \n",
       "885          0           5      117.0  \n",
       "886          0           0       54.0  \n",
       "887          0           0       19.0  \n",
       "888          1           3       64.5  \n",
       "889          0           0       26.0  \n",
       "890          0           0       96.0  \n",
       "\n",
       "[891 rows x 17 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have our data almost ready for machine learning. But most basic ML techniques will not work on strings, and in python they almost always require the data to be an array-- the implementations we will see in the `sklearn` package are not written to use a pandas dataframe. \n",
    "\n",
    "So the last two things we need to do are \n",
    "- (1) determine what columns we have left which are not numeric, and \n",
    "- (2) send our `pandas.DataFrame` back to a `numpy.array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name        object\n",
      "Sex         object\n",
      "Ticket      object\n",
      "Cabin       object\n",
      "Embarked    object\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[  0. ,   3. ,   1. , ...,   0. ,   1. ,  66. ],\n",
       "       [  1. ,   1. ,   1. , ...,   0. ,   1. ,  38. ],\n",
       "       [  1. ,   3. ,   0. , ...,   0. ,   0. ,  78. ],\n",
       "       ..., \n",
       "       [  0. ,   3. ,   1. , ...,   1. ,   3. ,  64.5],\n",
       "       [  1. ,   1. ,   0. , ...,   0. ,   0. ,  26. ],\n",
       "       [  0. ,   3. ,   0. , ...,   0. ,   0. ,  96. ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can require .dtypes to show only the columns which are 'object', which for pandas means it has strings:\n",
    "print (titanic.dtypes[titanic.dtypes.map(lambda x: x=='object')])\n",
    "\n",
    "train_data = titanic.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1) \n",
    "\n",
    "train_data = train_data.drop(['Age', 'PassengerId'], axis = 1)\n",
    "# Alternative to drop 'Age'\n",
    "# df = df.dropna()\n",
    "\n",
    "train_data = train_data.as_matrix()\n",
    "train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Example\n",
    "K nearest neighbors (kNN) is one of the simplest learning strategies: given a new, unknown observation, look up in your reference database which ones have the closest features and assign the predominant class.\n",
    "\n",
    "Let's try it out on our iris classification problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['versicolor']\n",
      "[[ 0.   0.8  0.2]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import neighbors, datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "\n",
    "# create the model\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "# fit the model\n",
    "knn.fit(X, y)\n",
    "\n",
    "# What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n",
    "# call the \"predict\" method:\n",
    "result = knn.predict([[3, 5, 4, 2],])\n",
    "\n",
    "print(iris.target_names[result])\n",
    "# You can also do probabilistic predictions:\n",
    "print(knn.predict_proba([[3, 5, 4, 2],]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_iris_knn(n_neighbors=3, pca=False):\n",
    "    import pylab as pl\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    # Create color maps for 3-class classification problem\n",
    "    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "    iris = datasets.load_iris()\n",
    "    if pca == False:\n",
    "        X = iris.data[:, :2]  # we only take the first two features. We could\n",
    "                        # avoid this ugly slicing by using a two-dim dataset\n",
    "        y = iris.target\n",
    "    else:\n",
    "        from sklearn.decomposition import PCA\n",
    "        pca = PCA(n_components=2)\n",
    "        X, y = iris.data, iris.target\n",
    "        pca.fit(X)\n",
    "        X = pca.transform(X)\n",
    "        \n",
    "    knn = neighbors.KNeighborsClassifier(n_neighbors)\n",
    "    knn.fit(X, y)\n",
    "\n",
    "    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n",
    "    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n",
    "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
    "                         np.linspace(y_min, y_max, 100))\n",
    "    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    pl.figure()\n",
    "    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "    # Plot also the training points\n",
    "    pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
    "    if pca:\n",
    "        pl.xlabel('principle component no.1')\n",
    "        pl.ylabel('principle component no.2')\n",
    "    else:\n",
    "        pl.xlabel('sepal length (cm)')\n",
    "        pl.ylabel('sepal width (cm)')\n",
    "    pl.axis('tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lS5wpKCgvIb1jRzGhoesYO3ZU9U6vvnqaFRRkFbD4uWbkpI0DICamlBUr1jF+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\nFm5uboiKiuq0X0VREBkZCQC4//77O33F7ddff219X/N//vM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1092dab50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_iris_knn(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also try to find the combinations of variables that explain the most variance using *PCA*.\n",
    "\n",
    "Consider the iris dataset. It cannot be visualized in a single 2D plot, as it has 4 features. We are going to extract 2 combinations of sepal and petal dimensions to visualize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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42xUkRC1qDfv+/fvTv39/QkJCcHZ2rm030YhOxsWRl5fHsEGDcJQZDUU9mM1m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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a15ded0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_iris_knn(1,pca=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other Dimensionality Reducting Routines\n",
    "Note that scikit-learn contains many other unsupervised dimensionality reduction routines: some you might wish to try are Other dimensionality reduction techniques which are useful to know about:\n",
    "\n",
    "- [sklearn.decomposition.PCA](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.PCA.html): Principal Component Analysis\n",
    "- [sklearn.decomposition.RandomizedPCA](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.RandomizedPCA.html): extremely fast approximate PCA implementation based on a randomized algorithm\n",
    "- [sklearn.decomposition.SparsePCA](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.SparsePCA.html): PCA variant including L1 penalty for sparsity\n",
    "- [sklearn.decomposition.FastICA](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.FastICA.html): Independent Component Analysis\n",
    "- [sklearn.decomposition.NMF](http://scikit-learn.org/0.13/modules/generated/sklearn.decomposition.NMF.html): non-negative matrix factorization\n",
    "- [sklearn.manifold.LocallyLinearEmbedding](http://scikit-learn.org/0.13/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html): nonlinear manifold learning technique based on local neighborhood geometry\n",
    "- [sklearn.manifold.IsoMap](http://scikit-learn.org/0.13/modules/generated/sklearn.manifold.Isomap.html): nonlinear manifold learning technique based on a sparse graph algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation Sets\n",
    "Above we made the mistake of testing our data on the same set of data that was used for training. This is not generally a good idea. If we optimize our estimator this way, we will tend to over-fit the data: that is, we learn the noise.\n",
    "\n",
    "A better way to test a model is to use a hold-out set which doesn't enter the training. `sklearn.cross_validation` offers various functions that can be used to reach our target. Also, functions from `sklearn.metrics` can help give numerical criteria on whether the learning model is successful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15  0  0]\n",
      " [ 0 11  0]\n",
      " [ 0  0 12]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "X, y = iris.data, iris.target\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y)\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(Xtrain, ytrain)\n",
    "ypred = knn.predict(Xtest)\n",
    "print(confusion_matrix(ytest, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can also be computed directly from the model.score method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(Xtest, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1.0\n",
      "5 1.0\n",
      "10 0.973684210526\n",
      "20 0.947368421053\n",
      "30 0.973684210526\n"
     ]
    }
   ],
   "source": [
    "for n_neighbors in [1, 5, 10, 20, 30]:\n",
    "    knn = KNeighborsClassifier(n_neighbors)\n",
    "    knn.fit(Xtrain, ytrain)\n",
    "    print(n_neighbors, knn.score(Xtest, ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see some other functionality package in sklearn:\n",
    "- `sklearn.preprocessing`: provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators.\n",
    "- `sklearn.pipeline`: implements utilities to build a composite estimator, as a chain of transforms and estimators.\n",
    "- `sklearn.learning_curve`: utilities to evaluate models with respect to a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "def PolynomialRegression(degree=2, **kwargs):\n",
    "    return make_pipeline(PolynomialFeatures(degree),\n",
    "                         LinearRegression(**kwargs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate these function, we introduce a problem that has long history, which is *overfitting*.\n",
    "\n",
    "We'll create a simple nonlinear function that we'd like to fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_func(x, err=0.5):\n",
    "    y = 10 - 1. / (x + 0.1)\n",
    "    if err > 0:\n",
    "        y = np.random.normal(y, err)\n",
    "    return y\n",
    "\n",
    "def make_data(N=40, error=1.0, random_seed=1):\n",
    "    # randomly sample the data\n",
    "    np.random.seed(1)\n",
    "    X = np.random.random(N)[:, np.newaxis]\n",
    "    y = test_func(X.ravel(), error)\n",
    "    \n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S/Y/qYSF9onz/fSlUcvToUdXU1OiJJ55QXl6eli9frpkzZ6q0tNRxY4BMxONxzZz5nDo6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0029d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_data(120, error=1.0)\n",
    "plt.scatter(X, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fh07Kyd7HXFIL9wByH3NJLdwD1MZ91MI9QO3cx4kqG/K33norN954I0eOHOGK\nK65gcHCQ++67b1q+PBQK5X/funUrd955JwMDA9TX15f9bHf38El9ZzybpT9dOClOk9nMUWD3scNs\njK4v+oyb7CRihk/q+ybT0hI56fuYK2rhHkDuYy6phXuA2riPWrgHqK37OFFlQ/6MM87gpz/9KYcO\nHcJxHFatWnVCJfmx49LH6+npobm5GYCdO3cCTCngT4VP8xVtW1LfzBuOQizRXfIzcStRkZAXQggh\nKqlsyAMYhsHatWtP+OQ333wz27dvZ2BggAsuuICbbroJy7JQFIX3v//9PPbYYzz00EPouo7f7+fv\n//7vT/g7TpSh6iiKiue5+W1L2kJ4ByLEg/04roOmagWfsRyLtJ3Gr/vHn04IIYSYs6YU8ifrK1/5\nyqT7P/jBD/LBD36wkpdQJDcpjlEwXn5Rix93ZxQ1NERvup/WYHPR5+JWQkJeCCFEValMb7I5bvx4\n+YBPw+fmmgk6k10lP5O0U0Wr2AkhhBBzWdmQ37lzJ9/97nfJZrNcf/31nHvuuVPuAT9XlZoUp9HM\nld4PD8SK9gEj89nLcDohhBDVo2zI33XXXZx++uk89thj+P1+HnnkEb71rW/NxLVVTKlJcZbUNeN5\ncGy4dEkeIJ5NTNqRUAghhJhLyoa867qcc845PPXUU1xyySUsXLgQx6nuZVhLTYqzpCWMlwozaPdN\nGOSu55K0UzNxiUIIIcQpKxvygUCA+++/n2eeeYYLL7yQBx54oGB8e7UaX2W/uNWPm4ziKjb9mYEJ\nPzeclfnshRBCVIeyIf/lL3+ZZDLJ1772Nerq6ujq6uKrX/3qTFxbRY0fL18XNtGzdQDEkqXHywNk\nnSxZJ1vRaxNCCCGmQ9mQb2trY9u2bTiOwx//+EcuuOACjhw5MhPXVlFmic53dUYjAO1DnZN+Vkrz\nQgghqkHZcfKf/vSn2bVrF62trfltiqLw4IMPVvTCKs3UjKJJcRaFWhgE3hicPOSTdor6EpPmCCGE\nEHNJ2ZDfvXs3v/zlL9G02gs0n2aQHjMpztKWKLt6g/TTg+d5+UV5xvM8j4SdJGrOzwUPhBBCVIey\n1fVbtmzh8OHDM3EtM258lf2iNj9eMoJNlqHs5IsZxLMyZl4IIea6+T7suWxJ/txzz+Wyyy6jtbUV\nTdPyJdwnnnhiJq6vonKd746HeUu9D1J1QCedyW7qfNEJP2u7Nik7RUAPVP5ChRBClOW4DpZrkXUt\nLMfK/04U7QzLAAAgAElEQVTIAs+YsHa2lpUN+fvuu48HHniARYsWzcT1zKjxw+hUVSGiNpIEjsW7\nWNewetLPD2fjEvJCCDHDPM/Dcm0sdyTInSyWa+O4pedwiWfipFIuzYFGVGV+zeZeNuQbGho4++yz\na/IJSFVUDM3Acqz8trZQCweBIwMxWDr559N2Bsu1MdSKrvMjhBDz1oSl8xOshU/babqSPbQEmuZV\np+my6bRhwwauueYa3va2t2EYRn77X/3VX1X0wmaKqZoFIb+kqY4DcT9ddGK7dtHMeOPFs3Ea/PWV\nvkwhhKhpnudhu3YuzF2L7EigT1Q6PxlZJ0tnspvWYHPZv9trRdm7XLRoUU1W1Y/yaSaJMQvPLGn1\n4xxeiLroILt793FGy8ZJPx+3ktT5ovOuCkgIIU6F53mk7DQpO43l5qrbZ6KTnO3auaAPNGNoRvkP\nVLmyId/e3s4999wzE9cyK8a3yy9o8eH2LIFFB3m+6+WyIe95LgkrScQMV/IyhRCiJqTtNAkrRdJO\nFcxTMpMc1yGW7KYl0IRf95X/QBUrW/zcu3cviUTtDhczRibFGWXqGs3BepzBJmKpGF2pnrLnkCVo\nhRBiYlnHYiAzSHu8g65kDwkrMWsBP8rzXLpTPaRqfNGxsiV5VVW58MILWblyJT7f8Seeap/xbiyf\nZpK20/n3m9dHefK1pWh1vTwXe5k/WXnhpJ+3HIu0ncav+yt9qUIIURVs1x4psScL+j3NJZ7n0Z3q\npdHfQNio/oXXSikb8v/n//yfmbiOWTU+5E9fG+Y3O9rA8vFa/x7eueztZdtu4lZCQl4IMa85rkPS\nTpG0kmSqZSEvD/pS/TiuS52v9mYxLRvyb3nLW2biOmbV+Jnvgn6DDasivNq9GBa9zss9e3hz2+mT\nniNpp3BkPnshxDzjem6+nT3lpE54aNtcMZgZxPWcmhstJV3CKe58Z+gKm9aEcbqWgAcvdb9S/iQe\nDFuyOp0QovaN9ozvTfXRHo/Rk+rLtW1XacCPGs7G6Un11dRUuPNjoGAZ4yfFUVBoa/KxtLmR2GAz\nXUoXHfEuFoZbJz1PwkpSZ0ZrcuIgIYTIOlkSVjJfc1mLklYS13NoDjTVxNDo6r+DaTK+NB/yG2xa\nE8Luyk1791zny2XPMdoeJYQQtcJybQYzQxyLx4gluhjOxms24Eel7Qxdye6auE8J+RHj2+UNXWXV\nkgBhewFe1seegb2kncwEnz4unpUqeyFE9UtaKY4NxeiIxxjMDGG79mxf0ozKOhadye6qv28J+RG5\nFekKhYMmm9ZGsbuXYHsWOztfK3uejJMlWy29SoUQYgzP80hYSToSnfSkeknb5Qs2tWx0drzsHB0C\nOBUS8iMMVS9qf/GbGhtXhaBvKXiws3fXlM41LGvNCyGqiOd5xLMJOhKd9Kb65uy49tnguA6dye6C\nYdbVREJ+jPFV9goK9RGTtYuacAZa6M308MZgrOx5knYSd5ZncxJCiHJcz2UoO8yxRIy+dH/VV01X\niue5dKV6SFrV1+dKQn6MUlX2Ib/O6WvD+Q54z3ftLHsez/NkqlshxJzlem6+M91AerAmOphVnAc9\nqV6Gq6zflYT8GL4Ss9ppqsrCVj9tvsW4GT/7BveTtMpX28Slyl4IMcc4rjMyh3yuM53UOJ64/vQA\ng5mh2b6MKZOQH8PUTCgxxD1Xmo/gdC/Fwealzt1lz2W7ds0vfCCEqA62a9OfHuBYIsZQZnjWF4ep\ndoOZIfrS/bN9GVMiIT+GqqiYanFp3qdrrF4axBxehucp7OzdhTOF/0iqrVpHCFFbLNemN9XPsUSM\n4Wy8pmZym23xbIKeVO+c/2cqIT/O+M53oyJBg00rmnD7Wxmw+jjcf6zsudJ2Bks6sgghZljWsehJ\n9dGRiJGwElU/3exclbRSdKV65nSzh4T8OKU63wEE/DqnrQ7h9OQ64L04lfnskclxhBAzJ+Nk6Ur2\nEEt0krSSEu4zIGNn6En1zfZlTEhCfpyJSvIqCo11PlbWL8FNBzgwfIDBdLLs+eKWDKcTQlRW2k7T\nmeymM9FVteO5q5nrzd3RCRLy45SaFGdUyK9z+ppcBzwXh53d5TvgeZ5blWMrhRBzX9JKEUt00ZXs\nITPPZ6cTpckqdCX4NF/JnvG6prJ0QYC6l1aQcPfxSs8uzlv0JnRt8jXkh604YTNUqcsVQtQQx3Vw\nPRfHc3FHXrnfC7c7niPj20VZEvIlmJo54fC3kF/njFVN/K6njaGmGAf6j7K+efmk57Mci7Sdwa+X\nbu8XQtQmtyioxwb06Hvn+D5caUcX00pCvoTxy84W7DM11i0P8sz+ZdAU48WuV1jXvAyl1AD7MeJW\nXEJeiDnAcR1cPDzPwxv3EzxcPHL/83BHto0/zoPj78d+dmQ4lYdHXB+gb1g63orZJSFfgqkZKIpS\ncvyjgkI0ZLChbRm7069w2DtIfyJBYyg86TmTdgrHddDUyav2hRAnx3FzJWLHc/JV2aMl5vw2z5mx\nkrJMOCPmAgn5ElRFJaAHckNQSgj4dTatDvPKjqWoS/ews/tVLgi9ZfKTerm2+XpfXQWuWIjaVRze\nLo5nz1p4C1FNJOQnEDaCE4a8pqi0NPhYbKyi093Ly72vct7is/AZk5fSE1aSOjOKokxetS9ErfI8\nr6CN2qOwrdr1XJx4mu7EkIS3ENNAQn4Cft2PruoTLr0Y9BucsbqJY4cXkGzuYH//ETa1rpz0nI7r\nkLJTBI1gJS5ZiBk1vjPZ2E5lnucV9Ah3R95PpQrbyHpknewM3IEQtU9CfhIhI8RgZrDkPlNXWb7Q\nT/DVFWSbO3ipaxcbmpejqZNPPTCcjUvIiznP9Vxs1869POf47yPDu6QXuBDVQUJ+EmEjyGB2cMK/\nzEIBk9OXLGNHaidHvUP0JoZpjUze5p5xsmQdC7PEsrZCzBTP8wrC2xnzu+U60mlMiBohIT8JTdXw\na/4Jp4n0+zQ2rAix4w/L8Jbs5qWuV3ln5Nyyw+mGs3GaAg2VuGQh8hzXKSqF297xUJeSuBC1T0K+\njLARmjDkVRTqIiarIms46O7h1f7dvC19NiH/5KX0pJ2kwZNe9mJ6uJ5LxsmScTI48TRdiUFs157z\nS2AKISpP5q4vI6D7Jx3bHvLrbF7diNO3gDRxDvQfLntOz/OIW4npvEwxz2SdLIOZYTqT3RyNH6M7\n2cNQZphkNonlWBLwQghASvJlKYpCUA8wPMGSsZqq0trko8lezSDHeKFrFxtaVmHq5TvgpS1ZLUpM\njeXaZOwMaSdN2s7IyoZCiCmRkJ+CsBGaMOQBwn6DLcuW8tRAmFjgCD3xQRbVT97m7rgOx4Y7yaSg\nwVcnM+GJAq7nkrbTpJ0MaTsz4VBOIYSYjIT8FBiagU8zyUwwdtdnaKxYEkA/vBw3uIsXO1+lre48\ntAmWrB0raSVJ2WnqfBEiRlgmypmnPM8j42TyoS7jxIUQ00Ha5KcoVGap2GjQ5LSmtXiOxp6h10im\nrCmf2/NcBtKDdCQ6J+zkJ2pP1rEYyg7TNdKu3jXSri4BL4SYLhLyUxTUAyiTlMwDPp1NKxtx+xeS\nVRLs6T2Ed4JjlGzXpivZQ0+qV6pna5DjOsStBD2pPtrjHcQSnQykB0nbGekoJ4SoCKmunyJVUQnq\nARIT9IpXFYX6iMFifQ0xjvJi1y42ta0mYJ74P+KklSJlp4maUaKmVOFXwuiUrI7r4HoOQxkl3+8i\nt3RobtFRIB/Ao9vJP74dPy63ecz2os950llOCDHjJORPQNgMTRjyACG/wZnLl/KLY1F6g+10xwdZ\n1th0Ut/leR6DmUESVoIGfx0BPXCylz2vjA9vO7/kqDtm+dHiOdSdRJr+dOkFiYQQolpJyJ8An2Zi\naAaWU7q93dBVFrX6CR5YQTq0k+c7drEw+naMMsPpJmO7Nt3JXgJ6gHp/HYY6P/+VuSPBnVuZrDCw\nJwtvIYSYz+ZnYpyCkBFkwCm9aA1AKGCwpXUdz1i7OJDYw3DqHBojp14KT9kp0ok0ETNM1IygTqHn\nfjXLOtbImPA0GScrbdZCCHESajspKiCkB5lsanq/qbFueR3KwCJsNcWenkO40xRQnucxlBkmlugi\naaWm5ZxzheM6JK0kvdIpTQghpo2E/AnSVI3gJO3jCgrhgMGq4HoAXuzeRTIzvT3lbdemJ9VLV7IH\nq0p74XueR9rOMJAZJJbooj3eQU+qj4SVxHGd2b48IYSoCVJdfxJCRmjSknTIr/PmlUvZf6COoeAx\nOof7CPvbpv060naajkSMqBmpiip827VJj0zNmrIz0n4uhBAVJiF/EkYXrZmoxKmpKg31Jo32KgaU\nF9jR/iqL65rwn8RwurI8GMoMk7CSNPjqCBrB6f+OkzS6OlraTpOy0zL2XwghZtjcLvrNYaEyYRry\n67x58Xo8W+dIZh/DqcrOYua4Dj2pPrqS3RP2/p8JlmMxnI3TleymPd5Bd7KH4WxcAl4IIWaBhPxJ\nChuTT3Pr0zWWtYUx4ktwtTS7ug5gO5Wvnk7bGTqSnfSnByo++YrnebiuO9Jhrp/2eAcdidx3S4c5\nIYSYfVJdf5J0Vcev+0jbmQmPCQcMNtRv4BUO8Urvq5y5cD11YbPyF+fllrKNW0nUcbPljQ3e4gj2\nxr3zJtsNQFwP0p+SSWSEEGIukpL8KShXmg/4dbYsW4yXqCeudXJsqG/ahtNNhTc6gcyYl+u5+ZdX\n9PIKXrm5Wce8hBBCVBUJ+VMQ0AOT9mhXUYiEDBaqa1EU2HHsFVLTPJxOCCGEmIiE/ClQFGVKHfDO\nWbYOzzbosA8wmJSlZIUQQswMaZM/RSEjlF+9rBRdU2lrCBE8uJRU+HV2dhygMXwGflObwasUQoja\n5XkeaSdDwkqQsJLHX3aShJWgsbuO5YHlLAy1zbtVPSXkT5GpGZiaQXaSYWshv87m5tPYnn6d1wZ2\nc05mg4S8EEKUkXWyRYEdH3mfHPkZtxMkrSTOZKOJegG2U2dG2dC4lg0Na2kLtsyLwJeQnwYhI0TW\nGZhwv9/U2bCojWdfbiQT6uaNvh7qgotm8AqFEGJusV2bWKKLnnRfPrTjdmFJ3HInn/NDUzRCRpDW\nYAthI0TICBLSg7mfo++NIGktwY4jL7Nv4HW2x55je+w5Gnx1bGhYy4bGdbQEmmo28CXkp0HICDKQ\nGZx0XHg4YLDcv45DPMOOY7tY2dxKywxeoxBCzKah7DDt8Q6OxWO0JzroTHaXnMtDIdfXqdFfT3Ak\nsMPjQjtkhAjpQXyaOaVwbmhYyAJ9EbZr8/rgYV7r38f+gYP8IbaDP8R20OhvGAn8tbQEmipx+7NG\nQn4aqIpKUA+QsCYeLx7w65yzbD0HDz9Hr3KQgcR59A9ncDwXbY7POS+EECfCdm06k935QD8WjzFs\nHe+7pCoqrYFmFocX0pYvhedCPKD7K7YOh67qrGtYzbqG1ViOxYHBQ7zWv48Dg4f4fcez/L7jWZr9\njWxoXMeGxrU0+Rsqch0zSUJ+moSM0KQhrykqjRE/9dYKBgP7eP7oXprq30w8niYc0AkGDNTJ1rAV\nQog5ajgb51gili+px5JdON7xtT2CeoC19atYFFrA4vBCFgRbMTRjFq8YDM3Itc83riXrZNk/eIjX\n+vby+uBh/vvYM/z3sWdoDTTn2/Ab/PWzer0nS0J+mvh1H7qqTzpHe9Bv8OaFm/j1wD5eT+wB3ozr\neQwlLRJpm0jQJOiTfyVCiLnLcR26Uj25QB8J9qHscH6/gkJrsDkf6IvDC6kzo3O6zdvUTE5rXMdp\njevIOBn2Dxxkd99eDg4d4Tftf+A37X9gQbA1X6Vf54vO9iVPmSTKNAoZIQYzgxPuN3WVVS0t/Laj\nGTvQw4FYJy2h3B8Wx/UYiGeIpyzqQiY+Q3rfCyFmX8JKFgR6LNlVUJgJ6H5W163IBXpoIQtCrZja\nDEzfXSE+zcempg1satpA2k6zb+B1dvft4/DwG8SSXTzV/jsWhtrygR81I7N9yZOSkJ9GYSPIYHZw\n0ilgg36DNeH1vOb08KvDT/LmxnPYsGBxfr/tuPQOpfEZGpGgialLe70QYmak7BSdyW46E93Ekl10\npbvpSx0fOaSg0BxoYnF4AYtCuVJ6g69uTpfST4Vf93NG82mc0XwaKTvF3v4D7O7bx5Hho3QkOvn1\n0f9mcWghpzdv4JLlF+LX/bN9yUUk5KeRpmoEtAApOzXhMX6fxpuXrOX1vXvJBrr5Y+o/eWF3E5sb\nN3Na68r8fywZyyEzmCLg04kGDTRVwl4IMX0SVpJYsovORDedyS5iya6CanfIldJXRZezKLyQxeEF\nLAy14dN8s3TFsyugB9jScjpbWk4nYSXZ23+A1/r3cmS4nfZEB2knwzXrrpztyyyieBVcD/S2227j\nqaeeoqmpiUcffbTkMXfddRe/+c1vCAQC3HvvvWzcuHFK5+7uHi5/0CxIWil6Ur2THjOUzDKczHJo\nMMYz7c9hBzsB0K0Im+o3s6llLZpyvLpeQSHo14kEjaJV5WZbQ0OQ/v7qX4VO7mPuqIV7gLlzH57n\nMWzFR0roXXQmc6X0uJUoOC6oB2gLtrIg2EJbqJW2YAsr2hYwMDBxoaVaVPLfRdxK8MbwUd7c+iZa\ngpUdftfScuJNAxUtyV911VX82Z/9GbfcckvJ/U8//TRHjhzh8ccf56WXXuKOO+7g4YcfruQlVVxA\n96OpGo7rTHhMyK8TT1lsWbqKlfUL2dPRwfNdO7HC7byU+B2vDD3HhujpnN68EVM18fBIpC1SGZtw\n0CDk11GkJ74QYhzP8xjKDo+U0LuIJXOl9OS42sWwEWJN3UraRgJ9QbCVsBEqqnav1Wr46RQ2Qmxp\nOb3iAX+yKhryZ599Nu3t7RPuf+KJJ7jyylz1xpYtWxgeHqanp4fm5uZKXlZFjS5aM5SZuKZBU9WC\naW3XL1zIugUL2NPey/OdO7HrjrArsYPdwy+xNrKB0xtPJ6gHcz3xE1kSaYtIQHriCzGfeZ5Hf2Yw\nH+idI4GedjIFx9WZUdbVL6It2EpbqIUFwdayC2uJ2jGrKdHV1cWCBQvy79va2ujs7KzqkAcI6ZOH\nPEAoYDB2sJ2iKGxY0sz6xRey5/Agz3W+glN/kD2Jl9kb38WK0GrOaNhMnVmP4+R64ifTFpGg9MQX\notp4noftOWSdLBknQ8bJjrxyv5fanh13TNrJFA3ZbfDVsyK6jAUjgd4WbCUwBzuDiZkjRcEKMDQD\nn2aScbITHuPTNVrqAjhZm2Taxh3pGqEoChtW1LNu2fnsPriZF47uxml+nYPKPg4m97HYv4zTGzbT\n6m8ja4/0xDc16oImuiad84SYTp7n4XgOtmtjuXb+p+Va2AXvbWzXKjhG6XQZSiZzoWxnybqFwV1q\nStdyVEXFp5n4NJMmo4Fmf2O+hN4WbJm3neLExGY15FtbW4nFYvn3sViMtra2KX32ZDogzCR/VKE7\nMXkHPIDli+txXI/hZJZ4ymLsf/fnvSnIOae38MKrm3n20Gu4zQdo5wjtHUdoCyzgrNazWBFdgaIo\npF0ImSrRsA9dndl2tIaG2qj6k/uYO072HizHJm1nxr3SRduyjoXlWGSdXFjn3ufC+/g2G8ux8CYb\nE3sCTM3Er5tEfCFa9EZ8ug//yMunmbnfDR9+bcz2Mcf4dRNd1We8nbwW/jxBZe/D1E1aonMzkyoe\n8pN13r/44ov54Q9/yLvf/W5efPFFotHolKvq52rv+lGu5zIQT+NN8rQ+vsdnQFOIZ62Ckj3A+pUB\nVi7Zwst7V7Fz7xsoLa/TSYxfHv4FUb2OTfWbWRlezdCQRqwrTiigEw7OzDS5c6UH8amS+5hdnueR\ndS2yTpZAWKOrb7Cgarp01fX4fZnJlxstw1B19JGXqfoI6SF0Vc9vN1Qj/95QDXRFy/3URo8xMBQt\n91PVaW6Ikkm4+DQTUzNPbj52F8iCnYU4FjD5qmzTrVr/PI1X6fswNQtfmSba6TDnetfffPPNbN++\nnYGBAS644AJuuukmLMtCURTe//73s3XrVp5++mm2bdtGIBDgnnvuqeTlzChVUQkZAeLZRPmD859R\niAZNwgGDeKow7E1D5axNdWxaE+al15aya1cMpfUgQ83H+EPPb3mxfwcbo6ezNroBL+WRTNtEggZ+\nU5Mx9mLa5UI5S9ax8uGcdbJk3ZESspsl4+S2W6P788dZYz6bHTn+5MJLV/WRUrCfOl90pCrbV/an\noRkjYZ0LZ13Rpr2E3BAJ0m9Xf0CK6lbRcfKVNNdL8gAZJ0tnomvC/eWeLl0vN3QukSos2QMkUw7P\nvzrMa2/0orYewmg9CpqNoRisjW5gY3QTQT0EgKYp+HQNn6lhGuq0rnonT/qzy3Edhq04g5khhq04\npl8jnkjheC6u6+J4Lo7n4Hou7pjfHc/F9ZzC/W7h9vHHuZ6L4zq5ID/JUB5lqAamZmCq5shPA3Ok\nxBsJBMDWxgXzyO967qep5rZp6tztdFqtf6bGqoV7gJkoyRssCE2tqflUzLmS/Hzn00wMzcByTu4v\nRFVRiARMQn6jKOyDAY23n1XP5vVhnt/VyL6XVqO1vIGy8DCvDr7Ma4O7WBlezZrIelp8rTiORzKT\n64mrayo+Q8Nn5EJ/rk2wI3I8zyPtZBjKDjGUGWYoG2coO8xgdoih7DDD2XjRhCbTRVVUNEUb+anm\n3/s0k4gZPh7SmlkyqM2CEM/9d5DfrxqTlpprJViEmAsk5CssbITodwbKHziJycI+Gta54K0NbNkQ\nZscrEQ6+sAKt6Rj+pYc4EN/Hgfg+glqIFaGVrAivotFsxnZcbMclkbZQUDB0BXNM6MtEOzNjtBQ+\nlB3OvTLxXKCPvs/GJywxq4pK1AyzLLKYqBkhYkaImhHqIyFSSQtN0QrCWVVUNDX3c3Tb8f3Hjxl9\nySQoQtQGCfkKC+oB+pWBSRetmarJwr6hzmDb+U1090XY8XKAN15YjFrXQ3BBF+loB68OvcKrQ68Q\n1iOsCK1iRXgV9UYDKJC1PbK2SzyVC33TUPEZKqahY+hKzYe+53nYrkPWsfBGq6hx81Xcx19e0baJ\njy38nOM5JKzkmAAfnrQU7tf8NPjqiPpy4R01o0TN8MjvkZKzk4GUgoUQhSTkK0xTNYJ6gKQ1ffM/\njw37ZNoiPibsWxpN/mRrMx3dGV7a7efovhZcbyNqXQ/+1hiJuk5eGXyJVwZfImrU5QI/tIo6sx4A\nDy+3OI7lABaqMhr6GqauYczRVfFyQW2TdkaGTI35mRr5mbEzpJyR4VROOv/+ZMcsnyxVUYkYYZaG\nFxH1RfPBPTbEq3mpTiHE3CEhPwNCRmhaQ36UqiiEAybBgEEyVRj2C1t8LGzxkbVcjsYyHDoa4sjB\nNrKOjVrXjdnSwVBdNzutF9g58AINZiPLQ6tYEVpJxIjmv8P1PNJZh3Q2Nxe/puaq9k1drfhMe2k7\nQ0+ql4Hs4PHxzk7meICPhPXo+xMZPqUqKn7NR0AP0OCrJ+jz49peQZX1+Jc2Uo2tKRoqyqTH5l4K\n6sixmqISMILUmRFCRvDkhlMJIcQJkpCfAVNZtOZUqEwc9qahsmppgFVLA7iux7GuDIfaoxx+YzGJ\n/Rm0hi70pg7663roz+7gxf4dNJnNrAivYnloJSE9XPBdjuuRytikRqbHznowPJxmtEZfKfp/GFur\nXFDFrOSOcVyHAWuA/kwffSOv/kw/cTs+6X0rKPne102+JnyaP//en/89N7mIL//y49d96Ipe0OGw\nvj7IwMAMVHO7kMo4QPGfhZItOuNGVZQ6Zuwhus8gmbHRVCX3QKKCoiozMmeCEGLukZCfIWEjxGBm\nqKLfURD2aZt40ioYeqeqCksW+FmywM/5b/bo6bc41N7A4fZl9B1IoTV0ojXG6K3rpTfbw3N9z9Li\na2NFaCXLQysJ6MUzRrluLvgLle6A4HoucTvOQLaPgWw/A1Y//dk+hq2holnFAlqQhYHF1BsNRI26\nkWFTI0OnVB+m5sNQJu+lXXwB4LmQsjzGTyriqRpDiYmnIa4Wqp5hKJ4p2q6goKq52h9VHXkpx7dp\nqoKiKmiKgqIyrcMshRCzR0J+hoSMYMVDfpSKQtifW5I2nXVIZWwyWbcgSBVFoaXRpKXR5JwzogzF\nbQ61N3G4fRWxg3HU+hhaU4zuSCfdmU7+2LedNv8CVoRWsSy0Ar828aIXnueRclIMWP3HA30k1B2v\nsARrKAbNvlbqzQYazAbqjQbqzQZ8k5xfnDgPD8cFB69UJUJJYx8Cjj8UFD9UKQW1OIUbJ3sEK/WA\npijgS9uksnbxvqld9pQ/Ufb5UBn/tsz5xr237Nwolqk4ocEM42rGpJZGTEZCfoboqo5f95G2i0tZ\nlaKgEDB1AqaO47mkM7nAz9rFf/FEwzqb10fYvD5CKt3IkWOtHDq2jqMHB6C+E62xg0466Ex3sL33\n9yz0L2JleBVr/avpSvfkQ3w01DNu4X2qqNSZ9fkQbzAbqTcbCGqle4mL2ed6HrkWppmdL8tBZWh4\n5v47qZSMC0PD098XZzwFBUXh+Gv0/UiTjUruYUBRQFVGfx99n2szU8eeQ5p3aoqE/AwKG6EZDfmx\nNEUl5FcJ+Q1sxyWZyZWWHKf4L/CAX2P9qhDrV4Ww7AbaY4s41L6Rw0f6cSLH0BpjdNBOR7qd3/f8\ntvjzSoRWvZWo1kDUqKdObyBiRNE1DU0dU0Kc4YV0hKhFHl6uX4Z3fMt0UBWFpO3m+tyMpZT8teBd\nqf44BUeO36GM+bSSqzNRCnYfP6Cwj8/I5hI1R2O/w5+xydhOvjlKVWp/aPAoCfkZFNADqIo6o8O1\nStE1lWjQJBo0ydgO6bRDKls8dS6AoausWBJgxZIArltPrGcxh9tTHNzbSzrQjhoexE0H8VJh3FQE\nLxUi5er0FZwlA3RPeD2jga8qud77+fcj21RVGfMf87jSysjfBgWlmfxxI39Z5I85XqIZfy7THMKy\nyrgbbHUAABVASURBVNdjn0qlQ/67x19L/lVmf0GJbfyxuW0N9S6eaxPwq/h9Gro2P/4iE9PP9bwJ\n+txMZO7OkO4qKkPjHlbUfP+TXH+U3GiY4iaqau/AKiE/gxRFIWQEGc5O3mt8Jvl0DV9YI4pBJuuQ\nzDpkMk7J5TVVVWFRq49FrT7O9eroG1zG4LBHPJnFdb3cy2Pk99xPZ+Tn6F8Yo9u8kWOd0c+5xz/n\njOyzLTe/DXK9yEdLLaPPI9W58sLMMQyFgE8l4NNGgl8l4Ndy2/y57X6/SsCn4jNVqV0R84br5fqn\nOCfwcDLagVUZ7aw60uQRNIFQ5a71VEjIz7CQEZpTIT9KQcFv6vhNHTc0OkyudPs95P6QN9UbrFwa\nmJF2x8l4nlcQ+mMfBvBy5YvRYzxGto17YAiH/MQT6THnnOJ3l/oLYoLPeuR69xdcT/7ljWwf8378\ntY8/dty5XBcURaV/KEM67ZLKuKTSDqmMy3AiO6V78vvU/AOAf+TBIDDmwcDnU/PtulBcy8BENQ8j\nBxfWPByvbRlbq2Lbbr4ZaXzV7PHf5WFEzLzRDqzgYY+t+HPn7kJJEvIzbHRRj6wzd4drqYpCyG8Q\n8hs47kj7fcbGLtF+PxeMVl2P23pC54hGDFSluEd3tYlGSj90eZ5HJjsa/C7pMQ8AuQcCJ78vmXbo\nH6qefxYTPgiM+b/8g8SYnerIRlUZ814Zec+491NqYincZ+havne9Mu67R881/roLRioox9uix7dx\nj36fbuRmpDQn/Jn7XWpo5i8J+VkQNkL0zeGQH0tTVSIBk0jAJGu7+RJ+qfZ7MXcpioLfp+H3aTRE\nyx/vOB7pbO5BID0S/qmMQybrFdcsjFSXjK9FKaw5KVFTkd93vEYCDzRNxRpTg+SNHDi+X1n+8wXb\nju8ce/zo72P/2BbeR/H1OQXbi+957Pu5LrcIVWHwG6MPA6Y64YOC6+kkkvak/UXGPxyNPvyIuUFC\nfhYEjQD9mVNbmW42mLqKqZtEQ0auVJi1T6kjmpi7NE0hFNAIBWa+GnKi2oi5rFSTSiQcYCieGtNk\n9P+3d6+xUVRtHMD/M7O3bu/QGyJ5JRCEIiDRYIREtEWgKbUQMCRKIKCiiSmgRLRtIiYijVy8K0Ji\nVYiBKLYkBtCEYimiUEEFTNMPJiBBKQilpaXdbmfnvB92d/bShe4W6HSn/19CZnd2ZnhOuz3PmTNn\nzgD+5kb4mJLQBovo0SgJbCcCx9OAblXA3a35/omwZdBrt3fZ4dLQ0qbe8YaJv14I7w3Rb+FDaG+I\nLPsH3QYPvA1aJ0VYJ0ceqBu8TvEtU5I9UFVVb8DYbTKslsHRw8EkbwBZkuG0JBgdRp95r98rcNgU\npKc74bRKIWdi/gpE0ysooS8DZ0u+dQitIIO39176EiFnan4irJYKqSwDa0PX9ThGUJl8I2p7c6uV\nY8Rr+BT3el4yCpwhDzRCCKge0aMBEKmhAMhwu9XI40VuNpYk7L2/LtB8dYTm20nzbaeqAl36AF3v\nYNv+6CGxWry/J7u/98ImhzQEIr222QJP6lTi4O4VJnmDJNuSIBQ3gPh/LKh+x2nIhcf4kp7uxNV+\nesKe3tUb1I8c0r2N0C5wf9MgvBEUfNbn39+ZYEGXSwm5o4ENCwomSRKsFglWC3rtqTGyVyX0bp2w\nO3Z8EzV5wu7c0e/OCdveYlFwrc0Nd7eGrpCGjXfZ3uGBuzv2cSiK4n0+iMOmYOFjCZgyLvsO/CRu\nDZO8QWyKDZmpQyG7HOjo7kSn2gG3p7v3HSnu6SPRcfsbRekpDshhU6l6hAbN460Y/ZWixxM4a9Ir\nxXi4uEyDhix7Z+vDbThbjqaxIoTwXv5we5O/tzHQ87W/cdDlDu39aO8cmPU3k7zBrLIFqfZkpNqT\noWoqOtROdHZ3oitOBubRwKdIMpQo/tIFRGAeA3+DQNMCZ06+BkHwdY8bXQoBIne3hq9jLwMNFJIU\nGJwYK6fVjol3330Horp1TPIDiEW2IMWWjBRbMjyaBx1qJzrUTnQZNBUuDS4SvBN8KAZdRvYn/PS0\nRDTbpPAPe2wX6bMex+zls+CPgy+fBPYNHjsS/FmE0R8idNvkRCs0NbQL+KadJTcrY4TNvIPvhO9O\nAAHha4wRBWOSH6AUWUGyLQnJtiR4NA86VRc61A64PF03rdSI4pV/LnFJQs/pQ6Ubvhmw40DSkuwQ\nUUyVfLvpg9dEoEdGiKAxGnqjIHgde1XMikk+DiiygiRbIpJsid6E73Gho7sTLo+LCZ+IQsiSBLkP\n17E14esV8DUENCGQkmKHpAUaKpF6LPTPQrtFfIvQSzsiaOPwSz2BWR5D51DwH5uNkL5hko8ziqwg\nSU5EkjURmtC8Z/i+hB/ehUhEFC3Zd+N68OWapAQrul1W44IKo+nzBATuPtEnUwq5WyX0dt3UZDuE\nxzs/gPDdcaLpt/v5ej3gveRhtgYFk3wckyUZiVYnEq1OaEKDS3V5B+6pTPhEZD6yfkdKbHemJDut\nULtsUW8fmNEwcJ+/ECLQIPA1IjTN+7nDwrnr6Q6TJRlOqxNOPeF3oUPtQEd3fM0cRkRktMADl6Jr\nSdiUgdPbEY5J3oS8CT8BTmsCOq2daHa1wKP1/wAgIiIy1sCbc5FuqwRLAnKcWXBa43caXSIi6hsm\n+UFAkRVkJAzF0IQhkCT+yomIBgvW+INIotWJuxKz4bDYjQ6FiIj6AZP8IKPICrKcmUh3pPGZz0RE\nJsckP0gl25KQk5gNmxL9bSVERBRfmOQHMatsQbYzE6n2lAE7NSgREfUdk/wgJ0kSUu0pyHFmwTqA\n7/UkIhqoZImT4dAAZ1NsyHFmoaWrFW3udqPDISIamCTAJttgV2ywK3bYFRsUmUme4oAkSUh3pCHB\nkoBm11Womtr7TkREJiZLMmxBCd2mWCHH0a3ITPLUg8NiR05iFq66WnG9+7rR4RAR9RuLbAk5S4/3\ny5hM8hSRLMkYmpAOp9XBaXGJyJwkwC7bQs7UB3LXe18wydNNeafFteFqVwsfdkNEcU2WZG8yt9j0\n6+pmny+ESZ565Z8W97qlA82uFgihGR1S/5P8C0l/431IlRS0SdBrSQrazb/e/4xr4XuUpW8Z9Bxs\nomDe75EEWZJ8T0bzLX2faUKDR2jQhBY3j5eWJBmKJEORFSiS3OP6thTl/bzRJmcJEjITU+FwJ8d9\n13tfMMlT1BKtTtgVG5pdV+FSu/r1/5bCKjl/pYew9+HbBS/loPfhFWdmWgqc3W0Rk3Z/tPR7JH7f\n86rD1wECmv4a0ISAgKYfI8mWgA6Lx1v5a94l3X49v49y6GtISHEkwWNXIn7fAktv0gv+jvqPH+vg\nLn/C9//eve89+jL8s9v78/Albj15K5Alxfde9r33Lo04c062J8GltPX7/zsQMMlTTCyyBVnOTLS5\n29HS1RrTvv6KS/8H71KRZUiQfa36sG0itPTvBIusGHotTm983GL9l5mUDKkz8GwCIUSg8heeQKWv\nhVb+/tdx06MgBXpVbtSj4k+iIeeGkp5yQxuMkgQJsv7a2yCUQxqQwYk8mkSV4UyGuN5/Z47+vxWr\n3Hu1Hv69CG4UeLTAOqti1a9T+5O0IilQZDmQxA1K3BQdJnnqk2RbEhwWBxSbii4r9OTs/4NXpODE\nLesVJPUv7+9CgQIFQO8Jx6P5k74nJAEE1mvehKl3G0shyVSWghOsLwn7EnL4tj33877PSk1BQndb\nj56UaJMr9S7a70VmajIc7sF5BmwWTPLUZ1bZgsykdMidrATMQpGjbxDcKRbFAksUZ6NE1Lv4uaOf\niIiIYsIkT0REZFJM8kRERCbFJE9ERGRSTPJEREQmxSRPRERkUkzyREREJsUkT0REZFJM8kRERCbF\nJE9ERGRSTPJEREQmxSRPRERkUkzyREREJsUkT0REZFJM8kRERCbFJE9ERGRSTPJEREQmxSRPRERk\nUkzyREREJsUkT0REZFKSEEIYHQQRERHdfjyTJyIiMikmeSIiIpNikiciIjIpJnkiIiKTYpInIiIy\nKSZ5IiIik4q7JF9XV4fZs2dj1qxZ2LZtm9HhxKypqQmLFy9GYWEhioqKsH37dqNDuiWapmHevHl4\n4YUXjA6lz9ra2rBixQoUFBSgsLAQJ0+eNDqkmH3xxReYM2cOioqKsHr1arjdbqNDikpZWRmmTp2K\noqIifV1rayuWLVuGWbNm4ZlnnkFbW5uBEUYnUjk2bNiAgoICFBcXo6SkBO3t7QZG2LtIZfCrrKzE\n2LFj0dLSYkBksblROXbs2IGCggIUFRVh06ZNBkUXnUhlaGxsxMKFCzF37lwsWLAAp0+fju5gIo54\nPB4xY8YMcf78eeF2u8UTTzwh/vrrL6PDismlS5dEQ0ODEEKI9vZ2MXPmzLgrQ7DPP/9crF69Wjz/\n/PNGh9Jnr776qti9e7cQQoju7m7R1tZmcESxaWpqEnl5eaKrq0sIIcTKlStFdXW1wVFF59dffxUN\nDQ1izpw5+roNGzaIbdu2CSGE2Lp1q9i4caNR4UUtUjmOHDkiPB6PEEKIjRs3ik2bNhkVXlQilUEI\nIS5cuCCWLVsmHnvsMXH16lWDootepHIcPXpULF26VHR3dwshhLhy5YpR4UUlUhmWLVsmDh8+LIQQ\nora2VixatCiqY8XVmfypU6fwv//9D8OHD4fVakVhYSFqamqMDismmZmZGDduHAAgMTERo0aNwqVL\nlwyOqm+amppw6NAhPPnkk0aH0mft7e04fvw45s+fDwCwWCxISkoyOKrYaZqGzs5OqKoKl8uFrKws\no0OKyoMPPoiUlJSQdTU1NZg3bx4AYN68eThw4IARocUkUjmmTp0KWfZWsffffz+ampqMCC1qkcoA\nAOvXr8eaNWsMiKhvIpVj586deO6552CxWAAAQ4YMMSK0qEUqgyRJeq9WW1sbsrOzozqW5bZHdwdd\nvHgRw4YN099nZ2dH32UxAJ0/fx6NjY2YOHGi0aH0if+PPx66U2/k/PnzSE9PR2lpKRobG3Hfffeh\nvLwcDofD6NCilp2djaVLl+LRRx9FQkICpk2bhqlTpxodVp81NzcjIyMDgLdR3NzcbHBEt2737t0o\nLCw0OoyY1dTUYNiwYbj33nuNDuWWnD17FsePH8e7774Lu92ONWvWYMKECUaHFZPS0lI8++yzePvt\ntyGEwK5du6LaL67O5M3k+vXrWLFiBcrKypCYmGh0ODGrra1FRkYGxo0bBxHHMyOrqoqGhgY89dRT\nqK6uhsPhiLuxHteuXUNNTQ1+/PFHHD58GB0dHfjuu++MDuu2kSTJ6BBuyZYtW2C1WiNe6x7IXC4X\ntm7dipKSEn1dvP6tezwetLa24uuvv8Yrr7yCVatWGR1SzHbu3Iny8nLU1taitLQUZWVlUe0XV0k+\nOzsb//77r/7+4sWLcdMtGUxVVaxYsQLFxcWYMWOG0eH0yW+//YaDBw8iPz8fq1evxrFjx+KqS88v\nJycHOTk5eqt+1qxZaGhoMDiq2Pz8888YMWIE0tLSoCgKHn/8cfz+++9Gh9VnQ4cOxeXLlwEA//33\n34DvWr2ZqqoqHDp0CJs3bzY6lJidO3cO//zzD4qLi5GXl4eLFy9i/vz5uHLlitGhxSwnJwczZ84E\nAEycOBGyLOPq1asGRxWbPXv26Pli9uzZOHXqVFT7xVWSnzBhgv7Fc7vd2Lt3L/Lz840OK2ZlZWUY\nPXo0lixZYnQoffbyyy+jtrYWNTU1eOedd/DQQw9hw4YNRocVs4yMDAwbNgxnzpwBABw9ehSjRo0y\nOKrY3HXXXTh58iS6uroghIi7MoSfHebl5aGqqgoAUF1dHTd/4+HlqKurw2effYYtW7bAZrMZFFVs\ngsswZswYHDlyBDU1NTh48CCys7NRXV2NoUOHGhhhdMJ/FzNmzMDRo0cBAGfOnIGqqkhPTzcitKiF\nlyE7Oxv19fUAgF9++QX33HNPVMeJu6fQ1dXV4a233oIQAgsWLMDy5cuNDikmJ06cwKJFizBmzBhI\nkgRJkvDSSy/hkUceMTq0Pquvr0dlZSU+/fRTo0Ppk8bGRpSXl0NVVYwYMQIVFRVITk42OqyYfPTR\nR9i7dy8sFgtyc3Oxbt06WK1Wo8Pqlb8XqKWlBRkZGSgpKcGMGTOwcuVKXLhwAcOHD8d7770XcUDY\nQBKpHFu3bkV3dzfS0tIAAJMmTcIbb7xhbKA3EakM/gGpAJCfn49vv/1WL89AFakcxcXF+rgbq9WK\n1157DVOmTDE61BuKVIaRI0di3bp10DQNdrsda9euRW5ubq/HirskT0RERNGJq+56IiIiih6TPBER\nkUkxyRMREZkUkzwREZFJMckTERGZFJM8ERGRSTHJEw1ipaWl2LNnj9FhENEdwiRPRERkUpwMh2iQ\nqaioQG1tLbKysqBpmv6o4O3bt0MIgfHjx+P111+HzWbDvn378OGHHyIhIQG5ubnweDyoqKhAXl4e\nJk2ahMbGRnz11Veoq6uLuP/hw4fxwQcfwOPx4O6778abb76J1NRUg38CRIMHz+SJBpEffvgBjY2N\n2L9/P95//32cO3cOHR0d+Oabb7Br1y5UV1djyJAhqKysRHNzMyoqKrB9+3ZUVVWhtbU15FjTp0/H\n/v370dzcfMP9N2/ejMrKSlRVVWHatGnYuHGjQSUnGpzi6nnyRHRr6uvrMXPmTMiyjCFDhmD69OkQ\nQuDvv//GwoULIYSAqqrIzc3FiRMnMHnyZGRmZgIA5s6diwMHDujHmjhxIgDg2LFjEfc/deoULly4\ngMWLF0MIAU3TBvy850RmwyRPNIhIkgRN0/T3sizD4/GgoKAA5eXlAIDOzk6oqor6+vqQbcM5HA4A\nuOn+DzzwAD755BMAgNvtxvXr1+9U0YgoAnbXEw0iDz/8ML7//nu43W60trbip59+AgAcOHAAzc3N\nEEJg7dq1+PLLLzF58mT8+eefuHz5MoQQ2LdvHyRJ6nHMKVOmRNx/0qRJ+OOPP3D27FkAwMcffxyX\njyMmimc8kycaRPLz83H69GkUFRUhMzMTo0ePRkpKCl588UUsWbIEQgiMGzcOy5cvh81mQ3l5OZYu\nXQq73Y7hw4frg+aCk/3YsWNvuP/69euxatUqaJqGnJwcXpMn6mccXU9EEbW0tGDHjh0oKSkBAKxb\ntw4jR47E008/bXBkRBQtnskTUURpaWm4du0aCgsLoSgKxo8fr99uR0TxgWfyREREJsWBd0RERCbF\nJE9ERGRSTPJEREQmxSRPRERkUkzyREREJsUkT0REZFL/Bw3loZ0Plz5RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109363050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.learning_curve import validation_curve\n",
    "\n",
    "def rms_error(model, X, y):\n",
    "    y_pred = model.predict(X)\n",
    "    return np.sqrt(np.mean((y - y_pred) ** 2))\n",
    "\n",
    "degree = np.arange(0, 18)\n",
    "val_train, val_test = validation_curve(PolynomialRegression(), X, y,\n",
    "                                       'polynomialfeatures__degree', degree, cv=7,\n",
    "                                       scoring=rms_error)\n",
    "\n",
    "def plot_with_err(x, data, **kwargs):\n",
    "    mu, std = data.mean(1), data.std(1)\n",
    "    lines = plt.plot(x, mu, '-', **kwargs)\n",
    "    plt.fill_between(x, mu - std, mu + std, edgecolor='none',\n",
    "                     facecolor=lines[0].get_color(), alpha=0.2)\n",
    "\n",
    "plot_with_err(degree, val_train, label='training scores')\n",
    "plot_with_err(degree, val_test, label='validation scores')\n",
    "plt.xlabel('degree'); plt.ylabel('rms error')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the trend here, which is common for this type of plot.\n",
    "\n",
    "- For a small model complexity, the training error and validation error are very similar. This indicates that the model is __under-fitting__ the data: it doesn't have enough complexity to represent the data. Another way of putting it is that this is a __high-bias__ model.\n",
    "\n",
    "- As the model complexity grows, the training and validation scores diverge. This indicates that the model is __over-fitting__ the data: it has so much flexibility, that it fits the noise rather than the underlying trend. Another way of putting it is that this is a __high-variance__ model.\n",
    "\n",
    "- Note that the training score (nearly) always improves with model complexity. This is because a more complicated model can fit the noise better, so the model improves. The validation data generally has a sweet spot, which here is around 5 terms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recap: Scikit-learn's estimator interface\n",
    "Scikit-learn strives to have a uniform interface across all methods, and we'll see examples of these below. Given a scikit-learn estimator object named model, the following methods are available:\n",
    "\n",
    "- Available in all Estimators\n",
    "    - `model.fit()` : fit training data. For supervised learning applications, this accepts two arguments: the data X and the labels y (e.g. `model.fit(X, y)`). For unsupervised learning applications, this accepts only a single argument, the data X (e.g. `model.fit(X)`).\n",
    "- Available in supervised estimators\n",
    "    - `model.predict()` : given a trained model, predict the label of a new set of data. This method accepts one argument, the new data X_new (e.g. `model.predict(X_new)`), and returns the learned label for each object in the array.\n",
    "    - `model.predict_proba()` : For classification problems, some estimators also provide this method, which returns the probability that a new observation has each categorical label. In this case, the label with the highest probability is returned by `model.predict()`.\n",
    "    - `model.score()` : for classification or regression problems, most estimators implement a score method. Scores are between 0 and 1, with a larger score indicating a better fit.\n",
    "- Available in unsupervised estimators\n",
    "    - `model.predict()` : predict labels in clustering algorithms.\n",
    "    - `model.transform()` : given an unsupervised model, transform new data into the new basis. This also accepts one argument X_new, and returns the new representation of the data based on the unsupervised model.\n",
    "    - `model.fit_transform()` : some estimators implement this method, which more efficiently performs a fit and a transform on the same input data."
   ]
  }
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