Feature Engineering- Normalization And Standardization- Day 5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Probability Ratio Encoding\n",
    "1. Probability of Survived based on Cabin--- Categorical Feature\n",
    "2. Probability of Not Survived---1-pr(Survived)\n",
    "3. pr(Survived)/pr(Not Survived)\n",
    "4. Dictonary to map cabin with probability\n",
    "5. replace with the categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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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>Cabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived Cabin\n",
       "0         0   NaN\n",
       "1         1   C85\n",
       "2         1   NaN\n",
       "3         1  C123\n",
       "4         0   NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv',usecols=['Cabin','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Cabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived    Cabin\n",
       "0         0  Missing\n",
       "1         1      C85\n",
       "2         1  Missing\n",
       "3         1     C123\n",
       "4         0  Missing"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Replacing\n",
    "df['Cabin'].fillna('Missing',inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Missing', 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
       "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
       "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
       "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
       "       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
       "       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
       "       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
       "       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
       "       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n",
       "       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n",
       "       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n",
       "       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n",
       "       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n",
       "       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
       "       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
       "       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
       "       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
       "       'C148'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Cabin</th>\n",
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      "text/plain": [
       "   Survived Cabin\n",
       "0         0     M\n",
       "1         1     C\n",
       "2         1     M\n",
       "3         1     C\n",
       "4         0     M"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin']=df['Cabin'].astype(str).str[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_df=df.groupby(['Cabin'])['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.466667</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.744681</td>\n",
       "    </tr>\n",
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       "      <th>C</th>\n",
       "      <td>0.593220</td>\n",
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       "      <th>D</th>\n",
       "      <td>0.757576</td>\n",
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       "      <th>E</th>\n",
       "      <td>0.750000</td>\n",
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       "      <td>0.615385</td>\n",
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       "      <td>0.299854</td>\n",
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       "      <th>T</th>\n",
       "      <td>0.000000</td>\n",
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      "text/plain": [
       "       Survived\n",
       "Cabin          \n",
       "A      0.466667\n",
       "B      0.744681\n",
       "C      0.593220\n",
       "D      0.757576\n",
       "E      0.750000\n",
       "F      0.615385\n",
       "G      0.500000\n",
       "M      0.299854\n",
       "T      0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_df=pd.DataFrame(prob_df)\n",
    "prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_df['Died']=1-prob_df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th>A</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.533333</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.744681</td>\n",
       "      <td>0.255319</td>\n",
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       "      <th>C</th>\n",
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       "      <th>D</th>\n",
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      "text/plain": [
       "       Survived      Died\n",
       "Cabin                    \n",
       "A      0.466667  0.533333\n",
       "B      0.744681  0.255319\n",
       "C      0.593220  0.406780\n",
       "D      0.757576  0.242424\n",
       "E      0.750000  0.250000"
      ]
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     "metadata": {},
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   "source": [
    "prob_df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <th>A</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.875000</td>\n",
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       "      <th>B</th>\n",
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       "      <td>0.255319</td>\n",
       "      <td>2.916667</td>\n",
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       "      <th>D</th>\n",
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       "      <td>0.750000</td>\n",
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      ],
      "text/plain": [
       "       Survived      Died  Probability_ratio\n",
       "Cabin                                       \n",
       "A      0.466667  0.533333           0.875000\n",
       "B      0.744681  0.255319           2.916667\n",
       "C      0.593220  0.406780           1.458333\n",
       "D      0.757576  0.242424           3.125000\n",
       "E      0.750000  0.250000           3.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_df['Probability_ratio']=prob_df['Survived']/prob_df['Died']\n",
    "prob_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_encoded=prob_df['Probability_ratio'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.428274</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1.458333</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1.458333</td>\n",
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      ],
      "text/plain": [
       "   Survived Cabin  Cabin_encoded\n",
       "0         0     M       0.428274\n",
       "1         1     C       1.458333\n",
       "2         1     M       0.428274\n",
       "3         1     C       1.458333\n",
       "4         0     M       0.428274"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin_encoded']=df['Cabin'].map(probability_encoded)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Cabin</th>\n",
       "      <th>Cabin_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>E</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>0.428274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived Cabin  Cabin_encoded\n",
       "0          0     M       0.428274\n",
       "1          1     C       1.458333\n",
       "2          1     M       0.428274\n",
       "3          1     C       1.458333\n",
       "4          0     M       0.428274\n",
       "5          0     M       0.428274\n",
       "6          0     E       3.000000\n",
       "7          0     M       0.428274\n",
       "8          1     M       0.428274\n",
       "9          1     M       0.428274\n",
       "10         1     G       1.000000\n",
       "11         1     C       1.458333\n",
       "12         0     M       0.428274\n",
       "13         0     M       0.428274\n",
       "14         0     M       0.428274\n",
       "15         1     M       0.428274\n",
       "16         0     M       0.428274\n",
       "17         1     M       0.428274\n",
       "18         0     M       0.428274\n",
       "19         1     M       0.428274"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Transformation of Features\n",
    "\n",
    "Why Transformation of Features Are Required?\n",
    "\n",
    "1. Linear Regression---Gradient Descent ----Global Minima\n",
    "2. Algorithms like KNN,K Means,Hierarichal Clustering--- Eucledian Distance\n",
    "\n",
    "Every Point has some vectors and Directiom\n",
    "\n",
    "Deep Learning Techniques(Standardization, Scaling)\n",
    "1.ANN--->GLobal Minima, Gradient\n",
    "2.CNN\n",
    "3.RNN\n",
    "\n",
    "0-255 pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Types Of Transformation\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",
    "   Logarithmic Transformation\n",
    "   Reciprocal Trnasformation\n",
    "   Square Root Transformation\n",
    "   Exponential Trnasformation\n",
    "   Box Cox Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Standardization\n",
    "We try to bring all the variables or features to a similar scale. standarisation means centering the variable at zero.\n",
    "z=(x-x_mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('titanic.csv', usecols=['Pclass','Age','Fare','Survived'])\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].fillna(df.Age.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Age         0\n",
       "Fare        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### standarisation: We use the Standardscaler from sklearn library\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "### fit vs fit_transform\n",
    "df_scaled=scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "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_scaled[:,1],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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([-2.22415608, -1.91837055, -1.61258503, -1.3067995 , -1.00101397,\n",
       "        -0.69522845, -0.38944292, -0.08365739,  0.22212813,  0.52791366,\n",
       "         0.83369919,  1.13948471,  1.44527024,  1.75105577,  2.05684129,\n",
       "         2.36262682,  2.66841235,  2.97419787,  3.2799834 ,  3.58576892,\n",
       "         3.89155445]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 13,
     "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_scaled[:,2],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([562., 170.,  67.,  39.,  15.,  16.,   2.,   0.,   9.,   2.,   6.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.]),\n",
       " array([-0.64842165, -0.13264224,  0.38313716,  0.89891657,  1.41469598,\n",
       "         1.93047539,  2.4462548 ,  2.96203421,  3.47781362,  3.99359303,\n",
       "         4.50937244,  5.02515184,  5.54093125,  6.05671066,  6.57249007,\n",
       "         7.08826948,  7.60404889,  8.1198283 ,  8.63560771,  9.15138712,\n",
       "         9.66716653]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 14,
     "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_scaled[:,3],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([562., 170.,  67.,  39.,  15.,  16.,   2.,   0.,   9.,   2.,   6.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.]),\n",
       " array([  0.     ,  25.61646,  51.23292,  76.84938, 102.46584, 128.0823 ,\n",
       "        153.69876, 179.31522, 204.93168, 230.54814, 256.1646 , 281.78106,\n",
       "        307.39752, 333.01398, 358.63044, 384.2469 , 409.86336, 435.47982,\n",
       "        461.09628, 486.71274, 512.3292 ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "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['Fare'],bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Min Max Scaling  (### CNN)---Deep Learning Techniques\n",
    "Min Max Scaling scales the values between 0 to 1.\n",
    "X_scaled = (X - X.min / (X.max - X.min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>0.014151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.472229</td>\n",
       "      <td>0.139136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.321438</td>\n",
       "      <td>0.015469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.434531</td>\n",
       "      <td>0.103644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.434531</td>\n",
       "      <td>0.015713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass       Age      Fare\n",
       "0       0.0     1.0  0.271174  0.014151\n",
       "1       1.0     0.0  0.472229  0.139136\n",
       "2       1.0     1.0  0.321438  0.015469\n",
       "3       1.0     0.0  0.434531  0.103644\n",
       "4       0.0     1.0  0.434531  0.015713"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max=MinMaxScaler()\n",
    "df_minmax=pd.DataFrame(min_max.fit_transform(df),columns=df.columns)\n",
    "df_minmax.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "        0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOpklEQVR4nO3cbYwd51nG8f9VO00LLdTBa8uyXWyQgTqItGUxEQHUNog4KcJBaiQXaK0qkoUIqEhI1OkHKoQspV9QhSCqrBDVCKhltSkxLRQsl1BQX9wN5M1JTZakOCtb8TYFSosUZPfmw07Fqb2bHe+es5vz+P+TrJl55pkz961dXTuec86kqpAkteUVq12AJGn4DHdJapDhLkkNMtwlqUGGuyQ1aO1qFwCwfv362rZt22qXIUlj5eGHH/5qVU3Mt+9lEe7btm1jampqtcuQpLGS5N8X2udtGUlqkOEuSQ3qFe5JvpLk8SSPJJnqxq5LcjzJ091y3cD8u5NMJzmd5JZRFS9Jmt+VXLm/tareWFWT3fYB4ERV7QBOdNsk2QnsBa4HdgP3JlkzxJolSYtYzm2ZPcDhbv0wcPvA+JGqerGqngWmgV3LOI8k6Qr1DfcC/i7Jw0n2d2Mbq+ocQLfc0I1vBp4bOHamG/sOSfYnmUoyNTs7u7TqJUnz6vtRyJuq6mySDcDxJF9+ibmZZ+yyR09W1SHgEMDk5KSPppSkIep15V5VZ7vleeATzN1meT7JJoBueb6bPgNsHTh8C3B2WAVLkha3aLgn+e4kr/32OvDzwBPAMWBfN20f8GC3fgzYm+TaJNuBHcDJYRcuSVpYn9syG4FPJPn2/L+oqk8n+RJwNMmdwBngDoCqOpXkKPAkcAG4q6oujqR6SXoZ2HbgU0s+9iv3vH2Ilfy/RcO9qp4Bbphn/AXg5gWOOQgcXHZ1kqQl8RuqktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUoN7hnmRNkn9J8slu+7okx5M83S3XDcy9O8l0ktNJbhlF4ZKkhV3Jlft7gacGtg8AJ6pqB3Ci2ybJTmAvcD2wG7g3yZrhlCtJ6qNXuCfZArwduG9geA9wuFs/DNw+MH6kql6sqmeBaWDXcMqVJPXR98r9Q8DvAN8aGNtYVecAuuWGbnwz8NzAvJlu7Dsk2Z9kKsnU7OzsFRcuSVrYouGe5BeA81X1cM/XzDxjddlA1aGqmqyqyYmJiZ4vLUnqY22POTcBv5jkNuBVwPck+TPg+SSbqupckk3A+W7+DLB14PgtwNlhFi1JemmLXrlX1d1VtaWqtjH3RulnqupXgWPAvm7aPuDBbv0YsDfJtUm2AzuAk0OvXJK0oD5X7gu5Bzia5E7gDHAHQFWdSnIUeBK4ANxVVReXXakkqbcrCveqegh4qFt/Abh5gXkHgYPLrE2StER+Q1WSGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhq0aLgneVWSk0keTXIqye9149clOZ7k6W65buCYu5NMJzmd5JZRNiBJulyfK/cXgbdV1Q3AG4HdSW4EDgAnqmoHcKLbJslOYC9wPbAbuDfJmlEUL0ma36LhXnO+0W1e0/0rYA9wuBs/DNzere8BjlTVi1X1LDAN7Bpq1ZKkl9TrnnuSNUkeAc4Dx6vqi8DGqjoH0C03dNM3A88NHD7TjV36mvuTTCWZmp2dXU4PkqRL9Ar3qrpYVW8EtgC7kvzoS0zPfC8xz2seqqrJqpqcmJjoV60kqZcr+rRMVf0n8BBz99KfT7IJoFue76bNAFsHDtsCnF12pZKk3vp8WmYiyeu69VcDPwd8GTgG7Oum7QMe7NaPAXuTXJtkO7ADODnswiVJC1vbY84m4HD3iZdXAEer6pNJPg8cTXIncAa4A6CqTiU5CjwJXADuqqqLoylfkjSfRcO9qh4D3jTP+AvAzQsccxA4uOzqJElL4jdUJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoPWrnYBw7DtwKeWfOxX7nn7ECuRpJcHr9wlqUGGuyQ1aNFwT7I1yd8neSrJqSTv7cavS3I8ydPdct3AMXcnmU5yOskto2xAknS5PlfuF4Dfrqo3ADcCdyXZCRwATlTVDuBEt023by9wPbAbuDfJmlEUL0ma36LhXlXnquqfu/X/Bp4CNgN7gMPdtMPA7d36HuBIVb1YVc8C08CuYRcuSVrYFd1zT7INeBPwRWBjVZ2DuT8AwIZu2mbguYHDZrqxS19rf5KpJFOzs7NXXrkkaUG9wz3Ja4CPA79VVV9/qanzjNVlA1WHqmqyqiYnJib6liFJ6qFXuCe5hrlg//OqeqAbfj7Jpm7/JuB8Nz4DbB04fAtwdjjlSpL66PNpmQB/AjxVVX8wsOsYsK9b3wc8ODC+N8m1SbYDO4CTwytZkrSYPt9QvQl4F/B4kke6sfcD9wBHk9wJnAHuAKiqU0mOAk8y90mbu6rq4tArlyQtaNFwr6p/Yv776AA3L3DMQeDgMuqSJC2D31CVpAY18eAwaZR8MJ3GkVfuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1aNFwT3J/kvNJnhgYuy7J8SRPd8t1A/vuTjKd5HSSW0ZVuCRpYX2u3D8C7L5k7ABwoqp2ACe6bZLsBPYC13fH3JtkzdCqlST1smi4V9Vnga9dMrwHONytHwZuHxg/UlUvVtWzwDSwa0i1SpJ6Wuo9941VdQ6gW27oxjcDzw3Mm+nGJEkraNhvqGaesZp3YrI/yVSSqdnZ2SGXIUlXt6WG+/NJNgF0y/Pd+AywdWDeFuDsfC9QVYeqarKqJicmJpZYhiRpPksN92PAvm59H/DgwPjeJNcm2Q7sAE4ur0RJ0pVau9iEJB8F3gKsTzIDfAC4Bzia5E7gDHAHQFWdSnIUeBK4ANxVVRdHVLskaQGLhntVvXOBXTcvMP8gcHA5RUmSlsdvqEpSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0MjCPcnuJKeTTCc5MKrzSJIuN5JwT7IG+GPgVmAn8M4kO0dxLknS5UZ15b4LmK6qZ6rqf4EjwJ4RnUuSdIm1I3rdzcBzA9szwE8OTkiyH9jfbX4jyellnG898NWlHJgPLuOsq2fJ/Y6xsex5mb9fY9nzMl11PeeDy+r5+xfaMapwzzxj9R0bVYeAQ0M5WTJVVZPDeK1xcLX1C/Z8tbDn4RnVbZkZYOvA9hbg7IjOJUm6xKjC/UvAjiTbk7wS2AscG9G5JEmXGMltmaq6kOQ3gL8F1gD3V9WpUZyrM5TbO2PkausX7PlqYc9DkqpafJYkaaz4DVVJapDhLkkNGptwX+xxBpnzh93+x5K8eTXqHKYePf9K1+tjST6X5IbVqHOY+j62IslPJLmY5B0rWd8o9Ok5yVuSPJLkVJJ/WOkah63H7/b3JvmrJI92Pb9nNeocliT3Jzmf5IkF9g8/v6rqZf+PuTdl/w34AeCVwKPAzkvm3Ab8DXOfsb8R+OJq170CPf8UsK5bv/Vq6Hlg3meAvwbesdp1r8DP+XXAk8Dru+0Nq133CvT8fuCD3foE8DXglatd+zJ6/lngzcATC+wfen6Ny5V7n8cZ7AH+tOZ8AXhdkk0rXegQLdpzVX2uqv6j2/wCc98nGGd9H1vxm8DHgfMrWdyI9On5l4EHquoMQFWNe999ei7gtUkCvIa5cL+wsmUOT1V9lrkeFjL0/BqXcJ/vcQablzBnnFxpP3cy95d/nC3ac5LNwC8BH17Bukapz8/5h4B1SR5K8nCSd69YdaPRp+c/At7A3JcfHwfeW1XfWpnyVsXQ82tUjx8YtkUfZ9Bzzjjp3U+StzIX7j890opGr0/PHwLeV1UX5y7qxl6fntcCPw7cDLwa+HySL1TVv466uBHp0/MtwCPA24AfBI4n+ceq+vqoi1slQ8+vcQn3Po8zaO2RB736SfJjwH3ArVX1wgrVNip9ep4EjnTBvh64LcmFqvrLlSlx6Pr+bn+1qr4JfDPJZ4EbgHEN9z49vwe4p+ZuSE8neRb4EeDkypS44oaeX+NyW6bP4wyOAe/u3nW+Efivqjq30oUO0aI9J3k98ADwrjG+ihu0aM9Vtb2qtlXVNuBjwK+PcbBDv9/tB4GfSbI2yXcx94TVp1a4zmHq0/MZ5v6nQpKNwA8Dz6xolStr6Pk1FlfutcDjDJL8Wrf/w8x9cuI2YBr4H+b+8o+tnj3/LvB9wL3dleyFGuMn6vXsuSl9eq6qp5J8GngM+BZwX1XN+5G6cdDz5/z7wEeSPM7cLYv3VdXYPgo4yUeBtwDrk8wAHwCugdHll48fkKQGjcttGUnSFTDcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP+D/Pazaz3GDzMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_minmax['Pclass'],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "        0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "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_minmax['Age'],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([562., 170.,  67.,  39.,  15.,  16.,   2.,   0.,   9.,   2.,   6.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.]),\n",
       " array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "        0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 20,
     "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_minmax['Fare'],bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Robust Scaler\n",
    "It is used to scale the feature to median and quantiles\n",
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0,1,2,3,4,5,6,7,8,9,10\n",
    "\n",
    "9-90 percentile---90% of all values in this group is less than 9\n",
    "1-10 precentile---10% of all values in this group is less than 1\n",
    "4-40%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass       Age      Fare\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"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "scaler=RobustScaler()\n",
    "df_robust_scaler=pd.DataFrame(scaler.fit_transform(df),columns=df.columns)\n",
    "df_robust_scaler.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 23,
     "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['Age'],bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([562., 170.,  67.,  39.,  15.,  16.,   2.,   0.,   9.,   2.,   6.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.]),\n",
       " array([-0.62600478,  0.48343237,  1.59286952,  2.70230667,  3.81174382,\n",
       "         4.92118096,  6.03061811,  7.14005526,  8.24949241,  9.35892956,\n",
       "        10.46836671, 11.57780386, 12.68724101, 13.79667816, 14.90611531,\n",
       "        16.01555246, 17.12498961, 18.23442675, 19.3438639 , 20.45330105,\n",
       "        21.5627382 ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 24,
     "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['Fare'],bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Guassian Transformation\n",
    "\n",
    "Some machine learning algorithms like linear and logistic assume that the features are normally distributed\n",
    "-Accuracy\n",
    "-Performance\n",
    "- logarithmic transformation\n",
    "- reciprocal transformation\n",
    "- square root transformation\n",
    "- exponential transformation (more general, you can use any exponent)\n",
    "- boxcox transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### fillnan\n",
    "df['Age']=df['Age'].fillna(df['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Age         0\n",
       "Fare        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stat\n",
    "import pylab "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### If you want to check whether feature is guassian or normal distributed\n",
    "#### Q-Q plot\n",
    "def plot_data(df,feature):\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.subplot(1,2,1)\n",
    "    df[feature].hist()\n",
    "    plt.subplot(1,2,2)\n",
    "    stat.probplot(df[feature],dist='norm',plot=pylab)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(df,'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logarithmic Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f/nKXX19C1fDhcMYZxTCuxx8fuKEKDFaStJJO1keVBobFi+FTnypWOt5llyJUbbXVcoe0tRVDrZ57rm8ftf76RaB69tmBHaYqGawkaWUdrY+6nIiYFBFzImLOY4891n+VSX3xxBOw++7w05/CMcfAJZfAuusud8iUKcWA9FxpMbrqTZ48OFqnOmKwkqQKleujdnVcZk7PzDGZOWbDDTfsp+qkPrjttmL6hOuugxkzioHqQ4e+tHvXXYsuv74MTm/v8mvFu/lqxWAlSctrXx/1PuBsYJdyfVSpdV14Iey0EyxcWEyl8LGPActPmzBrVu8u3d46lTm4uvw6Y7CSpApdrI8qtZ7MYh6DvfeG17++mPRzxx1payumOuhL69TGGxeXH8ytUx0xWEmSNBAtXAgHHghf/jJ89KNw7bW0zd6EIUNqM4bqwQdrV+pA4jxWktSJyvVRpZbywANFK9Vf/wrf/jZT7v08J63Z8aSfPXHGGXb1dcdgJUnSQHL99cWkny+8ABddxDZf2JPbb+/bJVdZBU47zVBVDbsCJUkaKE45Bd77XlhnHT644Z+JvfoWqlZZpWilWrzYUFUtg5UkSa1uyRI4+mg4/HDmrD2W9e65iYv/+YZeX26NNQxUvWWwkiSplT35ZLEczY9/zPS1PsuOT17KU6zXq0u1T53w/PMGqt5yjJUkSa3q9tth/HgW/esBjuAUTnvusF5dZuutYe7cGtc2SNliJUlSK7roIthxRx6/bwHvydmcRs9D1bhxRQuVoap2DFaSJLWSTDjhBJgwAV73OrZbOoc/s1OPLrHuusVlrrqqTjUOYgYrSZJaxcKFxeCnL32J3ww5gGG3/JEH2bRHlxg3Dv7znzrVJ4OVJEktYf58nnjju1l21tkcy7c4YGkbLzCs6tPPOMNWqv7g4HVJkprdDTfwyM77MCwXMp4LuYS9qj513XVtoepPtlhJktTMTjuNF98xlmdzODvy5x6FKrv9+p/BSpKkZrRkCXzuc3DYYfyRd7EDN3EHW1d9+uTJdvs1gl2BkiQ1m//8h2s2+gjvefFKfsSnOYbvs7QH/2RPngzTptWxPnXKYCVJUhN506p3cM6S8ezEPD7OrziVj1d9ruOpGs9gJUlSk9hryKX8MQ/kBdbgvVzNn9i5qvMMVM3DMVaSJDVaJl8c8h0uzL34J1uwPTdXHaoyDVXNxGAlSVIDbbTu85wx5GC+nV/kt+zPO7mOBxhZ1bmTJ9e5OPWYXYGSJDXI1q94kAuf2ZvtmcNxTOVbfAmI6s7d2gHqzcgWK0mS+llbG7w9bmTWM2N4PXcyngv4FsdRbaiaPNmFk5uVLVaSJPWjCDiYX3MNk3iQTdiVq7idbbo9zykUWoPBSpKkfjI0lvJdvsh/831msQsfZiZPsn6X5wwbVqy9rNZgsJIkqc6GDoW1lz3FxRzIB7iMH/MpjuH7LGHVLs8zVLUeg5UkSXU0dCi8dtldXMh4NudePsF0fsknuj0vsx+KU805eF2SpDpoayvGU71/2e+5kbezHv9hHLMMVQOcwUqSNOjtumsRgmr5OOig5Bi+x8Xsxb1szvbczHW8q8s6hgwxVLU6g5UkaVDbdVeYNau211ydF5jBIXyPz3MO+/FOruN+RnV6/KqrFoFq6dLa1qH+Z7CSJA1aU6bUPlRtxENcw3v4GKfzZb7BR/gNC1mr0+OHDYNFi2pbgxrHweuSpEFpyhQ46aTaXnN7buJ89mYdnmEfzuV89unyeO/6G3hssZIkDTr1CFUTOYNreTcvsjo7cUO3oeqMMwxVA5HBSpI0qNQ6VA1hKd/mC5zBwdzATmzPzdzGtl2ec8YZMHFi7WpQ87ArUJI0aKy5Jjz/fO2utw5PcxYHsge/52dM4TP8sNNJP4cMcXD6YGCLlSRpUOhpqBo2rLhTr9PHXf/H01u9nT1WuRJOPpmj8mcszlU7Pd5QNTgYrCRJA9Y227w8r1RPQ1WX458uvxx22AGeeKK4rfCII/pcqwYGg5UkaUDaZhu4/faen9dlqMqEE0+EPfaAUaPg5pvh3e/uU50aWAxWkqQBpy6h6oUX4LDD4JhjYJ994PrrYfTovpSpAchgJUkaUOoSqh5+GN77XpgxA772NZg5E4YP70uZGqC8K1CSNCCsthosXty7c7sMVTffXLRQ/ec/8LvfwX779bpGDXy2WEmSWl5fQlVmF6HqzDOLMVSrrAJ/+pOhSt0yWEmSWl5vQtWQIUWo6tDSpXDsscUsnjvsULRavfnNfapRg4PBSpLUknbd9eWpFHpi3Lhu5pV65hmYMAG+/W048ki48krYcMM+16vBwTFWklQhItYArgVWp/iN/F1mfrWxVWlFu+5aTB/VU+PGwVVXdXHA3XcXoeruu2HaNJg8udc1anAyWEnS8l4EdsnMBRGxKnBdRPw+M//c6MJU2GQTeOihnp+39dbdhKorr4QPfxiGDi1ejx3b2xI1iNkVKEkVsrCgfLtq+ehsJI76WV9C1dy5nezMhB/9CHbfHTbbrBhPZahSL9liJUkriIihwC3Aa4GfZeaNDS5JpZ6Gqk4Hp7d78cWiu+/UU4spFX79a+enUp/YYiVJK8jMpZm5HbApsENEvHHFYyJiUkTMiYg5jz32WP8XOYhssknvBqkPG9bNAf/+dzHp56mnwle+UsxRZahSHxmsJKkTmfkUMBvYvYN90zNzTGaO2dA7xuqmt11/3S6ifMstsP328Le/wW9/C1//ejH/gtRH3f4tiog1IuKmiPhbRMyNiK+X218ZEVdGxN3l83oV53wpIu6JiLsiYrd6/gEkqZYiYsOIWLd8PQzYFbizsVUNHpVTKET0PFS1T6XQZag6+2x45zuLIHX99fChD/WpZqlSNfG8/Q6ZNwPbAbtHxI7AscCszNwSmFW+JyK2Bg4AtqH4r7xp5XgFSWoFGwFXR8TfgZuBKzPz4gbXNCj0dgqFdt1OpbBsGRx/PBx4IIwZUwxS32673n+g1IFuB69nZgId3SEzARhbbp9B0Vz+xXL72Zn5InBvRNwD7ADcUMvCJakeMvPvwFsaXcdg1JdQNXRoN6HqmWfgoIPgoovgE5+An/60WAdHqrGq7grs6A6ZiBiRmQ8DZObDEfGq8vBNgMr5XuaX21a85iRgEsCIESOYPXv2Sp+7YMGCDrc3K+utr8FS7zHbLql9MaWu6mm171cDR1sbHHxw364xaVIXO//5Txg/Hu66qwhUU6b0fCS8VKWqglVmLgW2K8cdnNfRHTIVOvrbutINr5k5HZgOMGbMmBzbwZwhs2fPpqPtzcp662uw1HvosZfUvpjSfRPHdrqv1b7fShExBBiemc80uhb1TFtb0ZDUF5MnF5Okd2jWLNh//yJIXXEF7LJL3z5M6kaPboFY4Q6ZRyJiI4Dy+dHysPnAZhWnbQr04p4OSepcRJwZEetExFrA7cBdEfH5Rtelnjn++N6dd8YZxSD1zE5CVSb85Cew226w8cbFeCpDlfpBNXcFdnaHzIXAIeVhhwAXlK8vBA6IiNUjYnNgS+CmWhcuadDbumyh2hu4FBgJ9LFDSf2lfW6qefN6dl5EEaomTuzioEWLir7BT38a9twTbrgBXvOaPtUrVauarsCNgBnlOKshwMzMvDgibgBmRsThwP3A/gCZOTciZlL8F+QS4KiyK1GSamnVci2/vYGfZubiiHDpmRbQ07mphg6FJdUOPXzkEdhvv2IahS9/2fmp1O+quSuwwztkMvMJYFwn50wFpva5Oknq3M+B+4C/AddGxCjAMVYtoKdzU3U5ML3SX/8KEybA44/Db35TLKgs9TNjvKSWlJk/zsxNMnOPcuHkecB7G12XutbW1rPjuxyYXmnmTNh55+L19dcbqtQwLsIsqSVFxAjgf4GNM/MD5eTEOwG/amxl6kxP7wDsdgFlKCb9/OpX4ZvfLILVOefAiBG9rlHqK1usJLWq04DLgY3L9/8HfKZh1ahbPbkDcOutqzjo2Wdh332LUHX44cXUCoYqNZjBSlKr2iAzZwLLADJzCeCNMk2s2jsAt94a5s7t5qB//Qt22gkuvhh+/GP4xS9g9dX7XKPUV3YFSmpVz0XE+pQTEJdrmD7d2JLUmWrGVlXV9Qdw9dXFwsmZcNllxSKDUpOwxUpSq/ocxbx5W0TE9cCvgU81tiR1pJqxVVV1/WXCz34G73sfvPrVcNNNhio1HVusJLWkzPxLRLwH2IpiKa27MnNxg8tSB6oZW9Vt19+iRfCpT8H06fDBDxazhK6zTk3qk2rJYCWpJUXEx1bY9NaIIDN/3ZCC1Kn77+/jBR59tOj6++Mf4bjj4BvfcNJPNS2DlaRWtX3F6zUoJiz+C0WXoJrIWmvBggWd7x81qouTb721mPTz0UfhzDPhwANrXp9USwYrSS0pM5cbTxURrwBOb1A56sJzz3W+LwKmdrZOx+9+B4ccAuutB9ddB297W13qk2rJtlRJA8VCikXf1WS6utvv9NM7WFC5fdLP/feHN78Z5swxVKll2GIlqSVFxEWUUy1Q/Efi1sDMxlWkzgwdCks7mGFs6NAOQtWCBfCxj8F558Fhh8FJJzk/lVqKwUpSq/pexeslwLzMnN+oYtS5sWOLSdE72r6ce+8txlPNnQs//CF8+tNFX6HUQgxWklpSZl7T6BpUnXvuqWL77NnFnX9Ll8Lvfw/vf39/lCbVnMFKUkuJiGd5uQtwuV1AZqaTGzWZzqZbeGn7SScVrVOvfS1ceCFs6VA5tS6DlaSWkplrN7oG9czIkR2vE7jFZotg8tFw8smw557FFO2veEX/FyjVkMFKUkuLiFdRzGMFQGb2dTpK1VBbG8zvYOTbyGGPcd2a+8PJ18AXv1jMuTB0aP8XKNWYwUpSS4qI8cD3gY2BR4FRwB3ANo2sSy/rbI3Abfk7v18ynhH3PVIsTbPSrYFS63IeK0mt6hvAjsD/ZebmFDOvX9/YklSpozUC9+Fc/sQ7YPFiuPZaQ5UGHIOVpFa1ODOfAIZExJDMvBrYrtFF6WWVg9aDZXyFr3Mu+3Ebb2QMc2D77Ts/WWpRdgVKalVPRcRw4FqgLSIepZjPSk2ifdD6mjzHDA7hQ5zDaRzCkZzMiy8Pi5MGFFusJLWUiPhQRKwBTKBYxuazwGXAP4EPNrI2Le+1r4WRzON6dmYfzuNzfJ/DOJUXWYNx4xpdnVQftlhJajUTgWkUYeos4IrMnNHYktSRpVdfyxz2Y1UWsweXcgW7vbTvqqsaWJhUR7ZYSWopmbkP8FpgFvBp4IGIOCki3t3YyrSc6dO5Ytk4nmB9duCm5UKVNJAZrCS1nMx8JjNnZOYHgG2BW4GfRMQDDS5NixfDUUfBEUdwFe/j7dzI3bxuuUOcrkoDmV2BklpWRKwH7At8BHglcE5jKxrkHn8c9t+/WPfv85/n4me+xTM/XzlFTZrU/6VJ/cVgJamlRMTawN7AgcBbgQuBbwJXZ2ZHawiqP/zjHzBhAjz0ENcfeTq7/OggFi1a+bBx42DatP4vT+ovdgVKajX3ArsDJwGbZeakzPyDoaqBzj8fdtoJXniBy467lnee3HGoArj66mJGdmmgMlhJajUjM3NiZl6UmYsbXcyglgnf/Cbssw9svTXMmcORp+zQ5SnLlnU8I7s0UNgVKKmlZObCRtcg4Lnn4LDD4Le/LRYEnD4dhg1bbrb1zlRzjNSqbLGSJPXM/ffDO98J55wD3/0u/PrXMGwYUMy23p1qjpFalS1WkqTqXXcd7LsvvPgiXHwxfOADy+1+7WuLZWw6M2QITJ1a5xqlBjJYSWopEXER0OlA9cwc34/lDC6//CVMmQKbbw4XXACvf/1yu6dMgVmzOj99jTWKS0ycWOc6pQYyWElqNd8rn/cFXg2cUb4/ELivEQUNeIsXwzHHwE9+ArvtBmedBeutt9Jh06d3fomhQ+H55+tYo9QkDFaSWkpmXgMQEd/IzMplbC6KiGsbVNbA9cQT8OEPwx/+UISrE06AVTr+p2Pp0s4v09U+aSBx8LqkVrVhRLym/U1EbA5s2NeLRsRmEXF1RNwREXMj4ui+XrNlzZ0LO+xQjKuaMQO+971OQxV0vVSNy9hosLDFSuqF0cde0u0xx2y7hEOrOE699vW1ThAAABp8SURBVFlgdkT8q3w/GjiiBtddAhyTmX8pZ3m/JSKuzMzba3Dt1nHhhcVgqOHD4ZprYMcduz1l0iQ46aTO90mDgcFKUkvKzMsiYkugfQT1nZn5Yg2u+zDwcPn62Yi4A9gEGBzBKhO+9S348pfhbW8rZlXfZJOqTm1fqqYyXEXAkUe6jI0GD7sCJbWkiFgT+Dzwycz8GzAyIvaq8WeMBt4C3NjBvkkRMSci5jz22GO1/NjGWbgQDjywmBr9wAPh2murDlVQ3BV48skvvx8+HE4/3VClwcVgJalVnQosAnYq38+nWIy5JiJiOHAO8JnMfGbF/Zk5PTPHZOaYDTfs89CuxnvgAXjXu2DmTPj2t+GMM16a9LMaU6YULVWVKzYuWACHHuragBpcDFaSWtUWmfkdYDFAZj4PRC0uHBGrUoSqtsw8txbXbGp/+hOMGQN33w0XXQRf+ELRh9cDnU21sGSJawNqcDFYSWpViyJiGOVkoRGxBdDnMVYREcCvgDsy88S+Xq/pnXIKjB0L66wDN94Ie+7Zq8t0NZ2CawNqMDFYSWpVXwUuAzaLiDZgFvCFGlx3Z+BgYJeIuLV87FGD6zaXJUvgM5+Bww8vgtVNN8Eb3tDry3U1nYJrA2ow8a5ASS0nIoYA61HMvr4jRRfg0Zn5eF+vnZnXUaMuxab15JPwkY/AVVfBZz8L3/lOl/NTVaOzqRZWWcW1ATW4GKwktZzMXBYRn8zMmYCThfXE7bfD+PHFYPVTToHDDqvJZdvv/Dv55JcHsA8fXrx3bUANJnYFSmpVV0bEf5czpb+y/dHoopraxRcXE30uWABXX12zUNVu2jRYtqwIVpnw7LOGKg0+BitJrerjwFHAtcAt5WNOQytqVpnFGn/jx8PrXgc33wzveEdNP2LXXYsbCdsfu+5a08tLLcOuQEktKTM3b3QNLeH554sB6medBQccAL/6Fay5Zk0/YtddYdas5bfNmlVsv+qqmn6U1PRssZLUkiJizYj4ckRML99vWeuZ11ve/PnFpJ9nn10sU3PmmTUPVbByqOpuuzSQ2WIlqVWdStH9196nNR/4LXBxwypqJjfcAPvsUyxTc8EF8MEPNroiaVCwxUpSq6rbzOst77TTirmphg+HP//ZUCX1I4OVpFZVl5nXW9qSJfC5zxV3+73rXcWkn1tvXfePHTeuZ9ulgcxgJalV1Wvm9db0n/8Uy9H84Afw6U/DZZfBK/tn9omrrlo5RI0b58B1DU6OsZLUkjLzyoj4CzWeeb0l3XEHTJgA990Hv/xlcRdgPzNESQWDlaSWEhFvXWHTw+XzyIgYmZl/6e+aGurSS+HAA2GNNYpJP3feudEVSYOaXYGSWs33y8fPgBuB6cAvytc/bmBd/SuzWONvr71giy2KST8bEKqmTFl+YtAhQ4pt0mBlsJLUUjLzvZn5XmAe8NbMHJOZbwPeAtzT2Or6yfPPw8EHwxe/CPvvD9ddByNH9nsZU6asvPByZrHNcKXBymAlqVW9PjP/0f4mM28DtmtgPf3jwQfhPe+Btjb45jeLyT/rMOlnNaZP790+aSDrNliVC5xeHRF3RMTciDi63P7KiLgyIu4un9erOOdLEXFPRNwVEbvV8w8gadC6MyJ+GRFjI+I9EfEL4I5GF1VXN94I229fDFY//3w4/vii/61Bli7t3T5pIKumxWoJcExmvoHi7pujImJr4FhgVmZuSXGb87EA5b4DgG2A3YFpETG0HsVLGtQOBeYCRwOfAW4HDmtkQXX1618XLVXDhhWzqk+Y0OiKGNrFL3tX+6SBrNtglZkPt99lk5nPUvwX4SbABGBGedgMYO/y9QTg7Mx8MTPvpRjzsEOtC5c0eJX/sXZxZv4gM/cpHz/IzBcaXVvNLV0K//3fcMgh8I53FJN+vvGNja4KgEmTerdPGsh6NN1CRIymGCB6IzAiMx+GInxFxKvKwzYB/lxx2vxy24rXmgRMAhgxYgSzZ89e6fMWLFjQ4fZmZb311Uz1HrPtkm6PGTGsuuP6U1ffXzN9v93JzKURsTAiXpGZTze6nrp56qliKoXLLoNPfhJOPBFWXbXRVb1k2rTiuXIAewQceeTL+6TBpupgFRHDgXOAz2TmM9F5v35HO3KlDZnTKW6TZsyYMTl27NiVTpo9ezYdbW9W1ltfzVTvocde0u0xx2y7hO//o7mmirtv4thO9zXT91ulF4B/RMSVwHPtGzPz040rqYbuugvGj4d77y1Ggn/iE42uqEPTphmipEpV/epHxKoUoaotM88tNz8SERuVrVUbAY+W2+cDm1WcvinwUK0KlqTSJeVj4Pn974uWqtVWg1mzinX/JLWEau4KDOBXwB2ZeWLFrguBQ8rXhwAXVGw/ICJWj4jNgS2Bm2pXsiQB8BvgFmAO8JvMnJGZM7o5p7llwve/X0z6OXp0MemnoUpqKdW0WO0MHEzR5H5rue044ARgZkQcDtwP7A+QmXMjYibFHTpLgKMy0xtvJdVERKwC/C/wcYpJQocAm0bEqcDxmbm4kfX12gsvFCO+Tz8dPvQhOO00WGutRlclqYequSvwusyMzHxTZm5XPi7NzCcyc1xmblk+P1lxztTM3CIzt8rM39f3jyBpkPku8Epg88x8W2a+BdgCWBf4XkMr662HHiqmUjj9dPif/4GZM5syVLW1wfDhyy9hU/lYe+3iGGkwa66RtZLUvb2A12XmSzfFlDfUTAbupJjXqnXcfDPsvTc8/TScey7ss0+jK+pQWxt87GOwbFnnxyxYAIceWryeOLFfypKajkvaSGo1WRmqKjYupYM7kJvaGWcUY6hWW62Y9LNJQxUUk7x3FaraLVlSHCsNVgYrSa3m9oj42IobI+Igihar5rd0abGA8sEHw447Fq1W227b6Kq6dP/99TlWGmjsCpTUao4Czo2Ij1PcFZjA9sAwoHmbfNo9/TR89KNw6aUwZQr88IdNNelnZ0aOhHnzqj9WGqwMVpJaSmY+CLw9InahWJM0gN9n5qzGVlaF//u/YtLPf/4TTj4Zjjii0RVVberU7sdYAayySnGsNFgZrCS1pMz8A/CHRtfRIz//OTzxRDHp57vf3ehqeqR9MPoRR8Bzz3V8zPDhRV504LoGM4OVJPWXb30LPvMZ2Gyz7o9tQhMnGpqk7jh4XZL6y2qrtWyoklQdg5UkSVKNGKwkSZJqxGAlSZJUIwYrSZKkGjFYSZIk1YjBSpLUqSlTYMgQiFj+sfbaxcLMkpbnPFaSpA5NmQInndTxvgUL4NBDi9fObSW9zBYrSVKHpk/vev+SJXD88f1Ti9QqDFaSpA4tXdr9MfffX/86pFZisJKkChFxSkQ8GhG3NbqWRhs6tPtjRo6sfx1SKzFYSdLyTgN2b3QRzWDSpK73r7IKTJ3aP7VIrcJgJUkVMvNa4MlG19EMpk2DyZOLuwBXNHw4nHaaA9elFXlXoCSpU9OmFQ9J1bHFSpJ6ISImRcSciJjz2GOPNbocSU3CYCVJvZCZ0zNzTGaO2XDDDRtdjqQmYbCSJEmqEYOVJFWIiLOAG4CtImJ+RBze6JoktQ4Hr0tShcw8sNE1SGpdtlhJkiTViMFKkiSpRgxWkiRJNWKwkiRJqhGDlSRJUo0YrCRJkmrEYCVJklQjBitJGsTa2mCDDSCi68faaxfHSuqaE4RK0iDV1gaHHQaLF3d/7IIFcOihxeuJE+taltTSbLGSpEHq+OOrC1XtliwpzpHUOYOVJA1S99/fP+dIg4nBSpIGqZEj++ccaTAxWEnSIDV1Kqy6avXHr7JKcY6kzhmsJGmQmjgRTj0V1l+/+2OHD4fTTnPgutQd7wqUpEFs4kTDklRLtlhJkiTViMFKkiSpRgxWkiRJNWKwkiRJqhGDlSRJUo0YrCRJkmrEYCVJklQjBitJkqQaMVhJkiTViMFKkiSpRgxWkiRJNWKwkiRJqhGDlSRJUo0YrCRJkmrEYCVJklQjBitJkqQaMVhJkiTVSLfBKiJOiYhHI+K2im2vjIgrI+Lu8nm9in1fioh7IuKuiNitXoVLkiQ1m2parE4Ddl9h27HArMzcEphVvicitgYOALYpz5kWEUNrVq0kSVIT6zZYZea1wJMrbJ4AzChfzwD2rth+dma+mJn3AvcAO9SoVkmSpKa2Si/PG5GZDwNk5sMR8apy+ybAnyuOm19uW0lETAImAYwYMYLZs2evdMyCBQs63N6srLe+mqneY7Zd0u0xI4ZVd1x/6ur7a6bvV5JaVW+DVWeig23Z0YGZOR2YDjBmzJgcO3bsSsfMnj2bjrY3K+utr2aq99BjL+n2mGO2XcL3/1Hr/4v1zX0Tx3a6r5m+X0lqVb29K/CRiNgIoHx+tNw+H9is4rhNgYd6X54kSVLr6G2wuhA4pHx9CHBBxfYDImL1iNgc2BK4qW8lSpIktYZu+yki4ixgLLBBRMwHvgqcAMyMiMOB+4H9ATJzbkTMBG4HlgBHZebSOtUuSZLUVLoNVpl5YCe7xnVy/FRgal+KkiRJakXOvC5JK4iI3ctJju+JiGMbXU9vtbXBBhtARPePDTYojpfUN811y5Kkuhrdxd2Mx2y7pKq7HTtz3wl79vrcZlJOavwz4H0UN+TcHBEXZubtja2sZ9ra4LDDYPHi6o5/4gn4+MeL1xMn1q8uaaCzxUqSlrcDcE9m/iszFwFnU0x+3FKOP776UNVu0aLiPEm9Z7CSpOVtAjxQ8b7DiY4jYlJEzImIOY899li/FVet++/v3/MkFQxWkrS8qiY6zszpmTkmM8dsuOGG/VBW9draYEgvf91HjqxtLdJgY7CSpOW19ETHbW0waRIs7cVEN6utBlO9p1vqE4OVJC3vZmDLiNg8IlYDDqCY/LglHH88LFzY8/PWXx9OOcWB61JfeVegJFXIzCUR8UngcmAocEpmzm1wWVXrbIxUBCxb1r+1SIORwUqSVpCZlwKXNrqO3hg5EubN63i7pPqzK1CSWlhbG4weXQxW32ADePzxlY9Zc03HTkn9xRYrSWpR7QPV28dUPfHEysesvz786EeOnZL6iy1WktSiqhmoPny4oUrqTwYrSWpR1Uzm6YSfUv8yWElSi6pmQLqD1qX+ZbCSpCZXOUB99GiYMqV4njevmEahMw5al/qfg9clqYmtOEB93jw46aSX92cW4SqzGKgO8OSTRUvV1KmOr5L6m8FKkppYNQPUM2HUKLjvvn4pSVIX7AqUpCZW7eBzB6lLzcFgJUlNrNrB5w5Sl5qDwUqSmtjUqcUg9K44SF1qHgYrSWoiK94BCDB9ejGGKqJ4njx5+ffTpztIXWoWDl6XpCbR0R2AkyYVwcmB6VJrsMVKkppER3cALlxYbJfUGgxWktQkOruzzzv+pNZhsJKkJtHZnX3e8Se1DoOVJDWJju4A9I4/qbUYrCSpSUycuPIdgN7xJ7UW7wqUpCYycaJBSmpltlhJkiTViMFKkiSpRgxWkiRJNWKwkiRJqhGDlSRJUo0YrCRJkmrEYCVJklQjzmOlAWv0sZc0ugRJ0iBji5UkSVKNGKwkSZJqxGAlSZJUIwYrSZKkGjFYSZIk1YjBSpIkqUYMVpIkSTVisJKkftDWBqNHw5AhxXNbW6MrklQPThAqSXXW1gaTJsHChcX7efOK9wATJzauLkm1Z4uVJNXZ8ce/HKraLVxYbJc0sBisJKnO7r+/Z9sltS6DlSTV2ciRPdsuqXUZrCSpzqZOhTXXXH7bmmsW2yUNLA5eV0ONPvaSqo89ZtslHNqD46Weioj9ga8BbwB2yMw5tbhu+wD1448vuv9GjixClQPXpYHHYCVJL7sN2Bf4ea0vPHGiQUoaDAxW6lZPWpWkVpaZdwBERKNLkdSiHGMlSZJUI7ZYraAvrTPdjQG674Q9e31tSbUREVcBr+5g1/GZeUEPrjMJmAQw0tv7JJUMVv2onl1qhjapOpm5a42uMx2YDjBmzJisxTUltT67AiVJkmrEYCVJpYjYJyLmAzsBl0TE5Y2uSVJrqVtXYETsDvwIGAr8MjNPqNdnqXfdjM4LJS0vM88Dzmt0HZJaV11arCJiKPAz4APA1sCBEbF1PT5LkiSpWdSrxWoH4J7M/BdARJwNTABur8XFnVdJkiQ1o3qNsdoEeKDi/fxymyRJ0oAVmbW/S7hcb2u3zPyv8v3BFOtufarimJfmgAG2Au7q4FIbAI/XvMD6sd76st766u96R2Xmhv34eXUTEY8B8/p4mWb8+9JsNTVbPdB8NVlP92pRU6e/X/XqCpwPbFbxflPgocoDKueA6UxEzMnMMbUvrz6st76st75ard5mUouA2Izff7PV1Gz1QPPVZD3dq3dN9eoKvBnYMiI2j4jVgAOAC+v0WZIkSU2hLi1WmbkkIj4JXE4x3cIpmTm3Hp8lSZLULOo2j1VmXgpc2sfLdNlV2ISst76st75ard6Bphm//2arqdnqgearyXq6V9ea6jJ4XZIkaTBySRtJkqQaafpgFRH7R8TciFgWEU11Z0G7iNg9Iu6KiHsi4thG19OdiDglIh6NiNsaXUt3ImKziLg6Iu4o/x4c3eiauhIRa0TETRHxt7Lerze6pmpExNCI+GtEXNzoWgaziPhGRPw9Im6NiCsiYuMG1/PdiLizrOm8iFi3kfWUNTXFvwnN9rvfbL/rzfbb3Z+/zU0frIDbgH2BaxtdSEdadPme04DdG11ElZYAx2TmG4AdgaOa/Pt9EdglM98MbAfsHhE7NrimahwN3NHoIsR3M/NNmbkdcDHwlQbXcyXwxsx8E/B/wJcaXA80wb8JTfq7fxrN9bvebL/d/fbb3PTBKjPvyMyOJg9tFi8t35OZi4D25XuaVmZeCzzZ6DqqkZkPZ+ZfytfPUvzj37Sz+GdhQfl21fLR1AMZI2JTYE/gl42uZbDLzGcq3q5Fg//uZOYVmbmkfPtnijkJG6pJ/k1out/9Zvtdb7bf7v78bW76YNUCXL6nn0TEaOAtwI2NraRrZbfarcCjwJWZ2dT1Aj8EvgAsa3QhgoiYGhEPABNpfItVpY8Dv290EU3C3/0eaJbf7v76bW6KYBURV0XEbR08mrrlpxQdbGvqFopWFBHDgXOAz6zwX/VNJzOXll05mwI7RMQbG11TZyJiL+DRzLyl0bUMFt393mXm8Zm5GdAGfLLR9ZTHHE/RtdNW73qqranB/N2vUjP9dvfXb3Pd5rHqiczctdE19EG3y/eobyJiVYr/Y7Zl5rmNrqdamflURMymGPfQFANKO7AzMD4i9gDWANaJiDMy86AG1zVg9eD37kzgEuCrdSyn23oi4hBgL2Bc9tP8PC3wb4K/+1Vo1t/uev82N0WLVYtz+Z46iogAfgXckZknNrqe7kTEhu13TkXEMGBX4M7GVtW5zPxSZm6amaMp/u7+wVDVOBGxZcXb8TT4705E7A58ERifmQsbWUuT8Xe/G832292fv81NH6wiYp+ImA/sBFwSEZc3uqZK5cDO9uV77gBmNvvyPRFxFnADsFVEzI+IwxtdUxd2Bg4GdilvQb+1bF1pVhsBV0fE3yl+fK/MTKcwULVOKLu8/g68n+JuzUb6KbA2cGX5/72TG1xPU/yb0Iy/+034u95sv9399tvszOuSJEk10vQtVpIkSa3CYCVJklQjBitJkqQaMVhJkiTViMFKkiSpRgxWkqR+ERHrV9x6/++IeLB8/VRE3N7PtexduShwRPxPRPR4YtKIGB0RDZsAOCKOW+H9n8rnhtY1mBmsJEn9IjOfyMztymVFTgZ+UL7ejjqsVRkRXa0usjfwUrDKzK9k5lW1rqEfLBesMvMdjSpEBYOVJKkZDI2IX0TE3Ii4opwdm4jYIiIui4hbIuKPEfH6cvuoiJgVEX8vn0eW20+LiBMj4mrg2x2dHxHvoJjZ/rtli9kW5XkfKq+xfUT8KSL+FhE3RcTaZQvQHyPiL+WjywAThZ9GxO0RcUlEXFpx/fsiYoPy9ZhyeRUiYofyc/9aPm9Vbj80Is4t/xx3R8R3yu0nAMPKP0NbuW1BB7UMjYjvRsTN5fd1RLl9o4i4tjz/toh4Vx//NxRNslagJGnQ2xI4MDM/EREzgf2AM4DpwJGZeXdEvB2YBuxCMSv8rzNzRkR8HPgxRSsUwOuAXTNzaUTMWvH8zNwlIi4ELs7M3wEUK7BAFEvU/Ab4SGbeHBHrAM8DjwLvy8wXolh66CxgTBd/nn2ArYBtgRHA7cAp3XwHdwLvzswlZbfk/5bfAxStem8BXgTuioifZOaxEfHJstWvK4cDT2fm9hGxOnB9RFwB7AtcnplTI2IosGY311EVDFaSpGZwb2beWr6+BRgdEcOBdwC/bQ8+wOrl804UwQDgdOA7Fdf6bRmqujq/M1sBD2fmzQCZ+QxARKwF/DQitgOWUoS3rrwbOCszlwIPRcQfujke4BXAjDK4JbBqxb5Zmfl0WcvtwCjggSquCcXySG9qbzErP2dLiqVdToliseTzK75/9YHBSpLUDF6seL0UGEYxXOWpKlpkoAgi7Z4rn3tyfrtY4VrtPgs8Ary5vO4LPayp0hJeHoqzRsX2bwBXZ+Y+ETEamF2xb8Xvpyf/fgfwqcxcaV3FiHg3sCdwekR8NzN/3YPrqgOOsZIkNaWytejeiNgfXhq39OZy95+AA8rXE4Hrenj+sxQLTK/oTmDjiNi+PGftchD8KyhaspZRLC48tJvyrwUOKMc3bQS8t2LffcDbytf7VWx/BfBg+frQbq7fbnHZ4tSVy4HJ7cdFxOsiYq2IGAU8mpm/AH4FvLXKz1QXDFaSpGY2ETg8Iv4GzAUmlNs/DRwWEX+nCDpH9/D8s4HPlwPFt2g/ODMXAR8BflKecyVFq9I04JCI+DNFN+BzdO084G7gH8BJwDUV+74O/Cgi/kjR+tTuO8C3IuJ6ug9u7aYDf28fvN6JX1KM8fpLFFMw/JyixWsscGtE/JUi4P2oys9UFyKzs5ZKSZJUCxFxGhWD5TVw2WIlSZJUI7ZYSZIk1YgtVpIkSTVisJIkSaoRg5UkSVKNGKwkSZJqxGAlSZJUIwYrSZKkGvn/yAaFc/Bq/EgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df['Age_log']=np.log(df['Age'])\n",
    "plot_data(df,'Age_log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reciprocal Trnasformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age_reciprocal']=1/df.Age\n",
    "plot_data(df,'Age_reciprocal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Square Root Transformation\n",
    "df['Age_sqaure']=df.Age**(1/2)\n",
    "plot_data(df,'Age_sqaure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Exponential Transdormation\n",
    "df['Age_exponential']=df.Age**(1/1.2)\n",
    "plot_data(df,'Age_exponential')"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age_Boxcox'],parameters=stat.boxcox(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7964531473656952\n"
     ]
    }
   ],
   "source": [
    "print(parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(df,'Age_Boxcox')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(df,'Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Fare\n",
    "df['Fare_log']=np.log1p(df['Fare'])\n",
    "plot_data(df,'Fare_log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Fare_Boxcox'],parameters=stat.boxcox(df['Fare']+1)\n",
    "plot_data(df,'Fare_Boxcox')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}