Feature Engineering- Handling Categorical Feature- Day 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal Number Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "today_date=datetime.datetime.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 7, 16, 19, 13, 59, 571679)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 7, 13, 19, 13, 59, 571679)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_date-datetime.timedelta(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### List Comprehension\n",
    "days=[today_date-datetime.timedelta(x) for x in range(0,15)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data=pd.DataFrame(days)\n",
    "data.columns=[\"Day\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-16 19:13:59.571679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-15 19:13:59.571679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-14 19:13:59.571679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-13 19:13:59.571679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-12 19:13:59.571679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Day\n",
       "0 2020-07-16 19:13:59.571679\n",
       "1 2020-07-15 19:13:59.571679\n",
       "2 2020-07-14 19:13:59.571679\n",
       "3 2020-07-13 19:13:59.571679\n",
       "4 2020-07-12 19:13:59.571679"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-16 19:13:59.571679</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-15 19:13:59.571679</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-14 19:13:59.571679</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-13 19:13:59.571679</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-12 19:13:59.571679</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Day    weekday\n",
       "0 2020-07-16 19:13:59.571679   Thursday\n",
       "1 2020-07-15 19:13:59.571679  Wednesday\n",
       "2 2020-07-14 19:13:59.571679    Tuesday\n",
       "3 2020-07-13 19:13:59.571679     Monday\n",
       "4 2020-07-12 19:13:59.571679     Sunday"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['weekday']=data['Day'].dt.weekday_name\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary={'Monday':1,'Tuesday':2,'Wednesday':3,'Thursday':4,'Friday':5,'Saturday':6,'Sunday':7\n",
    "    \n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Monday': 1,\n",
       " 'Tuesday': 2,\n",
       " 'Wednesday': 3,\n",
       " 'Thursday': 4,\n",
       " 'Friday': 5,\n",
       " 'Saturday': 6,\n",
       " 'Sunday': 7}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['weekday_ordinal']=data['weekday'].map(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekday_ordinal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-16 19:13:59.571679</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-15 19:13:59.571679</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-14 19:13:59.571679</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-13 19:13:59.571679</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-12 19:13:59.571679</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-07-11 19:13:59.571679</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-10 19:13:59.571679</td>\n",
       "      <td>Friday</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-07-09 19:13:59.571679</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-07-08 19:13:59.571679</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-07 19:13:59.571679</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-07-06 19:13:59.571679</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-07-05 19:13:59.571679</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-07-04 19:13:59.571679</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-07-03 19:13:59.571679</td>\n",
       "      <td>Friday</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-07-02 19:13:59.571679</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Day    weekday  weekday_ordinal\n",
       "0  2020-07-16 19:13:59.571679   Thursday                4\n",
       "1  2020-07-15 19:13:59.571679  Wednesday                3\n",
       "2  2020-07-14 19:13:59.571679    Tuesday                2\n",
       "3  2020-07-13 19:13:59.571679     Monday                1\n",
       "4  2020-07-12 19:13:59.571679     Sunday                7\n",
       "5  2020-07-11 19:13:59.571679   Saturday                6\n",
       "6  2020-07-10 19:13:59.571679     Friday                5\n",
       "7  2020-07-09 19:13:59.571679   Thursday                4\n",
       "8  2020-07-08 19:13:59.571679  Wednesday                3\n",
       "9  2020-07-07 19:13:59.571679    Tuesday                2\n",
       "10 2020-07-06 19:13:59.571679     Monday                1\n",
       "11 2020-07-05 19:13:59.571679     Sunday                7\n",
       "12 2020-07-04 19:13:59.571679   Saturday                6\n",
       "13 2020-07-03 19:13:59.571679     Friday                5\n",
       "14 2020-07-02 19:13:59.571679   Thursday                4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Count Or Frequency Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                  1       2           3   4                    5   \\\n",
       "0  39          State-gov   77516   Bachelors  13        Never-married   \n",
       "1  50   Self-emp-not-inc   83311   Bachelors  13   Married-civ-spouse   \n",
       "2  38            Private  215646     HS-grad   9             Divorced   \n",
       "3  53            Private  234721        11th   7   Married-civ-spouse   \n",
       "4  28            Private  338409   Bachelors  13   Married-civ-spouse   \n",
       "\n",
       "                   6               7       8        9     10  11  12  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male  2174   0  40   \n",
       "1     Exec-managerial         Husband   White     Male     0   0  13   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male     0   0  40   \n",
       "3   Handlers-cleaners         Husband   Black     Male     0   0  40   \n",
       "4      Prof-specialty            Wife   Black   Female     0   0  40   \n",
       "\n",
       "               13      14  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data' , header = None,index_col=None)\n",
    "train_set.head()                                                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=[1,3,5,6,7,8,9,13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set=train_set[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set.columns=['Employment','Degree','Status','Designation','family_job','Race','Sex','Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employment</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Status</th>\n",
       "      <th>Designation</th>\n",
       "      <th>family_job</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Employment      Degree               Status         Designation  \\\n",
       "0          State-gov   Bachelors        Never-married        Adm-clerical   \n",
       "1   Self-emp-not-inc   Bachelors   Married-civ-spouse     Exec-managerial   \n",
       "2            Private     HS-grad             Divorced   Handlers-cleaners   \n",
       "3            Private        11th   Married-civ-spouse   Handlers-cleaners   \n",
       "4            Private   Bachelors   Married-civ-spouse      Prof-specialty   \n",
       "\n",
       "       family_job    Race      Sex         Country  \n",
       "0   Not-in-family   White     Male   United-States  \n",
       "1         Husband   White     Male   United-States  \n",
       "2   Not-in-family   White     Male   United-States  \n",
       "3         Husband   Black     Male   United-States  \n",
       "4            Wife   Black   Female            Cuba  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Employment : 9 labels\n",
      "Degree : 16 labels\n",
      "Status : 7 labels\n",
      "Designation : 15 labels\n",
      "family_job : 6 labels\n",
      "Race : 5 labels\n",
      "Sex : 2 labels\n",
      "Country : 42 labels\n"
     ]
    }
   ],
   "source": [
    "for feature in train_set.columns[:]:\n",
    "    print(feature,\":\",len(train_set[feature].unique()),'labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_map=train_set['Country'].value_counts().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>29170</td>\n",
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       "      <td>Self-emp-not-inc</td>\n",
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       "      <td>29170</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
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       "      <td>29170</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
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       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Private</td>\n",
       "      <td>9th</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
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       "      <td>Female</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>Male</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Private</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>29170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Employment         Degree                  Status  \\\n",
       "0           State-gov      Bachelors           Never-married   \n",
       "1    Self-emp-not-inc      Bachelors      Married-civ-spouse   \n",
       "2             Private        HS-grad                Divorced   \n",
       "3             Private           11th      Married-civ-spouse   \n",
       "4             Private      Bachelors      Married-civ-spouse   \n",
       "5             Private        Masters      Married-civ-spouse   \n",
       "6             Private            9th   Married-spouse-absent   \n",
       "7    Self-emp-not-inc        HS-grad      Married-civ-spouse   \n",
       "8             Private        Masters           Never-married   \n",
       "9             Private      Bachelors      Married-civ-spouse   \n",
       "10            Private   Some-college      Married-civ-spouse   \n",
       "11          State-gov      Bachelors      Married-civ-spouse   \n",
       "12            Private      Bachelors           Never-married   \n",
       "13            Private     Assoc-acdm           Never-married   \n",
       "14            Private      Assoc-voc      Married-civ-spouse   \n",
       "15            Private        7th-8th      Married-civ-spouse   \n",
       "16   Self-emp-not-inc        HS-grad           Never-married   \n",
       "17            Private        HS-grad           Never-married   \n",
       "18            Private           11th      Married-civ-spouse   \n",
       "19   Self-emp-not-inc        Masters                Divorced   \n",
       "\n",
       "           Designation      family_job                 Race      Sex  Country  \n",
       "0         Adm-clerical   Not-in-family                White     Male    29170  \n",
       "1      Exec-managerial         Husband                White     Male    29170  \n",
       "2    Handlers-cleaners   Not-in-family                White     Male    29170  \n",
       "3    Handlers-cleaners         Husband                Black     Male    29170  \n",
       "4       Prof-specialty            Wife                Black   Female       95  \n",
       "5      Exec-managerial            Wife                White   Female    29170  \n",
       "6        Other-service   Not-in-family                Black   Female       81  \n",
       "7      Exec-managerial         Husband                White     Male    29170  \n",
       "8       Prof-specialty   Not-in-family                White   Female    29170  \n",
       "9      Exec-managerial         Husband                White     Male    29170  \n",
       "10     Exec-managerial         Husband                Black     Male    29170  \n",
       "11      Prof-specialty         Husband   Asian-Pac-Islander     Male      100  \n",
       "12        Adm-clerical       Own-child                White   Female    29170  \n",
       "13               Sales   Not-in-family                Black     Male    29170  \n",
       "14        Craft-repair         Husband   Asian-Pac-Islander     Male      583  \n",
       "15    Transport-moving         Husband   Amer-Indian-Eskimo     Male      643  \n",
       "16     Farming-fishing       Own-child                White     Male    29170  \n",
       "17   Machine-op-inspct       Unmarried                White     Male    29170  \n",
       "18               Sales         Husband                White     Male    29170  \n",
       "19     Exec-managerial       Unmarried                White   Female    29170  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set['Country']=train_set['Country'].map(country_map)\n",
    "train_set.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Advantages\n",
    "1. Easy To Use\n",
    "2. Not increasing feature space\n",
    "##### Disadvantages\n",
    "1. It will provide same weight if the frequencies are same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Target Guided Ordinal Encoding\n",
    "1. Ordering the labels according to the target\n",
    "2. Replace the labels by the joint probability of being 1 or 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Survived Cabin\n",
       "0         0   NaN\n",
       "1         1   C85\n",
       "2         1   NaN\n",
       "3         1  C123\n",
       "4         0   NaN"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('titanic.csv', usecols=['Cabin','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cabin'].fillna('Missing',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cabin']=df['Cabin'].astype(str).str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived Cabin\n",
       "0         0     M\n",
       "1         1     C\n",
       "2         1     M\n",
       "3         1     C\n",
       "4         0     M"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin\n",
       "A    0.466667\n",
       "B    0.744681\n",
       "C    0.593220\n",
       "D    0.757576\n",
       "E    0.750000\n",
       "F    0.615385\n",
       "G    0.500000\n",
       "M    0.299854\n",
       "T    0.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Cabin'])['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['T', 'M', 'A', 'G', 'C', 'F', 'B', 'E', 'D'], dtype='object', name='Cabin')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Cabin'])['Survived'].mean().sort_values().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['T', 'M', 'A', 'G', 'C', 'F', 'B', 'E', 'D'], dtype='object', name='Cabin')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_labels=df.groupby(['Cabin'])['Survived'].mean().sort_values().index\n",
    "ordinal_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<enumerate at 0x24e376166c0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enumerate(ordinal_labels,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'T': 0, 'M': 1, 'A': 2, 'G': 3, 'C': 4, 'F': 5, 'B': 6, 'E': 7, 'D': 8}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_labels2={k:i for i,k in enumerate(ordinal_labels,0)}\n",
    "ordinal_labels2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Cabin_ordinal_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived Cabin  Cabin_ordinal_labels\n",
       "0         0     M                     1\n",
       "1         1     C                     4\n",
       "2         1     M                     1\n",
       "3         1     C                     4\n",
       "4         0     M                     1"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin_ordinal_labels']=df['Cabin'].map(ordinal_labels2)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ordinal=df.groupby(['Cabin'])['Survived'].mean().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 0.4666666666666667,\n",
       " 'B': 0.7446808510638298,\n",
       " 'C': 0.5932203389830508,\n",
       " 'D': 0.7575757575757576,\n",
       " 'E': 0.75,\n",
       " 'F': 0.6153846153846154,\n",
       " 'G': 0.5,\n",
       " 'M': 0.29985443959243085,\n",
       " 'T': 0.0}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ordinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Cabin_ordinal_labels</th>\n",
       "      <th>mean_ordinal_encode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0.299854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>0.593220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0.299854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>0.593220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0.299854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived Cabin  Cabin_ordinal_labels  mean_ordinal_encode\n",
       "0         0     M                     1             0.299854\n",
       "1         1     C                     4             0.593220\n",
       "2         1     M                     1             0.299854\n",
       "3         1     C                     4             0.593220\n",
       "4         0     M                     1             0.299854"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mean_ordinal_encode']=df['Cabin'].map(mean_ordinal)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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