Woe and IV values.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weight of evidence\n",
    "\n",
    "Weight of Evidence (WoE) was developed primarily for the credit and financial industries to help build more predictive models to evaluate the risk of loan default. That is, to predict how likely the money lent to a person or institution is to be lost. Thus, Weight of Evidence is a measure of the \"strength” of a grouping technique to separate good and bad risk (default).\n",
    "\n",
    "It is computed from the basic odds ratio: ln( (Proportion of Good Credit Outcomes) / (Proportion of Bad Credit Outcomes))\n",
    "\n",
    "WoE will be 0 if the P(Goods) / P(Bads) = 1. That is, if the outcome is random for that group. If P(Bads) > P(Goods) the odds ratio will be < 1 and the WoE will be < 0; if, on the other hand, P(Goods) > P(Bads) in a group, then WoE > 0.\n",
    "\n",
    "WoE is well suited for Logistic Regression, because the Logit transformation is simply the log of the odds, i.e., ln(P(Goods)/P(Bads)). Therefore, by using WoE-coded predictors in logistic regression, the predictors are all prepared and coded to the same scale, and the parameters in the linear logistic regression equation can be directly compared.\n",
    "\n",
    "The WoE transformation has three advantages:\n",
    "\n",
    "1. It establishes a monotonic relationship to the dependent variable.\n",
    "2. It orders the categories on a \"logistic\" scale which is natural for logistic regression\n",
    "3. The transformed variables, can then be compared because they are on the same scale. Therefore, it is possible to determine which one is more predictive.\n",
    "\n",
    "The WoE also has three drawbacks:\n",
    "\n",
    "1. May incur in loss of information (variation) due to binning to few categories (we will discuss this further in the discretisation section)\n",
    "2. It does not take into account correlation between independent variables\n",
    "3. Prone to cause over-fitting\n",
    "\n",
    "# IV Values\n",
    "\n",
    "IV= Sum of ((event%- non_event%) * WOE)\n",
    "\n",
    "where woe= ln( (event%) / (non_event%))\n",
    "\n",
    "\n",
    "Weight of evidence (WOE) and Information value (IV) are simple, yet powerful techniques to perform variable transformation and selection. These concepts have huge connection with the logistic regression modeling technique. It is widely used in credit scoring to measure the separation of good vs bad customers.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('tit_train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Missing</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare    Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500  Missing        S  \n",
       "1      0          PC 17599  71.2833      C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250  Missing        S  \n",
       "3      0            113803  53.1000     C123        S  \n",
       "4      0            373450   8.0500  Missing        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Cabin.fillna('Missing', inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500     M        S  \n",
       "1      0          PC 17599  71.2833     C        C  \n",
       "2      0  STON/O2. 3101282   7.9250     M        S  \n",
       "3      0            113803  53.1000     C        S  \n",
       "4      0            373450   8.0500     M        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cabin'] = data['Cabin'].astype(str).str[0]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "car= data.columns[data.dtypes=='object']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important\n",
    "The calculation of the WoE to replace the labels should be done considering the ONLY the training set, and then expanded it to the test set. See below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, data.Survived, test_size=0.3,\n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 12), (623,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def woe(col):\n",
    "    prob_df = pd.DataFrame()\n",
    "    prob_df['Survived']= X_train.groupby([col])['Survived'].mean()\n",
    "    prob_df['Died'] = 1-prob_df.Survived\n",
    "    prob_df.loc[prob_df.Survived == 0, 'Survived'] = 0.00001\n",
    "    prob_df.loc[prob_df.Died == 0, 'Survived'] = 0.00001\n",
    "    prob_df['WOE'] = np.log(prob_df.Survived/prob_df.Died)\n",
    "    \n",
    "    return prob_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Died</th>\n",
       "      <th>WOE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>-0.287682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>1.232144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.287682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.810930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>1.049822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.303609</td>\n",
       "      <td>0.696391</td>\n",
       "      <td>-0.830169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-11.512925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived      Died        WOE\n",
       "Cabin                               \n",
       "A      0.428571  0.571429  -0.287682\n",
       "B      0.774194  0.225806   1.232144\n",
       "C      0.571429  0.428571   0.287682\n",
       "D      0.692308  0.307692   0.810930\n",
       "E      0.740741  0.259259   1.049822\n",
       "F      0.666667  0.333333   0.693147\n",
       "G      0.500000  0.500000   0.000000\n",
       "M      0.303609  0.696391  -0.830169\n",
       "T      0.000010  1.000000 -11.512925"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe('Cabin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Died</th>\n",
       "      <th>WOE</th>\n",
       "      <th>S-D</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.753488</td>\n",
       "      <td>0.246512</td>\n",
       "      <td>1.117304</td>\n",
       "      <td>0.566447</td>\n",
       "      <td>1.424106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>-1.410987</td>\n",
       "      <td>0.857659</td>\n",
       "      <td>1.424106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived      Died       WOE       S-D        IV\n",
       "Sex                                                     \n",
       "female  0.753488  0.246512  1.117304  0.566447  1.424106\n",
       "male    0.196078  0.803922 -1.410987  0.857659  1.424106"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe('Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def woe(col):\n",
    "    prob_df = pd.DataFrame()\n",
    "    prob_df['count']= X_train.groupby([col])['Survived'].count()\n",
    "    prob_df['event']= X_train.groupby([col])['Survived'].sum() \n",
    "    prob_df['non_event']= prob_df['count'] - prob_df['event']\n",
    "    prob_df.loc[prob_df['event'] == 0, 'event'] = 0.00001\n",
    "    prob_df.loc[prob_df['non_event'] == 0, 'non_event'] = 0.00001\n",
    "    prob_df['event_rate']= prob_df['event']/prob_df['count']\n",
    "    prob_df['non_event_rate']= prob_df['non_event']/prob_df['count']\n",
    "    prob_df[\"dist_event\"] = (prob_df['event']/prob_df.sum().event)\n",
    "    #dist_event= no of event for group/ total sum of event of all group\n",
    "    prob_df[\"dist_non_event\"] = (prob_df['non_event']/prob_df.sum().non_event)\n",
    "    #dist_non_event= no of non_event for group/ total sum of non_event of all group\n",
    "    prob_df['WOE'] = np.log(prob_df['dist_event']/prob_df['dist_non_event'])\n",
    "    prob_df['IV']= sum((prob_df['dist_event'] - prob_df['dist_non_event'])*prob_df['WOE'])\n",
    "    prob_df['column']=col\n",
    "    return prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>count</th>\n",
       "      <th>event</th>\n",
       "      <th>non_event</th>\n",
       "      <th>event_rate</th>\n",
       "      <th>non_event_rate</th>\n",
       "      <th>dist_event</th>\n",
       "      <th>dist_non_event</th>\n",
       "      <th>WOE</th>\n",
       "      <th>IV</th>\n",
       "      <th>column</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>14</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>2.479339e-02</td>\n",
       "      <td>0.020997</td>\n",
       "      <td>0.166180</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>31</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>9.917355e-02</td>\n",
       "      <td>0.018373</td>\n",
       "      <td>1.686005</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>42</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>9.917355e-02</td>\n",
       "      <td>0.047244</td>\n",
       "      <td>0.741544</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>26</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>7.438016e-02</td>\n",
       "      <td>0.020997</td>\n",
       "      <td>1.264792</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>27</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>8.264462e-02</td>\n",
       "      <td>0.018373</td>\n",
       "      <td>1.503684</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>9</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.479339e-02</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>1.147009</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4.132231e-03</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>0.453862</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>471</td>\n",
       "      <td>143.00000</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.303609</td>\n",
       "      <td>0.696391</td>\n",
       "      <td>5.909091e-01</td>\n",
       "      <td>0.860892</td>\n",
       "      <td>-0.376307</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.132231e-08</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>-11.059064</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count      event  non_event  event_rate  non_event_rate    dist_event  \\\n",
       "Cabin                                                                          \n",
       "A         14    6.00000        8.0    0.428571        0.571429  2.479339e-02   \n",
       "B         31   24.00000        7.0    0.774194        0.225806  9.917355e-02   \n",
       "C         42   24.00000       18.0    0.571429        0.428571  9.917355e-02   \n",
       "D         26   18.00000        8.0    0.692308        0.307692  7.438016e-02   \n",
       "E         27   20.00000        7.0    0.740741        0.259259  8.264462e-02   \n",
       "F          9    6.00000        3.0    0.666667        0.333333  2.479339e-02   \n",
       "G          2    1.00000        1.0    0.500000        0.500000  4.132231e-03   \n",
       "M        471  143.00000      328.0    0.303609        0.696391  5.909091e-01   \n",
       "T          1    0.00001        1.0    0.000010        1.000000  4.132231e-08   \n",
       "\n",
       "       dist_non_event        WOE        IV column  \n",
       "Cabin                                              \n",
       "A            0.020997   0.166180  0.490246  Cabin  \n",
       "B            0.018373   1.686005  0.490246  Cabin  \n",
       "C            0.047244   0.741544  0.490246  Cabin  \n",
       "D            0.020997   1.264792  0.490246  Cabin  \n",
       "E            0.018373   1.503684  0.490246  Cabin  \n",
       "F            0.007874   1.147009  0.490246  Cabin  \n",
       "G            0.002625   0.453862  0.490246  Cabin  \n",
       "M            0.860892  -0.376307  0.490246  Cabin  \n",
       "T            0.002625 -11.059064  0.490246  Cabin  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe('Cabin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>event</th>\n",
       "      <th>non_event</th>\n",
       "      <th>event_rate</th>\n",
       "      <th>non_event_rate</th>\n",
       "      <th>dist_event</th>\n",
       "      <th>dist_non_event</th>\n",
       "      <th>WOE</th>\n",
       "      <th>IV</th>\n",
       "      <th>column</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>215</td>\n",
       "      <td>162.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.753488</td>\n",
       "      <td>0.246512</td>\n",
       "      <td>0.669421</td>\n",
       "      <td>0.139108</td>\n",
       "      <td>1.571166</td>\n",
       "      <td>1.340788</td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>408</td>\n",
       "      <td>80.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>0.330579</td>\n",
       "      <td>0.860892</td>\n",
       "      <td>-0.957125</td>\n",
       "      <td>1.340788</td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count  event  non_event  event_rate  non_event_rate  dist_event  \\\n",
       "Sex                                                                       \n",
       "female    215  162.0       53.0    0.753488        0.246512    0.669421   \n",
       "male      408   80.0      328.0    0.196078        0.803922    0.330579   \n",
       "\n",
       "        dist_non_event       WOE        IV column  \n",
       "Sex                                                \n",
       "female        0.139108  1.571166  1.340788    Sex  \n",
       "male          0.860892 -0.957125  1.340788    Sex  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe('Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>event</th>\n",
       "      <th>non_event</th>\n",
       "      <th>event_rate</th>\n",
       "      <th>non_event_rate</th>\n",
       "      <th>dist_event</th>\n",
       "      <th>dist_non_event</th>\n",
       "      <th>WOE</th>\n",
       "      <th>IV</th>\n",
       "      <th>column</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>108</td>\n",
       "      <td>61.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.564815</td>\n",
       "      <td>0.435185</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.123360</td>\n",
       "      <td>0.722887</td>\n",
       "      <td>0.122809</td>\n",
       "      <td>Embarked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>56</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.410714</td>\n",
       "      <td>0.589286</td>\n",
       "      <td>0.095833</td>\n",
       "      <td>0.086614</td>\n",
       "      <td>0.101147</td>\n",
       "      <td>0.122809</td>\n",
       "      <td>Embarked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>457</td>\n",
       "      <td>156.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>0.341357</td>\n",
       "      <td>0.658643</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.790026</td>\n",
       "      <td>-0.195094</td>\n",
       "      <td>0.122809</td>\n",
       "      <td>Embarked</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count  event  non_event  event_rate  non_event_rate  dist_event  \\\n",
       "Embarked                                                                    \n",
       "C           108   61.0       47.0    0.564815        0.435185    0.254167   \n",
       "Q            56   23.0       33.0    0.410714        0.589286    0.095833   \n",
       "S           457  156.0      301.0    0.341357        0.658643    0.650000   \n",
       "\n",
       "          dist_non_event       WOE        IV    column  \n",
       "Embarked                                                \n",
       "C               0.123360  0.722887  0.122809  Embarked  \n",
       "Q               0.086614  0.101147  0.122809  Embarked  \n",
       "S               0.790026 -0.195094  0.122809  Embarked  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe('Embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat= ['Sex','Cabin','Embarked','Pclass','Parch','SibSp']\n",
    "df= pd.DataFrame()\n",
    "for i in cat:\n",
    "    a= woe(i)\n",
    "    df= pd.concat([df,a],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>event</th>\n",
       "      <th>non_event</th>\n",
       "      <th>event_rate</th>\n",
       "      <th>non_event_rate</th>\n",
       "      <th>dist_event</th>\n",
       "      <th>dist_non_event</th>\n",
       "      <th>WOE</th>\n",
       "      <th>IV</th>\n",
       "      <th>column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>215</td>\n",
       "      <td>162.00000</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.753488</td>\n",
       "      <td>0.246512</td>\n",
       "      <td>6.694215e-01</td>\n",
       "      <td>0.139108</td>\n",
       "      <td>1.571166</td>\n",
       "      <td>1.340788</td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>408</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>3.305785e-01</td>\n",
       "      <td>0.860892</td>\n",
       "      <td>-0.957125</td>\n",
       "      <td>1.340788</td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>14</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>2.479339e-02</td>\n",
       "      <td>0.020997</td>\n",
       "      <td>0.166180</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>31</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>9.917355e-02</td>\n",
       "      <td>0.018373</td>\n",
       "      <td>1.686005</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>42</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>9.917355e-02</td>\n",
       "      <td>0.047244</td>\n",
       "      <td>0.741544</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>26</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>7.438016e-02</td>\n",
       "      <td>0.020997</td>\n",
       "      <td>1.264792</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>27</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>8.264462e-02</td>\n",
       "      <td>0.018373</td>\n",
       "      <td>1.503684</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>9</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.479339e-02</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>1.147009</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4.132231e-03</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>0.453862</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>471</td>\n",
       "      <td>143.00000</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.303609</td>\n",
       "      <td>0.696391</td>\n",
       "      <td>5.909091e-01</td>\n",
       "      <td>0.860892</td>\n",
       "      <td>-0.376307</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.132231e-08</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>-11.059064</td>\n",
       "      <td>0.490246</td>\n",
       "      <td>Cabin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>108</td>\n",
       "      <td>61.00000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.564815</td>\n",
       "      <td>0.435185</td>\n",
       "      <td>2.541667e-01</td>\n",
       "      <td>0.123360</td>\n",
       "      <td>0.722887</td>\n",
       "      <td>0.122809</td>\n",
       "      <td>Embarked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>56</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.410714</td>\n",
       "      <td>0.589286</td>\n",
       "      <td>9.583333e-02</td>\n",
       "      <td>0.086614</td>\n",
       "      <td>0.101147</td>\n",
       "      <td>0.122809</td>\n",
       "      <td>Embarked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>457</td>\n",
       "      <td>156.00000</td>\n",
       "      <td>301.0</td>\n",
       "      <td>0.341357</td>\n",
       "      <td>0.658643</td>\n",
       "      <td>6.500000e-01</td>\n",
       "      <td>0.790026</td>\n",
       "      <td>-0.195094</td>\n",
       "      <td>0.122809</td>\n",
       "      <td>Embarked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>156</td>\n",
       "      <td>97.00000</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.621795</td>\n",
       "      <td>0.378205</td>\n",
       "      <td>4.008264e-01</td>\n",
       "      <td>0.154856</td>\n",
       "      <td>0.951035</td>\n",
       "      <td>0.496465</td>\n",
       "      <td>Pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>2.644628e-01</td>\n",
       "      <td>0.178478</td>\n",
       "      <td>0.393237</td>\n",
       "      <td>0.496465</td>\n",
       "      <td>Pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335</td>\n",
       "      <td>81.00000</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.241791</td>\n",
       "      <td>0.758209</td>\n",
       "      <td>3.347107e-01</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.689023</td>\n",
       "      <td>0.496465</td>\n",
       "      <td>Pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>474</td>\n",
       "      <td>169.00000</td>\n",
       "      <td>305.0</td>\n",
       "      <td>0.356540</td>\n",
       "      <td>0.643460</td>\n",
       "      <td>6.983470e-01</td>\n",
       "      <td>0.800525</td>\n",
       "      <td>-0.136551</td>\n",
       "      <td>0.169129</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78</td>\n",
       "      <td>42.00000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>1.735537e-01</td>\n",
       "      <td>0.094488</td>\n",
       "      <td>0.608012</td>\n",
       "      <td>0.169129</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.457627</td>\n",
       "      <td>0.542373</td>\n",
       "      <td>1.115702e-01</td>\n",
       "      <td>0.083990</td>\n",
       "      <td>0.283963</td>\n",
       "      <td>0.169129</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.239669e-02</td>\n",
       "      <td>0.005249</td>\n",
       "      <td>0.859327</td>\n",
       "      <td>0.169129</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.132231e-08</td>\n",
       "      <td>0.005249</td>\n",
       "      <td>-11.752211</td>\n",
       "      <td>0.169129</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>4.132231e-03</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>-0.644751</td>\n",
       "      <td>0.169129</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.132231e-08</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>-11.059064</td>\n",
       "      <td>0.169129</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>427</td>\n",
       "      <td>150.00000</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0.351288</td>\n",
       "      <td>0.648712</td>\n",
       "      <td>6.198347e-01</td>\n",
       "      <td>0.727034</td>\n",
       "      <td>-0.159521</td>\n",
       "      <td>0.515549</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>3.305785e-01</td>\n",
       "      <td>0.167979</td>\n",
       "      <td>0.677005</td>\n",
       "      <td>0.515549</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>3.719008e-02</td>\n",
       "      <td>0.031496</td>\n",
       "      <td>0.166179</td>\n",
       "      <td>0.515549</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>8.264462e-03</td>\n",
       "      <td>0.020997</td>\n",
       "      <td>-0.932433</td>\n",
       "      <td>0.515549</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>4.132231e-03</td>\n",
       "      <td>0.026247</td>\n",
       "      <td>-1.848724</td>\n",
       "      <td>0.515549</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.132231e-08</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>-12.157676</td>\n",
       "      <td>0.515549</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.132231e-08</td>\n",
       "      <td>0.018373</td>\n",
       "      <td>-13.004974</td>\n",
       "      <td>0.515549</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count      event  non_event  event_rate  non_event_rate    dist_event  \\\n",
       "female    215  162.00000       53.0    0.753488        0.246512  6.694215e-01   \n",
       "male      408   80.00000      328.0    0.196078        0.803922  3.305785e-01   \n",
       "A          14    6.00000        8.0    0.428571        0.571429  2.479339e-02   \n",
       "B          31   24.00000        7.0    0.774194        0.225806  9.917355e-02   \n",
       "C          42   24.00000       18.0    0.571429        0.428571  9.917355e-02   \n",
       "D          26   18.00000        8.0    0.692308        0.307692  7.438016e-02   \n",
       "E          27   20.00000        7.0    0.740741        0.259259  8.264462e-02   \n",
       "F           9    6.00000        3.0    0.666667        0.333333  2.479339e-02   \n",
       "G           2    1.00000        1.0    0.500000        0.500000  4.132231e-03   \n",
       "M         471  143.00000      328.0    0.303609        0.696391  5.909091e-01   \n",
       "T           1    0.00001        1.0    0.000010        1.000000  4.132231e-08   \n",
       "C         108   61.00000       47.0    0.564815        0.435185  2.541667e-01   \n",
       "Q          56   23.00000       33.0    0.410714        0.589286  9.583333e-02   \n",
       "S         457  156.00000      301.0    0.341357        0.658643  6.500000e-01   \n",
       "1         156   97.00000       59.0    0.621795        0.378205  4.008264e-01   \n",
       "2         132   64.00000       68.0    0.484848        0.515152  2.644628e-01   \n",
       "3         335   81.00000      254.0    0.241791        0.758209  3.347107e-01   \n",
       "0         474  169.00000      305.0    0.356540        0.643460  6.983470e-01   \n",
       "1          78   42.00000       36.0    0.538462        0.461538  1.735537e-01   \n",
       "2          59   27.00000       32.0    0.457627        0.542373  1.115702e-01   \n",
       "3           5    3.00000        2.0    0.600000        0.400000  1.239669e-02   \n",
       "4           2    0.00001        2.0    0.000005        1.000000  4.132231e-08   \n",
       "5           4    1.00000        3.0    0.250000        0.750000  4.132231e-03   \n",
       "6           1    0.00001        1.0    0.000010        1.000000  4.132231e-08   \n",
       "0         427  150.00000      277.0    0.351288        0.648712  6.198347e-01   \n",
       "1         144   80.00000       64.0    0.555556        0.444444  3.305785e-01   \n",
       "2          21    9.00000       12.0    0.428571        0.571429  3.719008e-02   \n",
       "3          10    2.00000        8.0    0.200000        0.800000  8.264462e-03   \n",
       "4          11    1.00000       10.0    0.090909        0.909091  4.132231e-03   \n",
       "5           3    0.00001        3.0    0.000003        1.000000  4.132231e-08   \n",
       "8           7    0.00001        7.0    0.000001        1.000000  4.132231e-08   \n",
       "\n",
       "        dist_non_event        WOE        IV    column  \n",
       "female        0.139108   1.571166  1.340788       Sex  \n",
       "male          0.860892  -0.957125  1.340788       Sex  \n",
       "A             0.020997   0.166180  0.490246     Cabin  \n",
       "B             0.018373   1.686005  0.490246     Cabin  \n",
       "C             0.047244   0.741544  0.490246     Cabin  \n",
       "D             0.020997   1.264792  0.490246     Cabin  \n",
       "E             0.018373   1.503684  0.490246     Cabin  \n",
       "F             0.007874   1.147009  0.490246     Cabin  \n",
       "G             0.002625   0.453862  0.490246     Cabin  \n",
       "M             0.860892  -0.376307  0.490246     Cabin  \n",
       "T             0.002625 -11.059064  0.490246     Cabin  \n",
       "C             0.123360   0.722887  0.122809  Embarked  \n",
       "Q             0.086614   0.101147  0.122809  Embarked  \n",
       "S             0.790026  -0.195094  0.122809  Embarked  \n",
       "1             0.154856   0.951035  0.496465    Pclass  \n",
       "2             0.178478   0.393237  0.496465    Pclass  \n",
       "3             0.666667  -0.689023  0.496465    Pclass  \n",
       "0             0.800525  -0.136551  0.169129     Parch  \n",
       "1             0.094488   0.608012  0.169129     Parch  \n",
       "2             0.083990   0.283963  0.169129     Parch  \n",
       "3             0.005249   0.859327  0.169129     Parch  \n",
       "4             0.005249 -11.752211  0.169129     Parch  \n",
       "5             0.007874  -0.644751  0.169129     Parch  \n",
       "6             0.002625 -11.059064  0.169129     Parch  \n",
       "0             0.727034  -0.159521  0.515549     SibSp  \n",
       "1             0.167979   0.677005  0.515549     SibSp  \n",
       "2             0.031496   0.166179  0.515549     SibSp  \n",
       "3             0.020997  -0.932433  0.515549     SibSp  \n",
       "4             0.026247  -1.848724  0.515549     SibSp  \n",
       "5             0.007874 -12.157676  0.515549     SibSp  \n",
       "8             0.018373 -13.004974  0.515549     SibSp  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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