Assignement2.ipynb

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        "## **Requirements**\n",
        "\n",
        "1. When you work on this assignment, you should make a copy of this notebook in Google Colab. This can be done using the option `File > Save a copy in Drive` in Google Colab. \n",
        "\n",
        "2. You should fill in your answer for each task inside the code block right under the task. \n",
        "\n",
        "3. You should only insert your code into the designated code blocks, as mentioned above. Other than that, you shouldn't change anything else in the notebook, unless otherwise instructed.\n",
        "\n",
        "4.  For each code block, you are free to use multiple lines of code. \n",
        "\n",
        "5.   Even if you can only partially solve a task, you should include your code in the code block, which allows us to consider partial credit. \n",
        "\n",
        "6.   However, your code should not raise errors. Any code raising errors will not get partial credit.\n",
        "7.   The `Datasets.zip` contains 2 datasets in csv format  `employees.csv` and `Social_Network_Ads.csv` that you will work with them save them with your code in colab.\n",
        "\n",
        "8.   We will test your code in Google Colab. Make sure your code runs in Google Colab.\n",
        "\n",
        "\n",
        "9. To submit your assignment, download your Colab into a .ipynb file. This can be done using the option `Download > Download .ipynb` in Google Colab.\n",
        "\n",
        "10. Submit the downloaded .ipynb file into the Programming Assignment 2 entry in Canvas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EoRP98MpR-qj"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N-qiINBQSK2g"
      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MoUUdHhXrVh"
      },
      "source": [
        "## **Task 1: Exploratory Data Analysis (EDA) (30 points)**"
      ]
    },
    {
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      "execution_count": null,
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        "id": "hcnCdhhPl_qO",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00fb8d1c-564a-4a36-d75f-1c9137cc1b62"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RopL7tUZSQkT"
      },
      "source": [
        "## Importing the dataset(5pts)\n",
        "**(1.1)**We will explore a dataset about employees. This dataset has 1000 rows and 8 columns. The dataset is provided as a CSV file `employees.csv` load it into a pandas `DataFrame` named `employees`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WwEPNDWySTKm"
      },
      "outputs": [],
      "source": [
        "#Code task1\n",
        "employees = pd.read_csv('/content/drive/MyDrive/Data Science Assignment/employees.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFSCvXtJYrUH"
      },
      "source": [
        "## Understanding the dataset"
      ]
    },
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        "outputId": "803727a1-c78b-4337-e5bb-4ae4937edea9"
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      "outputs": [
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              "  FirstName  Gender   StartDate LastLogin Time  Salary  Bonus %  \\\n",
              "0   Douglas    Male    8/6/1993       12:42 PM   97308    6.945   \n",
              "1    Thomas    Male   3/31/1996        6:53 AM   61933    4.170   \n",
              "2     Maria  Female   4/23/1993       11:17 AM  130590   11.858   \n",
              "3     Jerry    Male    3/4/2005        1:00 PM  138705    9.340   \n",
              "4     Larry    Male   1/24/1998        4:47 PM  101004    1.389   \n",
              "5    Dennis    Male   4/18/1987        1:35 AM  115163   10.125   \n",
              "6      Ruby  Female   8/17/1987        4:20 PM   65476   10.012   \n",
              "7       NaN  Female   7/20/2015       10:43 AM   45906   11.598   \n",
              "8    Angela  Female  11/22/2005        6:29 AM   95570   18.523   \n",
              "9   Frances  Female    8/8/2002        6:51 AM  139852    7.524   \n",
              "\n",
              "  SeniorManagement                  Team  \n",
              "0             True             Marketing  \n",
              "1             True                   NaN  \n",
              "2            False               Finance  \n",
              "3             True               Finance  \n",
              "4             True       Client Services  \n",
              "5            False                 Legal  \n",
              "6             True               Product  \n",
              "7              NaN               Finance  \n",
              "8             True           Engineering  \n",
              "9             True  Business Development  "
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              "      <td>Douglas</td>\n",
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              "      <th>2</th>\n",
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              "      <th>4</th>\n",
              "      <td>Larry</td>\n",
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              "      <td>4:47 PM</td>\n",
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              "      <td>4/18/1987</td>\n",
              "      <td>1:35 AM</td>\n",
              "      <td>115163</td>\n",
              "      <td>10.125</td>\n",
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              "      <th>6</th>\n",
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              "      <td>8/17/1987</td>\n",
              "      <td>4:20 PM</td>\n",
              "      <td>65476</td>\n",
              "      <td>10.012</td>\n",
              "      <td>True</td>\n",
              "      <td>Product</td>\n",
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              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Female</td>\n",
              "      <td>7/20/2015</td>\n",
              "      <td>10:43 AM</td>\n",
              "      <td>45906</td>\n",
              "      <td>11.598</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>8</th>\n",
              "      <td>Angela</td>\n",
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              "      <td>11/22/2005</td>\n",
              "      <td>6:29 AM</td>\n",
              "      <td>95570</td>\n",
              "      <td>18.523</td>\n",
              "      <td>True</td>\n",
              "      <td>Engineering</td>\n",
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              "      <th>9</th>\n",
              "      <td>Frances</td>\n",
              "      <td>Female</td>\n",
              "      <td>8/8/2002</td>\n",
              "      <td>6:51 AM</td>\n",
              "      <td>139852</td>\n",
              "      <td>7.524</td>\n",
              "      <td>True</td>\n",
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      "source": [
        "employees.head(10)"
      ]
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              "(1000, 8)"
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        "employees.shape"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 8 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   FirstName         933 non-null    object \n",
            " 1   Gender            855 non-null    object \n",
            " 2   StartDate         1000 non-null   object \n",
            " 3   LastLogin Time    1000 non-null   object \n",
            " 4   Salary            1000 non-null   int64  \n",
            " 5   Bonus %           1000 non-null   float64\n",
            " 6   SeniorManagement  933 non-null    object \n",
            " 7   Team              957 non-null    object \n",
            "dtypes: float64(1), int64(1), object(6)\n",
            "memory usage: 62.6+ KB\n"
          ]
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      "source": [
        "employees.info()"
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              "              Salary      Bonus %\n",
              "count    1000.000000  1000.000000\n",
              "mean    90662.181000    10.207555\n",
              "std     32923.693342     5.528481\n",
              "min     35013.000000     1.015000\n",
              "25%     62613.000000     5.401750\n",
              "50%     90428.000000     9.838500\n",
              "75%    118740.250000    14.838000\n",
              "max    149908.000000    19.944000"
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              "      <td>35013.000000</td>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 402
        }
      ],
      "source": [
        "employees.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZGOD6m_B2wo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6eec47f-93c8-4fa0-ae2e-97def935fc10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FirstName            67\n",
              "Gender              145\n",
              "StartDate             0\n",
              "LastLogin Time        0\n",
              "Salary                0\n",
              "Bonus %               0\n",
              "SeniorManagement     67\n",
              "Team                 43\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 403
        }
      ],
      "source": [
        "employees.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KjE63mpZQL8"
      },
      "source": [
        "## FirstName, Gender, SeniorManagement and Team has null values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4iN3dMEgiY8l"
      },
      "source": [
        "**(1.2)**  Find out all the distinct values in the column Team. (5 points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OP_RqPu4kp06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e618096b-9545-4ecb-e510-f84d449abe95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Marketing\n",
            "nan\n",
            "Finance\n",
            "Client Services\n",
            "Legal\n",
            "Product\n",
            "Engineering\n",
            "Business Development\n",
            "Human Resources\n",
            "Sales\n",
            "Distribution\n"
          ]
        }
      ],
      "source": [
        "#code 1.2\n",
        "print(*employees['Team'].unique(), sep =\"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TsHbbwq1kt_z"
      },
      "source": [
        "**(1.3)** - Show how many data entries there are in each Team and the number of the senior Management for each team . (5 points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 394
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        {
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            "text/plain": [
              "                      Team  SeniorManagement\n",
              "Team                                        \n",
              "Business Development   101                54\n",
              "Client Services        106                42\n",
              "Distribution            90                38\n",
              "Engineering             92                49\n",
              "Finance                102                46\n",
              "Human Resources         91                45\n",
              "Legal                   88                41\n",
              "Marketing               98                49\n",
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              "Sales                   94                48"
            ],
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "#code 1.3\n",
        "Team_count = employees.groupby(['Team'])[['Team']].count()\n",
        "Senior_team = employees.groupby(['Team'])[['SeniorManagement']].sum()\n",
        "combined= pd.concat([Team_count,Senior_team], axis =1)\n",
        "combined"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nK2DAOV7pOTI"
      },
      "source": [
        "**(1.4)** - Show 3 random data instances (rows) for each class (Senior Management). (5 points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AhOJw_pepRB3",
        "colab": {
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          "height": 238
        },
        "outputId": "874e0da2-696a-47f0-827a-edd4a44ea99f"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "    FirstName  Gender   StartDate LastLogin Time  Salary  Bonus %  \\\n",
              "190     Carol  Female   3/19/1996        3:39 AM   57783    9.129   \n",
              "487    Cheryl  Female   12/3/2014        9:27 AM   71751   15.918   \n",
              "460      Tina  Female   1/17/2005        9:11 AM   88276   14.248   \n",
              "888   Marilyn  Female   10/8/2007       12:32 AM  115149   11.934   \n",
              "969     Linda  Female    2/4/2010        8:49 PM   44486   17.308   \n",
              "760     Shawn    Male  12/29/2006        3:05 AM   57871    4.020   \n",
              "\n",
              "    SeniorManagement                  Team  \n",
              "190            False               Finance  \n",
              "487            False  Business Development  \n",
              "460            False                 Legal  \n",
              "888             True                 Legal  \n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d255058a-0c19-4b3e-8d5b-3e11b4606e0e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 406
        }
      ],
      "source": [
        "#code 1.4\n",
        "employees.groupby(['SeniorManagement']).sample(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ia2qyD7xqFA9"
      },
      "source": [
        "**(1.5)** - Show the **number** of distinct values in each of these columns: `Start Date`, `Gender`, `Last Login Time`,`Bonus %`. (5 points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WW_AkW4lsNQO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "680b2b24-d50b-492b-fa1f-c1535f716257"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distinct values in StartDate = 972\n",
            "Distinct values in Gender = 2\n",
            "Distinct values in LastLogin Time = 720\n",
            "Distinct values in Bonus % = 971\n"
          ]
        }
      ],
      "source": [
        "#Code 1.5\n",
        "def distinct(data, column):\n",
        "  print(f'Distinct values in {column} = {data[column].nunique()}')\n",
        "\n",
        "distinct(employees, 'StartDate')\n",
        "distinct(employees, 'Gender')\n",
        "distinct(employees, 'LastLogin Time')\n",
        "distinct(employees, 'Bonus %')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WS3DjTG0sRvG"
      },
      "source": [
        "**(1.6)** - Find the minimum  and the maximum bonus in the `Bonus %` column. (5 points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xmOZhGqYssfx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4438afc-b197-415d-d21d-adcb8275dbce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Bonus = 19.944\n",
            "Minimum Bonus = 1.015\n"
          ]
        }
      ],
      "source": [
        "#Code 1.6\n",
        "print(f\"Maximum Bonus = {employees['Bonus %'].max()}\")\n",
        "print(f\"Minimum Bonus = {employees['Bonus %'].min()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgxA-xSvtfCm"
      },
      "source": [
        "## **Task 2: Data preprocessing (40 points)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhKP5Ttd1QwF"
      },
      "source": [
        "**(2.1)** cheking of missing data for each column (5points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A9vLq_KE1ibC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "682a7eba-ae5c-4ff7-91c7-d7862e61cbd1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FirstName            67\n",
              "Gender              145\n",
              "StartDate             0\n",
              "LastLogin Time        0\n",
              "Salary                0\n",
              "Bonus %               0\n",
              "SeniorManagement     67\n",
              "Team                 43\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 409
        }
      ],
      "source": [
        "#code2.1\n",
        "employees.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKOHnxRH1max"
      },
      "source": [
        "**(2.2)**Take care of missing data with expalnation (5points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZ21HkO8utRk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a5e91f4-ccdd-4ac6-a05d-8debdb60323b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FirstName            67\n",
              "Gender              145\n",
              "StartDate             0\n",
              "LastLogin Time        0\n",
              "Salary                0\n",
              "Bonus %               0\n",
              "SeniorManagement     67\n",
              "Team                 43\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 410
        }
      ],
      "source": [
        "#Checking for empty spaces and replaced it with NaN\n",
        "employees = employees.replace(r'^\\s*$', np.nan, regex=True)\n",
        "employees.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFcx7AtsvI0h"
      },
      "source": [
        "## SeniorManagement column has 67 missing values. This column is the dependent variable. So we cannot replace this 67 rows with anything. This will only reduce the model performance.\n",
        "The different approaches that we can do are:-\n",
        "1.   We can remove those 67 rows and train with remaining data. \n",
        "2.   We can train a model with the 933 rows of data and predict the for this 67 rows but, the performance of the new model depends on the prediction of these 67 rows.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YJv20KRp1q_f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a40417c-643b-4f43-beb2-b752ef62f32c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FirstName           0\n",
              "Gender              0\n",
              "StartDate           0\n",
              "LastLogin Time      0\n",
              "Salary              0\n",
              "Bonus %             0\n",
              "SeniorManagement    0\n",
              "Team                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 411
        }
      ],
      "source": [
        "#code2.2\n",
        "# Removing all rows with all the 4 columns with NaN values.\n",
        "employees = employees.dropna(subset=['FirstName', 'Gender', 'SeniorManagement', 'Team'], how='all')\n",
        "# Removing all rows with SeniorManagement as NaN values. \n",
        "employees = employees.dropna(subset=['SeniorManagement'])\n",
        "# We cannot replace anything for the First Name.\n",
        "employees['FirstName'].fillna('Unkown', inplace=True)\n",
        "# Many people could prefer to keep their gender a secret, and some of them prefer not to say. We can substitute the NaN values and treat it as \"Other\".\n",
        "employees['Gender'].fillna('Other', inplace=True)\n",
        "# NaN values in 'Team' column is replaced with mode of the data.\n",
        "employees['Team'].fillna(employees['Team'].mode()[0], inplace=True)\n",
        "employees.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i4t1OyVeYFD-"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5whq1-quR5h"
      },
      "source": [
        "**(2.3)** **- Nominal attributes**\n",
        "\n",
        "The columns `Team` and `Gender` are nominal attributes, as there isn't a meaningful order among the attribute values. Write a piece of code to transform these three attributes into numerical attributes using *label encoding*. More specifically, assign one distinct integer for each distinct value in such an attribute. Use `preprocessing.LabelEncoder()` from the `sklearn` library. \n",
        "(5 points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zCVlJ96_vqh2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "97b7336b-47c1-4c0b-fe82-2987fc17f833"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    FirstName  Gender   StartDate LastLogin Time  Salary  Bonus %  \\\n",
              "0     Douglas       1    8/6/1993       12:42 PM   97308    6.945   \n",
              "1      Thomas       1   3/31/1996        6:53 AM   61933    4.170   \n",
              "2       Maria       0   4/23/1993       11:17 AM  130590   11.858   \n",
              "3       Jerry       1    3/4/2005        1:00 PM  138705    9.340   \n",
              "4       Larry       1   1/24/1998        4:47 PM  101004    1.389   \n",
              "..        ...     ...         ...            ...     ...      ...   \n",
              "995     Henry       2  11/23/2014        6:09 AM  132483   16.655   \n",
              "996   Phillip       1   1/31/1984        6:30 AM   42392   19.675   \n",
              "997   Russell       1   5/20/2013       12:39 PM   96914    1.421   \n",
              "998     Larry       1   4/20/2013        4:45 PM   60500   11.985   \n",
              "999    Albert       1   5/15/2012        6:24 PM  129949   10.169   \n",
              "\n",
              "    SeniorManagement  Team  \n",
              "0               True     7  \n",
              "1               True     1  \n",
              "2              False     4  \n",
              "3               True     4  \n",
              "4               True     1  \n",
              "..               ...   ...  \n",
              "995            False     2  \n",
              "996            False     4  \n",
              "997            False     8  \n",
              "998            False     0  \n",
              "999             True     9  \n",
              "\n",
              "[933 rows x 8 columns]"
            ],
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 412
        }
      ],
      "source": [
        "#code 2.3\n",
        "from sklearn import preprocessing\n",
        "\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "# for the team column\n",
        "employees['Team']=label_encoder.fit_transform(employees.Team)\n",
        "# for the Gender column\n",
        "employees['Gender']=label_encoder.fit_transform(employees.Gender)\n",
        "employees"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0HUesh4gvvAJ"
      },
      "source": [
        "**(2.4)**convert the values in columns `Start Date`  to `datetime`   (5 points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "niHBYVFjxOJ3",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "55fac4de-a333-49f0-9e02-5146e7af239b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  FirstName  Gender  StartDate LastLogin Time  Salary  Bonus %  \\\n",
              "0   Douglas       1 1993-08-06       12:42 PM   97308    6.945   \n",
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      "source": [
        "#code2.4\n",
        "# This will create a datetime object.\n",
        "employees['StartDate'] = pd.to_datetime(employees['StartDate'])\n",
        "employees.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**(2.5)**Create separate columns for months,days and years---new columns `Start_month`, `Start_day` and `Start_year` to replace `Start Date`\n",
        "Check out this page to find out how to fetch the month, the day  and the year in a value of type `datatime`: https://docs.python.org/3/library/datetime.html#datetime.date.month.\n",
        "(5points)"
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              "  FirstName  Gender  StartDate LastLogin Time  Salary  Bonus %  \\\n",
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        "#code2.5\n",
        "employees['Start_month'], employees['Start_day'], employees['Start_year'] = employees['StartDate'].dt.month, employees['StartDate'].dt.day, employees['StartDate'].dt.year\n",
        "employees.head(5)\n"
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        "**(2.6)** - Remove columns `Start date`  from the `DataFrame` `employees`. We only need to use the 3 newly created columns from (3.3). (5points)"
      ]
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          },
          "metadata": {},
          "execution_count": 415
        }
      ],
      "source": [
        "#code2.6\n",
        "employees.drop(columns = 'StartDate', axis = 1, inplace = True)\n",
        "employees.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7zN7hkiz_dd"
      },
      "source": [
        "**(2.7)** Encoding the Dependent Variable(Senior Management)(5points)"
      ]
    },
    {
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      "execution_count": null,
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      "outputs": [
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            "text/plain": [
              "  FirstName  Gender LastLogin Time  Salary  Bonus %  Team  Start_month  \\\n",
              "0   Douglas       1       12:42 PM   97308    6.945     7            8   \n",
              "1    Thomas       1        6:53 AM   61933    4.170     1            3   \n",
              "2     Maria       0       11:17 AM  130590   11.858     4            4   \n",
              "3     Jerry       1        1:00 PM  138705    9.340     4            3   \n",
              "4     Larry       1        4:47 PM  101004    1.389     1            1   \n",
              "\n",
              "   Start_day  Start_year  Senior Management  \n",
              "0          6        1993                  1  \n",
              "1         31        1996                  1  \n",
              "2         23        1993                  0  \n",
              "3          4        2005                  1  \n",
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      ],
      "source": [
        "#code 2.7\n",
        "# True is represented as 1 and False is represented as 0\n",
        "employees['Senior Management'] = label_encoder.fit_transform(employees.SeniorManagement)\n",
        "# Dropping the SeniorManagment in the middle\n",
        "employees.drop(columns = 'SeniorManagement', axis = 1, inplace = True)\n",
        "employees.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yz94j-izzk6V"
      },
      "source": [
        "**(2.8)** Splitting the dataset into the Training set and Test set.Use 80% of the data for tarining and 20% for test.\n",
        "(5points) "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# These are the features takena as the independent variables. \n",
        "employees[['Gender', 'Salary', 'Bonus %','Team','Start_year']].head()"
      ],
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        "id": "sv2SseOaekhd",
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      "execution_count": null,
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        {
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              "   Gender  Salary  Bonus %  Team  Start_year\n",
              "0       1   97308    6.945     7        1993\n",
              "1       1   61933    4.170     1        1996\n",
              "2       0  130590   11.858     4        1993\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d00f0cda-7403-4cd2-9185-9f415b2516a5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d00f0cda-7403-4cd2-9185-9f415b2516a5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 417
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f7QspewyeBfx"
      },
      "outputs": [],
      "source": [
        "#code 2.8\n",
        "from sklearn.model_selection import train_test_split\n",
        "y = employees[['Senior Management']]\n",
        "X = employees[['Gender', 'Salary', 'Bonus %','Team','Start_year']] # First Name is not taken into consideration of the training process as it doesn't make adds anything to the SeniorManagement.\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state = 3) #Added a random_state to keep the training and testing data constant."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpGqbS4TqkIR"
      },
      "source": [
        "**(2.9)**Feature Scaling  (5points)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwj-Yce8Ynbq"
      },
      "source": [
        "## Feature Scaling will be only neccessary for Salary and bonus columns as they differ from other values by large differences and should be done in the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AxjSUXFQqo-3"
      },
      "outputs": [],
      "source": [
        "#Code 2.9\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scalar = preprocessing.StandardScaler()\n",
        "X_scaled_train = scalar.fit_transform(X_train[['Salary','Bonus %']]) # Applying fit and transform to the training data.\n",
        "X_scaled_test = scalar.transform(X_test[['Salary','Bonus %']]) # Applying the same transform to the test data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCvejKJi3D1H"
      },
      "source": [
        "## Applying the scaled values to X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G-OBeZzWdley"
      },
      "outputs": [],
      "source": [
        "X_train_new = pd.DataFrame(X_scaled_train) # Converting the array of the scaled results into a dataframe.\n",
        "X_train_new.columns = ['Salary','Bonus %'] # Changing name of the array from 0, 1 to 'Salary','Bonus %'\n",
        "X_train.drop(columns = ['Salary','Bonus %'], axis = 1, inplace = True) # Removing the unscaled values to add new values\n",
        "X_train['Salary'] = X_train_new['Salary'].values # Adding up the feature scaled value in the training data of 'Salary'\n",
        "X_train['Bonus %'] = X_train_new['Bonus %'].values # Adding up the feature scaled value in the training data of 'Bonus %'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3PyMp913I9o"
      },
      "source": [
        "## Applying the scaled values to X_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NaU0GQ30hqJX"
      },
      "outputs": [],
      "source": [
        "X_test_new = pd.DataFrame(X_scaled_test) # Converting the array of the scaled results into a dataframe.\n",
        "X_test_new.columns = ['Salary','Bonus %'] # Changing name of the array from 0, 1 to 'Salary','Bonus %'\n",
        "X_test.drop(columns = ['Salary','Bonus %'], axis = 1, inplace = True) # Removing the unscaled values to add new values\n",
        "X_test['Salary'] = X_test_new['Salary'].values # Adding up the feature scaled value in the training data of 'Salary'\n",
        "X_test['Bonus %'] = X_test_new['Bonus %'].values # Adding up the feature scaled value in the training data of 'Bonus %'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QVOx_CWB1vwX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2b22c567-7386-4071-aa14-17bf87ad26e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Team  Start_year    Salary   Bonus %\n",
              "323       0     9        1990  0.755387 -1.291943\n",
              "681       1     8        2002 -0.470381  1.286565\n",
              "738       1     0        2010  0.494929  0.449452\n",
              "874       0     5        1994  0.247058 -1.205017\n",
              "912       1     1        1998  1.071943 -1.654913"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5553839b-1164-44fa-b083-ae1b7e158413\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Team</th>\n",
              "      <th>Start_year</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Bonus %</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>323</th>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1990</td>\n",
              "      <td>0.755387</td>\n",
              "      <td>-1.291943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2002</td>\n",
              "      <td>-0.470381</td>\n",
              "      <td>1.286565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>738</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.494929</td>\n",
              "      <td>0.449452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>874</th>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1994</td>\n",
              "      <td>0.247058</td>\n",
              "      <td>-1.205017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>912</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1998</td>\n",
              "      <td>1.071943</td>\n",
              "      <td>-1.654913</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5553839b-1164-44fa-b083-ae1b7e158413')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5553839b-1164-44fa-b083-ae1b7e158413');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 422
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GXSMaDCD2_FE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4c0a4de7-664a-4be3-b081-a3c05f05cbf2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Team  Start_year    Salary   Bonus %\n",
              "859       0     0        2012 -1.479141  1.617207\n",
              "129       1     0        1988 -0.902490  0.514827\n",
              "798       1     7        1984  0.359950  1.386063\n",
              "564       1     1        1985 -1.430547 -0.479795\n",
              "561       0     3        1996 -0.582848 -0.298579"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d572ca8b-9d02-4229-9661-d12ec75d1dfc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Team</th>\n",
              "      <th>Start_year</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Bonus %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2012</td>\n",
              "      <td>-1.479141</td>\n",
              "      <td>1.617207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1988</td>\n",
              "      <td>-0.902490</td>\n",
              "      <td>0.514827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>798</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1984</td>\n",
              "      <td>0.359950</td>\n",
              "      <td>1.386063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1985</td>\n",
              "      <td>-1.430547</td>\n",
              "      <td>-0.479795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>561</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1996</td>\n",
              "      <td>-0.582848</td>\n",
              "      <td>-0.298579</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d572ca8b-9d02-4229-9661-d12ec75d1dfc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d572ca8b-9d02-4229-9661-d12ec75d1dfc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d572ca8b-9d02-4229-9661-d12ec75d1dfc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 423
        }
      ],
      "source": [
        "X_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m51CCwCV4_vY"
      },
      "source": [
        "## **Task 3: Grid Search**(30points)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FXSIBqY_5_U9"
      },
      "source": [
        "## Importing the dataset and training the model\n",
        "**(3.1)**We will explore a dataset about customers . The dataset is provided as a CSV file `Social_Network_Ads.csv` load it into a pandas `DataFrame` named `customers`.(2points)"
      ]
    },
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        {
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            "text/plain": [
              "   Age  EstimatedSalary  Purchased\n",
              "0   19            19000          0\n",
              "1   35            20000          0\n",
              "2   26            43000          0\n",
              "3   27            57000          0\n",
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            ]
          },
          "metadata": {},
          "execution_count": 424
        }
      ],
      "source": [
        "#code 3.1\n",
        "customers=pd.read_csv('/content/drive/MyDrive/Data Science Assignment/Social_Network_Ads .csv')\n",
        "customers.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S7yG6c4H2Xrd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b7947e2-a5ff-4145-aea4-0842eea1b6a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                0\n",
              "EstimatedSalary    0\n",
              "Purchased          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 425
        }
      ],
      "source": [
        "customers.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y0PFPinOVcd9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aca7ff56-4ed7-4436-e7f6-6f40948bc606"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    257\n",
              "1    143\n",
              "Name: Purchased, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 426
        }
      ],
      "source": [
        "customers['Purchased'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BedXa5N8VlPH"
      },
      "source": [
        "# **Consider 0 as Not Purchased and 1 as Purchased**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JK9702aKCI1s"
      },
      "source": [
        "**(3.2)**Splitting the dataset into the Training set and Test set.Use 80% of the data for tarining and 20% for test. (2points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BISztWfoDor6"
      },
      "outputs": [],
      "source": [
        "#code3.2\n",
        "y = customers[['Purchased']]\n",
        "X = customers.iloc[:,:-1]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.20, random_state= 10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8JQ2AqYCUgf"
      },
      "source": [
        "**(3.3)**Feature Scaling(2points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rZNxaHR7Dl0A"
      },
      "outputs": [],
      "source": [
        "#code 3.3\n",
        "Scaler = preprocessing.StandardScaler()\n",
        "X_scaled_train = Scaler.fit_transform(X_train) # Applying the Scaler on the training data\n",
        "X_scaled_test = Scaler.transform(X_test) # Applying the Scaler on the test data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCQhNtQ147Cz"
      },
      "source": [
        "**(3.4)**Training the Kernel SVM model on the Training set(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugd0wWcOYeKe"
      },
      "source": [
        "### SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v65nZ5L5Dfkr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3adbbdc3-b08e-472f-e2ed-421cbaeb1abc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy without Feature Scaling = 78.75\n",
            "Accuracy with Feature Scaling = 95.0\n"
          ]
        }
      ],
      "source": [
        "#code 3.4\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "y_train = y_train.values.ravel()\n",
        "y_test = y_test.values.ravel()\n",
        "\n",
        "def Train_model(model, X_train, X_test, y_train, y_test):\n",
        "  Model = model.fit(X_train, y_train)\n",
        "  y_predict = Model.predict(X_test)\n",
        "  return y_predict\n",
        "\n",
        "def Accuracy(Actual, Predicted):\n",
        "  return accuracy_score(Actual,Predicted)*100\n",
        "\n",
        "Predicted = Train_model(SVC(), X_train, X_test, y_train, y_test)\n",
        "print(f'Accuracy without Feature Scaling = {Accuracy(y_test, Predicted)}')\n",
        "\n",
        "Predicted = Train_model(SVC(), X_scaled_train, X_scaled_test, y_train, y_test)\n",
        "print(f'Accuracy with Feature Scaling = {Accuracy(y_test, Predicted)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgTbgRrjZokd"
      },
      "source": [
        "### Tried out other models to check how they perform with and without feature scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlboNnbBYizk"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7U8FMK4MW2W8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c64f0fac-b504-464f-e186-62259efd557f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy without Feature Scaling = 65.0\n",
            "Accuracy with Feature Scaling = 88.75\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "Predicted = Train_model(LogisticRegression(), X_train, X_test, y_train, y_test)\n",
        "print(f'Accuracy without Feature Scaling = {Accuracy(y_test, Predicted)}')\n",
        "\n",
        "Predicted = Train_model(LogisticRegression(), X_scaled_train, X_scaled_test, y_train, y_test)\n",
        "print(f'Accuracy with Feature Scaling = {Accuracy(y_test, Predicted)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opbZc5DxYozu"
      },
      "source": [
        "### Naive Bayes Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E4ij_EYoXh7Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de2f683f-7309-45d7-ff6b-f99433e84e26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy without Feature Scaling = 91.25\n",
            "Accuracy with Feature Scaling = 91.25\n"
          ]
        }
      ],
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "Predicted = Train_model(GaussianNB(), X_train, X_test, y_train, y_test)\n",
        "print(f'Accuracy without Feature Scaling = {Accuracy(y_test, Predicted)}')\n",
        "\n",
        "Predicted = Train_model(GaussianNB(), X_scaled_train, X_scaled_test, y_train, y_test)\n",
        "print(f'Accuracy with Feature Scaling = {Accuracy(y_test, Predicted)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nB-ZacP9D0ne"
      },
      "source": [
        "**(3.5)**Making the Confusion Matrix and calculate the accurancy(5points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JqLl6RyTD7mZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbfc7624-8ce0-412e-8e5d-8a2c79d95b5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mRows represent the Actual Values and Columns represent the Predicted Values in the below Confusion Matrix\u001b[0m\n",
            "╒═══════════════╤═════════════════╤═════════════╕\n",
            "│               │   Not Purchased │   Purchased │\n",
            "╞═══════════════╪═════════════════╪═════════════╡\n",
            "│ Not Purchased │              51 │           1 │\n",
            "├───────────────┼─────────────────┼─────────────┤\n",
            "│ Purchased     │              16 │          12 │\n",
            "╘═══════════════╧═════════════════╧═════════════╛\n",
            "TP = 12, TN = 51, FP = 1, FN = 16\n",
            "Accuracy without Feature Scaling= 78.75\n",
            "╒═══════════════╤═════════════════╤═════════════╕\n",
            "│               │   Not Purchased │   Purchased │\n",
            "╞═══════════════╪═════════════════╪═════════════╡\n",
            "│ Not Purchased │              48 │           4 │\n",
            "├───────────────┼─────────────────┼─────────────┤\n",
            "│ Purchased     │               0 │          28 │\n",
            "╘═══════════════╧═════════════════╧═════════════╛\n",
            "TP = 28, TN = 48, FP = 4, FN = 0\n",
            "Accuracy with Feature Scaling= 95.0\n"
          ]
        }
      ],
      "source": [
        "#code 3.5\n",
        "#Confusion matrix will be a matix between Actual values and the predicted values. Here the Actual values are the y_test and the Predicted values are y_predict.\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from tabulate import tabulate\n",
        "\n",
        "def table(model, X_T, X_t, y_T, y_t, Scaling):\n",
        "  labels = ['Not Purchased','Purchased']\n",
        "  y_predict = Train_model(model, X_T, X_t, y_T, y_t)\n",
        "  c_m = confusion_matrix(y_t, y_predict)\n",
        "  c_m_df = pd.DataFrame(c_m, index = labels, columns = labels)\n",
        "  print(tabulate(c_m_df, headers = 'keys', tablefmt = 'fancy_grid'))\n",
        "  print(f\"TP = {c_m_df.iloc[1,1]}, TN = {c_m_df.iloc[0,0]}, FP = {c_m_df.iloc[0,1]}, FN = {c_m_df.iloc[1,0]}\")\n",
        "  print(f\"Accuracy {Scaling} Feature Scaling= {Accuracy(y_test, y_predict)}\")\n",
        "\n",
        "\n",
        "print('\\033[1m' + 'Rows represent the Actual Values and Columns represent the Predicted Values in the below Confusion Matrix' + '\\033[0m')\n",
        "table(SVC(), X_train, X_test, y_train, y_test, 'without')\n",
        "table(SVC(), X_scaled_train, X_scaled_test, y_train, y_test, 'with')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qlICPXznD9bv"
      },
      "source": [
        "**(3.6)** Applying k-Fold Cross Validation with k=10 and display the  accurancy (5point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6jmCxQRPEN7_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbd6802f-3f6f-48d4-d9f4-d30746eb917e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of LogisticRegression = 64.25\n",
            "Accuracy of DecisionTreeClassifier = 84.25\n",
            "Accuracy of RandomForestClassifier = 91.25\n",
            "Accuracy of SVC = 77.0\n",
            "Accuracy of KNeighborsClassifier = 80.0\n",
            "Accuracy of GaussianNB = 88.75\n"
          ]
        }
      ],
      "source": [
        "#code 3.6\n",
        "#code 3.6\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn import tree\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "model = [LogisticRegression(), tree.DecisionTreeClassifier(), RandomForestClassifier(max_depth=2, random_state=0), SVC(), KNeighborsClassifier(n_neighbors=3), GaussianNB()]\n",
        "for i in model:\n",
        "  cv = KFold(n_splits=10, shuffle=True)\n",
        "  accuracy_scores = cross_val_score(i, X, y, scoring='accuracy', cv=cv, n_jobs=-1)*100\n",
        "  p = str(i).split(\"(\")[0]\n",
        "  print(f\"Accuracy of {p} = {accuracy_scores.mean()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtHXnEQBhwfc"
      },
      "source": [
        "## Accuracy of SVM is only 77.25% after doing kfold cross validation but there are other classifiers that performs better than SVM."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrfkUbw1EvqS"
      },
      "source": [
        "**(3.7)** Applying Grid Search to find the best model and the best parameters (c and gamma of the svm)(8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dnktwK2RFRBM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08b1a56b-bd95-42ff-d735-f72bc590af90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=SVC(),\n",
              "             param_grid=[{'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
              "                          'gamma': [0.001, 0.01, 0.1, 1, 10, 100],\n",
              "                          'kernel': ['rbf', 'linear', 'sigmoid']}])"
            ]
          },
          "metadata": {},
          "execution_count": 434
        }
      ],
      "source": [
        "#code 3.7\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "parameters =[{'kernel': ['rbf', 'linear', 'sigmoid'],\n",
        "               'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
        "               'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}]\n",
        "GSCV = GridSearchCV(SVC(), parameters,cv=5, refit=True)\n",
        "GSCV.fit(X_scaled_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XZZrSlWglpAf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c136bce9-faf7-454e-a3e5-22faf4f2ab92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters are  {'C': 1, 'gamma': 1, 'kernel': 'rbf'}\n",
            "Accuracy = 90.3125\n"
          ]
        }
      ],
      "source": [
        "print(\"Best parameters are \",GSCV.best_params_)\n",
        "print(f\"Accuracy = {GSCV.best_score_*100}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training with the best Hyperparamters"
      ],
      "metadata": {
        "id": "XnTGp70NpdT7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "table(SVC(C = GSCV.best_params_['C'], gamma = GSCV.best_params_['gamma'], kernel = GSCV.best_params_['kernel']), X_scaled_train, X_scaled_test, y_train, y_test, 'with')"
      ],
      "metadata": {
        "id": "3RahYtuopP5A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edd911e8-a4ae-4cb0-be40-4dbeca283bcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "╒═══════════════╤═════════════════╤═════════════╕\n",
            "│               │   Not Purchased │   Purchased │\n",
            "╞═══════════════╪═════════════════╪═════════════╡\n",
            "│ Not Purchased │              48 │           4 │\n",
            "├───────────────┼─────────────────┼─────────────┤\n",
            "│ Purchased     │               0 │          28 │\n",
            "╘═══════════════╧═════════════════╧═════════════╛\n",
            "TP = 28, TN = 48, FP = 4, FN = 0\n",
            "Accuracy with Feature Scaling= 95.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "on9rZ2BQFPfU"
      },
      "source": [
        "**(3.8)**Visualising the Training set results(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S8w6SzgLFiDd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "0c7487f0-5937-4167-8e18-ff60d70a416c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualising the Training set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_scaled_train, y_train\n",
        "#meshgrid is to create a rectangular grid out of an array of x values and an array of y values.\n",
        "X_array, Y_array = np.meshgrid(np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01))\n",
        "svm = SVC(C = GSCV.best_params_['C'], gamma = GSCV.best_params_['gamma'], kernel = GSCV.best_params_['kernel']).fit(X_scaled_train, y_train)\n",
        "plt.contourf(X_array, Y_array, svm.predict(np.array([Y_array.ravel(), X_array.ravel()]).T).reshape(X_array.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('darkorange', 'white')))\n",
        "plt.xlim(X_array.min(), X_array.max())\n",
        "plt.ylim(Y_array.min(), Y_array.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 1], X_set[y_set == j, 0],\n",
        "                color = ListedColormap(('black', 'lawngreen'))(i), label = j)\n",
        "plt.title('SVM (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iI_yuaWnFcrF"
      },
      "source": [
        "**(3.9)**Visualising the Test set results(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f57e8nFeFkBT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a69bcf0d-ff9c-4096-84e9-cfa46ffbf2b0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#code3.9\n",
        "\n",
        "from matplotlib.colors import ListedColormap\n",
        "#code\n",
        "X_set, y_set = X_scaled_test, y_test\n",
        "X_array, Y_array = np.meshgrid(np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01),\n",
        "                               np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01))\n",
        "plt.contourf(X_array, Y_array, svm.predict(np.array([Y_array.ravel(), X_array.ravel()]).T).reshape(X_array.shape),alpha = 0.75, cmap = ListedColormap(('darkorange', 'white')))\n",
        "plt.xlim(X_array.min(), X_array.max())\n",
        "plt.ylim(Y_array.min(), Y_array.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                color = ListedColormap(('black', 'lawngreen'))(i), label = j)\n",
        "\n",
        "plt.title('Kernel SVM (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qaxqUlVA23HJ"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "m51CCwCV4_vY"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}