diff --git a/backlog/Analyzing_Employment_Insights_survey_results_public_2018_.ipynb b/backlog/Analyzing_Employment_Insights_survey_results_public_2018_.ipynb
new file mode 100644
index 0000000..81f493b
--- /dev/null
+++ b/backlog/Analyzing_Employment_Insights_survey_results_public_2018_.ipynb
@@ -0,0 +1,828 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jipwlZQUjHXG"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns"
+      ],
+      "metadata": {
+        "id": "yDIEa-hMjRMp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load the dataset\n",
+        "df = pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/StockOverflow/survey_results_public_2018.csv')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qx538MUcjkJp",
+        "outputId": "300c9cf7-0ff9-404d-c62b-ba51bb594aed"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-14-c242cc7acb8e>:2: DtypeWarning: Columns (8,12,13,14,15,16,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+            "  df = pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/StockOverflow/survey_results_public_2018.csv')\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Display the first few rows of the dataframe\n",
+        "print(df.head())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wSJdECYqjl9_",
+        "outputId": "c836c66d-fca8-4176-b389-b6229449a0ba"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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’s degree (BA, BS, B.Eng., etc.)   \n",
+            "1  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+            "2  Employed full-time                                   Associate degree   \n",
+            "3  Employed full-time           Bachelor’s 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’s degree (BA, BS, B.Eng., etc.)   \n",
+            "1           Bachelor’s 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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Select relevant columns for employment insights analysis\n",
+        "employment_columns = ['Employment', 'CompanySize', 'YearsCoding', 'JobSatisfaction', 'CareerSatisfaction']"
+      ],
+      "metadata": {
+        "id": "UPAPIySPjRPy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Filter the dataset with selected columns\n",
+        "employment_df = df[employment_columns]"
+      ],
+      "metadata": {
+        "id": "aGFfuouJjwHt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Drop rows with missing values in target columns\n",
+        "employment_df = employment_df.dropna(subset=['JobSatisfaction', 'CareerSatisfaction'])"
+      ],
+      "metadata": {
+        "id": "3GR07WZcjxQe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display the first few rows of the filtered dataframe\n",
+        "print(employment_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J51HySV5jyOA",
+        "outputId": "564f6ad2-8a6e-4731-c7bd-ef9df6302b72"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "           Employment               CompanySize       YearsCoding  \\\n",
+            "0  Employed part-time        20 to 99 employees         3-5 years   \n",
+            "1  Employed full-time  10,000 or more employees  30 or more years   \n",
+            "2  Employed full-time        20 to 99 employees       24-26 years   \n",
+            "3  Employed full-time      100 to 499 employees       18-20 years   \n",
+            "4  Employed full-time  10,000 or more employees         6-8 years   \n",
+            "\n",
+            "                      JobSatisfaction                  CareerSatisfaction  \n",
+            "0                 Extremely satisfied                 Extremely satisfied  \n",
+            "1             Moderately dissatisfied  Neither satisfied nor dissatisfied  \n",
+            "2                Moderately satisfied                Moderately satisfied  \n",
+            "3  Neither satisfied nor dissatisfied               Slightly dissatisfied  \n",
+            "4                  Slightly satisfied                Moderately satisfied  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to plot satisfaction metrics\n",
+        "def plot_satisfaction_metric(column, title):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    sns.boxplot(x=employment_df[column], y=employment_df['JobSatisfaction'])\n",
+        "    plt.title(f'Job Satisfaction by {title}')\n",
+        "    plt.xlabel(title)\n",
+        "    plt.ylabel('Job Satisfaction')\n",
+        "    plt.xticks(rotation=45)\n",
+        "    plt.show()\n",
+        "\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    sns.boxplot(x=employment_df[column], y=employment_df['CareerSatisfaction'])\n",
+        "    plt.title(f'Career Satisfaction by {title}')\n",
+        "    plt.xlabel(title)\n",
+        "    plt.ylabel('Career Satisfaction')\n",
+        "    plt.xticks(rotation=45)\n",
+        "    plt.show()\n",
+        "\n",
+        "# Plot satisfaction metrics\n",
+        "plot_satisfaction_metric('Employment', 'Employment Status')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Rpdr6k8VjRSZ",
+        "outputId": "2e24b6b0-8b52-42b5-9f13-4e5f8e6bd40f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_satisfaction_metric('CompanySize', 'Company Size')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "yAgegWmxj8DV",
+        "outputId": "601f8402-23b1-4ea3-b393-6e6563a62f57"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_satisfaction_metric('YearsCoding', 'Years Coding')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "tBjrkqBHj9V7",
+        "outputId": "082edf7b-1ff7-4880-e5f6-7fa88a03a9ee"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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a7733nnx9fWVnZ6d169Zp6tSpmT4kZ3UXh/T0dDVu3FhDhgzJ8hxBQUGmdl5eXqYPkPe690PMg96dws3NTY0bN1bjxo1la2urBQsWaPfu3QoJCdGiRYsUERGhli1bavDgwfLy8pKNjY1iYmIUHx//QOdzdHTUtm3b9O2332rt2rXasGGDPvvsMzVq1EgbN27M8ZZ/48eP14gRI9StWzeNGTNGBQsWVL58+TRw4MBcXw3xICy9+iG752DMwzue29jYqGPHjpo3b55mzpypnTt36s8//9Rrr71mVb8Z4zho0KBsr4jJ+CCc29cjq/do/fr1FR8fry+//FIbN27URx99pKlTp2r27Nnq0aNHtnWWLl1aBw4cUGpqqtnVBo8at6wEYCkCBgAAnkLNmjXT3Llz9f3336tWrVo5tl2zZo1SUlK0evVqs79S3ztdISclSpRQcnKy6YqFnNpt3rxZderU+UdubSlJ1apV04IFC3T27FlJ0rJlyxQQEKAVK1aYfbjO+Et2htxOO8iXL5+ef/55Pf/883rvvfc0fvx4DRs2TN9++22O47Js2TI1bNhQ8+fPN9t+5coVPfPMM6bHJUqU0O7du3Xr1i2zhTAftGY/Pz+lp6frxIkTKlOmjGn7uXPndOXKFfn5+VnclyWymi5w/Phx+fv7m23r3LmzpkyZojVr1mj9+vXy9PS0eJpMdjL+Um9ra3vf96ilr8f9FCxYUF27dlXXrl2VnJys+vXrKyoqKseAoXnz5vr++++1fPlyvfLKKzn2n/H6HDt2zOxKhNTUVCUkJJieZ0a7EydOqFGjRqZ2t27dUkJCQp7dvtPPz0/ffvutrl+/bnYVw8mTJ/OkfwBPBqZIAADwFBoyZIicnZ3Vo0cPnTt3LtP++Ph4TZ8+XdL//3Xy7r9EJyYmKjY21uLztW/fXt9//72+/vrrTPuuXLmi27dvm9qlpaVpzJgxmdrdvn07y9vrWeL69ev6/vvvs9y3fv16Sf9/KXlWz3f37t2Zjs/4kGRJTZcuXcq0rXLlypKU6ZaP97Kxscl0FcAXX3yR6ZaMbdq00d9//60PPvggUx8Zx+em5iZNmkiS2R0+JOm9996TJDVt2vS+feTGqlWrzJ7Tnj17tHv3br344otm7SpWrKiKFSvqo48+0vLly/Xyyy9bPS3Ey8tLDRo00Jw5c0xB090uXLhg+t7S1yMnFy9eNHvs4uKikiVL3ve90KdPH/n4+Ojtt9/W8ePHM+0/f/68xo4dK0kKDQ2VnZ2dZsyYYVbv/PnzlZiYaHr9qlWrJk9PT82ePVupqammdnFxcQ/885aVsLAw3bp1S/PmzTNtS09P14cffphn5wDw+OMKBgAAnkIlSpTQkiVL1KFDB5UpU0adO3dW+fLllZqaql27dumLL75QRESEJOmFF16QnZ2dmjdvrt69eys5OVnz5s2Tl5dXlh/GsjJ48GCtXr1azZo1M9268Nq1azp8+LCWLVumU6dO6ZlnnlFISIh69+6tmJgYHThwQC+88IJsbW114sQJffHFF5o+fbrFt+i72/Xr11W7dm3VrFlT4eHh8vX11ZUrV7Rq1Spt375dLVu2NN36sFmzZlqxYoVatWqlpk2bKiEhQbNnz1bZsmWVnJxs6tPR0VFly5bVZ599pqCgIBUsWFDly5dX+fLlM51/9OjR2rZtm5o2bSo/Pz+dP39eM2fO1LPPPqu6devmWHuzZs00evRode3aVbVr19bhw4e1ePHiTPPjO3furE8++URvvfWW9uzZo3r16unatWvavHmzXn/9db300ku5qrlSpUrq0qWL5s6dqytXrigkJER79uzRggUL1LJlSzVs2DDXr0NOSpYsqbp166pv375KSUnRtGnTVKhQoSyn1XTu3FmDBg2SJKunR2T48MMPVbduXVWoUEE9e/ZUQECAzp07p++//17/+9//dPDgQUmWvx45KVu2rBo0aKCqVauqYMGC2rdvn5YtW6b+/fvneFyBAgW0cuVKNWnSRJUrV9Zrr72mqlWrSpJ+/PFHffrpp6Yrkjw9PRUZGano6GiFh4erRYsWOnbsmGbOnKnq1aubxs3W1lZjx45V79691ahRI3Xo0EEJCQmKjY3N1XO6n5YtW6pGjRp6++23dfLkSZUuXVqrV682hW8PshApgCfQo7uBBQAAeNiOHz9u7Nmzp9Hf399oZ2dndHV1NdapU8f4/vvvm92ub/Xq1caKFSsaHRwcjP7+/saJEyeabouYkJBgaufn55ft7feuXr1qjIyMNJYsWdJoZ2dnfOaZZ4y1a9c2Tp482ZiammrWdu7cucaqVasaHR0dja6ursYKFSoYhwwZYvzzzz8tOte9bt26ZZw3b56xZcuWRj8/P6O9vb3RycnJGBwcbHz33XfNbs+Xnp5uHD9+vKldcHCw8auvvjJ26dLF6OfnZ9bvrl27jFWrVjXa2dmZ3f7x3ls+btmyxfjSSy8ZixQpYrSzszMWKVLE+Morr2S6baeyuU3l22+/bfTx8TE6Ojoa69SpY/z++++NISEhZrcQNBrv3E502LBhxuLFixttbW2NhQsXNrZt29YYHx+f65ozxi06OtrUn6+vrzEyMtLsvWE0Zv9aZFXjvTJuffjuu+8ap0yZYvT19TXa29sb69WrZzx48GCWx5w9e9ZoY2NjDAoKyrHv7ISEhBjLlSuXaXt8fLyxc+fOxsKFCxttbW2NRYsWNTZr1sy4bNkyUxtLX4+M21R+8cUXmc4zduxYY40aNYweHh5GR0dHY+nSpY3jxo3L9HOQnT///NP45ptvGoOCgowODg5GJycnY9WqVY3jxo0zJiYmmrX94IMPjKVLlzba2toavb29jX379jVevnw5U58zZ840Fi9e3Ghvb2+sVq2acdu2bZmeU3a3qXR2ds7UX1bvpwsXLhg7duxodHV1Nbq7uxsjIiKMO3fuNEoyLl261KLnDuDJZjAa83BlHgAAAMBKf//9t3x8fDRy5EiNGDHiUZcDK6xatUqtWrXSjh07VKdOnUddDoCHjDUYAAAA8FiJi4tTWlqaOnXq9KhLQS7cuHHD7HFaWpref/99ubm5qUqVKo+oKgD/JNZgAAAAwGPhm2++0ZEjRzRu3Di1bNky0x0m8Hj7z3/+oxs3bqhWrVpKSUnRihUrtGvXLo0fP/4fu2sMgEeLKRIAAAB4LDRo0EC7du1SnTp1tGjRIhUtWvRRl4RcWLJkiaZMmaKTJ0/q5s2bKlmypPr27XvfxS0BPD0IGAAAAAAAgNVYgwEAAAAAAFiNgAEAAAAAAFiNRR4BZCk9PV1//vmnXF1dZTAYHnU5AAAAAB4Ro9Goq1evqkiRIsqXL/vrFAgYAGTpzz//lK+v76MuAwAAAMBj4vfff9ezzz6b7X4CBgBZcnV1lXTnl4ibm9sjrgYAAADAo5KUlCRfX1/TZ4TsEDAAyFLGtAg3NzcCBgAAAAD3nTrNIo8AAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAwAAAAAAMBqBAx4amzdulUGg0FXrlx51KWYiYqKUuXKlS1uP3fuXPn6+ipfvnyaNm1aro/PyqlTp2QwGHTgwAGr+gEAAACA7BAw/AtERETIYDBk+goPD7e4jwYNGmjgwIEPr8inhMFg0KpVq8y2DRo0SFu2bLHo+KSkJPXv31/vvPOO/vjjD/Xq1StXxwMAAADAo5L/UReAf0Z4eLhiY2PNttnb2+fpOYxGo9LS0pQ/P2+ru7m4uMjFxcWitmfOnNGtW7fUtGlT+fj4mPUBAAAAAI8zrmD4l7C3t1fhwoXNvgoUKCDpztQCOzs7bd++3dR+0qRJ8vLy0rlz5xQREaHvvvtO06dPN139cOrUKdOUhPXr16tq1aqyt7fXjh07lJ6erpiYGBUvXlyOjo6qVKmSli1bZuo747ivv/5awcHBcnR0VKNGjXT+/HmtX79eZcqUkZubmzp27Kjr16+bjrtfv3e7du2a3NzcMu1ftWqVnJ2ddfXq1SyPW7ZsmSpUqCBHR0cVKlRIoaGhunbtmiRp7969aty4sZ555hm5u7srJCREP/74o+lYf39/SVKrVq1kMBhMj++d4rB161bVqFFDzs7O8vDwUJ06dXT69GnFxcWpQoUKkqSAgADTOGc1ReKjjz5SmTJl5ODgoNKlS2vmzJlm+/fs2aPg4GA5ODioWrVq+umnn7J8vgAAAACQV/hTM0zTHzp16qSDBw/qt99+04gRI/TFF1/I29tb06dP1/Hjx1W+fHmNHj1akuTp6alTp05JkoYOHarJkycrICBABQoUUExMjBYtWqTZs2crMDBQ27Zt02uvvSZPT0+FhISYzhsVFaUPPvhATk5Oat++vdq3by97e3stWbJEycnJatWqld5//3298847kmRxv5Lk7Oysl19+WbGxsWrbtq1pe8ZjV1fXTONw9uxZvfLKK5o0aZJatWqlq1evavv27TIajZKkq1evqkuXLnr//fdlNBo1ZcoUNWnSRCdOnJCrq6v27t0rLy8vxcbGKjw8XDY2NpnOcfv2bbVs2VI9e/bUp59+qtTUVO3Zs0cGg0EdOnSQr6+vQkNDtWfPHvn6+srT0zNTH4sXL9bIkSP1wQcfKDg4WD/99JN69uwpZ2dndenSRcnJyWrWrJkaN26sRYsWKSEhQW+88cZ93wcpKSlKSUkxPU5KSrrvMbl18+ZNnTlzJs/7vZ9ixYrJwcHhHz+vpRiX7DE2WWNcsvaoxkVibLLDuGSPscka45K1x31cJMYmO/+2cSFg+Jf46quvMl1m/9///lf//e9/JUljx47Vpk2b1KtXL/3888/q0qWLWrRoIUlyd3eXnZ2dnJycVLhw4Ux9jx49Wo0bN5Z050Pq+PHjtXnzZtWqVUvSnb/G79ixQ3PmzDELAsaOHas6depIkrp3767IyEjFx8crICBAktS2bVt9++23euedd3LVb4YePXqodu3aOnv2rHx8fHT+/HmtW7dOmzdvznKMzp49q9u3b6t169by8/OTJNMVBZLUqFEjs/Zz586Vh4eHvvvuOzVr1swUBnh4eGQ5TtKdD+2JiYlq1qyZSpQoIUkqU6aMaX+hQoUk3Qlwsutj1KhRmjJlilq3bi1JKl68uI4cOaI5c+aoS5cuWrJkidLT0zV//nw5ODioXLly+t///qe+fftm2V+GmJgYRUdH59jGWmfOnFGvXr0e6jmyMnfuXAUFBf3j57UU45I9xiZrjEvWHtW4SIxNdhiX7DE2WWNcsva4j4vE2GTn3zYuBAz/Eg0bNtSsWbPMthUsWND0vZ2dnRYvXqyKFSvKz89PU6dOtbjvatWqmb4/efKkrl+/bgocMqSmpio4ONhsW8WKFU3fe3t7y8nJyRQuZGzbs2dPrvvNUKNGDZUrV04LFizQ0KFDtWjRIvn5+al+/fpZtq9UqZKef/55VahQQWFhYXrhhRfUtm1b01SSc+fOafjw4dq6davOnz+vtLQ0Xb9+PVeJZMGCBRUREaGwsDA1btxYoaGhat++vdl6Czm5du2a4uPj1b17d/Xs2dO0/fbt23J3d5ckHT16VBUrVjRLLDNCmZxERkbqrbfeMj1OSkqSr6+vpU/NIsWKFdPcuXNzfdzp06c1btw4DRs2zBT+5Pa8jzPGJXuMTdYYl6w96LhIjE12GJesWTsuGed+nPGeyRrjkj3GJmv/tnEhYPiXcHZ2VsmSJXNss2vXLknSpUuXdOnSJTk7O1vcd4bk5GRJ0tq1a1W0aFGzdvcuKmlra2v63mAwmD3O2Jaenp7rfu/Wo0cPffjhhxo6dKhiY2PVtWtXGQyGLNva2Nho06ZN2rVrlzZu3Kj3339fw4YN0+7du1W8eHF16dJFFy9e1PTp0+Xn5yd7e3vVqlVLqamp2Z4/K7GxsRowYIA2bNigzz77TMOHD9emTZtUs2bN+x6bMQ7z5s3Tc889l6l+a9jb2+f5wp/3cnBwsCpJ9fPze6wT6gfFuGSPscka45I1a8dFYmyyw7hk7WkdF4mxyQ7jkj3GJmv/tnFhkUdIkuLj4/Xmm2+aPrh26dLF9OFeunOFQ1pa2n37KVu2rOzt7XXmzBmVLFnS7Muav4Y/aL+vvfaaTp8+rRkzZujIkSPq0qVLjucxGAyqU6eOoqOj9dNPP8nOzk4rV66UJO3cuVMDBgxQkyZNVK5cOdnb2+vvv/82O97W1taicQoODlZkZKR27dql8uXLa8mSJRaMwp2rOooUKaLffvst0zgUL15c0p0pF4cOHdLNmzdNx/3www8W9Q8AAAAAD4orGP4lUlJS9Ndff5lty58/v5555hmlpaXptddeU1hYmLp27arw8HBVqFBBU6ZM0eDBgyXduUPC7t27derUKbm4uJhNr7ibq6urBg0apDfffFPp6emqW7euEhMTtXPnTrm5ud33A352HrTfAgUKqHXr1ho8eLBeeOEFPfvss9meY/fu3dqyZYteeOEFeXl5affu3bpw4YJpjYTAwEAtXLhQ1apVU1JSkgYPHixHR0ezPvz9/bVlyxbVqVNH9vb2pukVGRISEjR37ly1aNFCRYoU0bFjx3TixAl17tzZ4rGIjo7WgAED5O7urvDwcKWkpGjfvn26fPmy3nrrLXXs2FHDhg1Tz549FRkZqVOnTmny5MkW9w8AAAAAD4IrGP4lNmzYIB8fH7OvunXrSpLGjRun06dPa86cOZIkHx8fzZ07V8OHD9fBgwclSYMGDZKNjY3Kli0rT0/PHNcdGDNmjEaMGKGYmBiVKVNG4eHhWrt2rekv7A/qQfvt3r27UlNT1a1btxzbubm5adu2bWrSpImCgoI0fPhwTZkyRS+++KIkaf78+bp8+bKqVKmiTp06acCAAfLy8jLrY8qUKdq0aZN8fX2zXBvCyclJv/76q9q0aaOgoCD16tVL/fr1U+/evS0ehx49euijjz5SbGysKlSooJCQEMXFxZnGwcXFRWvWrNHhw4cVHBysYcOGaeLEiRb3DwAAAAAPxAg85T755BNjoUKFjCkpKY+6lCdKYmKiUZIxMTHxUZdiPHbsmDEkJMR47NixR13KY4VxyR5jkzXGJXuMTdYYl6wxLtljbLLGuGSPscna4zYuln42YIoEnlrXr1/X2bNnNWHCBPXu3Vt2dnaPuiQAAAAAeGoxRQJPrUmTJql06dIqXLiwIiMjH3U5AAAAAPBUI2DAUysqKkq3bt3Sli1b5OLi8qjLAQAAAICnGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACwGgEDAAAAAACw2lMTMPj7+2vatGk5tomKilLlypX/kXoeFkueZ4br16+rTZs2cnNzk8Fg0JUrV3J1fHaehHG893kaDAatWrXqkdXToEEDDRw40KK2RqNRvXr1UsGCBWUwGHTgwIFcHZ+duLg4eXh4WNUHAAAAAGQn/6M8eUREhBYsWKCYmBgNHTrUtH3VqlVq1aqVjEajxX3t3btXzs7OpscGg0ErV65Uy5Yt87Lkf0xcXJwGDhyoK1eumG2/93nmZMGCBdq+fbt27dqlZ555Ru7u7rk6/mly9uxZFShQ4KGfZ+vWrWrYsKEuX75s9mF+xYoVsrW1taiPDRs2KC4uTlu3blVAQICeeeaZXB0PAAAAAI/CIw0YJMnBwUETJ05U7969rfoA6OnpmYdVWc5oNCotLU358/8zQ5mb5xkfH68yZcqofPnyD3T84+bWrVsP/CG7cOHCeVxN7hQsWNDitvHx8fLx8VHt2rUf6HgAAAAAeBQe+RSJ0NBQFS5cWDExMTm227Fjh+rVqydHR0f5+vpqwIABunbtmmn/3ZfE+/v7S5JatWolg8Fgepxh4cKF8vf3l7u7u15++WVdvXrVtC89PV0xMTEqXry4HB0dValSJS1btsy0f+vWrTIYDFq/fr2qVq0qe3t77dixI1O9qamp6t+/v3x8fOTg4CA/Pz+z5/jee++pQoUKcnZ2lq+vr15//XUlJyebztG1a1clJibKYDDIYDAoKioq0/M0Go2KiopSsWLFZG9vryJFimjAgAGS7lySP2XKFG3btk0Gg0ENGjTIdLwkXblyRT169JCnp6fc3NzUqFEjHTx40Oy5TJgwQd7e3nJ1dVX37t118+bNHF+rjDHasmWLqlWrJicnJ9WuXVvHjh0zazdr1iyVKFFCdnZ2KlWqlBYuXGi232AwaNasWWrRooWcnZ01bty4LM93/vx5NW/eXI6OjipevLgWL16cqc3dUyRyem1yGlPpznunWrVqcnV1VeHChdWxY0edP39eknTq1Ck1bNhQklSgQAEZDAZFRERIyjxFYubMmQoMDJSDg4O8vb3Vtm1bSXeu6vnPf/6jM2fOmL137z0+JSVFgwYNUtGiReXs7KznnntOW7duNXvOcXFxKlasmJycnNSqVStdvHgxy/EDAAAAgLzwyK9gsLGx0fjx49WxY0cNGDBAzz77bKY28fHxCg8P19ixY/Xxxx/rwoUL6t+/v/r376/Y2NhM7ffu3SsvLy/FxsYqPDxcNjY2Zn2tWrVKX331lS5fvqz27dtrwoQJpg+vMTExWrRokWbPnq3AwEBt27ZNr732mjw9PRUSEmLqZ+jQoZo8ebICAgKyvPJixowZWr16tT7//HMVK1ZMv//+u37//XfT/nz58mnGjBkqXry4fvvtN73++usaMmSIZs6cqdq1a2vatGkaOXKk6UO5i4tLpnMsX75cU6dO1dKlS1WuXDn99ddfpnBgxYoVGjp0qH7++WetWLFCdnZ2WY5/u3bt5OjoqPXr18vd3V1z5szR888/r+PHj6tgwYL6/PPPFRUVpQ8//FB169bVwoULNWPGDAUEBGTZ392GDRumKVOmyNPTU3369FG3bt20c+dOSdLKlSv1xhtvaNq0aQoNDdVXX32lrl276tlnnzV9SJfurPcwYcIETZs2LdurRCIiIvTnn3/q22+/la2trQYMGGD60J+VnF6bnMZUunMVxZgxY1SqVCmdP39eb731liIiIrRu3Tr5+vpq+fLlatOmjY4dOyY3Nzc5OjpmOv++ffs0YMAALVy4ULVr19alS5e0fft2SdL06dNVokQJzZ07V3v37jV7796tf//+OnLkiJYuXaoiRYpo5cqVCg8P1+HDhxUYGKjdu3ere/fuiomJUcuWLbVhwwaNGjUqx9crJSVFKSkppsdJSUk5tj937pwSExNzbJNXTp8+bfbff4K7u7u8vb1zfRzjAuSNf/JnSeLn6WnwtP/+lXjPAI/a0/57Ji9+xzzygEG6c6VB5cqVNWrUKM2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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5779J8U1jRVR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Select features and target for Job Satisfaction prediction\n",
+        "X = employment_df[['Employment', 'CompanySize', 'YearsCoding']]\n",
+        "y_job = employment_df['JobSatisfaction']\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "R-J9yP8rkGko"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Preprocessing pipelines for numerical and categorical data\n",
+        "numerical_features = X.select_dtypes(include=['int64', 'float64']).columns\n",
+        "categorical_features = X.select_dtypes(include=['object']).columns\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "4cDo6INJkIuN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "numerical_pipeline = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='median')),\n",
+        "    ('scaler', StandardScaler())\n",
+        "])\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TEGwsDS3jRX6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "categorical_pipeline = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
+        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
+        "])\n",
+        "\n",
+        "# Combine preprocessing pipelines\n",
+        "preprocessor = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num', numerical_pipeline, numerical_features),\n",
+        "        ('cat', categorical_pipeline, categorical_features)\n",
+        "    ]\n",
+        ")\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "YPf97B0zjRbG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y_job, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# Create and fit the pipeline with Logistic Regression\n",
+        "model_job = Pipeline(steps=[\n",
+        "    ('preprocessor', preprocessor),\n",
+        "    ('classifier', LogisticRegression(max_iter=1000, random_state=42))\n",
+        "])\n",
+        "\n",
+        "model_job.fit(X_train, y_train)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 227
+        },
+        "id": "wdVkxiCkjRdR",
+        "outputId": "85606db2-d455-488f-aebd-34031d7de180"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "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(['Employment', 'CompanySize', 'YearsCoding'], dtype='object'))])),\n",
+              "                ('classifier',\n",
+              "                 LogisticRegression(max_iter=1000, random_state=42))])"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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;Employment&#x27;, &#x27;CompanySize&#x27;, &#x27;YearsCoding&#x27;], dtype=&#x27;object&#x27;))])),\n",
+              "                (&#x27;classifier&#x27;,\n",
+              "                 LogisticRegression(max_iter=1000, 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 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 sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><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;Employment&#x27;, &#x27;CompanySize&#x27;, &#x27;YearsCoding&#x27;], dtype=&#x27;object&#x27;))])),\n",
+              "                (&#x27;classifier&#x27;,\n",
+              "                 LogisticRegression(max_iter=1000, 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 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 sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><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;Employment&#x27;, &#x27;CompanySize&#x27;, &#x27;YearsCoding&#x27;], 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 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 sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><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 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 sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><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 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 sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Employment&#x27;, &#x27;CompanySize&#x27;, &#x27;YearsCoding&#x27;], 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 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 sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><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 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 sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Make predictions\n",
+        "y_pred_job = model_job.predict(X_test)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "IP9I0tVzjRgU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Evaluate the model\n",
+        "print('Job Satisfaction Prediction')\n",
+        "print('Accuracy:', accuracy_score(y_test, y_pred_job))\n",
+        "print('Confusion Matrix:')\n",
+        "print(confusion_matrix(y_test, y_pred_job))\n",
+        "print('Classification Report:')\n",
+        "print(classification_report(y_test, y_pred_job))\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6VHHDlIfjRjG",
+        "outputId": "c5cf792c-1912-49c2-b1c1-9e777db663ce"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Job Satisfaction Prediction\n",
+            "Accuracy: 0.3756495381062356\n",
+            "Confusion Matrix:\n",
+            "[[   0    0    0  497    0    0    0]\n",
+            " [   0    0    0 2459    0    0    0]\n",
+            " [   0    0    0 1278    0    0    0]\n",
+            " [   0    0    0 5205    0    0    0]\n",
+            " [   0    0    0 1043    0    0    0]\n",
+            " [   0    0    0 1406    0    0    0]\n",
+            " [   0    0    0 1968    0    0    0]]\n",
+            "Classification Report:\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                    precision    recall  f1-score   support\n",
+            "\n",
+            "            Extremely dissatisfied       0.00      0.00      0.00       497\n",
+            "               Extremely satisfied       0.00      0.00      0.00      2459\n",
+            "           Moderately dissatisfied       0.00      0.00      0.00      1278\n",
+            "              Moderately satisfied       0.38      1.00      0.55      5205\n",
+            "Neither satisfied nor dissatisfied       0.00      0.00      0.00      1043\n",
+            "             Slightly dissatisfied       0.00      0.00      0.00      1406\n",
+            "                Slightly satisfied       0.00      0.00      0.00      1968\n",
+            "\n",
+            "                          accuracy                           0.38     13856\n",
+            "                         macro avg       0.05      0.14      0.08     13856\n",
+            "                      weighted avg       0.14      0.38      0.21     13856\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Select features and target for Career Satisfaction prediction\n",
+        "y_career = employment_df['CareerSatisfaction']\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "NqElvzHjkVfj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y_career, test_size=0.2, random_state=42)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "2o-vrJ3_kXDw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create and fit the pipeline with Logistic Regression\n",
+        "model_career = Pipeline(steps=[\n",
+        "    ('preprocessor', preprocessor),\n",
+        "    ('classifier', LogisticRegression(max_iter=1000, random_state=42))\n",
+        "])\n",
+        "\n",
+        "model_career.fit(X_train, y_train)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 227
+        },
+        "id": "dNeCUaf4kXG3",
+        "outputId": "34130b73-76e0-4236-bcff-e0da0a9e089d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "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(['Employment', 'CompanySize', 'YearsCoding'], dtype='object'))])),\n",
+              "                ('classifier',\n",
+              "                 LogisticRegression(max_iter=1000, random_state=42))])"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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;Employment&#x27;, &#x27;CompanySize&#x27;, &#x27;YearsCoding&#x27;], dtype=&#x27;object&#x27;))])),\n",
+              "                (&#x27;classifier&#x27;,\n",
+              "                 LogisticRegression(max_iter=1000, 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><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;Employment&#x27;, &#x27;CompanySize&#x27;, &#x27;YearsCoding&#x27;], dtype=&#x27;object&#x27;))])),\n",
+              "                (&#x27;classifier&#x27;,\n",
+              "                 LogisticRegression(max_iter=1000, 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><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;Employment&#x27;, &#x27;CompanySize&#x27;, &#x27;YearsCoding&#x27;], 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Employment&#x27;, &#x27;CompanySize&#x27;, &#x27;YearsCoding&#x27;], 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Make predictions\n",
+        "y_pred_career = model_career.predict(X_test)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "print('Career Satisfaction Prediction')\n",
+        "print('Accuracy:', accuracy_score(y_test, y_pred_career))\n",
+        "print('Confusion Matrix:')\n",
+        "print(confusion_matrix(y_test, y_pred_career))\n",
+        "print('Classification Report:')\n",
+        "print(classification_report(y_test, y_pred_career))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bMZZxB_ikXJz",
+        "outputId": "47cef9b1-248f-4f50-f7f4-913b2c2ffdc3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Career Satisfaction Prediction\n",
+            "Accuracy: 0.374566974595843\n",
+            "Confusion Matrix:\n",
+            "[[   0    0    0  439    0    0    0]\n",
+            " [   0    0    0 2653    0    0    0]\n",
+            " [   0    0    0  948    0    0    0]\n",
+            " [   0    0    0 5190    0    0    0]\n",
+            " [   0    0    0  969    0    0    0]\n",
+            " [   0    0    0 1189    0    0    0]\n",
+            " [   0    0    0 2468    0    0    0]]\n",
+            "Classification Report:\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                    precision    recall  f1-score   support\n",
+            "\n",
+            "            Extremely dissatisfied       0.00      0.00      0.00       439\n",
+            "               Extremely satisfied       0.00      0.00      0.00      2653\n",
+            "           Moderately dissatisfied       0.00      0.00      0.00       948\n",
+            "              Moderately satisfied       0.37      1.00      0.54      5190\n",
+            "Neither satisfied nor dissatisfied       0.00      0.00      0.00       969\n",
+            "             Slightly dissatisfied       0.00      0.00      0.00      1189\n",
+            "                Slightly satisfied       0.00      0.00      0.00      2468\n",
+            "\n",
+            "                          accuracy                           0.37     13856\n",
+            "                         macro avg       0.05      0.14      0.08     13856\n",
+            "                      weighted avg       0.14      0.37      0.20     13856\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "jrVgTK7TkXMt"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file