Feature-Engineering/feature_transformation_basics.ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is Feature Engineering?\n",
    "\n",
    "It is the process of using domain knowledge to select and transform the most relevant variables from raw data to improve the performance of machine learning algorithms or statistical modeling. \n",
    "\n",
    "> The goal of feature engineering and selection is to improve the performance of machine learning (ML) algorithms.\n",
    "\n",
    "#### **Types of Feature Engineering:** \n",
    "\n",
    "Feature engineering consists of creation, transformation, extraction, and selection of features(variables), to creating an accurate ML algorithm. These processes entail:\n",
    "\n",
    "1. **Feature Construction:** Creating more useful features to improve the predictive model. It requires domain knowledge to manipulate Existing features via addition, subtraction, multiplication, and ratio, etc to create new derived features that have greater predictive power.  \n",
    "\n",
    "2. **Feature Transformations:** It is a mathematical transformation in which we apply a mathematical formula to a particular column (feature) and transform the values, which are useful for our further analysis e.g. avoiding computational errors by ensuring all features are within an acceptable range/ scale for the model. \n",
    "\n",
    "3. **Feature Extraction:** Feature extraction aims to reduce data complexity (often known as “data dimensionality”) while retaining as much relevant information as possible. This helps to improve the performance and efficiency of machine learning algorithms and simplify the analysis process using techniques such as Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-DSNE).\n",
    "\n",
    "4. **Feature Selection:** Identify and select the most relevant features to improve model interpretability and efficiency using techniques like univariate feature selection or recursive feature elimination. \n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "#### **Feature Engineering in ML Lifecycle**\n",
    "\n",
    "Feature engineering involves transforming raw data into a format that enhances the performance of machine learning models. The key steps in feature engineering include:\n",
    "\n",
    "**1. Imputation**\n",
    "\n",
    "Missing values are among the most common problems faced when it requires getting your data prepared for machine learning. Imputation is a method of dealing with missing values. It is intended for managing anomalies inside the dataset. There are two kinds of imputations: \n",
    "\n",
    "* *Numerical imputation*: Numerical imputation is implemented to fill gaps in assessments or polls when particular data bits are unavailable. \n",
    "* *Categorical data imputation*: Missing values in categorical data imputation could be replaced with the highest value that occurs in a column.\n",
    "\n",
    "**2. Outlier Handling**\n",
    "\n",
    "A method to eliminate outliers from the data set is known as outlier handling. This technique can be applied to various levels to provide more precise data representations. This phase must be carried out before beginning model training. The Z-score and standard deviation might be used for identifying outliers. There are certain ways to handle outliers: \n",
    "\n",
    "* *Removal*: The distribution is cleaned up by removing items that contain outliers.\n",
    "* *Replacing Values*: Outliers can be interpreted as equivalent missing data and substituted with appropriate imputation.\n",
    "* *Capping*: Replacing the largest and smallest numbers with an arbitrary value or that comes from a variable range.\n",
    "* *Discretization*: It is the method of turning continuous variables, models, and functions into discrete variables, models, and functions. \n",
    "\n",
    "**3. One-Hot Encoding**\n",
    "\n",
    "One-hot encoding is a form of encoding wherein each member of a finite set is expressed by its index, with only one component having its index set to “1” and the remainder of the elements being given indices that fall within a specific range. It is a method that transforms categorical data into a format that machine learning algorithms can easily understand and use to produce accurate predictions. \n",
    "\n",
    "**4. Transformers:**\n",
    "\n",
    "Like Linear and Logistic regression, some data science models assume that the variables follow a normal distribution. More likely, variables in real datasets will follow a skewed distribution. By applying some transformations to these skewed variables, we can map this skewed distribution to a normal distribution to increase the performance of our models.\n",
    "\n",
    "* *Log Transformation*: The log transform is commonly used to convert a skewed distribution into a normal or less skewed one. This transformation is not applied to those features which have negative values. This transformation is mostly applied `toright-skewed` data. Convert data from the addictive scale to multiplicative scale, i.e., linearly distributed data.\n",
    "\n",
    "* *Reciprocal Transformation*: This transformation is not defined for zero. It is a powerful transformation with a radical effect. This transformation reverses the order among values of the same sign, so large values become smaller and vice-versa.\n",
    "\n",
    "* *Square Transformation*: This transformation mostly applies to left-skewed data.\n",
    "\n",
    "* *Square Root Transformation*: This transformation is defined only for positive numbers. This can be used for reducing the skewness of right-skewed data. This transformation is weaker than Log Transformation.\n",
    "\n",
    "* *Custom Transformation*: A Function Transformer forwards its X (and optionally y) arguments to a user-defined function or function object and returns this function's result. The resulting transformer will not be pickleable if lambda is used as the function. This is useful for stateless transformations such as taking the log of frequencies, doing custom scaling, etc.\n",
    "\n",
    "* *Power Transformations*: Power transforms are a family of parametric, monotonic transformations that make data more Gaussian-like. The optimal parameter for stabilizing variance and minimizing skewness is estimated through maximum likelihood. This is useful for modeling issues related to non-constant variance or other situations where normality is desired. Currently, Power Transformer supports the Box-Cox transform and the Yeo-Johnson transform.\n",
    "\n",
    "By default, zero-mean, unit-variance normalization is applied to the transformed data.\n",
    "\n",
    "a. *Box-Cox Transformation*: It requires the input data to be strictly positive (not even zero is acceptable). Sqrt/sqr/log are the special cases of this transformation.\n",
    "b. *Yeo-Johnson Transformation*: It is a variation of the Box-Cox and supports both positive and negative data.\n",
    "\n",
    "\n",
    "**5. Feature Scaling**\n",
    "\n",
    "Standardize or normalize numerical features to ensure they are on a similar scale, improving model performance. There are two standard methods of scaling: \n",
    "\n",
    "* *Normalization:* In this process, all values are scaled between a particular range between 0 and 1.\n",
    "* *Standardization:* It is also known as Z-score normalization. It is the process of measuring values while considering their standard deviation.\n",
    "\n",
    "**6. Binning**\n",
    "\n",
    "Among the key problems that affect the effectiveness of the model in machine learning is overfitting, which happens because of more parameters and inaccurate data. Binning is a method for converting continuously varying variables to categorical variables. In this procedure, the continuous variable's spectrum of values is divided into numerous bins, and each one gets allocated a category value.\n",
    "\n",
    "**7. Text Data Processing** \n",
    "\n",
    "If dealing with text data, perform tasks such as tokenization, stemming, and removing stop words.\n",
    "\n",
    "**8. Time Series Features**\n",
    "\n",
    "Extract relevant timebased features such as lag features or rolling statistics for time series data.\n",
    "\n",
    "**9. Vector Features**\n",
    "\n",
    "Vector features are commonly used for training in machine learning. In machine learning, data is represented in the form of features, and these features are often organized into vectors. A vector is a mathematical object that has both magnitude and direction and can be represented as an array of numbers.\n",
    "\n",
    "\n",
    "Feature engineering is often an iterative process; evaluate, refine, and repeat as needed to achieve optimal results. These steps may vary based on the nature of the data and the machine learning task, but collectively, they contribute to creating a robust and effective feature set for model training.\n",
    "\n",
    "\n",
    ">> Cross-validation: Selecting features prior to cross-validation can introduce significant bias. Evaluate the impact of feature engineering on model performance using cross-validation techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing and Understanding the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Id', 'Requester id', 'Group', 'Status', 'Priority', 'Via',\n",
      "       'Created at', 'Updated at', 'Assigned at', 'Initially assigned at',\n",
      "       'Solved at', 'Resolution time', 'Satisfaction Score', 'Reopens',\n",
      "       'Replies', 'First reply time in minutes within business hours',\n",
      "       'First resolution time in minutes',\n",
      "       'First resolution time in minutes within business hours',\n",
      "       'Full resolution time in minutes',\n",
      "       'Full resolution time in minutes within business hours',\n",
      "       'Requester wait time in minutes',\n",
      "       'Requester wait time in minutes within business hours',\n",
      "       'Manual Tagging of Categories [list]'],\n",
      "      dtype='object')\n",
      "(16476, 23)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"DAAssignment.csv\")\n",
    "print(data.columns)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Requester id</th>\n",
       "      <th>Group</th>\n",
       "      <th>Status</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Via</th>\n",
       "      <th>Satisfaction Score</th>\n",
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       "      <th>First resolution time in minutes</th>\n",
       "      <th>First resolution time in minutes within business hours</th>\n",
       "      <th>Full resolution time in minutes</th>\n",
       "      <th>Full resolution time in minutes within business hours</th>\n",
       "      <th>Requester wait time in minutes</th>\n",
       "      <th>Requester wait time in minutes within business hours</th>\n",
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       "      <th>0</th>\n",
       "      <td>10300555531549</td>\n",
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       "      <td>Reimbursement Claims</td>\n",
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       "      <td>Support</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
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       "      <td>9</td>\n",
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       "      <td>18221.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Support</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
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       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
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       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36304.0</td>\n",
       "      <td>17739.0</td>\n",
       "      <td>7427.0</td>\n",
       "      <td>4008.0</td>\n",
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       "      <td>11283774362653</td>\n",
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       "      <td>692.0</td>\n",
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       "    <tr>\n",
       "      <th>16472</th>\n",
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       "      <td>Support</td>\n",
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       "      <td>897.0</td>\n",
       "      <td>341.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16476 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Requester id                 Group  Status Priority   Via  \\\n",
       "0      10300555531549               Support  Solved      Low  Mail   \n",
       "1      10420228868125  Reimbursement Claims  Closed      Low  Mail   \n",
       "2      10991633548957               Support  Solved      Low  Mail   \n",
       "3      10376247288477               Support  Solved      Low  Mail   \n",
       "4       7302858920989               Support  Closed      Low  Mail   \n",
       "...               ...                   ...     ...      ...   ...   \n",
       "16471  11283774362653               Support  Solved      Low  Mail   \n",
       "16472   7302298964893               Support  Solved      Low  Mail   \n",
       "16473   7303060862237               Support    Hold      Low  Mail   \n",
       "16474  10098421736733  Reimbursement Claims  Solved      Low  Mail   \n",
       "16475  11285001510813  Reimbursement Claims  Solved      Low  Mail   \n",
       "\n",
       "      Satisfaction Score  Reopens  Replies  \\\n",
       "0                Offered       11       11   \n",
       "1                    NaN       10       10   \n",
       "2                      4       10       11   \n",
       "3                Offered        9        9   \n",
       "4                      4        9        9   \n",
       "...                  ...      ...      ...   \n",
       "16471            Offered        0        1   \n",
       "16472            Offered        0        3   \n",
       "16473                NaN        0        1   \n",
       "16474                NaN        0        2   \n",
       "16475                NaN        0        1   \n",
       "\n",
       "       First reply time in minutes within business hours  \\\n",
       "0                                                  173.0   \n",
       "1                                                 1527.0   \n",
       "2                                                   61.0   \n",
       "3                                                  381.0   \n",
       "4                                                   37.0   \n",
       "...                                                  ...   \n",
       "16471                                               69.0   \n",
       "16472                                               90.0   \n",
       "16473                                               69.0   \n",
       "16474                                              280.0   \n",
       "16475                                              340.0   \n",
       "\n",
       "       First resolution time in minutes  \\\n",
       "0                               12975.0   \n",
       "1                                2984.0   \n",
       "2                                1563.0   \n",
       "3                                2888.0   \n",
       "4                                  37.0   \n",
       "...                                 ...   \n",
       "16471                             692.0   \n",
       "16472                            6422.0   \n",
       "16473                               NaN   \n",
       "16474                           12548.0   \n",
       "16475                             897.0   \n",
       "\n",
       "       First resolution time in minutes within business hours  \\\n",
       "0                                                 6488.0        \n",
       "1                                                 1527.0        \n",
       "2                                                  843.0        \n",
       "3                                                 1448.0        \n",
       "4                                                   37.0        \n",
       "...                                                  ...        \n",
       "16471                                               69.0        \n",
       "16472                                             2929.0        \n",
       "16473                                                NaN        \n",
       "16474                                             6223.0        \n",
       "16475                                              340.0        \n",
       "\n",
       "       Full resolution time in minutes  \\\n",
       "0                              96218.0   \n",
       "1                              45893.0   \n",
       "2                              19993.0   \n",
       "3                              91313.0   \n",
       "4                              36304.0   \n",
       "...                                ...   \n",
       "16471                            692.0   \n",
       "16472                           6422.0   \n",
       "16473                              NaN   \n",
       "16474                          12548.0   \n",
       "16475                            897.0   \n",
       "\n",
       "       Full resolution time in minutes within business hours  \\\n",
       "0                                                47978.0       \n",
       "1                                                22853.0       \n",
       "2                                                 9913.0       \n",
       "3                                                45953.0       \n",
       "4                                                17739.0       \n",
       "...                                                  ...       \n",
       "16471                                               69.0       \n",
       "16472                                             2929.0       \n",
       "16473                                                NaN       \n",
       "16474                                             6223.0       \n",
       "16475                                              340.0       \n",
       "\n",
       "       Requester wait time in minutes  \\\n",
       "0                             17853.0   \n",
       "1                             30113.0   \n",
       "2                              7115.0   \n",
       "3                             18221.0   \n",
       "4                              7427.0   \n",
       "...                               ...   \n",
       "16471                           692.0   \n",
       "16472                          6422.0   \n",
       "16473                           653.0   \n",
       "16474                         12547.0   \n",
       "16475                           897.0   \n",
       "\n",
       "       Requester wait time in minutes within business hours  \n",
       "0                                                 8516.0     \n",
       "1                                                15139.0     \n",
       "2                                                 3335.0     \n",
       "3                                                 9401.0     \n",
       "4                                                 4008.0     \n",
       "...                                                  ...     \n",
       "16471                                               69.0     \n",
       "16472                                             2930.0     \n",
       "16473                                               69.0     \n",
       "16474                                             6223.0     \n",
       "16475                                              341.0     \n",
       "\n",
       "[16476 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(columns=['Id', 'Created at', 'Updated at', 'Assigned at', 'Initially assigned at',\n",
    "       'Solved at', 'Resolution time', 'Manual Tagging of Categories [list]'], axis= 1)\n",
    "\n",
    "data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16476 entries, 0 to 16475\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                                  Non-Null Count  Dtype  \n",
      "---  ------                                                  --------------  -----  \n",
      " 0   Requester id                                            16476 non-null  int64  \n",
      " 1   Group                                                   16476 non-null  object \n",
      " 2   Status                                                  16476 non-null  object \n",
      " 3   Priority                                                16476 non-null  object \n",
      " 4   Via                                                     16476 non-null  object \n",
      " 5   Satisfaction Score                                      5453 non-null   object \n",
      " 6   Reopens                                                 16476 non-null  int64  \n",
      " 7   Replies                                                 16476 non-null  int64  \n",
      " 8   First reply time in minutes within business hours       12760 non-null  float64\n",
      " 9   First resolution time in minutes                        14834 non-null  float64\n",
      " 10  First resolution time in minutes within business hours  14834 non-null  float64\n",
      " 11  Full resolution time in minutes                         14409 non-null  float64\n",
      " 12  Full resolution time in minutes within business hours   14409 non-null  float64\n",
      " 13  Requester wait time in minutes                          16327 non-null  float64\n",
      " 14  Requester wait time in minutes within business hours    16327 non-null  float64\n",
      "dtypes: float64(7), int64(3), object(5)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Closed     12270\n",
       "Solved      2139\n",
       "Hold         790\n",
       "Pending      681\n",
       "Open         470\n",
       "New          126\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Status\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low       16282\n",
       "Urgent      182\n",
       "Normal       12\n",
       "Name: Priority, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Priority\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Endorsements            6449\n",
       "Support                 5670\n",
       "Reimbursement Claims    3827\n",
       "Onboardings              530\n",
       "Name: Group, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Group\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mail                      13081\n",
       "Internal Communication     2028\n",
       "OutBound                   1051\n",
       "Closed Ticket               316\n",
       "Name: Via, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Via\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Offered    4640\n",
       "5           409\n",
       "4           311\n",
       "3            64\n",
       "1            15\n",
       "2            14\n",
       "Name: Satisfaction Score, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Satisfaction Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Offered', nan, '4', '1', '2', '3', '5'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Satisfaction Score\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the target variable\n",
    "data['Satisfaction Category'] = data['Satisfaction Score'].apply(lambda x: 'Offered' if x in ['Offered', '4', '1', '2', '3', '5'] else 'Not Offered')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Requester id</th>\n",
       "      <th>Group</th>\n",
       "      <th>Status</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Via</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Reopens</th>\n",
       "      <th>Replies</th>\n",
       "      <th>First reply time in minutes within business hours</th>\n",
       "      <th>First resolution time in minutes</th>\n",
       "      <th>First resolution time in minutes within business hours</th>\n",
       "      <th>Full resolution time in minutes</th>\n",
       "      <th>Full resolution time in minutes within business hours</th>\n",
       "      <th>Requester wait time in minutes</th>\n",
       "      <th>Requester wait time in minutes within business hours</th>\n",
       "      <th>Satisfaction Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10300555531549</td>\n",
       "      <td>Support</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>Offered</td>\n",
       "      <td>11</td>\n",
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       "      <td>173.0</td>\n",
       "      <td>12975.0</td>\n",
       "      <td>6488.0</td>\n",
       "      <td>96218.0</td>\n",
       "      <td>47978.0</td>\n",
       "      <td>17853.0</td>\n",
       "      <td>8516.0</td>\n",
       "      <td>Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10420228868125</td>\n",
       "      <td>Reimbursement Claims</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>2984.0</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>45893.0</td>\n",
       "      <td>22853.0</td>\n",
       "      <td>30113.0</td>\n",
       "      <td>15139.0</td>\n",
       "      <td>Not Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10991633548957</td>\n",
       "      <td>Support</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>19993.0</td>\n",
       "      <td>9913.0</td>\n",
       "      <td>7115.0</td>\n",
       "      <td>3335.0</td>\n",
       "      <td>Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10376247288477</td>\n",
       "      <td>Support</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>Offered</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>381.0</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>91313.0</td>\n",
       "      <td>45953.0</td>\n",
       "      <td>18221.0</td>\n",
       "      <td>9401.0</td>\n",
       "      <td>Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7302858920989</td>\n",
       "      <td>Support</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36304.0</td>\n",
       "      <td>17739.0</td>\n",
       "      <td>7427.0</td>\n",
       "      <td>4008.0</td>\n",
       "      <td>Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16471</th>\n",
       "      <td>11283774362653</td>\n",
       "      <td>Support</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>Offered</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16472</th>\n",
       "      <td>7302298964893</td>\n",
       "      <td>Support</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>Offered</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>6422.0</td>\n",
       "      <td>2929.0</td>\n",
       "      <td>6422.0</td>\n",
       "      <td>2929.0</td>\n",
       "      <td>6422.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16473</th>\n",
       "      <td>7303060862237</td>\n",
       "      <td>Support</td>\n",
       "      <td>Hold</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>653.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Not Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16474</th>\n",
       "      <td>10098421736733</td>\n",
       "      <td>Reimbursement Claims</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>280.0</td>\n",
       "      <td>12548.0</td>\n",
       "      <td>6223.0</td>\n",
       "      <td>12548.0</td>\n",
       "      <td>6223.0</td>\n",
       "      <td>12547.0</td>\n",
       "      <td>6223.0</td>\n",
       "      <td>Not Offered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16475</th>\n",
       "      <td>11285001510813</td>\n",
       "      <td>Reimbursement Claims</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>340.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>Not Offered</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16476 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Requester id                 Group  Status Priority   Via  \\\n",
       "0      10300555531549               Support  Solved      Low  Mail   \n",
       "1      10420228868125  Reimbursement Claims  Closed      Low  Mail   \n",
       "2      10991633548957               Support  Solved      Low  Mail   \n",
       "3      10376247288477               Support  Solved      Low  Mail   \n",
       "4       7302858920989               Support  Closed      Low  Mail   \n",
       "...               ...                   ...     ...      ...   ...   \n",
       "16471  11283774362653               Support  Solved      Low  Mail   \n",
       "16472   7302298964893               Support  Solved      Low  Mail   \n",
       "16473   7303060862237               Support    Hold      Low  Mail   \n",
       "16474  10098421736733  Reimbursement Claims  Solved      Low  Mail   \n",
       "16475  11285001510813  Reimbursement Claims  Solved      Low  Mail   \n",
       "\n",
       "      Satisfaction Score  Reopens  Replies  \\\n",
       "0                Offered       11       11   \n",
       "1                    NaN       10       10   \n",
       "2                      4       10       11   \n",
       "3                Offered        9        9   \n",
       "4                      4        9        9   \n",
       "...                  ...      ...      ...   \n",
       "16471            Offered        0        1   \n",
       "16472            Offered        0        3   \n",
       "16473                NaN        0        1   \n",
       "16474                NaN        0        2   \n",
       "16475                NaN        0        1   \n",
       "\n",
       "       First reply time in minutes within business hours  \\\n",
       "0                                                  173.0   \n",
       "1                                                 1527.0   \n",
       "2                                                   61.0   \n",
       "3                                                  381.0   \n",
       "4                                                   37.0   \n",
       "...                                                  ...   \n",
       "16471                                               69.0   \n",
       "16472                                               90.0   \n",
       "16473                                               69.0   \n",
       "16474                                              280.0   \n",
       "16475                                              340.0   \n",
       "\n",
       "       First resolution time in minutes  \\\n",
       "0                               12975.0   \n",
       "1                                2984.0   \n",
       "2                                1563.0   \n",
       "3                                2888.0   \n",
       "4                                  37.0   \n",
       "...                                 ...   \n",
       "16471                             692.0   \n",
       "16472                            6422.0   \n",
       "16473                               NaN   \n",
       "16474                           12548.0   \n",
       "16475                             897.0   \n",
       "\n",
       "       First resolution time in minutes within business hours  \\\n",
       "0                                                 6488.0        \n",
       "1                                                 1527.0        \n",
       "2                                                  843.0        \n",
       "3                                                 1448.0        \n",
       "4                                                   37.0        \n",
       "...                                                  ...        \n",
       "16471                                               69.0        \n",
       "16472                                             2929.0        \n",
       "16473                                                NaN        \n",
       "16474                                             6223.0        \n",
       "16475                                              340.0        \n",
       "\n",
       "       Full resolution time in minutes  \\\n",
       "0                              96218.0   \n",
       "1                              45893.0   \n",
       "2                              19993.0   \n",
       "3                              91313.0   \n",
       "4                              36304.0   \n",
       "...                                ...   \n",
       "16471                            692.0   \n",
       "16472                           6422.0   \n",
       "16473                              NaN   \n",
       "16474                          12548.0   \n",
       "16475                            897.0   \n",
       "\n",
       "       Full resolution time in minutes within business hours  \\\n",
       "0                                                47978.0       \n",
       "1                                                22853.0       \n",
       "2                                                 9913.0       \n",
       "3                                                45953.0       \n",
       "4                                                17739.0       \n",
       "...                                                  ...       \n",
       "16471                                               69.0       \n",
       "16472                                             2929.0       \n",
       "16473                                                NaN       \n",
       "16474                                             6223.0       \n",
       "16475                                              340.0       \n",
       "\n",
       "       Requester wait time in minutes  \\\n",
       "0                             17853.0   \n",
       "1                             30113.0   \n",
       "2                              7115.0   \n",
       "3                             18221.0   \n",
       "4                              7427.0   \n",
       "...                               ...   \n",
       "16471                           692.0   \n",
       "16472                          6422.0   \n",
       "16473                           653.0   \n",
       "16474                         12547.0   \n",
       "16475                           897.0   \n",
       "\n",
       "       Requester wait time in minutes within business hours  \\\n",
       "0                                                 8516.0      \n",
       "1                                                15139.0      \n",
       "2                                                 3335.0      \n",
       "3                                                 9401.0      \n",
       "4                                                 4008.0      \n",
       "...                                                  ...      \n",
       "16471                                               69.0      \n",
       "16472                                             2930.0      \n",
       "16473                                               69.0      \n",
       "16474                                             6223.0      \n",
       "16475                                              341.0      \n",
       "\n",
       "      Satisfaction Category  \n",
       "0                   Offered  \n",
       "1               Not Offered  \n",
       "2                   Offered  \n",
       "3                   Offered  \n",
       "4                   Offered  \n",
       "...                     ...  \n",
       "16471               Offered  \n",
       "16472               Offered  \n",
       "16473           Not Offered  \n",
       "16474           Not Offered  \n",
       "16475           Not Offered  \n",
       "\n",
       "[16476 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Not Offered    11023\n",
       "Offered         5453\n",
       "Name: Satisfaction Category, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Satisfaction Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df, train_target, test_target = train_test_split(data.drop(columns=[\"Requester id\", \"Satisfaction Category\"]), data[\"Satisfaction Category\"], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identifying Input and Target Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_df.shape (13180, 14)\n",
      "test_df.shape (3296, 14)\n"
     ]
    }
   ],
   "source": [
    "print(\"train_df.shape\", train_df.shape)\n",
    "print(\"test_df.shape\", test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>index</th>\n",
       "      <th>Group</th>\n",
       "      <th>Status</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Via</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Reopens</th>\n",
       "      <th>Replies</th>\n",
       "      <th>First reply time in minutes within business hours</th>\n",
       "      <th>First resolution time in minutes</th>\n",
       "      <th>First resolution time in minutes within business hours</th>\n",
       "      <th>Full resolution time in minutes</th>\n",
       "      <th>Full resolution time in minutes within business hours</th>\n",
       "      <th>Requester wait time in minutes</th>\n",
       "      <th>Requester wait time in minutes within business hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14863</td>\n",
       "      <td>Endorsements</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>6508.0</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>6508.0</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>6508.0</td>\n",
       "      <td>3510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>Support</td>\n",
       "      <td>Solved</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>1231.0</td>\n",
       "      <td>51606.0</td>\n",
       "      <td>25686.0</td>\n",
       "      <td>6843.0</td>\n",
       "      <td>3243.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10872</td>\n",
       "      <td>Endorsements</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1603.0</td>\n",
       "      <td>6061.0</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>6061.0</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>1603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14364</td>\n",
       "      <td>Endorsements</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>OutBound</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12239</td>\n",
       "      <td>Support</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>Offered</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>181.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13175</th>\n",
       "      <td>11284</td>\n",
       "      <td>Support</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>Offered</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>6440.0</td>\n",
       "      <td>2906.0</td>\n",
       "      <td>6440.0</td>\n",
       "      <td>2906.0</td>\n",
       "      <td>6440.0</td>\n",
       "      <td>2906.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13176</th>\n",
       "      <td>11964</td>\n",
       "      <td>Endorsements</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>24044.0</td>\n",
       "      <td>11804.0</td>\n",
       "      <td>24044.0</td>\n",
       "      <td>11804.0</td>\n",
       "      <td>3889.0</td>\n",
       "      <td>1729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13177</th>\n",
       "      <td>5390</td>\n",
       "      <td>Support</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13178</th>\n",
       "      <td>860</td>\n",
       "      <td>Support</td>\n",
       "      <td>Closed</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>1336.0</td>\n",
       "      <td>2771.0</td>\n",
       "      <td>1331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13179</th>\n",
       "      <td>15795</td>\n",
       "      <td>Support</td>\n",
       "      <td>Hold</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13180 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index         Group  Status Priority       Via Satisfaction Score  \\\n",
       "0      14863  Endorsements  Closed      Low      Mail                NaN   \n",
       "1         33       Support  Solved      Low      Mail                  4   \n",
       "2      10872  Endorsements  Closed      Low      Mail                NaN   \n",
       "3      14364  Endorsements  Closed      Low  OutBound                NaN   \n",
       "4      12239       Support  Closed      Low      Mail            Offered   \n",
       "...      ...           ...     ...      ...       ...                ...   \n",
       "13175  11284       Support  Closed      Low      Mail            Offered   \n",
       "13176  11964  Endorsements  Closed      Low      Mail                NaN   \n",
       "13177   5390       Support  Closed      Low      Mail                  5   \n",
       "13178    860       Support  Closed      Low      Mail                  4   \n",
       "13179  15795       Support    Hold      Low      Mail                NaN   \n",
       "\n",
       "       Reopens  Replies  First reply time in minutes within business hours  \\\n",
       "0            0        1                                             3510.0   \n",
       "1            6        8                                               72.0   \n",
       "2            0        2                                             1603.0   \n",
       "3            0        0                                                NaN   \n",
       "4            0        2                                              181.0   \n",
       "...        ...      ...                                                ...   \n",
       "13175        0        1                                              192.0   \n",
       "13176        0        2                                             1729.0   \n",
       "13177        0        2                                               45.0   \n",
       "13178        1        9                                               84.0   \n",
       "13179        0        1                                               20.0   \n",
       "\n",
       "       First resolution time in minutes  \\\n",
       "0                                6508.0   \n",
       "1                                2671.0   \n",
       "2                                6061.0   \n",
       "3                                   0.0   \n",
       "4                                 952.0   \n",
       "...                                 ...   \n",
       "13175                            6440.0   \n",
       "13176                           24044.0   \n",
       "13177                              87.0   \n",
       "13178                            1574.0   \n",
       "13179                               NaN   \n",
       "\n",
       "       First resolution time in minutes within business hours  \\\n",
       "0                                                 3510.0        \n",
       "1                                                 1231.0        \n",
       "2                                                 3062.0        \n",
       "3                                                    0.0        \n",
       "4                                                  232.0        \n",
       "...                                                  ...        \n",
       "13175                                             2906.0        \n",
       "13176                                            11804.0        \n",
       "13177                                               87.0        \n",
       "13178                                              854.0        \n",
       "13179                                                NaN        \n",
       "\n",
       "       Full resolution time in minutes  \\\n",
       "0                               6508.0   \n",
       "1                              51606.0   \n",
       "2                               6061.0   \n",
       "3                                  0.0   \n",
       "4                                952.0   \n",
       "...                                ...   \n",
       "13175                           6440.0   \n",
       "13176                          24044.0   \n",
       "13177                             87.0   \n",
       "13178                           2776.0   \n",
       "13179                              NaN   \n",
       "\n",
       "       Full resolution time in minutes within business hours  \\\n",
       "0                                                 3510.0       \n",
       "1                                                25686.0       \n",
       "2                                                 3062.0       \n",
       "3                                                    0.0       \n",
       "4                                                  232.0       \n",
       "...                                                  ...       \n",
       "13175                                             2906.0       \n",
       "13176                                            11804.0       \n",
       "13177                                               87.0       \n",
       "13178                                             1336.0       \n",
       "13179                                                NaN       \n",
       "\n",
       "       Requester wait time in minutes  \\\n",
       "0                              6508.0   \n",
       "1                              6843.0   \n",
       "2                              3163.0   \n",
       "3                                 0.0   \n",
       "4                               952.0   \n",
       "...                               ...   \n",
       "13175                          6440.0   \n",
       "13176                          3889.0   \n",
       "13177                            88.0   \n",
       "13178                          2771.0   \n",
       "13179                            20.0   \n",
       "\n",
       "       Requester wait time in minutes within business hours  \n",
       "0                                                 3510.0     \n",
       "1                                                 3243.0     \n",
       "2                                                 1603.0     \n",
       "3                                                    0.0     \n",
       "4                                                  232.0     \n",
       "...                                                  ...     \n",
       "13175                                             2906.0     \n",
       "13176                                             1729.0     \n",
       "13177                                               88.0     \n",
       "13178                                             1331.0     \n",
       "13179                                               20.0     \n",
       "\n",
       "[13180 rows x 15 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.dropna(inplace=True)\n",
    "test_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_target Column:  14863    Not Offered\n",
      "33           Offered\n",
      "10872    Not Offered\n",
      "14364    Not Offered\n",
      "12239        Offered\n",
      "            ...     \n",
      "11284        Offered\n",
      "11964    Not Offered\n",
      "5390         Offered\n",
      "860          Offered\n",
      "15795    Not Offered\n",
      "Name: Satisfaction Category, Length: 13180, dtype: object\n",
      "test_target Column:  12811    Not Offered\n",
      "2816     Not Offered\n",
      "10336    Not Offered\n",
      "15619    Not Offered\n",
      "2545     Not Offered\n",
      "            ...     \n",
      "14092    Not Offered\n",
      "753          Offered\n",
      "8916         Offered\n",
      "4359         Offered\n",
      "12933    Not Offered\n",
      "Name: Satisfaction Category, Length: 3296, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"train_target Column: \", train_target)\n",
    "print(\"test_target Column: \", test_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using `ColumnTransformer` to apply different preprocessing to different columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How to use `ColumnTransformer`?\n",
    "\n",
    "* select from DataFrame columns by name or by index\n",
    "* passthrough or drop unspecified columns/ columns where preprocessing is not required"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "### **Q: Encode categorical features using OneHotEncoder or OrdinalEncoder**\n",
    "\n",
    "Two common ways to encode categorical features:\n",
    "\n",
    "1. `OneHotEncoder` for unordered (nominal) data\n",
    "\n",
    "2. `OrdinalEncoder` for ordered (ordinal) data\n",
    "\n",
    "P.S. `LabelEncoder` is for labels, not features!\n",
    "\n",
    "### **Q: For a one-hot encoded feature, what can you do if new data contains categories that weren't seen during training?**\n",
    "\n",
    "A: Set handle_unknown='ignore' to encode new categories as all zeros.\n",
    "\n",
    "See example 👇\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "trf = ColumnTransformer(transformers=[\n",
    "    # One Hot Encoding\n",
    "\n",
    "    (\"ohe_group\", OneHotEncoder(sparse_output= False, handle_unknown=\"ignore\", drop=\"first\"),[0]),\n",
    "    # *Handle unknown categories with OneHotEncoder by encoding them as zeros*\n",
    "    \n",
    "    \n",
    "    # Ordinal Encoding\n",
    "\n",
    "    (\"oe_status\", OrdinalEncoder(categories=[['Solved', 'Closed', 'Hold', 'Pending', 'Open', 'New']]), [1]),\n",
    "    (\"oe_priority\", OrdinalEncoder(categories=[['Low', 'Urgent', 'Normal']]), [2]),\n",
    "    \n",
    "    # One Hot Encoding\n",
    "\n",
    "    (\"ohe_via\", OneHotEncoder(sparse_output= False, handle_unknown=\"ignore\", drop=\"first\"),[3]),\n",
    "    ('satisfaction_imputer', SimpleImputer(strategy='constant', fill_value='Not Mentioned'), [4]),\n",
    "    ('satisfaction_encoder', OrdinalEncoder(categories=[['Not Mentioned', '1', '2', '3', '4', '5', 'Offered']]), [4]),\n",
    "    \n",
    "    # Imputation Transformer\n",
    "    \n",
    "    (\"imputing_first_reply_bh\", SimpleImputer(strategy=\"mean\"),[7]),\n",
    "    (\"imputing_first_resolution\", SimpleImputer(strategy=\"mean\"),[8]),\n",
    "    (\"imputing_first_resolution_bh\", SimpleImputer(strategy=\"mean\"),[9]),\n",
    "    (\"imputing_full_resolution\", SimpleImputer(strategy=\"mean\"),[10]),\n",
    "    (\"imputing_full_resolution_bh\", SimpleImputer(strategy=\"mean\"),[11]),\n",
    "    (\"imputing_requester_wait\", SimpleImputer(strategy=\"mean\"),[12]),\n",
    "    (\"imputing_requester_wait_bh\", SimpleImputer(strategy=\"mean\"),[13])\n",
    "],remainder=\"passthrough\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0, 0.0, 0.0, ..., 3243.0, 6, 8],\n",
       "       [1.0, 0.0, 0.0, ..., 232.0, 0, 2],\n",
       "       [1.0, 0.0, 0.0, ..., 2091.0, 0, 4],\n",
       "       ...,\n",
       "       [1.0, 0.0, 0.0, ..., 2906.0, 0, 1],\n",
       "       [1.0, 0.0, 0.0, ..., 88.0, 0, 2],\n",
       "       [1.0, 0.0, 0.0, ..., 1331.0, 1, 9]], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.fit_transform(train_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;ohe_group&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                               handle_unknown=&#x27;ignore&#x27;,\n",
       "                                               sparse_output=False),\n",
       "                                 [0]),\n",
       "                                (&#x27;oe_status&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;Solved&#x27;, &#x27;Closed&#x27;,\n",
       "                                                             &#x27;Hold&#x27;, &#x27;Pending&#x27;,\n",
       "                                                             &#x27;Open&#x27;, &#x27;New&#x27;]]),\n",
       "                                 [1]),\n",
       "                                (&#x27;oe_priority&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;Low&#x27;, &#x27;Urgent&#x27;,\n",
       "                                                             &#x27;Normal&#x27;]]),\n",
       "                                 [2]),\n",
       "                                (&#x27;ohe_via&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;f...\n",
       "                                (&#x27;imputing_first_reply_bh&#x27;, SimpleImputer(),\n",
       "                                 [7]),\n",
       "                                (&#x27;imputing_first_resolution&#x27;, SimpleImputer(),\n",
       "                                 [8]),\n",
       "                                (&#x27;imputing_first_resolution_bh&#x27;,\n",
       "                                 SimpleImputer(), [9]),\n",
       "                                (&#x27;imputing_full_resolution&#x27;, SimpleImputer(),\n",
       "                                 [10]),\n",
       "                                (&#x27;imputing_full_resolution_bh&#x27;, SimpleImputer(),\n",
       "                                 [11]),\n",
       "                                (&#x27;imputing_requester_wait&#x27;, SimpleImputer(),\n",
       "                                 [12]),\n",
       "                                (&#x27;imputing_requester_wait_bh&#x27;, SimpleImputer(),\n",
       "                                 [13])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;ohe_group&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                               handle_unknown=&#x27;ignore&#x27;,\n",
       "                                               sparse_output=False),\n",
       "                                 [0]),\n",
       "                                (&#x27;oe_status&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;Solved&#x27;, &#x27;Closed&#x27;,\n",
       "                                                             &#x27;Hold&#x27;, &#x27;Pending&#x27;,\n",
       "                                                             &#x27;Open&#x27;, &#x27;New&#x27;]]),\n",
       "                                 [1]),\n",
       "                                (&#x27;oe_priority&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;Low&#x27;, &#x27;Urgent&#x27;,\n",
       "                                                             &#x27;Normal&#x27;]]),\n",
       "                                 [2]),\n",
       "                                (&#x27;ohe_via&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;f...\n",
       "                                (&#x27;imputing_first_reply_bh&#x27;, SimpleImputer(),\n",
       "                                 [7]),\n",
       "                                (&#x27;imputing_first_resolution&#x27;, SimpleImputer(),\n",
       "                                 [8]),\n",
       "                                (&#x27;imputing_first_resolution_bh&#x27;,\n",
       "                                 SimpleImputer(), [9]),\n",
       "                                (&#x27;imputing_full_resolution&#x27;, SimpleImputer(),\n",
       "                                 [10]),\n",
       "                                (&#x27;imputing_full_resolution_bh&#x27;, SimpleImputer(),\n",
       "                                 [11]),\n",
       "                                (&#x27;imputing_requester_wait&#x27;, SimpleImputer(),\n",
       "                                 [12]),\n",
       "                                (&#x27;imputing_requester_wait_bh&#x27;, SimpleImputer(),\n",
       "                                 [13])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">ohe_group</label><div class=\"sk-toggleable__content fitted\"><pre>[0]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">oe_status</label><div class=\"sk-toggleable__content fitted\"><pre>[1]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Solved&#x27;, &#x27;Closed&#x27;, &#x27;Hold&#x27;, &#x27;Pending&#x27;, &#x27;Open&#x27;,\n",
       "                            &#x27;New&#x27;]])</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">oe_priority</label><div class=\"sk-toggleable__content fitted\"><pre>[2]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Low&#x27;, &#x27;Urgent&#x27;, &#x27;Normal&#x27;]])</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">ohe_via</label><div class=\"sk-toggleable__content fitted\"><pre>[3]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">satisfaction_imputer</label><div class=\"sk-toggleable__content fitted\"><pre>[4]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(fill_value=&#x27;Not Mentioned&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">satisfaction_encoder</label><div class=\"sk-toggleable__content fitted\"><pre>[4]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Not Mentioned&#x27;, &#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;,\n",
       "                            &#x27;Offered&#x27;]])</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">imputing_first_reply_bh</label><div class=\"sk-toggleable__content fitted\"><pre>[7]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">imputing_first_resolution</label><div class=\"sk-toggleable__content fitted\"><pre>[8]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">imputing_first_resolution_bh</label><div class=\"sk-toggleable__content fitted\"><pre>[9]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">imputing_full_resolution</label><div class=\"sk-toggleable__content fitted\"><pre>[10]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">imputing_full_resolution_bh</label><div class=\"sk-toggleable__content fitted\"><pre>[11]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">imputing_requester_wait</label><div class=\"sk-toggleable__content fitted\"><pre>[12]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">imputing_requester_wait_bh</label><div class=\"sk-toggleable__content fitted\"><pre>[13]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Reopens&#x27;, &#x27;Replies&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(remainder='passthrough',\n",
       "                  transformers=[('ohe_group',\n",
       "                                 OneHotEncoder(drop='first',\n",
       "                                               handle_unknown='ignore',\n",
       "                                               sparse_output=False),\n",
       "                                 [0]),\n",
       "                                ('oe_status',\n",
       "                                 OrdinalEncoder(categories=[['Solved', 'Closed',\n",
       "                                                             'Hold', 'Pending',\n",
       "                                                             'Open', 'New']]),\n",
       "                                 [1]),\n",
       "                                ('oe_priority',\n",
       "                                 OrdinalEncoder(categories=[['Low', 'Urgent',\n",
       "                                                             'Normal']]),\n",
       "                                 [2]),\n",
       "                                ('ohe_via',\n",
       "                                 OneHotEncoder(drop='f...\n",
       "                                ('imputing_first_reply_bh', SimpleImputer(),\n",
       "                                 [7]),\n",
       "                                ('imputing_first_resolution', SimpleImputer(),\n",
       "                                 [8]),\n",
       "                                ('imputing_first_resolution_bh',\n",
       "                                 SimpleImputer(), [9]),\n",
       "                                ('imputing_full_resolution', SimpleImputer(),\n",
       "                                 [10]),\n",
       "                                ('imputing_full_resolution_bh', SimpleImputer(),\n",
       "                                 [11]),\n",
       "                                ('imputing_requester_wait', SimpleImputer(),\n",
       "                                 [12]),\n",
       "                                ('imputing_requester_wait_bh', SimpleImputer(),\n",
       "                                 [13])])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 0.0, 1.0, ..., 61.0, 0, 2],\n",
       "       [1.0, 0.0, 0.0, ..., 3333.0, 0, 4],\n",
       "       [1.0, 0.0, 1.0, ..., 229.0, 0, 1],\n",
       "       ...,\n",
       "       [0.0, 0.0, 0.0, ..., 6467.0, 2, 5],\n",
       "       [1.0, 0.0, 0.0, ..., 64.0, 0, 1],\n",
       "       [1.0, 0.0, 0.0, ..., 1035.0, 0, 4]], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.transform(test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" checked><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LabelEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.LabelEncoder.html\">?<span>Documentation for LabelEncoder</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LabelEncoder()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label Encoder (for Categorical Targets Only)\n",
    "le = LabelEncoder()\n",
    "le.fit(train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Not Offered', 'Offered'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_target = le.transform(train_target)\n",
    "test_target = le.transform(test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 1, 1, 0])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **What is the difference between \"fit\" and \"transform\"?**\n",
    "\n",
    "-- `fit()`: Learn and estimate the parameters of the transformation\n",
    "\n",
    "\t* transformer.fit(train_data)\n",
    "\n",
    "-- `transform()`:\tApply the learned transformation to new data\t\n",
    "\n",
    "\t* transformed_data = estimator.transform(test_data)\n",
    "\n",
    "-- `fit_transform()`:\tLearn the parameters and apply the transformation to new data\n",
    "\n",
    "\t* transformed_data = estimator.fit_transform(data)\n",
    "\n",
    ">>>> Note: Use \"fit_transform\" on training data, but \"transform\" (only) on testing/new data\t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> *Want more tips? View all tips on **GitHub**.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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