Predictive_Analytics_using_python (1).ipynb

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      "name": "Predictive Analytics using python.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tg1C6wjY6R0m"
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      "source": [
        "## **PREDICTIVE ANALYTICS USING PYTHON**\r\n",
        "#### Predictive Analytics is the type of Data Analaytics where we use the PAST DATA to  make predictions about the future happennings.\r\n",
        "#### It is based on HISTORICAL DATA and assumes a static business plan/model.\r\n",
        "#### Helps business decisions to be automated using algorithms.\r\n",
        "#### Predictive Analytics includes steps:\r\n",
        "#### 1.Data Exploration\r\n",
        "#### 2.Data cleaning\r\n",
        "#### 3.Data visualization\r\n",
        "#### 4.Data Modelling and Prediction\r\n",
        "#### 5.Performance Analysis(Accuracy Check)\r\n",
        "##### I have taken an employee report dataset which has details like employee id,gender, department,total working days, total working hours, yearly bonus,total leaves.Using the fields of the dataset, I will predict the salary of the employee using train-test-split method linear Regression model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RedOnwH197AC"
      },
      "source": [
        "## **DATA EXPLORATION**\r\n",
        "#### The first step is to explore the dataset which is to be used for Predictive Analytics.With help of data exploration we can explore the data and find about its shape(total number of rows and columns), total entries,columns & their datatypes and so on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fhd9Nd_v2DeT"
      },
      "source": [
        "# importing all the required libraries and modules.\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "%matplotlib inline\r\n",
        "from sklearn import linear_model\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn import metrics"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
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        "id": "JpvtcJno5tJ8",
        "outputId": "48334a66-9a67-497d-e5f4-1e72965d7991"
      },
      "source": [
        "#Reading data using read_csv  \r\n",
        "data=pd.read_csv('/content/emp_report.csv')\r\n",
        "data"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>female</td>\n",
              "      <td>management</td>\n",
              "      <td>%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20</td>\n",
              "      <td>13900</td>\n",
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              "      <td>1192</td>\n",
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              "      <td>367200</td>\n",
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              "      <td>16160</td>\n",
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              "      <td>107</td>\n",
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              "      <td>1368</td>\n",
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              "      <td>##</td>\n",
              "      <td>1456</td>\n",
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              "      <td>14620</td>\n",
              "      <td>407720</td>\n",
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              "      <td>201</td>\n",
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              "      <td>marketing</td>\n",
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              "      <td>1496</td>\n",
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              "      <td>16880</td>\n",
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              "      <td>423180</td>\n",
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              "      <td>203</td>\n",
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              "      <td>##</td>\n",
              "      <td>16</td>\n",
              "      <td>15900</td>\n",
              "      <td>412440</td>\n",
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              "      <td>1408</td>\n",
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              "      <td>207</td>\n",
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              "      <td>181</td>\n",
              "      <td>1448</td>\n",
              "      <td>19</td>\n",
              "      <td>14330</td>\n",
              "      <td>405260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>179</td>\n",
              "      <td>1432</td>\n",
              "      <td>21</td>\n",
              "      <td>13710</td>\n",
              "      <td>400340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166</td>\n",
              "      <td>1328</td>\n",
              "      <td>34</td>\n",
              "      <td>9120</td>\n",
              "      <td>367660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186</td>\n",
              "      <td>1488</td>\n",
              "      <td>14</td>\n",
              "      <td>16120</td>\n",
              "      <td>##</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165</td>\n",
              "      <td>1320</td>\n",
              "      <td>#</td>\n",
              "      <td>8810</td>\n",
              "      <td>365200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189</td>\n",
              "      <td>1512</td>\n",
              "      <td>11</td>\n",
              "      <td>17600</td>\n",
              "      <td>425840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150</td>\n",
              "      <td>1200</td>\n",
              "      <td>50</td>\n",
              "      <td>5260</td>\n",
              "      <td>329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender    Emp_dept  ... Emp_yearly_leaves Emp_year_bonus Emp_salary\n",
              "0      101     female  management  ...                20          13900     402700\n",
              "1      102     female       sales  ...                 #           6900     352500\n",
              "2      103       male   technical  ...                51           5160     326940\n",
              "3      104     female    accounts  ...                42           6300     347580\n",
              "4      105       male   technical  ...                30             aa     367200\n",
              "5      106       male  management  ...                14          16160     417860\n",
              "6      107       male   technical  ...                32           6150     369030\n",
              "7      108       male   marketing  ...                29           9800     379160\n",
              "8      109       male   technical  ...                18          14620     407720\n",
              "9      201     female   marketing  ...                13          16880     420720\n",
              "10     202       male  management  ...                12              #     423180\n",
              "11     203       male    accounts  ...                47           5580     335980\n",
              "12     204       male   technical  ...                16          15900     412440\n",
              "13     205       male  management  ...                33           9570          %\n",
              "14     206     female   technical  ...                aa          12800     392960\n",
              "15     207     female       sales  ...                19          14330     405260\n",
              "16     208       male    accounts  ...                21          13710     400340\n",
              "17     209       male   marketing  ...                34           9120     367660\n",
              "18     301       male       sales  ...                14          16120         ##\n",
              "19     302       male   marketing  ...                 #           8810     365200\n",
              "20     303     female   technical  ...                11          17600     425840\n",
              "21     304       male       sales  ...                50           5260     329200\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "lhYF82wt553u",
        "outputId": "6b57b994-2941-4523-afe7-e55a571049e7"
      },
      "source": [
        "#To display first 5 rows of the dataset\r\n",
        "data.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>female</td>\n",
              "      <td>management</td>\n",
              "      <td>%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20</td>\n",
              "      <td>13900</td>\n",
              "      <td>402700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>160</td>\n",
              "      <td>1280</td>\n",
              "      <td>#</td>\n",
              "      <td>6900</td>\n",
              "      <td>352500</td>\n",
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              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>149</td>\n",
              "      <td>1192</td>\n",
              "      <td>51</td>\n",
              "      <td>5160</td>\n",
              "      <td>326940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>female</td>\n",
              "      <td>accounts</td>\n",
              "      <td>158</td>\n",
              "      <td>1264</td>\n",
              "      <td>42</td>\n",
              "      <td>6300</td>\n",
              "      <td>347580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170</td>\n",
              "      <td>1290</td>\n",
              "      <td>30</td>\n",
              "      <td>aa</td>\n",
              "      <td>367200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Emp_id Emp_gender    Emp_dept  ... Emp_yearly_leaves Emp_year_bonus Emp_salary\n",
              "0     101     female  management  ...                20          13900     402700\n",
              "1     102     female       sales  ...                 #           6900     352500\n",
              "2     103       male   technical  ...                51           5160     326940\n",
              "3     104     female    accounts  ...                42           6300     347580\n",
              "4     105       male   technical  ...                30             aa     367200\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "6IAyiFnw59Ti",
        "outputId": "6eeff768-3dee-44c7-e020-507a095461f6"
      },
      "source": [
        "#To display last 5 rows of the dataset\r\n",
        "data.tail()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166</td>\n",
              "      <td>1328</td>\n",
              "      <td>34</td>\n",
              "      <td>9120</td>\n",
              "      <td>367660</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186</td>\n",
              "      <td>1488</td>\n",
              "      <td>14</td>\n",
              "      <td>16120</td>\n",
              "      <td>##</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165</td>\n",
              "      <td>1320</td>\n",
              "      <td>#</td>\n",
              "      <td>8810</td>\n",
              "      <td>365200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189</td>\n",
              "      <td>1512</td>\n",
              "      <td>11</td>\n",
              "      <td>17600</td>\n",
              "      <td>425840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150</td>\n",
              "      <td>1200</td>\n",
              "      <td>50</td>\n",
              "      <td>5260</td>\n",
              "      <td>329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender   Emp_dept  ... Emp_yearly_leaves Emp_year_bonus Emp_salary\n",
              "17     209       male  marketing  ...                34           9120     367660\n",
              "18     301       male      sales  ...                14          16120         ##\n",
              "19     302       male  marketing  ...                 #           8810     365200\n",
              "20     303     female  technical  ...                11          17600     425840\n",
              "21     304       male      sales  ...                50           5260     329200\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B12EwCruNiOl",
        "outputId": "02a7ca92-6db0-47e2-c50d-fb91dbe626d3"
      },
      "source": [
        "#to display some basic information about the dataset such as memoray usage, columns and their datatypes,entries etc.\r\n",
        "data.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 22 entries, 0 to 21\n",
            "Data columns (total 8 columns):\n",
            " #   Column                   Non-Null Count  Dtype \n",
            "---  ------                   --------------  ----- \n",
            " 0   Emp_id                   22 non-null     int64 \n",
            " 1   Emp_gender               22 non-null     object\n",
            " 2   Emp_dept                 22 non-null     object\n",
            " 3   Emp_total_working_days   22 non-null     object\n",
            " 4   Emp_total_working_hours  21 non-null     object\n",
            " 5   Emp_yearly_leaves        22 non-null     object\n",
            " 6   Emp_year_bonus           22 non-null     object\n",
            " 7   Emp_salary               22 non-null     object\n",
            "dtypes: int64(1), object(7)\n",
            "memory usage: 1.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gapW7I506A3d",
        "outputId": "0438eb11-6bbd-4516-a7f9-2b061fbd58ac"
      },
      "source": [
        "#To display Column names of the dataset.\r\n",
        "data.columns"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Emp_id', 'Emp_gender', 'Emp_dept', 'Emp_total_working_days',\n",
              "       'Emp_total_working_hours', 'Emp_yearly_leaves', 'Emp_year_bonus',\n",
              "       'Emp_salary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGV7FE1R6Gr2",
        "outputId": "e27176d9-1fa9-4ab9-f80a-cbc37ad29878"
      },
      "source": [
        "#Lets see the shape of the dataset that is total  number of rows and columns. \r\n",
        "data.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prUun4K9qJIZ",
        "outputId": "3a9f2520-f886-4a0a-caee-84df2d3d90db"
      },
      "source": [
        "data.count()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Emp_id                     22\n",
              "Emp_gender                 22\n",
              "Emp_dept                   22\n",
              "Emp_total_working_days     22\n",
              "Emp_total_working_hours    21\n",
              "Emp_yearly_leaves          22\n",
              "Emp_year_bonus             22\n",
              "Emp_salary                 22\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsNUQHTOp0Kr",
        "outputId": "6ea22315-4c19-4e9d-d9e9-35b5ce5190d1"
      },
      "source": [
        "\r\n",
        "data[\"Emp_gender\"].value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      15\n",
              "female     7\n",
              "Name: Emp_gender, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etgCThhIkqyd"
      },
      "source": [
        "#### The above output tells us that 15 employees are male and 7 employees are female."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HosI5Pwbpss7",
        "outputId": "fdf6d9c9-4fc3-40cf-98e2-e36015390e70"
      },
      "source": [
        "data['Emp_dept'].unique()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['management', 'sales', 'technical', 'accounts', 'marketing'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEQUjlg3leu_"
      },
      "source": [
        "#### The above output tells us that there are 5 unique employee departments as management, sales, technical,accounts and marketing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDnSdSU8uNCQ",
        "outputId": "1abce77d-b584-43d9-a851-356b50bc6b9f"
      },
      "source": [
        "data[\"Emp_dept\"].value_counts()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "technical     7\n",
              "marketing     4\n",
              "management    4\n",
              "sales         4\n",
              "accounts      3\n",
              "Name: Emp_dept, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzdciTBAk6wH"
      },
      "source": [
        "#### The above output tells us that there are 7 employees in technical department, 4 employees each in marketing, sales and management and 3 employees in accounts department."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SncfTRHRpuVr",
        "outputId": "d1c794a0-b4ae-4ba9-9203-4efdfe05afd5"
      },
      "source": [
        "data['Emp_yearly_leaves'].unique()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['20', '#', '51', '42', '30', '14', '32', '29', '18', '13', '12',\n",
              "       '47', '16', '33', 'aa', '19', '21', '34', '11', '50'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmgObn6dudVz",
        "outputId": "74c42bb8-87e3-4517-ddaa-93377ff1d046"
      },
      "source": [
        "data['Emp_year_bonus'].unique()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['13900', '6900', '5160', '6300', 'aa', '16160', '6150', '9800',\n",
              "       '14620', '16880', '#', '5580', '15900', '9570', '12800', '14330',\n",
              "       '13710', '9120', '16120', '8810', '17600', '5260'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTm06NKemAyw"
      },
      "source": [
        "#### In the last two outputs we can see that symbols such as #,aa are included in unique data values of the columns which are inappropraite values.We have to either remove them or replace them by central tendencies. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "CiHnRKha6J0v",
        "outputId": "ffe2abd5-3b7b-4a7a-c964-9d46d552b643"
      },
      "source": [
        "#To display the statistical summary of numerical columns we have describe function.\r\n",
        "data.describe()\r\n",
        " "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>181.818182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>74.427105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>101.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>106.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>202.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>207.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>304.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Emp_id\n",
              "count   22.000000\n",
              "mean   181.818182\n",
              "std     74.427105\n",
              "min    101.000000\n",
              "25%    106.250000\n",
              "50%    202.500000\n",
              "75%    207.750000\n",
              "max    304.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rngpsGNCBWLd"
      },
      "source": [
        "#### In the above output, statistical summary is shown only for Emp_id column whereas it dosen't show that for Emp_total_working_days,Emp_total_working_hours,Emp_yearly_leaves,Emp_year_bonus and Emp_salary.\r\n",
        "#### This simply means that these columns have some missing data or inappropraite data which has to be cleaned before moving to prediction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26yuIiXGmfBO"
      },
      "source": [
        "## **DATA CLEANING**\r\n",
        "#### In data exploration, we have explored the data that we will be using for prediction.We have seen that there are some inappropriate values in the data that has to be cleaned.Therefore, we will clean the data before prediction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "FHAn7ay06QPy",
        "outputId": "bb3486c4-ade7-4e71-a992-f694c2f0a225"
      },
      "source": [
        "#Lets now calculate the null values using isnull function.\r\n",
        "data.isnull()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id  Emp_gender  Emp_dept  ...  Emp_yearly_leaves  Emp_year_bonus  Emp_salary\n",
              "0    False       False     False  ...              False           False       False\n",
              "1    False       False     False  ...              False           False       False\n",
              "2    False       False     False  ...              False           False       False\n",
              "3    False       False     False  ...              False           False       False\n",
              "4    False       False     False  ...              False           False       False\n",
              "5    False       False     False  ...              False           False       False\n",
              "6    False       False     False  ...              False           False       False\n",
              "7    False       False     False  ...              False           False       False\n",
              "8    False       False     False  ...              False           False       False\n",
              "9    False       False     False  ...              False           False       False\n",
              "10   False       False     False  ...              False           False       False\n",
              "11   False       False     False  ...              False           False       False\n",
              "12   False       False     False  ...              False           False       False\n",
              "13   False       False     False  ...              False           False       False\n",
              "14   False       False     False  ...              False           False       False\n",
              "15   False       False     False  ...              False           False       False\n",
              "16   False       False     False  ...              False           False       False\n",
              "17   False       False     False  ...              False           False       False\n",
              "18   False       False     False  ...              False           False       False\n",
              "19   False       False     False  ...              False           False       False\n",
              "20   False       False     False  ...              False           False       False\n",
              "21   False       False     False  ...              False           False       False\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQs8VOpq4ocC"
      },
      "source": [
        "#### In the above output NaN values are TRUE whereas symbols like #, ##,%, aa are False. This means that we have to convert these symbols into NaN(Not a number) and then replace or remove them to make sure our data is cleaned and proper for prediction. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9KQ780L6d3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "outputId": "5aa6a584-b08f-4fc4-b358-dca88e293e93"
      },
      "source": [
        "#By taking symbols #, ##,%,aa into a list and replacing them by NaN.\r\n",
        "missing_value_list=['#','##','%','aa']\r\n",
        "data=pd.read_csv('/content/emp_report.csv',na_values=missing_value_list)\r\n",
        "data"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>female</td>\n",
              "      <td>management</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>13900.0</td>\n",
              "      <td>402700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6900.0</td>\n",
              "      <td>352500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5160.0</td>\n",
              "      <td>326940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>female</td>\n",
              "      <td>accounts</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6300.0</td>\n",
              "      <td>347580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>367200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>106</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>186.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
              "      <td>417860.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>107</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
              "      <td>369030.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>108</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9800.0</td>\n",
              "      <td>379160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>407720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>female</td>\n",
              "      <td>marketing</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>420720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>423180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>335980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>184.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>412440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12800.0</td>\n",
              "      <td>392960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14330.0</td>\n",
              "      <td>405260.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13710.0</td>\n",
              "      <td>400340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>367660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>365200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>425840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>329200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender  ... Emp_year_bonus  Emp_salary\n",
              "0      101     female  ...        13900.0    402700.0\n",
              "1      102     female  ...         6900.0    352500.0\n",
              "2      103       male  ...         5160.0    326940.0\n",
              "3      104     female  ...         6300.0    347580.0\n",
              "4      105       male  ...            NaN    367200.0\n",
              "5      106       male  ...        16160.0    417860.0\n",
              "6      107       male  ...         6150.0    369030.0\n",
              "7      108       male  ...         9800.0    379160.0\n",
              "8      109       male  ...        14620.0    407720.0\n",
              "9      201     female  ...        16880.0    420720.0\n",
              "10     202       male  ...            NaN    423180.0\n",
              "11     203       male  ...         5580.0    335980.0\n",
              "12     204       male  ...        15900.0    412440.0\n",
              "13     205       male  ...         9570.0         NaN\n",
              "14     206     female  ...        12800.0    392960.0\n",
              "15     207     female  ...        14330.0    405260.0\n",
              "16     208       male  ...        13710.0    400340.0\n",
              "17     209       male  ...         9120.0    367660.0\n",
              "18     301       male  ...        16120.0         NaN\n",
              "19     302       male  ...         8810.0    365200.0\n",
              "20     303     female  ...        17600.0    425840.0\n",
              "21     304       male  ...         5260.0    329200.0\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "o3iUeLyQ610n",
        "outputId": "a1de5b08-7d40-4a64-cf98-c048e1aba90e"
      },
      "source": [
        "#Again check null values using isnull.\r\n",
        "data.isnull()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
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              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>False</td>\n",
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              "      <th>6</th>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id  Emp_gender  Emp_dept  ...  Emp_yearly_leaves  Emp_year_bonus  Emp_salary\n",
              "0    False       False     False  ...              False           False       False\n",
              "1    False       False     False  ...               True           False       False\n",
              "2    False       False     False  ...              False           False       False\n",
              "3    False       False     False  ...              False           False       False\n",
              "4    False       False     False  ...              False            True       False\n",
              "5    False       False     False  ...              False           False       False\n",
              "6    False       False     False  ...              False           False       False\n",
              "7    False       False     False  ...              False           False       False\n",
              "8    False       False     False  ...              False           False       False\n",
              "9    False       False     False  ...              False           False       False\n",
              "10   False       False     False  ...              False            True       False\n",
              "11   False       False     False  ...              False           False       False\n",
              "12   False       False     False  ...              False           False       False\n",
              "13   False       False     False  ...              False           False        True\n",
              "14   False       False     False  ...               True           False       False\n",
              "15   False       False     False  ...              False           False       False\n",
              "16   False       False     False  ...              False           False       False\n",
              "17   False       False     False  ...              False           False       False\n",
              "18   False       False     False  ...              False           False        True\n",
              "19   False       False     False  ...               True           False       False\n",
              "20   False       False     False  ...              False           False       False\n",
              "21   False       False     False  ...              False           False       False\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3s3Lcowxhrwu",
        "outputId": "612fc534-3091-4d1e-d5eb-897a56a9f6f2"
      },
      "source": [
        "#Now, Lets check for null values in the columns.\r\n",
        "data.isnull().sum()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Emp_id                     0\n",
              "Emp_gender                 0\n",
              "Emp_dept                   0\n",
              "Emp_total_working_days     2\n",
              "Emp_total_working_hours    3\n",
              "Emp_yearly_leaves          3\n",
              "Emp_year_bonus             2\n",
              "Emp_salary                 2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUpqs8-e58IR"
      },
      "source": [
        "#### The above output shows that colmns Emp_total_working_days, Emp_total_working_hours,Emp_yearly_leaves,Emp_year_bonus and Emp_salary has 2,3,3,2,2 NaN values or improper values each.\r\n",
        "#### Before moving to prediction, we have to clean the data and make sure it is proper to make accurate prediction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "geqE8Q4k65Fn",
        "outputId": "0cb3d280-4564-4f73-fbff-563bd11ee2a7"
      },
      "source": [
        "data.isnull().sum().sum()\r\n",
        "#total number of missing values"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CW3TEn9f6zbZ"
      },
      "source": [
        "#### There are total 12 missing values in the dataset.\r\n",
        "#### We have to clean the data because these inappropraite values might dsiturb our prediction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JaBTEfkiNIp",
        "outputId": "7859791e-520f-4e45-89ab-5cc585ac25f9"
      },
      "source": [
        "data['Emp_total_working_hours'].isnull().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAOdmEl_7JXC",
        "outputId": "1bbad85d-8add-42f9-bbad-faa36a6e84ad"
      },
      "source": [
        "data['Emp_total_working_hours'].median()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1344.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrokngJ87Nmq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "outputId": "663090f9-fe7c-47ad-8bd3-c1be7b4209dc"
      },
      "source": [
        "#replacing missing values of column 'Emp_total_working_hours' with its median value. \r\n",
        "median_value=data['Emp_total_working_hours'].median()\r\n",
        "data['Emp_total_working_hours'].fillna(median_value,inplace=True)\r\n",
        "data"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>female</td>\n",
              "      <td>management</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>13900.0</td>\n",
              "      <td>402700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6900.0</td>\n",
              "      <td>352500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5160.0</td>\n",
              "      <td>326940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>female</td>\n",
              "      <td>accounts</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6300.0</td>\n",
              "      <td>347580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>367200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>106</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
              "      <td>417860.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>107</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
              "      <td>369030.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>108</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9800.0</td>\n",
              "      <td>379160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>407720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>female</td>\n",
              "      <td>marketing</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>420720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>423180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>335980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>184.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>412440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12800.0</td>\n",
              "      <td>392960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14330.0</td>\n",
              "      <td>405260.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13710.0</td>\n",
              "      <td>400340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>367660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>365200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>425840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>329200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender  ... Emp_year_bonus  Emp_salary\n",
              "0      101     female  ...        13900.0    402700.0\n",
              "1      102     female  ...         6900.0    352500.0\n",
              "2      103       male  ...         5160.0    326940.0\n",
              "3      104     female  ...         6300.0    347580.0\n",
              "4      105       male  ...            NaN    367200.0\n",
              "5      106       male  ...        16160.0    417860.0\n",
              "6      107       male  ...         6150.0    369030.0\n",
              "7      108       male  ...         9800.0    379160.0\n",
              "8      109       male  ...        14620.0    407720.0\n",
              "9      201     female  ...        16880.0    420720.0\n",
              "10     202       male  ...            NaN    423180.0\n",
              "11     203       male  ...         5580.0    335980.0\n",
              "12     204       male  ...        15900.0    412440.0\n",
              "13     205       male  ...         9570.0         NaN\n",
              "14     206     female  ...        12800.0    392960.0\n",
              "15     207     female  ...        14330.0    405260.0\n",
              "16     208       male  ...        13710.0    400340.0\n",
              "17     209       male  ...         9120.0    367660.0\n",
              "18     301       male  ...        16120.0         NaN\n",
              "19     302       male  ...         8810.0    365200.0\n",
              "20     303     female  ...        17600.0    425840.0\n",
              "21     304       male  ...         5260.0    329200.0\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7woLJLBiByF",
        "outputId": "77f17c8b-9dfc-4b6e-a09b-714e956c46ad"
      },
      "source": [
        "data['Emp_total_working_hours'].isnull().sum()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMlZmTyUnuWX"
      },
      "source": [
        "#### All the missing values of Emp_total_working_hours column have been replaced."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_81x2YOiaB7",
        "outputId": "bc7ae77b-3e5f-4afc-f45f-8c9ab6165654"
      },
      "source": [
        "data['Emp_total_working_days'].isnull().sum()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhTb9V85ih_-",
        "outputId": "a08c0af6-fc72-43dc-ed70-bced2c8f0524"
      },
      "source": [
        "data['Emp_total_working_hours'].median()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1344.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "ykSIF8CrirHu",
        "outputId": "dc075fea-a3ae-4b03-c9d4-a61755d56b40"
      },
      "source": [
        "#replace missing values of column 'Student_sub1' with its median value. \r\n",
        "median_value=data['Emp_total_working_days'].median()\r\n",
        "data['Emp_total_working_days'].fillna(median_value,inplace=True)\r\n",
        "data"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>female</td>\n",
              "      <td>management</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>13900.0</td>\n",
              "      <td>402700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6900.0</td>\n",
              "      <td>352500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5160.0</td>\n",
              "      <td>326940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>female</td>\n",
              "      <td>accounts</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6300.0</td>\n",
              "      <td>347580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>367200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>106</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
              "      <td>417860.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>107</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
              "      <td>369030.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>108</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9800.0</td>\n",
              "      <td>379160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>407720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>female</td>\n",
              "      <td>marketing</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>420720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>423180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>335980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>184.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>412440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12800.0</td>\n",
              "      <td>392960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14330.0</td>\n",
              "      <td>405260.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13710.0</td>\n",
              "      <td>400340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>367660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>365200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>425840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>329200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender  ... Emp_year_bonus  Emp_salary\n",
              "0      101     female  ...        13900.0    402700.0\n",
              "1      102     female  ...         6900.0    352500.0\n",
              "2      103       male  ...         5160.0    326940.0\n",
              "3      104     female  ...         6300.0    347580.0\n",
              "4      105       male  ...            NaN    367200.0\n",
              "5      106       male  ...        16160.0    417860.0\n",
              "6      107       male  ...         6150.0    369030.0\n",
              "7      108       male  ...         9800.0    379160.0\n",
              "8      109       male  ...        14620.0    407720.0\n",
              "9      201     female  ...        16880.0    420720.0\n",
              "10     202       male  ...            NaN    423180.0\n",
              "11     203       male  ...         5580.0    335980.0\n",
              "12     204       male  ...        15900.0    412440.0\n",
              "13     205       male  ...         9570.0         NaN\n",
              "14     206     female  ...        12800.0    392960.0\n",
              "15     207     female  ...        14330.0    405260.0\n",
              "16     208       male  ...        13710.0    400340.0\n",
              "17     209       male  ...         9120.0    367660.0\n",
              "18     301       male  ...        16120.0         NaN\n",
              "19     302       male  ...         8810.0    365200.0\n",
              "20     303     female  ...        17600.0    425840.0\n",
              "21     304       male  ...         5260.0    329200.0\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iG5n-oHgi7bF",
        "outputId": "7c9a7c32-9a49-4a83-b30a-9bf6cc6a48a1"
      },
      "source": [
        "data['Emp_total_working_days'].isnull().sum()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFVnf-6PoA4D"
      },
      "source": [
        "#### The above output tells us that all the missing values of Emp_total_working_days column have been replaced. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYx8LggijDvR",
        "outputId": "8ca1f3f6-8e8a-4317-f9e7-51d00c4aaf3a"
      },
      "source": [
        "data['Emp_yearly_leaves'].isnull().sum()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "uPgzS81mjDmu",
        "outputId": "c0d1a8cd-984e-4c44-d9e5-bfd663249b49"
      },
      "source": [
        "median_value=data['Emp_yearly_leaves'].median()\r\n",
        "data['Emp_yearly_leaves'].fillna(median_value,inplace=True)\r\n",
        "data"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>female</td>\n",
              "      <td>management</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>13900.0</td>\n",
              "      <td>402700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6900.0</td>\n",
              "      <td>352500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5160.0</td>\n",
              "      <td>326940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>female</td>\n",
              "      <td>accounts</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6300.0</td>\n",
              "      <td>347580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>367200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>106</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
              "      <td>417860.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>107</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
              "      <td>369030.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>108</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9800.0</td>\n",
              "      <td>379160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>407720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>female</td>\n",
              "      <td>marketing</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>420720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>423180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>335980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>184.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>412440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12800.0</td>\n",
              "      <td>392960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14330.0</td>\n",
              "      <td>405260.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13710.0</td>\n",
              "      <td>400340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>367660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>365200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>425840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>329200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender  ... Emp_year_bonus  Emp_salary\n",
              "0      101     female  ...        13900.0    402700.0\n",
              "1      102     female  ...         6900.0    352500.0\n",
              "2      103       male  ...         5160.0    326940.0\n",
              "3      104     female  ...         6300.0    347580.0\n",
              "4      105       male  ...            NaN    367200.0\n",
              "5      106       male  ...        16160.0    417860.0\n",
              "6      107       male  ...         6150.0    369030.0\n",
              "7      108       male  ...         9800.0    379160.0\n",
              "8      109       male  ...        14620.0    407720.0\n",
              "9      201     female  ...        16880.0    420720.0\n",
              "10     202       male  ...            NaN    423180.0\n",
              "11     203       male  ...         5580.0    335980.0\n",
              "12     204       male  ...        15900.0    412440.0\n",
              "13     205       male  ...         9570.0         NaN\n",
              "14     206     female  ...        12800.0    392960.0\n",
              "15     207     female  ...        14330.0    405260.0\n",
              "16     208       male  ...        13710.0    400340.0\n",
              "17     209       male  ...         9120.0    367660.0\n",
              "18     301       male  ...        16120.0         NaN\n",
              "19     302       male  ...         8810.0    365200.0\n",
              "20     303     female  ...        17600.0    425840.0\n",
              "21     304       male  ...         5260.0    329200.0\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukw-v59CjDXr",
        "outputId": "432c9ec2-9f7d-4fcd-a6b5-07fdc0172b63"
      },
      "source": [
        "data['Emp_year_bonus'].isnull().sum()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "4QNjZKUbjDEc",
        "outputId": "a697e9f3-4f5f-4153-e427-aa433b4d6afd"
      },
      "source": [
        "median_value=data['Emp_year_bonus'].median()\r\n",
        "data['Emp_year_bonus'].fillna(median_value,inplace=True)\r\n",
        "data"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>female</td>\n",
              "      <td>management</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>13900.0</td>\n",
              "      <td>402700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6900.0</td>\n",
              "      <td>352500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5160.0</td>\n",
              "      <td>326940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>female</td>\n",
              "      <td>accounts</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6300.0</td>\n",
              "      <td>347580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11300.0</td>\n",
              "      <td>367200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>106</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
              "      <td>417860.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>107</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
              "      <td>369030.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>108</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9800.0</td>\n",
              "      <td>379160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>407720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>female</td>\n",
              "      <td>marketing</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>420720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11300.0</td>\n",
              "      <td>423180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>335980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>184.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>412440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12800.0</td>\n",
              "      <td>392960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14330.0</td>\n",
              "      <td>405260.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13710.0</td>\n",
              "      <td>400340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>367660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>365200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>425840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>329200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender  ... Emp_year_bonus  Emp_salary\n",
              "0      101     female  ...        13900.0    402700.0\n",
              "1      102     female  ...         6900.0    352500.0\n",
              "2      103       male  ...         5160.0    326940.0\n",
              "3      104     female  ...         6300.0    347580.0\n",
              "4      105       male  ...        11300.0    367200.0\n",
              "5      106       male  ...        16160.0    417860.0\n",
              "6      107       male  ...         6150.0    369030.0\n",
              "7      108       male  ...         9800.0    379160.0\n",
              "8      109       male  ...        14620.0    407720.0\n",
              "9      201     female  ...        16880.0    420720.0\n",
              "10     202       male  ...        11300.0    423180.0\n",
              "11     203       male  ...         5580.0    335980.0\n",
              "12     204       male  ...        15900.0    412440.0\n",
              "13     205       male  ...         9570.0         NaN\n",
              "14     206     female  ...        12800.0    392960.0\n",
              "15     207     female  ...        14330.0    405260.0\n",
              "16     208       male  ...        13710.0    400340.0\n",
              "17     209       male  ...         9120.0    367660.0\n",
              "18     301       male  ...        16120.0         NaN\n",
              "19     302       male  ...         8810.0    365200.0\n",
              "20     303     female  ...        17600.0    425840.0\n",
              "21     304       male  ...         5260.0    329200.0\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0JzB8vZjkoq",
        "outputId": "7600ad09-fde1-4058-8aa4-2d3ddb2e445f"
      },
      "source": [
        "data['Emp_salary'].isnull().sum()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXi_1dfcd_gM",
        "outputId": "d5fcaacc-1522-436f-d3dc-60678727e513"
      },
      "source": [
        "data['Emp_salary'].median()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "386060.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "XIfaRZeWjkim",
        "outputId": "2d7dda52-d1a1-4f75-b165-909873c394be"
      },
      "source": [
        "median_value=data['Emp_salary'].median()\r\n",
        "data['Emp_salary'].fillna(median_value,inplace=True)\r\n",
        "data"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_gender</th>\n",
              "      <th>Emp_dept</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>female</td>\n",
              "      <td>management</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>13900.0</td>\n",
              "      <td>402700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6900.0</td>\n",
              "      <td>352500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5160.0</td>\n",
              "      <td>326940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>female</td>\n",
              "      <td>accounts</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6300.0</td>\n",
              "      <td>347580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11300.0</td>\n",
              "      <td>367200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>106</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
              "      <td>417860.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>107</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
              "      <td>369030.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>108</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9800.0</td>\n",
              "      <td>379160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>407720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>female</td>\n",
              "      <td>marketing</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>420720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11300.0</td>\n",
              "      <td>423180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>335980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>184.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>412440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>386060.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12800.0</td>\n",
              "      <td>392960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14330.0</td>\n",
              "      <td>405260.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13710.0</td>\n",
              "      <td>400340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>367660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>386060.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>365200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>425840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>329200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender  ... Emp_year_bonus  Emp_salary\n",
              "0      101     female  ...        13900.0    402700.0\n",
              "1      102     female  ...         6900.0    352500.0\n",
              "2      103       male  ...         5160.0    326940.0\n",
              "3      104     female  ...         6300.0    347580.0\n",
              "4      105       male  ...        11300.0    367200.0\n",
              "5      106       male  ...        16160.0    417860.0\n",
              "6      107       male  ...         6150.0    369030.0\n",
              "7      108       male  ...         9800.0    379160.0\n",
              "8      109       male  ...        14620.0    407720.0\n",
              "9      201     female  ...        16880.0    420720.0\n",
              "10     202       male  ...        11300.0    423180.0\n",
              "11     203       male  ...         5580.0    335980.0\n",
              "12     204       male  ...        15900.0    412440.0\n",
              "13     205       male  ...         9570.0    386060.0\n",
              "14     206     female  ...        12800.0    392960.0\n",
              "15     207     female  ...        14330.0    405260.0\n",
              "16     208       male  ...        13710.0    400340.0\n",
              "17     209       male  ...         9120.0    367660.0\n",
              "18     301       male  ...        16120.0    386060.0\n",
              "19     302       male  ...         8810.0    365200.0\n",
              "20     303     female  ...        17600.0    425840.0\n",
              "21     304       male  ...         5260.0    329200.0\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06NcDTmrjkdf",
        "outputId": "af205d62-343d-4f59-fb12-2730fd42f8f7"
      },
      "source": [
        "data['Emp_salary'].isnull().sum()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCyFcuy-oSr9"
      },
      "source": [
        "#### After replacing missing values in the columns by their respective median, \r\n",
        " ####  lets now check for total missing values in our dataset.to ensure that there are no missing values missed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItWlmnlL7SFv",
        "outputId": "3df2184d-164d-460b-9978-d4f6f50d14cb"
      },
      "source": [
        "data.isnull().sum()\r\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Emp_id                     0\n",
              "Emp_gender                 0\n",
              "Emp_dept                   0\n",
              "Emp_total_working_days     0\n",
              "Emp_total_working_hours    0\n",
              "Emp_yearly_leaves          0\n",
              "Emp_year_bonus             0\n",
              "Emp_salary                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glgNQ0Cuo7fW"
      },
      "source": [
        "#### The above output shows that the data is clean for prediction as there are no missing values in it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fY_BLqIHpIyD"
      },
      "source": [
        "#### Lets check for statistical summary for all the numerical columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "-MTcsuce7aV4",
        "outputId": "c8737b23-71e6-4fdd-8f96-a87824be13f1"
      },
      "source": [
        "data.describe()\r\n",
        "#describe function after data cleaning.\r\n",
        "#all numerical columns are shown below."
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>181.818182</td>\n",
              "      <td>171.545455</td>\n",
              "      <td>1360.090909</td>\n",
              "      <td>25.863636</td>\n",
              "      <td>11239.545455</td>\n",
              "      <td>382799.545455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>74.427105</td>\n",
              "      <td>12.529878</td>\n",
              "      <td>97.661178</td>\n",
              "      <td>12.532987</td>\n",
              "      <td>4184.762014</td>\n",
              "      <td>31099.991192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>101.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>1192.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>5160.000000</td>\n",
              "      <td>326940.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>106.250000</td>\n",
              "      <td>165.250000</td>\n",
              "      <td>1297.500000</td>\n",
              "      <td>16.500000</td>\n",
              "      <td>7377.500000</td>\n",
              "      <td>365700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>202.500000</td>\n",
              "      <td>170.500000</td>\n",
              "      <td>1344.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>11300.000000</td>\n",
              "      <td>386060.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>207.750000</td>\n",
              "      <td>183.250000</td>\n",
              "      <td>1444.000000</td>\n",
              "      <td>32.750000</td>\n",
              "      <td>14547.500000</td>\n",
              "      <td>407105.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>304.000000</td>\n",
              "      <td>189.000000</td>\n",
              "      <td>1512.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>17600.000000</td>\n",
              "      <td>425840.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Emp_id  Emp_total_working_days  ...  Emp_year_bonus     Emp_salary\n",
              "count   22.000000               22.000000  ...       22.000000      22.000000\n",
              "mean   181.818182              171.545455  ...    11239.545455  382799.545455\n",
              "std     74.427105               12.529878  ...     4184.762014   31099.991192\n",
              "min    101.000000              149.000000  ...     5160.000000  326940.000000\n",
              "25%    106.250000              165.250000  ...     7377.500000  365700.000000\n",
              "50%    202.500000              170.500000  ...    11300.000000  386060.000000\n",
              "75%    207.750000              183.250000  ...    14547.500000  407105.000000\n",
              "max    304.000000              189.000000  ...    17600.000000  425840.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oh7cdnddpUrV"
      },
      "source": [
        "#### Handling categorical data/column "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "WWER-Bu57eKd",
        "outputId": "d5d88956-07d8-49cd-944b-402f0a9849ae"
      },
      "source": [
        "dummies=pd.get_dummies(data.Emp_dept)\r\n",
        "dummies\r\n",
        "#convert categorical into numerical data."
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    accounts  management  marketing  sales  technical\n",
              "0          0           1          0      0          0\n",
              "1          0           0          0      1          0\n",
              "2          0           0          0      0          1\n",
              "3          1           0          0      0          0\n",
              "4          0           0          0      0          1\n",
              "5          0           1          0      0          0\n",
              "6          0           0          0      0          1\n",
              "7          0           0          1      0          0\n",
              "8          0           0          0      0          1\n",
              "9          0           0          1      0          0\n",
              "10         0           1          0      0          0\n",
              "11         1           0          0      0          0\n",
              "12         0           0          0      0          1\n",
              "13         0           1          0      0          0\n",
              "14         0           0          0      0          1\n",
              "15         0           0          0      1          0\n",
              "16         1           0          0      0          0\n",
              "17         0           0          1      0          0\n",
              "18         0           0          0      1          0\n",
              "19         0           0          1      0          0\n",
              "20         0           0          0      0          1\n",
              "21         0           0          0      1          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "id": "40Fick_f7p83",
        "outputId": "39e441c4-82c3-4ab5-c477-472923b7f0e2"
      },
      "source": [
        "merged= pd.concat([data,dummies],axis='columns')\r\n",
        "merged\r\n",
        "#merging data, with categorical data converted into numerical."
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>2</th>\n",
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              "      <td>technical</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>186.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
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              "      <td>technical</td>\n",
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              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
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              "      <th>7</th>\n",
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              "      <td>1368.0</td>\n",
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              "      <td>379160.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>407720.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>female</td>\n",
              "      <td>marketing</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>420720.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11300.0</td>\n",
              "      <td>423180.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>335980.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>male</td>\n",
              "      <td>technical</td>\n",
              "      <td>184.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>412440.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>male</td>\n",
              "      <td>management</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>386060.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12800.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>female</td>\n",
              "      <td>sales</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>male</td>\n",
              "      <td>accounts</td>\n",
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              "      <td>13710.0</td>\n",
              "      <td>400340.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>367660.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>386060.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>male</td>\n",
              "      <td>marketing</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>365200.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>female</td>\n",
              "      <td>technical</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>425840.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>male</td>\n",
              "      <td>sales</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>329200.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id Emp_gender    Emp_dept  ...  marketing  sales  technical\n",
              "0      101     female  management  ...          0      0          0\n",
              "1      102     female       sales  ...          0      1          0\n",
              "2      103       male   technical  ...          0      0          1\n",
              "3      104     female    accounts  ...          0      0          0\n",
              "4      105       male   technical  ...          0      0          1\n",
              "5      106       male  management  ...          0      0          0\n",
              "6      107       male   technical  ...          0      0          1\n",
              "7      108       male   marketing  ...          1      0          0\n",
              "8      109       male   technical  ...          0      0          1\n",
              "9      201     female   marketing  ...          1      0          0\n",
              "10     202       male  management  ...          0      0          0\n",
              "11     203       male    accounts  ...          0      0          0\n",
              "12     204       male   technical  ...          0      0          1\n",
              "13     205       male  management  ...          0      0          0\n",
              "14     206     female   technical  ...          0      0          1\n",
              "15     207     female       sales  ...          0      1          0\n",
              "16     208       male    accounts  ...          0      0          0\n",
              "17     209       male   marketing  ...          1      0          0\n",
              "18     301       male       sales  ...          0      1          0\n",
              "19     302       male   marketing  ...          1      0          0\n",
              "20     303     female   technical  ...          0      0          1\n",
              "21     304       male       sales  ...          0      1          0\n",
              "\n",
              "[22 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "RgyYGLlJ7xoH",
        "outputId": "6279001b-c644-4019-b3cb-c9e3ba8d4f62"
      },
      "source": [
        "final=merged.drop(['Emp_gender','Emp_dept'],axis='columns')\r\n",
        "final\r\n",
        "#dropping or removing of categorical columns as we  have already converted them into numerical columns."
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>Emp_salary</th>\n",
              "      <th>accounts</th>\n",
              "      <th>management</th>\n",
              "      <th>marketing</th>\n",
              "      <th>sales</th>\n",
              "      <th>technical</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1344.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5160.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6300.0</td>\n",
              "      <td>347580.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>106</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
              "      <td>417860.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>107</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
              "      <td>369030.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>108</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9800.0</td>\n",
              "      <td>379160.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>407720.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>420720.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11300.0</td>\n",
              "      <td>423180.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>335980.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>184.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>412440.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>386060.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12800.0</td>\n",
              "      <td>392960.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14330.0</td>\n",
              "      <td>405260.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13710.0</td>\n",
              "      <td>400340.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>367660.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>386060.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>365200.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>425840.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>329200.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id  Emp_total_working_days  ...  sales  technical\n",
              "0      101                   170.5  ...      0          0\n",
              "1      102                   160.0  ...      1          0\n",
              "2      103                   149.0  ...      0          1\n",
              "3      104                   158.0  ...      0          0\n",
              "4      105                   170.0  ...      0          1\n",
              "5      106                   186.0  ...      0          0\n",
              "6      107                   168.0  ...      0          1\n",
              "7      108                   171.0  ...      0          0\n",
              "8      109                   170.5  ...      0          1\n",
              "9      201                   187.0  ...      0          0\n",
              "10     202                   188.0  ...      0          0\n",
              "11     203                   153.0  ...      0          0\n",
              "12     204                   184.0  ...      0          1\n",
              "13     205                   167.0  ...      0          0\n",
              "14     206                   176.0  ...      0          1\n",
              "15     207                   181.0  ...      1          0\n",
              "16     208                   179.0  ...      0          0\n",
              "17     209                   166.0  ...      0          0\n",
              "18     301                   186.0  ...      1          0\n",
              "19     302                   165.0  ...      0          0\n",
              "20     303                   189.0  ...      0          1\n",
              "21     304                   150.0  ...      1          0\n",
              "\n",
              "[22 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVXruZY6phxG"
      },
      "source": [
        "## **DATA VISUALIZATION**\r\n",
        "#### After data exploration and cleaning, lets visualize the data for better understanding and finding about the relationship between columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NDUfqF_B9vkU",
        "outputId": "3ee0dbe5-78a6-4e95-80d9-118bafda2717"
      },
      "source": [
        "# data visualization \r\n",
        "Empsal=final['Emp_salary']\r\n",
        "Empwork=final['Emp_total_working_days']\r\n",
        "plt.scatter(Empwork,Empsal,label='Total working days', color='purple')\r\n",
        "plt.title('Salary vs Total working days of the employee ')\r\n",
        "plt.ylabel(' Salary of the employee')\r\n",
        "plt.xlabel('Total working days of the employee ')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hlpL0wGcqX9o"
      },
      "source": [
        "#### The above visualization shows that with increasing number of total working days of the employee, salary of employee also increases. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0dbaI_BK1Pby",
        "outputId": "5b65c2ff-9e87-451a-86f1-40b1756f6e9f"
      },
      "source": [
        "Empwork=final['Emp_total_working_days']\r\n",
        "bins=[135,145,155,165,175,185,195]\r\n",
        "plt.hist(Empwork,bins,histtype='bar',rwidth=0.8)\r\n",
        "plt.xlabel('Total working days of the employees')\r\n",
        "plt.ylabel(' Number of Employees ')\r\n",
        "plt.title('Employee and their total working days')\r\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-UqjiXtIrAM4"
      },
      "source": [
        "#### The above visualization shows that maximum number of employees have worked for around 170 days."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVBgzgOAeZmv",
        "outputId": "f249c58a-2107-48e3-b555-bd337af17380"
      },
      "source": [
        "final['Emp_total_working_hours']\r\n",
        "#looking at the values/data in Emp_total_working_hours column."
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1344.0\n",
              "1     1280.0\n",
              "2     1192.0\n",
              "3     1264.0\n",
              "4     1290.0\n",
              "5     1344.0\n",
              "6     1344.0\n",
              "7     1368.0\n",
              "8     1456.0\n",
              "9     1496.0\n",
              "10    1504.0\n",
              "11    1224.0\n",
              "12    1344.0\n",
              "13    1336.0\n",
              "14    1408.0\n",
              "15    1448.0\n",
              "16    1432.0\n",
              "17    1328.0\n",
              "18    1488.0\n",
              "19    1320.0\n",
              "20    1512.0\n",
              "21    1200.0\n",
              "Name: Emp_total_working_hours, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "9kVqsUi51UfL",
        "outputId": "215a5050-bd89-4c47-c443-0e11bcbebc38"
      },
      "source": [
        "Empsal=final['Emp_salary']\r\n",
        "Empwork=final['Emp_total_working_hours']\r\n",
        "plt.scatter(Empsal,Empwork,label='Total Working hours', color='red')\r\n",
        "plt.title('Salary vs Total working hours of the employee ')\r\n",
        "plt.ylabel('Salary of the employee')\r\n",
        "plt.xlabel('Total working hours of the employee ')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sd-V9nWhrm65"
      },
      "source": [
        "#### The above visualization shows that with increasing number of total working hours of the employee, salary of employee also increases. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "EX3AHim21Tm-",
        "outputId": "f53611f7-d8f2-48e6-ad56-b0fbaf8ffd44"
      },
      "source": [
        "Empwork=final['Emp_total_working_hours']\r\n",
        "bins=[1180,1230,1280,1330,1380,1430,1480,1530,1580]\r\n",
        "plt.hist(Empwork,bins,histtype='bar',rwidth=0.8)\r\n",
        "plt.xlabel('Total working hours of the employees')\r\n",
        "plt.ylabel(' Number of Employees ')\r\n",
        "plt.title('Employee and their total working hours')\r\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBNkG99gtivu"
      },
      "source": [
        "#### The above visualization shows that maximum number of employees have worked for around 1350 hours."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1SQRz4OdZbR",
        "outputId": "08612dfe-7653-4fb3-bdec-0d11f4156360"
      },
      "source": [
        "final['Emp_year_bonus']\r\n",
        "#looking for data/values in Emp_year_bonus column."
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     13900.0\n",
              "1      6900.0\n",
              "2      5160.0\n",
              "3      6300.0\n",
              "4     11300.0\n",
              "5     16160.0\n",
              "6      6150.0\n",
              "7      9800.0\n",
              "8     14620.0\n",
              "9     16880.0\n",
              "10    11300.0\n",
              "11     5580.0\n",
              "12    15900.0\n",
              "13     9570.0\n",
              "14    12800.0\n",
              "15    14330.0\n",
              "16    13710.0\n",
              "17     9120.0\n",
              "18    16120.0\n",
              "19     8810.0\n",
              "20    17600.0\n",
              "21     5260.0\n",
              "Name: Emp_year_bonus, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KEk_aERz-m-v",
        "outputId": "28500386-2c57-4727-9154-29c768333fbc"
      },
      "source": [
        "Empsal=final['Emp_salary']\r\n",
        "Empbonus=final['Emp_year_bonus']\r\n",
        "plt.scatter(Empsal,Empbonus,label='Yearly Bonus', color='green')\r\n",
        "plt.title('Salary-Yearly bonus of the employees ')\r\n",
        "plt.ylabel('Salary of the employee')\r\n",
        "plt.xlabel('Yearly bonus')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Myy67LCIiXn311Rg5cmSsWLEi3njjjXjzzTcjIuLpp5+OhvLccccdUVFREc8991zj8fr37x8REXfeeWcceuihERGxfPnyqKqqilWrVq1x7ueffz769esXY8aMiW222Sbe//73R21tbUREnHXWWTF16tSIiJg/f34MGzYs3nzzzbj44otjxIgRsXz58njzzTdj+PDhsWjRojXOHRFxzTXXxOTJkyMiYtSoUVFXV9dYrkLr8l6YWesuf/TyqDy7MnSqovLsypK/q4A50cR3ao8bxXd9TZ89vbGnQ4OVq1Yyffb09e7t0L9/f4488kg23nhjrr76am666SbOOussILsaWLRoEVtvvTVf//rXefjhh+nduzdPP/104/7jxo1r8h6LffbZh+OOO44lS5Zw3XXX8elPf5o+fdZ+6xuqswCuuuoqpk2bxq233so999zD8ccfD8COO+5IZWVl43knTJjAgAHZ9NM77bQTtbW1DBs2bK1jN/jIRz7ClClTOOKIIzj88MNLfKXMrFiTRk9qs55YTXEQWUflbqjq1asXvXr1IiK47rrr2GGHHdZYf+qppzJ48GAeeeQR3nvvPfr169e4rn///s0e95hjjuHyyy/nyiuv5OKLL241H4cccghTp05tdbsNN9ywcbl3796N7TH57rr5ez/OP/987r//fm6++WZ233135s6dyxZbbNHqecysc3KbyDpqj4YqgP33359f/vKXROqC/c9//hOA+vp6ttpqK3r16sVll13Gu+++W9TxpkyZwowZ2Qy2hW0mTbnnnnvYdtttAdhrr72oqcnafZ5++mkWLVq0VnArNHjwYObPn897773H9ddf35j+7LPPsscee3D66aczaNAgFi9e3MJRzKyzcxBZR+3SUAX88Ic/ZNWqVXzwgx9k55135oc//CEAxx13HJdccgljxozhySefbPHqI2/w4MF84AMfaPHq4tlnn23s4nvyySdzwQUXNJ7zvffeY/To0Rx55JHMmjVrjSuQpvz0pz/loIMOYvz48Wy11VaN6f/zP//D6NGjGTVqFOPHj2fMmDFF5d/MOiffbMi63+BWM6+G6bOns6h+EcMHDKd6QnVZ6xzbwsqVKxk9ejQPPfRQYxtGZ+SbDc06p+ZuNnSbSAnK3VDV1v76179y7LHH8q1vfatTBxAza1pn/uHqINIDfOITn6C2tsmZLc2sk2uP8a/Wh9tEkp5WrdcZ+T0wW1tLtxV0Bg4iZJMhLV261F9iHSjSfCL5Lstm1j7jX60PV2cBQ4cOpa6ujiVLlnR0Vnq0hpkNzWy14QOGNzkSb1vfVlAqBxGgb9++nk3PzDql6gnVa7SJQHluKyiVq7PMzDqxSaMnMfPgmVQOqESIygGVzDx4ZqdoVAffJ2JmZkXwpFRmZtbmHETMzKxkDiJmZlYyBxEzsw5UjplS25ODiFkP1dW/vLqDhiFNautrCaJxSJOu9F44iJj1QN3hy6s76OxDmhSjbEFE0kWSXpb0WEH68ZKelPS4pJ/l0k+StEDSU5L2z6VPTGkLJJ2YSx8h6f6UfpWkDcpVFrPupjt8eXUHnX1Ik2KU80pkFjAxnyDpY8ChwJiI2Bk4K6XvBHwO2Dntc56k3pJ6A78GDgB2Ao5K2wKcCZwdEdsBrwLHlrEsZt1Kd/jy6gzWt0qwvWZKLaeyBZGIuBtYVpD8VeCnEfF22ubllH4ocGVEvB0RzwMLgHHpsSAinouI/wBXAocqm8D748C1af9LgMPKVRaz7qY7fHl1tLaoEmyvmVLLqb3bRLYH9krVUHdJ+lBKHwLkJ9uuS2nNpW8BLI+IdwrSmyRpmqQ5kuZ4kEWz7vHl1dHaokqwsw9pUoz2HoCxD7A5sCfwIeBqSduU+6QRMROYCdmwJ+U+n1ln1/Al1Vlny+sK2qpKsKvNlFqovYNIHfCHyAbsekDSe8CWwAvAsNx2Q1MazaQvBQZK6pOuRvLbm1kRuvqXV0fr7EO0t5f2rs76I/AxAEnbAxsArwA3Ap+TtKGkEcBI4AHgQWBk6om1AVnj+40pCN0BfCYddzJwQ7uWxMx6NFcJZsrZxfcK4B/ADpLqJB0LXARsk7r9XglMjszjwNXAE8CtwNci4t10lfF14DZgPnB12hbg+8C3JS0gayO5sFxlMTMr1B3aM9qCh4I3M7NWeSh4MzNrcw4iZmZWMgcRMzMrmYOImZmVzEHEzMxK5iBiZmYlcxAxM7OSOYiYmVnJHETMzKxkDiJmZlYyBxEzMytZUUFE0kaSdih3ZszMrGtpNYhIOhh4mGx0XSTtIunGcmfMzMw6v2KuRE4lm+t8OUBEPAyMKGOezMzaVc28GqpmVNHrtF5Uzahap3nSe7piZjZcFRH1kvJpPWv8eDPrtmrm1TDtpmmN86XX1tcy7aZpAD1ubpBSFHMl8rikzwO9JY2U9Evg3jLny8ysXUyfPb0xgDRYuWol02dP76AcdS3FBJHjgZ2Bt4HfA/XACeXMlJlZe1lUv2id0m1NrQaRiFgZEdOBfSLiQxHxg4h4qx3yZmbWJlpq8xg+YHiT+zSXbmsqpnfWeElPAE+m52MknVf2nJmZtYGGNo/a+lqCaGzzaAgk1ROqqehbscY+FX0rqJ5Q3RHZ7XKKqc46G9gfWAoQEY8Ae5czU2ZmbaW1No9Joycx8+CZVA6oRIjKAZXMPHimG9WLVEzvLCJicUHvrHfLkx0zs7ZVTJvHpNGTHDRKVMyVyGJJ44GQ1FfSd4H5Zc6XmVmbcJtHeRUTRL4CfA0YArwA7JKet0jSRZJelvRYE+u+IykkbZmeS9K5khZIelTSbrltJ0t6Jj0m59J3lzQv7XOuCi6VzMzAbR7lVkwQiYiYFBGDI+J9EXF0RCwtYr9ZwMTCREnDgP2A/DXmAcDI9JgG/CZtuzlwCrAH2V3zp0jaLO3zG+BLuf3WOpeZmds8yquYIHKfpGskHbAuv/Yj4m5gWROrzga+x5p3vR8KXBqZ+4CBkrYia9C/PSKWRcSrwO3AxLRu04i4LyICuBQ4rNi8mVn30tqwJZNGT2LhCQt575T3WHjCQgeQNlRMENkemAkcAzwj6SeSti/lZJIOBV5IPbzyhgCLc8/rUlpL6XVNpDd33mmS5kias2TJklKybmadVGtdeK28irnZMCLi9og4iqz6aDLwgKS7JH242BNJqgBOBn5Ucm5LFBEzI2JsRIwdNGhQe5/ezMrIw5Z0rGJuNtxC0jclzQG+SzYMypbAd8iGQSnWtmSj/z4iaSEwFHhI0vvJGuyH5bYdmtJaSh/aRLqZdVPNVVl52JKOVcx9Iv8ALgMOi4h8FdIcSecXe6KImAe8r+F5CiRjI+KVND/J1yVdSdaIXh8RL0q6DfhJrjF9P+CkiFgm6TVJewL3k1W1/bLYvJhZ19LSSLvDBwyntr52rX3chbd9FNMmskNEnAEsl7RxfkVEnNncTpKuIAtAO0iqk3RsC+e4BXgOWAD8DjguHX8ZcAbwYHqcntJI21yQ9nkW+HMRZTGzLqilKit34e1YxVyJ7CzpMmBzsls6lgCTI2Kt+z/yUhtKS+urcstBM/eeRMRFwEVNpM8BRrWaezPr8lqqsmroaTV99nQW1S9i+IDhVE+odg+sdlJMEJkJfDsi7gCQtG9KG1/GfJmZNWqtysrDlnScYqqz+jcEEICIuBPoX7YcmZkVcJVV51VMEHlO0g8lVaXHD8jaL8zM2oXvOu+8lDVHtLBB1jPqNOCjKenvwKnpDvIuZ+zYsTFnzpyOzoaZWZciaW5EjC1Mb7VNJAWLb5QlV2Zm1qU1G0Qk3cSa41utISIOKUuOzMysy2jpSuSsdsuFmZl1Sc0GkYi4q2FZ0gbAjmRXJk9FxH/aIW9mZtbJtdomIulTwPlkd4ULGCHpyxHhO8TNzHq4Ym42/AXwsYhYACBpW+BmPMyImVmPV8x9Iq83BJDkOeD1MuXHzMy6kGKCyBxJt0iakuY4vwl4UNLhkg4vc/7MrAtpbYZB636Kqc7qB7wE7JOeLwE2Ag4ma2j/Q3myZmZdSUvDtfvO8u6r1TvWuxvfsW5WHlUzqpocJLFyQCULT1jY/hmyNlXyHeuSRpDNZliV3943G5pZnmcY7JmKqc76I3AhWVvIe+XNjpl1VZ5hsGcqpmH9rYg4NyLuiIi7Gh5lz5mZdSkerr1nKuZK5BxJpwB/Ad5uSIyIh8qWKzPrcjzDYM9UTBAZDXwB+Dirq7MiPTcza+QZBnueYoLIZ4FtPF6WmZkVKqZN5DFgYLkzYmZmXU8xVyIDgSclPciabSLu4mtm1sMVE0ROKXsuzMysS2q1Oit1510I9E3LDwKt9sySdJGklyU9lkv7uaQnJT0q6XpJA3PrTpK0QNJTkvbPpU9MaQsknZhLHyHp/pR+VZrzxMzM2lGrQUTSl4Brgd+mpCFkNyC2ZhYwsSDtdmBURHwQeBo4KZ1jJ+BzwM5pn/Mk9ZbUG/g1cACwE3BU2hbgTODsiNgOeBU4tog8mZlZGyqmYf1rwEeA1wAi4hngfa3tFBF3A8sK0v4SEe+kp/cBQ9PyocCVEfF2RDwPLADGpceCiHgu9Q67EjhUksi6GF+b9r8EOKyIspiZWRsqJoi8ne/eK6kP2X0i6+uLrJ7YagiwOLeuLqU1l74FsDwXkBrSmyRpmqQ5kuYsWbKkDbJuZmZQXBC5S9LJwEaSPglcQzaOVskkTQfeAdplsoGImBkRYyNi7KBBg9rjlGZmPUIxQeREsjlE5gFfBm4BflDqCSVNAQ4CJsXqcehfAIblNhua0ppLXwoMTFdF+XQzM2tHrXbxjYj3gN+lx3qRNBH4HrBPRKzMrboR+L2k/wO2BkYCDwACRqbh6F8ga3z/fESEpDuAz5C1k0wGbljf/JmZ2bop5kqkJJKuAP4B7CCpTtKxwK+ATYDbJT0s6XyAiHgcuBp4ArgV+FpEvJvaPL4O3AbMB65O2wJ8H/i2pAVkbSQXlqssZmbWNM9saGZmrWpuZsOir0QkVbS+lZmZ9STF3Gw4XtITwJPp+RhJ55U9Z2Zm1ukVcyVyNrA/WY8oIuIRYO9yZsqsI9XMq6FqRhW9TutF1Ywqaua1S090s5J09Oe1mAEYiYjF2U3ijd4tT3bMOlbNvBqm3TSNlauyzoO19bVMu2kagCdbsk6nM3xei7kSWSxpPBCS+kr6LllPKbNuZ/rs6Y3/kA1WrlrJ9NnTOyhHZs3rDJ/XYoLIV8jGzxpCdq/GLum5WbezqH7ROqWbdaTO8Hkt5mbDVwBfx1uPMHzAcGrra5tMN+tsOsPntZjeWYMknSxpZpoj5CJJF7VH5szaW/WEair6rtmbvaJvBdUTqjsoR2bN6wyf12Ia1m8A/g78FTeoWzfX0Bg5ffZ0FtUvYviA4VRPqHajunVKneHz2uod65Iejohd2ik/Zec71s3M1t363LH+J0kHliFPZmbWxTVbnSXpdbLJpwScLOltYFV6HhGxaftk0czMOqtmg0hEbNKeGTEzs66nmN5Zs4tJMzOznqel6qx+QH9gS0mbkVVjAWxKC/OZm5lZz9FSF98vAyeQzTQ4l9VB5DWyyaXMzKyHa6lN5BzgHEnHR8Qv2zFPZmbWRbTaJuIAYmZmzSnbHOtmZtb9NRtEJH0k/d2w/bJjZmZdSUtXIuemv/9oj4yYmVnX01LvrFWSZgJDJJ1buDIivlG+bJmZWVfQUhA5CPgE2fzqc9snO2Zm1pW01MX3FeBKSfMj4pF1PXCac+Qg4OWIGJXSNgeuAqqAhcAREfGqsgnczwEOBFYCUyLiobTPZOAH6bA/johLUvruwCxgI+AW4JvR2pDEZmbWporpnbVU0vWSXk6P6yQNLWK/WcDEgrQTgdkRMRKYnZ4DHACMTI9pwG+gMeicAuwBjANOSXfPk7b5Um6/wnOZmVmZFRNELgZuJLtzfWvgppTWooi4G1hWkHwocElavgQ4LJd+aWTuAwZK2oqsKu32iFgWEa8CtwMT07pNI+K+dPVxae5YZmbWTooJIu+LiIsj4p30mAUMKvF8gyPixbT8b2BwWh4CLM5tV5fSWkqvayK9SZKmSZojac6SJUtKzLpZx6mZV0PVjCp6ndaLqhlV1Myr6egsmQHFBZFXJDtY45EAABFZSURBVB0tqXd6HA0sXd8TpyuIdmnDiIiZETE2IsYOGlRq/DPrGDXzaph20zRq62sJgtr6WqbdNM2BxDqFYoLIF4EjyK4cXgQ+A0wt8Xwvpaoo0t+XU/oLwLDcdkNTWkvpQ5tIN+t2ps+ezspVK9dIW7lqJdNnT++gHJmtVszYWbURcUhEDIqI90XEYRGxqMTz3QhMTsuTgRty6ccosydQn6q9bgP2k7RZalDfD7gtrXtN0p6pZ9cxuWOZdSuL6pv+d2su3aw9lW3sLElXkN3tvoOkOknHAj8FPinpGbJ7UH6aNr8FeA5YAPwOOA4gIpYBZwAPpsfpKY20zQVpn2eBP5erLGYdafiA4euUXiy3s1hbUE+7tWLs2LExZ86cjs6GWdEa2kTyVVoVfSuYefBMJo2e1GmOad2bpLkRMbYwvZjpcXuXJ0tmVoxJoycx8+CZVA6oRIjKAZXr/WXvdhZrKy0Ne9LgGUnXARdHxBPlzpCZrW3S6ElteoXgdhZrK8W0iYwBngYukHRfuudi0zLny8zKqFztLNbzFNM76/WI+F1EjAe+TzYMyYuSLpG0XdlzaGZtrnpCNRV9K9ZIq+hbQfWE6g7KkXVVRbWJSDpE0vXADOAXwDZkw5/cUub8mVkZlKOdxXqmotpEgDuAn0fEvbn0ayXtXZ5smXWsmnk1TJ89nUX1ixg+YDjVE6q73RdsW7ezWM/UYhBJPbNmRcTpTa33xFTWHRV2f20YZgTwl65ZgRarsyLiXbI5Qcx6DHd/NSteMdVZ/0/Sr8gmk3qjIbFh0iiz7sbdX82KV0wQ2SX9zVdpBfDxts+OWccbPmA4tfW1Taab2ZqK6eL7sSYeDiDW5TU3dpS7v5oVr5grESR9CtgZ6NeQ1lxju1lXUEzjeXfvnWXWFlodgFHS+UAF8DGyUXM/AzwQEceWP3ttzwMwGkDVjKomq6wqB1Sy8ISF7Z8hs06u5AEYgfERcQzwakScBnwY2L6tM2jWntx4btY2igkib6a/KyVtDawCtipflszKz2NHmbWNYoLInyQNBH4OPAQsBK4oZ6bMys2N52Zto9WG9Yg4Iy1eJ+lPQL+IqC9vtszKy43nZm2j2YZ1SYe3tGNE/KEsOSozN6ybma275hrWW7oSObiFdQF0ySBiZmZtp9kgEhFT2zMjZmbW9fhmQzMzK1kxk1KdDxwJHA8I+CxQWeZ8mZlZF+CbDc3MrGQdcrOhpG9JelzSY5KukNRP0ghJ90taIOkqSRukbTdMzxek9VW545yU0p+StP/65MnMzNZdqTcb/r7UE0oaAnwDGBsRo4DewOeAM4GzI2I74FWgYWyuY8mugrYDzk7bIWmntN/OwETgvDQTo5mZtZNihoI/IyKWR8R1ZG0hO0bEj9bzvH2AjST1IRvc8UWy+UmuTesvAQ5Ly4em56T1EyQppV8ZEW9HxPPAAmDceubLzMzWQbNBRNKHJL0/9/wY4GrgDEmbl3rCiHgBOAtYRBY86oG5wPKIeCdtVgcMSctDgMVp33fS9lvk05vYp7As0yTNkTRnyZIlpWbdzMwKtHQl8lvgPwCS9gZ+ClxK9iU+s9QTStqM7CpiBLA10J+sOqpsImJmRIyNiLGDBg0q56nMzHqUloJI74hYlpaPBGZGxHUR8UNgu/U45yeA5yNiSUSsIrvz/SPAwFS9BTAUeCEtvwAMA0jrBwBL8+lN7GNmZu2gxSCS+1KfAPwtt66omxSbsQjYU1JFatuYADwB3EE24RXAZOCGtHxjek5a/7fIBvy6Efhc6r01AhgJPLAe+TIzs3XUUjC4ArhL0itk3Xz/DiBpO7IqrZJExP2SriXr6fUO8E+y6rGbgSsl/TilXZh2uRC4TNICYBlZjywi4nFJV5MFoHeAr0XEu6Xmy8zM1l2L0+NK2pPsnpC/RMQbKW17YOOIeKh9sti2PIqvmdm6K2UUXyLivibSnm7LjJmZWddVzM2GZmZmTXIQMTOzkjmImJlZyRxEzMysZA4iZmZWMgcRMzMrmYOImZmVzEHEyqpmXg1VM6rodVovqmZUUTOvpqOzZGZtaH3GwDJrUc28GqbdNI2Vq1YCUFtfy7SbpgEwafSkjsyambURX4lY2UyfPb0xgDRYuWol02dP76AcmVlbcxCxsllUv2id0s2s63EQsbIZPmD4OqWbWdfjIGJlUz2hmoq+FWukVfStoHpCdQflyMzamoOIlc2k0ZOYefBMKgdUIkTlgEpmHjzTjepm3UiL84l0R55PxMxs3TU3n4ivRMzMrGQOImZmVjIHEWuR7zg3s5b4jnVrlu84N7PW+ErEmuU7zlfzFZlZ03wlYs3yHecZX5GZNa9DrkQkDZR0raQnJc2X9GFJm0u6XdIz6e9maVtJOlfSAkmPStotd5zJaftnJE3uiLJ0Z77jPOMrMrPmdVR11jnArRGxIzAGmA+cCMyOiJHA7PQc4ABgZHpMA34DIGlz4BRgD2AccEpD4LG24TvOM74iM2teuwcRSQOAvYELASLiPxGxHDgUuCRtdglwWFo+FLg0MvcBAyVtBewP3B4RyyLiVeB2YGI7FqXb8x3nGV+RmTWvI9pERgBLgIsljQHmAt8EBkfEi2mbfwOD0/IQYHFu/7qU1ly6taFJoyf1uKBRqHpC9RptItAzr8jMmtIR1Vl9gN2A30TErsAbrK66AiCysVjabDwWSdMkzZE0Z8mSJW11WOshfEVm1ryOuBKpA+oi4v70/FqyIPKSpK0i4sVUXfVyWv8CMCy3/9CU9gKwb0H6nU2dMCJmAjMhGzurbYphPYmvyMya1u5XIhHxb2CxpB1S0gTgCeBGoKGH1WTghrR8I3BM6qW1J1Cfqr1uA/aTtFlqUN8vpZmZWTvpqPtEjgdqJG0APAdMJQtoV0s6FqgFjkjb3gIcCCwAVqZtiYhlks4AHkzbnR4Ry9qvCOVTM6+G6bOns6h+EcMHDKd6QrV/BZtZp+Sh4DuZwhvbIGvEdR28mXUkDwXfRfjGNjPrShxEOhnf2GZmXYmDSCfjG9vMrCtxEFlPbT26q4caMbOuxEFkPTQ0gtfW1xJE4+iu6xNIfGObmXUl7p1VhOa63FbNqKK2vnat7SsHVLLwhIVtlGMzs47XXO8szyfSipbmknAjuJn1dK7OakVLXW7dCG5mPZ2DSCtautpwI7iZ9XQOIq1o6WrDjeBm1tO5TaQVrc0l4dFdzawn85VIK3y1YWbWPHfxNTOzVnkARjMza3MOImZmVjIHETMzK5mDiJmZlcxBxMzMStbjemdJWkI2h3t3tCXwSkdnogP0xHL3xDJDzyx3ZylzZUQMKkzscUGkO5M0p6kueN1dTyx3Tywz9Mxyd/YyuzrLzMxK5iBiZmYlcxDpXmZ2dAY6SE8sd08sM/TMcnfqMrtNxMzMSuYrETMzK5mDiJmZlcxBpBOQ1E/SA5IekfS4pNMK1p8raUXu+YaSrpK0QNL9kqpy605K6U9J2j+XPjGlLZB0Yi59RDrGgnTMDcpb2jXK1WS5lamW9LSk+ZK+kUs/N+X1UUm75Y41WdIz6TE5l767pHlpn3MlKaVvLun2tP3tkjbr4DJPkPSQpIcl3SNpu5TeLd7rdP7ekv4p6U8t5ac7lTnlobDcNSmvj0m6SFLflN41P98R4UcHPwABG6flvsD9wJ7p+VjgMmBFbvvjgPPT8ueAq9LyTsAjwIbACOBZoHd6PAtsA2yQttkp7XM18Lm0fD7w1Y4uNzAVuBTolda9L/09EPhz2m9P4P6UvjnwXPq7WVreLK17IG2rtO8BKf1nwIlp+UTgzA4u89PAB3Lv76zu9F6nc34b+D3wp5by053K3Ey5D0yfAwFX5MrdJT/f7fpi+lHUB64CeAjYI/1z3AFsxZpB5Dbgw2m5D9ndrAJOAk4q3C49bsuln5QeSvv2SelrbNeB5X4A2K6JbX4LHJV7/lR6bY4Cflu4XVr3ZC69cbuGfdPyVsBTHVzmp4A9cu/PT7rTew0MBWYDHwf+1FJ+ukuZmyp3E+u/BVR35c+3q7M6iXTJ+zDwMnB7RNwPfB24MSJeLNh8CLAYICLeAeqBLfLpSV1Kay59C2B5OkY+vd00U+5tgSMlzZH0Z0kj0+brWr4habkwHWBw7nX9NzC4DYvVombK/N/ALZLqgC8AP02bd5f3egbwPeC99Lyl/HSXMsPa5W6UqrG+ANyakrrk59tBpJOIiHcjYheyXy7jJO0NfBb4ZcfmrLyaKPcosuqKtyIb6uF3wEVlzkMA7dbXvZkyfws4MCKGAhcD/9de+Sk3SQcBL0fE3I7OS3sqotznAXdHxN/LmY9yf74dRDqZiFhOVoX1MWA7YIGkhUCFpAVpsxeAYQCS+gADgKX59GRoSmsufSkwMB0jn97ucuWeSPaL6g9p1fXAB9PyupbvhbRcmA7wkqStANLfl9uqLMXKlfkAYEy6IgG4ChiflrvDe/0R4JD0Ob6SrGrnnBby0x3KDE2UW9LlAJJOAQaRtZc06Jqf7/asH/Sj2XrTQcDAtLwR8HfgoIJt8m0iX2PNhser0/LOrNnw+BxZu0qftDyC1Q2PO6d9rmHNhsfjOrrcZFU5X0zp+wIPpuVPsWbD4wMpfXPgebJGx83S8uZpXWHD44Ep/ees2fD4sw4u8yvA9in9WOC67vRe58q/L6sbmJvMT3crcxPl/m/gXmCjgm265Oe73V9MP5r8gH0Q+CfwKPAY8KMmtskHkX7pn2NB+hBtk1s3naynylOknhop/UCyHkDPAtNz6dukYyxIx9ywo8sNDARuBuYB/yD7lU76R/l1KsM8YGzuWF9MZVgATM2lj03Hfhb4FatHadiCrMHzGeCvDf+UHVjm/0plegS4s+E97S7vdS4P+7L6y7TJ/HS3MjdR7ndSPh9Oj4bPQJf8fHvYEzMzK5nbRMzMrGQOImZmVjIHETMzK5mDiJmZlcxBxMzMSuYgYpakUVTvkXRALu2zkm5tab8ijrui9a0at923YbRXs66gT+ubmPUMERGSvgJcI+kOsv+Pn5DdRb/OJPWJ1eM2mXVLvhIxy4mIx4CbgO8DPwIuB6YrmwPkn5IOBZBUJenvyuYAeUjS+JS+b0q/EXgif2xJl0o6LPe8puF4BTaVdHOac+J8Sb3S9keluSMek3Rm7jgrlM2/8oik+yQNTumzJH0mv136u5Wku5XNXfKYpL3a5MWzHslBxGxtpwGfJxvTqh/wt4gYRzae2c8l9Scbi+iTEbEbcCRwbm7/3YBvRsT2Bce9EJgCIGkA2fhYNzdx/nHA8WTzZ2wLHC5pa+BMsnGndgE+lAtI/YH7ImIMcDfwpVbK93myIdF3AcaQ3TVtVhJXZ5kViIg3JF0FrACOAA6W9N20uh8wHPgX8CtJuwDvAvmA8UBEPN/Ece+SdJ6kQcCnycbHaqq664GIeA5A0hXAR4FVwJ0RsSSl1wB7A38E/kM2RwfAXOCTrRTxQaBhRr0/RoSDiJXMQcSsae+lh4BPR8RT+ZWSTgVeIvsl3wt4K7f6jRaOeylwNNnAglOb2aZwLKLWxiZaFavHL3qX1f/X76S8karENgCIiLvTVAOfAmZJ+r+IuLSVc5g1ydVZZi27DTg+N3f1ril9APBiRLxHNrFQ7yKPNws4ASAinmhmm3HK5gbvRVZVdg/ZIIL7SNpSUm+yWezuauVcC4Hd0/IhZNPxIqkSeCkifgdcQFb9ZlYSBxGzlp1B9uX7qKTH03PIJhSaLOkRYEdavvpoFBEvAfPJJp5qzoNkI7LOJxv2+/rIZqk7kWz+kUeAuRFxQyun+x1Z4HmEbGrYhjzuCzwi6Z9kQeqcYvJu1hSP4mvWjiRVkA3zvVtE1Hd0fszWl69EzNqJpE+QXV380gHEugtfiZiZWcl8JWJmZiVzEDEzs5I5iJiZWckcRMzMrGQOImZmVrL/Dxbt3j6kmoX8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmxsF_V7sfpD"
      },
      "source": [
        "#### The above visualization shows that with increasing yearly bonus of the employee, salary of employee also increases. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Y0NTaWrh3J_K",
        "outputId": "95bb6110-dbda-4150-a89c-65087d6f3f76"
      },
      "source": [
        "Empwork=final['Emp_year_bonus']\r\n",
        "bins=[4000,6000,8000,10000,12000,14000,16000,18000]\r\n",
        "plt.hist(Empwork,bins,histtype='bar',rwidth=0.8)\r\n",
        "plt.xlabel('Yearly bonus of the employees')\r\n",
        "plt.ylabel(' Number of Employees ')\r\n",
        "plt.title('Employee and their yearly bonus')\r\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5x4lIinsuXH0"
      },
      "source": [
        "#### The above visualization shows that maximum number of employees have got yearly bonus around 8000 to 10000 and 16000 to 18000."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBZbNz5iBV-e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3d0191b4-d570-4541-80af-fe79af7549e2"
      },
      "source": [
        "Empsal=final['Emp_salary']\r\n",
        "Empleaves=final['Emp_yearly_leaves']\r\n",
        "plt.scatter(Empleaves,Empsal,label='Yearly leaves', color='orange')\r\n",
        "plt.title('Yearly leaves-salary of employee ')\r\n",
        "plt.xlabel('Yearly leaves of the employee')\r\n",
        "plt.ylabel('Salary of the employee')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrOt1P4sszJB"
      },
      "source": [
        "#### The above visualization shows that with decreasing number of yearly leaves of the employee, salary of employee increases.Which simply means that the employee with less number of leaves have got more salary as compared to teh employee with more number of leaves."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-tG8BPBtQhI"
      },
      "source": [
        "## **DATA MODELLING AND PREDICTION**\r\n",
        "#### By using train-test-split method of linear regression model, we have to first split the data into training data and testing data.Since, we are using labelled data, we'll drop the column that is target column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsckCRtU76O5"
      },
      "source": [
        "#train data and mention target column.\r\n",
        "train=final.drop(['Emp_salary'], axis=1)\r\n",
        "test=final['Emp_salary']"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5w3-LljT8TaE"
      },
      "source": [
        "#mentioning train-test ratio  \r\n",
        "X_train,X_test,y_train, y_test=train_test_split(train, test, test_size=0.2, random_state=0)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jWOAFNcpvrQ6"
      },
      "source": [
        "#### Here data is split into training data and testing data by the ratio of 8:2. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfZEB9ig8YuR",
        "outputId": "331d1258-80f8-4168-9d07-6ab6a9a4282f"
      },
      "source": [
        "len(X_train) #length of training data"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "7_6opBV78b0T",
        "outputId": "ee89dbed-cea1-4c8c-ce7c-222ec2a342f2"
      },
      "source": [
        "X_train #train data"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>accounts</th>\n",
              "      <th>management</th>\n",
              "      <th>marketing</th>\n",
              "      <th>sales</th>\n",
              "      <th>technical</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>304</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5260.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>203</td>\n",
              "      <td>153.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5580.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>302</td>\n",
              "      <td>165.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>8810.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>109</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1456.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>14620.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>107</td>\n",
              "      <td>168.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6150.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>208</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1432.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13710.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11300.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5160.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>106</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16160.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>209</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>201</td>\n",
              "      <td>187.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>108</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9800.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>301</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16120.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>158.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6300.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>170.5</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>13900.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>207</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14330.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>204</td>\n",
              "      <td>184.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15900.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id  Emp_total_working_days  ...  sales  technical\n",
              "21     304                   150.0  ...      1          0\n",
              "11     203                   153.0  ...      0          0\n",
              "19     302                   165.0  ...      0          0\n",
              "8      109                   170.5  ...      0          1\n",
              "6      107                   168.0  ...      0          1\n",
              "16     208                   179.0  ...      0          0\n",
              "4      105                   170.0  ...      0          1\n",
              "2      103                   149.0  ...      0          1\n",
              "5      106                   186.0  ...      0          0\n",
              "17     209                   166.0  ...      0          0\n",
              "9      201                   187.0  ...      0          0\n",
              "7      108                   171.0  ...      0          0\n",
              "18     301                   186.0  ...      1          0\n",
              "3      104                   158.0  ...      0          0\n",
              "0      101                   170.5  ...      0          0\n",
              "15     207                   181.0  ...      1          0\n",
              "12     204                   184.0  ...      0          1\n",
              "\n",
              "[17 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJi5q3d1wA5n"
      },
      "source": [
        "#### The above is the training data which will be used by the model to train it self."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtHTkU0E8ftO",
        "outputId": "962cdf2b-d528-4677-9168-e975395db013"
      },
      "source": [
        "len(X_test)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "ObG8AjtyIbKY",
        "outputId": "d090bfe9-1335-4802-ed18-d5848269e2ef"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_id</th>\n",
              "      <th>Emp_total_working_days</th>\n",
              "      <th>Emp_total_working_hours</th>\n",
              "      <th>Emp_yearly_leaves</th>\n",
              "      <th>Emp_year_bonus</th>\n",
              "      <th>accounts</th>\n",
              "      <th>management</th>\n",
              "      <th>marketing</th>\n",
              "      <th>sales</th>\n",
              "      <th>technical</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>303</td>\n",
              "      <td>189.0</td>\n",
              "      <td>1512.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>17600.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>202</td>\n",
              "      <td>188.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11300.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>206</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12800.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>205</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9570.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6900.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Emp_id  Emp_total_working_days  ...  sales  technical\n",
              "20     303                   189.0  ...      0          1\n",
              "10     202                   188.0  ...      0          0\n",
              "14     206                   176.0  ...      0          1\n",
              "13     205                   167.0  ...      0          0\n",
              "1      102                   160.0  ...      1          0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQRFo3bDwNxQ"
      },
      "source": [
        "#### We will test the model using the data above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zYK9pna8kpB"
      },
      "source": [
        "reg=LinearRegression()"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SjkDqpswUcT"
      },
      "source": [
        "#### Now, fit the data into linear regression model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_Hoo8Hw8pEi",
        "outputId": "0a096cfb-2ebb-4a0c-aced-05419591b9d0"
      },
      "source": [
        "reg.fit(X_train, y_train)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ja_t0KIgws6k"
      },
      "source": [
        "#### Since, this is linear regression using multiple variables.\r\n",
        "#### Employee salary is dependent on total working days, hours, bonus and leaves taken.\r\n",
        "#### Mathematically,\r\n",
        "#### y=m1*x1+m2*x2+m3*x3+m4*x4+c\r\n",
        "#### where y(Emp salary)is dependent on m1(total working days),m2(total working hours),m3(yearly bonus) and m4(yearly leaves).\r\n",
        "#### x1,x2,x3 and x4 are coefficients and c is the intercept.\r\n",
        "#### Lets check coefficients and intercepts."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjE1KtgInHOW",
        "outputId": "9922a723-7a88-4321-87ca-4944f41c9433"
      },
      "source": [
        "reg.coef_"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-4.14409257e+01,  4.10844844e+02,  5.83912631e+01, -6.44756117e+02,\n",
              "        2.64493871e+00, -4.51561930e+02,  7.52286699e+03,  4.41187520e+01,\n",
              "       -6.76908311e+03, -3.46340693e+02])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NtNyupLnN-Q",
        "outputId": "245dc096-1ad4-4fef-879d-81a8ed8a361a"
      },
      "source": [
        "reg.intercept_"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "226542.7430284008"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tDpzsAa3yWeZ"
      },
      "source": [
        "###**PREDICTION**\r\n",
        "#### Let's check the prediction now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EtiJJZN8r1x"
      },
      "source": [
        "pred=reg.predict(X_test)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tW3hZsTy846x",
        "outputId": "f59b8cf7-c684-4762-a058-482f2b6ed95f"
      },
      "source": [
        "pred"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([419035.67109152, 412904.56734991, 392498.49958832, 376227.14822155,\n",
              "       360732.87589395])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSzhi4qryqp0"
      },
      "source": [
        "#### The test data was of length 5, we got 5 predictions.\r\n",
        "#### Lets compare actual data and predicted data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "ardZu_gJBwy0",
        "outputId": "8710735e-08ae-4b39-ffc3-4a62625e3435"
      },
      "source": [
        "# Comparing Actual and  Predicted values.\r\n",
        "data1 = pd.DataFrame({'Actual': y_test, 'Predicted': pred})  \r\n",
        "data1"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>425840.0</td>\n",
              "      <td>419035.671092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>423180.0</td>\n",
              "      <td>412904.567350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>392960.0</td>\n",
              "      <td>392498.499588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>386060.0</td>\n",
              "      <td>376227.148222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>352500.0</td>\n",
              "      <td>360732.875894</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual      Predicted\n",
              "20  425840.0  419035.671092\n",
              "10  423180.0  412904.567350\n",
              "14  392960.0  392498.499588\n",
              "13  386060.0  376227.148222\n",
              "1   352500.0  360732.875894"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "baIqZqdWy_jc"
      },
      "source": [
        "##**PERFORMANCE ANALYSIS OF THE MODEL (ACCURACY CHECK)**\r\n",
        "#### Let's check the score of the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6LjGcZbp855_",
        "outputId": "d33363e2-a690-4cf5-e218-57b0ccf53e84"
      },
      "source": [
        "reg.score(X_train, y_train)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9490279794815379"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOlD5Tmv8_IE",
        "outputId": "bb93531d-1ad8-48d9-daf7-111cd621f242"
      },
      "source": [
        "# To calculate mean absolute error.\r\n",
        "print('Mean Absolute Error:',metrics .mean_absolute_error(y_test, pred))"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 7121.397928529268\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krbG90PzzR_C"
      },
      "source": [
        "## **CONCLUSION**\r\n",
        "### Predictive analytics helps us to find what likely is about to happen by using the past data.\r\n",
        "#### Predictive Analytics is performed on employee report dataset which includes steps as:\r\n",
        "#### 1.Data exploration-where we explore the data that is being used for predictive anayltics.It gives us fair idea about the shape,basic information,unique columns & their datatypes etc.\r\n",
        "#### 2.Data cleaning-Before moving to data modelling or prediction, it is important to ensure that the data has no missing or inappropriate values whcih may affect the prediction.If there are any missing or inappropriate values, they have to be removed or replaced.\r\n",
        "#### 3.Data visualization-For better understanding of the data being used, visualization helps us to understand the relationship between columns and many more.\r\n",
        "#### 4.Data modelling or Prediction-once the data is cleaned, we can move forward with data modelling and then prediction.\r\n",
        "#### I have used train-test-split method of linear regression model where data was split into training data and testing data in the ratio of 8:2.\r\n",
        "#### 5.Performance analysis (Accuracy check)-once the predictions are made, the efficiency of model is to be checked by score of the model.\r\n",
        "#### Other techniques apart from linear regression can be used are time series analysis, data mining etc.\r\n"
      ]
    }
  ]
}