ProjectData_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a0cc2485",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f9d407d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22750, 9)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employee ID</th>\n",
       "      <th>Date of Joining</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company Type</th>\n",
       "      <th>WFH Setup Available</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Resource Allocation</th>\n",
       "      <th>Mental Fatigue Score</th>\n",
       "      <th>Burn Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fffe32003000360033003200</td>\n",
       "      <td>2008-09-30</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fffe3700360033003500</td>\n",
       "      <td>2008-11-30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fffe31003300320037003900</td>\n",
       "      <td>2008-03-10</td>\n",
       "      <td>Female</td>\n",
       "      <td>Product</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.49</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fffe32003400380032003900</td>\n",
       "      <td>2008-11-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fffe31003900340031003600</td>\n",
       "      <td>2008-07-24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Employee ID Date of Joining  Gender Company Type  \\\n",
       "0  fffe32003000360033003200      2008-09-30  Female      Service   \n",
       "1      fffe3700360033003500      2008-11-30    Male      Service   \n",
       "2  fffe31003300320037003900      2008-03-10  Female      Product   \n",
       "3  fffe32003400380032003900      2008-11-03    Male      Service   \n",
       "4  fffe31003900340031003600      2008-07-24  Female      Service   \n",
       "\n",
       "  WFH Setup Available  Designation  Resource Allocation  Mental Fatigue Score  \\\n",
       "0                  No          2.0                  3.0                   3.8   \n",
       "1                 Yes          1.0                  2.0                   5.0   \n",
       "2                 Yes          2.0                  NaN                   5.8   \n",
       "3                 Yes          1.0                  1.0                   2.6   \n",
       "4                  No          3.0                  7.0                   6.9   \n",
       "\n",
       "   Burn Rate  \n",
       "0       0.16  \n",
       "1       0.36  \n",
       "2       0.49  \n",
       "3       0.20  \n",
       "4       0.52  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our input dataset\n",
    "attrition_df = pd.read_csv('train.csv')\n",
    "print(attrition_df.shape)\n",
    "attrition_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f49bbf3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Employee ID                0\n",
       "Date of Joining            0\n",
       "Gender                     0\n",
       "Company Type               0\n",
       "WFH Setup Available        0\n",
       "Designation                0\n",
       "Resource Allocation     1381\n",
       "Mental Fatigue Score    2117\n",
       "Burn Rate               1124\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count rows with NaN values\n",
    "attrition_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c1c5938e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18590, 9)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employee ID</th>\n",
       "      <th>Date of Joining</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company Type</th>\n",
       "      <th>WFH Setup Available</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Resource Allocation</th>\n",
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       "      <th>0</th>\n",
       "      <td>fffe32003000360033003200</td>\n",
       "      <td>2008-09-30</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fffe3700360033003500</td>\n",
       "      <td>2008-11-30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fffe32003400380032003900</td>\n",
       "      <td>2008-11-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fffe31003900340031003600</td>\n",
       "      <td>2008-07-24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fffe3300350037003500</td>\n",
       "      <td>2008-11-26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Product</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fffe33003300340039003100</td>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fffe32003600320037003400</td>\n",
       "      <td>2008-10-31</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fffe33003100330032003700</td>\n",
       "      <td>2008-03-16</td>\n",
       "      <td>Male</td>\n",
       "      <td>Product</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fffe3400310035003800</td>\n",
       "      <td>2008-05-12</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fffe33003100330036003300</td>\n",
       "      <td>2008-05-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>Product</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Employee ID Date of Joining  Gender Company Type  \\\n",
       "0   fffe32003000360033003200      2008-09-30  Female      Service   \n",
       "1       fffe3700360033003500      2008-11-30    Male      Service   \n",
       "3   fffe32003400380032003900      2008-11-03    Male      Service   \n",
       "4   fffe31003900340031003600      2008-07-24  Female      Service   \n",
       "5       fffe3300350037003500      2008-11-26    Male      Product   \n",
       "6   fffe33003300340039003100      2008-01-02  Female      Service   \n",
       "7   fffe32003600320037003400      2008-10-31  Female      Service   \n",
       "10  fffe33003100330032003700      2008-03-16    Male      Product   \n",
       "11      fffe3400310035003800      2008-05-12    Male      Service   \n",
       "14  fffe33003100330036003300      2008-05-14    Male      Product   \n",
       "\n",
       "   WFH Setup Available  Designation  Resource Allocation  \\\n",
       "0                   No          2.0                  3.0   \n",
       "1                  Yes          1.0                  2.0   \n",
       "3                  Yes          1.0                  1.0   \n",
       "4                   No          3.0                  7.0   \n",
       "5                  Yes          2.0                  4.0   \n",
       "6                   No          3.0                  6.0   \n",
       "7                  Yes          2.0                  4.0   \n",
       "10                 Yes          2.0                  3.0   \n",
       "11                 Yes          0.0                  1.0   \n",
       "14                 Yes          1.0                  3.0   \n",
       "\n",
       "    Mental Fatigue Score  Burn Rate  \n",
       "0                    3.8       0.16  \n",
       "1                    5.0       0.36  \n",
       "3                    2.6       0.20  \n",
       "4                    6.9       0.52  \n",
       "5                    3.6       0.29  \n",
       "6                    7.9       0.62  \n",
       "7                    4.4       0.33  \n",
       "10                   5.3       0.50  \n",
       "11                   1.8       0.12  \n",
       "14                   5.8       0.51  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows with null values\n",
    "attrition_df = attrition_df.dropna()\n",
    "print(attrition_df.shape)\n",
    "attrition_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d6b046ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Service', 'Product'], dtype=object)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count unique variables in \"Company Type\" column\n",
    "\n",
    "pd.unique(attrition_df[\"Company Type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4eb975b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date of Joining</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company Type</th>\n",
       "      <th>WFH Setup Available</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Resource Allocation</th>\n",
       "      <th>Mental Fatigue Score</th>\n",
       "      <th>Burn Rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-09-30</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.16</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-11-30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.36</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-11-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>Service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-07-24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Service</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008-11-26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Product</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date of Joining  Gender Company Type WFH Setup Available  Designation  \\\n",
       "0      2008-09-30  Female      Service                  No          2.0   \n",
       "1      2008-11-30    Male      Service                 Yes          1.0   \n",
       "3      2008-11-03    Male      Service                 Yes          1.0   \n",
       "4      2008-07-24  Female      Service                  No          3.0   \n",
       "5      2008-11-26    Male      Product                 Yes          2.0   \n",
       "\n",
       "   Resource Allocation  Mental Fatigue Score  Burn Rate  \n",
       "0                  3.0                   3.8       0.16  \n",
       "1                  2.0                   5.0       0.36  \n",
       "3                  1.0                   2.6       0.20  \n",
       "4                  7.0                   6.9       0.52  \n",
       "5                  4.0                   3.6       0.29  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove employee ID (not needed) + make date of joining a datetype datatype\n",
    "attrition_df = attrition_df.drop(columns=[\"Employee ID\"])\n",
    "attrition_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bff57f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change date datatype to datetime\n",
    "attrition_df[\"Date of Joining\"]=pd.to_datetime(attrition_df['Date of Joining'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "63494392",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date of Joining</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Company Type</th>\n",
       "      <th>WFH Setup Available</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Resource Allocation</th>\n",
       "      <th>Mental Fatigue Score</th>\n",
       "      <th>Burn Rate</th>\n",
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       "      <th>0</th>\n",
       "      <td>2008-09-30</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.16</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-11-30</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-11-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-07-24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008-11-26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date of Joining  Gender  Company Type  WFH Setup Available  Designation  \\\n",
       "0      2008-09-30       0             1                    0          2.0   \n",
       "1      2008-11-30       1             1                    1          1.0   \n",
       "3      2008-11-03       1             1                    1          1.0   \n",
       "4      2008-07-24       0             1                    0          3.0   \n",
       "5      2008-11-26       1             0                    1          2.0   \n",
       "\n",
       "   Resource Allocation  Mental Fatigue Score  Burn Rate  \n",
       "0                  3.0                   3.8       0.16  \n",
       "1                  2.0                   5.0       0.36  \n",
       "3                  1.0                   2.6       0.20  \n",
       "4                  7.0                   6.9       0.52  \n",
       "5                  4.0                   3.6       0.29  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write a for loop that loops through variables with object datatypes and encode them\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "encoded_df = attrition_df.copy()\n",
    "\n",
    "for col in attrition_df:\n",
    "    if encoded_df[col].dtypes == \"object\":\n",
    "        encoded_df[col] = le.fit_transform(encoded_df[col])\n",
    "\n",
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a5fb5267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date of Joining         datetime64[ns]\n",
       "Gender                           int32\n",
       "Company Type                     int32\n",
       "WFH Setup Available              int32\n",
       "Designation                    float64\n",
       "Resource Allocation            float64\n",
       "Mental Fatigue Score           float64\n",
       "Burn Rate                      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6ba21f7c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The range for Designation is 0.0-5.0\n",
      "The range for Resource Allocation is 1.0-10.0\n",
      "The range for Mental Fatigue Score is 0.0-10.0\n",
      "The range for Burn Rate is 0.0-1.0\n"
     ]
    }
   ],
   "source": [
    "# Find range of values in continuous data columns\n",
    "\n",
    "for col in encoded_df:\n",
    "    if encoded_df[col].dtypes == \"float64\":\n",
    "        print(f\"The range for {col} is {encoded_df.min(axis=0)[col]}-{encoded_df.max(axis=0)[col]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65e10814",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unimportant columns\n",
    "\n",
    "#ncoded_df = encoded_df.drop(columns=[\"Date of Joining\", \"Company Type\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ec6a336",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dist of values in each bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "504f4fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test for normality of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99326470",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use unsupervised model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a6a5cff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "229d6486",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a069abf8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6038cb11",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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