job_postings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7UeWmCTb5SJp"
   },
   "source": [
    "# Import library and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "zy2okzAM5aUL",
    "outputId": "9b84cacd-515a-45d3-d453-608ab03f01ce"
   },
   "outputs": [],
   "source": [
    "# !pip install tensorflow-gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "bnluCIQw5SJq",
    "outputId": "dd75700e-0fba-4a3b-8996-5ea31bd68ebc"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Also data visualization \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "# sklearn for splitting the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# tensorflow \n",
    "import tensorflow as tf # version=2.1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "H_L3rL4D5554",
    "outputId": "d0006cd2-f22f-4c5c-d823-f62992c2b7d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "K1pWiDRT5SJu",
    "outputId": "1b98250d-90da-4e3c-b231-43230975a2c2"
   },
   "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>job_id</th>\n",
       "      <th>title</th>\n",
       "      <th>location</th>\n",
       "      <th>department</th>\n",
       "      <th>salary_range</th>\n",
       "      <th>company_profile</th>\n",
       "      <th>description</th>\n",
       "      <th>requirements</th>\n",
       "      <th>benefits</th>\n",
       "      <th>telecommuting</th>\n",
       "      <th>has_company_logo</th>\n",
       "      <th>has_questions</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>required_experience</th>\n",
       "      <th>required_education</th>\n",
       "      <th>industry</th>\n",
       "      <th>function</th>\n",
       "      <th>fraudulent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Marketing Intern</td>\n",
       "      <td>US, NY, New York</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>We're Food52, and we've created a groundbreaki...</td>\n",
       "      <td>Food52, a fast-growing, James Beard Award-winn...</td>\n",
       "      <td>Experience with content management systems a m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Internship</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Customer Service - Cloud Video Production</td>\n",
       "      <td>NZ, , Auckland</td>\n",
       "      <td>Success</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90 Seconds, the worlds Cloud Video Production ...</td>\n",
       "      <td>Organised - Focused - Vibrant - Awesome!Do you...</td>\n",
       "      <td>What we expect from you:Your key responsibilit...</td>\n",
       "      <td>What you will get from usThrough being part of...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marketing and Advertising</td>\n",
       "      <td>Customer Service</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   job_id                                      title          location  \\\n",
       "0       1                           Marketing Intern  US, NY, New York   \n",
       "1       2  Customer Service - Cloud Video Production    NZ, , Auckland   \n",
       "\n",
       "  department salary_range                                    company_profile  \\\n",
       "0  Marketing          NaN  We're Food52, and we've created a groundbreaki...   \n",
       "1    Success          NaN  90 Seconds, the worlds Cloud Video Production ...   \n",
       "\n",
       "                                         description  \\\n",
       "0  Food52, a fast-growing, James Beard Award-winn...   \n",
       "1  Organised - Focused - Vibrant - Awesome!Do you...   \n",
       "\n",
       "                                        requirements  \\\n",
       "0  Experience with content management systems a m...   \n",
       "1  What we expect from you:Your key responsibilit...   \n",
       "\n",
       "                                            benefits  telecommuting  \\\n",
       "0                                                NaN              0   \n",
       "1  What you will get from usThrough being part of...              0   \n",
       "\n",
       "   has_company_logo  has_questions employment_type required_experience  \\\n",
       "0                 1              0           Other          Internship   \n",
       "1                 1              0       Full-time      Not Applicable   \n",
       "\n",
       "  required_education                   industry          function  fraudulent  \n",
       "0                NaN                        NaN         Marketing           0  \n",
       "1                NaN  Marketing and Advertising  Customer Service           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data=pd.read_csv('/gdrive/My Drive/Bangkit/fake_job_postings.csv') #if using colab\n",
    "data=pd.read_csv('fake_job_postings.csv')\n",
    "\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17014, 18), (866, 18))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of record for each class\n",
    "\n",
    "(data[data.fraudulent == 0].shape), (data[data.fraudulent == 1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fraudulent presence distribution \n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "f = sns.countplot(x='fraudulent', data=data)\n",
    "f.set_title(\"Fraudulent presence distribution\")\n",
    "f.set_xticklabels(['Real job postings', 'Fake job postings'])\n",
    "plt.xlabel(\"\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Employement type ditribution of REAL job postings')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Employement type of FAKE/REAL job postings\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "data[data['fraudulent']==1].groupby('employment_type')['employment_type'].count().plot(kind='bar')\n",
    "plt.title('Employement type ditribution of FAKE job postings')\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "data[data['fraudulent']==0].groupby('employment_type')['employment_type'].count().plot(kind='bar')\n",
    "plt.title('Employement type ditribution of REAL job postings')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G3SUTHnS5SJx"
   },
   "source": [
    "# Selecting feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iOUQN3DK5SJy"
   },
   "outputs": [],
   "source": [
    "# Drop unused field to making a model\n",
    "\n",
    "df = data.drop(columns=['job_id','title','location','department','company_profile','description','requirements','benefits'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "id": "Qx4xjXPq5SJ0",
    "outputId": "5ca9b88a-c06c-461e-e2d7-477cccdb63c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drop some field of the dataset\n",
      "\n",
      "Shape of dataset before :  (17880, 18)\n",
      "Shape of dataset after :  (17880, 10)\n"
     ]
    }
   ],
   "source": [
    "print('Drop some field of the dataset\\n')\n",
    "print('Shape of dataset before : ',data.shape)\n",
    "print('Shape of dataset after : ',df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2e1vhfKQ5SJ3"
   },
   "source": [
    "# Checking NaN value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 551
    },
    "colab_type": "code",
    "id": "l7J1T1Zn5SJ3",
    "outputId": "7f4cc16d-fbc1-44b1-eda1-7560eae69f38"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check target nan value in the data \n",
    "\n",
    "combined = df.copy()\n",
    "nan_percentage = combined.isnull().sum().sort_values(ascending=False) / combined.shape[0]\n",
    "missing_val = nan_percentage[nan_percentage > 0]\n",
    "\n",
    "plt.figure(figsize=(5,7))\n",
    "sns.barplot(x=missing_val.index.values, y=missing_val.values * 100, palette=\"Reds_r\");\n",
    "plt.title(\"Percentage of missing values in data\");\n",
    "plt.ylabel(\"%\");\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sSNB9KmV5SJ6"
   },
   "source": [
    "# Taking care NaN value\n",
    "\n",
    "```\n",
    "Because salary_range has more than 50% NaN value of the data, so we will drop the feature of salary range. And the rest that contain NaN value is required_education, required_experience, function, industry, and employment_type. Those field which contain NaN value less than 50% will not dropped and will be replaced by string '-'. The reason why those value is not dropped or not replaced by other string that have rasional value is because in this case we think NaN value is not a missing value. So instead of we dropped or replace with rasional value, we think that NaN value is literally still NaN value.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lXZACu2-5SJ6"
   },
   "outputs": [],
   "source": [
    "# Drop column salary range \n",
    "\n",
    "df = df.drop(['salary_range'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1TSUiuxD5SJ9"
   },
   "outputs": [],
   "source": [
    "# Replace NaN with '-' string\n",
    "\n",
    "df = df.fillna('-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "tv0sHNUQ5SKB",
    "outputId": "2f3c07f6-3157-417e-ad95-741551ac6ba5"
   },
   "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>telecommuting</th>\n",
       "      <th>has_company_logo</th>\n",
       "      <th>has_questions</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>required_experience</th>\n",
       "      <th>required_education</th>\n",
       "      <th>industry</th>\n",
       "      <th>function</th>\n",
       "      <th>fraudulent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Internship</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>-</td>\n",
       "      <td>Marketing and Advertising</td>\n",
       "      <td>Customer Service</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Mid-Senior level</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Computer Software</td>\n",
       "      <td>Sales</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Mid-Senior level</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Hospital &amp; Health Care</td>\n",
       "      <td>Health Care Provider</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   telecommuting  has_company_logo  has_questions employment_type  \\\n",
       "0              0                 1              0           Other   \n",
       "1              0                 1              0       Full-time   \n",
       "2              0                 1              0               -   \n",
       "3              0                 1              0       Full-time   \n",
       "4              0                 1              1       Full-time   \n",
       "\n",
       "  required_experience required_education                   industry  \\\n",
       "0          Internship                  -                          -   \n",
       "1      Not Applicable                  -  Marketing and Advertising   \n",
       "2                   -                  -                          -   \n",
       "3    Mid-Senior level  Bachelor's Degree          Computer Software   \n",
       "4    Mid-Senior level  Bachelor's Degree     Hospital & Health Care   \n",
       "\n",
       "               function  fraudulent  \n",
       "0             Marketing           0  \n",
       "1      Customer Service           0  \n",
       "2                     -           0  \n",
       "3                 Sales           0  \n",
       "4  Health Care Provider           0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qav7k7Vd5SKD"
   },
   "source": [
    "# Count categorical variable from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 77
    },
    "colab_type": "code",
    "id": "pPT1JY0-5SKE",
    "outputId": "0d3109fd-712d-41d9-85cf-554526fca4b1"
   },
   "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>employment_type</th>\n",
       "      <th>required_experience</th>\n",
       "      <th>required_education</th>\n",
       "      <th>industry</th>\n",
       "      <th>function</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>quantity of unique value</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>132</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          employment_type  required_experience  \\\n",
       "quantity of unique value                6                    8   \n",
       "\n",
       "                          required_education  industry  function  \n",
       "quantity of unique value                  14       132        38  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([{'employment_type': len(df['employment_type'].value_counts()),    \n",
    "               'required_experience': len(df['required_experience'].value_counts()),\n",
    "               'required_education': len(df['required_education'].value_counts()),\n",
    "               'industry': len(df['industry'].value_counts()),\n",
    "               'function': len(df['function'].value_counts()),\n",
    "              }], columns = ['employment_type', 'required_experience', 'required_education', 'industry', 'function'], \n",
    "              index = ['quantity of unique value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "18O6b7d25SKG"
   },
   "source": [
    "# Creating feature columns from our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "colab_type": "code",
    "id": "oI2-a2Cj5SKG",
    "outputId": "136c5ecb-2b18-4d0d-fd9c-f122e1ccc7db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17880 entries, 0 to 17879\n",
      "Data columns (total 9 columns):\n",
      "telecommuting          17880 non-null int64\n",
      "has_company_logo       17880 non-null int64\n",
      "has_questions          17880 non-null int64\n",
      "employment_type        17880 non-null object\n",
      "required_experience    17880 non-null object\n",
      "required_education     17880 non-null object\n",
      "industry               17880 non-null object\n",
      "function               17880 non-null object\n",
      "fraudulent             17880 non-null int64\n",
      "dtypes: int64(4), object(5)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NS6WVAls5SKJ"
   },
   "outputs": [],
   "source": [
    "CATEGORICAL_COLUMNS = ['employment_type', 'required_experience', 'required_education', 'industry', 'function']\n",
    "NUMERIC_COLUMNS = ['telecommuting', 'has_company_logo', 'has_questions']\n",
    "\n",
    "feature_columns = []\n",
    "for feature_name in CATEGORICAL_COLUMNS:\n",
    "    vocabulary = df[feature_name].unique()\n",
    "    feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))\n",
    "\n",
    "for feature_name in NUMERIC_COLUMNS:\n",
    "    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hgmYNKf05SKM"
   },
   "source": [
    "# Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q3plOl8h5SKM"
   },
   "outputs": [],
   "source": [
    "X=df.drop(\"fraudulent\", axis=1)\n",
    "y=df[\"fraudulent\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uBXTPmpZ5SKO"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.33, random_state=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AnEORONg5SKQ"
   },
   "source": [
    "# Define input function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zrS4NqnA5SKR"
   },
   "outputs": [],
   "source": [
    "def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n",
    "    def input_function():\n",
    "        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n",
    "        if shuffle:\n",
    "            ds = ds.shuffle(1000)\n",
    "        ds = ds.batch(batch_size).repeat(num_epochs)\n",
    "        return ds\n",
    "    return input_function\n",
    "\n",
    "train_input_fn = make_input_fn(X_train, y_train)\n",
    "eval_input_fn = make_input_fn(X_test, y_test, num_epochs=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "a73hpEF65SKT",
    "outputId": "220c4482-84ed-4737-fa0a-11d4a6d83272"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some feature keys: ['telecommuting', 'has_company_logo', 'has_questions', 'employment_type', 'required_experience', 'required_education', 'industry', 'function']\n",
      "\n",
      "A batch of class: [b'Full-time' b'-' b'Contract' b'Contract' b'-' b'Full-time' b'-'\n",
      " b'Full-time' b'Full-time' b'Full-time']\n",
      "\n",
      "A batch of Labels: [0 0 0 1 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "ds = make_input_fn(X_train, y_train, batch_size=10)()\n",
    "for feature_batch, label_batch in ds.take(1):\n",
    "    print('Some feature keys:', list(feature_batch.keys()))\n",
    "    print()\n",
    "    print('A batch of class:', feature_batch['employment_type'].numpy())\n",
    "    print()\n",
    "    print('A batch of Labels:', label_batch.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit linear classifier algorithm and evaluate the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "jdGJHk8w5SKV",
    "outputId": "d10ee18b-3f4a-461c-bdc1-b62b358ba1cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': 0.9539061, 'accuracy_baseline': 0.9493306, 'auc': 0.87659985, 'auc_precision_recall': 0.39700872, 'average_loss': 0.14240184, 'label/mean': 0.05066938, 'loss': 0.14211349, 'precision': 0.754717, 'prediction/mean': 0.045631398, 'recall': 0.13377926, 'global_step': 3750}\n"
     ]
    }
   ],
   "source": [
    "linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)\n",
    "linear_est.train(train_input_fn)\n",
    "result = linear_est.evaluate(eval_input_fn)\n",
    "\n",
    "clear_output()\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "tz9r3E3E5SKY",
    "outputId": "3ff79fc5-14f4-4d49-d194-664c06d99083"
   },
   "outputs": [],
   "source": [
    "res_prediction = list(linear_est.predict(eval_input_fn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FgULA9z36vty"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'logits': array([-4.0011406], dtype=float32),\n",
       "  'logistic': array([0.01796607], dtype=float32),\n",
       "  'probabilities': array([0.98203385, 0.01796607], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-3.0466862], dtype=float32),\n",
       "  'logistic': array([0.04536076], dtype=float32),\n",
       "  'probabilities': array([0.95463926, 0.04536076], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.6196244], dtype=float32),\n",
       "  'logistic': array([0.1652567], dtype=float32),\n",
       "  'probabilities': array([0.8347433 , 0.16525668], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-7.2237806], dtype=float32),\n",
       "  'logistic': array([0.00072851], dtype=float32),\n",
       "  'probabilities': array([9.9927145e-01, 7.2850980e-04], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-5.5480204], dtype=float32),\n",
       "  'logistic': array([0.00388005], dtype=float32),\n",
       "  'probabilities': array([0.99612   , 0.00388005], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.6973195], dtype=float32),\n",
       "  'logistic': array([0.06313171], dtype=float32),\n",
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       "  'classes': array([b'0'], dtype=object),\n",
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       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.590064], dtype=float32),\n",
       "  'logistic': array([0.06978063], dtype=float32),\n",
       "  'probabilities': array([0.9302194 , 0.06978063], dtype=float32),\n",
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       "  'classes': array([b'0'], dtype=object),\n",
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       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.0011406], dtype=float32),\n",
       "  'logistic': array([0.01796607], dtype=float32),\n",
       "  'probabilities': array([0.98203385, 0.01796607], dtype=float32),\n",
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       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.9030252], dtype=float32),\n",
       "  'logistic': array([0.05200422], dtype=float32),\n",
       "  'probabilities': array([0.9479958 , 0.05200422], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
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       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-5.5298634], dtype=float32),\n",
       "  'logistic': array([0.00395086], dtype=float32),\n",
       "  'probabilities': array([0.9960491 , 0.00395086], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
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       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-6.316834], dtype=float32),\n",
       "  'logistic': array([0.0018024], dtype=float32),\n",
       "  'probabilities': array([0.9981976, 0.0018024], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.5061736], dtype=float32),\n",
       "  'logistic': array([0.07542652], dtype=float32),\n",
       "  'probabilities': array([0.9245735 , 0.07542652], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-0.8668782], dtype=float32),\n",
       "  'logistic': array([0.2959043], dtype=float32),\n",
       "  'probabilities': array([0.70409566, 0.29590428], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-6.5271683], dtype=float32),\n",
       "  'logistic': array([0.00146101], dtype=float32),\n",
       "  'probabilities': array([0.998539  , 0.00146101], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-3.7413466], dtype=float32),\n",
       "  'logistic': array([0.02317244], dtype=float32),\n",
       "  'probabilities': array([0.9768275 , 0.02317244], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-3.1762793], dtype=float32),\n",
       "  'logistic': array([0.0400682], dtype=float32),\n",
       "  'probabilities': array([0.9599318, 0.0400682], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.5116234], dtype=float32),\n",
       "  'logistic': array([0.18069834], dtype=float32),\n",
       "  'probabilities': array([0.81930166, 0.18069832], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.912249], dtype=float32),\n",
       "  'logistic': array([0.00730221], dtype=float32),\n",
       "  'probabilities': array([0.9926978 , 0.00730221], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-5.9398594], dtype=float32),\n",
       "  'logistic': array([0.00262549], dtype=float32),\n",
       "  'probabilities': array([0.99737453, 0.00262549], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.848994], dtype=float32),\n",
       "  'logistic': array([0.00777533], dtype=float32),\n",
       "  'probabilities': array([0.9922247 , 0.00777533], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-6.381066], dtype=float32),\n",
       "  'logistic': array([0.00169045], dtype=float32),\n",
       "  'probabilities': array([0.9983095 , 0.00169045], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.0011406], dtype=float32),\n",
       "  'logistic': array([0.01796607], dtype=float32),\n",
       "  'probabilities': array([0.98203385, 0.01796607], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.6193645], dtype=float32),\n",
       "  'logistic': array([0.06790251], dtype=float32),\n",
       "  'probabilities': array([0.9320975, 0.0679025], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.6196244], dtype=float32),\n",
       "  'logistic': array([0.1652567], dtype=float32),\n",
       "  'probabilities': array([0.8347433 , 0.16525668], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.401881], dtype=float32),\n",
       "  'logistic': array([0.01210592], dtype=float32),\n",
       "  'probabilities': array([0.9878941 , 0.01210592], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-5.16592], dtype=float32),\n",
       "  'logistic': array([0.00567542], dtype=float32),\n",
       "  'probabilities': array([0.9943245 , 0.00567542], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.808155], dtype=float32),\n",
       "  'logistic': array([0.00809681], dtype=float32),\n",
       "  'probabilities': array([0.99190325, 0.00809681], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-5.9398594], dtype=float32),\n",
       "  'logistic': array([0.00262549], dtype=float32),\n",
       "  'probabilities': array([0.99737453, 0.00262549], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-5.772876], dtype=float32),\n",
       "  'logistic': array([0.00310115], dtype=float32),\n",
       "  'probabilities': array([0.9968989 , 0.00310115], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.0310404], dtype=float32),\n",
       "  'logistic': array([0.26288244], dtype=float32),\n",
       "  'probabilities': array([0.7371176 , 0.26288247], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-6.8243585], dtype=float32),\n",
       "  'logistic': array([0.00108579], dtype=float32),\n",
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       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
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       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-5.3560266], dtype=float32),\n",
       "  'logistic': array([0.00469745], dtype=float32),\n",
       "  'probabilities': array([0.99530256, 0.00469745], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-3.0276744], dtype=float32),\n",
       "  'logistic': array([0.04619118], dtype=float32),\n",
       "  'probabilities': array([0.9538088 , 0.04619117], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.819107], dtype=float32),\n",
       "  'logistic': array([0.05630036], dtype=float32),\n",
       "  'probabilities': array([0.9436996 , 0.05630035], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.331019], dtype=float32),\n",
       "  'logistic': array([0.01298335], dtype=float32),\n",
       "  'probabilities': array([0.9870166 , 0.01298335], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.7893708], dtype=float32),\n",
       "  'logistic': array([0.05790127], dtype=float32),\n",
       "  'probabilities': array([0.94209874, 0.05790127], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.361894], dtype=float32),\n",
       "  'logistic': array([0.01259359], dtype=float32),\n",
       "  'probabilities': array([0.98740643, 0.01259359], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-3.9050002], dtype=float32),\n",
       "  'logistic': array([0.0197433], dtype=float32),\n",
       "  'probabilities': array([0.9802566, 0.0197433], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.205419], dtype=float32),\n",
       "  'logistic': array([0.09926491], dtype=float32),\n",
       "  'probabilities': array([0.9007351, 0.0992649], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-3.1333585], dtype=float32),\n",
       "  'logistic': array([0.04175203], dtype=float32),\n",
       "  'probabilities': array([0.95824796, 0.04175203], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.5716298], dtype=float32),\n",
       "  'logistic': array([0.07098675], dtype=float32),\n",
       "  'probabilities': array([0.92901325, 0.07098675], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-2.7158685], dtype=float32),\n",
       "  'logistic': array([0.06204347], dtype=float32),\n",
       "  'probabilities': array([0.9379566 , 0.06204347], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.0994697], dtype=float32),\n",
       "  'logistic': array([0.01631101], dtype=float32),\n",
       "  'probabilities': array([0.98368907, 0.01631101], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-1.0921135], dtype=float32),\n",
       "  'logistic': array([0.2512205], dtype=float32),\n",
       "  'probabilities': array([0.7487795, 0.2512205], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-4.4943542], dtype=float32),\n",
       "  'logistic': array([0.01104846], dtype=float32),\n",
       "  'probabilities': array([0.9889515 , 0.01104846], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " {'logits': array([-6.3341684], dtype=float32),\n",
       "  'logistic': array([0.00177148], dtype=float32),\n",
       "  'probabilities': array([0.9982285 , 0.00177148], dtype=float32),\n",
       "  'class_ids': array([0], dtype=int64),\n",
       "  'classes': array([b'0'], dtype=object),\n",
       "  'all_class_ids': array([0, 1]),\n",
       "  'all_classes': array([b'0', b'1'], dtype=object)},\n",
       " ...]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "colab_type": "code",
    "id": "Gf_H5Fae70t4",
    "outputId": "7c0d98b6-be6f-4b6d-9aa9-0a744dd61abf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b04a03ce10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs = pd.Series([pred['probabilities'][0] for pred in res_prediction])\n",
    "\n",
    "probs.plot(kind='hist', bins=20, title='predicted probabilities')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TKw5gFsb8bD-"
   },
   "source": [
    "<h3>The result of the prediction give a conclusion that for each data that predicted using this model, have a high accuracy that proofing the prediction is correct</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qKPBppBi8E2M"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mEPcwH0i-K3c"
   },
   "source": [
    "### Receiver Operating Characteristic (ROC) of the results\n",
    "it is show a better idea of the tradeoff between the true positive rate and false positive rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "colab_type": "code",
    "id": "3GndXJxs9Ru3",
    "outputId": "d2c5af56-b7b1-453f-a961-3b7e45ca2da8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.05)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p7uE4gxIRKXbb6pppbGlnxqQRuQ6lR1H6KL5B0JH9HHA78DZ33xt3YCIixWx19W6AgqhRRGkYawBOcve1cQcjIlIq1lTXUzGgnImVQ3IdSo8OmijM7Bx3fxh4BphnZvPSj7v7b+MOTkSkWK2urufICcMpK8u/9Se6ylSjeAdBX8T13RxLAUoUIiKHoLWtg03b9vCGk/JvAsDuHDRRuPu14Y+fdPcn0o+Z2XmxRiUiUsTWb2mgI5lixsT878iGzE1P8wjmZ7ojXFeis37Un2ACv6PjD09EpPisqa4HCqMjGzI3PX0AOJ9gFtj0ZqZ21OwkInLIVlfvpmrkQIYPGZDrUCLJ1PR0HYCZfcHdP5O9kEREitua6npsyshchxFZpFFPZvaWrsc16klEpPd21rdQ19BaMM1OEG3U04e6OaZRTyIih6Czf6IQ3sju1OOoJ3c/O32/mfV397a4AxMRKUarq3fTr7yMKWMLZ+G1KFN4nE6wEt1/AUuB48zsKnf/ZcyxiYgUndXV9UwfP4x+5ZGm2ssLUSL9b+Bx4M1ALTAH+Jc4gxIRKUbtHUnWb2koqP4JiJYoyt39IYKhsr9393VAeaxRiYgUoY3b9tDWniyo/gmImCjMbBFwIfCgmR1L8NKdiIj0wr6O7CKsUdxCsErdD8IZZO8B9F6FiEgvranezYihAxg1rCLXofRKj53Z4fsSvzWzfmbWHzjK3TviD01EpLisrq5nxsQRJBL5P2Nsuh5rFGY21syWAI1AC0Hz08TYIxMRKSINTXvZVtdccM1OEK3p6VvAP4BxBMugLiWYFFBERCIqtIkA00VZ4W6mu789bfuzZrYyroBERIrR6up6yhIJpo8vvEQRpUbR38wGdm6Y2WCCKTxERCSiNdW7mTx2CBUDCu/tgig1il8AD5nZjwgSxNXAXbFGJSJSRFrbOnhxXR1nz5uU61AOSZRRTzeb2SbgDQQv2v0Y+EHMcYmIFI3lr+wAYOq4wpnfKV2UGgXAH4DdBIsW/cXd1fQkIhLR+i0NABw/Y0yOIzk0UYbHXgq8CnwE+ATwqpmdnflTIiICwfxODz61kVHDKhg5tDBWtOsqSo3iFuAMd38ewMzmA7cD8+MMTESkGKRSKTqSKc6cO7HgXrTrFGXUU1NnkgBw92fQqCcRkUg6ksHjsrysMJMERKtR3GtmnyR48a4D+CfgBTMbBSTcfWecAYqIFLJV63cB0N5RuN+voySKGwhGO/1nl/1XEtQsCm9QsIhIFjy6fDM/uc8BOGpyYU0tni7K8FhNKS4i0ktt7cl9SeLSM45k1tSROY7o0EUdHisiIr1QW98CwKnHjufiU6fnNpjDFOuirWZ2hZm9aGavmNkHM5x3oZmtjTMWEZFs6uhIAnD8jMocR3L4YksUZjaJYGjt6cBc4Dozm9PNeeOArwCFOyRARKSL10Y7xfp9PCsilcDMJpvZBWZWbmZTI177POBhd9/p7o0E80O9tZvzbgc+H/GaIiIFoRiGxXbqsY/CzC4kWH+iAzgVeNHM3unud/fw0YlATdp2DbCoy7U/DDwDPN6boDtVVhbmvCl9papqWK5DyCmVv3TLXwhlr21qA2D06MF9Hm+2yx+lM/tG4CRgibvXmNnpwB1AT4mijP1fzEsAyc4NMzsWuAw4F5jcm6A71dbuIZks3LHJh6OqahjbtzfkOoycUflLt/yFUvba2kYAGhpa+jTewyl/WVnikL5gR2l6Knf3fTUDd19OtDezNwET0rbHA9Vp228Ljz8FLAEmmtnSCNcVEcl7nU1P/Uqh6QloCvslUgBmtphg7eyePAR8zsyqCNbbvgy4rvOgu38W+Gx4zenAo+6+uFfRi4jkqY5k0IBSVgSJIkqN4gbgAWCGmT0G/A74ZE8fcvfNwKeBR4DlwJ3u/oSZLTGzhYcRs4hI3mtp7QCKY9RTlDezl5nZycApBNN1PO7uO6Jc3N3vBO7ssu+Cbs5bB0yPck0RkXy3fVcz//v7FwAY0K/wE0WU9SjmA0cCWwn6GKaG+0REpBv1jXsBWDR7LBOrhuQ4msMXpY/iN2k/D+C1DuhF3Z8uIlK61tbUc8tPnwZg8fETKSvQNSjSRWl6OiJ928zOAt4ZV0AiIoWqqaWd237/AqOHV3D2vEkFPWNsul43nrn7o8CCvg9FRKRwpVIp7rhvFTvrW3n/m47lwlOmU9G/OFZhiPJmdnp/RAJYCAyKLSIRkQL0l+eqeXLVNi4788iiqUl06m0fRQrYBnwgnnBERArPpm17+PlDr3DMEaN548nTch1On4uSKD7m7r+PPRIRkQLUureD2+5+gcEV/bjmojlF0XndVZQ+iltij0JEpMC07u2gqaWNnz3obKlt4tqL5zBiyIBchxWLKDWK583s08BSYE/nTnd/JraoRETy2CPPbOKnD7y8b/uiU6czZ/roHEYUryiJ4qTwzzVp+1IEL+GJiJSUmtrGfUnivIWTOWL8cBbNGZvjqOIVJVEsdvdN6TvM7JiY4hERyWvPr9kJwNvPPoo3nBR1HbfCdtBEYWad9ag/hS/ZJQhqEgMIRkLNij06EZE8dcYJE3o+qUhkqlH8HDg//Lk2bX87wbKmIiIlo7Wtg/uf2MDvl64N9xTf6KaDOWiicPfXA5jZD9396uyFJCKSX5LJFB/5+lL2tgdrTLz59CMYPDBKy31xiDLXk5KEiJSkVCrFbXevZPkrO2jvCJLETVcvYvLY3i8nWshKJyWKiPTSd+5eyVOrtgFwyjHjefvZMxgxtCLHUWWfEoWISCiVStEcrkz3/JpangyTxFevP42RJZggOilRiEjJ60gm+ceLW7ln2Xq27mza79iHLjuupJMEKFGISAnrSCZ57IWt/PGxdWyra2Zy1VDedtYMysuCEU1Txg5ldhG/cR2VEoWIlJz2jiTLXtjCnx5bx/ZdLUwdN5Tr33Icc48eU5ST+h0uJQoRKVrPvLyd59fU7rcvlYIX1+1kx+4Wpo8fxjsum8kJR1WSUII4KCUKESk6O3Y185VfLGfbrmaAA2Z1HTtqEO963UyOO1IJIgolChEpOq9s3s22Xc3MPWoM5y6czDHqZzgsShQiUlRWrK7l+/e8CMBbzjySyVWl9XJcHJQoRKRoPO3buP+JjVQMKOfMEyYyacyQXIdUFJQoRKRgNbe2A/Do8s08tWoba2saAFg0eyyXn3t0LkMrKkoUIlJwduxu5ht3rWDT9sb99tuUkVxy+hHMnDoyR5EVJyUKESkoL2+o4xO3PbZv+/+dcxQAs6eNYuq4YbkKq6gpUYhIQfn7c9UAnHrseK58nVExoDzHERU/JQoRKSivbNxFWSLBNRfNyXUoJUOJQkQKQjKZ4o/L1vH86h0MrtCjK5v0ty0ieWvV+jpeXL8TgJc37OLlTbuZN7OKNyyakuPISkusicLMrgA+A/QHbnX3b3c5fgnweYLFZ9cCV7l7XZwxiUhh+N49K3l85VYAyhIJBlWU894LZ/Pmc2ayfXtDjqMrLbElCjObBNwCLABagWVm9oi7vxgeHw7cBpzo7pvN7Cbgc8BH4opJRApDMpni8ZVbmVA5mEsXH8nCWWNzHVJJK4vx2ucBD7v7TndvBO4C3pp2vD/wQXffHG6vAKbGGI+IFIBUKsWL64LmptOPn6AkkQfibHqaCNSkbdcAizo33L0W+B2AmQ0CbgC+GWM8IpLHmlraWLGmlt88upra+lYATpo9LsdRCcSbKMqAVNp2Akh2PcnMRhAkjOfc/Y7e3KCysrQn+6qqKu2Xi1T+4il/MpniQ/+xhMaWYEqO4UMG8L5Lj8NmVHV7fjGV/VBku/xxJopNwOK07fFAdfoJZjYBuB94GPhYb29QW7uHZDLV84lFqKpqWEl36Kn8hV/+9VsaqG/aC0BD014aW9pJAF9838mMGTGQ8rKybstYDGU/HIdT/rKyxCF9wY4zUTwEfM7MqoBG4DLgus6DZlYO3AP8yt2/EGMcIpJHGlva+PlDr7DshS0HHPvUlQsYN2pwDqKSTGJLFOFIpk8DjwADgNvd/QkzWwLcCEwB5gP9zKyzk/spd78mrphEJDeeeGkrv3z4VZKpFLv37N23//Jzj2bGxOEAVPQvZ1KVpgXPR7G+R+HudwJ3dtl3QfjjU8Q76kpE8sSa6nrqG/dy2nETABg8sB9vOeNI+pXrEVAI9Ga2iPS5ZCrFV37+LA3NbSSAXXv20q9fGe9546xchyaHQIlCRPrc/f/YwKoNuwCYP7OKsaMGM21caY9SLGRKFCLSJ9bW1HPXo6tJpVL7ksS3PnoGgwfqMVPo9BsUkcPW2tbBzXc8BcDUcUOZOXkEs6ePVpIoEvotishhW7/ltbWq33/JsTmORvqaEoWI9FprWwevbtrNXX9ZvS9JALz1rBk5jEriokQhIr32P79Yzqubd+/bvuDkacyZPooxIwblMCqJixKFiPSorqGV3Y3BRH0/XrKKDdv2APDvVy5gYuVgBg/sn8vwJGZKFCJyUCvX7uTljbu4Z9m6A47ddPUiJo/VkNdSoEQhIgDsrG/h1l8/F24lANi0fc++4yfPGcei2eMgATMnj9SIphKi37SIAPCdu1eyaXsjlcMrmDoumMa6auRAFh8/kblHj8lxdJJLShQiJay9I8namnpeWl/Hui31zJwykk+8Yx5lZYlchyZ5RIlCpIQkUyk2bt3DS+vreGl9HS9v3EVrWwcJYPqEYbz3wtlKEnIAJQqRIpZKpdiys4kX19Wxan0dqzbU7VtFbkLlYE49bjyzp45i1rRRDB2kkUvSPSUKkSKzY3czL62r46UNQa2hc/2HyuEVzDu6itnTgsQwalhFjiOVQqFEIVLgdjfu5aX1O1kVNidt39UCwPDB/Zk1bRSzwz9VIweRSKhZSXpPiUIkz7W1J6ne0cgfH1vHq5t2U1aWoKwsQTKZIplMsbsxqDEMqujHrKkjOW/hFGZPG8WkMUOUGKRPKFGI5Lnf/GU1Dzy5cd/24uMnMHBgf1pa2gAYO2oQc6aPZtq4YeqIllgoUYjkiWQqxa8feZWVa+tIrwjsrG9h6KD+XHXBLKaNG8bo4QOpqhrG9u0NB7+YSB9SohDJE7f85GnW1tQDMC/tBbcxIwYyc8pI5h1dlavQpMQpUYjkSGtbB6Re2+5MErdcexITKofkKCqRAylRiGRBQ9Ne1m1pYF1NPeu2NLC2pp5d4bDVdBefOl1JQvKOEoVIH2tqaWP9lgbWpiWGHbtb9h0fP3ows6eNYuKYIft1PpclEpw0Z1wuQhbJSIlC5DC07G1nw9Y9rKup35cYttY17zteNXIgR0wYztnzJ3HE+OFMGz+MQRX6ZyeFRf/HivTC0hXVrN/SQHNrBxu2NlBd20gq7GcYPbyC6eOHc9pxE5g+YRjTxw/XtBhSFJQoRHrhF39+hebWDkYNq2DK2KEsnDWW6eOHMX38MEYM1ZQYUpyUKER6oSyR4NwFk3nn+TNzHYpI1ihRSEnrSCbZ1bCXJ1dt4/GVW3o8v6m1PQtRieQXJQopGe0dSR5buYW9bcl9+3758Ku0dwTbiQScMCPzSm6VIwZy4qyxscYpkm+UKKRgtXck6UimDnr8oac28tL6un3ba2saaO6mRtCvPMGVrzOOOWI0o4cPjCVWkUKmRCF5bfOORjZt23PA/sa91fzsvlWRrnHUpBEATBozhH7lCa6+YDYVA8r3HR8ysL8m0xPJQIlC8tp37n6BzdsbD3r85DnjmDJ26EGPHzejkslVBz8uIj1ToigxO3Y1s3VXc88nHqKVa3by8DObSAF98R19b3uShbPGcuniI/bbP2rUEJr2tKipSCQLYk0UZnYF8BmgP3Cru3+7y/G5wO3AcOCvwPvdveiGlaRSKe57YgM7d7f22TUHDepPc3Nbrz/352c29VkMmbzuxCl90pyTAE45ZvwB8x9VVQ1je9lhX15EIogtUZjZJOAWYAHQCiwzs0fc/cW0034GXOPuj5vZD4BrgdviiiluTS3tbK1rYumKGp5fvWPfxKCNLe207u0AYMjAvvkr71zhrLcqBpSzYGYVZ5wwsU/i6M7o4RWMGTEotuuLSHbFWaM4D3jY3XcCmNldwFuBm8LtacAgd388PP/HwOeJlijKAZZg/mleAAAIEElEQVS9sIXGlt5/q47Lg+EqZGWJBHOPrtqvw7R/vzLesGhqn83zU1k5lNraAzt5S0mpd0CXcvlLuexw6OVP+1x5pvO6ijNRTARq0rZrgEU9HJ8c8doTAC4+86jDia/PXf762Vm9X2VlaXfSqvylW/5SLjv0SfknAKujnhxnoihjv2VZSADJXhzP5ElgMUFy6TiMGEVESkk5QZJ4sjcfijNRbCJ4mHcaD1R3OT4hw/FMWoG/HVZ0IiKlKXJNolOc40YeAs41syozGwxcBtzXedDd1wMtZnZauOtK4N4Y4xERkUMQW6Jw983Ap4FHgOXAne7+hJktMbOF4WnvBL5mZquAocA34opHREQOTSKV6v0QSxERKR16ZUlERDJSohARkYyUKEREJCMlChERySjvZ48t9YkFI5T/EoKpTxLAWuAqd6874EIFqqfyp513IfAtdz+iu+OFKMLv3oDvAqOALcDlpfS7N7P5BOUfAGwE3uXuu7IeaIzMbDiwDLjI3dd1OZa1Z19e1yjSJhY8HZgLXGdmc7qc9jPgenefSfCwvDa7Ucanp/KH/xPdBlzo7icAK4DP5SDUWET8/WNm44Cv0Dczm+eFCL/7BPAH4Evh7/5Z4IZcxBqHiL/7rwM3huV34F+zG2W8zOwkgheLZx7klKw9+/I6UZA2saC7NwKdEwsCB51Y8G1ZjzI+GctP8E3rg+E7KxAkiqlZjjFOPZW/0+0Etapi0lPZ5wON7t75EusXgW5rWwUqyu++nODbNMBgIL6FVnLjWuCDdDNjRbafffne9BTnxIKFIGP53b0W+B2AmQ0i+Eb5zWwGGLOefv+Y2YeBZ4DHKS49lf0oYEs4Pf884CXgQ9kLL3Y9/u6BjwMPmNmtQCNwUpZiywp3vwYgaGE8QFafffleo4hzYsFCEKl8ZjYC+BPwnLvfkaXYsiFj+c3sWIKpYW7OclzZ0NPvvh9wFnCbu88H1gBfzVp08evpdz8I+AFwnrtPAP4X+ElWI8ytrD778j1R9DRx4OFMLFgIeiyfmU0AlhI0O12TvdCyoqfyvy08/hSwBJhoZkuzF16seir7FuAVd38q3P45B37jLmQ9lf9YoNndnwi3v0uQOEtFVp99+Z4oSn1iwYzlN7Ny4B7gV+7+UXcvtvlYevr9f9bdZ7r7XOACoNrdFx/kWoUmY9kJRsJUmdkJ4fbFwNNZjjFOPZX/VWCKvdYucwm9nDq7kGX72ZfXiaLUJxaMUP43EXRqvtXMlod/bs9hyH0q4u+/KPVUdndvBi4Fvm9mK4FzgH/JXcR9K0L564D3AL8ysxXA1cBVOQs4S3L17NOkgCIiklFe1yhERCT3lChERCQjJQoREclIiUJERDJSohARkYyUKCSvmdltZrbWzG7JcM5ZZvZCNuOKwswWmtld4c8nmtl3uu7PcjzXmNk/Z/u+Uvjyfa4nkfcBU919U64D6a3wrenOieyOIZyLp8v+bDodyLuEKvlPiULyVjgdRwK4N/wmPAL4d4L1B8YCd7j7f3T5zOkEcx6VE8yF85/u/hszGwB8GTgzPPYs8GF3r+/y+c8RTLg3hWCKhOXANe5eb2bHAN8CKsNr/4+7/8TMhgI/Ao4mmG/naYIEd0Z4/huBm4ARZvYj4I5w/2kE6yjMdPct4f3/QTBV/J97Ee8pBJPEPUfw0t13gXEE0zqsB94e3utNwPlm1uzu3zazTxO88VwGrAP+2d2LaQoc6SNqepK8lTYdx9kE8/L/C/Bud18InAx8yszGdPnY54GvuvsCgrd1zwn33wC0AwvC9QuqgS8d5NZnEjxcZ4WfudHM+hGs//BNdz+e4OH/RTM7heAN6WHhVCInhtc4Mq0cG4EbgaXuflXa/t0Es/++C8DMZhM83O/vZbzTgHnu/i7gcuAxdz8ljKEJuNLdfxfG/7UwSfwTcBywKIx7CcF07SIHUKKQghDOY3UxsMDMPktQa0gAQ7qc+ivg22b2f8ACghoIwEUE8wE9a2bLgTcDByyCFPq1u2919yTBDKWvJ1g8ZqC7/zaMpxr4DfAGgiR2jJk9SvCAv9XdX41YtNuBd4c/XwX8MLxvb+J9vHNlM3f/OrDMzD5OMKPqsQTTO3R1EUGyfSq8/oeAbuezFlGikIJgZkMIml/mE6w/8W9AG11WtXP37xJ8U36Q4AG/wswGEjTffMTd54bfoBdx8H6C9OUky4AOXmvKosux/u6+lqC56j8JFtJ5yMwujlIud18K9DOzRcAVwA/DQ72Jd0/nD2b2ZYJmru3A94AH6H7lv3Lgy2nXX0jQPCVyACUKKRRHEzyEP+Pu9xBMKV1B8MDbx8yWETTD/Bi4DhjJa80515vZADMrA75P8GDvziVmNiI871qCGXpXAW1m9pbwPhMJ2vcfNLMPEPRRPODunwzvNb/LNdsJViTszu0EC06tCJup6GW86V5PUKP5KbANOJ/X/o7SY7gfuCZcTheC5PLTCNeXEqREIYViBfBHYJWZvUTQDPUiwTf5dJ8AbjKzZ4FHgc+Hi9LfTNBh+2z4uQQHn211K0Gb/UvAbuCL7t5G0PzzkXC20oeAm9z9EYIFc8qBF83saYJO964zeT4OHGlmv+3mfncQrAud3kfQm3jT3QR8JYzxDwTNYp1/R/cC7zezT4X3+iPweDj77PEEs7GKHECzx4qkCUcRjXH363Mdi0i+UI1CREQyUo1CREQyUo1CREQyUqIQEZGMlChERCQjJQoREclIiUJERDJSohARkYz+PzJfEdT5MN7CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, probs)\n",
    "plt.plot(fpr, tpr)\n",
    "plt.title('ROC curve')\n",
    "plt.xlabel('false positive rate')\n",
    "plt.ylabel('true positive rate')\n",
    "plt.xlim(0,)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "serving_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(\n",
    "  tf.feature_column.make_parse_example_spec(feature_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0405 02:46:34.963313  1660 deprecation.py:323] From C:\\Users\\PERSONALISE NOTEBOOK\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\saved_model\\signature_def_utils_impl.py:145: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b'model\\\\1586029594'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_est.export_saved_model(export_dir_base='model', serving_input_receiver_fn=serving_input_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build some testing\n",
    "\n",
    "```\n",
    "We trying to predict new data which actually the data is from data testing which is X_test and already have the label. \n",
    "In this section we will set the label with -1 and see the prediction of our model.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pd.DataFrame([[0,1,1,'Full-time','Mid-Senior level','High School or equivalent','Oil & Energy','Other', -1], \n",
    "                     [0,1,1,'Full-time','-',\"Bachelor's Degree\",'Information Technology and Services','Engineering', -1]],\n",
    "                    columns=['telecommuting', 'has_company_logo', 'has_questions', 'employment_type',\n",
    "       'required_experience', 'required_education', 'industry', 'function', \"fraud\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>telecommuting</th>\n",
       "      <th>has_company_logo</th>\n",
       "      <th>has_questions</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>required_experience</th>\n",
       "      <th>required_education</th>\n",
       "      <th>industry</th>\n",
       "      <th>function</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Mid-Senior level</td>\n",
       "      <td>High School or equivalent</td>\n",
       "      <td>Oil &amp; Energy</td>\n",
       "      <td>Other</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>-</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>Information Technology and Services</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   telecommuting  has_company_logo  has_questions employment_type  \\\n",
       "0              0                 1              1       Full-time   \n",
       "1              0                 1              1       Full-time   \n",
       "\n",
       "  required_experience         required_education  \\\n",
       "0    Mid-Senior level  High School or equivalent   \n",
       "1                   -          Bachelor's Degree   \n",
       "\n",
       "                              industry     function  fraud  \n",
       "0                         Oil & Energy        Other     -1  \n",
       "1  Information Technology and Services  Engineering     -1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pred = pred.drop(['fraud'], axis=1)\n",
    "y_pred = pred['fraud']\n",
    "\n",
    "test_input_fn = make_input_fn(pred, y_pred, num_epochs=1, shuffle=False, batch_size=2)\n",
    "\n",
    "prediction = list(linear_est.predict(test_input_fn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [b'0']\n",
       "1    [b'0']\n",
       "Name: classes, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = pd.DataFrame(prediction)\n",
    "prediction['classes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze the result\n",
    "\n",
    "```\n",
    "Unfortunately, instead of produce class 1 our model predict those 2 rows with 0. Why?\n",
    "When our team trying to get deep dive with the data, we know that the data is not good enough because \n",
    "the difference between real job and fake job posting is so far which 17014-866 = 16148 (its only 4.84% for the \n",
    "data that labelled as fake job posting). Means that we very deficiency of fake job posting's data.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "But, our accuracy for data evaluation is very good which s 95%. It's because the data validation that we used for \n",
    "calculate the accuracy is only contain 299 rows of class = 1 (fake job posting) and for class = 0 (real job posting) \n",
    "is contain 5602 rows.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "5602+299 = 5901 rows\n",
    "299/5901 *100% = 5.06693781% \n",
    "Our accuracy baseline is 0.9493306 = 100% - 5.06693781% \n",
    "\n",
    "BOOM\n",
    "\n",
    "After that, we wondering is the model can predict rows with label class = 1? And the answer is below\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_1 = pd.concat([X_test, y_test], axis=1)\n",
    "eval_1 = eval_1[eval_1.fraudulent == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = eval_1.drop(\"fraudulent\", axis=1)\n",
    "y_test = eval_1[\"fraudulent\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_input_fn = make_input_fn(X_test, y_test, num_epochs=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': 0.13377926, 'accuracy_baseline': 1.0, 'auc': 0.0, 'auc_precision_recall': 0.99999994, 'average_loss': 2.0681415, 'label/mean': 1.0, 'loss': 2.0447621, 'precision': 1.0, 'prediction/mean': 0.23362413, 'recall': 0.13377926, 'global_step': 3750}\n"
     ]
    }
   ],
   "source": [
    "result = linear_est.evaluate(eval_input_fn)\n",
    "\n",
    "clear_output()\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##### After we do some evaluation for the label = 1, we got the accuracy = 13.37%. that accuracy is calculated with only using label = 1. And we got the result that it is not because the training model was bad, but because the data is still less information and very less fake job description's data to making very good prediction for fake or real job postings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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