Transformation-of-Features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ef72ed8",
   "metadata": {},
   "source": [
    "## Types Of Transformation for Features\n",
    "    1. Normalization And Standardization\n",
    "    2. Scaling to Minimum And Maximum values\n",
    "    3. Scaling To Median And Quantiles\n",
    "    4. Guassian Transformation\n",
    "    5. Logarithmic Transformation\n",
    "    6. Reciprocal Trnasformation\n",
    "    7. Square Root Transformation\n",
    "    8. Exponential Trnasformation\n",
    "    9. Box Cox Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8319f966",
   "metadata": {},
   "source": [
    "## Standardization(Why standardization?)\n",
    "Standardization comes into picture when features of input data set have large differences between their ranges, or simply when they are measured in different measurement units (e.g., Pounds, Meters, Miles … etc).\n",
    "\n",
    "We try to bring all the variables or features to a similar scale. standarisation means centering the variable at zero. z=(x-x_mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e5ee1b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anagrams',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'gammas',\n",
       " 'geyser',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'titanic']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b612c342",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</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>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass   age     fare\n",
       "0         0       3  22.0   7.2500\n",
       "1         1       1  38.0  71.2833\n",
       "2         1       3  26.0   7.9250\n",
       "3         1       1  35.0  53.1000\n",
       "4         0       3  35.0   8.0500"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = sns.load_dataset('titanic')\n",
    "dataframe = dataframe.iloc[:,[0,1,3,6]]\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6a6471ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 4)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c6705675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived      0\n",
       "pclass        0\n",
       "age         177\n",
       "fare          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "4cfa9cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived    0\n",
       "pclass      0\n",
       "age         0\n",
       "fare        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## for now handling the missing values inside age column\n",
    "dataframe['age'].fillna(dataframe.age.median(),inplace=True)\n",
    "dataframe.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "23a6e943",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Separating out the independent and dependent feature\n",
    "\n",
    "x = dataframe.iloc[:,1:]\n",
    "y = dataframe.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b62e1dcf",
   "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>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass   age     fare\n",
       "0         3  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         3  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         3  35.0   8.0500\n",
       "..      ...   ...      ...\n",
       "886       2  27.0  13.0000\n",
       "887       1  19.0  30.0000\n",
       "888       3  28.0  23.4500\n",
       "889       1  26.0  30.0000\n",
       "890       3  32.0   7.7500\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "eed2e5f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d1c16d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e260d21d",
   "metadata": {},
   "outputs": [
    {
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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>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>41.5792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10.5167</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>120.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>77.2875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>596 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass   age      fare\n",
       "6         1  54.0   51.8625\n",
       "718       3  28.0   15.5000\n",
       "685       2  25.0   41.5792\n",
       "73        3  26.0   14.4542\n",
       "882       3  22.0   10.5167\n",
       "..      ...   ...       ...\n",
       "106       3  21.0    7.6500\n",
       "270       1  28.0   31.0000\n",
       "860       3  41.0   14.1083\n",
       "435       1  14.0  120.0000\n",
       "102       1  21.0   77.2875\n",
       "\n",
       "[596 rows x 3 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "25665ea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6      0\n",
       "718    0\n",
       "685    0\n",
       "73     0\n",
       "882    0\n",
       "      ..\n",
       "106    1\n",
       "270    0\n",
       "860    0\n",
       "435    1\n",
       "102    0\n",
       "Name: survived, Length: 596, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "bd5c2610",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>15.2458</td>\n",
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       "    <tr>\n",
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       "      <td>31.0</td>\n",
       "      <td>10.5000</td>\n",
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       "    <tr>\n",
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       "      <td>7.9250</td>\n",
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       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0000</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>3</td>\n",
       "      <td>40.5</td>\n",
       "      <td>7.7500</td>\n",
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       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.7417</td>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.2458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>295 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass   age     fare\n",
       "709       3  28.0  15.2458\n",
       "439       2  31.0  10.5000\n",
       "840       3  20.0   7.9250\n",
       "720       2   6.0  33.0000\n",
       "39        3  14.0  11.2417\n",
       "..      ...   ...      ...\n",
       "715       3  19.0   7.6500\n",
       "525       3  40.5   7.7500\n",
       "381       3   1.0  15.7417\n",
       "140       3  28.0  15.2458\n",
       "173       3  21.0   7.9250\n",
       "\n",
       "[295 rows x 3 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b36c6f85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "709    1\n",
       "439    0\n",
       "840    0\n",
       "720    1\n",
       "39     1\n",
       "      ..\n",
       "715    0\n",
       "525    0\n",
       "381    1\n",
       "140    0\n",
       "173    0\n",
       "Name: survived, Length: 295, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1eeb4f",
   "metadata": {},
   "source": [
    "## 1. MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f7c38eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Standardistaion -- We use the StandardScaler from sklearn library\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f7501e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f1ed3afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.62580285,  1.91349292,  0.38784185],\n",
       "       [ 0.80576177, -0.09452019, -0.31908053],\n",
       "       [-0.41002054, -0.32621401,  0.18792449],\n",
       "       ...,\n",
       "       [ 0.80576177,  0.90948636, -0.34613654],\n",
       "       [-1.62580285, -1.17575802,  1.71250117],\n",
       "       [-1.62580285, -0.63513911,  0.8821286 ]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "6f31ac1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ 0.80576177, -0.09452019, -0.46974808],\n",
       "       [ 0.80576177, -0.55790784, -0.47987294],\n",
       "       [-1.62580285,  1.60456783,  1.97787045],\n",
       "       [ 0.80576177, -0.17175147, -0.48481483],\n",
       "       [ 0.80576177, -0.55790784, -0.46391578],\n",
       "       [-1.62580285,  1.6817991 ,  0.8951696 ],\n",
       "       [-0.41002054,  0.52332999, -0.4162854 ],\n",
       "       [-0.41002054, -0.01728892, -0.11495032],\n",
       "       [-1.62580285, -0.55790784,  2.32586387],\n",
       "       [-1.62580285,  1.60456783,  1.44858996],\n",
       "       [-0.41002054,  0.83225509, -0.36768297],\n",
       "       [ 0.80576177, -0.17175147, -0.45200819],\n",
       "       [-1.62580285, -0.09452019, -0.62041562],\n",
       "       [-1.62580285,  2.76303693, -0.10425778],\n",
       "       [ 0.80576177, -0.9440642 , -0.45200819],\n",
       "       [-1.62580285, -0.09452019, -0.10425778],\n",
       "       [-1.62580285, -1.94807076,  0.9709894 ],\n",
       "       [ 0.80576177, -0.09452019, -0.46691358],\n",
       "       [ 0.80576177, -2.17976458, -0.40397343],\n",
       "       [-0.41002054,  0.52332999, -0.08092861],\n",
       "       [-0.41002054,  1.06394891, -0.11009007],\n",
       "       [-1.62580285,  0.67779254,  2.36304473],\n",
       "       [-0.41002054,  0.13717363, -0.11009007],\n",
       "       [ 0.80576177, -0.01728892, -0.46391578],\n",
       "       [ 0.80576177, -0.86683293, -0.45905554],\n",
       "       [-0.41002054, -0.09452019, -0.32782897],\n",
       "       [-1.62580285,  0.75502382,  1.53526365],\n",
       "       [-0.41002054,  0.75502382, -0.36768297],\n",
       "       [ 0.80576177, -0.24898274, -0.45200819],\n",
       "       [ 0.80576177, -0.71237038, -0.48335676],\n",
       "       [-1.62580285, -0.09452019,  1.97787045],\n",
       "       [ 0.80576177,  1.52733655, -0.62041562],\n",
       "       [-0.41002054, -0.48067656, -0.32791062],\n",
       "       [ 0.80576177, -2.17976458,  0.15114801],\n",
       "       [ 0.80576177, -0.09452019, -0.4672363 ],\n",
       "       [ 0.80576177, -0.09452019, -0.1645248 ],\n",
       "       [-0.41002054, -2.02530203, -0.11495032],\n",
       "       [ 0.80576177, -1.94807076, -0.40397343],\n",
       "       [ 0.80576177, -0.78960166, -0.47169217],\n",
       "       [ 0.80576177,  0.87087073, -0.46974808],\n",
       "       [ 0.80576177, -2.17976458, -0.31438165],\n",
       "       [ 0.80576177, -0.09452019, -0.32402243],\n",
       "       [ 0.80576177, -0.63513911, -0.46634591]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_scaled = scaler.transform(x_test)\n",
    "x_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "000db4ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Model Building\n",
    "\n",
    "## fit() method is used for training and predict() method is used for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "433f125a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logit_model = LogisticRegression()\n",
    "\n",
    "logit_model.fit(x_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f316fc3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "56484c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled = StandardScaler()\n",
    "df_scaled=df_scaled.fit_transform(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "3d3e7e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.78927234,  0.82737724, -0.56573646, -0.50244517],\n",
       "       [ 1.2669898 , -1.56610693,  0.66386103,  0.78684529],\n",
       "       [ 1.2669898 ,  0.82737724, -0.25833709, -0.48885426],\n",
       "       ...,\n",
       "       [-0.78927234,  0.82737724, -0.1046374 , -0.17626324],\n",
       "       [ 1.2669898 , -1.56610693, -0.25833709, -0.04438104],\n",
       "       [-0.78927234,  0.82737724,  0.20276197, -0.49237783]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "79073622",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "e17ff68f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([216.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 184.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 491.]),\n",
       " array([-1.56610693, -1.44643272, -1.32675851, -1.2070843 , -1.08741009,\n",
       "        -0.96773588, -0.84806167, -0.72838747, -0.60871326, -0.48903905,\n",
       "        -0.36936484, -0.24969063, -0.13001642, -0.01034222,  0.10933199,\n",
       "         0.2290062 ,  0.34868041,  0.46835462,  0.58802883,  0.70770304,\n",
       "         0.82737724]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot histogram for pclass feature\n",
    "plt.hist(df_scaled[:,1],bins=20)    ## 1 is column index for pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "a7951c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 30.,  17.,  15.,   9.,  29.,  64.,  67.,  70., 260.,  62.,  51.,\n",
       "         40.,  35.,  27.,  35.,  23.,  15.,   9.,  11.,  11.,   4.,   2.,\n",
       "          3.,   1.,   1.]),\n",
       " array([-2.22415608, -1.97952766, -1.73489924, -1.49027082, -1.24564239,\n",
       "        -1.00101397, -0.75638555, -0.51175713, -0.26712871, -0.02250029,\n",
       "         0.22212813,  0.46675655,  0.71138498,  0.9560134 ,  1.20064182,\n",
       "         1.44527024,  1.68989866,  1.93452708,  2.1791555 ,  2.42378392,\n",
       "         2.66841235,  2.91304077,  3.15766919,  3.40229761,  3.64692603,\n",
       "         3.89155445]),\n",
       " <BarContainer object of 25 artists>)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting histogram for 'age' feature\n",
    "plt.hist(df_scaled[:,2],bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "760290d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([519., 197.,  55.,  47.,  20.,  15.,   7.,   9.,   2.,   0.,   5.,\n",
       "          4.,   8.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   3.]),\n",
       " array([  0.      ,  20.493168,  40.986336,  61.479504,  81.972672,\n",
       "        102.46584 , 122.959008, 143.452176, 163.945344, 184.438512,\n",
       "        204.93168 , 225.424848, 245.918016, 266.411184, 286.904352,\n",
       "        307.39752 , 327.890688, 348.383856, 368.877024, 389.370192,\n",
       "        409.86336 , 430.356528, 450.849696, 471.342864, 491.836032,\n",
       "        512.3292  ]),\n",
       " <BarContainer object of 25 artists>)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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enuSdb+4DHwZeZLX7Xe13mZf5DvVHGHzC4sfAp1Z7PivU01eBq8B/M/gL/zjwu8Bp4GLbbhga/6nW/wXgz1d7/ovs9Y8Z/O/oD4Hn289HeuwX+EPgB63XF4G/a/Xuer2h7w/wf5+W6bJXBp/Ye6H9nHszi1a7X28/IEkdWsuXZSRJ8zDcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUof+B3w7Rq45ErhoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dataframe['fare'],bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9d30467",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bc578c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b778a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "816ee2fb",
   "metadata": {},
   "source": [
    "## 2. Min Max Scaling (### CNN)---Deep Learning Techniques\n",
    "Min Max Scaling scales the values between 0 to 1. X_scaled = (X - X.min / (X.max - X.min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "06bee1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "855c8430",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.673285</td>\n",
       "      <td>0.101229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.346569</td>\n",
       "      <td>0.030254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.308872</td>\n",
       "      <td>0.081157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.321438</td>\n",
       "      <td>0.028213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>0.020527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0         1         2\n",
       "0  0.0  0.673285  0.101229\n",
       "1  1.0  0.346569  0.030254\n",
       "2  0.5  0.308872  0.081157\n",
       "3  1.0  0.321438  0.028213\n",
       "4  1.0  0.271174  0.020527"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max = MinMaxScaler()\n",
    "df_minmax = pd.DataFrame(min_max.fit_transform(x_train))\n",
    "df_minmax.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "684b75c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_min_max = pd.DataFrame(min_max.fit_transform(dataframe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d8e41355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f66c4436c70>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## pair plot\n",
    "import seaborn as sns\n",
    "\n",
    "sns.pairplot(df_min_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e38b8d51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df_minmax[0],bins=20)\n",
    "plt.xlabel(\"pclass\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "16ec094e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAHgCAYAAACmdasDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAXOElEQVR4nO3df6yleV3Y8fdHFk0tWtAdCeWHg2TFgpVVp7SWYhFq5UcjYtHu1ii1tCupNDb2D1ebiKkhwVaqaS3QVTdgovxQpFLBH4RYqFHEWV3XBUQXWHFlZQfW+DuYXb79Y87aK51xLnPuuXNn5vVKbu453/Occz7hycy+eeY555m1VgAAcLn7hAs9AAAAHAXCGAAAEsYAAFAJYwAAqIQxAABUwhgAAKq64kIPUHXllVeu48ePX+gxAAC4xN10000fWmsdO9NjRyKMjx8/3smTJy/0GAAAXOJm5rfP9phTKQAAIGEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKDaRxjPzI0zc9fM3Lpn7dUzc/Pm5/aZuXmzfnxm/mzPYy/b4ewAAHBgrtjHNi+vvq/6ofsW1lr/7L7bM/Pi6g/2bP+etdbVBzQfAAAcinOG8VrrrTNz/EyPzcxUX109+YDnAgCAQ7XtOcZPrD641vqtPWuPnJlfnZm3zMwTz/bEmbluZk7OzMlTp05tOQYAAGxn2zC+tnrlnvt3Vo9Ya31+9c3Vj8zMp57piWutG9ZaJ9ZaJ44dO7blGAAAsJ3zDuOZuaL6yurV962ttT6y1vrw5vZN1Xuqz952SAAA2LX9fPjubP5R9RtrrTvuW5iZY9Xda617Z+azqquq9245I3AIjl//hp2+/u0vesZOXx8AtrWfr2t7ZfWL1aNn5o6Zee7moWv6y6dRVH1xdcvM/Fr1Y9Xz1lp3H+TAAACwC/v5Voprz7L+L86w9trqtduPBQAAh8uV7wAAIGEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAADVPsJ4Zm6cmbtm5tY9a98xM787Mzdvfp6+57FvnZnbZubdM/NluxocAAAO0n6OGL+8euoZ1r9nrXX15ueNVTPzmOqa6rGb57xkZu53UMMCAMCunDOM11pvre7e5+s9s3rVWusja633VbdVj99iPgAAOBTbnGP8/Jm5ZXOqxYM2aw+tfmfPNnds1gAA4Eg73zB+afWo6urqzurFm/U5w7brTC8wM9fNzMmZOXnq1KnzHAMAAA7GeYXxWuuDa61711ofrb6//3e6xB3Vw/ds+rDqA2d5jRvWWifWWieOHTt2PmMAAMCBOa8wnpmH7Ln7rOq+b6x4fXXNzHzSzDyyuqp6+3YjAgDA7l1xrg1m5pXVk6orZ+aO6gXVk2bm6k6fJnF79Q1Va613zMxrqndW91TfuNa6dyeTAwDAATpnGK+1rj3D8g/+Fdu/sHrhNkMBAMBhc+U7AABIGAMAQCWMAQCgEsYAAFAJYwAAqIQxAABUwhgAACphDAAAlTAGAIBKGAMAQCWMAQCgEsYAAFAJYwAAqIQxAABUwhgAACphDAAAlTAGAIBKGAMAQCWMAQCgEsYAAFAJYwAAqIQxAABUwhgAACphDAAAlTAGAIBKGAMAQCWMAQCgEsYAAFAJYwAAqIQxAABUwhgAACphDAAAlTAGAIBKGAMAQCWMAQCgEsYAAFAJYwAAqIQxAABUwhgAACphDAAAlTAGAIBKGAMAQCWMAQCgEsYAAFAJYwAAqIQxAABUwhgAACphDAAAlTAGAIBKGAMAQCWMAQCg2kcYz8yNM3PXzNy6Z+0/z8xvzMwtM/O6mXngZv34zPzZzNy8+XnZDmcHAIADs58jxi+vnvoxa2+qPnet9XnVb1bfuuex96y1rt78PO9gxgQAgN06Zxivtd5a3f0xaz+71rpnc/dt1cN2MBsAAByagzjH+F9WP7Xn/iNn5ldn5i0z88SzPWlmrpuZkzNz8tSpUwcwBgAAnL+twnhm/kN1T/XDm6U7q0estT6/+ubqR2bmU8/03LXWDWutE2utE8eOHdtmDAAA2Np5h/HMPKf6J9XXrLVW1VrrI2utD29u31S9p/rsgxgUAAB26bzCeGaeWn1L9eVrrT/ds35sZu63uf1Z1VXVew9iUAAA2KUrzrXBzLyyelJ15czcUb2g099C8UnVm2am6m2bb6D44uo/zsw91b3V89Zad5/xhQEA4Ag5Zxivta49w/IPnmXb11av3XYoAAA4bK58BwAACWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKj2EcYzc+PM3DUzt+5Z+7SZedPM/Nbm94P2PPatM3PbzLx7Zr5sV4MDAMBB2s8R45dXT/2YteurN6+1rqrevLnfzDymuqZ67OY5L5mZ+x3YtAAAsCPnDOO11luruz9m+ZnVKza3X1F9xZ71V621PrLWel91W/X4gxkVAAB253zPMX7wWuvOqs3vz9isP7T6nT3b3bFZAwCAI+2gP3w3Z1hbZ9xw5rqZOTkzJ0+dOnXAYwAAwMfnfMP4gzPzkKrN77s263dUD9+z3cOqD5zpBdZaN6y1Tqy1Thw7duw8xwAAgINxvmH8+uo5m9vPqX5iz/o1M/NJM/PI6qrq7duNCAAAu3fFuTaYmVdWT6qunJk7qhdUL6peMzPPrd5ffVXVWusdM/Oa6p3VPdU3rrXu3dHsAABwYM4Zxmuta8/y0FPOsv0LqxduMxQAABw2V74DAICEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAACquuJ8nzgzj65evWfps6pvrx5Y/evq1Gb929Zabzzf9wEAgMNw3mG81np3dXXVzNyv+t3qddXXV9+z1vrugxgQAAAOw0GdSvGU6j1rrd8+oNcDAIBDdVBhfE31yj33nz8zt8zMjTPzoDM9YWaum5mTM3Py1KlTZ9oEAAAOzdZhPDOfWH159aObpZdWj+r0aRZ3Vi8+0/PWWjestU6stU4cO3Zs2zEAAGArB3HE+GnVr6y1Pli11vrgWuvetdZHq++vHn8A7wEAADt1EGF8bXtOo5iZh+x57FnVrQfwHgAAsFPn/a0UVTPzydWXVt+wZ/k/zczV1apu/5jHAADgSNoqjNdaf1p9+sesfe1WEwEAwAXgyncAAJAwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAShgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAAAqYQwAAJUwBgCAqq7Y5skzc3v1R9W91T1rrRMz82nVq6vj1e3VV6+1fn+7MQEAYLcO4ojxl6y1rl5rndjcv75681rrqurNm/sAAHCk7eJUimdWr9jcfkX1FTt4DwAAOFDbhvGqfnZmbpqZ6zZrD15r3Vm1+f0ZW74HAADs3FbnGFdPWGt9YGY+o3rTzPzGfp+4Cenrqh7xiEdsOQYAAGxnqyPGa60PbH7fVb2uenz1wZl5SNXm911nee4Na60Ta60Tx44d22YMAADY2nmH8cz89Zn5lPtuV/+4urV6ffWczWbPqX5i2yEBAGDXtjmV4sHV62bmvtf5kbXWT8/ML1evmZnnVu+vvmr7MQEAYLfOO4zXWu+tHneG9Q9XT9lmKAAAOGzbfvgOOATHr3/DhR4BAC55LgkNAAAJYwAAqIQxAABUwhgAACphDAAAlTAGAIBKGAMAQCWMAQCgEsYAAFAJYwAAqFwSGg6ESzaf267/N7r9Rc/Y6esDcOlzxBgAABLGAABQCWMAAKiEMQAAVD58x2XCh+MAgHNxxBgAABLGAABQCWMAAKiEMQAAVD58B1wiDuMDlq6uB3Bpc8QYAAASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFTCGAAAKmEMAACVMAYAgEoYAwBAJYwBAKASxgAAUAljAACohDEAAFR1xYUeAIBLx/Hr37DT17/9Rc/Y6esDl7fzPmI8Mw+fmZ+bmXfNzDtm5ps2698xM787Mzdvfp5+cOMCAMBubHPE+J7q36+1fmVmPqW6aWbetHnse9Za3739eAAAcDjOO4zXWndWd25u/9HMvKt66EENBgAAh+lAPnw3M8erz69+abP0/Jm5ZWZunJkHneU5183MyZk5eerUqYMYAwAAztvWYTwzD6heW/27tdYfVi+tHlVd3ekjyi8+0/PWWjestU6stU4cO3Zs2zEAAGArW4XxzNy/01H8w2utH69aa31wrXXvWuuj1fdXj99+TAAA2K1tvpViqh+s3rXW+i971h+yZ7NnVbee/3gAAHA4tvlWiidUX1v9+szcvFn7turambm6WtXt1Tds8R4AAHAotvlWip+v5gwPvfH8xzl8voweAIBySWgAAKhcEpojYNdH7eFi4V+wAC4sR4wBACBhDAAAlTAGAIBKGAMAQOXDdwD75oOiAJc2R4wBACBhDAAAlTAGAIBKGAMAQOXDdwCXDR8eBPirOWIMAAAJYwAAqIQxAABUwhgAACphDAAAlTAGAIBKGAMAQCWMAQCgEsYAAFAJYwAAqIQxAABUdcWFHgAA9uv49W/Y6evf/qJn7PT1gaPNEWMAAMgRYwD4C7s+Il2OSsNR5ogxAAAkjAEAoHIqxUXPP/sBABwMR4wBACBHjAHgkuIr7eD8OWIMAAA5Yrxzh3EOMAAA23PEGAAAEsYAAFAJYwAAqIQxAABUwhgAACphDAAAlTAGAIDK9xgDwKHy/fZwdDliDAAAOWLMPji6AcB9DuO/Cbe/6Bk7fw84E0eMAQAgYQwAAJUwBgCAShgDAEAljAEAoBLGAABQ+bo2AOAys+uvnPN1cxevnR0xnpmnzsy7Z+a2mbl+V+8DAAAHYSdHjGfmftV/r760uqP65Zl5/Vrrnbt4PwCAo8KFsc7tqB5V39UR48dXt6213rvW+vPqVdUzd/ReAACwtV2F8UOr39lz/47NGgAAHEm7+vDdnGFt/aUNZq6rrtvc/eOZefeOZjmXK6sPXaD35vDYz5c++/jyYD9fBua77OdL3QXex595tgd2FcZ3VA/fc/9h1Qf2brDWuqG6YUfvv28zc3KtdeJCz8Fu2c+XPvv48mA/Xx7s50vfUd3HuzqV4perq2bmkTPzidU11et39F4AALC1nRwxXmvdMzPPr36mul9141rrHbt4LwAAOAg7u8DHWuuN1Rt39foH6IKfzsGhsJ8vffbx5cF+vjzYz5e+I7mPZ6117q0AAOASt7Mr3wEAwMXksgjjc12eek77r5vHb5mZL7gQc7Kdfeznr9ns31tm5hdm5nEXYk62s9/Lzc/M35mZe2fm2Yc5HwdjP/t5Zp40MzfPzDtm5i2HPSPb2cff2X9jZv7XzPzaZh9//YWYk+3MzI0zc9fM3HqWx49Ug13yYbzn8tRPqx5TXTszj/mYzZ5WXbX5ua566aEOydb2uZ/fV/3DtdbnVd/ZET2/ibPb536+b7vv6vQHgLnI7Gc/z8wDq5dUX77Wemz1VYc9J+dvn3+Wv7F651rrcdWTqhdvvumKi8vLq6f+FY8fqQa75MO4/V2e+pnVD63T3lY9cGYectiDspVz7ue11i+stX5/c/dtnf5+bS4u+73c/L+tXlvddZjDcWD2s5//efXja633V6217OuLy3728ao+ZWamekB1d3XP4Y7JttZab+30vjubI9Vgl0MY7+fy1C5hffH7ePfhc6uf2ulE7MI59/PMPLR6VvWyQ5yLg7WfP8+fXT1oZv73zNw0M193aNNxEPazj7+v+ludvkDYr1fftNb66OGMxyE6Ug22s69rO0LOeXnqfW7D0bbvfTgzX9LpMP4HO52IXdjPfv7e6lvWWveePtDERWg/+/mK6gurp1R/rfrFmXnbWus3dz0cB2I/+/jLqpurJ1ePqt40M/9nrfWHO56Nw3WkGuxyCONzXp56n9twtO1rH87M51U/UD1trfXhQ5qNg7Of/XyietUmiq+snj4z96y1/uehTMhB2O/f2x9aa/1J9Scz89bqcZUwvjjsZx9/ffWidfp7ZW+bmfdVn1O9/XBG5JAcqQa7HE6l2M/lqV9ffd3mk5F/r/qDtdadhz0oWznnfp6ZR1Q/Xn2to0oXrXPu57XWI9dax9dax6sfq/6NKL7o7Ofv7Z+onjgzV8zMJ1d/t3rXIc/J+dvPPn5/p/9FoJl5cPXo6r2HOiWH4Ug12CV/xPhsl6eemedtHn9Zp6/Q9/TqtupPO/3/UrmI7HM/f3v16dVLNkcT71lrnbhQM/Px2+d+5iK3n/281nrXzPx0dUv10eoH1lpn/Doojp59/ln+zurlM/Prnf7n9m9Za33ogg3NeZmZV3b6W0WunJk7qhdU96+j2WCufAcAAF0ep1IAAMA5CWMAAEgYAwBAJYwBAKASxgAAUAljgCNtZp41M2tmPudCzwJwqRPGAEfbtdXPd/oCCADskDAGOKJm5gHVE6rntgnjmfmEmXnJzLxjZn5yZt44M8/ePPaFM/OWmblpZn5mZh5yAccHuOgIY4Cj6yuqn95cwvzumfmC6iur49Xfrv5V9UVVM3P/6r9Vz15rfWF1Y/XCCzAzwEXrkr8kNMBF7Nrqeze3X7W5f//qR9daH61+b2Z+bvP4o6vPrd60ueT5/ao7D3VagIucMAY4gmbm06snV587M6vTobuq153tKdU71lpfdEgjAlxynEoBcDQ9u/qhtdZnrrWOr7UeXr2v+lD1TzfnGj+4etJm+3dXx2bmL06tmJnHXojBAS5WwhjgaLq2///o8Gurv1ndUd1a/Y/ql6o/WGv9eadj+rtm5teqm6u/f2jTAlwCZq11oWcA4OMwMw9Ya/3x5nSLt1dPWGv93oWeC+Bi5xxjgIvPT87MA6tPrL5TFAMcDEeMAQAg5xgDAEAljAEAoBLGAABQCWMAAKiEMQAAVMIYAACq+r/WcAZXI3o9fQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df_minmax[1],bins=25)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "99becd3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df_minmax[2],bins=20)\n",
    "plt.xlabel(\"fare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88ec0306",
   "metadata": {},
   "source": [
    "## 3. Robust Scaler\n",
    "    Robust Scaler are robust to outliers.It is used to scale the feature to median and quantiles Scaling using median and quantiles consists of substracting the median to all the observations, and then dividing by the interquantile difference. The interquantile difference is the difference between the 75th and 25th quantile:\n",
    "\n",
    "    IQR = 75th quantile - 25th quantile\n",
    "\n",
    "    X_scaled = (X - X.median) / IQR\n",
    "\n",
    "    In example data : 0,1,2,3,4,5,6,7,8,9,10\n",
    "\n",
    "        a. 9-90 percentile---90% of all values in this group is less than 9.\n",
    "        b. 1-10 precentile---10% of all values in this group is less than 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "a960276e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ea25bd98",
   "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>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>41.5792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10.5167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>120.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>77.2875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>596 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass   age      fare\n",
       "6         1  54.0   51.8625\n",
       "718       3  28.0   15.5000\n",
       "685       2  25.0   41.5792\n",
       "73        3  26.0   14.4542\n",
       "882       3  22.0   10.5167\n",
       "..      ...   ...       ...\n",
       "106       3  21.0    7.6500\n",
       "270       1  28.0   31.0000\n",
       "860       3  41.0   14.1083\n",
       "435       1  14.0  120.0000\n",
       "102       1  21.0   77.2875\n",
       "\n",
       "[596 rows x 3 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1441472e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6      0\n",
       "718    0\n",
       "685    0\n",
       "73     0\n",
       "882    0\n",
       "      ..\n",
       "106    1\n",
       "270    0\n",
       "860    0\n",
       "435    1\n",
       "102    0\n",
       "Name: survived, Length: 596, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "b48b942d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.2458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.5000</td>\n",
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       "    <tr>\n",
       "      <th>840</th>\n",
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       "    <tr>\n",
       "      <th>720</th>\n",
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       "      <td>6.0</td>\n",
       "      <td>33.0000</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.2417</td>\n",
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       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>3</td>\n",
       "      <td>40.5</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.7417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.2458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>295 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass   age     fare\n",
       "709       3  28.0  15.2458\n",
       "439       2  31.0  10.5000\n",
       "840       3  20.0   7.9250\n",
       "720       2   6.0  33.0000\n",
       "39        3  14.0  11.2417\n",
       "..      ...   ...      ...\n",
       "715       3  19.0   7.6500\n",
       "525       3  40.5   7.7500\n",
       "381       3   1.0  15.7417\n",
       "140       3  28.0  15.2458\n",
       "173       3  21.0   7.9250\n",
       "\n",
       "[295 rows x 3 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "2668e456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "709    1\n",
       "439    0\n",
       "840    0\n",
       "720    1\n",
       "39     1\n",
       "      ..\n",
       "715    0\n",
       "525    0\n",
       "381    1\n",
       "140    0\n",
       "173    0\n",
       "Name: survived, Length: 295, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4a6376eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.602069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.230769</td>\n",
       "      <td>1.161670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.153846</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.461538</td>\n",
       "      <td>-0.168630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0         1         2\n",
       "0 -2.0  2.000000  1.602069\n",
       "1  0.0  0.000000  0.044788\n",
       "2 -1.0 -0.230769  1.161670\n",
       "3  0.0 -0.153846  0.000000\n",
       "4  0.0 -0.461538 -0.168630"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "scaler = RobustScaler()\n",
    "df_robust = pd.DataFrame(scaler.fit_transform(x_train))\n",
    "df_robust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "36d7fbbb",
   "metadata": {},
   "outputs": [
    {
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       "       [-2.00000000e+00,  1.46153846e+00,  1.02979872e+00],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -2.12171306e-01],\n",
       "       [-2.00000000e+00,  9.23076923e-01,  7.08599572e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  1.60795717e+00],\n",
       "       [ 0.00000000e+00, -5.76923077e-01, -3.08531049e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  3.19700214e+00],\n",
       "       [-2.00000000e+00,  6.15384615e-01,  5.06779443e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  8.87901499e+00],\n",
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       "       [-1.00000000e+00, -4.61538462e-01,  6.22946467e-01],\n",
       "       [ 0.00000000e+00,  1.53846154e+00,  8.53139186e-01],\n",
       "       [-1.00000000e+00, -3.84615385e-01, -1.26518201e-01],\n",
       "       [-2.00000000e+00,  3.07692308e-01,  2.64828694e+00],\n",
       "       [-2.00000000e+00,  2.69230769e+00,  2.71966167e+00],\n",
       "       [-1.00000000e+00, -2.09000000e+00,  6.22946467e-01],\n",
       "       [ 0.00000000e+00, -2.30769231e-01, -3.08531049e-01],\n",
       "       [-2.00000000e+00,  5.38461538e-01,  5.18021413e-01],\n",
       "       [-2.00000000e+00,  1.61538462e+00,  4.91434690e-01],\n",
       "       [-2.00000000e+00, -3.07692308e-01,  2.77283940e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.75588865e-04],\n",
       "       [ 0.00000000e+00, -5.38461538e-01, -2.74269807e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.81584582e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  3.39014989e-02],\n",
       "       [-1.00000000e+00,  0.00000000e+00, -6.19023555e-01],\n",
       "       [ 0.00000000e+00, -1.76923077e+00, -8.47623126e-02],\n",
       "       [-2.00000000e+00,  1.61538462e+00,  1.81905782e+00],\n",
       "       [ 0.00000000e+00, -1.00000000e+00,  0.00000000e+00],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  4.12972591e+00],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.86047109e-01],\n",
       "       [ 0.00000000e+00,  1.07692308e+00, -2.95683084e-01],\n",
       "       [ 0.00000000e+00, -7.69230769e-01,  1.43289079e-01],\n",
       "       [ 0.00000000e+00, -5.38461538e-01, -2.57854390e-01],\n",
       "       [-1.00000000e+00,  1.23076923e+00,  4.94466809e-01],\n",
       "       [-1.00000000e+00,  0.00000000e+00, -6.22783726e-02],\n",
       "       [-1.00000000e+00,  6.92307692e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  8.46153846e-01,  6.28299786e-01],\n",
       "       [-1.00000000e+00,  4.61538462e-01,  7.72839400e-01],\n",
       "       [-1.00000000e+00,  1.53846154e-01, -6.22783726e-02],\n",
       "       [-1.00000000e+00,  2.92307692e+00, -1.69344754e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  5.65595717e+00],\n",
       "       [ 0.00000000e+00,  1.15384615e+00, -2.74269807e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.45897216e-01],\n",
       "       [ 0.00000000e+00, -3.07692308e-01, -2.98004283e-01],\n",
       "       [ 0.00000000e+00, -7.69230769e-01, -2.85156317e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  4.15237687e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.74269807e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.17096360e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.80049679e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.85511777e-01],\n",
       "       [-1.00000000e+00,  6.15384615e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  3.07692308e-01,  4.47880086e-02],\n",
       "       [ 0.00000000e+00, -3.46153846e-01, -3.09421842e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  5.18021413e-01],\n",
       "       [-1.00000000e+00,  8.46153846e-01, -6.22783726e-02],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  2.90006852e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.56783726e-01],\n",
       "       [-2.00000000e+00,  2.84615385e+00,  2.03533191e+00],\n",
       "       [-1.00000000e+00, -3.84615385e-01, -1.69344754e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  1.60206852e+00],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -2.80873662e-01],\n",
       "       [ 0.00000000e+00,  5.38461538e-01, -3.17096360e-01],\n",
       "       [-2.00000000e+00, -3.84615385e-01,  2.09439400e+00],\n",
       "       [-1.00000000e+00, -7.69230769e-01, -6.22783726e-02],\n",
       "       [-2.00000000e+00,  6.15384615e-01,  5.11062099e-01],\n",
       "       [ 0.00000000e+00, -1.46153846e+00,  3.39014989e-02],\n",
       "       [ 0.00000000e+00, -8.46153846e-01, -3.13884368e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.09601713e-01],\n",
       "       [ 0.00000000e+00,  7.69230769e-01,  7.25194861e-01],\n",
       "       [-1.00000000e+00, -8.46153846e-01, -1.69344754e-01],\n",
       "       [ 0.00000000e+00, -1.84615385e+00,  9.61798715e-02],\n",
       "       [-2.00000000e+00,  1.34615385e+00,  6.01533191e-01],\n",
       "       [-1.00000000e+00, -3.84615385e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00,  3.07692308e-01, -2.79623126e-01],\n",
       "       [ 0.00000000e+00, -1.53846154e-01, -2.82655246e-01],\n",
       "       [ 0.00000000e+00, -1.69230769e+00,  7.20376874e-01],\n",
       "       [-2.00000000e+00, -3.07692308e-01,  9.98143897e+00],\n",
       "       [-2.00000000e+00,  1.30769231e+00,  5.18021413e-01],\n",
       "       [-1.00000000e+00,  7.69230769e-02,  5.68162741e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.80873662e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  9.01319058e-01],\n",
       "       [-1.00000000e+00,  1.07692308e+00, -6.22783726e-02],\n",
       "       [ 0.00000000e+00,  6.15384615e-01,  4.15237687e-01],\n",
       "       [-1.00000000e+00,  3.84615385e-01, -9.33276231e-02],\n",
       "       [ 0.00000000e+00, -8.46153846e-01, -3.16916488e-01],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.12171306e-01],\n",
       "       [-1.00000000e+00,  1.69230769e+00,  4.94466809e-01],\n",
       "       [-2.00000000e+00,  5.38461538e-01,  3.23536617e+00],\n",
       "       [-2.00000000e+00,  7.69230769e-01,  9.12508779e+00],\n",
       "       [-1.00000000e+00,  4.61538462e-01, -6.22783726e-02],\n",
       "       [-2.00000000e+00, -8.46153846e-01,  1.82208994e+00],\n",
       "       [ 0.00000000e+00, -1.30769231e+00,  7.20376874e-01],\n",
       "       [ 0.00000000e+00,  2.53846154e+00, -3.51892934e-01],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -2.48038544e-01],\n",
       "       [-1.00000000e+00, -1.61538462e+00,  5.05173448e-01],\n",
       "       [ 0.00000000e+00,  2.69230769e+00, -2.08423983e-01],\n",
       "       [ 0.00000000e+00, -6.15384615e-01, -3.09421842e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  3.38505353e-01],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.12886510e-01],\n",
       "       [-2.00000000e+00,  6.15384615e-01,  4.52016274e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.75588865e-04],\n",
       "       [ 0.00000000e+00,  1.69230769e+00, -2.74269807e-01],\n",
       "       [-2.00000000e+00, -7.69230769e-02,  8.43879229e+00],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -3.08531049e-01],\n",
       "       [-2.00000000e+00,  3.84615385e-01,  3.23536617e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.88188437e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  4.71627409e-01],\n",
       "       [ 0.00000000e+00, -2.00000000e+00,  2.83546039e-01],\n",
       "       [-1.00000000e+00, -2.30769231e-01,  6.65773019e-01],\n",
       "       [-2.00000000e+00,  1.76923077e+00,  2.00963597e+00],\n",
       "       [-1.00000000e+00, -2.30769231e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.08531049e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.80873662e-01],\n",
       "       [-1.00000000e+00, -3.07692308e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00, -7.69230769e-01,  2.46608137e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  6.87186296e-01],\n",
       "       [ 0.00000000e+00, -2.30769231e-01, -3.17096360e-01],\n",
       "       [-1.00000000e+00, -3.07692308e-01,  1.96145610e-03],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -2.96933619e-01],\n",
       "       [-2.00000000e+00, -2.08307692e+00,  5.87134047e+00],\n",
       "       [-1.00000000e+00, -3.07692308e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00, -1.53846154e-01, -2.79623126e-01],\n",
       "       [-1.00000000e+00,  4.61538462e-01,  2.80334047e-01],\n",
       "       [-2.00000000e+00, -5.38461538e-01,  1.06175931e+01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.87117773e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.09421842e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.48038544e-01],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -2.86047109e-01],\n",
       "       [-2.00000000e+00,  2.61538462e+00,  2.80710064e+00],\n",
       "       [ 0.00000000e+00, -7.69230769e-01, -1.97537473e-01],\n",
       "       [-2.00000000e+00,  1.07692308e+00,  9.12508779e+00],\n",
       "       [-1.00000000e+00,  2.23076923e+00, -9.01156317e-02],\n",
       "       [ 0.00000000e+00, -6.92307692e-01, -6.19023555e-01],\n",
       "       [-1.00000000e+00,  1.07692308e+00,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -3.09601713e-01],\n",
       "       [ 0.00000000e+00, -6.15384615e-01, -2.48038544e-01],\n",
       "       [ 0.00000000e+00,  5.00000000e-01, -3.43327623e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.12171306e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.74269807e-01],\n",
       "       [ 0.00000000e+00, -3.84615385e-01, -2.80873662e-01],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -3.08531049e-01],\n",
       "       [-2.00000000e+00,  1.84615385e+00,  6.87186296e-01],\n",
       "       [ 0.00000000e+00,  1.53846154e-01, -8.47623126e-02],\n",
       "       [-1.00000000e+00,  6.53846154e-01,  4.94466809e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.87117773e-01],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -3.09421842e-01],\n",
       "       [-2.00000000e+00,  1.69230769e+00,  5.10474518e+00],\n",
       "       [ 0.00000000e+00, -7.69230769e-02, -3.20308351e-01],\n",
       "       [ 0.00000000e+00, -4.61538462e-01, -2.74269807e-01],\n",
       "       [-2.00000000e+00,  1.76923077e+00,  2.71966167e+00],\n",
       "       [-1.00000000e+00,  6.15384615e-01, -1.69344754e-01],\n",
       "       [-1.00000000e+00,  7.69230769e-02,  4.94466809e-01],\n",
       "       [-2.00000000e+00, -4.61538462e-01,  5.87134047e+00],\n",
       "       [-2.00000000e+00,  1.69230769e+00,  3.93879229e+00],\n",
       "       [-1.00000000e+00,  9.23076923e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00, -7.69230769e-02, -2.48038544e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00, -6.19023555e-01],\n",
       "       [-2.00000000e+00,  2.84615385e+00,  5.18021413e-01],\n",
       "       [ 0.00000000e+00, -8.46153846e-01, -2.48038544e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  5.18021413e-01],\n",
       "       [-2.00000000e+00, -1.84615385e+00,  2.88668522e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.80873662e-01],\n",
       "       [ 0.00000000e+00, -2.07692308e+00, -1.42222698e-01],\n",
       "       [-1.00000000e+00,  6.15384615e-01,  5.69413276e-01],\n",
       "       [-1.00000000e+00,  1.15384615e+00,  5.05173448e-01],\n",
       "       [-2.00000000e+00,  7.69230769e-01,  5.95324625e+00],\n",
       "       [-1.00000000e+00,  2.30769231e-01,  5.05173448e-01],\n",
       "       [ 0.00000000e+00,  7.69230769e-02, -2.74269807e-01],\n",
       "       [ 0.00000000e+00, -7.69230769e-01, -2.63563169e-01],\n",
       "       [-1.00000000e+00,  0.00000000e+00,  2.55160600e-02],\n",
       "       [-2.00000000e+00,  8.46153846e-01,  4.12972591e+00],\n",
       "       [-1.00000000e+00,  8.46153846e-01, -6.22783726e-02],\n",
       "       [ 0.00000000e+00, -1.53846154e-01, -2.48038544e-01],\n",
       "       [ 0.00000000e+00, -6.15384615e-01, -3.17096360e-01],\n",
       "       [-2.00000000e+00,  0.00000000e+00,  5.10474518e+00],\n",
       "       [ 0.00000000e+00,  1.61538462e+00, -6.19023555e-01],\n",
       "       [-1.00000000e+00, -3.84615385e-01,  2.53361884e-02],\n",
       "       [ 0.00000000e+00, -2.07692308e+00,  1.08065525e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -2.81584582e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  3.85259101e-01],\n",
       "       [-1.00000000e+00, -1.92307692e+00,  4.94466809e-01],\n",
       "       [ 0.00000000e+00, -1.84615385e+00, -1.42222698e-01],\n",
       "       [ 0.00000000e+00, -6.92307692e-01, -2.91400428e-01],\n",
       "       [ 0.00000000e+00,  9.61538462e-01, -2.87117773e-01],\n",
       "       [ 0.00000000e+00, -2.07692308e+00,  5.51391863e-02],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  3.39014989e-02],\n",
       "       [ 0.00000000e+00, -5.38461538e-01, -2.79623126e-01]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "8846bd26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f66bf65de50>"
      ]
     },
     "execution_count": 143,
     "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": [
    "import seaborn as sns\n",
    "sns.pairplot(df_robust)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "9160c720",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.461538</td>\n",
       "      <td>-0.312011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>2.461242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.153846</td>\n",
       "      <td>-0.282777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>1.673732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>-0.277363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.076923</td>\n",
       "      <td>-0.062981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-0.692308</td>\n",
       "      <td>0.673281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-0.153846</td>\n",
       "      <td>0.673281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>-0.290356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1         2         3\n",
       "0    0.0  0.0 -0.461538 -0.312011\n",
       "1    1.0 -2.0  0.769231  2.461242\n",
       "2    1.0  0.0 -0.153846 -0.282777\n",
       "3    1.0 -2.0  0.538462  1.673732\n",
       "4    0.0  0.0  0.538462 -0.277363\n",
       "..   ...  ...       ...       ...\n",
       "886  0.0 -1.0 -0.076923 -0.062981\n",
       "887  1.0 -2.0 -0.692308  0.673281\n",
       "888  0.0  0.0  0.000000  0.389604\n",
       "889  1.0 -2.0 -0.153846  0.673281\n",
       "890  0.0  0.0  0.307692 -0.290356\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_robust_scaler = pd.DataFrame(scaler.fit_transform(dataframe))\n",
    "df_robust_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "20c20577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f66bf2be460>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.pairplot(df_robust_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "4bf5f3af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f66bf5d3700>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "19dc80f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 40.,  14.,  15.,  31.,  79.,  98., 262.,  84.,  73.,  45.,  35.,\n",
       "         35.,  29.,  16.,  13.,  11.,   4.,   5.,   1.,   1.]),\n",
       " array([-2.12153846, -1.81546154, -1.50938462, -1.20330769, -0.89723077,\n",
       "        -0.59115385, -0.28507692,  0.021     ,  0.32707692,  0.63315385,\n",
       "         0.93923077,  1.24530769,  1.55138462,  1.85746154,  2.16353846,\n",
       "         2.46961538,  2.77569231,  3.08176923,  3.38784615,  3.69392308,\n",
       "         4.        ]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAODklEQVR4nO3df6jd9X3H8edr0dkxHVMSXZaEXSnZqJY1wsU5/MfVMjMtTR04IkwCE9I/IigIW9L+0e6PgGOr3WCzI62iMKcLqCizXZs6hxQ27dVlNjFmDTXT2wRzOze0DByJ7/1xv1mPyb25954fObmfPB8Q7jnf8z3nvL9onvnme77nm1QVkqS2/My4B5AkDZ9xl6QGGXdJapBxl6QGGXdJatAF4x4AYOXKlTUxMTHuMSRpWXn55Zd/XFWr5nrsnIj7xMQEU1NT4x5DkpaVJP8x32MelpGkBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBp0T31DV8jGx/dm+n3v4vluGOImkM3HPXZIaZNwlqUHGXZIaZNwlqUHGXZIatGDck6xL8nySA0n2J7m7W/6lJD9Ksrf7dXPPc3YkOZTkYJKbRrkBkqTTLeZUyOPAvVX1SpJLgJeT7Oke+0pV/VnvykmuAjYDVwO/DHwnya9W1YlhDi5Jmt+Ce+5VdbSqXuluvwccANac4SmbgMer6v2qegM4BFw7jGElSYuzpGPuSSaAa4AXu0V3JXk1yUNJLu2WrQHe6nnaNHP8YZBka5KpJFMzMzNLn1ySNK9Fxz3JxcATwD1V9S7wVeCjwAbgKPDlk6vO8fQ6bUHVrqqarKrJVavm/PddJUl9WlTck1zIbNgfraonAarq7ao6UVUfAF/jp4depoF1PU9fCxwZ3siSpIUs5myZAA8CB6rq/p7lq3tWuxXY191+Btic5KIkVwLrgZeGN7IkaSGLOVvmeuAO4PtJ9nbLPg/cnmQDs4dcDgOfA6iq/Ul2A68xe6bNNs+UkaSza8G4V9V3mfs4+jfO8JydwM4B5pIkDcBvqEpSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSgxaMe5J1SZ5PciDJ/iR3d8svS7InyQ+6n5f2PGdHkkNJDia5aZQbIEk63WL23I8D91bVx4DrgG1JrgK2A89V1Xrgue4+3WObgauBjcADSVaMYnhJ0twWjHtVHa2qV7rb7wEHgDXAJuCRbrVHgM92tzcBj1fV+1X1BnAIuHbIc0uSzmBJx9yTTADXAC8CV1TVUZj9AwC4vFttDfBWz9Omu2WnvtbWJFNJpmZmZvoYXZI0n0XHPcnFwBPAPVX17plWnWNZnbagaldVTVbV5KpVqxY7hiRpERYV9yQXMhv2R6vqyW7x20lWd4+vBo51y6eBdT1PXwscGc64kqTFWMzZMgEeBA5U1f09Dz0DbOlubwGe7lm+OclFSa4E1gMvDW9kSdJCLljEOtcDdwDfT7K3W/Z54D5gd5I7gTeB2wCqan+S3cBrzJ5ps62qTgx7cEnS/BaMe1V9l7mPowPcOM9zdgI7B5hLkjQAv6EqSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUoAXjnuShJMeS7OtZ9qUkP0qyt/t1c89jO5IcSnIwyU2jGlySNL/F7Lk/DGycY/lXqmpD9+sbAEmuAjYDV3fPeSDJimENK0lanAXjXlUvAO8s8vU2AY9X1ftV9QZwCLh2gPkkSX0Y5Jj7XUle7Q7bXNotWwO81bPOdLfsNEm2JplKMjUzMzPAGJKkU/Ub968CHwU2AEeBL3fLM8e6NdcLVNWuqpqsqslVq1b1OYYkaS59xb2q3q6qE1X1AfA1fnroZRpY17PqWuDIYCNKkpaqr7gnWd1z91bg5Jk0zwCbk1yU5EpgPfDSYCNKkpbqgoVWSPIYcAOwMsk08EXghiQbmD3kchj4HEBV7U+yG3gNOA5sq6oTI5lckjSvBeNeVbfPsfjBM6y/E9g5yFCSpMH4DVVJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJatCCcU/yUJJjSfb1LLssyZ4kP+h+Xtrz2I4kh5IcTHLTqAaXJM1vMXvuDwMbT1m2HXiuqtYDz3X3SXIVsBm4unvOA0lWDG1aSdKiLBj3qnoBeOeUxZuAR7rbjwCf7Vn+eFW9X1VvAIeAa4czqiRpsfo95n5FVR0F6H5e3i1fA7zVs950t+w0SbYmmUoyNTMz0+cYkqS5XDDk18scy2quFatqF7ALYHJycs51NBoT258d9wiSRqzfPfe3k6wG6H4e65ZPA+t61lsLHOl/PElSP/qN+zPAlu72FuDpnuWbk1yU5EpgPfDSYCNKkpZqwcMySR4DbgBWJpkGvgjcB+xOcifwJnAbQFXtT7IbeA04DmyrqhMjml2SNI8F415Vt8/z0I3zrL8T2DnIUJKkwfgNVUlqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYN+/ID0rwGuezB4ftuGeIkUvvcc5ekBhl3SWqQcZekBnnMfRnykr2SFuKeuyQ1yLhLUoOMuyQ1yLhLUoP8QFXLwqAfIvslKJ1v3HOXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lq0ECX/E1yGHgPOAEcr6rJJJcBfwdMAIeB36uq/xpsTEnSUgxjz/23qmpDVU1297cDz1XVeuC57r4k6SwaxWGZTcAj3e1HgM+O4D0kSWcwaNwL+HaSl5Ns7ZZdUVVHAbqfl8/1xCRbk0wlmZqZmRlwDElSr0H/mb3rq+pIksuBPUleX+wTq2oXsAtgcnKyBpxDktRjoD33qjrS/TwGPAVcC7ydZDVA9/PYoENKkpam77gn+fkkl5y8Dfw2sA94BtjSrbYFeHrQISVJSzPIYZkrgKeSnHydv62qf0jyPWB3kjuBN4HbBh9TGszE9mf7fu7h+24Z4iTS2dF33Kvqh8An5lj+n8CNgwwlSRqM31CVpAYZd0lq0KCnQp4TPJ4qSR/mnrskNci4S1KDjLskNaiJY+7SKA3ymc4g/DxIg3DPXZIaZNwlqUEelhmTcf1VX9L5wT13SWqQe+7SOcov52kQ7rlLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoP8hqrUoEGvXeQ3XJc/4z4AL/4l6Vx13sfdQEtq0Xkfd0mn86Jly59xlzRU/sFwbvBsGUlqkHGXpAYZd0lqkHGXpAb5gaqkJvhB7oeNLO5JNgJ/AawAvl5V943qvSS1we+dDM9IDsskWQH8FfA7wFXA7UmuGsV7SZJON6o992uBQ1X1Q4AkjwObgNdG9H6S1Ldx/o1hVIeERhX3NcBbPfengd/oXSHJVmBrd/cnSQ6OYI6VwI9H8LpnUwvbAG7HuaSFbYBGtiN/MtB2/Mp8D4wq7pljWX3oTtUuYNeI3n92iGSqqiZH+R6j1sI2gNtxLmlhG8DtWMioToWcBtb13F8LHBnRe0mSTjGquH8PWJ/kyiQ/C2wGnhnRe0mSTjGSwzJVdTzJXcC3mD0V8qGq2j+K91rASA/7nCUtbAO4HeeSFrYB3I4zSlUtvJYkaVnx8gOS1CDjLkkNajruSf40yetJXk3yVJJfHPdM/UhyW5L9ST5IsqxO/UqyMcnBJIeSbB/3PP1K8lCSY0n2jXuWfiVZl+T5JAe6/5/uHvdM/UjykSQvJfm3bjv+eNwz9SvJiiT/muTvh/3aTccd2AN8vKp+Hfh3YMeY5+nXPuB3gRfGPchSNHYZioeBjeMeYkDHgXur6mPAdcC2Zfrf433gk1X1CWADsDHJdeMdqW93AwdG8cJNx72qvl1Vx7u7/8Ls+fbLTlUdqKpRfIN31P7/MhRV9b/AyctQLDtV9QLwzrjnGERVHa2qV7rb7zEblTXjnWrpatZPursXdr+W3ZkhSdYCtwBfH8XrNx33U/wB8M1xD3GemesyFMsuJi1KMgFcA7w45lH60h3O2AscA/ZU1XLcjj8H/hD4YBQvvuyv557kO8AvzfHQF6rq6W6dLzD7V9JHz+ZsS7GY7ViGFrwMhc6+JBcDTwD3VNW7456nH1V1AtjQfY72VJKPV9Wy+TwkyaeBY1X1cpIbRvEeyz7uVfWpMz2eZAvwaeDGOodP6l9oO5YpL0NxjklyIbNhf7Sqnhz3PIOqqv9O8k/Mfh6ybOIOXA98JsnNwEeAX0jyN1X1+8N6g6YPy3T/YMgfAZ+pqv8Z9zznIS9DcQ5JEuBB4EBV3T/uefqVZNXJM9+S/BzwKeD1sQ61RFW1o6rWVtUEs78v/nGYYYfG4w78JXAJsCfJ3iR/Pe6B+pHk1iTTwG8Czyb51rhnWozuw+yTl6E4AOwe02UoBpbkMeCfgV9LMp3kznHP1IfrgTuAT3a/H/Z2e47LzWrg+SSvMrsDsaeqhn4q4XLn5QckqUGt77lL0nnJuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXo/wBe2XYb3m2/pwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_robust_scaler[2],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "1cbb36b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([519., 197.,  55.,  47.,  20.,  15.,   7.,   9.,   2.,   0.,   5.,\n",
       "          4.,   8.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   3.]),\n",
       " array([-0.62600478,  0.26154494,  1.14909466,  2.03664438,  2.9241941 ,\n",
       "         3.81174382,  4.69929353,  5.58684325,  6.47439297,  7.36194269,\n",
       "         8.24949241,  9.13704213, 10.02459185, 10.91214157, 11.79969129,\n",
       "        12.68724101, 13.57479073, 14.46234045, 15.34989017, 16.23743989,\n",
       "        17.12498961, 18.01253933, 18.90008904, 19.78763876, 20.67518848,\n",
       "        21.5627382 ]),\n",
       " <BarContainer object of 25 artists>)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_robust_scaler[3],bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e14503",
   "metadata": {},
   "source": [
    "## 4. Guassian Transformation\n",
    "Some machine learning algorithms like linear and logistic assume that the features are normally distributed(-Accuracy -Performance)::\n",
    "\n",
    "    1. logarithmic transformation\n",
    "    2. reciprocal transformation\n",
    "    3. square root transformation\n",
    "    4. exponential transformation (more general, you can use any exponent)\n",
    "    5. boxcox transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "ce363137",
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived   Age     Fare\n",
       "0           0  22.0   7.2500\n",
       "1           1  38.0  71.2833\n",
       "2           1  26.0   7.9250\n",
       "3           1  35.0  53.1000\n",
       "4           0  35.0   8.0500\n",
       "..        ...   ...      ...\n",
       "886         0  27.0  13.0000\n",
       "887         1  19.0  30.0000\n",
       "888         0   NaN  23.4500\n",
       "889         1  26.0  30.0000\n",
       "890         0  32.0   7.7500\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '/home/ashish/projects/Feature_transformations/titanic_train.csv'\n",
    "data = pd.read_csv(path,usecols=['Age','Fare','Survived'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "85e402ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = data['Age'].fillna(data['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "7571979a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Age         0\n",
       "Fare        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "55725ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b737f03a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## If you want to check whether feature is guassian or normal distributed use Q-Q plot\n",
    "\n",
    "def plot_data(df,feature):\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.subplot(1,2,1)\n",
    "    df[feature].hist()\n",
    "    plt.subplot(1,2,2)\n",
    "    stats.probplot(df[feature],dist='norm',plot=pylab)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "4d0e0c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(data,'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb61b69",
   "metadata": {},
   "source": [
    "## Logarithmic Transformation on 'Age'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "9b2c9f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JqqtXXoF994U77oArr4QPfahfh2trg49/HF54oXf7RRSTd7aKXgWrzJwHzKs8fwbYvot204AGXd2UJEn9smxZMaXCnDnF+n977NGnw0yZUkzM3tP0CN2px9xTZXLmdUmS9JrMYvLPH/8YTj8dDjusV7u3tb02keeMGf0LVRGNG4TeV+XO6iVJklrbV74CZ58Nn/40fOYzVe82ZUoRpMoSAUcd1ZzjqLpjj5UkSSrMmAFf/CIcemjRW1WFtjZYbbVyQlX7bOljxsBFF8H06f0/Zr0ZrCRJElx2GRxzDOy2G3z/+z2u/zdlStHkwAOLce79MWYMXHxxMbQrs7gJsdV6qtp5KVCSpMHuppuKhLTttkXA6mH9vzIu+40YUQxsb9UA1RV7rCRJGsx+/3vYay9429vgmmuqWv/vnHP6frr11it6p55/fuCFKjBYSZI0eD3wAOy8M2ywQTEB6Lrrdtm0ra3oZWqf8bw3RowowlRmMZH7QAxU7QxWktSJiBgaEXdGxLWNrkWqiYULi6Vqhg4t5qvacMMum7a1FVcKezu550DvneqMwUqSOnc8cH+ji5Bq4plnilD1z3/CDTfAm9+8UpOOPVQHHti7wx999ODoneqMwUqSVhARGwO7Aj9odC1S6V54objz769/hauugq23XqlJWxscdFDve6jaL/m14jQJZTFYSdLKvg2cCHS5QllETI6I2yPi9qeeeqpuhUn90r7+3+9+Bz/6EUyY0Gmz44/v/Tiqo48eXJf8umKwkqQOImI34MnMvKO7dpk5MzPHZ+b4kSNH1qk6qR+WLSuWp5k9G2bOLO4E7MSUKcWVwt4YN25w91J1ZLCSpOVtC+wREY8APwK2i4iLG1uS1E+Z8MlPwiWXwKmnwhFHrNRkhx1eW9+vN44+Gu69t6Q6BwCDlSR1kJmfzcyNM3MssD9wc2b2cuiu1GS+9jU466wiXJ100r82t8+eHgFz5/bukOPGFXnNnqrlGawkSRrIvvc9+Pzni9HoZ5zxr6Vq+jN7ur1UXXNJG0nqQmbOA+Y1uAyp737ykyIF7bornHsuDCn6U9ra+haqxowp1vFT1+yxkiRpILr55uIWvfe9r1j/b9gw2tqKbNXbeamg2G/atPLLHGgMVpIkDTR33AF77glveQtcey1TPj38XxN99nYaBSjWZL7wQqdSqIaXAiVJGkj+/Odi/b/11oPZs5ky9XV9Hku1yipw/vkGqt6wx0qSpIHib38rlqqBYv2/N76Rc87p/WFWX72YQf3VVw1VvWWwkiRpIPj732GnnYrH669ni73fQkTfZlB/8UUDVV95KVCSpFa3eDHsvjs8+GARqg7+N+67r/eHOfpo56XqL4OVJEmt7NVXYb/94Le/hcsuY4evbdfrUDVunPNSlcVgJUlSq1q2DA4/HK67Dr73PTY6bl8ee6z63bffHm66qXblDUaOsZIkqRVlwgknFKPMv/pVptw12VDVBOyxkiSpFZ12Gnz723D88WzR9jnuu7/6Xddd11BVK/ZYSZLUan7wA/jc52DSJFY7+5vcd39Uvev228M//lHD2gY5g5UkSa3kyivh4x+HnXfmXX/4Ia8sqe5XeURx1dCeqtryUqAkSa3illvggANgm21Y/5bLeealYVXvetFFzk1VDwYrSZJawZ13Fuv/vfnNbHTXLJ55ac2qd+3L+oDqGy8FSpLU7B58ECZO5IVVX8fG983msZdeX/Wu229fw7q0EnusJElqZo89BjvuyNNPLWPbnMPf2LjqXZ1Sof4MVpIkNat//AMmTmTR/KeZmLfwZ95a1W7OpN44BitJkppR+/p/f/oTe+Us7mB8VbsNG2aoaiTHWEmS1GxefRU++lH49a/55AYXM5cdqtpt3XXhlVdqW5q6Z4+VJEnNZNkyOPJIuPZajmY65yzcr6rd1l3XiT+bgT1WkiQ1i0w48US48EK+wJc5h6Or2s1Q1TzssZIkqVl8/evwjW/wHY7lK/xPVbs4R1VzscdKkqRmcN55cNJJXMr+HM+ZQM/r/x1dXYeW6sgeK0mSGu2qq1h6xMe4iR05hAvIKvs9pk+vcV3qNYOVJEmNdOutvLTXR7mL97AvV/Aqq1a1m71VzclgJUlSo9x1F8/9++4s5E3syixeYERVux19tL1VzcpgJUlSI/zlLzzx7om8zDrsxGz+zno97mKgan4OXpckqc6u+O7j/OXNOzI0l7Ajc1jIJj3uY6hqDfZYSZJUR5fNfJa3/PfOjOIJtuNm/sTbum2/xhrF6jZqDQYrSZLq5cUXGfXxPRnHfezGtfyebbptbqhqPV4KlCSpHpYs4arh+/NBfsFBXMSN7Nht8yFDDFWtyGAlSVKNDR2SnDdsMntyNf/Nd7iMj/a4z9KldShMpTNYSZJUQ0OHwtfyZA7nh3yJLzKdY3rcx2VqWpfBSpKkGmhrgwj45LIzOInTOZspfJkvdrvPkCGGqlZnsJIkqWRtbXDggXAwF3AGn+HHfITjOIvu1v8bNszLfwOBdwVKkga9HXaAuXPLPeZuXMO5HMGN7MDBXMgyhnbZdtgweOWVcs+vxrDHSpI0qNUiVH2AX3AZH+EPvJt9uJJXWK3LtmusYagaSAxWkqRBrexQtSV3cw27M58x7MosFrFWl22dp2rgMVhJkgatKVPKPd5YHmY2O7GIEezIHJ5mZJdtL77YUDUQOcZKkjQoTZkCM2aUd7wNeIIb+U9W42U+wC95lNFdtr34Ypg0qbxzq3kYrCRJg07ZoWpt/skNTGRDHmd75nI/4zpt56W/gc9gJUkaVMoOVavxElexJ+/gHnbnGm7jvZ22M1QNDo6xkiQNKjNnVt82s4efV5fw0l4HMIGfM6ztAm7IiV22NVQNDgYrSdKgMXx49ZNwrrFGDw0y4aij4Gc/gzPPhP/6r/6WpwHAYCVJGhSGD4cXX6yubVWX7aZOhXPPhc9/Ho47rt/1aWAwWEmSBoVSQ9W3vgWnngof/zicckq/a9PAYbCSJA1YW2xRLIQcXS/Rt5yqQtVFF8GnPgX77gtnn139wTUoGKwkSQPSFlvAfff1bp8eQ9WsWXDYYbDddsVKy0O7Xv9Pg5PBSpI04PQlVPU4WP1Xv4L99oOttioGrK/W9fp/GrwMVpKkAaWvoarb3qp77oHddoNNNoHrroO1ul7/T4ObE4RKkgaU3oaqzB4aPPII7LRTcVvh7NmwwQZ9LU2DgMFKkjQgrLoqvPpq7/YZ0tN1myefhB13LLqzfvELGDu2r+VpkDBYSZJaXl9DVbeThT73HOy8MyxcCDfdBO94R79q1ODgGCtJUsvrTajafvvi8l+3oeqll2CvveCPf4Sf/ATe//7+lqhBwmAlSWpJO+zQuzmqoAhVN93UQ6OlS2HSJLjlFjj/fNhll/6UqUHGYCVJajk77ABz5/Z+vx5DVSZMmQJXXlnMrn7ggX2qT4OXwUqS1HL6EqrGjaui0Re+ADNnwmc/C5/4RO9PokHPYCVJHUTE6hHxu4j4Y0TcGxFfbnRN6r9x4+Dee3todNZZ8NWvwpFHwrRpdalLA493BUrS8l4GtsvMRRExDPhlRFyfmb9tdGEqbLRR9W17nKOqXVsbHH887L03zJjh+n/qM4OVJHWQmQksqrwcVvmp9tezamyjjeCxx6pr2+MSNe2uvx4OPRQmTIBLLoFV/NWovvNSoCStICKGRsRdwJPAjZl5WydtJkfE7RFx+1NPPVX3Gger3oSqHhdUBvjNb2DffWHLLeGqq2D11ftVn2SwkqQVZObSzNwK2BjYJiJWmhkyM2dm5vjMHD9y5Mi61ziYbLRR76ZVyKwyVN17L+y6a3GC66+HtdfuV50SGKwkqUuZ+SwwD5jY2EoGr95c+uuV+fOL9f9WWw3mzIFRo2pwEg1GBitJ6iAiRkbEupXnawA7AA80tKhBrLehavvtq2j01FPF+n+LFhWLKm+6aZ9qkzrTY7Dq6tbjiHh9RNwYEQ9WHl/XYZ/PRsRDEfGniNipln8ASSrZhsAtEXE38HuKMVbXNrimQaPjbOq9vTGvqlnVn3++mEl9wQK49lp45zv7XKvUmWpufej01mNgH2BuZp4WEScDJwMnRcQ4YH9gC+CNwE0R8ZbM7G5VJklqCpl5N7B1o+sYjPo6mzrA0KFVhKqXXy6mU7jzTvjZz+ADH+jbyaRu9NhjlYXObj3eE7igsv0CYK/K8z2BH2Xmy5n5MPAQsE2ZRUuSBp6+hiqAyZN7aLB0KRx0UHGS886D3Xbr+8mkblQ1xqqLW49HZebjAJXHDSrNNwIe7bD7wso2SZJKd/TRMH16Nw0y4dhj4fLL4Ywz4OCD61abBp+qZkGrXMbbqjKg86ed3XrcQWdXxVeaXC8iJgOTAUaNGsW8efNW2mnRokWdbm9W1ltbg6XeE7ZcUn4xFd3V02qfrwaOtraiM6k3xoyBRx6psvGXvgTnnAMnnggnnNDL6qTe6dX0spn5bETMo7j1+ImI2DAzH4+IDSl6s6Doodqkw24bAyvd15GZM4GZAOPHj88JEyasdL558+bR2fZmZb21NVjqPfTkWeUXU/HIpAldvtdqn68GhrY2OPDA3u9X9VJ+3/0unHIKHH44nHZa708k9VI1dwV2devx1cAhlWaHAFdVnl8N7B8Rq0XEpsDmwO9KrluSNABMndq79hFw8cUwaVIVjS+9FI47DvbcE773Pdf/U11U02O1IXBBRAylCGKXZea1EfEb4LKIOAJYAOwHkJn3RsRlwH3AEuAY7wiUJHVmwYLq2lW9mHK72bOLsVQf/GARsFz/T3XS49+0rm49zsxngE6nYsvMaUC1HbWSpEGmNzOqDx3ay4Pfdlux/t8WW8DVV/diNWap/5x5XZJUV71dpqbHqRQ6uv/+YgLQUaPghhtgnXV6XZ/UHwYrSVJd9SZU9TiVQkePPlosVTNsWLH+3xve0Kf6pP7worMkqSn1alzV008Xoeq55+DWW2GzzWpWl9Qdg5UkqW7a2mpw0EWLYNdd4eGHi56qd72rBieRqmOwkiTVRW/mrBo3rsqDvvJKMVD99tvhyivhQx/qc31SGQxWkqS6qHbOqnHj4N57q2i4bFkxpcKcOcX6f3vu2a/6pDIYrCRJddHTnFW9GlOVWUz++eMfw//+Lxx2WL9qk8riXYGSWl5EDImItRtdh7r3+teXeLCvfAXOPhs+/eliDUCpSRisJLWkiLgkItaOiDUpVnr4U0R8ptF1qXNtbfDMM12/X/WYKoAZM+CLX4RDDoHTT+93bVKZDFaSWtW4zHwO2Au4DhgNHNTQitSpagatVzWmCuCyy+CYY2C33eAHP3D9PzUdg5WkVjUsIoZRBKurMvNVoLcryqkOehq0XvWSNTfdVCS0bbctxla5/p+akMFKUqv6HvAIsCZwa0SMAZ5raEXqVE+D1pcureIgv/897LUXvO1tcM01MHx4GaVJpTNYSWpJmXlWZm6UmbtkYT7wH42uSysbPbr798eM6eEADzwAO+8MI0fC7Nmw7rpllSaVzmAlqSVFxKiIODcirq+8Hgcc0uCy1Ilp07p+L6L791m4sFiqZuhQuPFG2HDD0uuTymSwktSqzgdmA2+svP4z8IlGFaOu/epXnW+PgIsugkmTutjxmWeKUPXss3DDDfDmN9eqRKk0BitJrWr9zLwMWAaQmUuAakbrqM5mzux8+5Ah3YSqF14o7vz761/h6qth661rVp9UJm+pkNSqXoiI9ajcCRgR7wX+2diS1JmuBqd3OWi9ff2/3/0OfvITmDChVqVJpTNYSWpVnwKuBjaLiF8BI4EPN7YkdWbo0M5DVKfTLCxbVixPM3s2fP/7sPfeNa9PKpOXAiW1pMz8A/DvwPuBjwNbZObdja1KnZk8ucrtmfDJT8Ill8Cpp8KRR9a8Nqls9lhJakkRcfAKm94dEWTmhQ0pSF3adlv43veKzqh2Q4YU25fzta/BWWcV4eqkk+pao1QWe6wktar3dPj5IPAlYI9GFqTOTZ26fKiC4vVyM7J/73vw+c8XM6ufcYZL1ahl2WMlqSVl5n93fB0R6wAXNagcdaOrmdf/tf0nP4Gjj4ZddoHzziu6s6QW5d9eSQPFYmDzRhehlXU18/ro0cDNNxdzLrzvfXD55TBsWF1rk8pmj5WklhQR1/DaostDgHHAZY2rSF2ZNq0YqL548Wvbhg+HGUfeAXvuCW95C1x7rev/aUAwWElqVWd0eL4EmJ+ZCxtVjDrX1gaHH15MTdVu9dXh0i//mZ1P3xnWW6+YVf11r2tckVKJDFaSWlJm/rzRNah7bW3FWPQVvf6lv7HN/+wIawFz5sBGG9W9NqlWDFaSWkpEPM9rlwCXewvIzFy7ziWpC8vd9VfxOv7ObHZizZeegV/OKy4DSgOIwUpSS8nMtRpdg6qz4t2Aa7CYa9idzXmQnbmem//t3xpTmFRDBitJLS0iNgBWb3+dmV3c3K96Gz0a5s8vnq/Cq1zOfryP37Afl3ML2zW2OKlGnG5BUkuKiD0i4kHgYeDnwCPA9Q0tSsuZNq14DJZxHoezK9dxFOdwJfuy/faNrU2qFYOVpFb1FeC9wJ8zc1Nge+BXjS1JHU2aBNtvl3yDEziIi5nKV/k+k9l+e7jppkZXJ9WGwUpSq3o1M58BhkTEkMy8BdiqwTWpgylT4D03n8Yn+TZnchxf43OA49U1sBmsJLWqZyNiBHAr0BYRZ1LMZ6UmseScH3Aqn6ON/+KTfIvixk2YObOxdUm1ZLCS1FIi4sMRsTqwJ8UyNp8EbgD+AuzeyNrUwU9/yoz8ONczkcP4Idnh183SpQ2sS6ox7wqU1GomAdMpwtSlwJzMvKCxJWk58+bBAQfwe7bhw/yEV1l1ubeHDm1MWVI92GMlqaVk5t7Am4G5wHHAoxExIyI+1NjKBMCdd8Iee8Bmm/GTw2axmDVXajJ5cgPqkurEHitJLScznwMuAC6IiPWADwPfiYjXZ+Ymja1uEHvwQZg4sVj3b/Zsztj49SxevRhTtXRp0VM1eTJMn97oQqXaMVhJalkR8TpgH+CjwOuBKxpb0SD22GOw446wbBlXHzuH/TbbeLmFlwGGDYNtt21MeVK9GKwktZSIWAvYCzgAeDdwNfBV4JbM7GwNQdXaP/5R9FQ99RTXn3gLe5741k6bvfQSHHxw8XzSpDrWJ9WRY6wktZqHgYnADGCTzJycmTcbqhpk8WLYfXd44AH42c84+rz3dNt82bLOF2eWBgp7rCS1mtGZubjRRQh49VX46Efh17+GH/8YdthhpYWXO1NNG6lV2WMlqaUYqprEsmVw5JFw7bVw9tmw335AsfByT6ppI7Uqg5UkqfdOOgkuvBC+/GU4+uh/bW5feLkrQ4b03EZqZQYrSVLvnH46nHEGHHss/M//LPfWpElw8cWw6qor77b66kUWc+C6BjLHWElqKRFxDdDlQPXM3KOO5Qw+551X9Fbtvz+ceSZErNRk0iTDkwYve6wktZozgG9Q3B34IvD9ys8i4J4G1jXwXXUVfOxjxXxVF1xQXNdbwZQpRdZq/xkypNgmDRb2WElqKZn5c4CI+EpmdlzG5pqIuLVBZQ18t95a3AE4fjxccUWn1/qmTIEZM5bflvnaNmdc12Bgj5WkVjUyIt7U/iIiNgVGNrCegeuuu4q5qjbdFGbNghEjOm02c2bXh+juPWkgscdKUqv6JDAvIv5aeT0W+Hjjyhmg/vKXYlb1tdeGOXNg/fW7bLp0adeH6e49aSAxWElqSZl5Q0RsDrytsumBzHy5v8eNiE2AC4E3AMuAmZl5Zn+P25Ief7wYT7VkCdxyC2zS/frWQ4d2HaCGDq1BfVIT8lKgpJYUEcOBzwDHZuYfgdERsVsJh14CnJCZbwfeCxwTEeNKOG5refZZ2HlneOIJuO46ePvbe9xl8uS+vScNJPZYSX0w9uRZPbY5YcslHFpFO/XZD4E7gPdVXi8ELgeu7c9BM/Nx4PHK8+cj4n5gI+C+/hy3pbz4Iuy5J9x3XzGz+jbbVLVb++D0jgPYI+Cooxy4rsHDHitJrWqzzDwdeBUgM18EVp5UqR8iYiywNXBbmcdtakuWFHNU/eIXxWyeO+7Yq92nTy/uBGz/WbbMUKXBxWAlqVW9EhFrUJksNCI2A/o9xqpdRIwArgA+kZnPdfL+5Ii4PSJuf+qpp8o6bWNlFtfsrr4azjqrCFiSesVgJalVfRG4AdgkItqAucCJZRw4IoZRhKq2zLyyszaZOTMzx2fm+JEjB8gsDyefDD/8IXzhC8VyNb00ZUoxIWj75KBrrQVtbTWoU2pijrGS1HIiYgjwOmAfigHmARyfmU+XcOwAzgXuz8xv9vd4LeOMM4o1AI8+Gr70pV7v3tnkoIsWwaGHFs9d4kaDhT1WklpOZi6juBvwmcyclZnXlhGqKrYFDgK2i4i7Kj+7lHTs5nTBBfCZz8BHPgLf+U6n6//1pKsJQJcsgalT+1mf1ELssZLUqm6MiE8DPwZeaN+YmX/vz0Ez85eUPAi+qV1zDRxxBOywQzFYvY8TTnU3AeiCBX2sTWpBBitJrerwyuMxHbYl8KZO2qozv/xl0Uu19dZw5ZWw2mp9PlR3k4OOHt3nw0otx0uBklpSZm7ayY+hqlp33w277QZjxhQTgK61Vr8O19UEoKusAtOm9evQUksxWElqSRExPCI+HxEzK683L2nm9YHv4Ydhp52KxZRnz4YS7mqcPr0Y995xeNaIEXD++Q5c1+BisJLUqn4IvAK8v/J6IfDVxpXTIp54Av7zP+Hll4tQNWZMaYeePr2YELR9ctDnnzdUafAxWElqVTWfeX3A+ec/YeLEYnHlWbNgiy0aXZE04Dh4XVKrqunM6wPOSy8V6//dc09xJ+D73tfzPpJ6zR4rSa2qZjOvDzhLlsABB8DPf17MWTVxYumn2GGH12ZcjyheS4ORPVaSWlJm3hgRf6DkmdcHnEw46ij42c/gzDPhv/6r9FPssAPMnbv8trlzi+033VT66aSmZrCS1FIi4t0rbHq88jg6IkZn5h/qXVNTmzoVzj0XPv95OO64mpxixVDV03ZpIDNYSWo136g8rg6MB/5I0WP1TuA24AMNqqv5fOtbcOqpxSRTp5zS6GqkQcExVpJaSmb+R2b+BzAfeHdmjs/MfwO2Bh5qbHVN5KKL4FOfgn33LeZB6MP6f5J6z2AlqVW9LTP/r/1FZt4DbNW4cprIrFlw2GGw3XbQ1tbn9f+qtf32vdsuDWQGK0mt6oGI+EFETIiIf4+I7wP3N7qohvvVr2C//eBd74Kf/rRf6/9V66abVg5R22/vwHUNTo6xktSqDgWOBo6vvL4VmNGwaprBPfcU6/9tvDFcfz2svXbdTm2IkgoGK0ktJyKGAtdm5g7AtxpdT1N45JFi/b/hw2HOHNhgg0ZXJA1KXgqU1HIycymwOCLWaXQtTeHJJ2HHHWHx4mL9v7FjG12RNGjZYyWpVb0E/F9E3Ai80L4xM2szWVOzeu452HlnWLgQbrwR3vGORlckDWoGK0mtalblZ/B66SXYay/44x/hqqtg220bXZE06HkpUFKr+jFwB3A78OPMvCAzL2hwTfWzdCkceCDccgv88Iew664NKWPKlOXXCBwypNgmDVYGK0ktJSJWiYjTgYXABcDFwKMRcXpEDGtsdXWSWaSXK66Ab34TDjqoIWVMmQIzVrgPM7PYZrjSYGWwktRqvg68Htg0M/8tM7cGNgPWBc5oZGF184UvwMyZ8NnPwic/2bAyZs7s23vSQGawktRqdgM+lpnPt2/IzOco5rTapWFV1ctZZ8FXvwpHHgnTpjW0lKVL+/aeNJD1GKwiYpOIuCUi7o+IeyPi+Mr210fEjRHxYOXxdR32+WxEPBQRf4qInWr5B5A06GRmZicblwIrbR9Q2trg+ONh772L620NXv+vu5VyaryKjtS0qumxWgKckJlvB94LHBMR44CTgbmZuTkwt/Kaynv7A1sAE4Hplcn8JKkM90XEwStujIgDgQcaUE99XH89HHooTJgAl1wCqzT+pu7Jk/v2njSQ9fh/ZmY+Djxeef58RNwPbATsCUyoNLsAmAecVNn+o8x8GXg4Ih4CtgF+U3bxkgalY4ArI+JwirsCE3gPsAawdyMLq5nf/Ab23Re23LKYVmH11RtdEQDTpxePHQewR8BRR732njTY9OqfPBExFtgauA0YVQldZObjEdG+fsJGwG877Lawsk2S+i0z/wb8v4jYjqJnPIDrM3NuYyurkXvvLaZSeOMb677+XzWmTzdESR1VHawiYgRwBfCJzHwuur6239kbK417iIjJwGSAUaNGMW/evJV2WrRoUafbm5X11lYz1XvClkt6bDNqjera1VN3n18zfb7VyMybgZsbXUdNzZ9frP+32mrF+n+jRjW6Ikk9qCpYVeaGuQJoy8wrK5ufiIgNK71VGwJPVrYvBDbpsPvGwGMrHjMzZwIzAcaPH58TJkxY6bzz5s2js+3Nynprq5nqPfTknif8PmHLJXzj/xo/DqajRyZN6PK9Zvp8BTz1VLH+36JFcOut8KY3NboiSVWo5q7AAM4F7s/Mb3Z462rgkMrzQ4CrOmzfPyJWi4hNgc2B35VXsiQNcM8/D7vsAgsWwDXXwDvf2eiKJFWpmn9ObwscRLHY6V2VbZ8DTgMui4gjgAXAfgCZeW9EXAbcR3FH4TGV26AlST15+eViOoU774Sf/hQ++MFGVySpF6q5K/CXdD5uCmD7LvaZBjR25jpJajVLlxbL08ydC+efD7vv3uiKJPWSM69LUjPIhGOPhcsvhzPOgEMO6XkfSU3HYCVJzeBLX4JzzoETT4QTTmh0NZL6yGAlSY323e/CKafAYYfBaac1upoutbXBiBHFJKCd/ay1VtFGGsya615wSRpsfvQjOO442GMPmDmz4ev/daWtDQ4+GJYt67rNokXFqjsAkybVpSyp6dhjJUmNMmdOkVY++MEiYDXB+n9dmTq1+1DVbsmSoq00WBmsJKkRbrsN9tkHxo2Dq6+GNdZodEXdWrCgNm2lgcZgJUn1dv/9xQSgo0bBDTfAOus0uqIejR5dm7bSQGOwkqR6evTRYqmaYcOKS4FveEOjK6rKtGkwpIrfGKusUrSVBiuDlSTVy9NPF6HqueeKnqrNNmt0RVWbNAkuvBDWXLPrNiNGFPOaOnBdg1nzjpSUpIFm6lR4+OGip2qrrRpdTa9NmmRoknpij5Uk1cs3vgE33ggf+lCjK5FUIwYrSaqXESNcVFka4AxWkiRJJTFYSZIklcRgJUmSVBKDlSRJUkkMVpIkSSUxWEmSJJXEYCVJklQSg5UkSVJJDFaSJEklMVhJkro0ZQoMGQIRy/+stRa0tTW6Oqn5uAizJKlTU6bAjBmdv7doERx6aPHchZml19hjJUnq1MyZ3b+/ZAlMnVqfWqRWYbCSJHVq6dKe2yxYUPs6pFZisJIkdWro0J7bjB5d+zqkVmKwkqQOIuK8iHgyIu5pdC2NNnly9++vsgpMm1afWqRWYbCSpOWdD0xsdBHNYPp0OPro4i7AFY0YAeef78B1aUUGK0nqIDNvBf7e6DqaxfTpsGwZZC7/8/zzhiqpMwYrSZKkkhisJKkPImJyRNweEbc/9dRTjS5HUpMwWElSH2TmzMwcn5njR44c2ehyJDUJg5UkSVJJDFaS1EFEXAr8BnhrRCyMiCMaXZOk1uFagZLUQWYe0OgaJLUue6wkSZJKYrCSJEkqicFKkiSpJAYrSZKkkhisJEmSSmKwkiRJKonBSpIkqSQGK0mSpJIYrCRJkkpisJIkSSqJwUqSJKkkBitJkqSSGKwkaRBra4P114eI7n/WWqtoK6l7qzS6AElSY7S1wWGHwauv9tx20SI49NDi+aRJNS1Lamn2WEnSIDV1anWhqt2SJcU+krpmsJKkQWrBgvrsIw0mBitJGqRGj67PPtJgYrCSpEFq2jQYNqz69qusUuwjqWsGK0kapCZNgh/+ENZbr+e2I0bA+ec7cF3qiXcFStIgNmmSYUkqkz1WkiRJJTFYSZIklcRgJUmSVBKDlSRJUkkMVpIkSSUxWEmSJJXEYCVJklQSg5UkSVJJDFaSJEklMVhJkiSVxGAlSZJUEoOVJElSSQxWkiRJJTFYSZIklcRgJUmSVBKDlSRJUkkMVpIkSSUxWEmSJJXEYCVJklQSg5UkSVJJDFaSJEklMVhJkiSVxGAlSZJUEoOVJElSSQxWkiRJJekxWEXEeRHxZETc02Hb6yPixoh4sPL4ug7vfTYiHoqIP0XETrUqXJIkqdlU02N1PjBxhW0nA3Mzc3NgbuU1ETEO2B/YorLP9IgYWlq1kiRJTazHYJWZtwJ/X2HznsAFlecXAHt12P6jzHw5Mx8GHgK2KadUSZKk5tbXMVajMvNxgMrjBpXtGwGPdmi3sLJNkiRpwFul5ONFJ9uy04YRk4HJAKNGjWLevHkrtVm0aFGn25uV9dZWM9V7wpZLemwzao3q2tVTd59fM32+ktSq+hqsnoiIDTPz8YjYEHiysn0hsEmHdhsDj3V2gMycCcwEGD9+fE6YMGGlNvPmzaOz7c3Kemurmeo99ORZPbY5YcslfOP/yv63S/88MmlCl+810+crSa2qr5cCrwYOqTw/BLiqw/b9I2K1iNgU2Bz4Xf9KlCRJag09/nM6Ii4FJgDrR8RC4IvAacBlEXEEsADYDyAz742Iy4D7gCXAMZm5tEa1S5IkNZUeg1VmHtDFW9t30X4aMK0/RUmSJLUiZ16XJEkqicFKkiSpJAYrSZKkkhisJGkFETGxst7pQxFxcqPrkdQ6DFaS1EFlfdOzgZ2BccABlXVQW05bG6y/PkT0/LP++kV7Sf3TXLMXSqqpsd1MbHrClkuqmvi0K4+ctmuf920y2wAPZeZfASLiRxTroN7X0Kp6qa0NDjsMXn21uvbPPAOHH148nzSpdnVJA509VpK0vAGx5unUqdWHqnavvFLsJ6nvDFaStLyq1jyNiMkRcXtE3P7UU0/VoazeWbCgvvtJKhisJGl5Va15mpkzM3N8Zo4fOXJk3Yqr1ujR9d1PUsFgJUnL+z2weURsGhGrAvtTrIPaMtraYNGi3u+36qowzXUzpH5x8LokdZCZSyLiWGA2MBQ4LzPvbXBZVWtrg8mTYfHi3u233npw5pkOXJf6y2AlSSvIzOuA6xpdR19Mndp5qBozBh55pO7lSIOOlwIlaQDpavC5g9Kl+jBYSdIA0tXgcwelS/VhsJKkAWTaNBg+fPltw4c7KF2qF4OVJA0gkybBzJnFmKqI4nHmTAelS/VisJKkFtbWBmPHwpAhxXp/668PBx1UvHfRRcWAdUOVVD/eFShJLWrFqRWeeea19+bPL94Dg5VUT/ZYSVKL6mpqhXaLF7v2n1RvBitJalHVTKHgNAtSfRmsJKlFVTOFgtMsSPVlsJKkFtXZ1AodOc2CVH8GK0lqUStOrbDeesWP0yxIjeNdgZLU5NraikHoCxYUl/Z22QWuu+6119OmGaCkZmGwkqQmtuKUCvPnw4wZr73vtApSc/FSoCQ1sZ6mVACnVZCaicFKkppYtdMlOK2C1BwMVpLUxKqdLsFpFaTmYLCSpCbW05QK4LQKUjMxWElSE1txSoUxY+Doo5d/7bQKUvPwrkBJaiIrTq3QPpWCwUlqDQYrSWoSnU2t4FQKUmvxUqAkNYnOplZwKgWptRisJKlJdDVlglMpSK3DYCVJTaKrKROcSkFqHQYrSWoSnU2t4FQKUmsxWElSk+hsagWnUpBai3cFSlITcWoFqbXZYyVJklQSg5UkSVJJDFaSJEklMVhJkiSVxGAlSZJUEoOVJElSSQxWkiRJJTFYSZIklcRgJUmSVBKDlSRJUkkMVpIkSSUxWEmSJJXERZg1YI09eVajS5AkDTL2WEmSJJXEYCVJklQSg5UkSVJJDFaSJEklMVhJkiSVxGAlSZJUEoOVJElSSQxWkiRJJTFYSZIklcRgJUmSVBKDlSTVQVsbjB0LQ4YUj21tja5IUi24VqAk1VhbG0yeDIsXF6/nzy9eA0ya1Li6JJXPHitJqrGpU18LVe0WLy62SxpYDFaSVGMLFvRuu6TWZbCSpBobPbp32yW1LoOVJNXYtGkwfPjy24YPL7ZLGlgMVpJUY5MmwcyZMGYMRBSPM2c6cF0aiLwrUA019uRZVbc9YcslHNqL9lIzmTTJICUNBvZYSVJFROwXEfdGxLKIGN/oeiS1HoOVJL3mHmAf4NZGFyKpNXkpUD3qzeU6qZVl5v0AEdHoUiS1KHusJEmSSmKP1Qr60zvT0+DqR07btc/HllSOiLgJeEMnb03NzKt6cZzJwGSA0U5IJanCYCVpUMnMHUo6zkxgJsD48eOzjGNKan0Gqzqq5Vgle8MkSWo8x1hJUkVE7B0RC4H3AbMiYnaja5LUWuyxkqSKzPwp8NNG1yGpdRmsBoi+XGZ0JnNJksrlpUBJkqSS1CxYRcTEiPhTRDwUESfX6jySJEnNoibBKiKGAmcDOwPjgAMiYlwtziVJktQsajXGahvgocz8K0BE/AjYE7ivjIO7xIokSWpGtboUuBHwaIfXCyvbJEmSBqzILH/C4IjYD9gpM4+svD4I2CYz/7tDm38tBwG8FfhTJ4daH3i69AJrx3pry3prq971jsnMkXU8X81ExFPA/H4ephn/vjRbTc1WDzRfTdbTszJq6vL7q1aXAhcCm3R4vTHwWMcGHZeD6EpE3J6Z48svrzast7ast7Zard5mUkZAbMbPv9lqarZ6oPlqsp6e1bqmWl0K/D2weURsGhGrAvsDV9foXJIkSU2hJj1WmbkkIo4FZgNDgfMy895anEuSJKlZ1Gzm9cy8Driun4fp9lJhE7Le2rLe2mq1egeaZvz8m62mZqsHmq8m6+lZTWuqyeB1SZKkwcglbSRJkkrS9MEqIvaLiHsjYllENNWdBe1abfmeiDgvIp6MiHsaXUtPImKTiLglIu6v/D04vtE1dSciVo+I30XEHyv1frnRNVUjIoZGxJ0RcW2jaxnMIuIrEXF3RNwVEXMi4o0NrufrEfFApaafRsS6jaynUlNT/E5otu/9Zvteb7bv7np+Nzd9sALuAfYBbm10IZ1p0eV7zgcmNrqIKi0BTsjMtwPvBY5p8s/3ZWC7zHwXsBUwMSLe29iSqnI8cH+jixBfz8x3ZuZWwLXAFxpcz43AOzLzncCfgc82uB5ogt8JTfq9fz7N9b3ebN/ddftubvpglZn3Z2Znk4c2i38t35OZrwDty/c0rcy8Ffh7o+uoRmY+npl/qDx/nuKXf9PO4p+FRZWXwyo/TT2QMSI2BnYFftDoWga7zHyuw8s1afDfncyck5lLKi9/SzEnYUM1ye+Epvveb7bv9Wb77q7nd3PTB6sW4PI9dRIRY4GtgdsaXEq3KpfV7gKeBG7MzKauF/g2cCKwrMF1CIiIaRHxKDCJxvdYdXQ4cH2ji2gSfu/3QrN8d9fru7kpglVE3BQR93Ty09Q9PxXRybam7qFoRRExArgC+MQK/6pvOpm5tHIpZ2Ngm4h4R4NL6lJE7AY8mZl3NLqWwaKn77vMnJqZmwBtwLGNrqfSZirFpZ22WtdTbU0N5vd+lZrpu7te3801m8eqNzJzh0bX0A89Lt+j/omIYRT/Y7Zl5pWNrqdamflsRMyjGPfQFANKO7EtsEdE7AKsDqwdERdn5oENrmvA6sX33SXALOCLNSynx3oi4hBgN2D7rNP8PC3wO8Hv/So063d3rb+bm6LHqsW5fE8NRUQA5wL3Z+Y3G11PTyJiZPudUxGxBrAD8EBDi+pGZn42MzfOzLEUf3dvNlQ1TkRs3uHlHjT4705ETAROAvbIzMWNrKXJ+L3fg2b77q7nd3PTB6uI2DsiFgLvA2ZFxOxG19RRZWBn+/I99wOXNfvyPRFxKfAb4K0RsTAijmh0Td3YFjgI2K5yC/pdld6VZrUhcEtE3E3x5XtjZjqFgap1WuWS193AjhR3azbSd4G1gBsr/++d0+B6muJ3QjN+7zfh93qzfXfX7bvZmdclSZJK0vQ9VpIkSa3CYCVJklQSg5UkSVJJDFaSJEklMVhJkiSVxGAlSaqLiFivw633/19E/K3y/NmIuK/OtezVcVHgiDglIno9MWlEjI2Ihk0AHBGfW+H1ryuPDa1rMDNYSZLqIjOfycytKsuKnAN8q/J8K2qwVmVEdLe6yF7Av4JVZn4hM28qu4Y6WC5YZeb7G1WICgYrSVIzGBoR34+IeyNiTmV2bCJis4i4ISLuiIhfRMTbKtvHRMTciLi78ji6sv38iPhmRNwC/G9n+0fE+ylmtv96pcdss8p+H64c4z0R8euI+GNE/C4i1qr0AP0iIv5Q+ek2wEThuxFxX0TMiojrOhz/kYhYv/J8fGV5FSJim8p576w8vrWy/dCIuLLy53gwIk6vbD8NWKPyZ2irbFvUSS1DI+LrEfH7yuf18cr2DSPi1sr+90TEB/v531A0yVqBkqRBb3PggMz8WERcBuwLXAzMBI7KzAcj4v8B04HtKGaFvzAzL4iIw4GzKHqhAN4C7JCZSyNi7or7Z+Z2EXE1cG1m/gQgolhXOYolan4MfDQzfx8RawMvAk8C/5mZL0Wx9NClwPhu/jx7A28FtgRGAfcB5/XwGTwAfCgzl1QuS36t8jlA0au3NfAy8KeI+E5mnhwRx1Z6/bpzBPDPzHxPRKwG/Coi5gD7ALMzc1pEDAWG93AcVcFgJUlqBg9n5l2V53cAYyNiBPB+4PL24AOsVnl8H0UwALgIOL3DsS6vhKru9u/KW4HHM/P3AJn5HEBErAl8NyK2ApZShLfufAi4NDOXAo9FxM09tAdYB7igEtwSGNbhvbmZ+c9KLfcBY4BHqzgmFMsjvbO9x6xyns0plnY5L4rFkn/W4fNXPxisJEnN4OUOz5cCa1AMV3m2ih4ZKIJIuxcqj73Zv12scKx2nwSeAN5VOe5LvaypoyW8NhRn9Q7bvwLckpl7R8RYYF6H91b8fHrz+zuA/87MldZVjIgPAbsCF0XE1zPzwl4cV51wjJUkqSlVeosejoj94F/jlt5VefvXwP6V55OAX/Zy/+cpFphe0QPAGyPiPZV91qoMgl+HoidrGcXiwkN7KP9WYP/K+KYNgf/o8N4jwL9Vnu/bYfs6wN8qzw/t4fjtXq30OHVnNnB0e7uIeEtErBkRY4AnM/P7wLnAu6s8p7phsJIkNbNJwBER8UfgXmDPyvbjgMMi4m6KoHN8L/f/EfCZykDxzdobZ+YrwEeB71T2uZGiV2k6cEhE/JbiMuALdO+nwIPA/wEzgJ93eO/LwJkR8QuK3qd2pwOnRsSv6Dm4tZsJ3N0+eL0LP6AY4/WHKKZg+B5Fj9cE4K6IuJMi4J1Z5TnVjcjsqqdSkiSVISLOp8NgeQ1c9lhJkiSVxB4rSZKkkthjJUmSVBKDlSRJUkkMVpIkSSUxWEmSJJXEYCVJklQSg5UkSVJJ/n8H2lRmAL0BkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data['Age_log'] = np.log(data['Age'])\n",
    "plot_data(data,'Age_log') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e433dd74",
   "metadata": {},
   "source": [
    "The logarthmic Transfromation is not giving a very efficient normal distribution as we can see large no. of points are away from the red line."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb95bc3e",
   "metadata": {},
   "source": [
    "## Reciprocal Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "671267ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age_recip'] = 1/data['Age']\n",
    "plot_data(data,'Age_recip')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092225c9",
   "metadata": {},
   "source": [
    "## Square-Root Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c4f25639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Square_Age'] = data['Age']**(1/2)\n",
    "plot_data(data,'Square_Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f64b52e",
   "metadata": {},
   "source": [
    "## Exponential Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "70052058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['expo_age'] = data['Age']**(1/1.2)\n",
    "plot_data(data,'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3756fcb",
   "metadata": {},
   "source": [
    "## BoxCOx Transformation\n",
    "The Box-Cox transformation is defined as:\n",
    "\n",
    "T(Y)=(Y exp(λ)−1)/λ\n",
    "\n",
    "where Y is the response variable and λ is the transformation parameter. λ varies from -5 to 5. In the transformation, all values of λ are considered and the optimal value for a given variable is selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "99013ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "## the second returned argument i.e parameters is the lambda that maximizes the log-likelihood function.\n",
    "data['Age_BoxCox'],parameters = stats.boxcox(data['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "505292f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7964531473656952\n"
     ]
    }
   ],
   "source": [
    "print(parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "e5498f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(data,'Age_BoxCox')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06af837e",
   "metadata": {},
   "source": [
    "NOW,  Take another Feature --> Fare and try to transfrom it for better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "aff5f613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fI0mSNCjUG6wSuDwiboqI6WXbmMxcDFA+b162jwMerDl2UdkmSZI0pK1R5367ZeZDEbE5cEVE3NXNvtFJW662UxHQpgOMGTOG9vb2OkuBMevAR7dbVvf+vdGbOnpjyZIlDTt3o1jzwLBmSRo66gpWmflQ+fxIRFxAcWnv4YgYm5mLI2Is8Ei5+yJgy5rDtwAe6uScs4BZAJMnT84pU6bUXfSpbRdx8u31ZsLeuW9a/XX0Rnt7O735GVuBNQ8Ma5akoaPHS4ERsV5EbNCxDfw7cAcwFzik3O0Q4KJyey4wNSLWiohtgAnA9VUXLkmS1Grq6fYZA1wQER37/zAzL4uIG4A5EXEE8ABwEEBmzo+IOcACYBlwTGYub0j1kiRJLaTHYJWZfwF26KT9cWDPLo6ZCczsd3WSJEmDiDOvSxqyImLtiLg+Im6NiPkR8fmy3QmOJTWEwUrSUPY88JbM3AHYEdgnIl6HExxLahCDlaQhKwtLypejykfiBMeSGsRgJWlIi4iREXELxZQwV2TmdTjBsaQGMVhJGtIyc3lm7kgxp96uEbFtN7vXNcExFJMcR8SNEXHjo48+WkGlkoYCg5WkYSEznwDaKcZOPVxObExfJjguzzcrMydn5uTRo0c3qmxJg4zBStKQFRGjI2KjcnsdYC/gLpzgWFKDNGZdGElqDWOB2eWdfSOAOZl5cUT8Hic4ltQABitJQ1Zm3gbs1Em7ExxLaggvBUqSJFXEYCVJklQRg5UkSVJFDFaSJEkVMVhJkiRVxGAlSZJUEYOVJElSRQxWkiRJFTFYSZIkVcRgJUmSVBGDlSRJUkUMVpIkSRUxWEmSJFXEYCVJklQRg5WklhIRIyJiw2bXIUl9YbCS1HQR8cOI2DAi1gMWAHdHxMebXZck9ZbBSlIrmJiZTwEHAJcCWwHvaWpFktQHBitJrWBURIyiCFYXZeZSIJtbkqSB1NYGm20GEcVj5MjieY01iufNNiseI0asvD1+fHFsq1ij2QVIEnAmcB9wK3BNRGwNPNXUiiQNmLY2OOwwWLr0xbYVK4rn5cuL58cff/G92u3774fp04vtadMaW2c97LGS1HSZ+a3MHJeZ+2bhfmCPZtclaWCccMLKoaq3nn22OEcrMFhJarqIGBMR34uIX5avJwKHNLksSQPkgQda4xxVMFhJagXfB34FvLR8/Sfgw80qRtLA2mqr1jhHFQxWklrBZpk5B1gBkJnLgOXNLUnSQJk5E0aN6vvx665bnKMVGKwktYJnImJTyjsBI+J1wJPNLUnSQJk2Dc45Bzbd9MW2EWVCGTmyeN500+IRsfL21lvDrFmtMXAdvCtQUmv4H2Au8PKIuBYYDRzY3JIkDaRp01onHPWHwUpS02XmzRHxZuCVQAB3l3NZSdKgYrCS1HQR8d5VmnaOCDLz3KYUJEl95BgrSa1gl5rHm4DPAW9vZkGSGm/V2dY326y1ZlHvC3usJDVdZn6w9nVEvAT4QZPKkTQAOptt/fHH4fDDi+3BOt7KHitJrehZYEKzi5DUOF3Ntv7CC60zi3pf2GMlqeki4he8uOjyCGAiMKd5FUlqtO5mSm+VWdT7wmAlqRV8rWZ7GXB/Zi5qVjGSGm+rrYoFlLt6b7AyWElqusy8utk1SBpYM2euPsYKYM01W2cW9b4wWElqmoh4mhcvAa70FpCZueEAlyRpgHQMTj/22GLQOhSzqZ9yyuAduA4GK0lNlJkbNLsGSc0zVGZbr2WwktQyImJzYO2O15k5iIewShqOnG5BUtNFxNsj4h7gXuBq4D7gl00tSpL6wGAlqRV8EXgd8KfM3AbYE7i2uSVJUu8ZrCS1gqWZ+TgwIiJGZOavgR2bXJMk9VrdwSoiRkbEHyPi4vL1JhFxRUTcUz5vXLPv8RGxMCLujoi9G1G4pCHliYhYH7gGaIuIUyjms5KkQaU3PVbHAnfWvD4OmJeZE4B55WsiYiIwFZgE7AOcFhEjqylX0lASEQdGxNrA/hTL2HwEuAz4M/CfzaxNUrVWXXA5AkaOLJ7Hjx/8iy93qCtYRcQWwNuAs2qa9wdml9uzgQNq2s/PzOcz815gIbBrJdVKGmqmAQ8ApwN7U8xdNTszv1VeGpQ0BHQsuPz4Kv9Xr1hRPN9/P0yfPjTCVWR2NjffKjtF/BT4MrAB8LHM3C8insjMjWr2+UdmbhwR3wb+kJnnle3fA36ZmT9d5ZzTgekAY8aMec35559fd9GP/P1JHv5n3bv3ynbjXtKQ8y5ZsoT111+/IeduFGseGMO15j322OOmzJwcERsC76Do6d4BuAj4UWZe0/9KB8bkyZPzxhtvbHYZUssaP77r5Wtqbb013Hdfo6vpv4i4KTMnd/Zej/NYRcR+wCOZeVNETKnn8zppWy29ZeYsYBYUv5SmTKnn1IVT2y7i5NsbMwXXfdPqr6M32tvb6c3P2AqseWAM95oz8ymKXu/ZEbEpcCBwakRskplbVvIhkpqq3kWVB/Piyx3qSSe7AW+PiH0pJu7bMCLOAx6OiLGZuTgixgKPlPsvAmp/GW4BPFRl0ZKGnvIGmHcC/w1sAvysuRVJqsomm6x+GbAzg3nx5Q49jrHKzOMzc4vMHE/RVX9VZh4MzAUOKXc7hKL7nrJ9akSsFRHbABOA6yuvXNKgFxEbRMR7IuJSiptjdgH+D9gqMz/c1OIkVaKtDZ56quf91l13cC++3KE/19NOBOZExBEUg08PAsjM+RExB1hAcbv0MZm5vN+VShqK7gV+RTF4/bLMXNrD/pIGmRNOgKVd/J89YkQxgH3rrYtQNRTWDexVsMrMdqC93H6cYnbkzvabCQyB3CmpwbbKzGebXYSkxulq3FQELB+C3S7OvC6paQxV0tDX1bipoTCeqjMGK0mS1DAzZxbjp2oNlfFUnTFYSZKkhpk2DWbNKsZRRRTPs2YNjfFUnWnMZFCSVIeI+AWdzHPXITPfPoDlSKpQWxscdRQ888yLbeuvP3QGqXfFYCWpmb5WPr8T+P+A88rX7wbua0ZBkvqvrQ3e+94Xl6zpsGQJHHposT1Uw5XBSlLTZObVABHxxczcveatX0TEoFnSRtLKTjhh9VDVYdmy4v2hGqwcYyWpFYyOiJd1vCgnFx7dxHok9UNPS9MMhaVrumKPlaRW8BGgPSL+Ur4eDxzVvHIk9cdWW3W/6PJQnWoBDFaSWkBmXhYRE4BXlU13ZebzzaxJUt/NnNn5GCuANdYYulMtgJcCJbWAiFgX+Djwgcy8FdgqIvZrclmS+mjaNDj3XFhvvZXb118fvv/9oTu+CgxWklrDOcALwOvL14soFmPul4jYMiJ+HRF3RsT8iDi2bN8kIq6IiHvK541rjjk+IhZGxN0RsXd/a5CGq2nTirsAM198PP300A5VYLCS1BpenplfBZYCZOY/gajgvMuAj2bmq4HXAcdExETgOGBeZk4A5pWvKd+bCkwC9gFOi4iRFdQhaZgwWElqBS9ExDqUk4VGxMuBfo+xyszFmXlzuf00cCcwDtgfmF3uNhs4oNzeHzg/M5/PzHuBhcCu/a1D0vBhsJLUCj4LXAZsGRFtFL1In6jyAyJiPLATcB0wJjMXQxG+gM3L3cYBD9Yctqhsk6S6eFegpKaKiBHAxhSzr7+O4hLgsZn5WIWfsT7wM+DDmflURJdXGTt7o9MldyJiOjAdYKuhfO+4pF6xx0pSU2XmCoq7AR/PzEsy8+KKQ9UoilDVlpk/L5sfjoix5ftjgUfK9kXAljWHbwE81EXdszJzcmZOHj3auUwlFQxWklrBFRHxsfIuvk06Hv09aRRdU98D7szMr9e8NRc4pNw+BLiopn1qRKxVzv4+Abi+v3VIGj68FCipFRxePh9T05bAyzrZtzd2A94D3B4Rt5RtnwJOBOZExBHAA8BBAJk5PyLmAAso7ig8JjOX97MGScOIwUpS02XmNg0672/petqGPbs4ZiYwhOeFltRIXgqU1HQRsW5E/G9EzCpfT3DmdUmDkcFKUivomHn9DeXrSmZel6SBZrCS1AoaNfO6JA0og5WkVtCQmdclaaA5eF1SK1h15vXdgEObWpEk9YHBSlLTZeYVEXEzDZp5XZIGisFKUtNExM6rNC0un7eKiK06FlCWpMHCYCWpmU4un9cGJgO3UvRYbU+xWPIbm1SXJPWJg9clNU1m7pGZewD3AzuXa++9BtgJWNjc6iSp9wxWklrBqzLz9o4XmXkHsGPzypGkvvFSoKRWcFdEnAWcRzHlwsHAnc0tSZJ6z2AlqRUcCswAji1fXwOc3rRqJKmPDFaSmioiRgIXZ+ZewDeaXY8k9YdjrCQ1VWYuB56NiJc0uxZJ6i97rCS1gueA2yPiCuCZjsbM/FDzSpKk3rPHSlIruAT4NMXYqptqHpIGmbY22GwziCgem21WtA0X9lhJagU/Bv6N4o7AP2fmc02uR1IftLXBYYfB0qUvtj3+OBx+eLE9bVpz6hpI9lhJapqIWCMivgosAmZTTLfwYER8NSJGNbc6Sb11wgkrh6oOL7xQvDccGKwkNdNJwCbANpn5mszcCXg5sBHwtWYWJqn3Hnigb+8NJQYrSc20H/C+zHy6oyEzn6KY02rfplUlqU+22qpv7w0lBitJzZSZmZ00LqcYbyVpEJk5E0Z1chF/zTWL94YDg5WkZloQEe9dtTEiDgbuakI9kvph2jQ45xzYdNMX2zbdFM4+e3gMXAfvCpTUXMcAP4+IwymmV0hgF2Ad4B3NLExS30ybNnxCVGfssZLUNJn518x8LfAF4D7gAeALmblrZv61qcVJ6pVV568ajnNYgT1WklpAZl4FXNXsOiT13tFHw+ldLJk+3OawAnusJElSH3UXqjoMpzmswGAlSZL6aNas+vYbLnNYgcFKkiT10fLl9e03XOawgjqCVUSsHRHXR8StETE/Ij5ftm8SEVdExD3l88Y1xxwfEQsj4u6I2LuRP4AkSRp4Rx9d337DaQ4rqK/H6nngLZm5A7AjsE9EvA44DpiXmROAeeVrImIiMBWYBOwDnBYRIxtQuyRJapJ6LgOuv/4gmcPq6afh+ecrOVWPwSoLS8qXo8pHAvtTLJpK+XxAub0/cH5mPp+Z9wILgV0rqVaSJLWE7i4DbropnHdekVdaPlRdcQVst11l3Wp1jbGKiJERcQvwCHBFZl4HjMnMxQDl8+bl7uOAB2sOX1S2SZKkIWJkF9eiRo6Exx4bBIHqiSfgyCPh3/8d1l4b9q5m5FJd81iV63btGBEbARdExLbd7B6dnWK1nSKmA9MBxowZQ3t7ez2lADBmHfjodsvq3r83elNHbyxZsqRh524Uax4Y1ixpMJo+vfOpFqZPH/haeu3ii+Goo+Bvf4PjjoPPfrYIVxXo1QShmflERLRTjJ16OCLGZubiiBhL0ZsFRQ/VljWHbQE81Mm5ZgGzACZPnpxTpkypu45T2y7i5NsbM7fpfdPqr6M32tvb6c3P2AqseWBYs6TB6LTTiudZs4rLgiNHFqGqo70lPfYYfPjDxXTw220HF10EkydX+hH13BU4uuypIiLWAfaiWBx1LnBIudshwEXl9lxgakSsFRHbABOA6yutWpIkNd1pp8GyZZBZPLd0qPrJT2DiRPjxj+Fzn4Mbb6w8VEF9Y6zGAr+OiNuAGyjGWF0MnAi8NSLuAd5aviYz5wNzgAXAZcAx5aVESZI0yLW1FXf71a4JOGJE/dMvDLi//Q0OPBDe9a5iQq2bbiou/a25ZkM+rsfraZl5G7BTJ+2PA3t2ccxMYBjNWiFJ0tDX1RI2mS+2t0yvVWZxa+Kxx8Kzz8KJJ8JHPwprNHaZZGdelyRJPWpr63ldwHqXuGm4RYtgv/3gve+FV70KbrkFPvnJhocqMFhJkqQ61LOQcr1L3DRMJnz3uzBpErS3wze/Cb/5TRGuBkjjo5skSRr06llIuau5rQbEvffC+94H8+bBHnvAWWfBy1424GXYYyVJknpUz0LKTZnDasUKOPVU2HZbuP56OOMMuPLKpoQqMFhJkqQ6zJwJo0Z1/f6MGU0YuP6nP8Huu8OHPgRvfjPMn19M/DmiefHGYCVJkno0bRqcc06xDmCHjjUBMwc4VC1bBiedBDvsUISp2bPhkktgyy17PrbBHGMlSZLqMm1aC6wBeMcdcPjhcMMNcMABRaIbO7bJRb3IHitJktT6XngBvvAF2HnnYqD6j38MP/95S4UqMFhJkqQ67LXXyrOtr7NOMbfVgLjpJthll2LG9AMPhAULipnUIwaogPoZrCRJUrf22quYxaDWc88V8282NFw99xx86lPw2tfCo4/ChRfCD38Io0c38EP7x2AlSZK6tWqo6rBiRX0Th/bJ738PO+0EX/5ykeDmz4f992/Qh1XHYCVJkvqsnolDe+XZZ+F//gd2263YvuwyOPts2Hjjij+oMbwrUJIkdamnS331TBxat/Z2OPJI+POfixWfTzwRNtigwg9oPHusJElSp9raiqtwXRkxopg4tN+efroIUnvsUbxub4fvfGfQhSowWEmSpC68//3FOKqunHtuBfNa/epXxXI0Z5xRXAK87bZiFvVBymAlSZJWM2kSLFnS/T79ClX/+Accdhjssw+stx5cey2cfDKsu24/Ttp8BitJkrSSo48uporqzsiR/fiAiy6CiRPhBz8oplO4+WZ4/ev7ccLW4eB1SZK0kjPP7Hmf6dP7cOJHHy0WTD7/fNh++2J9v5137sOJWpc9VpIk6V/a2rofV9WhV4suZxZL0EycCD/7WbE0zQ03DLlQBfZYSZKkGvVM+DljRi9OuHhxcW3xwguLZWnOPrsYrD5E2WMlSZL+pacJP2fMqLO3KhNmzy56qS67DE46CX73uyEdqsBgJWmIi4izI+KRiLijpm2TiLgiIu4pnzeuee/4iFgYEXdHxN7NqVpqnu4m/Nx66zpD1QMPwL77wqGHFkHq1lvhYx+DNYb+hTKDlaSh7vvAPqu0HQfMy8wJwLzyNRExEZgKTCqPOS0i+nPvkzTozJwJo0at3r7mmnVMBrpiRTEf1aRJ8JvfwKmnwtVXwyte0ZBaW5HBStKQlpnXAH9fpXl/YHa5PRs4oKb9/Mx8PjPvBRYCuw5EnVKrmDYNzjkHNt30xbZNNy2GRnU7b9Wf/wx77VVcK3zta+H22+EDHyimZx9GhtdPK0mFMZm5GKB83rxsHwc8WLPforJtNRExPSJujIgbH3300YYWKw20adPgsceKYVKZxXaXoWr5cjjllGL6hJtugu9+F664ArbZZkBrbhUGK0l6UXTSlp3tmJmzMnNyZk4ePXp0g8uSWtRdd8Gb3gQf/jBMmQLz5xeLKEdn/ysNDwYrScPRwxExFqB8fqRsXwRsWbPfFsBDA1yb1HRHH12MM48ono8+epUdli2DE0+EHXeEu+8uZlC/+GLYYotmlNtSDFaShqO5wCHl9iHARTXtUyNirYjYBpgAXN+E+qSmmTQJTj+9uMIHxfPpp9eEq9tug9e9Do4/Hvbbr1j75uCDh3UvVa2hf9+jpGEtIn4ETAE2i4hFwGeBE4E5EXEE8ABwEEBmzo+IOcACYBlwTGYub0rhUhOMGwcPddFHe86ZL3Da5l8qbg3cZBP4yU/gwAMHtsBBwGAlaUjLzHd38daeXew/E+jppnJpyOkuVL2GGzl7xeHw+duL3qlvfnPl2wb1L14KlCRpmNtrr85D1dr8ky9zHNfxWjblcfjFL4rxVIaqLhmsJEkaxtraYN681dvfwLX8kZ04jq9wNofz9cPnF2Oq1C0vBUqSNIwdeeTKr9flGb7Ep/ggp/IAW/FWLufXI9/Ksu81p77BxmAlSdIwNW4cPPfci6/34CrO4khexr2cygc4ni/zDOtz3uyuz6GVeSlQkqRh6OijXxxXtSFPcgZHcRV7spyRvIlr+BCnFqHqvB6WstFKDFaSJA0jbW3F8n2nn1683odfcgfbciRncRIfYwdu5be8CSiW/TNU9Y6XAiVJGiZqp1TYmL/zDT7CIZzLHUziv/gZN6yy5vhppzWhyEHOYCVJ0jCw5pqwdGmxfQAXcDoz2JTH+QKfZiYn8AJrrbT/jBlNKHIIMFhJkjTEjRtXhKrRPMKpfJD/Zg43sxP7cBm3suNq+++5p71VfeUYK0mShrBikHoylR+xgIkcwIV8ipm8lus6DVURcOWVA1/nUGGPlSRJQ1RbG1x4+kNcyAz2Zy5/4LUcztncycQuj3n/+wewwCHIYCVJ0lCUyXVHncMC/oe1eJ7/4WRO4VhWMLLLQ2bM8BJgfxmsJEkaau6/nxt3fh/feuYKrmZ3juQsFjKh01333NNLf1UyWEmSNFSsWAFnnMGzH/okr1qeHM13OIP3k10Mqc4c4PqGAYOVJElDwcKFcMQRcM01/Ja3Mp1Z3M/4Lnef2PUwK/WDdwVKkjSYLV8OX/86bL893HornH02e/OrbkMVwPz5A1PecGOwkiRpsFqwAHbbDT76UdhrL1iwgKNvOAyIbg9bb72BKW84MlhJkjTYLF0KX/oS7LRTcQnwhz+Eiy6Cl76UM87o+fAzz2x8icNVj8EqIraMiF9HxJ0RMT8iji3bN4mIKyLinvJ545pjjo+IhRFxd0Ts3cgfQJKkYeWWW2DXXeGEE+CAA4peq3e/GyJoa+t5QPp557mwciPV02O1DPhoZr4aeB1wTERMBI4D5mXmBGBe+ZryvanAJGAf4LSI6HrSDEmS1LPnn4fPfAZ22QUWL4af/Qx+/GPYfPN/7XLssd2fYsYMQ1Wj9RisMnNxZt5cbj8N3AmMA/YHZpe7zQYOKLf3B87PzOcz815gIayyXLYkSarf9dfDa14DX/wi/L//V/RSvfOdK+0yaRI8/nj3p3Hyz8br1RiriBgP7ARcB4zJzMVQhC+gIzKPAx6sOWxR2SZJknrjn/+Ej38cXv96ePJJuOQSmD0bNtlkpd3WXbfIWt2J7sezqyJ1z2MVEesDPwM+nJlPRdf/hTp7Y7UrvhExHZgOMGbMGNrb2+sthTHrwEe3W1b3/r3Rmzp6Y8mSJQ07d6NY88CwZkmd+s1vinmp7rkHjjoKvvpV2HDD1XYbN67IXz1xDcCBUVewiohRFKGqLTN/XjY/HBFjM3NxRIwFHinbFwFb1hy+BfDQqufMzFnALIDJkyfnlClT6i761LaLOPn2xsxtet+0+uvojfb2dnrzM7YCax4Y1ixpJUuWwPHHw7e/DdtsU6w3s+eeq+129NFw+un1n9bLgAOjnrsCA/gecGdmfr3mrbnAIeX2IcBFNe1TI2KtiNgGmABcX13JkiQNUVdeCdttB9/5TjES/fbbKwlVGjj1dPvsBrwHuD0ibinbPgWcCMyJiCOAB4CDADJzfkTMARZQ3FF4TGYur7pwSZKGjCefhI99DM46C17xiuIy4G67dbl7b0PVjBn9rE916zFYZeZv6XoK19VjdHHMTGBmP+qSJGl4uPjiYgDU4sXwyU/CZz8L66zT5e5tbb07/YwZXgYcSC7CLElSMzz+OHz4w8WMndtuCxdcUMxR1YMjj6zv9C99Kfz1r/0rUb3nkjaSJA20n/4UJk6E888veqhuuqmuUHX00fDccz2ffp11DFXNYrCSJGmgPPwwHHggHHQQbLFFEag+9zlYc826Dp81q+d9Jk6EZ5/tX5nqO4OVJEmNlllc8ps4sRhT9eUvw3XXwfbb9+o0y3u4FWzPPWH+/H7UqX5zjJUkSY20aFExOP2SS4oZ1M8+G171ql6fpp5B61de2Yf6VCl7rCRJaoTMYvqESZPgqqvgm98splHoY6g6+ODu95k4sW9lqlr2WEmSVLV774X3vQ/mzYMpU4qA9fKX9/l0PYUq8BJgq7DHSpKkqqxYUSxFs912cP31cMYZRbjqR6gaN67C+tRw9lhJklSFP/2pWDT5t7+FffaBM8+Erbbq1ymPPhoeWm213dWNHNmvj1GF7LGSJKk/li2Dk06CHXaAO+6A738fLr2036Gqra3+pWtmz+7XR6lC9lhJktRXd9wBhx8ON9wA++9fJKGxY/t92jXXhKVL69v3vPNg2rR+f6QqYrCSJKm3li6FE0+EL34RXvKSYgb1d70LoquldXs2blx9l/1qZfb549QgBitJknrj5puLXqpbb4WpU+Fb34LRo/t8unXXhX/+s/fH7blnnz9SDeQYK0mS6vHcc3DCCbDrrvDII3DhhfCjH/U6VLW1FR1bHY++hKoRI5wMtFXZYyVJUk/+8Ieil+rOO+Gww+Dkk2Hjjes6dK+9ihkXqtTT0jZqHoOVJEldefZZ+PSn4RvfKBZNvuwy2Hvvug/vx5CrLp13XvXnVHUMVpIkdebqq4t5qf78Z5gxoxisvuGGdR9edaiKgB/8wDsAW53BSpKkWk8/DZ/8ZDF1wsteBr/+dbEsTZ16M1VCPUaNghdeqO58aiwHr0uS1OHyy2HbbYulaD7yEbjttrpD1dFHF71KVYWqPfcsplMwVA0uBitJkv7xj2Jw+t57F/MfXHstfP3rsN56dR1+9NH1z5Lek/POKwKVd/0NTl4KlCQNb3PnwvvfX0yh8KlPFYPV1167rkPb2uDgg/v38V7qG1oMVpKk4emxx+BDHyrmotp+e7j4Yth55x4P6+8YqnXWKW421NDkpUBJ0vCSCXPmwMSJ8NOfwhe+UKz1t0qo2muvlSfy7Hj0J1TtuaehaqgzWEmSho/Fi+G//gv++79h/PhieZpPf7rohipNmlQEqKon9Zwxw3FTw4HBSpI09GXCuecWqenSS+GrX4Xf/Q623Xa1JWYWLKj2o2fMKD7+tNOqPa9ak8FKkjQkdQSmLeNBLh3xNjjkEH77j4m88vlbiU98nBi1BhH9H3zenRkzDFTDjYPXJUmraWuDY4+Fxx9vdiX9kbyP7/I1PsZIlvNBvsV3OIYcoD4FQ9XwZLCSJAHFXExnnFFcthrstuEvnMWRvIVfM4+3cCRncR/bNPxzR4xwgeThzkuBkjTMtbXBWmsVE1wO9lA1guV8iFO4ne2YzI28j1nsxZUNDVUdE3pmGqpkj5UkDWttbXDYYdWubdcsr+QuvscR7MbvuIR9OYoz+StbVHb+jTYqJmiXumOPlSStIiL2iYi7I2JhRBzX7Hoa6YQTBn+oGskyPsFXuIUdeTV3cjA/YD8urixUbbRR0RtlqFI9DFaSVCMiRgLfAf4DmAi8OyIm9ve8bW3FtEkjRhTPbW3177dq29FHv/h6s82KR+12BKyxRvE8YkTnk1x2PO6/v78/WXNty+38gdfxFY7jEt7GRBbQxsFA9Pmco0a9eGnPQKXe8lKgJK1sV2BhZv4FICLOB/YH+jy7UVsbTJ/+4ozb999fvAaYNq37/Q47rAhAHWvJ3X//yov91t61V7vdMdZnsI+Z6sooXuB4vswJzOQJNuJAfsLPOLDbY1xKRgPBHitJWtk44MGa14vKtj474YTVv9CffbZo72m/pUubv0DvppuuPEC76Y8bb+KF7SbzeT7HmtPexeaPLuCneWCPxxmqNBAMVpK0ss6uIa3W7xMR0yPixoi48dFHH+32hA88UF97V/s1Q22QeuyxlXvWmua55+C44+C1ry265+bOLQrdbLNmVyb9i8FKkla2CNiy5vUWwEOr7pSZszJzcmZOHj16dLcn3Gqr+tq72m+gbb11iwSpWr/7Hey4I3zlK3DooTB/Pvznfza7Kmk1BitJWtkNwISI2CYi1gSmAnP7c8KZM2HddVduW3fdor2n/UaNWml94IZbc83V62qqZ56BD38Y3vjGosfq8svhrLOKW/WkFmSwkqQambkM+ADwK+BOYE5mzu/POadNg1mzip6giOJ51qzVe4U62++cc+Dss1dumzHjxdebblo8arcBRo4snqMXN8dtumnxWS3TW/XrX8P228MppxS3Qt5+O7z1rc2uSuqWdwVK0ioy81Lg0irPOW1afYGlq/1aJuwMhKeegk98As48E/7t3+Dqq2H33ZtdlVQXe6wkSa3jl7+ESZPgu9+Fj30Mbr3VUKVBxWAlSWq+v/+9GJS+776w4YbFYPWTTlp90JnU4gxWkqTmuuCCopfqvPPgf/8Xbr65mFJBGoQcYyVJao5HHoEPfhDmzCmmUrj0Uthpp2ZXJfWLPVaSpIGVCeefX/RSXXgh/N//wfXXG6o0JNhjJUkaOA89VEydcNFFsOuuxfwOkyY1uyqpMvZYSZIaL7OYlGviRPjVr+Dkk4sB6oYqDTH2WEmSGuv++2H69GLW9N13L2ZOnzCh2VVJDdFjj1VEnB0Rj0TEHTVtm0TEFRFxT/m8cc17x0fEwoi4OyL2blThkqQWt2IFnH46bLstXHstfOc7xWzqhioNYfX0WH0f+DZwbk3bccC8zDwxIo4rX38yIiZSrKs1CXgpcGVEvCIzl1dbduOMP+6Shpz3o9stY0pDzixJLWjhQjjyyGLW9Le+tVirZ/z4ZlclNVyPPVaZeQ3w91Wa9wdml9uzgQNq2s/PzOcz815gIbBrNaVKklre8uXwjW8Ua/zdcgt873vFmCpDlYaJvo6xGpOZiwEyc3FEbF62jwP+ULPforJNkjTU3XknHH44/OEPsN9+cMYZMM6vAA0vVQ9e72wd9ex0x4jpwHSAMWPG0N7eXveHjFmnuLQ2mIxZh179jK1gyZIl1jwArFmD3tKl8LWvwec+BxtsAG1t8O53Q3T2lSANbX0NVg9HxNiyt2os8EjZvgjYsma/LYCHOjtBZs4CZgFMnjw5p0yZUveHn9p2ESffPrhuaPzodst4Vy9+xlbQ3t5Ob/67tAJrHhiDsWY1yC23FL1Uf/wjHHQQfPvbsPnmPR4mDVV9ncdqLnBIuX0IcFFN+9SIWCsitgEmANf3r0RJUst5/nn4zGdgl12KST9/+tNiaRpDlYa5Hrt9IuJHwBRgs4hYBHwWOBGYExFHAA8ABwFk5vyImAMsAJYBxwymOwIlSXW4/vqil2r+fHjve4vB6pts0uyqpJbQY7DKzHd38daeXew/E5jZn6IkSS3on/+Ez362mDX9pS+FSy6BffdtdlVSSxlcA5UkSc3x298WvVT33FPMov7Vr8JLXtLsqqSW41qBkqSuLVkCH/pQsRTN0qVw5ZVw5pmGKqkLBitJUufmzYPttivu9PvgB+H222HPTkeBSCoZrCRJK3vyyeJy3157wZprwjXXwCmnwPrrN7syqeUZrCRJL7rkEpg0qViK5hOfKOapeuMbm12VNGgYrCRJ8Pjj8J73FEvRbLRRsSzNV74C66zT7MqkQcVgJUnD3c9+BhMnwvnnF5N+3nRTMfGnpF5zugVJGq4efhg+8IFi1vSdd4bLL4cddmh2VdKgZo+VJA03mcVCyRMnwty58OUvw3XXGaqkCthjJUnDyV//Cu9/P1x8Mbz+9cUg9Ve/utlVSUOGPVaSNBxkFiFq4sRifqpvfAN+8xtDlVQxe6wkaai77z543/uKWdOnTIGzzoKXv7zZVUlDkj1WkjRUrVhRzJq+7bbF9Amnn170VhmqpIaxx0qShqJ77oEjjigu9+29N8yaBVtt1eyqpCHPHitJGkqWL4evfQ22375Y2+/734df/tJQJQ0Qe6wkaaiYPx8OPxyuvx7237+49Dd2bLOrkoYVe6wkabBbuhT+7/9gp53gL38pZlC/4AJDldQE9lhJ0mD2xz8WvVS33AJTp8K3vgWjRze7KmnYssdKkgaj55+H//3fYk2/v/2t6KH60Y8MVVKT2WMlSYPNddfBYYfBnXfCoYfC178OG2/c7KokYY+VJA0ezz4LH/sYvOENsGRJcbffOecYqqQWYo+VJA0G11xTzEu1cCHMmAEnnggbbtjsqiStwh4rSWplTz8NxxwDb35zMZP6VVfBaacZqqQWZbCSpFZ1+eXFcjSnnw4f+QjcdhvssUezq5LUDYOVJLWaJ54oLvvtvTesuy5ce20xQH299ZpdmaQeGKwkqZXMnQuTJsHs2XD88cU8Va9/fbOrklQng5UktYLHHoNp04qlaDbbrJhS4UtfgrXXbnZlknrBYCVJzZQJP/kJTJxYPH/+83DDDfCa1zS7Mkl94HQLktQsf/tbccffz38OkyfDvHmw3XbNrkpSP9hjJUkDLRPOPbfopbrkEvjKV+D3vzdUSUOAPVaSNJAefBCOOqqYNf0Nb4Czz4ZXvrLZVUmqiD1WkjRQfvCD4o6/q6+GU04pZlM3VElDij1WkjRQli+HXXaB734XXvayZlcjqQEMVpI0UA45pHhENLsSSQ1isBpA44+7pGHnvu/EtzXs3JIqYqCShjzHWEmSJFXEYCVJklQRg5UkSVJFDFaSJEkVMVhJkiRVxGAlSZJUEYOVpCEpIg6KiPkRsSIiJq/y3vERsTAi7o6IvWvaXxMRt5fvfSvC+REk9Y7BStJQdQfwTuCa2saImAhMBSYB+wCnRcTI8u3TgenAhPKxz4BVK2lIMFhJGpIy887MvLuTt/YHzs/M5zPzXmAhsGtEjAU2zMzfZ2YC5wIHDFzFkoYCg5Wk4WYc8GDN60Vl27hye9X2TkXE9Ii4MSJufPTRRxtSqKTBxyVtJA1aEXEl8P918tYJmXlRV4d10pbdtHcqM2cBswAmT57c5X6ShheDlXrkGodqVZm5Vx8OWwRsWfN6C+Chsn2LTtolqW5eCpQ03MwFpkbEWhGxDcUg9eszczHwdES8rrwb8L1AV71ektSphvVYRcQ+wCnASOCszDyxUZ+lwavq3rCPbreMQ8tz2hs2vEXEO4BTgdHAJRFxS2bunZnzI2IOsABYBhyTmcvLw2YA3wfWAX5ZPiSpbg0JVuWty98B3krRvX5DRMzNzAWN+DxJWlVmXgBc0MV7M4GZnbTfCGzb4NIkDWGN6rHaFViYmX8BiIjzKW5xNlhJTVRVD2Ftz2AHewglqXHBqrPbmV/boM8SjRlg3tmXpyRJ6loU8+BVfNKIg4C9M/PI8vV7gF0z84M1+0ynmOEY4JVAZxP5dWUz4LGKyh0o1jwwrHlgVFHz1pk5uopimi0iHgXu7+dpWvHvQavV1Gr1QOvVZD09a+jvr0b1WHV1O/O/1M4B01sRcWNmTu55z9ZhzQPDmgfGYKy5kaoIiK34Z9pqNbVaPdB6NVlPzxpdU6OmW7gBmBAR20TEmhTrcs1t0GdJkiS1hIb0WGXmsoj4APAriukWzs7M+Y34LEmSpFbRsHmsMvNS4NIGnb5PlxCbzJoHhjUPjMFYc6trxT/TVqup1eqB1qvJenrW0JoaMnhdkiRpOHJJG0mSpIoMqmAVEftExN0RsTAijmt2PR0i4uyIeCQi7qhp2yQiroiIe8rnjWveO778Ge6OiL2bVPOWEfHriLgzIuZHxLGtXndErB0R10fErWXNn2/1mmvqGBkRf4yIiwdDzRFxX0TcHhG3RMSNg6HmoSAivhgRt5V/7pdHxEubXM9JEXFXWdMFEbFRM+spazqo/P9/RUQ07W6zVvs+6ux7qJm6+o5pYj2dfn80RGYOigfFIPg/Ay8D1gRuBSY2u66ytt2BnYE7atq+ChxXbh8HfKXcnljWvhawTfkzjWxCzWOBncvtDYA/lbW1bN1AAOuX26OA64DXtXLNNbX/D/BD4OJB8vfjPmCzVdpauuah8AA2rNn+EHBGk+v5d2CNcvsrHf/Nm1zTqynmPmwHJjephpb7Purse6jJ9XT6HdPEejr9/mjEZw2mHqt/LZOTmS8AHcvkNF1mXgP8fZXm/YHZ5fZs4ICa9vMz8/nMvBdYSPGzDajMXJyZN5fbTwN3UsyY37J1Z2FJ+XJU+UhauGaAiNgCeBtwVk1zS9fchcFY86CSmU/VvFyP4u9302Tm5Zm5rHz5B4o5CZsqM+/MzN5MKN0ILfd91MX3UNN08x3TrHq6+v6o3GAKVp0tk9O0/0h1GJOZi6H4CwZsXra33M8REeOBnSgSfEvXXV5SuwV4BLgiM1u+ZuCbwCeAFTVtrV5zApdHxE3lKgnQ+jUPCRExMyIeBKYBn2l2PTUOB37Z7CJahH/ne2GV75hm1tHZ90flBlOwik7aBuMtjS31c0TE+sDPgA+v8q/l1XbtpG3A687M5Zm5I8W/nHeNiG272b3pNUfEfsAjmXlTvYd00taMvx+7ZebOwH8Ax0TE7t3s2yo1DwoRcWVE3NHJY3+AzDwhM7cE2oAPNLuecp8TgGVlTQ1XT01N5t/5OvXiO6bhevn90WcNm8eqAXpcJqfFPBwRYzNzcUSMpUjI0EI/R0SMovgL35aZPy+bW75ugMx8IiLagX1o7Zp3A94eEfsCawMbRsR5tHbNZOZD5fMjEXEBxaWPlq55sMjMverc9YfAJcBnG1hOj/VExCHAfsCeWQ5QabRe/Bk1i3/n69DFd0zTrfL9Uflg/8HUYzXYlsmZCxxSbh8CXFTTPjUi1oqIbYAJwPUDXVxEBPA94M7M/HrNWy1bd0SM7rgrKSLWAfYC7mrlmjPz+MzcIjPHU/ydvSozD27lmiNivYjYoGObYgDzHa1c81ARERNqXr6d4u9300TEPsAngbdn5rPNrKXFDLbvowHXzXdMs+rp6vujeo0eiV/lA9iX4s6CPwMnNLuemrp+BCwGllL8S+YIYFNgHnBP+bxJzf4nlD/D3cB/NKnmN1J0Xd8G3FI+9m3luoHtgT+WNd8BfKZsb9maV6l/Ci/eFdiyNVPc6XRr+Zjf8f9aK9c8VB4U/7q/o/w7/gtgXJPrWUgxlqjjd0RT71Isa3pH+Xv2eeBh4FdNqqOlvo86+x5qcj2dfsc0sZ5Ovz8a8XDmdUmSpIoMpkuBkiRJLc1gJUmSVBGDlSRJUkUMVpIkSRUxWEmSJFXEYCVJGhARsWlE3FI+/hYRfy23n4iIBQNcywERMbHm9RciotcTk0bE+IiofJLJXnz+p1Z5/bvyual1DWcGK0nSgMjMxzNzxyyWFTkD+Ea5vSMrr6VZiYjobnWRA4B/BavM/ExmXll1DQNgpWCVmW9oViEqGKwkSa1gZER8NyLmR8Tl5ezYRMTLI+KyckHw30TEq8r2rSNiXkTcVj5vVbZ/PyK+HhG/Br7S2fER8QaKme1PKnvMXl4ed2B5jl0i4ncRcWtEXB8RG5Q9QL+JiJvLR7cBJgrfjogFEXFJRFxac/77ImKzcntyubwKEbFr+bl/LJ9fWbYfGhE/L3+OeyLiq2X7icA65c/QVrYt6aSWkRFxUkTcUP55HVW2j42Ia8rj74iIN/Xzv6EYXGsFSpKGrgnAuzPzfRExB/gv4DxgFvD+zLwnIl4LnAa8Bfg2cG5mzo6Iw4FvUfRCAbwC2Cszl0fEvFWPz8y3RMRcipUQfgpQrMAC5RI1Pwb+OzNviIgNgX9SrI351sx8rlx66EfA5G5+nncArwS2A8YAC4Cze/gzuAvYPTOXlZclv1T+OUDRq7cTxYzzd0fEqZl5XER8oOz1684RwJOZuUtErAVcGxGXA++kmLl+ZkSMBNbt4Tyqg8FKktQK7s3MW8rtm4DxEbE+8AbgJx3BB1irfH49RTAA+AHw1Zpz/aQMVd0d35VXAosz8waAzHwK/rVu5rcjYkdgOUV4687uwI8ycznwUERc1cP+AC8BZpfBLYFRNe/Ny8wny1oWAFtTLDdUj38Htu/oMSs/ZwLFmodnR7FY8oU1f/7qB4OVJKkVPF+zvRxYh2K4yhN19MhAEUQ6PFM+9+b4DrHKuTp8hGJtwh3K8z7Xy5pqLePFoThr17R/Efh1Zr4jIsYD7TXvrfrn05vv7wA+mJm/Wu2NiN2BtwE/iIiTMvPcXpxXnXCMlSSpJZW9RfdGxEHwr3FLO5Rv/w6YWm5PA37by+OfBjbo5GPvAl4aEbuUx2xQDoJ/CUVP1grgPcDIHsq/Bphajm8aC+xR8959wGvK7f+qaX8J8Ndy+9Aezt9hadnj1J1fATM69ouIV0TEehGxNfBIZn4X+B6wc52fqW4YrCRJrWwacERE3ArMB/Yv2z8EHBYRt1EEnWN7efz5wMfLgeIv79g5M18A/hs4tTzmCopepdOAQyLiDxSXAZ+hexcA9wC3A6cDV9e893nglIj4DUXvU4evAl+OiGvpObh1mAXc1jF4vQtnUYzxujmKKRjOpOjxmgLcEhF/pAh4p9T5mepGZHbVUylJkqoQEd+nZrC8hi57rCRJkipij5UkSVJF7LGSJEmqiMFKkiSpIgYrSZKkihisJEmSKmKwkiRJqojBSpIkqSL/Pwkwcnve7kwlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(data,'Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "d4d390de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Logarthmic transformation\n",
    "data['log_fare'] = np.log1p(data['Fare'])\n",
    "plot_data(data,'log_fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b1cc894e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## the second returned argument i.e.parameters is the lambda that maximizes the log-likelihood function.\n",
    "data['BoxCox_Fare'],parameters = stats.boxcox(data['Fare']+1)\n",
    "plot_data(data,'BoxCox_Fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54b821b",
   "metadata": {},
   "source": [
    "And So on Feature Transformations can be applied to many of the features present in a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97ad292c",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}