2. Pre-Processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "  <h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Notebook Content</h3>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Data-Preprocessing\" role=\"tab\" aria-controls=\"profile\">Data Preprocessing<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "     <div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "  <a class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" href=\"#Missing-Value-Treatments\" role=\"tab\" aria-controls=\"home\">Missing Value Treatment</a>\n",
    " <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Mean-or-median-or-other-summary-statistic-substitution\" role=\"tab\" aria-controls=\"messages\"> Missing Value Treatment with mean<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Forward-fill-and-backward-fill-(can-be-used-according-to-business-problem)\" role=\"tab\" aria-controls=\"messages\">Forward and Backward fill<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "   <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Nearest-neighbors-imputation\" role=\"tab\" aria-controls=\"messages\">Nearest neighbors imputation<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#MultiOutputRegressor\" role=\"tab\" aria-controls=\"messages\">MultiOutputRegressor<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#IterativeImpute\" role=\"tab\" aria-controls=\"messages\">IterativeImpute<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Time-Series-Specific-Methods\" role=\"tab\" aria-controls=\"messages\">Time-Series Specific Methods<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "  <a class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" href=\"#Rescalling-Data\" role=\"tab\" aria-controls=\"home\">Rescalling</a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#MinMaxScaler\" role=\"tab\" aria-controls=\"settings\">MinMaxScaler<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#MaxAbsScaler\" role=\"tab\" aria-controls=\"settings\">MaxAbsScaler<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Robust-Scaler\" role=\"tab\" aria-controls=\"settings\">Robust Scaler<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#StandardScaler\" role=\"tab\" aria-controls=\"settings\">StandardScaler Scaler<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    " <div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "  <a class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" href=\"#Data-Transformation\" role=\"tab\" aria-controls=\"home\">Data Transformation</a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Quantile-Transformation\" role=\"tab\" aria-controls=\"settings\">Quantile Transformation<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Power-Transformation\" role=\"tab\" aria-controls=\"settings\">Power Transformation<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Custom-Transformation\" role=\"tab\" aria-controls=\"settings\">Custom Transformation<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Data-Normalization\" role=\"tab\" aria-controls=\"settings\">Data-Normalization<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "  <a class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" href=\"#Handling-Categorical-Variable\" role=\"tab\" aria-controls=\"home\">Handling Categorical Variable</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-processing refers to the **transformations applied to data** before feeding it to the algorithm. Data Preprocessing is a process that can be used to **convert the raw data into a clean dataset**. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for analysis; pre-processing heps us to bring our data to a desired format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Need for Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For achieving better results** from the applied model in Machine Learning projects the format of the data has to be in a proper manner. Some specified Machine Learning models need information in a specified format, for example, Random Forest algorithm does not support null values, therefore to execute random forest algorithm null values have to be managed from the original raw data set. Another aspect is that data set should be formatted in such a way that more than one Machine Learning and Deep Learning algorithms are executed in one data set, and best out of them is chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different data preprocesses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The different pre processing techniques are listed below; we will look into each of it in detail:"
   ]
  },
  {
   "attachments": {
    "Types%20of%20Pre_Processing.PNG": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Types%20of%20Pre_Processing.PNG](attachment:Types%20of%20Pre_Processing.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Missing Value Treatments__\n",
    "The methods used to handle missing values are as follows:<br>\n",
    "1. Drop missing values\n",
    "2. Fill missing value with test statistic\n",
    "3. Predict missing value with maching learning algoritm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   First Score  Second Score  Third Score\n",
      "0        100.0          30.0          NaN\n",
      "1         90.0          45.0         40.0\n",
      "2          NaN          56.0         98.0\n",
      "3         95.0           NaN         98.0\n",
      "4         75.0           NaN         56.0\n",
      "\n",
      "No of null values:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "First Score     1\n",
       "Second Score    2\n",
       "Third Score     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import  numpy as np\n",
    "# Check missing values in a dataset \n",
    "dict = {'First Score':[100, 90, np.nan, 95, 75], \n",
    "\n",
    "        'Second Score': [30, 45, 56, np.nan, np.nan], \n",
    "\n",
    "        'Third Score':[np.nan, 40, 98, 98, 56]} \n",
    "\n",
    "# creating a dataframe from list \n",
    "df = pd.DataFrame(dict)\n",
    "print(df)\n",
    "print('\\nNo of null values:')\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "2          0.0          56.0         98.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If the missing value isn’t identified as NaN , then we have to first convert or replace such non NaN entry with a NaN\n",
    "df_2 = df.copy()\n",
    "df_2['First Score'].replace(np.nan,0, inplace= True)\n",
    "df_2[df_2['First Score'] == 0].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputation vs Removing Data\n",
    "Before jumping to the methods of data imputation, we have to understand the reason why data goes missing.\n",
    "1. **Missing completely at random**: This is a case when the probability of missing variable is same for all observations. For example: respondents of data collection process decide that they will declare their earning after tossing a fair coin. If an head occurs, respondent declares his / her earnings & vice versa. Here each observation has equal chance of missing value.\n",
    "2. **Missing at random**: This is a case when variable is missing at random and missing ratio varies for different values / level of other input variables. For example: We are collecting data for age and female has higher missing value compare to male.\n",
    "3. **Missing that depends on unobserved predictors**: This is a case when the missing values are not random and are related to the unobserved input variable. For example: In a medical study, if a particular diagnostic causes discomfort, then there is higher chance of drop out from the study. This missing value is not at random unless we have included “discomfort” as an input variable for all patients.\n",
    "4. **Missing that depends on the missing value itself**: This is a case when the probability of missing value is directly correlated with missing value itself. For example: People with higher or lower income are likely to provide non-response to their earning.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Simple approaches**<br>\n",
    "A number of simple approaches exist. For basic use cases, these are often enough.<br><br>\n",
    "**Dropping rows with null values**\n",
    "1. If the number of data points is sufficiently high that dropping some of them will not cause lose generalizability in the models built (to determine whether or not this is the case, a learning curve can be used)\n",
    "2. Dropping too much data is also dangerous\n",
    "3. If in a large data set is present and missinng values is in range of 5-3%; then droping missing values is feasible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "1         90.0          45.0         40.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3=df.copy()\n",
    "df_3.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropping features with high nullity**\n",
    "\n",
    "A feature that has a high number of empty values is unlikely to be very useful for prediction. It can often be safely dropped.\n",
    "<br>**Note:** \"But before deciding the variable is not usefull we should perform feature importance test for validation\", tree based method can be used "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.drop(['Second Score'], axis= 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean or median or other summary statistic substitution\n",
    "When to use example:\n",
    "1. Check outlier, if less outliers is present then mean imputation can be used\n",
    "2. When outliers are more median impuation can be used \n",
    "3. For categorical variables mode imputaion can be used\n",
    "\n",
    "<br>**NOTE:**- Ok to use if missing data is less than 3%, otherwise introduces too much bias and artificially lowers variability of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7.   2.   3. ]\n",
      " [ 4.   3.5  6. ]\n",
      " [10.   3.5  9. ]]\n"
     ]
    }
   ],
   "source": [
    "# Simple illustration for missing value imputation with mean \n",
    "# The imputation strategies are mean, mode & median \n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer\n",
    "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])\n",
    "SimpleImputer()\n",
    "#This will look for all columns where we have NaN value and replace the NaN value with specified test statistic.\n",
    "X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]\n",
    "print(imp_mean.transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL    8.0   black  class 1  22.0\n",
       "1    L    NaN    gray  class 2  20.0\n",
       "2   XL   10.0    blue  class 2  19.0\n",
       "3    M    NaN  orange  class 1  17.0\n",
       "4    M   11.0   green  class 3   NaN\n",
       "5    M    7.0     red  class 1  22.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df=pd.DataFrame([[\"XXL\", 8, \"black\", \"class 1\", 22],\n",
    "[\"L\", np.nan, \"gray\", \"class 2\", 20],\n",
    "[\"XL\", 10, \"blue\", \"class 2\", 19],\n",
    "[\"M\", np.nan, \"orange\",\"class 1\", 17],\n",
    "[\"M\", 11, \"green\", \"class 3\", np.nan],\n",
    "[\"M\", 7, \"red\", \"class 1\", 22]])\n",
    "\n",
    "df.columns=[\"size\", \"price\", \"color\", \"class\", \"boh\"]\n",
    "df_copy= df.copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL    8.0   black  class 1  22.0\n",
       "1    L    NaN    gray  class 2  20.0\n",
       "2   XL   10.0    blue  class 2  19.0\n",
       "3    M    NaN  orange  class 1  17.0\n",
       "4    M   11.0   green  class 3  20.0\n",
       "5    M    7.0     red  class 1  22.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# imputation is done with respect to one column by using mean, mode and median stragey \n",
    "from sklearn.impute import SimpleImputer\n",
    "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imp_mean.fit(df[['boh']])\n",
    "df[\"boh\"]=imp_mean.transform(df[[\"boh\"]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>9.0</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL    8.0   black  class 1  22.0\n",
       "1    L    9.0    gray  class 2  20.0\n",
       "2   XL   10.0    blue  class 2  19.0\n",
       "3    M    9.0  orange  class 1  17.0\n",
       "4    M   11.0   green  class 3  20.0\n",
       "5    M    7.0     red  class 1  22.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some other column \n",
    "imp_mean.fit(df[['price']])\n",
    "df[\"price\"]=imp_mean.transform(df[[\"price\"]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  size  price   color    class   boh  First score  Second Score\n",
      "0  XXL    8.0   black  class 1  22.0          8.0           8.0\n",
      "1    L    9.0    gray  class 2  20.0          9.0           9.0\n",
      "2   XL   10.0    blue  class 2  19.0         10.0          10.0\n",
      "3    M    9.0  orange  class 1  17.0          9.0           9.0\n",
      "4    M   11.0   green  class 3  20.0         11.0          11.0\n",
      "5    M    7.0     red  class 1  22.0          7.0           7.0\n"
     ]
    }
   ],
   "source": [
    "# Columns specific imputation in a dataframe \n",
    "df_4 = df.copy()\n",
    "mean_value=df_4['price'].mean()\n",
    "df_4['First score']=df_4['price'].fillna(mean_value)\n",
    "#this will replace all NaN values with the mean of the non null values\n",
    "#For Median\n",
    "median_value=df_4['price'].median()\n",
    "df_4['Second Score']=df_4['price'].fillna(median_value)\n",
    "print(df_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forward fill and backward fill (can be used according to business problem)\n",
    "Forward filling means fill missing values with previous data. Backward filling means fill missing values with next data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series  :\n",
      " 2010-10-31    100.0\n",
      "2010-11-30      NaN\n",
      "2010-12-31      NaN\n",
      "2011-01-31     18.0\n",
      "2011-02-28     65.0\n",
      "2011-03-31      NaN\n",
      "2011-04-30     32.0\n",
      "2011-05-31     10.0\n",
      "2011-06-30      5.0\n",
      "2011-07-31     24.0\n",
      "2011-08-31     60.0\n",
      "Freq: M, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Creating the Series \n",
    "sr = pd.Series([100, None, None, 18, 65, None, 32, 10, 5, 24, 60]) \n",
    "\n",
    "# Create the Index \n",
    "index_ = pd.date_range('2010-10-09', periods = 11, freq ='M')   \n",
    "\n",
    "# set the index\n",
    "sr.index = index_   \n",
    "\n",
    "# Print the series\n",
    "print('Series  :\\n',sr) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series after forward fill :\n",
      " 2010-10-31    100.0\n",
      "2010-11-30    100.0\n",
      "2010-12-31    100.0\n",
      "2011-01-31     18.0\n",
      "2011-02-28     65.0\n",
      "2011-03-31     65.0\n",
      "2011-04-30     32.0\n",
      "2011-05-31     10.0\n",
      "2011-06-30      5.0\n",
      "2011-07-31     24.0\n",
      "2011-08-31     60.0\n",
      "Freq: M, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "result = sr.fillna(method = 'ffill')\n",
    "print('Series after forward fill :\\n',result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series after backward fill :\n",
      " 2010-10-31    100.0\n",
      "2010-11-30     18.0\n",
      "2010-12-31     18.0\n",
      "2011-01-31     18.0\n",
      "2011-02-28     65.0\n",
      "2011-03-31     32.0\n",
      "2011-04-30     32.0\n",
      "2011-05-31     10.0\n",
      "2011-06-30      5.0\n",
      "2011-07-31     24.0\n",
      "2011-08-31     60.0\n",
      "Freq: M, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "result = sr.fillna(method = 'bfill')\n",
    "print('Series after backward fill :\\n',result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nearest neighbors imputation\n",
    "It can be used for data that are continuous, discrete, ordinal and categorical which makes it particularly useful for dealing with all kind of missing data. The assumption behind using KNN for missing values is that a point value can be approximated by the values of the points that are closest to it, based on other variables. <br><br>The distance metric varies according to the type of data:\n",
    "1. **Continuous Data**: The commonly used distance metrics for continuous data are Euclidean, Manhattan and Cosine\n",
    "2. **Categorical Data**: Hamming distance is generally used in this case. It takes all the categorical attributes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 2. , 4. ],\n",
       "       [3. , 4. , 3. ],\n",
       "       [5.5, 6. , 5. ],\n",
       "       [8. , 8. , 7. ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.impute import KNNImputer\n",
    "X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]]\n",
    "imputer = KNNImputer(n_neighbors=2)\n",
    "imputer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>9.0</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL    8.0   black  class 1  22.0\n",
       "1    L    9.0    gray  class 2  20.0\n",
       "2   XL   10.0    blue  class 2  19.0\n",
       "3    M    9.0  orange  class 1  17.0\n",
       "4    M   11.0   green  class 3  20.0\n",
       "5    M    7.0     red  class 1  22.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>9.0</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL    8.0   black  class 1  22.0\n",
       "1    L    9.0    gray  class 2  20.0\n",
       "2   XL   10.0    blue  class 2  19.0\n",
       "3    M    9.0  orange  class 1  17.0\n",
       "4    M   11.0   green  class 3  20.0\n",
       "5    M    7.0     red  class 1  22.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN Imputer for a dataframe \n",
    "import numpy as np\n",
    "from sklearn.impute import KNNImputer\n",
    "imputer = KNNImputer(n_neighbors=5)\n",
    "q = imputer.fit_transform(df[['boh']])\n",
    "df['boh'] = q\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>9.0</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL    8.0   black  class 1  22.0\n",
       "1    L    9.0    gray  class 2  20.0\n",
       "2   XL   10.0    blue  class 2  19.0\n",
       "3    M    9.0  orange  class 1  17.0\n",
       "4    M   11.0   green  class 3  20.0\n",
       "5    M    7.0     red  class 1  22.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = imputer.fit_transform(df[['price']])\n",
    "df['price'] = q\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MultiOutputRegressor\n",
    "\n",
    "\n",
    "This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  5. 162.  60.]\n",
      " [  2. 110.  60.]\n",
      " [ 12. 101. 101.]\n",
      " [ 12. 105.  37.]\n",
      " [ 13. 155.  58.]\n",
      " [  4. 101.  42.]\n",
      " [  8. 101.  38.]\n",
      " [  6. 125.  40.]\n",
      " [ 15. 200.  40.]\n",
      " [ 17. 251. 250.]\n",
      " [ 17. 120.  38.]\n",
      " [ 13. 210. 115.]\n",
      " [ 14. 215. 105.]\n",
      " [  1.  50.  50.]\n",
      " [  6.  70.  31.]\n",
      " [ 12. 210. 120.]\n",
      " [  4.  60.  25.]\n",
      " [ 11. 230.  80.]\n",
      " [ 15. 225.  73.]\n",
      " [  2. 110.  43.]]\n",
      "[[191.  36.  50.]\n",
      " [189.  37.  52.]\n",
      " [193.  38.  58.]\n",
      " [162.  35.  62.]\n",
      " [189.  35.  46.]\n",
      " [182.  36.  56.]\n",
      " [211.  38.  56.]\n",
      " [167.  34.  60.]\n",
      " [176.  31.  74.]\n",
      " [154.  33.  56.]\n",
      " [169.  34.  50.]\n",
      " [166.  33.  52.]\n",
      " [154.  34.  64.]\n",
      " [247.  46.  50.]\n",
      " [193.  36.  46.]\n",
      " [202.  37.  62.]\n",
      " [176.  37.  54.]\n",
      " [157.  32.  52.]\n",
      " [156.  33.  54.]\n",
      " [138.  33.  68.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_linnerud\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "X, y = load_linnerud(return_X_y=True)\n",
    "print(X)\n",
    "print(y)\n",
    "clf = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)\n",
    "pred =clf.predict(X[[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[176.16484296,  35.0548407 ,  57.09000136]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IterativeImpute"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a Multivariate imputer that estimates each feature from all the others. It applies a strategy for imputing missing values by modeling each feature with missing values as a function of other features in a round-robin fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.95847623,  2.        ,  3.        ],\n",
       "       [ 4.        ,  2.6000004 ,  6.        ],\n",
       "       [10.        ,  4.99999933,  9.        ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "imp_mean = IterativeImputer(random_state=0)\n",
    "imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])\n",
    "IterativeImputer(random_state=0)\n",
    "X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]\n",
    "imp_mean.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-Series Specific Methods\n",
    "1. **Last Observation Carried Forward (LOCF) & Next Observation Carried Backward (NOCB)**\n",
    "<br>This is a common statistical approach to the analysis of longitudinal repeated measured data where some follow-up observations may be missing. Longitudinal data track the same sample at different points in time. Both these methods can introduce bias in analysis and perform poorly when data has a visible trend\n",
    "2. **Data without trend and seasonality**\n",
    "mean, mode, median and random sample imputation can be used \n",
    "3. **Linear Interpolation**\n",
    "This method works well for a time series with some **trend** but is not suitable for **seasonal data**\n",
    "4. **Seasonal Adjustment + Linear Interpolation**\n",
    "This method works well for data with both **trend and seasonality**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rescalling Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When data is comprised of **attributes with varying scales**, many machine learning algorithms can benefit from rescaling the attributes to all have the same scale. This is useful for optimization algorithms used in the core of machine learning algorithms like gradient descent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also useful for algorithms that weight inputs like regression and neural networks and algorithms that use distance measures like K-Nearest Neighbors. \n",
    "Rescaling of data using different techniques, some of which are listed below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When faced with features which are very different in scale / units, it is quite clear to see that classifiers / regressors which rely on euclidean distance such as k-nearest neighbours will fail or be sub-optimal. Same goes for other regressors. Especially the ones that rely on gradient descent based optimisation such as logistic regressions, Support Vector Machines and Neural networks. The only classifiers/regressors which are immune to impact of scale are the tree based regressors.\n",
    "\n",
    "**NOTE:** \n",
    "1. Before performing scalling one should check oultier and Treat the outlier\n",
    "2. Check the EDA Notebook for various outlier treament method "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform features by **scaling each feature to a given range**.\n",
    "This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.<br><br>The transformation is given by:<br>X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))<br>X_scaled = X_std * (max - min) + min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinMaxScaler()\n",
      "[[0.   0.  ]\n",
      " [0.25 0.25]\n",
      " [0.5  0.5 ]\n",
      " [1.   1.  ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]\n",
    "scaler = MinMaxScaler()\n",
    "print(scaler.fit(data))\n",
    "MinMaxScaler()\n",
    "print(scaler.transform(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>9.0</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL    8.0   black  class 1  22.0\n",
       "1    L    9.0    gray  class 2  20.0\n",
       "2   XL   10.0    blue  class 2  19.0\n",
       "3    M    9.0  orange  class 1  17.0\n",
       "4    M   11.0   green  class 3  20.0\n",
       "5    M    7.0     red  class 1  22.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>0.25</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>0.50</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>0.75</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.50</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1.00</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>0.00</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL   0.25   black  class 1  22.0\n",
       "1    L   0.50    gray  class 2  20.0\n",
       "2   XL   0.75    blue  class 2  19.0\n",
       "3    M   0.50  orange  class 1  17.0\n",
       "4    M   1.00   green  class 3  20.0\n",
       "5    M   0.00     red  class 1  22.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# minmax scaler on cloumn of a dataframe\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "df['price'] = scaler.fit_transform(df[['price']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>0.25</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>0.50</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>0.75</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.50</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1.00</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>0.00</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL   0.25   black  class 1  22.0\n",
       "1    L   0.50    gray  class 2  20.0\n",
       "2   XL   0.75    blue  class 2  19.0\n",
       "3    M   0.50  orange  class 1  17.0\n",
       "4    M   1.00   green  class 3  20.0\n",
       "5    M   0.00     red  class 1  22.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  MaxAbsScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This estimator **scales and translates each feature individually** such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.<br><br>\n",
    "This scaler can also be applied to sparse CSR or CSC matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5, -1. ,  1. ],\n",
       "       [ 1. ,  0. ,  0. ],\n",
       "       [ 0. ,  1. , -0.5]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "X = [[ 1., -1.,  2.],\n",
    "     [ 2.,  0.,  0.],\n",
    "     [ 0.,  1., -1.]]\n",
    "transformer = MaxAbsScaler().fit(X)\n",
    "transformer\n",
    "MaxAbsScaler()\n",
    "transformer.transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>0.25</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>0.50</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>0.75</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.50</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1.00</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>0.00</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL   0.25   black  class 1  22.0\n",
       "1    L   0.50    gray  class 2  20.0\n",
       "2   XL   0.75    blue  class 2  19.0\n",
       "3    M   0.50  orange  class 1  17.0\n",
       "4    M   1.00   green  class 3  20.0\n",
       "5    M   0.00     red  class 1  22.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>0.25</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>0.50</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>0.75</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.50</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1.00</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>0.00</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL   0.25   black  class 1  22.0\n",
       "1    L   0.50    gray  class 2  20.0\n",
       "2   XL   0.75    blue  class 2  19.0\n",
       "3    M   0.50  orange  class 1  17.0\n",
       "4    M   1.00   green  class 3  20.0\n",
       "5    M   0.00     red  class 1  22.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "transformer = MaxAbsScaler().fit(df[['price']])\n",
    "df['price'] = transformer.transform(df[['price']])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Robust Scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale features using statistics that are **robust to outliers**. RobustScaler transforms the feature vector by subtracting the median and then dividing by the interquartile range (75% value — 25% value)<br><br>\n",
    "**Centering and scaling happen independently on each feature** by computing the relevant statistics on the samples in the training set. Median and interquartile range are then stored to be used on later data using the transform method.<br>\n",
    "Standardization of a dataset is a common requirement for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the interquartile range often give better results<br><br>**Use RobustScaler, to reduce the effects of outliers**, relative to MinMaxScaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. , -2. ,  0. ],\n",
       "       [-1. ,  0. ,  0.4],\n",
       "       [ 1. ,  0. , -1.6]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "X = [[ 1., -2.,  2.],\n",
    "     [ -2.,  1.,  3.],\n",
    "     [ 4.,  1., -2.]]\n",
    "transformer = RobustScaler().fit(X)\n",
    "transformer\n",
    "RobustScaler()\n",
    "transformer.transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>0.25</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>0.50</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>0.75</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.50</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1.00</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>0.00</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL   0.25   black  class 1  22.0\n",
       "1    L   0.50    gray  class 2  20.0\n",
       "2   XL   0.75    blue  class 2  19.0\n",
       "3    M   0.50  orange  class 1  17.0\n",
       "4    M   1.00   green  class 3  20.0\n",
       "5    M   0.00     red  class 1  22.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>0.25</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>0.50</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>0.75</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>-0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.50</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>-1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1.00</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>0.00</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class       boh\n",
       "0  XXL   0.25   black  class 1  0.888889\n",
       "1    L   0.50    gray  class 2  0.000000\n",
       "2   XL   0.75    blue  class 2 -0.444444\n",
       "3    M   0.50  orange  class 1 -1.333333\n",
       "4    M   1.00   green  class 3  0.000000\n",
       "5    M   0.00     red  class 1  0.888889"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# robustscaler for dataframe\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "transformer = RobustScaler().fit(df[['boh']])\n",
    "df['boh'] = transformer.transform(df[['boh']])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### StandardScaler\n",
    "\n",
    "StandardScaler standardizes a feature by subtracting the mean and then scaling to unit variance. Unit variance means dividing all the values by the standard deviation. StandardScaler does not meet the strict definition of scale I introduced earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**When to use**\n",
    "it can be used when to transform a feature so it is close to normally distributed \n",
    "**NOTE**\n",
    "1. Results in the distribution with a Standard deviation equal to 1\n",
    "2. If there are outliers in the feature, normalize the data and scale most of the data to a small interval\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DescribeResult(nobs=4, minmax=(-1.3416407864998738, 1.3416407864998738), mean=0.0, variance=1.3333333333333333, skewness=0.0, kurtosis=-1.3599999999999999)\n",
      "DescribeResult(nobs=4, minmax=(-1.2828087129930659, 1.3778315806221817), mean=-5.551115123125783e-17, variance=1.3333333333333335, skewness=0.11003776770595125, kurtosis=-1.394993095506219)\n",
      "DescribeResult(nobs=4, minmax=(-1.155344148338584, 1.53471088361394), mean=0.0, variance=1.3333333333333333, skewness=0.48089217736510326, kurtosis=-1.1471008824318165)\n",
      "DescribeResult(nobs=4, minmax=(-1.2604572012883055, 1.2668071116222517), mean=-5.551115123125783e-17, variance=1.3333333333333333, skewness=0.0056842140599118185, kurtosis=-1.6438177182479734)\n",
      "DescribeResult(nobs=4, minmax=(-1.338945389819976, 1.3434309690153527), mean=5.551115123125783e-17, variance=1.3333333333333333, skewness=0.005374558840039456, kurtosis=-1.3619131970819205)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "data= [[1, 1, 1, 1, 1],[2, 5, 10, 50, 100],[3, 10, 20, 150, 200],[4, 15, 40, 200, 300]]\n",
    "\n",
    "df = pd.DataFrame(data, columns=['N0', 'N1', 'N2', 'N3', 'N4']).astype('float64')\n",
    "\n",
    "sc_X = StandardScaler()\n",
    "df = sc_X.fit_transform(df)\n",
    "\n",
    "# df = pd.DataFrame(df, columns=['N0', 'N1', 'N2', 'N3', 'N4'])\n",
    "# Get the dataframe for further analysis\n",
    "\n",
    "\n",
    "\n",
    "# From this stats infromation can be obtanined\n",
    "num_cols = len(df[0,:])\n",
    "for i in range(num_cols):\n",
    "    col = df[:,i]\n",
    "    col_stats = ss.describe(col)\n",
    "    print(col_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two types of transformations are available: quantile transforms and power transforms.<br> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Quantile Transformation\n",
    "Quantile transformation can be used for __uniform data__. By performing a rank transformation, a quantile transform smooths out unusual distributions and is less influenced by outliers than scaling methods. It does, however, distort correlations and distances within and across features. <br><br> An example of Quantile Transformation is given below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        ],\n",
       "       [0.09871873],\n",
       "       [0.10643612],\n",
       "       [0.11754671],\n",
       "       [0.21017437],\n",
       "       [0.21945445],\n",
       "       [0.23498666],\n",
       "       [0.32443642],\n",
       "       [0.33333333],\n",
       "       [0.41360794],\n",
       "       [0.42339464],\n",
       "       [0.46257841],\n",
       "       [0.47112236],\n",
       "       [0.49834237],\n",
       "       [0.59986536],\n",
       "       [0.63390302],\n",
       "       [0.66666667],\n",
       "       [0.68873101],\n",
       "       [0.69611125],\n",
       "       [0.81280699],\n",
       "       [0.82160354],\n",
       "       [0.88126439],\n",
       "       [0.90516028],\n",
       "       [0.99319435],\n",
       "       [1.        ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "rng = np.random.RandomState(0)\n",
    "X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0)\n",
    "qt = QuantileTransformer(n_quantiles=10, random_state=0)\n",
    "qt.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.34164079, -1.28280871, -1.15534415, -1.2604572 , -1.33894539],\n",
       "       [-0.4472136 , -0.52262577, -0.53456222, -0.63816599, -0.45080071],\n",
       "       [ 0.4472136 ,  0.4276029 ,  0.15519548,  0.63181608,  0.44631513],\n",
       "       [ 1.34164079,  1.37783158,  1.53471088,  1.26680711,  1.34343097]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Power Transformation\n",
    "Power transforms are a family of parametric transformations that aim to __map data from any distribution to as close to a Gaussian distribution__ as possible in order to stabilize variance and minimize skewness<br><br>There are two methods for power transformation: __Yeo Johnson and Box-cox__<br><br> Box-Cox can only be applied to strictly positive data. In both methods, the transformation is parameterized by \n",
    "λ, which is determined through maximum likelihood estimation. Here is an example of using Box-Cox to map samples drawn from a lognormal distribution to a normal distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.49024349,  0.17881995, -0.1563781 ],\n",
       "       [-0.05102892,  0.58863195, -0.57612414],\n",
       "       [ 0.69420009, -0.84857822,  0.10051454]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "pt = PowerTransformer(method='box-cox', standardize=False)\n",
    "X_lognormal = np.random.RandomState(616).lognormal(size=(3, 3))\n",
    "pt.fit_transform(X_lognormal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class   boh\n",
       "0  XXL    8.0   black  class 1  22.0\n",
       "1    L    NaN    gray  class 2  20.0\n",
       "2   XL   10.0    blue  class 2  19.0\n",
       "3    M    NaN  orange  class 1  17.0\n",
       "4    M   11.0   green  class 3   NaN\n",
       "5    M    7.0     red  class 1  22.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>7546.664382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>5524.660719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>4670.996737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>3245.914188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>7546.664382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class          boh\n",
       "0  XXL    8.0   black  class 1  7546.664382\n",
       "1    L    NaN    gray  class 2  5524.660719\n",
       "2   XL   10.0    blue  class 2  4670.996737\n",
       "3    M    NaN  orange  class 1  3245.914188\n",
       "4    M   11.0   green  class 3          NaN\n",
       "5    M    7.0     red  class 1  7546.664382"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "pt = PowerTransformer(method='box-cox', standardize=False)\n",
    "df_copy['boh'] = pt.fit_transform(df_copy[['boh']])\n",
    "df_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Transformation\n",
    "We might want to __convert an existing Python function into a transformer__ to assist in data cleaning or processing. A transformer from an arbitrary function with FunctionTransformer can be implemented. <br><br>For example, to build a transformer that applies a log transformation in a pipeline, the following can be done:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.69314718],\n",
       "       [1.09861229, 1.38629436]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "transformer = FunctionTransformer(np.log1p, validate=True)\n",
    "X = np.array([[0, 1], [2, 3]])\n",
    "transformer.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>7546.664382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gray</td>\n",
       "      <td>class 2</td>\n",
       "      <td>5524.660719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>4670.996737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orange</td>\n",
       "      <td>class 1</td>\n",
       "      <td>3245.914188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>green</td>\n",
       "      <td>class 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>7546.664382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price   color    class          boh\n",
       "0  XXL    8.0   black  class 1  7546.664382\n",
       "1    L    NaN    gray  class 2  5524.660719\n",
       "2   XL   10.0    blue  class 2  4670.996737\n",
       "3    M    NaN  orange  class 1  3245.914188\n",
       "4    M   11.0   green  class 3          NaN\n",
       "5    M    7.0     red  class 1  7546.664382"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-38-539a8f993a59>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_copy['boh'] = transformer.fit_transform(df_copy[['boh']])\n"
     ]
    },
    {
     "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>8.928993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>8.449342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>8.928993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price  color    class       boh\n",
       "0  XXL    8.0  black  class 1  8.928993\n",
       "2   XL   10.0   blue  class 2  8.449342\n",
       "5    M    7.0    red  class 1  8.928993"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "transformer = FunctionTransformer(np.log1p, validate=True)\n",
    "df_copy = df_copy.dropna()\n",
    "df_copy['boh'] = transformer.fit_transform(df_copy[['boh']])\n",
    "df_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization is the process of **scaling individual samples to have unit norm**. The function normalize provides a quick and easy way to perform this operation on a single array-like dataset, either using the l1 or l2 norms. Normalizer __works on the rows, not the columns!__ \n",
    "<br><br>By default, L2 normalization is applied to each observation so the that the values in a row have a unit norm. Unit norm with L2 means that if each element were squared and summed, the total would equal 1. Alternatively, L1 (aka taxicab or Manhattan) normalization can be applied instead of L2 normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.40824829, -0.40824829,  0.81649658],\n",
       "       [ 1.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.70710678, -0.70710678]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "X = [[ 1., -1.,  2.],\n",
    "     [ 2.,  0.,  0.],\n",
    "     [ 0.,  1., -1.]]\n",
    "X_normalized = preprocessing.normalize(X, norm='l2')\n",
    "X_normalized\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The preprocessing module further provides a utility class Normalizer that implements the same operation using the Transformer API (even though the fit method is useless in this case: the class is stateless as this operation treats samples independently)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.40824829, -0.40824829,  0.81649658],\n",
       "       [ 1.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.70710678, -0.70710678]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalizer = preprocessing.Normalizer().fit(X)  # fit does nothing\n",
    "normalizer.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>8.928993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>8.449342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>8.928993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price  color    class       boh\n",
       "0  XXL    8.0  black  class 1  8.928993\n",
       "2   XL   10.0   blue  class 2  8.449342\n",
       "5    M    7.0    red  class 1  8.928993"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-42-178b15806e9c>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_copy['price'] = preprocessing.normalize(df_copy[['price']], norm='l1')\n"
     ]
    },
    {
     "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>size</th>\n",
       "      <th>price</th>\n",
       "      <th>color</th>\n",
       "      <th>class</th>\n",
       "      <th>boh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>black</td>\n",
       "      <td>class 1</td>\n",
       "      <td>8.928993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>blue</td>\n",
       "      <td>class 2</td>\n",
       "      <td>8.449342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>red</td>\n",
       "      <td>class 1</td>\n",
       "      <td>8.928993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  size  price  color    class       boh\n",
       "0  XXL    1.0  black  class 1  8.928993\n",
       "2   XL    1.0   blue  class 2  8.449342\n",
       "5    M    1.0    red  class 1  8.928993"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "df_copy['price'] = preprocessing.normalize(df_copy[['price']], norm='l1')\n",
    "df_copy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize and Normalizer accept both dense array-like and sparse matrices from scipy.sparse as input. For sparse input the data is converted to the Compressed Sparse Rows representation (see scipy.sparse.csr_matrix) before being fed to efficient Cython routines. To avoid unnecessary memory copies, it is recommended to choose the CSR representation upstream."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __To summarize__\n",
    "• Use MinMaxScaler as the default if we are transforming a feature. It’s non-distorting.\n",
    "<br>• Use RobustScaler if we have outliers and want to reduce their influence. However, we might be better off removing the outliers, instead.\n",
    "<br>• Use StandardScaler if one need a relatively normal distribution.\n",
    "<br>• Use Normalizer sparingly — it normalizes sample rows, not feature columns. It can use l2 or l1 normalization."
   ]
  },
  {
   "attachments": {
    "Scalers.png": {
     "image/png": 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/gAMaXkhs0qRJtNtTCSGPDhGaoa5bt658O0pf1q9fL4W0t+hCwCnCjR6JCSDi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MF3U8bsBEOnHkFKchYEmVPgUVK1eYmrRvQK27t+D9gSVOfHu4jJd/vDRSfgkfPpyR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RL0DTN7IB7YKHDSNx61+dCuzui0fQPpLnIsQy5UKJyKxkSJ3B+IWZOG+nyfVZys\ncawyc8Jgj108X/o3PZu1Q8YtMk2fWaaSem8SGX3BDR4GW54T3RdYsZe4CRyZ+wydRvML2r9ysXRU\nT+gT7ZX+axc3dHHJk2STfdjB5u5ERQ+RayRsNEL+oj+jjbFub1A5ryfFN/rAvNUHPBvCEicyLVTd\nNRz9yK4Nwk1CWTfk15xnJB8qepfRSSGc84TE7vxqV0d0S9+ITuyuWYDbdgwz7K5J3B5/bs4xkVyD\nBwJ1hP4oMToQ5taljStg0eHFzXpyQjYtep+kZHmv0050UrCYb5rDYGkPgd11NZyPB2hjAfQcPNkj\nZzv3Hk1jAWC8yMNpodgJkKZzjGZHwSEyKbMLK8/QPwHkJ4IO0fVDbCeAHQjrhyMf79TecRepkxxH\nFHKlnSJpN0idNoto003NIjJv2kiaxxpXKyhK5Uks9mxbQ+8bcWP/wvLxc4TdXLntLE0SEgh5fa52\nNHaTNr3E7BX7KA+xngYtunk6NzoxTlDBSNGx9yh6D8DjDMocBNSEuZtIsZSNgg7zz9++QvaZZ6J6\nUcoTmOBpXaoehEUcOWWFpwPjdzBgUKaxs9Z5Js5nYqoee07LJr1o/YEFYKc+6kCHMQKTCZ4TJ0uj\n1JvX61qWXe9BqNwcPckTCnZo0WGAaNS6J3Va/B4GuFSOylVtKMbPXCdeeOFFev04I4Wt7IuBeD+R\nmk4E9bVgphrBDMALFqHl9X1J1mWRUlXIKdCoPAP8rXWXwXQNoYgHwGZm/49m+ZxIw4ZX575jtVfq\nWEN/RftNWbDN019lP0G/m7pou+d0FZB9C+D9+asPeoztIHvuIp5IeeAbXRhc9JdhExb7CDmMdfl9\nAMb0ifM2U453PdjgGDdrvedkBxaF8v+gzExZsNVzuhphbOUpAnwGG/64TzgELFx3RPRQJkj5t9Mm\n+cj19QylferCbSKPdpoUv4u2AWir4ZOWeiJTSIKN14eVoro6h5HslVdf016phEt+SfRyCZM9ZCtO\neeA70X6QLwDjDCd8O+nK4qSvMtEH9zV7QNeYvXwfySE5z8jfk2DO/UiRR889/4L2ji/+5qennlJV\ntlDnF2B3HoMReuDoeZ60QUDqfw1bdhcNW6kO5PoUiRK792NXTwLoh1hQSGcy/I+sA8xz05fs9EkR\nBpz8DhNaH8fCpZ/SH6VMQJ+T8zS+a+j4RaJyTfUkkyQc+qu+z0kZ46+fBsJsLOp5Rvv7m3HjixFT\nlnt0N/Q1LMrxm1j8zVt1gJwaAOpDOkNIpKETlK3quxkkcaIDBaJO446iRcf+nnpBmSWNlfZo0Nz3\nxH7sN+OKUdNWee4RoAz4f8iaLIpeC4zjyWx9qMesjvXv2Z0XgFzrmQHDyeyV+0kOSOT3gbpNO4th\nSj8NRqht7q9vybqSfQtgXYixI3V6yD4pnzE/Y5yAmM9aWz86kY1O10bAznreCDYCKtX0GtY+vRI4\nzZoEZclXxKsbpNBOckv0zllZsnmjbYFI2i8CrdVCQbaF7E+B4PUwr4f90bXfOI8xEqAtUW7wQeIU\nYvHGEyJNem+Kb7O6RnsPHb/Qo79hHsDGMe4Vcn3B2kOieYd+9Dd8983PrtK1xJ9dDHoriGHQf1t0\nHCDKVVENpehPcm4roegnfT9SU3bG1OYIp2WTvx0IO+UA8j5lWxnvKxBSN7fbNx6W+dAM2eeNMsaO\nzhhK3/Onu9Rv3lVUqO6NNIwxgd9HFgTUgSSGbh4INv6crHHM0PdLyCzUEcom+wDup/fQaZ6NISOI\nYiBBWdA/7BDsPkPRY83mVSc2c7u6rySYvQSY2fX12NEN8P1jZqwhOQtQT7JP9B42nZ6BP1uDkWC6\nuFnZ9OjL5vZ6hHEOdJ2Jil4KnVW/PkMbS9DuaP85Kz82jSblRKb506uM8xjwNy7c1mWc2CCCYXVc\nhLJuQNlKlfdGTNFHcdHj1jyhx6dtFOzaZOzoiG7pG3qM5Qd2dEa9XPO99s6l+j5gd80CnNgxJGZr\nLCBlud5WYGdNAtyW427NMZFcgwcjdwFdpKTMvmmg9ZG6s+dVo3jrMZOFwM252Ar6NTfav0Q5r3NW\nAYNTeiD7GTDrj3b6qH7NoydNuiyeNsWzjGYWCLvr6vWr53uibkPWJNdSFAL8Zo/BqpMrxvSEET3p\nGoRiJ8iljMVBY+d7nI70+ib6ARzEJE7sBJGaW/21cSDkulSvN/gjmdYWcAjbv2dTxO0GNeq1EbUa\nqYcI0f4oB34L86q0QT4dw3fdH+r+xVP3H/z535HDar7p/8XPRs/BQKob3DAevyrXyBsMR673lEIE\nWgz8+eC++PbOLfGqsrB4Wbd4QZi3zz67Iu7/8bsyMaem75MgdBx+54X/vejzvlPgUYs0HMjNjE4I\nZTSQMSVcZUNjXb92Rfzyy48iqaKMwIARKZD68cqlMyLW62+Id99LbLqQfBT57lt1wfFmHO8iJJyg\nLyHNDzxqseH87nuJPBNIIDDZXr18jvoUPGrRD/2BsXbj+ifKOPtJJEueNooDpBnos59+clH8hrEZ\nNx6FnQ00LtEf/vnnH9pkMyqz8Fi9fesL8fVXN8Q3X39BmzxvxX9XxH/7PRrzwcCYv3j+JH0PcnD7\nKz/G443PP6EJIGnyND7eu8gp/83tL2mMWfXqRTjIyxdOU0hCTERI2+VkAokk4ei/kZStAKEgEeIa\n34v+8c67iXwUfiN2+yrjDtzX7Pc1J3UGuQe5duXiGdKNIf9SpMpA4dfDhZ35Bdidx1DvMLIgN/hr\nig6RRNFfnBjCreBUT4Lcv3DuBJXrbUX+B5urolMfe5iIZB9HP0LaK2koRoQovQHZH4+6/gqdCGsQ\nbEJi4Zbg3YQ+axDc3wPlM3CGC7Q2CYabOhDS26ONf//trnjxxZdIl3vhfy9pf40KwuhDNqNtIYPe\nUdop2L34Wx/axe68EAyELP/i5jWqAxiQcT926zFSbS6BPMf9YwwmS5kuJPnsRDY6XRuFCub4u3d/\nEa/HjhNSm9shknpPoLWaEV4Pq9jV+3g97BzI/U+Utvz5p+9pfkiqjIdAstysrvXyG/WDdBt6+fXL\nzz9SSgg4Z7qhdyJVKuoa/Tdl6owB9ZZwls1OOdzEbt94FOdDfJ8VndFp+wbSXfDbkFnYAEyeKr3l\nMRsMu2ucQOC+keoFEcxQR7jvQBsH0AkQJQD0HzlblDCk4XaT6Lbl2dV9owvMKdeV9vtGmZ8wNyRS\n1lNoU0TZAuv2XKHxZAW3dHE9j6JN9lHAiZ6HOUbug/304/eKTHqZZFrCRCks6c1OZJrZXO8EN/sR\n5J4TG4QbRGrd4OY84Q+r8yuwqyO6oW9EJ07WLECvi7hhx/CH1TWJxK3x5+YcE8k1eKQJx1z8pOL2\nutofodoJ0J/Pnz1OchRj8uVXYml/8YX3zb2Ec21uBuxc+L0/fv9NJE2hyHWlnvt2biC2rFtCztTI\nQmiGnbkSQL9z5KjFMA8jkTZMM4ybcP9lIgX3NftwnTGPO9zHGYZhHn1YljMMwzBus2bZLDG0dyuK\nhLD10I2IOUUzvmDDZ+zQrnSNiDVIe6Zn/oxRlIIcHL58lx2jHkNYz2MYhmEYhgkfowZ1FJ9evSBS\np8siWnXyTeEM5zakQMQhFqRNR0ReN4B+F/pRJYZhGIZhGIZhGIZhGIZhGIZhHmlwGv2TK+fExfMn\nxKxJQ+m9yrWasZNWNIKolUjRg8fwfm3Fzc8/ofcR0WHnllUeJy2kZGEnLYZhGIZhGIaxByLaQdee\nP32U2LJ+iScVKaJk9e3SiJy0QL7CZejZLTiiFvPYwCdLmEcZ7r9MpOC+Zh+uM+Zxh/s4wzDMow/L\ncoZhGMYNzp46IhpXK6C9EhRNa/WOC+L1N9QUiEz0oI+aBdAucsMIIP3wiq1nH7kU8Yw1WM9jGIZh\nGIYJHz9+/62oUyGnJ1UygH790w/faa+Eq9G0APQ7jqjFMAzDMAzDMAzDMAzDMAzDME84zz3/PDkB\ngQ8SpxAT5mxkJ62HgNqNOoh+I2aLd99PQq+lk1act94WDVt2F2t2XmQnLYZhGIZhGIZxANY7M5bs\nFmUq1dXeEeSkhXVRxmx5xfQlu1x10pJwRC3msYFPljCPMtx/mUjBfc0+XGfM4w73cYZhmEcfluUM\nwzAM82Tw4P498fNPP9CGUsyYz2rvMo8zrOcxDMMwDMNEhv/++4+ctJ6OEUPEei229q77QL/jiFoM\nwzAMwzAMwzAMwzAMwzAMwzAPOc89/4KIGy8BO2kxDMMwDMMwjMs89dRTdCAinE5aEnbUYhiGYRiG\nYRiGYRiGYRiGYRiGYRiGYRiGYRiGCTPsqMUwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDBNm2FGL\nYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRgmzLCjFsMwDMMwDMMwDMMwDMMwDMMwDMMwDMMwDMMw\nTJiJ0at3735fffUlvYj5cgJ6ZphHkT9+v0vPL774Cj0zzKME918mUnBfsw/XGfO4E0ofv3/vD3Hv\n3u/i33//ETFjPqu9ywSD6+3hws32+O+//8Rvd38Rfz64L5555hnx9NOhn436999/xdXLZ8WNz66K\neG+/J/7552/x+2936Teee+557VPMk87joK+gT//xx2/iX6WPy7H44P49ce8Plpf+uKfUF2SYInwU\nmRNTe9cZqmz5ldrh2WefE0899ZTPe07ljZM2DFdZniQgE+4rdY+6ixHjGe3d0Pn1l5/EuTNHxdNP\nPS1efuVV7V3GH3KMuqUTuMHff/9FesRffz4Qz0bTWJL18pRyHUOpGyb68Ke7chs9XATT88KxBtHD\n/SEwWK/J+o+p6GNPPSTyPhj//vOP+E3TrfCIESOG0ndiaH81R6+PGecRt/oJvgPrc/zGP8qcFVPR\nBYMhfxv/47buY5fv7twSJ4/tF/t2bhBXLp0RP/7wrXj99TfFc8+/oH0isoRbPjzs3FV01/Nnjom9\nO9Yrz0fFra9vKLL0ZUWPjaV94tHCbJ0UHVgpx8NW90/6WGAePqDfcS9kGIZhGIZhGMYRE0b0FIUy\nxxO9O9bX3mGswPX2cOFme8BQhe/CAwapUJk77SNRMNNbok65HKJFneLk6HBo3zbPbzDM48TC2WOp\nXzevXUJ7R4gZEwfTex2bVtLeYfR0bFaZ6mfmpKHaO845d/qoR7ZgswscObjL8x4285zgpA3DVZYn\niTrlc1FdrVk2S3snNG59dUM0qJJPFM4SX7SsU0JsWb9Y+wsTiLaNylE7zJkyXHsn+sFmGcoE/SK6\naFKzKJVh0Zxx2jtMdOFPd+U2erRwew1ihPtDYH78/o6n/j+5el579+Hni5vXPOXGw8pctXCWqq/j\ngXnk5uefaH9xr58M7tXS8xv5M8Sh/h0IOGjlS/+m538G92qh/SWywCmrdN7EolSeRKJTs0pi4ohe\nYtTAjnRdSNGfurauofSVb7VPR45wy4eHlV9+/lE0q12M6h62lLFDu9Kjd4d6omyB5KJGmaziysXT\n2qcfHczWSdFBoHI8rHX/pI4F5uGGHbUYhmEYhmEYhgnI6CGdyUA2rG8b7Z3Hk29ufUn3iYfe4Mgw\n0cGXNz8Tk0f19Zygf/udD8RTdD6ZYRiGYSLL7CnDxQVtQ+N/L74sXo0Vm64jweOgn/3999/aVeTY\nuWUV1Vmlomm1dx4/noR7dAte5zBM6Dzu42jz2uBO2Ns2LNeuVCIxvx3ct1W7MufYod3alcpff/6p\nXUWOk8c+psNV337zNb3G2j1rrkIiWaoM9Brs2bZGVC+TmaKTMuEF7dCwSn5x6uh+ev1a7DdFxmx5\nRbqMOUiPBdeunhd1yucUZ04eotcS1i1CI5S6Z5gnEXbUYhiGYRiGYRgmIH/8/hs5iyBNyuMMohXh\nPvFA+gKGsQtCvjdq3ZMebyd4X3vXGZcvntKuhFix7axYs+uSeDpG4FQUDPO4kS13YRpPpSvW0d5h\nIsl77yeh+m/Strdj+eNWG7pRFsY5J4/so+cS5WqIPafuiIrVG9PrSMD6mTP+/PNPqrPvv/tGe+fh\noVyV+jSeM2TNq73jjIf5Hh82/I0jN3VXJvrgdowMj/t89PWXn1NUKH/AOQ0OFv5wS7Yb2bl5tXZl\nzq6tgf8eCQZ1b0rPcEqZtXwfrd0nztkoFqw5JHYevyWq1G5Of//ph+/E1LH96ZoJH3OmfiS+uPEp\nXfcbMVtsPnBdTF2wTcxYultpj69F90GT6G9gUPdmlMZPwrpFaIRS9wzzJMKOWgzDMAzDMAzDMAzj\nAs8+97xo0roXPd6MG197N3Te+yCpdsUwTxZZsuen8VSybHXtHSaSJHg3IdV/o5Y9tHfs41YbulEW\nJnRSpc0snnqKozsyoQFHP4zn9BlzaO8w0UW4dFcmsnA7Mm6xd/s67Soqe7av1a7MCZds37tjnbj7\n68/aK1+Q9nDLuiXaq+gBEYTg5Ab6DJ8h0qTPSteSV159TXTsNUrkLVyGXluJXMaExpGPd9BznSYd\naQ0SI8Yz9Bo880xMUb5KA9G+x0f0Gk5FiGbOuAPXPcPY46n7D/7878hhNbzc/+Jno2eGeRT57ls1\nrOibcd6mZ4Z5lOD+y0QK7mv24TpjHncC9XEsmHdtXSU2rlpIC+gPEqcQJcpWE0lTpBc58xYVIwa0\nFysWTiWD05CxC8SaZbPE8cN7xZ3bX4l3308iChT9UBQqXsF0Qw8GtY1rFoorF0+Lq5fOitix44jU\n6bOIoqWrKv+bWPuUCsLInz99WCRPlVGkzZCN/u/YoT3ix+/viIxZ84gPK9Wj//nt7i9i6byJ4szJ\nw+K3X38RSVOmEzUbtLHk5LJ98wpx5cJpsXDmGHpdrV4r8XrsN0XV2i3EC/97id7DqdkjB3aKbRuX\niZufqakO3kuYRBQtU1XkyF1EPP20tXMwodTb6RMHxZa1i8VXX1wXPyj3HzdeAqU90oqyleuLd95L\nRJ/5/LMrYt/O9XRdtFQVEd/kVPXF8yfEcS1FQOWazcSLL71C1+fPHBMH926hNsEJwhSpMoj0WXKL\n4so9Wr0/GCl3KPX56mtviHJKuXB95OOd4tOrF2iDt1CJCrRxj/pct3KuOHZwt7h5/RPqX2Uq1hbZ\ncxfWvkmp83/+EUvmTxR//fnA9F5+/ukHsXb5LLquXre1eO75F+hasm/nBnHq+Md0P/f/+EMkTp5a\nlKvSQKROl0X7RGjtYQSnAZcoffDvv/5U6qy6eCv+O/T+prWLxHd3vhb5i5QVzz33gti0ZoE4cXS/\n+PPBfZEwSUpRoXojkTJ1JvrsX8r/Lpo9Vnxy+bzYuXklvdeio3rqtk7jTuLAns2iU/PK9PrYJ/fo\nOdR6strufyrfv2z+JLrPanVaUZvu3LJSnDp2QLwa63WRJFlqZcy1FW/Eiaf9hy9W2kPy33//id3b\n1ohzp4+Iy+dPif+U/pJK+VyegqVEpmzuntJ+HAimrxjb7srls5R+4+yJwyL+O++LHHmKiNIValM/\nP6rIhh2bVohL506KeG+/K3Io8r5C1YamUYzsyHLJtU8ukAH3wN6tFAEifeaciqxoINaumC2mjukv\nUqbJLOau+pg+e/zIXnHx7DFF1r0jSnzo6+hjRR5K9PNI+kw5lfEwm8agk3EuQV1u27hCHD2wg+bL\n+/fuUV1myJJLlKlQhzZl9NiRA3rwO2dPHRGH929X5PYe+g3MfeWrNhCt65cRJ5Q6qtu0s2jZcYD2\nH4H5/tvb4rBW/yhPBmWsFylVWdz743fRtIYqf/ee/pZSU0g9ANRt0lncuH7V9vxy6cIpv20YSlnQ\nVqHIJNT7qeMHlN/fTp9PpugMWXMVFEVKVBIrF08Tv//2q9InKkbpS/5wMhbs9GEJ6mzzusXKb5wT\nn1+7TGM0XaYcpK/oZTvStUB369h7lChYtBz1eYyDYP3NyIkj+8SFs0fF3KkjKbpArvwllN/Lrozb\n3DSWQDjvPZB+hnHkZO5bt2Ku+OnHb0XuAqXEyy+/qtTnIvHx7s2iXrMuIq8yxwC35qAmSj8+o9xr\ngxbdaL7buHo+9TfI09z5i5N8lXrqra9uiO2b1JRS/vreSUVmnD9zhK5rNmgnYsZ8lq71SD3izPFD\n4tD+bfQe9IhXXn1dVKjWiFL69Ghbi96HHoFIJAeVMXjl4hnqT5BhGLtGXQG4VS+4T9xv+ix5PBv6\nTnQlf/cosVteK33DKLeQygeR5l5+OZYyJyxV5rXnRY36arp4qROD2o06+GxWAoyZI8oc8nrsuMp6\npq72rgrGxuqlM5VxfoXqClFa3n0vMf1Whsy5tE+pPLh/T9E/J9A12u6mMva3bVimyOK9YtzMteL0\nyYN+xxEcfMx019rlc4qrSvmad+gn6jfrSu9JkP5t64aliq56Ttz47Kp4P1Ey0uXKVq7nWVMEI1iZ\npbNRpOZ6tNUhZZ49uGeLuP/gnsiSI79SH9XEH3/8Rjpy7DfjKXN7be3TKk5kn1OC6XmYB83aEVxX\n5orVS2Yoc/gnyvr5W9Ij0F5VajXzpKEKRqD+YKeNEJEJzj4vKeOlUo0mYq+iU+zZvk58980tMXn+\nFvoMxu2lCydJ7zmqrCFfU+o1S878olS5mtTGnyl6ZKp0WWk9qcdOv4Sue1nRV5KkSCcyKrrHVqXv\n7dm2ltaqTdv20T7lS6D5CJHHS+ZOSO/PX3tY/P33X2L7xuXi9PGDQfWRSOmTZkCvq1I8PV1jLbxf\nWavFeettsWHfp6bjpGrJjKR7yM+CJZtOikTK74JQZbukd8f6ijxYSvIVuh30j/4jZ0fRIwH6UJcW\nVakvo99hzi1cshKt6/VYlal6fTNh4pQib6HSdC25/fVNsgeBRElTizwFSvrMq6t3XqTDBWboP7ft\nyJfitdffoGsJ+j3mHPQzyC3YR/IVLiWKlKwcpT3szBGwkxXM9BZdT1+yy6O/SezYgKzMk4H44bs7\nYo2im51T1ja4R8gL2PjKKWvNN95UyyixIi/MwDwtx2Pfj2aS7DBD/7mPJi8TufIVt6Rb2Kl7vW0M\nDkqQI0f277Ckc9lZJ0nslM3qPGynHE7rPn/hD+laYmcswIkT93xJ0fNwzzFjxlTq9n36fAFlDaTX\nlYONhXDPIwxjBPodO2oxjw28kc48ynD/ZSIF9zX7cJ0xjzuB+jickto0UE/96SlTqa7oPWSqx8El\nvbLgh5H/2MFd2ie8yM/qgSNRt9Y1yMhnBIv7EVOW+xhe5e+UKFudDGcXzhzT/qIC48NHk5aL4X3b\nmIbin71yv0idNqojiJ56FfMoC/sT2isvCFn/9jsf0PWYoV3EkjmqIcNIpZpNRZe+Y7VXgXFab/L/\n/DFyygoyJKJ+q5bIQO81adNLNGrVk671dGpRhYyr2LRYse0sbVzNnT6CnCTMgLGzz9Bp4vkX/qe9\n4x9pgMR3Z8tdyLTMI6euFLuUz5mdfu3cdwwZigA2QPKmi03Xk+ZviWKQh/NC42oF6HrX8VviZc2Q\nDWPu8H5t/Z6u7dRntKhSS00/4LQ9zPBn+JEb5o1b9xQrF0+nlAdGpCHr3h+/iXzp39Te9eXw5bu0\nmWp01HJaT3DwstPueoMaNv9HDexI10YWbzwhEidNpb2y1x7g7i8/iSF9WlMfMaNNt6GiVoN22isG\nBNNX9G3XtvswMW5oN7rWA3nxSqzXxcgBHbR3vGBz5aNJS7VXKnZlOcAGTq/2vpvRAPIic478tJGo\nd9SaOLKXmD99lMisfI/eIG9VHko880i5GuL2VzdpE8eI1XEOMOaa1SpmOm8A3P/CdUd8NmrsyAE9\nw5S5DfVipFCJiuL7O7eU8X3YsqMWNuQw32FMGkH7Tx8/iK6lsf3g/m2ifaNy9N6Ry7+RAd/u/DJp\nVG/TNgy1LHB0cSqTsCnaqXkV2pQygo1UOEKhfcbOXEeO6cFwMhbs9mEAvQy/Y1ZnqO9Zy/fShjCQ\n/Q3OQdhsQrQHI4E2UCSTR/cVc6eqp971NGvfVzRo3i3s9x5IP4v9RlxHc1+NMllJX8Rp/kWzx3nq\nRtaHm3OQdNSCgxvuw2zcj5q2ijZ69f3Z35huXK0gjXm9nDTiT4+Avrzt8Bc+G8V1m3YS86aNpGs9\n2BCbt+qAj97nZr2YOX24oSvJewROyhusb3z26SVRvZS5AwTSsk4fN5D6PeQWwCZe63rqxvWB8z+R\nnqln3vSRYtLI3iJZqgyUHkvib66U1G7cQbTuPFh7JcSvyr0WzqJuqA4aM8/nf+E0gNf+xhFkhpnu\n6s8xZ//uTaJPx/qmcgj9ZuyMtbT5HIxgZcb8Gam5/szJQ6JJ9ULaKy/oT2Uq1qE5zDjmnMi+UAim\n5/lbg6xYNE2M6G8+LnF/i9Yd9XvAQo+//mC3jdB/OjWrROvrSrWaevRR/didP2OUmDiiF13rwVot\n1mtvUHQlo4y02y/HDetGY/zDyvXIqVc6HeHQQp9h0+naSKD56DllbOvl97xpI+jaiFEfiaQ+aYbe\nUavfR7NEvy4N6Xru6gNRnKf0NgakM+vXuQFd6x21QpXtEumohTZPkjwN9TE42I6aulL7hBf5Wdhj\n4JR46uj+KI5admQq2qRW2exUZrBsy2nxQaLkdI2DZq0UmY5DEkC2Jw4jdVb0X9Cy00BRt0knujYC\nJ+NflP4GXo8dx3MIBt+7eM44MX64edRY6Py4H+mgYneO8Ccf7NoCQLB5MhCw5dUul8N0nKKvz1P6\nnf6QpRV5YQbGdNFsCega/QZrWKOjtITSG/73n3j5lVhKO/wTVLewW/d621iuAsVN7YlmOpfddRJw\nW3eA3LFbDqd1Lx3V7I6FL25cE3UUuWNWPgAZMn7Wek/dBnLUisQ8wjBGoN9ZOxLNMAzDMAzDMMwT\nR5JkaUSHniPIMA0Sa6+LlKxEryUwgsO5pUCx8rTAh9EGGw5gw8p5ZPyW4NRWs1pFPUbtqnVa0P/A\nOQcGECyKW9YpQQtdI3DygJNWsTLV6H8q11KdeWDsw0YcDEY43dlbWRRjcSwZPcjcUKanRoPWZLiQ\nIEw37lWeYIWRWxpVYPzHphqcjXANVip/x2fsYKfejh3e4zGCwziBU2ezlu+jjWmcegV9OjWgE8gw\nJMo227E56uYUDDLSiFCuWgMycqxeNstjoMP/wnEGZZL1iMhOsyYPo2urwLiJMuP70CYw2EojEgxu\naE/cb9f+4+kEpvzbiP7tqYyhMLx/O49TEAwnMCY3bNndU1dwRLn2yUW6lthpD6fMmDCY+isM+Ni4\nQD3r7xsRXHDSuEu/cVQOCfoiHk8/HTWiUSiE0u5wiEDZ0QcxFjBmJMYxZ7c99BurqAf0H2we4FQ1\nGD+sOzltMM6AkRvyFu3dY/BkMjwCGFrRFvgbxivaFkZlgE0xOIlInMhyRFqSBmD8ZvdBk8TQ8Yto\nYwXywswZyQw78tAIIj5grBcpVUUMGbeQ+qK8fzvjfP7M0Z5NNfT9yfO3ionzNpPBGuD+4eBihhU5\nIFmxaKqnXjC/IZoAxkL2PEVojMBhwyqQq+0bl6ey4fdadxksRkxeTm0NpKE9EE7mFzPcKIsROzIJ\nG8DSSQvyDn2h5+ApdG8wuJttIvrDyVhw0ocRVQDO8/g+fAbf32vIVHKiBBhDowdH1XcQUQabaNBx\nhk1YTPMtygWM/c2MAkXL0vwjwe/hdZYcBSNy78H0s1AYM6QL1Q1+F/1AyrtwzEHob+hXuJfhk5ZS\nHcn77di0Im2Aw1kCYxtsWbc4igxDWeWYL1XB/2ao1COgL0tQZy06RHX8gpMW9HvMBdhkxaYaQJsu\nnjueriWRmpvt6EqB7jGU8pr1jV9+/pH0V4DytOg4gOYwGfUUTlpugMgQcq6EHopxPm/1QSo/NhzB\nghmjo+ixEv3/VqjemCJAuDWOPlXWWqgDjG2Mccx5kJ9wwADoN93bBHcOMWJW5kjN9Yh20k5zAsY9\nYWxijKL/oR/CSctIKOvZSILIP9JJK2feYmLg6Lli9PTVyr11oj6M+xvhx8HZCqG0EepdOl1kzVVI\nlK+qOgjt37XR46SFsYp+j7pFW6JNoY8aCaVfrl8xl/QW1AfuAU7l/rA6juCkhe+DMyjKAYdpiVEf\niZQ+aYU34sYTGbVIg4gKY0SmPYQciqu1rx2clBeHyQoWr0DXH+/eRA64eu7d+52ctEBBRc7D8ciI\nXZkKh47+o2bTNRjYvSk5+ANEA5dOWrAVSKc7RGqUwPm2hTL+t6xfQo5J+v6PyD6Y6/HQRyretGah\nxzEFbYCytes+XKTWUihiLluvyC4Q6hyhJxRbgD8dyh963R/jtNuACWLKgm1kC5JyE7LY2MbAn7zw\nR6zXYpNuA9Bv4CyINdWVS2co2p0eRPFCe8BRKJhuEUrdQ0+HPVHaxoZNXOLR4Y06l5N1Ujh0Byfl\ncFr3EjtjAQzq0czTp9p0HUKHdDGHyz6MeQNR/YIRqXmEYcxgRy2GYRiGYRiGYUyJ/WZcUa1uK0pP\nBhIlTUWv9anpJDAsDp+wmFLPVK/XWsxYvNNjeDt/Wk3RAlYtmUEGOjBm5lrRsdco+p/KNZuJFVvP\nev5n9VLzDXv8zsBRc+h/OvcZQxvXEkTcGjFpGaWlwAkmpCMAcO5CeqRA4PsqVm+svRKiVPladK84\n3YXw4dLIXb1+a9H/o1kU+QCh3XEt05vgMzDG2cFqvSFNGIDRoP9HMyk0eJr0WSnVQtN2qvEWRoVv\nbn1B16UrqoYJGBSwuasHJz4lxUtXpagE0iAOo8L0xTsouhHKhHrERhSAId6uwRdGoelLdlKb4FRt\ns3beEOAw5k2et5nqvUWH/qLvR942t2JY9AeMLNigAdg86zV4iiip9A2EH5+1TDWsAqR9M2K1PUIB\nTn44AY7T5ajnfiNm0vtov+vXrpDRFqH9C5XwOmqhL+Lhz+nBCaG2O+pl2eZT1AcxFlp1GkTjA8CA\nLQ3adtsDzjxyYxXOXGgP9J+S5WqIaYt20EYTWDTTWgQ7Jipou4Vrj1B7Iz1p/xFqajDJuFnrabyi\nbacrY0By8dxx7cqZLJ80ojc9Y+zPXbmf0j8gBRGiEcKwahW78tBIrUbtxeAx80ThEhWpL06au0n7\ni/Vxvne7mv4Pxlj0/czZ84msOQpQhClpGD4XwIkqmBwASAmDjSuACCCY35DyBWNhzPTVPvOfFZDO\nDxsdACfWkYYrn/IdaGtsrFrFzvziD7fKoseqTMLvwhkLwPgOeYe+gJQWUxZu9TiiWcXJWHDSh6eO\nVWUy/mfeqoP0/UiVhpPq0nEdDrHYtDSCjXmcoMcmJubbMTNUeavvb/5IkTojzT8yuijST+E1yhuJ\ne8f3+dPP3ADzPlI8oR+gHOGcg7DZhHYoUKQs1dHiDcepHoDc7IIuC7D5efXyWbqW7Nu1UbuCA53q\nWGKG1COQVhGgHVBn6ONG4AgBeYy5AJEwEK1EOmudO+mVh5Gem63qSv7u0Y3yGvvGioVTPHJr9op9\nol7TzjSH1WvaxbHcMkPvRNR3+HQa5xiHKH/vod5DIUjL4w9spCJCVzdsgL/+hmvjSG7oo9/OXfkx\nzXmQn41b9xLTl6opb+F0glR4djGWOVJz/ewpw+l70IeWbjxJYxNjFP0Pm7RmhLqejRRHdFGC4XxR\nTJmXc+cvodzbQFG7sapfQP+W86NdQm0j/N+KbWfFxDkblTWi+vnBvVrQM5wxpizcRv0edYu2lHOd\nkVD7JdZBWw5eF0OV9oaM8IfVcYT2X77lNEXsQzl6DJpEjgZAr4+ASOiTVoFDUdFSatRm6BN4rWfz\nGnU9V7S0GjnKCU7Kmz5TDs+Y2r9nMz1LkI4N4O9IIWeGE5maStEFpbMcbEnLF0xR+vGXYmivlvQe\n+gz6uQQOJ3A4kqCd+3ZqICoWSS0KZIwruretJZbMnRBFbwaI4IUIwgDRNzEeUDbYmGYu3eNxgNq4\naj49uzFHADdsQMZ5MhDjP+pBzkpoqzkr9lMqQaQgxpjCa7yPOXbMUN/UqhIzeREIjDvZb/C9WFPV\nKZdD5Ez5imhVv7SYOXGwOKOMLX0/D6ZbhFr30LlQz/g80pIPn7jE49Sk17mcrJPCoTs4Xa85qXtg\ndyxgzQNHLAB5X6the8qkgDm85+DJHofhyxdO0XMgIjWPMIwZ7KjFMAzDMAzDMEzINGjua1DBSUR5\n6glGLcnqJaoxDlF1cmmbJBKcRm3YUj0pt2Gluvg2Ur9ZF+1KJVsub5qK+koZ9I4s+jDWP//4vXZl\nH3nKFTRs0V278tKghVpmcN6QljEYVustQ5bclK5s0Jj5UdKmxH/7Pe1KiPv31VR4hYp5nXxwkk2P\njGyEjTicYDu4bxsZR0HvoVOjfL90RMNnDu7bStdWgZH+2Wef014JkSl7Pu1KiFqN2/sYttOmz65d\nIU3bLe3KPhtXq2kOYByqXlfdpJfEjZeANu6x4fyjtsGix2p7OAWOa3Dy05MtZ0HtSog7t0P/DauE\n2u61Gral+tSTI483RZjcwLLbHpvXqCceYViDM5ceGE8btlJPWMIAfuf2V3TN2KNWo3bizbhqmgOQ\nNEU6jzEVJ4iTp1RToIDX34jjMYp+d+c2PQO7shwn3NFmAJEcXvjfS3QtqVijiXYVHLvy0EhDncwG\nkIPSOcfqOC9frSGVoXl79USxnvgJ1DL8dtd8U8OqHED6FglSqOjnN6SQgLHcDpfOq0ZqRILUpxUB\n2FzBpqgV7Mwv/nCrLHqsyiS98ws2X/S88MKLikzyfS8YTvQau30YUVmk0wn0IDjS64FjMeQoHsY+\njNPo2JjXk0wZ85JQ5p1I3Hs4gWzDxrh+bIVrDsLGD5xF9KCO5FwrHUfyFdKlCNM5ZoEdm1bQM5yM\nsCnsBs07DojSDmkyZKNnHFSQRHJudkNXCrW8Zn3j8oXT9Ay5lVCLziRxKrfMgBzDGIFjX+Kk6mEZ\nyZs6ufrggfkYgTMLNlLdBqk5pcxv2224iKcbryB9xhweB7htG5bRs1XMyhypuf7sCXVzu0aDNjRO\n9cARTzqp6gl1PRspEB1GcvniaZ9N8RrKvIGN9jmrPvablioYobYRHPX1OsCP33/rmavhAPLMMzHp\nWoJNeCNu9MsBo+ZE0UtDAU5w0kFAkiqd14lF3iOIhD5pBzhQATgLXzzntYEg7SucbEB+h/LFaXnR\nP8tohwR2bFTnQclOLbprqfI1/fZjpzIVEUgRXQggelTnluoBBKyZ4JiEuUQPHI6QSk0fjQlgLQ0d\nDt9RuVg6ShGOSEWSowd3efpE07a9ffr9008/LZq0601y6Pfff6MxHOocIQnVFmA2Twbi5BHVsQdr\n//gJ3qdrCV5X1w5ZHjukOsUYMcqLYKROl0UsWn+U7sMo2xFFHQ7yTaoVFNVKZRKfXAns1CYJte6b\nKeNcX8+ou9wFVB1dr3M5WSeFQ3dwul5zWvd2x8Jff/5J94xH6fJqSm8J5IHsZw/u36dnfzyM8wjz\nZMGOWgzDMAzDMAzDhASM3nKTXw/CZYN///2XnnF6VBr4sFjetXV1lAfSigCcvDKe7sXvyO+UwJFG\nIp0JJO+8n1i7Co1L507SM9LRwPhuBO/JVDVXLgY/rSWxWm8ARs2a9dtSOsqjh3aLtSvmiGnjBoje\nHeuL5rW9GwTSUAaDiIy2snPzanoGMEJIZ4kylerQs69R6GSUNoHRQhqbb311k56tkjyldzMYPP+8\nt72Sa4ZPiXHj2SlffH6NnnESUh9GXdK++0diza5Loms/NaKKxE57OCVdJjXdjh69Qeff/0L/DauE\n2u4p0kaNOoNQ9xK5GWS3PW5+rsoI1LuxTHh8eUP9PvANO2o5IkWaTNqVCgyfzz2vGo3TZIh6GjpR\nMl9jrxNZ/uUX3miDyVJ5HcEkcJCRUVyCYVce6jGbRwDSvQCr47xyzaZUhhgxYoj9ylhBSgecFG/f\npIJpGgg9VuWA/sTzuybzWVrNmcIq0hEExnMzcuXz3Wz2h535xR9ulUWPVZn0ySXVUQsn2M2iyWTO\nZj1dhVO9xm4f/upLr7zOnCNq+RK8m5DkKB5IT6kHGylG9JuZTuedSN17OMmWO+oGU7jmIMg3s03k\nZJpjLDbF4ZAHPQQbYmD7huX0DDC+ZNSAEuV8Hb5CIVmKtNqVlxdfUnUh2WYgknOzG7pSqOU16xsn\nj+6n55SGOVTiRG6ZkTR5WhojRUpUFGdPHRGb1y0Rs6cME4N6NheVinnby98YgfNMOPhapwv++P03\npvUa4xm1j+t1TCuYlTkScz0iykg5pndSl+C7c+Uvrr1ScSr7ooP8hb2On0jFCCcR6CrQW/7+6y+a\nHxA5yCmhynOZhlSi37DXOxRL4r39bpT1fqj9Et/nluOrJHnqjNqVl/+9+KJ25dVHQCT0STugLhAZ\nBshUh0Cf9jCQI34gQimvTH94aP82T0pRjF+k5gOIGOoPpzIVTiIDRnpTIF69qDrrdh84QbwV/x26\nNoKDgoj+fvjyXTF/7WFKo1e4ZCUfGwNSpDWrVcwjG+QaCZ8x2kdA0ZKVSb9bvOEYlTHUOUISqi3A\nbJ70B2Qi5CHInlu1mxnJmU+VtdCH9M6MEqO8sAKcehDNbuuhm2LVjguUQh5p/fQOuIgQXOvDbJQq\nNhih1n1ykzXw65ojk36ucLJOCofuEMp6zUnd2x0LsMPinnEY8Mbnn4idW1aJhbPHihED2ot6FfN4\n9OZgPIzzCPNkwY5aDMMwDMMwDMOEhDR6BwNGF8m2DUspx7/xMX/6KO0TvqdNQaDfwcYxnA3CAQxX\nwHgyVo9cmF/WnLqsYLXeAAyR0ycMEkWzJRCt65USQ3q2ELMmDaV69IcMuY2IYDJawJ4d6+gZxg95\nKkxvbDBrEzxk2+nb0ApmmySSF3ROdhK9EdMpNz5T0ybEecsbMcgKdtrDKbHfcMcZzQ1CbffYseNo\nV16kAUuP3faQn4fhzqxM/bo0pL+D7+7Y64+MilnbSfTOlBKjbHUiy+/o/idWLK/zjB59lK9AOJGH\nEn/j3N8pfH/c/voL2ogsXyil6NSsEqV0WDhzjDi4d4v2Cf9YlQPfaJEF9AZtPTBQ+/ubkQf373nm\n1FdjvU7PRuw4y1qdX8xwuywSqzJJbga8EcfcoB7rdfP+aYaTsQDs9uFbX93QrnAq3t7cFq55J1L3\nHk4QfcBIuOYgf/Lt5Ve9zoLfadFEkeIUwBlEpoLGBr4EzhFuEUhP0xPJudmNPhtqeY19A3JLRh5B\nOiAz3DpwACcibLCWLZhcNK5WQPTr3IBS8qxfMddUHzMSN565A0GoyE12MH54D9N6lREpvjbZyAyE\nWZkjMdd/e8cbwfc1P7qRsV2dyr7oAPPxR5OXeZybIFOgq0BvKZQlPm3gf6erA7uEKs+NDj/66HZm\nh6OAcT0ear+M/45vZB83CKRnG4mEPmmXIqXV9IeIiiydGreuU9u0WBn/aa2DEUp502XI7onMI9vz\n0P7t9Ix+nj6zN5K6kVBkKpzfq9bxpjhEhC2kEA0GZA2cP5FGb8jYBWLXiVuUJlX232tXz3vShX/9\nhdqHzXQSM0KdIySh2gKslhdcu3pBu1LGsB/5LB2WwLVPVd1Hj1MHQYA10zvvJSL9Cmn9yNln43GK\nOiuRadEDEWrdm+lcxrWK03WS27qDW+s1O3VvdywAOKU1qJyPUiv2aFtLjB/WndJ16rMiBONhnEeY\nJwt21GIYhmEYhmEYJiI89ZR3+VGgWHnRsGX3gA/lH7RPBwcnmsOFNAr9/pu6QWPGX3/9Rc9vWHQ0\nsMvAHs3FzAmD6RpGylqN2oveQ6eJGUv30GlNM5CKQzo+fbxnMz1vWq2mgilftYEnJeHTT6th+/FZ\ns3bQP8xOwkYn2CAwIjdhwtknnPJUmJwJg2FWTyG3u8Xx6bQ9YAg3K4v+Ea7NyMeeAG2nP+XvDyey\nPIHOoejXX73pPvTIqF7BcCIP3eT+vT9E4+oFPRuR6KtN2vQSg8bMo9/vOXgKve8Pq3IgwbuJ6Pmn\nAKl7jWlK/IFodnJz6NdffqZnI1a/C1idX8xwuyweLMokbBaAX38274dSNlrBqV5jtw8/rfsd9D87\nhGveidS9h4LZ3GcVt+egZ599Vrvy5f49bwqat7UULVly5PeMr/271I1cmd6pdIXanr9FB5GYm93s\ns26VN1xy694fv2tXXtYsm0UbrHKDFOn1OvcdKybM3SS2HLzhcVaINDFieNsFqavM6lI+SmtpykIh\nErIiXvx3tSus88zTyxmdu8K5ng0H+Qt/KJZvPSPGz94gajfuQNEkJdjAr185r8cZxy5utxGiQ0ru\n34s6NsAzMX3TIUa6X1rCYptHSp+0S94CaoRbyKDzZ46Ja59c8ESRCyWtaijlRZpB6SS2Q4uitXOL\nGtW1dIVaUcapnlBkKqJZblilpvEHiKoloyvqGdKrpejetpZHJzaC8kF3nrJgm/aON3KuPBDz5wNr\na2W35ohI2oBi6Rycf9ecno38+ecD7UqRJ34coq2ybMFkao+500Zo70QF6QEnz9viqS95ODMQkZif\nneobbpfNaTlCqXu7Y+HTq+fJKU06ZSF6bZuuQ8TIKSvEyu3nSWe2wkM5jzBPFOGZzRmGYRiGYRiG\nYQwgRLzcXMqep7Bo2rZPlEf95l1FpRpN6RGuU6J2SZVOTQlx5cIpv04MlzXjQqq05ulQQgEnnWVY\n/yq1m4stBz4XbboMEWUq1hHpMman982AcaVEWTUqw+6ta+jUpDRilCxfk55BitTesOJmbYJHtbqt\nqE3cjOJgF7NNBLMQ9Wm0dGBmYckBQqKPHtKZ0nM8jlitp0i1u932SJcpFz2/nzCZaZmatOntkRHJ\nDKk1mcjgRJa/pdsM9dcXvrpp/r4ep/LQTfbv2eQ5mTxk3EKxYM0h0ahVT1G0VBU6PX//vj1HGn8k\nTKymsEMUFzNnLcxHiBZjFZmK58ub3pRfem6bpDXxh9X5xR9ulsUuCZOmoucb16+apqT68ftvtavg\nOBkLTvqwTI8HbmuR1vT8dvcXkqN4WDk17waRunerWJ37ghGuOej2V19oV77Ik/nYDJP1iQ1dODyC\nbRuWU5+UaUVLlgs+vsLBozY3h6O8chw6lVtmffRLk3kP0XQAnDb2n/1B9Bo8hdKjZctZkDY15SZs\npEmS3Js6qVyVBqb1WrtRe0+9hkKk5npsMstoU1/e9KZo1mOUqY/SehbzHDayMUdkz11YtO48mFJG\nbT74uajTpCN9Bvd36tjHdG2HcLSRPnXvV7oIJ3pufemNMAki2S/dJlL6pF0QJUemP9y9bbXYu12N\nmIoIONGZ3kumNzzy8Q4ar7uUNSSQaRH94VSmQs8e3KulJ5qiHPd9OzeI4th54exxKs/Hu80dtSQ4\nLCC/R36H1IkR1eevv/6kaz1XLp0h/W7iyF5UJrfmiEjagN5PmFS7EuL6p5e0K1/0axpjGm+7IDof\n2mPlomnaO+bAMShz9gJ0baXeIjU/O1knhaNsTsoRSt3bHQurl8zU/iLEuj1XxKipK0Wthu1F3kKl\nlbk9sfj1V3MHMyOP8jzCPB48rT0zDMMwDMMwDMOEnQxZc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rczFvA70K8liHKJ/guZOmHu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Kw1yxQg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\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hTDEJWQCZoAcyA2mGgX6MSqzqbYFwMn8jGwZAfzDahkJdR+qJzjWSvs9UrauuZfUg64BE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\nHj64L3XLFVLHRk6dLxmzZFdpd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DaEcQSLZk2S7/Z/q9IAzzl3ylglC6Bk+Spq6w1FSpVV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\n+db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2oo27d8nsz5bL4A+mSrM+vdXwQYa7+UNAVvy4jmFx7JC5CRBQ5urpOE/pksjlK1d0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\nREREREREREREREREREQUGiL/B8eCtsVBiJz1AAAAAElFTkSuQmCC\n"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Scalers.png](attachment:Scalers.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Handling Categorical Variable__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding\n",
    "In this method, each category is maped to a vector that contains 1 and 0 denoting the presence or absence of the feature. The number of vectors depends on the number of categories for features.<br><br>This method produces a lot of columns that slows down the learning significantly if the number of the category is very high for the feature.<br><br>One Hot Encoding is very popular. All categories can be represented by **N-1 (N= No of Category)** as that is sufficient to encode the one that is not included. Usually, for **Regression, N-1** (drop first or last column of One Hot Coded new feature ) is used, **but for classification, the recommendation is to use all N columns without as most of the tree-based algorithm builds a tree based on all available variables.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\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>y</th>\n",
       "      <th>x</th>\n",
       "      <th>dummy_a</th>\n",
       "      <th>dummy_b</th>\n",
       "      <th>dummy_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y  x  dummy_a  dummy_b  dummy_c\n",
       "0  5  1     True    False    False\n",
       "1  3  3    False     True    False\n",
       "2  1  2    False     True    False\n",
       "3  3  1     True    False    False\n",
       "4  4  2    False     True    False\n",
       "5  7  1    False    False     True\n",
       "6  7  1    False    False     True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = np.array([[5, 'a', 1],\n",
    "                    [3, 'b', 3],\n",
    "                    [1, 'b', 2],\n",
    "                    [3, 'a', 1],\n",
    "                    [4, 'b', 2],\n",
    "                    [7, 'c', 1],\n",
    "                    [7, 'c', 1]])                \n",
    "\n",
    "df = pd.DataFrame(data=my_data, columns=['y', 'dummy', 'x'])\n",
    "df = pd.get_dummies(df, columns = ['dummy'])\n",
    "df\n",
    "# Dummy variable are created \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "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>y</th>\n",
       "      <th>x</th>\n",
       "      <th>dummy_b</th>\n",
       "      <th>dummy_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y  x  dummy_b  dummy_c\n",
       "0  5  1    False    False\n",
       "1  3  3     True    False\n",
       "2  1  2     True    False\n",
       "3  3  1    False    False\n",
       "4  4  2     True    False\n",
       "5  7  1    False     True\n",
       "6  7  1    False     True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = np.array([[5, 'a', 1],\n",
    "                    [3, 'b', 3],\n",
    "                    [1, 'b', 2],\n",
    "                    [3, 'a', 1],\n",
    "                    [4, 'b', 2],\n",
    "                    [7, 'c', 1],\n",
    "                    [7, 'c', 1]])                \n",
    "\n",
    "\n",
    "df = pd.DataFrame(data=my_data, columns=['y', 'dummy', 'x'])\n",
    "df = pd.get_dummies(df, columns = ['dummy'], drop_first = True)\n",
    "# to run the regression we want to get rid of the strings 'a', 'b', 'c' (obviously)\n",
    "# and we want to get rid of one dummy variable to avoid the dummy variable trap\n",
    "# arbitrarily chose \"a\", coefficients on \"c\" an \"b\" would show effect of \"c\" and \"b\"\n",
    "# relative to \"a\"\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding\n",
    "In this encoding, __each category is assigned a value from 1 through N__; here N is the number of categories for the feature. One major issue with this approach is that there is no relation or order between these classes, but the algorithm might consider them as some order, or there is some relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>y</th>\n",
       "      <th>dummy</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>c</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>c</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y dummy  x\n",
       "0  5     a  1\n",
       "1  3     b  3\n",
       "2  1     b  2\n",
       "3  3     a  1\n",
       "4  4     b  2\n",
       "5  7     c  1\n",
       "6  7     c  1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = np.array([[5, 'a', 1],\n",
    "                    [3, 'b', 3],\n",
    "                    [1, 'b', 2],\n",
    "                    [3, 'a', 1],\n",
    "                    [4, 'b', 2],\n",
    "                    [7, 'c', 1],\n",
    "                    [7, 'c', 1]])                \n",
    "\n",
    "df = pd.DataFrame(data=my_data, columns=['y', 'dummy', 'x'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
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       "\n",
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       "\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>y</th>\n",
       "      <th>dummy</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y  dummy  x\n",
       "0  5      0  1\n",
       "1  3      1  3\n",
       "2  1      1  2\n",
       "3  3      0  1\n",
       "4  4      1  2\n",
       "5  7      2  1\n",
       "6  7      2  1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "df['dummy'] = le.fit_transform(df.dummy)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Pre-Processing\" role=\"tab\" aria-controls=\"settings\">Go to Top<span class=\"badge badge-primary badge-pill\"></span></a>"
   ]
  }
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