Job Satisfaction Analysis/JobSatisfaction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "eOEX0amSNBuA"
   },
   "outputs": [],
   "source": [
    "#imorting neccessory libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0FRoQw7_O5fu",
    "outputId": "fbb15331-be13-4390-b5b9-b11ee41d173b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running in local system\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "  import google.colab\n",
    "  IN_COLAB = True\n",
    "except:\n",
    "  IN_COLAB = False\n",
    "\n",
    "if IN_COLAB:\n",
    "    print(\"Running in google colab\\n\")\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive')\n",
    "    df=pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/StockOverflow/survey_results_public_2018.csv')\n",
    "\n",
    "else:\n",
    "    print(\"Running in local system\")\n",
    "    file_path = r\"/Users/ls/Desktop/Stackoverflow-Analysis/Data/survey_results_sample_2018.csv\"  # Replace with your file path\n",
    "    df=pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3y-4rilZPa-4",
    "outputId": "8d10e4d5-3240-4709-ec96-12c4031884db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Respondent Hobby OpenSource         Country         Student  \\\n",
      "0           1   Yes         No           Kenya              No   \n",
      "1           3   Yes        Yes  United Kingdom              No   \n",
      "2           4   Yes        Yes   United States              No   \n",
      "3           5    No         No   United States              No   \n",
      "4           7   Yes         No    South Africa  Yes, part-time   \n",
      "\n",
      "           Employment                                    FormalEducation  \\\n",
      "0  Employed part-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
      "1  Employed full-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
      "2  Employed full-time                                   Associate degree   \n",
      "3  Employed full-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
      "4  Employed full-time  Some college/university study without earning ...   \n",
      "\n",
      "                                      UndergradMajor  \\\n",
      "0                          Mathematics or statistics   \n",
      "1  A natural science (ex. biology, chemistry, phy...   \n",
      "2  Computer science, computer engineering, or sof...   \n",
      "3  Computer science, computer engineering, or sof...   \n",
      "4  Computer science, computer engineering, or sof...   \n",
      "\n",
      "                CompanySize  \\\n",
      "0        20 to 99 employees   \n",
      "1  10,000 or more employees   \n",
      "2        20 to 99 employees   \n",
      "3      100 to 499 employees   \n",
      "4  10,000 or more employees   \n",
      "\n",
      "                                             DevType  ...  \\\n",
      "0                               Full-stack developer  ...   \n",
      "1  Database administrator;DevOps specialist;Full-...  ...   \n",
      "2           Engineering manager;Full-stack developer  ...   \n",
      "3                               Full-stack developer  ...   \n",
      "4  Data or business analyst;Desktop or enterprise...  ...   \n",
      "\n",
      "                     Exercise Gender         SexualOrientation  \\\n",
      "0        3 - 4 times per week   Male  Straight or heterosexual   \n",
      "1   Daily or almost every day   Male  Straight or heterosexual   \n",
      "2                         NaN    NaN                       NaN   \n",
      "3  I don't typically exercise   Male  Straight or heterosexual   \n",
      "4        3 - 4 times per week   Male  Straight or heterosexual   \n",
      "\n",
      "                                    EducationParents  \\\n",
      "0         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
      "1         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
      "2                                                NaN   \n",
      "3  Some college/university study without earning ...   \n",
      "4  Some college/university study without earning ...   \n",
      "\n",
      "                  RaceEthnicity                Age Dependents  MilitaryUS  \\\n",
      "0   Black or of African descent  25 - 34 years old        Yes         NaN   \n",
      "1  White or of European descent  35 - 44 years old        Yes         NaN   \n",
      "2                           NaN                NaN        NaN         NaN   \n",
      "3  White or of European descent  35 - 44 years old         No          No   \n",
      "4  White or of European descent  18 - 24 years old        Yes         NaN   \n",
      "\n",
      "                          SurveyTooLong     SurveyEasy  \n",
      "0  The survey was an appropriate length      Very easy  \n",
      "1  The survey was an appropriate length  Somewhat easy  \n",
      "2                                   NaN            NaN  \n",
      "3  The survey was an appropriate length  Somewhat easy  \n",
      "4  The survey was an appropriate length  Somewhat easy  \n",
      "\n",
      "[5 rows x 129 columns]\n"
     ]
    }
   ],
   "source": [
    "# Display the first few rows of the dataframe\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "smR6ZcZ6OIzF",
    "outputId": "89365fe3-5451-4578-de35-68c8d3ba8e4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Respondent  AssessJob1  AssessJob2  AssessJob3  AssessJob4  AssessJob5  \\\n",
      "count   99.000000   64.000000   64.000000   64.000000   64.000000   64.000000   \n",
      "mean    74.252525    5.750000    6.437500    6.312500    4.359375    3.218750   \n",
      "std     42.168598    2.817181    2.695528    2.641999    2.674792    2.458989   \n",
      "min      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
      "25%     40.000000    4.000000    4.750000    4.000000    2.000000    1.000000   \n",
      "50%     77.000000    6.000000    7.000000    7.000000    4.000000    2.000000   \n",
      "75%    111.500000    8.000000    9.000000    8.000000    6.000000    4.000000   \n",
      "max    143.000000   10.000000   10.000000   10.000000   10.000000   10.000000   \n",
      "\n",
      "       AssessJob6  AssessJob7  AssessJob8  AssessJob9  ...  \\\n",
      "count    64.00000   64.000000   64.000000   64.000000  ...   \n",
      "mean      5.03125    5.500000    4.343750    7.781250  ...   \n",
      "std       2.41009    2.949307    2.533701    2.446045  ...   \n",
      "min       1.00000    1.000000    1.000000    1.000000  ...   \n",
      "25%       3.00000    3.000000    2.000000    6.750000  ...   \n",
      "50%       5.00000    5.500000    4.000000    9.000000  ...   \n",
      "75%       6.25000    8.000000    6.000000   10.000000  ...   \n",
      "max      10.00000   10.000000   10.000000   10.000000  ...   \n",
      "\n",
      "       JobEmailPriorities7        Salary  ConvertedSalary  AdsPriorities1  \\\n",
      "count            42.000000  4.900000e+01        46.000000       56.000000   \n",
      "mean              4.904762  4.666699e+05     80993.739130        2.803571   \n",
      "std               1.736404  2.175638e+06    131734.984542        1.891797   \n",
      "min               1.000000  0.000000e+00         0.000000        1.000000   \n",
      "25%               3.250000  1.600000e+04     30603.000000        1.000000   \n",
      "50%               5.000000  7.000000e+04     56313.500000        2.500000   \n",
      "75%               6.000000  1.200000e+05     84943.000000        4.000000   \n",
      "max               7.000000  1.520000e+07    900000.000000        7.000000   \n",
      "\n",
      "       AdsPriorities2  AdsPriorities3  AdsPriorities4  AdsPriorities5  \\\n",
      "count       56.000000       56.000000       56.000000       56.000000   \n",
      "mean         4.285714        2.964286        3.928571        4.392857   \n",
      "std          1.691844        1.628879        1.895997        2.154729   \n",
      "min          1.000000        1.000000        1.000000        1.000000   \n",
      "25%          3.000000        2.000000        2.000000        2.000000   \n",
      "50%          4.000000        2.500000        4.000000        5.000000   \n",
      "75%          6.000000        4.000000        5.000000        6.000000   \n",
      "max          7.000000        7.000000        7.000000        7.000000   \n",
      "\n",
      "       AdsPriorities6  AdsPriorities7  \n",
      "count       56.000000       56.000000  \n",
      "mean         4.964286        4.660714  \n",
      "std          1.953684        1.781489  \n",
      "min          1.000000        1.000000  \n",
      "25%          4.000000        3.000000  \n",
      "50%          5.000000        5.000000  \n",
      "75%          7.000000        6.000000  \n",
      "max          7.000000        7.000000  \n",
      "\n",
      "[8 rows x 43 columns]\n"
     ]
    }
   ],
   "source": [
    "# Summary statistics\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bvc8e8D7PfFC",
    "outputId": "4f08b6d0-73ca-40cc-c70b-5a0b0b70fdb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent        0\n",
      "Hobby             0\n",
      "OpenSource        0\n",
      "Country           0\n",
      "Student           1\n",
      "                 ..\n",
      "Age              32\n",
      "Dependents       32\n",
      "MilitaryUS       80\n",
      "SurveyTooLong    32\n",
      "SurveyEasy       32\n",
      "Length: 129, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "9SdbszuTPfXT",
    "outputId": "1d71f563-51e2-45fc-e154-0d925b9624f5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9p/34kjp0md5tv5sz77mfqf7y440000gn/T/ipykernel_51256/2208408659.py:8: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.countplot(x='JobSatisfaction', data=df, palette='viridis')\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create the count plot\n",
    "ax = sns.countplot(x='JobSatisfaction', data=df, palette='viridis')\n",
    "\n",
    "# Add title and labels\n",
    "plt.title('Job Satisfaction Distribution', fontsize=16)\n",
    "plt.xlabel('Job Satisfaction Level', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "\n",
    "# Display counts on top of the bars\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                ha='center', va='baseline', fontsize=12, color='black', xytext=(0, 5), \n",
    "                textcoords='offset points')\n",
    "\n",
    "# Rotate x-axis labels if necessary\n",
    "plt.xticks(rotation=45, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "7x73vib8Obvk"
   },
   "outputs": [],
   "source": [
    "# Select relevant features for prediction , which are relatade to job\n",
    "features = ['Hobby', 'OpenSource', 'Country', 'Student', 'Employment', 'FormalEducation',\n",
    "            'UndergradMajor', 'CompanySize', 'DevType', 'YearsCoding', 'YearsCodingProf',\n",
    "            'JobSatisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "5ivNFXwbO2Vl"
   },
   "outputs": [],
   "source": [
    "# Filter the dataset with selected features\n",
    "df = df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "id": "5IC3YEsDObyf"
   },
   "outputs": [],
   "source": [
    "# Drop rows with missing target value\n",
    "df = df.dropna(subset=['JobSatisfaction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe = OrdinalEncoder(categories=[['Extremely dissatisfied','Moderately dissatisfied','Slightly dissatisfied','Neither satisfied nor dissatisfied', 'Slightly satisfied', 'Moderately satisfied', 'Extremely satisfied']])\n",
    "df['JobSatisfaction'] = oe.fit_transform(df[['JobSatisfaction']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Hobby</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>Employment</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>DevType</th>\n",
       "      <th>YearsCoding</th>\n",
       "      <th>YearsCodingProf</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Mathematics or statistics</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>Full-stack developer</td>\n",
       "      <td>3-5 years</td>\n",
       "      <td>3-5 years</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
       "      <td>30 or more years</td>\n",
       "      <td>18-20 years</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Associate degree</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>Engineering manager;Full-stack developer</td>\n",
       "      <td>24-26 years</td>\n",
       "      <td>6-8 years</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>100 to 499 employees</td>\n",
       "      <td>Full-stack developer</td>\n",
       "      <td>18-20 years</td>\n",
       "      <td>12-14 years</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Yes, part-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Data or business analyst;Desktop or enterprise...</td>\n",
       "      <td>6-8 years</td>\n",
       "      <td>0-2 years</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Hobby OpenSource         Country         Student          Employment  \\\n",
       "0   Yes         No           Kenya              No  Employed part-time   \n",
       "1   Yes        Yes  United Kingdom              No  Employed full-time   \n",
       "2   Yes        Yes   United States              No  Employed full-time   \n",
       "3    No         No   United States              No  Employed full-time   \n",
       "4   Yes         No    South Africa  Yes, part-time  Employed full-time   \n",
       "\n",
       "                                     FormalEducation  \\\n",
       "0         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
       "1         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
       "2                                   Associate degree   \n",
       "3         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
       "4  Some college/university study without earning ...   \n",
       "\n",
       "                                      UndergradMajor  \\\n",
       "0                          Mathematics or statistics   \n",
       "1  A natural science (ex. biology, chemistry, phy...   \n",
       "2  Computer science, computer engineering, or sof...   \n",
       "3  Computer science, computer engineering, or sof...   \n",
       "4  Computer science, computer engineering, or sof...   \n",
       "\n",
       "                CompanySize  \\\n",
       "0        20 to 99 employees   \n",
       "1  10,000 or more employees   \n",
       "2        20 to 99 employees   \n",
       "3      100 to 499 employees   \n",
       "4  10,000 or more employees   \n",
       "\n",
       "                                             DevType       YearsCoding  \\\n",
       "0                               Full-stack developer         3-5 years   \n",
       "1  Database administrator;DevOps specialist;Full-...  30 or more years   \n",
       "2           Engineering manager;Full-stack developer       24-26 years   \n",
       "3                               Full-stack developer       18-20 years   \n",
       "4  Data or business analyst;Desktop or enterprise...         6-8 years   \n",
       "\n",
       "  YearsCodingProf  JobSatisfaction  \n",
       "0       3-5 years              6.0  \n",
       "1     18-20 years              1.0  \n",
       "2       6-8 years              5.0  \n",
       "3     12-14 years              3.0  \n",
       "4       0-2 years              4.0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9p/34kjp0md5tv5sz77mfqf7y440000gn/T/ipykernel_51256/2208408659.py:8: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.countplot(x='JobSatisfaction', data=df, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create the count plot\n",
    "ax = sns.countplot(x='JobSatisfaction', data=df, palette='viridis')\n",
    "\n",
    "# Add title and labels\n",
    "plt.title('Job Satisfaction Distribution', fontsize=16)\n",
    "plt.xlabel('Job Satisfaction Level', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "\n",
    "# Display counts on top of the bars\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                ha='center', va='baseline', fontsize=12, color='black', xytext=(0, 5), \n",
    "                textcoords='offset points')\n",
    "\n",
    "# Rotate x-axis labels if necessary\n",
    "plt.xticks(rotation=45, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "id": "z7tM54s9Ob1I"
   },
   "outputs": [],
   "source": [
    "# Split features and target\n",
    "X = df.drop('JobSatisfaction', axis=1)\n",
    "y = df['JobSatisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "id": "mb_yUbbWOb37"
   },
   "outputs": [],
   "source": [
    "# Define categorical and numerical features\n",
    "categorical_features = X.select_dtypes(include=['object']).columns\n",
    "numerical_features = X.select_dtypes(include=['int64', 'float64']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "5luiMUypOI3h"
   },
   "outputs": [],
   "source": [
    "# Preprocessing pipelines for numerical and categorical data\n",
    "numerical_pipeline = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "wzSnaqicPyd8"
   },
   "outputs": [],
   "source": [
    "categorical_pipeline = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "id": "GN5N9QGrPyg-"
   },
   "outputs": [],
   "source": [
    "# Combine preprocessing pipelines\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numerical_pipeline, numerical_features),\n",
    "        ('cat', categorical_pipeline, categorical_features)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "Hb2Osu0ZPykj"
   },
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "id": "5oOnOyFaQTsA"
   },
   "outputs": [],
   "source": [
    "# Create and fit the pipeline\n",
    "model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', RandomForestClassifier(random_state=42))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "XOaOUYi1P2yA"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  Index([&#x27;Hobby&#x27;, &#x27;OpenSource&#x27;, &#x27;Country&#x27;, &#x27;Student&#x27;, &#x27;Employment&#x27;,\n",
       "       &#x27;FormalEducation&#x27;, &#x27;UndergradMajor&#x27;, &#x27;CompanySize&#x27;, &#x27;DevType&#x27;,\n",
       "       &#x27;YearsCoding&#x27;, &#x27;YearsCodingProf&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  Index([&#x27;Hobby&#x27;, &#x27;OpenSource&#x27;, &#x27;Country&#x27;, &#x27;Student&#x27;, &#x27;Employment&#x27;,\n",
       "       &#x27;FormalEducation&#x27;, &#x27;UndergradMajor&#x27;, &#x27;CompanySize&#x27;, &#x27;DevType&#x27;,\n",
       "       &#x27;YearsCoding&#x27;, &#x27;YearsCodingProf&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([], dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 Index([&#x27;Hobby&#x27;, &#x27;OpenSource&#x27;, &#x27;Country&#x27;, &#x27;Student&#x27;, &#x27;Employment&#x27;,\n",
       "       &#x27;FormalEducation&#x27;, &#x27;UndergradMajor&#x27;, &#x27;CompanySize&#x27;, &#x27;DevType&#x27;,\n",
       "       &#x27;YearsCoding&#x27;, &#x27;YearsCodingProf&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>Index([], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;Hobby&#x27;, &#x27;OpenSource&#x27;, &#x27;Country&#x27;, &#x27;Student&#x27;, &#x27;Employment&#x27;,\n",
       "       &#x27;FormalEducation&#x27;, &#x27;UndergradMajor&#x27;, &#x27;CompanySize&#x27;, &#x27;DevType&#x27;,\n",
       "       &#x27;YearsCoding&#x27;, &#x27;YearsCodingProf&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([], dtype='object')),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  Index(['Hobby', 'OpenSource', 'Country', 'Student', 'Employment',\n",
       "       'FormalEducation', 'UndergradMajor', 'CompanySize', 'DevType',\n",
       "       'YearsCoding', 'YearsCodingProf'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier', RandomForestClassifier(random_state=42))])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "id": "y6mT8lvLQbtg"
   },
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "id": "ZCSpSAuDdNgi"
   },
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "yksyXWGZdR4_",
    "outputId": "3deaf8a9-8e7f-4d48-9562-60b99aa96e01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the model to a file\n",
    "joblib.dump(model, 'model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "fw6wBmNwQko4",
    "outputId": "486c5704-db44-44db-8d00-c6928a43810e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.18\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.00      0.00      0.00         3\n",
      "         2.0       0.00      0.00      0.00         3\n",
      "         3.0       0.00      0.00      0.00         2\n",
      "         4.0       0.00      0.00      0.00         1\n",
      "         5.0       0.27      1.00      0.43         3\n",
      "         6.0       0.00      0.00      0.00         5\n",
      "\n",
      "    accuracy                           0.18        17\n",
      "   macro avg       0.05      0.17      0.07        17\n",
      "weighted avg       0.05      0.18      0.08        17\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/opt/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/opt/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/opt/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/opt/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/opt/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#Adding more libraries required\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc\n",
    "\n",
    "\n",
    "\n",
    "# Printing accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')\n",
    "\n",
    "# Printing classification report\n",
    "print('Classification Report:')\n",
    "report = classification_report(y_test, y_pred, output_dict=True)\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Converting classification report to a DataFrame for better readability\n",
    "report_df = pd.DataFrame(report).transpose()\n",
    "\n",
    "# Plotting confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "# If the model is a binary classifier, plot the ROC curve\n",
    "if len(set(y_test)) == 2:\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "huZqOC5mTaJm"
   },
   "outputs": [],
   "source": [
    "#Testing the Model\n",
    "def predict_job_satisfaction(user_input):\n",
    "    # Convert user input to DataFrame\n",
    "    input_df = pd.DataFrame([user_input])\n",
    "\n",
    "    # Ensure the input has the same columns as the training data\n",
    "    input_df = input_df[X.columns]\n",
    "\n",
    "    # Make prediction\n",
    "    prediction = model.predict(input_df)\n",
    "\n",
    "    return prediction[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "yHhyCWmNTi-Y"
   },
   "outputs": [],
   "source": [
    "# Example user input\n",
    "user_input_example = {\n",
    "    'Hobby': 'Yes',\n",
    "    'OpenSource': 'Yes',\n",
    "    'Country': 'United States',\n",
    "    'Student': 'No',\n",
    "    'Employment': 'Employed full-time',\n",
    "    'FormalEducation': 'Bachelor’s degree (BA, BS, B.Eng., etc.)',\n",
    "    'UndergradMajor': 'Computer science, computer engineering, or software engineering',\n",
    "    'CompanySize': '100 to 499 employees',\n",
    "    'DevType': 'Developer, back-end',\n",
    "    'YearsCoding': '6-8 years',\n",
    "    'YearsCodingProf': '3-5 years'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "vvGywGhnTlRM",
    "outputId": "f00c365e-1f2b-4c39-cee5-41a68383c64c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Job Satisfaction: [['Moderately satisfied']]\n"
     ]
    }
   ],
   "source": [
    "# Predict job satisfaction for the example input\n",
    "predicted_satisfaction = predict_job_satisfaction(user_input_example)\n",
    "print(f'Predicted Job Satisfaction: {oe.inverse_transform([[predicted_satisfaction]])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}