02. Label Encoding.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "7e9ff81c",
   "metadata": {},
   "source": [
    "# Label Encoding\n",
    "This process is used for converting categorical data into numeric data and is necessary step while preparing the data to feed the machines (computer program) since the machine do not understand any other language than the language of numbers. Label encoding helps us train our model in better manner since the categorical features would be dropped otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a734a65b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e53f3d8b",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital  education default  balance housing loan  \\\n",
       "0   30   unemployed  married    primary      no     1787      no   no   \n",
       "1   33     services  married  secondary      no     4789     yes  yes   \n",
       "2   35   management   single   tertiary      no     1350     yes   no   \n",
       "3   30   management  married   tertiary      no     1476     yes  yes   \n",
       "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown  no  \n",
       "1  cellular   11   may       220         1    339         4  failure  no  \n",
       "2  cellular   16   apr       185         1    330         1  failure  no  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown  no  \n",
       "4   unknown    5   may       226         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data\n",
    "df=pd.read_csv('bank.csv',\";\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8329912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['married', 'single', 'divorced'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the unique values for encoding\n",
    "df['marital'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de7186ba",
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
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       "      <th>campaign</th>\n",
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       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
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       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
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       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
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       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
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       "      <td>339</td>\n",
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       "      <td>failure</td>\n",
       "      <td>no</td>\n",
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       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>0</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
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       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>1</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>1</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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      "text/plain": [
       "   age          job  marital  education default  balance housing loan  \\\n",
       "0   30   unemployed        1    primary      no     1787      no   no   \n",
       "1   33     services        1  secondary      no     4789     yes  yes   \n",
       "2   35   management        0   tertiary      no     1350     yes   no   \n",
       "3   30   management        1   tertiary      no     1476     yes  yes   \n",
       "4   59  blue-collar        1  secondary      no        0     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown  no  \n",
       "1  cellular   11   may       220         1    339         4  failure  no  \n",
       "2  cellular   16   apr       185         1    330         1  failure  no  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown  no  \n",
       "4   unknown    5   may       226         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label Encoding\n",
    "df['marital']=df['marital'].map({'single':0, 'married':1,'divorced':2})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5616b9e4",
   "metadata": {},
   "source": [
    "It is easy to encode this way when the unique values of the particular column are less, but what if there are lot of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e42d68d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unemployed', 'services', 'management', 'blue-collar',\n",
       "       'self-employed', 'technician', 'entrepreneur', 'admin.', 'student',\n",
       "       'housemaid', 'retired', 'unknown'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the unique values for encoding\n",
    "df['job'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22cf29dc",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th>education</th>\n",
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       "      <th>balance</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
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       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
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       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>secondary</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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      "text/plain": [
       "   age   job  marital  education default  balance housing loan   contact  day  \\\n",
       "0   30  10.0        1    primary      no     1787      no   no  cellular   19   \n",
       "1   33   0.0        1  secondary      no     4789     yes  yes  cellular   11   \n",
       "2   35   1.0        0   tertiary      no     1350     yes   no  cellular   16   \n",
       "3   30   1.0        1   tertiary      no     1476     yes  yes   unknown    3   \n",
       "4   59   2.0        1  secondary      no        0     yes   no   unknown    5   \n",
       "\n",
       "  month  duration  campaign  pdays  previous poutcome   y  \n",
       "0   oct        79         1     -1         0  unknown  no  \n",
       "1   may       220         1    339         4  failure  no  \n",
       "2   apr       185         1    330         1  failure  no  \n",
       "3   jun       199         4     -1         0  unknown  no  \n",
       "4   may       226         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['job'].replace(\n",
    "    {'unknown':np.nan, \n",
    "     'services':0,\n",
    "     'management':1,\n",
    "     'blue-collar':2,\n",
    "     'self-employed':3,\n",
    "     'technician':4,\n",
    "     'entrepreneur':5,\n",
    "     'admin.':6,\n",
    "     'student':7,\n",
    "     'housemaid':8,\n",
    "     'retired':9,\n",
    "     'unemployed':10 },\n",
    "    inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cebc6ac",
   "metadata": {},
   "source": [
    "In case when you have more unique values you can still encode them using the same method but this becomes time consuming process. In such cases we can use a function offered by `Sklearn` known as `LabelEncoder` for encoding our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e76522",
   "metadata": {},
   "source": [
    "#### Encoding with Label Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0dacb773",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing function from the library\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a6e3ee",
   "metadata": {},
   "source": [
    "Let's replace all the months with numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b978c353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['oct', 'may', 'apr', 'jun', 'feb', 'aug', 'jan', 'jul', 'nov',\n",
       "       'sep', 'mar', 'dec'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month'].unique()\n",
    "#Month doesn't have `unknown` values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d10fe904",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>marital</th>\n",
       "      <th>education</th>\n",
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       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
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       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   job  marital  education default  balance housing loan   contact  day  \\\n",
       "0   30  10.0        1    primary      no     1787      no   no  cellular   19   \n",
       "1   33   0.0        1  secondary      no     4789     yes  yes  cellular   11   \n",
       "2   35   1.0        0   tertiary      no     1350     yes   no  cellular   16   \n",
       "3   30   1.0        1   tertiary      no     1476     yes  yes   unknown    3   \n",
       "4   59   2.0        1  secondary      no        0     yes   no   unknown    5   \n",
       "\n",
       "   month  duration  campaign  pdays  previous poutcome   y  \n",
       "0     10        79         1     -1         0  unknown  no  \n",
       "1      8       220         1    339         4  failure  no  \n",
       "2      0       185         1    330         1  failure  no  \n",
       "3      6       199         4     -1         0  unknown  no  \n",
       "4      8       226         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"month\"]=le.fit_transform(df[\"month\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "003479d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df[\"contact\"]=le.fit_transform(df[\"contact\"])\n",
    "#df.head()\n",
    "#Doing this will also replace the `unknown` i.e. the missing value with some number\n",
    "#We should take care of that using if-else block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "879cf7a6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   job  marital  education default  balance housing loan   contact  day  \\\n",
       "0   30  10.0        1    primary      no     1787      no   no  cellular   19   \n",
       "1   33   0.0        1  secondary      no     4789     yes  yes  cellular   11   \n",
       "2   35   1.0        0   tertiary      no     1350     yes   no  cellular   16   \n",
       "3   30   1.0        1   tertiary      no     1476     yes  yes       NaN    3   \n",
       "4   59   2.0        1  secondary      no        0     yes   no       NaN    5   \n",
       "\n",
       "   month  duration  campaign  pdays  previous poutcome   y  \n",
       "0     10        79         1     -1         0      NaN  no  \n",
       "1      8       220         1    339         4  failure  no  \n",
       "2      0       185         1    330         1  failure  no  \n",
       "3      6       199         4     -1         0      NaN  no  \n",
       "4      8       226         1     -1         0      NaN  no  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replacing `unknown` with np.nan wiz missing values\n",
    "for column in df.columns:\n",
    "    df[column].replace({'unknown':np.nan}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15bc5c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'failure', 'other', 'success'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['poutcome'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a585674",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
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       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   job  marital  education default  balance housing loan   contact  day  \\\n",
       "0   30  10.0        1    primary      no     1787      no   no  cellular   19   \n",
       "1   33   0.0        1  secondary      no     4789     yes  yes  cellular   11   \n",
       "2   35   1.0        0   tertiary      no     1350     yes   no  cellular   16   \n",
       "3   30   1.0        1   tertiary      no     1476     yes  yes       NaN    3   \n",
       "4   59   2.0        1  secondary      no        0     yes   no       NaN    5   \n",
       "\n",
       "   month  duration  campaign  pdays  previous  poutcome   y  \n",
       "0     10        79         1     -1         0         3  no  \n",
       "1      8       220         1    339         4         0  no  \n",
       "2      0       185         1    330         1         0  no  \n",
       "3      6       199         4     -1         0         3  no  \n",
       "4      8       226         1     -1         0         3  no  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['poutcome']=le.fit_transform(df['poutcome'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec690fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 1, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['poutcome'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2150455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['cellular', nan, 'telephone'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['contact'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e37e432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
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       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>79</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   job  marital  education default  balance housing loan  contact  day  \\\n",
       "0   30  10.0        1    primary      no     1787      no   no        0   19   \n",
       "1   33   0.0        1  secondary      no     4789     yes  yes        0   11   \n",
       "2   35   1.0        0   tertiary      no     1350     yes   no        0   16   \n",
       "3   30   1.0        1   tertiary      no     1476     yes  yes        2    3   \n",
       "4   59   2.0        1  secondary      no        0     yes   no        2    5   \n",
       "\n",
       "   month  duration  campaign  pdays  previous  poutcome   y  \n",
       "0     10        79         1     -1         0         3  no  \n",
       "1      8       220         1    339         4         0  no  \n",
       "2      0       185         1    330         1         0  no  \n",
       "3      6       199         4     -1         0         3  no  \n",
       "4      8       226         1     -1         0         3  no  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['contact']=le.fit_transform(df['contact'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58aa815e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['contact'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9192188a",
   "metadata": {},
   "source": [
    "You can see that it encodes the null values with highest digit, we will use this fact to replace the null value again with `np.nan`,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e74c4192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital  education default  balance housing loan  \\\n",
       "0   30   unemployed  married    primary      no     1787      no   no   \n",
       "1   33     services  married  secondary      no     4789     yes  yes   \n",
       "2   35   management   single   tertiary      no     1350     yes   no   \n",
       "3   30   management  married   tertiary      no     1476     yes  yes   \n",
       "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  cellular   19   oct        79         1     -1         0      NaN  no  \n",
       "1  cellular   11   may       220         1    339         4  failure  no  \n",
       "2  cellular   16   apr       185         1    330         1  failure  no  \n",
       "3       NaN    3   jun       199         4     -1         0      NaN  no  \n",
       "4       NaN    5   may       226         1     -1         0      NaN  no  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data again while null values are replaced with np.nan\n",
    "df=pd.read_csv('bank.csv',';')\n",
    "for column in df.columns:\n",
    "    df[column].replace({'unknown':np.nan}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6bc9c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorical columns\n",
    "categorical_columns=['job','marital','education','default','housing','loan','contact','month','poutcome','y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f411aea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job', 'education', 'contact', 'poutcome']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#categorical columns with null values\n",
    "null_cat_cols=[]\n",
    "for col in categorical_columns:\n",
    "    if np.nan in list(df[col].unique()):\n",
    "        null_cat_cols.append(col)\n",
    "null_cat_cols       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db5b845a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
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       "      <th>loan</th>\n",
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       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1787</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
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       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4789</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1476</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   30   10        1          0        0     1787        0     0        0   \n",
       "1   33    7        1          1        0     4789        1     1        0   \n",
       "2   35    4        2          2        0     1350        1     0        0   \n",
       "3   30    4        1          2        0     1476        1     1        2   \n",
       "4   59    1        1          1        0        0        1     0        2   \n",
       "\n",
       "   day  month  duration  campaign  pdays  previous  poutcome  y  \n",
       "0   19     10        79         1     -1         0         3  0  \n",
       "1   11      8       220         1    339         4         0  0  \n",
       "2   16      0       185         1    330         1         0  0  \n",
       "3    3      6       199         4     -1         0         3  0  \n",
       "4    5      8       226         1     -1         0         3  0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we will encode this using label encoder\n",
    "for column in categorical_columns:\n",
    "    df[column]=le.fit_transform(df[column])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cefba93",
   "metadata": {},
   "source": [
    "While using the fact that null values are replaced at the end therefore having the highest numeric value in the respective column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b238aeb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1787</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>79</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4789</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1476</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   30  10.0        1        0.0        0     1787        0     0      0.0   \n",
       "1   33   7.0        1        1.0        0     4789        1     1      0.0   \n",
       "2   35   4.0        2        2.0        0     1350        1     0      0.0   \n",
       "3   30   4.0        1        2.0        0     1476        1     1      NaN   \n",
       "4   59   1.0        1        1.0        0        0        1     0      NaN   \n",
       "\n",
       "   day  month  duration  campaign  pdays  previous  poutcome  y  \n",
       "0   19     10        79         1     -1         0       NaN  0  \n",
       "1   11      8       220         1    339         4       0.0  0  \n",
       "2   16      0       185         1    330         1       0.0  0  \n",
       "3    3      6       199         4     -1         0       NaN  0  \n",
       "4    5      8       226         1     -1         0       NaN  0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in null_cat_cols:\n",
    "    df[col].replace({max(df[col]):np.nan}, inplace=True)\n",
    "df.head()\n",
    "#tadaa we got our missing values back"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5a92fe",
   "metadata": {},
   "source": [
    "**Note** : It is always better to fill the missing values prior to the label encoding which saves lots of trouble and efforts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e58940b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the data for next session\n",
    "df.to_csv('bank_encoded.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffbf660",
   "metadata": {},
   "source": [
    "The End"
   ]
  }
 ],
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