session 7 convert categorical data to numerical data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5c4c65d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "04a58503",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
       "      <td>West</td>\n",
       "      <td>592.2029</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
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       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>25480 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "      case_status  \n",
       "0          Denied  \n",
       "1       Certified  \n",
       "2          Denied  \n",
       "3          Denied  \n",
       "4       Certified  \n",
       "...           ...  \n",
       "25475   Certified  \n",
       "25476   Certified  \n",
       "25477   Certified  \n",
       "25478   Certified  \n",
       "25479   Certified  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "717ff702",
   "metadata": {},
   "source": [
    "- In machine learning algorithms will develop models by using maths\n",
    "\n",
    " - maths allows only numbers\n",
    "    \n",
    "- so it is very important , you need to pass numerical data only\n",
    "\n",
    "- so we need to convert categorical data to numerical data\n",
    "\n",
    "- For that we have encoding methods\n",
    "\n",
    "- Encoding\n",
    "\n",
    "  - Label Encoder\n",
    "    \n",
    "     - map method\n",
    "     - np.where()\n",
    "     - LabelEncoder package from sklearn\n",
    "        \n",
    "  - One hot encoder\n",
    "\n",
    "     - pd.get_dummies()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351043fd",
   "metadata": {},
   "source": [
    "## Label Encoder\n",
    "\n",
    "#### Map Method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1578de77",
   "metadata": {},
   "source": [
    "- read any categorical column: case_status\n",
    "- check how many unique labels are there\n",
    "- create a dictionary with those unique labels as keys by providing a number as values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a78262de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        1\n",
       "3        1\n",
       "4        0\n",
       "        ..\n",
       "25475    0\n",
       "25476    0\n",
       "25477    0\n",
       "25478    0\n",
       "25479    0\n",
       "Name: case_status, Length: 25480, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Read the data##########################################\n",
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n",
    "\n",
    "##############################Map#############################\n",
    "\n",
    "visa_df['case_status'].unique()   # two unique labels are there\n",
    "dict1={'Certified':0,'Denied':1}\n",
    "visa_df['case_status'].map(dict1)\n",
    "\n",
    "# do you want overrite existed column\n",
    "# do you want create a new column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73bc0a8",
   "metadata": {},
   "source": [
    "### Create a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7d55bc1",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
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       "      <th>case_status_num</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
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       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
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       "      <td>EZYV25479</td>\n",
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       "      <td>Master's</td>\n",
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       "      <td>Y</td>\n",
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       "      <td>86154.7700</td>\n",
       "      <td>Year</td>\n",
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       "      <td>0</td>\n",
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       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "      case_status  case_status_num  \n",
       "0          Denied                1  \n",
       "1       Certified                0  \n",
       "2          Denied                1  \n",
       "3          Denied                1  \n",
       "4       Certified                0  \n",
       "...           ...              ...  \n",
       "25475   Certified                0  \n",
       "25476   Certified                0  \n",
       "25477   Certified                0  \n",
       "25478   Certified                0  \n",
       "25479   Certified                0  \n",
       "\n",
       "[25480 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Read the data##########################################\n",
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n",
    "\n",
    "##############################Map#############################\n",
    "\n",
    "visa_df['case_status'].unique()   # two unique labels are there\n",
    "dict1={'Certified':0,'Denied':1}\n",
    "visa_df['case_status_num']=visa_df['case_status'].map(dict1)\n",
    "visa_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74907b2",
   "metadata": {},
   "source": [
    "### Drop the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd8f944a",
   "metadata": {},
   "outputs": [],
   "source": [
    "visa_df.drop('case_status_num',axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ccce70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
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       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
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       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
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       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
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       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
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       "    <tr>\n",
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       "      <th>25479</th>\n",
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       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "      case_status  \n",
       "0          Denied  \n",
       "1       Certified  \n",
       "2          Denied  \n",
       "3          Denied  \n",
       "4       Certified  \n",
       "...           ...  \n",
       "25475   Certified  \n",
       "25476   Certified  \n",
       "25477   Certified  \n",
       "25478   Certified  \n",
       "25479   Certified  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff14773",
   "metadata": {},
   "source": [
    "## overwrite on same column(preferable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8dc6f843",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>44444</td>\n",
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       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>Bachelor's</td>\n",
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       "      <td>1082</td>\n",
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       "      <td>Y</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
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       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
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       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
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       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "       case_status  \n",
       "0                1  \n",
       "1                0  \n",
       "2                1  \n",
       "3                1  \n",
       "4                0  \n",
       "...            ...  \n",
       "25475            0  \n",
       "25476            0  \n",
       "25477            0  \n",
       "25478            0  \n",
       "25479            0  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Read the data##########################################\n",
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n",
    "\n",
    "##############################Map#############################\n",
    "\n",
    "visa_df['case_status'].unique()   # two unique labels are there\n",
    "dict1={'Certified':0,'Denied':1}\n",
    "visa_df['case_status']=visa_df['case_status'].map(dict1)\n",
    "\n",
    "visa_df\n",
    "# in the map method inplace=True is not there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "51f6c826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asia', 'Africa', 'North America', 'Europe', 'South America',\n",
       "       'Oceania'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1=visa_df['continent'].unique()\n",
    "l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d3dd64ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asia', 'Africa', 'North America', 'Europe', 'South America',\n",
       "       'Oceania'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df['continent'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "25acedea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Asia': 0,\n",
       " 'Africa': 1,\n",
       " 'North America': 2,\n",
       " 'Europe': 3,\n",
       " 'South America': 4,\n",
       " 'Oceania': 5}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1=visa_df['continent'].unique()\n",
    "dict1={}\n",
    "for i in range(len(l1)):\n",
    "    dict1[l1[i]]=i\n",
    "dict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e3587010",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asia 0\n",
      "Africa 1\n",
      "North America 2\n",
      "Europe 3\n",
      "South America 4\n",
      "Oceania 5\n"
     ]
    }
   ],
   "source": [
    "num=visa_df['continent'].unique()\n",
    "dict1={}\n",
    "for i in range(len(num)):\n",
    "    print(num[i],i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "30a5b255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Asia': 0,\n",
       " 'Africa': 1,\n",
       " 'North America': 2,\n",
       " 'Europe': 3,\n",
       " 'South America': 4,\n",
       " 'Oceania': 5}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels=visa_df['continent'].unique()\n",
    "num=len(visa_df['continent'].unique())\n",
    "dict1={}\n",
    "for i in range(num):\n",
    "    dict1[labels[i]]=i\n",
    "    \n",
    "dict1\n",
    "\n",
    "labels=visa_df['continent'].unique()\n",
    "num=len(visa_df['continent'].unique())\n",
    "\n",
    "{labels[i]:i for i in range(num)}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c96d00",
   "metadata": {},
   "source": [
    "### Method:-2: np.where()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ebd7a123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['case_id', 'continent', 'education_of_employee', 'has_job_experience',\n",
       "       'requires_job_training', 'no_of_employees', 'yr_of_estab',\n",
       "       'region_of_employment', 'prevailing_wage', 'unit_of_wage',\n",
       "       'full_time_position', 'case_status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8571342b",
   "metadata": {},
   "source": [
    "- np.where() is applicable for binary condition\n",
    "- which means it is applicable only for two labels\n",
    "- np.where(<condition>,<Truevalue>,<False_value>)\n",
    "- for example case_status has two labels\n",
    "- condition:  <col_values>=='certified'\n",
    "- True value: Replace all certified values with 0\n",
    "- False value: Replace all certified values with 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d11d8cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
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       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
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       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
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       "      <td>2601</td>\n",
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       "      <td>77092.5700</td>\n",
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       "    <tr>\n",
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       "      <td>EZYV25477</td>\n",
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       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
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       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>25480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "       case_status  \n",
       "0                1  \n",
       "1                0  \n",
       "2                1  \n",
       "3                1  \n",
       "4                0  \n",
       "...            ...  \n",
       "25475            0  \n",
       "25476            0  \n",
       "25477            0  \n",
       "25478            0  \n",
       "25479            0  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df['case_status']=np.where((visa_df['case_status']=='Certified'),0,1)\n",
    "visa_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0f0d88f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.7700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       case_id continent education_of_employee has_job_experience  \\\n",
       "0            0      Asia           High School                  N   \n",
       "1            0      Asia              Master's                  Y   \n",
       "2            0      Asia            Bachelor's                  N   \n",
       "3            0      Asia            Bachelor's                  N   \n",
       "4            0    Africa              Master's                  Y   \n",
       "...        ...       ...                   ...                ...   \n",
       "25475        0      Asia            Bachelor's                  Y   \n",
       "25476        0      Asia           High School                  Y   \n",
       "25477        0      Asia              Master's                  Y   \n",
       "25478        0      Asia              Master's                  Y   \n",
       "25479        0      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "       case_status  \n",
       "0                1  \n",
       "1                0  \n",
       "2                1  \n",
       "3                1  \n",
       "4                0  \n",
       "...            ...  \n",
       "25475            0  \n",
       "25476            0  \n",
       "25477            0  \n",
       "25478            0  \n",
       "25479            0  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con=visa_df['case_status']=='Certified'\n",
    "visa_df['case_id']=np.where(con,1,0)\n",
    "visa_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334ce510",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "47d861fb",
   "metadata": {},
   "source": [
    "### labels-encoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c811df0",
   "metadata": {},
   "source": [
    "- LabelEncoder is a method from sklearn\n",
    "- under sklearn we have sub models\n",
    "- one of the submodule: preprocessing\n",
    "- Any sklearn packages we have only 3 steps\n",
    "\n",
    "- step:-1: read the packages\n",
    "- step:-2: save the packages\n",
    "- step:-3: apply fit transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c9ca594f",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
       "      <td>West</td>\n",
       "      <td>592.2029</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.7700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "       case_status  \n",
       "0                1  \n",
       "1                0  \n",
       "2                1  \n",
       "3                1  \n",
       "4                0  \n",
       "...            ...  \n",
       "25475            0  \n",
       "25476            0  \n",
       "25477            0  \n",
       "25478            0  \n",
       "25479            0  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n",
    "\n",
    "# step:-1\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# step:-2\n",
    "le=LabelEncoder()\n",
    "# step:-3\n",
    "visa_df['case_status']=le.fit_transform(visa_df['case_status'])\n",
    "visa_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "60ad3d10",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2574268201.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[34], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    $fit--Transform:\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "$fit--Transform:\n",
    "    - fit and transform two different definations\n",
    "age: 1 2 3 4 5\n",
    "\n",
    "nwe age: by adding each observation with mean value : x+mean\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1533190",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       continent case_status\n",
      "0           Asia      Denied\n",
      "1           Asia   Certified\n",
      "2           Asia      Denied\n",
      "3           Asia      Denied\n",
      "4         Africa   Certified\n",
      "5           Asia   Certified\n",
      "6           Asia   Certified\n",
      "7  North America      Denied\n",
      "8           Asia   Certified\n",
      "9         Europe   Certified\n",
      "   continent case_status\n",
      "0          1      Denied\n",
      "1          1   Certified\n",
      "2          1      Denied\n",
      "3          1      Denied\n",
      "4          0   Certified\n",
      "5          1   Certified\n",
      "6          1   Certified\n",
      "7          3      Denied\n",
      "8          1   Certified\n",
      "9          2   Certified\n"
     ]
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "print(visa_df[['continent','case_status']].head(10))\n",
    "\n",
    "# step:-1\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# step:-2\n",
    "le=LabelEncoder()\n",
    "# step:-3\n",
    "visa_df['continent']=le.fit_transform(visa_df['continent'])\n",
    "print(visa_df[['continent','case_status']].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6d0eacc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "4    0\n",
      "Name: continent, dtype: int32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Asia', 'Asia', 'Asia', ..., 'Asia', 'Asia', 'Asia'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(visa_df['continent'][:5])\n",
    "le.inverse_transform(visa_df['continent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "48c4ba48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ea9b7be6",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
       "      <td>West</td>\n",
       "      <td>592.2029</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.7700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "      case_status  \n",
       "0          Denied  \n",
       "1       Certified  \n",
       "2          Denied  \n",
       "3          Denied  \n",
       "4       Certified  \n",
       "...           ...  \n",
       "25475   Certified  \n",
       "25476   Certified  \n",
       "25477   Certified  \n",
       "25478   Certified  \n",
       "25479   Certified  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b507cc",
   "metadata": {},
   "source": [
    "### one hot encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4988f546",
   "metadata": {},
   "source": [
    "- one hot encoder means at a time only one will be ON(1/True), others are OFF(0/False)\n",
    "- suppose case status has two unique labels\n",
    "- certified and denied \n",
    "- like below\n",
    "- In one hot encoder it will create two new column\n",
    "- like below\n",
    "|case_status|certified|denied|\n",
    "|-----------|---------|------|\n",
    "|certified|1|0|\n",
    "|denied|0|1|\n",
    "\n",
    "**Advantages**\n",
    "\n",
    "- one hot encoder new column are orthogonal to each other\n",
    "\n",
    "- orthogonality means 90 degrees phase shift\n",
    "\n",
    "- 90 degrees phase shift means perpendicular each other\n",
    "\n",
    "- perpendicular means independent each other \n",
    "\n",
    "- Independent means no relation \n",
    "\n",
    "- It is very important properties that it is to avoid relation between variables\n",
    "\n",
    "**Draw Back**\n",
    "\n",
    "- Assume that you have 100 unique labels for a column\n",
    "\n",
    "- so it will create 100 new columms\n",
    "\n",
    "- To process 100 columns we require more memory,more time,more hardware\n",
    "\n",
    "- This is called curse of Dimensionality\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e568c22a",
   "metadata": {},
   "source": [
    "**pd.get_dummies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7fa68117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status_Certified</th>\n",
       "      <th>case_status_Denied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.7700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "       case_status_Certified  case_status_Denied  \n",
       "0                      False                True  \n",
       "1                       True               False  \n",
       "2                      False                True  \n",
       "3                      False                True  \n",
       "4                       True               False  \n",
       "...                      ...                 ...  \n",
       "25475                   True               False  \n",
       "25476                   True               False  \n",
       "25477                   True               False  \n",
       "25478                   True               False  \n",
       "25479                   True               False  \n",
       "\n",
       "[25480 rows x 13 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n",
    "\n",
    "pd.get_dummies(visa_df,columns=['case_status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b1d4f61a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>case_id</th>\n",
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       "      <td>1897</td>\n",
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       "      <td>83434.0300</td>\n",
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       "      <td>N</td>\n",
       "      <td>1082</td>\n",
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       "      <td>Year</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>EZYV25480</td>\n",
       "      <td>Bachelor's</td>\n",
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       "      <td>3195</td>\n",
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       "      <td>Midwest</td>\n",
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "         case_id education_of_employee has_job_experience  \\\n",
       "0         EZYV01           High School                  N   \n",
       "1         EZYV02              Master's                  Y   \n",
       "2         EZYV03            Bachelor's                  N   \n",
       "3         EZYV04            Bachelor's                  N   \n",
       "4         EZYV05              Master's                  Y   \n",
       "...          ...                   ...                ...   \n",
       "25475  EZYV25476            Bachelor's                  Y   \n",
       "25476  EZYV25477           High School                  Y   \n",
       "25477  EZYV25478              Master's                  Y   \n",
       "25478  EZYV25479              Master's                  Y   \n",
       "25479  EZYV25480            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "      case_status  continent_Africa  continent_Asia  continent_Europe  \\\n",
       "0          Denied             False            True             False   \n",
       "1       Certified             False            True             False   \n",
       "2          Denied             False            True             False   \n",
       "3          Denied             False            True             False   \n",
       "4       Certified              True           False             False   \n",
       "...           ...               ...             ...               ...   \n",
       "25475   Certified             False            True             False   \n",
       "25476   Certified             False            True             False   \n",
       "25477   Certified             False            True             False   \n",
       "25478   Certified             False            True             False   \n",
       "25479   Certified             False            True             False   \n",
       "\n",
       "       continent_North America  continent_Oceania  continent_South America  \n",
       "0                        False              False                    False  \n",
       "1                        False              False                    False  \n",
       "2                        False              False                    False  \n",
       "3                        False              False                    False  \n",
       "4                        False              False                    False  \n",
       "...                        ...                ...                      ...  \n",
       "25475                    False              False                    False  \n",
       "25476                    False              False                    False  \n",
       "25477                    False              False                    False  \n",
       "25478                    False              False                    False  \n",
       "25479                    False              False                    False  \n",
       "\n",
       "[25480 rows x 17 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n",
    "\n",
    "pd.get_dummies(visa_df,columns=['continent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "184226f6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>N</td>\n",
       "      <td>98</td>\n",
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       "      <td>83434.0300</td>\n",
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       "      <td>N</td>\n",
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       "      <td>Certified</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.7700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id education_of_employee has_job_experience  \\\n",
       "0         EZYV01           High School                  N   \n",
       "1         EZYV02              Master's                  Y   \n",
       "2         EZYV03            Bachelor's                  N   \n",
       "3         EZYV04            Bachelor's                  N   \n",
       "4         EZYV05              Master's                  Y   \n",
       "...          ...                   ...                ...   \n",
       "25475  EZYV25476            Bachelor's                  Y   \n",
       "25476  EZYV25477           High School                  Y   \n",
       "25477  EZYV25478              Master's                  Y   \n",
       "25478  EZYV25479              Master's                  Y   \n",
       "25479  EZYV25480            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "      case_status  continent_Africa  continent_Asia  continent_Europe  \\\n",
       "0          Denied                 0               1                 0   \n",
       "1       Certified                 0               1                 0   \n",
       "2          Denied                 0               1                 0   \n",
       "3          Denied                 0               1                 0   \n",
       "4       Certified                 1               0                 0   \n",
       "...           ...               ...             ...               ...   \n",
       "25475   Certified                 0               1                 0   \n",
       "25476   Certified                 0               1                 0   \n",
       "25477   Certified                 0               1                 0   \n",
       "25478   Certified                 0               1                 0   \n",
       "25479   Certified                 0               1                 0   \n",
       "\n",
       "       continent_North America  continent_Oceania  continent_South America  \n",
       "0                            0                  0                        0  \n",
       "1                            0                  0                        0  \n",
       "2                            0                  0                        0  \n",
       "3                            0                  0                        0  \n",
       "4                            0                  0                        0  \n",
       "...                        ...                ...                      ...  \n",
       "25475                        0                  0                        0  \n",
       "25476                        0                  0                        0  \n",
       "25477                        0                  0                        0  \n",
       "25478                        0                  0                        0  \n",
       "25479                        0                  0                        0  \n",
       "\n",
       "[25480 rows x 17 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n",
    "\n",
    "pd.get_dummies(visa_df,columns=['continent'],dtype='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb6fb93c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7a76905",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d46c0093",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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