Day 1 Feature Scaling on Titanic Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=sns.load_dataset('titanic')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass   age  parch\n",
       "0         0       3  22.0      0\n",
       "1         1       1  38.0      0\n",
       "2         1       3  26.0      0\n",
       "3         1       1  35.0      0\n",
       "4         0       3  35.0      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df[['survived','pclass','age','parch']]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2.fillna(df2.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X:  (891, 3)\n",
      "shape of Y:  (891,)\n"
     ]
    }
   ],
   "source": [
    "X=df3.drop('survived',axis=1)\n",
    "Y=df3['survived']\n",
    "print('shape of X: ',X.shape)\n",
    "print('shape of Y: ',Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X_train:  (712, 3)\n",
      "shape of X_test:  (179, 3)\n",
      "shape of Y_train:  (712,)\n",
      "shape of Y_test:  (179,)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=51)\n",
    "print('shape of X_train: ',X_train.shape)\n",
    "print('shape of X_test: ',X_test.shape)\n",
    "print('shape of Y_train: ',Y_train.shape)\n",
    "print('shape of Y_test: ',Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc=StandardScaler()\n",
    "sc.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.30617978, 29.55409121,  0.39185393])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.mean_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.84405789, 12.99162985,  0.79647463])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.306180</td>\n",
       "      <td>29.554091</td>\n",
       "      <td>0.391854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.844651</td>\n",
       "      <td>13.000763</td>\n",
       "      <td>0.797035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pclass         age       parch\n",
       "count  712.000000  712.000000  712.000000\n",
       "mean     2.306180   29.554091    0.391854\n",
       "std      0.844651   13.000763    0.797035\n",
       "min      1.000000    0.420000    0.000000\n",
       "25%      1.750000   22.000000    0.000000\n",
       "50%      3.000000   29.699118    0.000000\n",
       "75%      3.000000   35.000000    0.000000\n",
       "max      3.000000   71.000000    5.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sc = sc.transform(X_train)\n",
    "X_test_sc = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.8220055 , -0.42751304, -0.49198545],\n",
       "       [ 0.8220055 ,  1.997125  , -0.49198545],\n",
       "       [ 0.8220055 , -1.42815732, -0.49198545],\n",
       "       ...,\n",
       "       [ 0.8220055 ,  1.41983023,  3.27461284],\n",
       "       [ 0.8220055 ,  0.01116307,  0.76354731],\n",
       "       [ 0.8220055 , -0.08113618, -0.49198545]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sc = pd.DataFrame(X_train_sc,columns=['pclass','age','parch'])\n",
    "X_test_sc = pd.DataFrame(X_test_sc,columns = ['pclass','age','parch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.822005</td>\n",
       "      <td>-0.427513</td>\n",
       "      <td>-0.491985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.822005</td>\n",
       "      <td>1.997125</td>\n",
       "      <td>-0.491985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.822005</td>\n",
       "      <td>-1.428157</td>\n",
       "      <td>-0.491985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.822005</td>\n",
       "      <td>-0.889349</td>\n",
       "      <td>-0.491985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.822005</td>\n",
       "      <td>0.011163</td>\n",
       "      <td>2.019080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass       age     parch\n",
       "0  0.822005 -0.427513 -0.491985\n",
       "1  0.822005  1.997125 -0.491985\n",
       "2  0.822005 -1.428157 -0.491985\n",
       "3  0.822005 -0.889349 -0.491985\n",
       "4  0.822005  0.011163  2.019080"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>712.00</td>\n",
       "      <td>712.00</td>\n",
       "      <td>712.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.55</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.82</td>\n",
       "      <td>3.19</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pclass     age   parch\n",
       "count  712.00  712.00  712.00\n",
       "mean     0.00    0.00    0.00\n",
       "std      1.00    1.00    1.00\n",
       "min     -1.55   -2.24   -0.49\n",
       "25%     -0.66   -0.58   -0.49\n",
       "50%      0.82    0.01   -0.49\n",
       "75%      0.82    0.42   -0.49\n",
       "max      0.82    3.19    5.79"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sc.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmc = MinMaxScaler()\n",
    "mmc.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_mmc = mmc.transform(X_train)\n",
    "X_test_mmc = mmc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.33408898, 0.        ],\n",
       "       [1.        , 0.78039105, 0.        ],\n",
       "       [1.        , 0.14990082, 0.        ],\n",
       "       ...,\n",
       "       [1.        , 0.67412865, 0.6       ],\n",
       "       [1.        , 0.4148359 , 0.2       ],\n",
       "       [1.        , 0.39784642, 0.        ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_mmc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_mmc = pd.DataFrame(X_train_mmc,columns=['pclass','age','parch'])\n",
    "X_test_mmc = pd.DataFrame(X_test_mmc,columns = ['pclass','age','parch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>712.00</td>\n",
       "      <td>712.00</td>\n",
       "      <td>712.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pclass     age   parch\n",
       "count  712.00  712.00  712.00\n",
       "mean     0.65    0.41    0.08\n",
       "std      0.42    0.18    0.16\n",
       "min      0.00    0.00    0.00\n",
       "25%      0.38    0.31    0.00\n",
       "50%      1.00    0.41    0.00\n",
       "75%      1.00    0.49    0.00\n",
       "max      1.00    1.00    1.00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_mmc.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x21864d398d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x218672f7f60>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(X_train_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x21867942b70>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(X_train_mmc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original data not changed# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}