Session 8 Normalization and standardization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b0f5910",
   "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": "markdown",
   "id": "b0137add",
   "metadata": {},
   "source": [
    "- In every dataset we have different columns has different units\n",
    "\n",
    "- In every dataset we have have different columns has values varies from -infinity to infinity\n",
    "\n",
    "- It is very important standardize the data, make sure all the columns values under same range\n",
    "\n",
    "- To achive this we have two methods:\n",
    "    \n",
    "    - Normalization\n",
    "    \n",
    "    - Standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b9d7d7a",
   "metadata": {},
   "source": [
    "**Normalization:**\n",
    "\n",
    "- min max scalar"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "83df561c",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c4b842",
   "metadata": {},
   "source": [
    "**Standardization**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "fe1f14a6",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dd8148a",
   "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": 3,
   "id": "6a12f145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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": 3,
     "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": "code",
   "execution_count": 4,
   "id": "f7554797",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step-1: calcualte min value\n",
    "# step-2: calculayte max value\n",
    "# step-3: Dr=max_wage - min_wage\n",
    "# step-4: Nr=visa_df['prevailing_wage'] - min_wage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1bbd648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.001849\n",
       "1        0.261345\n",
       "2        0.385312\n",
       "3        0.261371\n",
       "4        0.469616\n",
       "           ...   \n",
       "25475    0.241505\n",
       "25476    0.874579\n",
       "25477    0.458311\n",
       "25478    0.269895\n",
       "25479    0.222033\n",
       "Name: prevailing_wage_norm, Length: 25480, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Min=round(visa_df['prevailing_wage'].min(),2)\n",
    "Max=round(visa_df['prevailing_wage'].max(),2)\n",
    "Dr=Max-Min\n",
    "Nr=visa_df['prevailing_wage']-Min\n",
    "visa_df['prevailing_wage_norm']=Nr/Dr\n",
    "visa_df['prevailing_wage_norm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a18d8feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>prevailing_wage_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>592.2029</td>\n",
       "      <td>0.001849</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83425.6500</td>\n",
       "      <td>0.261345</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122996.8600</td>\n",
       "      <td>0.385312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83434.0300</td>\n",
       "      <td>0.261371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149907.3900</td>\n",
       "      <td>0.469616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>25475</th>\n",
       "      <td>77092.5700</td>\n",
       "      <td>0.241505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>279174.7900</td>\n",
       "      <td>0.874579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>146298.8500</td>\n",
       "      <td>0.458311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>86154.7700</td>\n",
       "      <td>0.269895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>70876.9100</td>\n",
       "      <td>0.222033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       prevailing_wage  prevailing_wage_norm\n",
       "0             592.2029              0.001849\n",
       "1           83425.6500              0.261345\n",
       "2          122996.8600              0.385312\n",
       "3           83434.0300              0.261371\n",
       "4          149907.3900              0.469616\n",
       "...                ...                   ...\n",
       "25475       77092.5700              0.241505\n",
       "25476      279174.7900              0.874579\n",
       "25477      146298.8500              0.458311\n",
       "25478       86154.7700              0.269895\n",
       "25479       70876.9100              0.222033\n",
       "\n",
       "[25480 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df[['prevailing_wage','prevailing_wage_norm']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a703bdc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, -1.033808255447724e-08)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df['prevailing_wage_norm'].max(),visa_df['prevailing_wage_norm'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10ab6928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(319210.27, 2.1367)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df['prevailing_wage'].max(),visa_df['prevailing_wage'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "462374a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21077, 20575)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_id=visa_df['prevailing_wage_norm'].idxmax()\n",
    "min_id=visa_df['prevailing_wage_norm'].idxmin()\n",
    "max_id,min_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "33742a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>prevailing_wage_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21077</th>\n",
       "      <td>319210.2700</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20575</th>\n",
       "      <td>2.1367</td>\n",
       "      <td>-1.033808e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prevailing_wage  prevailing_wage_norm\n",
       "21077      319210.2700          1.000000e+00\n",
       "20575           2.1367         -1.033808e-08"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df[['prevailing_wage','prevailing_wage_norm']].iloc[[max_id,min_id]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23dedec8",
   "metadata": {},
   "source": [
    "**MinMaxScalar**\n",
    "\n",
    "- MinMaxScalar is a method from sklearn preprocessing\n",
    "- Read the packlages\n",
    "- Save the packages\n",
    "- Apply fit transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e3f8e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "############################# Read the Data########################\n",
    "\n",
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n",
    "# step:-1\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# step:-2\n",
    "mms=MinMaxScaler()\n",
    "# step:-3\n",
    "visa_df['prevailing_wage_norm1']=mms.fit_transform(visa_df[['prevailing_wage']])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39fe772f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>prevailing_wage_norm1</th>\n",
       "      <th>prevailing_wage</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001849</td>\n",
       "      <td>592.2029</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.261345</td>\n",
       "      <td>83425.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.385312</td>\n",
       "      <td>122996.8600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.261371</td>\n",
       "      <td>83434.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.469616</td>\n",
       "      <td>149907.3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>0.241505</td>\n",
       "      <td>77092.5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>0.874579</td>\n",
       "      <td>279174.7900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>0.458311</td>\n",
       "      <td>146298.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>0.269895</td>\n",
       "      <td>86154.7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>0.222033</td>\n",
       "      <td>70876.9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       prevailing_wage_norm1  prevailing_wage\n",
       "0                   0.001849         592.2029\n",
       "1                   0.261345       83425.6500\n",
       "2                   0.385312      122996.8600\n",
       "3                   0.261371       83434.0300\n",
       "4                   0.469616      149907.3900\n",
       "...                      ...              ...\n",
       "25475               0.241505       77092.5700\n",
       "25476               0.874579      279174.7900\n",
       "25477               0.458311      146298.8500\n",
       "25478               0.269895       86154.7700\n",
       "25479               0.222033       70876.9100\n",
       "\n",
       "[25480 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df[['prevailing_wage_norm1','prevailing_wage']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "222f8bf8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "102a3ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# array\n",
    "\n",
    "v1=np.array([[[1,2,3,4]]])\n",
    "v1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f56c08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Z Score\n",
    "\n",
    "# step:1 calculate mean\n",
    "# step:2 calculate std\n",
    "# step:3 Nr= x-mean\n",
    "# step:4 Nr/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00fc5e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=round(visa_df['prevailing_wage'].mean(),2)\n",
    "std=round(visa_df['prevailing_wage'].std(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6c58f10b",
   "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",
       "      <th>prevailing_wage_norm1</th>\n",
       "      <th>wage_standardization</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",
       "      <td>0.001849</td>\n",
       "      <td>-1.398510</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",
       "      <td>0.261345</td>\n",
       "      <td>0.169832</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",
       "      <td>0.385312</td>\n",
       "      <td>0.919061</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>0.261371</td>\n",
       "      <td>0.169991</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>0.469616</td>\n",
       "      <td>1.428576</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",
       "    </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",
       "      <td>0.241505</td>\n",
       "      <td>0.049924</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",
       "      <td>0.874579</td>\n",
       "      <td>3.876083</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",
       "      <td>0.458311</td>\n",
       "      <td>1.360253</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",
       "      <td>0.269895</td>\n",
       "      <td>0.221504</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",
       "      <td>0.222033</td>\n",
       "      <td>-0.067762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 14 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  prevailing_wage_norm1  wage_standardization  \n",
       "0          Denied               0.001849             -1.398510  \n",
       "1       Certified               0.261345              0.169832  \n",
       "2          Denied               0.385312              0.919061  \n",
       "3          Denied               0.261371              0.169991  \n",
       "4       Certified               0.469616              1.428576  \n",
       "...           ...                    ...                   ...  \n",
       "25475   Certified               0.241505              0.049924  \n",
       "25476   Certified               0.874579              3.876083  \n",
       "25477   Certified               0.458311              1.360253  \n",
       "25478   Certified               0.269895              0.221504  \n",
       "25479   Certified               0.222033             -0.067762  \n",
       "\n",
       "[25480 rows x 14 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_score=(visa_df['prevailing_wage']-mean)/std\n",
    "visa_df['wage_standardization']=z_score\n",
    "visa_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5d04246e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>prevailing_wage</th>\n",
       "      <th>wage_standardization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>592.2029</td>\n",
       "      <td>-1.398510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83425.6500</td>\n",
       "      <td>0.169832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122996.8600</td>\n",
       "      <td>0.919061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83434.0300</td>\n",
       "      <td>0.169991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149907.3900</td>\n",
       "      <td>1.428576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>77092.5700</td>\n",
       "      <td>0.049924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>279174.7900</td>\n",
       "      <td>3.876083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>146298.8500</td>\n",
       "      <td>1.360253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>86154.7700</td>\n",
       "      <td>0.221504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>70876.9100</td>\n",
       "      <td>-0.067762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       prevailing_wage  wage_standardization\n",
       "0             592.2029             -1.398510\n",
       "1           83425.6500              0.169832\n",
       "2          122996.8600              0.919061\n",
       "3           83434.0300              0.169991\n",
       "4          149907.3900              1.428576\n",
       "...                ...                   ...\n",
       "25475       77092.5700              0.049924\n",
       "25476      279174.7900              3.876083\n",
       "25477      146298.8500              1.360253\n",
       "25478       86154.7700              0.221504\n",
       "25479       70876.9100             -0.067762\n",
       "\n",
       "[25480 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df[['prevailing_wage','wage_standardization']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2c65e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(visa_df['prevailing_wage'])\n",
    "plt.subplot(1,2,2)\n",
    "plt.boxplot(visa_df['wage_standardization'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41804d49",
   "metadata": {},
   "source": [
    "**StandardScalar**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fc866e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss=StandardScaler()\n",
    "visa_df=pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c27b310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.39853722],\n",
       "       [ 0.1698353 ],\n",
       "       [ 0.91907852],\n",
       "       ...,\n",
       "       [ 1.36027953],\n",
       "       [ 0.22150859],\n",
       "       [-0.06776315]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wage_ss=ss.fit_transform(visa_df[['prevailing_wage']])\n",
    "wage_ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "35ebb841",
   "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",
       "      <th>wage_standard</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",
       "      <td>-1.398537</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",
       "      <td>0.169835</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",
       "      <td>0.919079</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>0.169994</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>1.428604</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>Certified</td>\n",
       "      <td>0.049924</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",
       "      <td>3.876159</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",
       "      <td>1.360280</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",
       "      <td>0.221509</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",
       "      <td>-0.067763</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  wage_standard  \n",
       "0          Denied      -1.398537  \n",
       "1       Certified       0.169835  \n",
       "2          Denied       0.919079  \n",
       "3          Denied       0.169994  \n",
       "4       Certified       1.428604  \n",
       "...           ...            ...  \n",
       "25475   Certified       0.049924  \n",
       "25476   Certified       3.876159  \n",
       "25477   Certified       1.360280  \n",
       "25478   Certified       0.221509  \n",
       "25479   Certified      -0.067763  \n",
       "\n",
       "[25480 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df['wage_standard']=wage_ss\n",
    "visa_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cbbba96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07370d44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32875cd0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e031061e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1cc5ed1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f25523d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66b2937e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cfe139d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8ed7ac2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d609131",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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