employee_performance_predictor.ipynb

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        "# **Employee Performance predictor**\n",
        "## By [Bhavneek Singh](https://bhavneek.netlify.app/), IIT Delhi\n",
        "\n",
        "### Dataset Description\n",
        "\n",
        "This dataset includes important attributes of the garment manufacturing process and the productivity of the employees which had been collected manually and also been validated by the industry experts.\n",
        "\n",
        "day : Day of the Week\n",
        "\n",
        "quarter : A portion of the month. A month was divided into four quarters\n",
        "\n",
        "department : Associated department with the instance\n",
        "\n",
        "team no. : Associated team number with the instance \n",
        "\n",
        "noofworkers : Number of workers in each team \n",
        "\n",
        "noofstylechange : Number of changes in the style of a particular product\n",
        "\n",
        "targetedproductivity : Targeted productivity set by the Authority for each team for each day. \n",
        "\n",
        "smv : Standard Minute Value, it is the allocated time for a task \n",
        "\n",
        "wip : Work in progress. Includes the number of unfinished items for products \n",
        "\n",
        "overtime : Represents the amount of overtime by each team in minutes\n",
        "\n",
        "incentive : Represents the amount of financial incentive (in BDT) that enables or motivates a particular course of action.\n",
        "\n",
        "idletime : The amount of time when the production was interrupted due to several reasons \n",
        "\n",
        "idlemen : The number of workers who were idle due to production interruption\n",
        "\n",
        "actual_productivity : The actual % of productivity that was delivered by the workers. It ranges from 0-1."
      ]
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/kaggle/input/employee-performance-prediction/train_dataset.csv\n",
            "/kaggle/input/employee-performance-prediction/test_dataset.csv\n"
          ]
        }
      ],
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ]
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "df = pd.read_csv(\"/kaggle/input/employee-performance-prediction/train_dataset.csv\")"
      ]
    },
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1017 entries, 0 to 1016\n",
            "Data columns (total 26 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   team                   1017 non-null   int64  \n",
            " 1   targeted_productivity  1017 non-null   float64\n",
            " 2   smv                    1017 non-null   float64\n",
            " 3   wip                    594 non-null    float64\n",
            " 4   over_time              1017 non-null   int64  \n",
            " 5   incentive              1017 non-null   int64  \n",
            " 6   idle_time              1017 non-null   float64\n",
            " 7   idle_men               1017 non-null   int64  \n",
            " 8   no_of_style_change     1017 non-null   int64  \n",
            " 9   no_of_workers          1017 non-null   float64\n",
            " 10  month                  1017 non-null   int64  \n",
            " 11  quarter_Quarter1       1017 non-null   int64  \n",
            " 12  quarter_Quarter2       1017 non-null   int64  \n",
            " 13  quarter_Quarter3       1017 non-null   int64  \n",
            " 14  quarter_Quarter4       1017 non-null   int64  \n",
            " 15  quarter_Quarter5       1017 non-null   int64  \n",
            " 16  department_finishing   1017 non-null   int64  \n",
            " 17  department_finishing   1017 non-null   int64  \n",
            " 18  department_sweing      1017 non-null   int64  \n",
            " 19  day_Monday             1017 non-null   int64  \n",
            " 20  day_Saturday           1017 non-null   int64  \n",
            " 21  day_Sunday             1017 non-null   int64  \n",
            " 22  day_Thursday           1017 non-null   int64  \n",
            " 23  day_Tuesday            1017 non-null   int64  \n",
            " 24  day_Wednesday          1017 non-null   int64  \n",
            " 25  actual_productivity    1017 non-null   float64\n",
            "dtypes: float64(6), int64(20)\n",
            "memory usage: 206.7 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>targeted_productivity</th>\n",
              "      <th>smv</th>\n",
              "      <th>wip</th>\n",
              "      <th>over_time</th>\n",
              "      <th>incentive</th>\n",
              "      <th>idle_time</th>\n",
              "      <th>idle_men</th>\n",
              "      <th>no_of_style_change</th>\n",
              "      <th>no_of_workers</th>\n",
              "      <th>...</th>\n",
              "      <th>department_finishing</th>\n",
              "      <th>department_finishing</th>\n",
              "      <th>department_sweing</th>\n",
              "      <th>day_Monday</th>\n",
              "      <th>day_Saturday</th>\n",
              "      <th>day_Sunday</th>\n",
              "      <th>day_Thursday</th>\n",
              "      <th>day_Tuesday</th>\n",
              "      <th>day_Wednesday</th>\n",
              "      <th>actual_productivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>594.000000</td>\n",
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              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.443461</td>\n",
              "      <td>0.730747</td>\n",
              "      <td>15.150492</td>\n",
              "      <td>1183.183502</td>\n",
              "      <td>4532.940020</td>\n",
              "      <td>40.689282</td>\n",
              "      <td>0.564405</td>\n",
              "      <td>0.398230</td>\n",
              "      <td>0.160275</td>\n",
              "      <td>34.846116</td>\n",
              "      <td>...</td>\n",
              "      <td>0.201573</td>\n",
              "      <td>0.214356</td>\n",
              "      <td>0.584071</td>\n",
              "      <td>0.161259</td>\n",
              "      <td>0.152409</td>\n",
              "      <td>0.164208</td>\n",
              "      <td>0.165192</td>\n",
              "      <td>0.171091</td>\n",
              "      <td>0.185841</td>\n",
              "      <td>0.736509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.472473</td>\n",
              "      <td>0.097384</td>\n",
              "      <td>10.946096</td>\n",
              "      <td>1793.836719</td>\n",
              "      <td>3275.997333</td>\n",
              "      <td>173.240655</td>\n",
              "      <td>10.093731</td>\n",
              "      <td>3.351712</td>\n",
              "      <td>0.440199</td>\n",
              "      <td>22.185292</td>\n",
              "      <td>...</td>\n",
              "      <td>0.401373</td>\n",
              "      <td>0.410577</td>\n",
              "      <td>0.493124</td>\n",
              "      <td>0.367950</td>\n",
              "      <td>0.359594</td>\n",
              "      <td>0.370647</td>\n",
              "      <td>0.371536</td>\n",
              "      <td>0.376774</td>\n",
              "      <td>0.389169</td>\n",
              "      <td>0.174304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.233705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>3.940000</td>\n",
              "      <td>770.500000</td>\n",
              "      <td>1440.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.651515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>15.260000</td>\n",
              "      <td>1039.000000</td>\n",
              "      <td>4080.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.773333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>24.260000</td>\n",
              "      <td>1254.750000</td>\n",
              "      <td>6900.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.850170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>54.560000</td>\n",
              "      <td>23122.000000</td>\n",
              "      <td>15120.000000</td>\n",
              "      <td>3600.000000</td>\n",
              "      <td>270.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.108125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>8 rows × 26 columns</p>\n",
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            ],
            "text/plain": [
              "              team  targeted_productivity          smv           wip  \\\n",
              "count  1017.000000            1017.000000  1017.000000    594.000000   \n",
              "mean      6.443461               0.730747    15.150492   1183.183502   \n",
              "std       3.472473               0.097384    10.946096   1793.836719   \n",
              "min       1.000000               0.070000     2.900000      7.000000   \n",
              "25%       3.000000               0.700000     3.940000    770.500000   \n",
              "50%       7.000000               0.750000    15.260000   1039.000000   \n",
              "75%       9.000000               0.800000    24.260000   1254.750000   \n",
              "max      12.000000               0.800000    54.560000  23122.000000   \n",
              "\n",
              "          over_time    incentive    idle_time     idle_men  \\\n",
              "count   1017.000000  1017.000000  1017.000000  1017.000000   \n",
              "mean    4532.940020    40.689282     0.564405     0.398230   \n",
              "std     3275.997333   173.240655    10.093731     3.351712   \n",
              "min        0.000000     0.000000     0.000000     0.000000   \n",
              "25%     1440.000000     0.000000     0.000000     0.000000   \n",
              "50%     4080.000000     0.000000     0.000000     0.000000   \n",
              "75%     6900.000000    50.000000     0.000000     0.000000   \n",
              "max    15120.000000  3600.000000   270.000000    45.000000   \n",
              "\n",
              "       no_of_style_change  no_of_workers  ...  department_finishing  \\\n",
              "count         1017.000000    1017.000000  ...           1017.000000   \n",
              "mean             0.160275      34.846116  ...              0.201573   \n",
              "std              0.440199      22.185292  ...              0.401373   \n",
              "min              0.000000       2.000000  ...              0.000000   \n",
              "25%              0.000000       9.000000  ...              0.000000   \n",
              "50%              0.000000      34.000000  ...              0.000000   \n",
              "75%              0.000000      57.000000  ...              0.000000   \n",
              "max              2.000000      89.000000  ...              1.000000   \n",
              "\n",
              "       department_finishing   department_sweing   day_Monday  day_Saturday  \\\n",
              "count            1017.000000        1017.000000  1017.000000   1017.000000   \n",
              "mean                0.214356           0.584071     0.161259      0.152409   \n",
              "std                 0.410577           0.493124     0.367950      0.359594   \n",
              "min                 0.000000           0.000000     0.000000      0.000000   \n",
              "25%                 0.000000           0.000000     0.000000      0.000000   \n",
              "50%                 0.000000           1.000000     0.000000      0.000000   \n",
              "75%                 0.000000           1.000000     0.000000      0.000000   \n",
              "max                 1.000000           1.000000     1.000000      1.000000   \n",
              "\n",
              "        day_Sunday  day_Thursday  day_Tuesday  day_Wednesday  \\\n",
              "count  1017.000000   1017.000000  1017.000000    1017.000000   \n",
              "mean      0.164208      0.165192     0.171091       0.185841   \n",
              "std       0.370647      0.371536     0.376774       0.389169   \n",
              "min       0.000000      0.000000     0.000000       0.000000   \n",
              "25%       0.000000      0.000000     0.000000       0.000000   \n",
              "50%       0.000000      0.000000     0.000000       0.000000   \n",
              "75%       0.000000      0.000000     0.000000       0.000000   \n",
              "max       1.000000      1.000000     1.000000       1.000000   \n",
              "\n",
              "       actual_productivity  \n",
              "count          1017.000000  \n",
              "mean              0.736509  \n",
              "std               0.174304  \n",
              "min               0.233705  \n",
              "25%               0.651515  \n",
              "50%               0.773333  \n",
              "75%               0.850170  \n",
              "max               1.108125  \n",
              "\n",
              "[8 rows x 26 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "execution": {
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        },
        "id": "_pasroxHj-Cq",
        "outputId": "965f9c5a-1669-4e3a-a822-f9468715ffdd"
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      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>targeted_productivity</th>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <th>4</th>\n",
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              "      <td>0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.790003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   team  targeted_productivity    smv    wip  over_time  incentive  idle_time  \\\n",
              "0     9                   0.75   3.94    NaN        960          0        0.0   \n",
              "1     7                   0.65  30.10  909.0       7080          0        0.0   \n",
              "2     3                   0.80   4.15    NaN       1440          0        0.0   \n",
              "3     1                   0.65  22.53  762.0       5040          0        0.0   \n",
              "4     4                   0.70  30.10  767.0       3300         50        0.0   \n",
              "\n",
              "   idle_men  no_of_style_change  no_of_workers  ...  department_finishing  \\\n",
              "0         0                   0            8.0  ...                     1   \n",
              "1         0                   1           59.0  ...                     0   \n",
              "2         0                   0            7.0  ...                     0   \n",
              "3         0                   1           42.0  ...                     0   \n",
              "4         0                   1           57.0  ...                     0   \n",
              "\n",
              "   department_finishing   department_sweing  day_Monday  day_Saturday  \\\n",
              "0                      0                  0           0             0   \n",
              "1                      0                  1           0             0   \n",
              "2                      1                  0           0             0   \n",
              "3                      0                  1           0             0   \n",
              "4                      0                  1           1             0   \n",
              "\n",
              "   day_Sunday  day_Thursday  day_Tuesday  day_Wednesday  actual_productivity  \n",
              "0           0             0            0              1             0.755167  \n",
              "1           0             1            0              0             0.535678  \n",
              "2           0             0            0              1             0.820833  \n",
              "3           0             0            0              1             0.581131  \n",
              "4           0             0            0              0             0.790003  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UPggfgRfj-Cr"
      },
      "source": [
        "### Dealing with NaN values\n",
        "We can see that a column named 'wip' has many Nan values, from the documentation of dataset, wip means work in progress i.e number of unfinished items. So its safe to assume it can be filled with 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:27:54.725432Z",
          "iopub.status.busy": "2022-12-21T06:27:54.725045Z",
          "iopub.status.idle": "2022-12-21T06:27:54.731413Z",
          "shell.execute_reply": "2022-12-21T06:27:54.730366Z",
          "shell.execute_reply.started": "2022-12-21T06:27:54.725399Z"
        },
        "id": "wgRR-toOj-Cs"
      },
      "outputs": [],
      "source": [
        "df['wip'] = df['wip'].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:27:55.460200Z",
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        {
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              "  <thead>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.820833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.65</td>\n",
              "      <td>22.53</td>\n",
              "      <td>762.0</td>\n",
              "      <td>5040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>42.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.581131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>30.10</td>\n",
              "      <td>767.0</td>\n",
              "      <td>3300</td>\n",
              "      <td>50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>57.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.790003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   team  targeted_productivity    smv    wip  over_time  incentive  idle_time  \\\n",
              "0     9                   0.75   3.94    0.0        960          0        0.0   \n",
              "1     7                   0.65  30.10  909.0       7080          0        0.0   \n",
              "2     3                   0.80   4.15    0.0       1440          0        0.0   \n",
              "3     1                   0.65  22.53  762.0       5040          0        0.0   \n",
              "4     4                   0.70  30.10  767.0       3300         50        0.0   \n",
              "\n",
              "   idle_men  no_of_style_change  no_of_workers  ...  department_finishing  \\\n",
              "0         0                   0            8.0  ...                     1   \n",
              "1         0                   1           59.0  ...                     0   \n",
              "2         0                   0            7.0  ...                     0   \n",
              "3         0                   1           42.0  ...                     0   \n",
              "4         0                   1           57.0  ...                     0   \n",
              "\n",
              "   department_finishing   department_sweing  day_Monday  day_Saturday  \\\n",
              "0                      0                  0           0             0   \n",
              "1                      0                  1           0             0   \n",
              "2                      1                  0           0             0   \n",
              "3                      0                  1           0             0   \n",
              "4                      0                  1           1             0   \n",
              "\n",
              "   day_Sunday  day_Thursday  day_Tuesday  day_Wednesday  actual_productivity  \n",
              "0           0             0            0              1             0.755167  \n",
              "1           0             1            0              0             0.535678  \n",
              "2           0             0            0              1             0.820833  \n",
              "3           0             0            0              1             0.581131  \n",
              "4           0             0            0              0             0.790003  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_5L0m9Dj-Cv"
      },
      "source": [
        "## Getting general idea of data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:27:58.274143Z",
          "iopub.status.busy": "2022-12-21T06:27:58.273733Z",
          "iopub.status.idle": "2022-12-21T06:27:58.279905Z",
          "shell.execute_reply": "2022-12-21T06:27:58.278435Z",
          "shell.execute_reply.started": "2022-12-21T06:27:58.274108Z"
        },
        "id": "C_AicTAZj-Cw"
      },
      "outputs": [],
      "source": [
        "num_cols=['no_of_workers','no_of_style_change','idle_men','idle_time','incentive','over_time','wip','smv','targeted_productivity','actual_productivity']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:27:59.164664Z",
          "iopub.status.busy": "2022-12-21T06:27:59.163721Z",
          "iopub.status.idle": "2022-12-21T06:28:00.096900Z",
          "shell.execute_reply": "2022-12-21T06:28:00.095680Z",
          "shell.execute_reply.started": "2022-12-21T06:27:59.164623Z"
        },
        "id": "hQ_IGiEbj-Cx",
        "outputId": "cd33f735-4662-4697-db15-c14f0a4da003"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,12))\n",
        "sns.heatmap(df[num_cols].corr(),annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:28:02.843497Z",
          "iopub.status.busy": "2022-12-21T06:28:02.842794Z",
          "iopub.status.idle": "2022-12-21T06:28:03.328378Z",
          "shell.execute_reply": "2022-12-21T06:28:03.327105Z",
          "shell.execute_reply.started": "2022-12-21T06:28:02.843445Z"
        },
        "id": "jas4r9aij-Cy",
        "outputId": "9cb21a1d-3764-4a28-c795-a14c2d7cb3b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='targeted_productivity', ylabel='actual_productivity'>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='targeted_productivity',  y='actual_productivity',data=df, hue='team', palette='viridis', alpha=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:28:03.647753Z",
          "iopub.status.busy": "2022-12-21T06:28:03.647077Z",
          "iopub.status.idle": "2022-12-21T06:28:04.084025Z",
          "shell.execute_reply": "2022-12-21T06:28:04.082546Z",
          "shell.execute_reply.started": "2022-12-21T06:28:03.647712Z"
        },
        "id": "Y4rqIM3Gj-Cz",
        "outputId": "d217ace7-4aa5-4fb1-8a75-32b3263242da"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='no_of_workers', ylabel='actual_productivity'>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='no_of_workers',  y='actual_productivity',data=df, hue='team', palette='viridis', alpha=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:28:04.748977Z",
          "iopub.status.busy": "2022-12-21T06:28:04.748578Z",
          "iopub.status.idle": "2022-12-21T06:28:05.192229Z",
          "shell.execute_reply": "2022-12-21T06:28:05.190909Z",
          "shell.execute_reply.started": "2022-12-21T06:28:04.748945Z"
        },
        "id": "hDRxq9N9j-C0",
        "outputId": "3fc712b0-c78b-48df-ca3c-e26539ab9620"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='wip', ylabel='actual_productivity'>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='wip',  y='actual_productivity',data=df, hue='team', palette='viridis', alpha=0.6)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59vXhCG8j-C1"
      },
      "source": [
        "### Initial Asessments\n",
        "Plotting a correlation matrix and some scatterplots, we can see that there seems to be no apparent linear/polynomial pattern with singular feature. As many of the features we are dealing with are discrete, algebra based regression models may or may not perform as well as tree based regression methods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:28:29.346662Z",
          "iopub.status.busy": "2022-12-21T06:28:29.346199Z",
          "iopub.status.idle": "2022-12-21T06:28:29.353647Z",
          "shell.execute_reply": "2022-12-21T06:28:29.352055Z",
          "shell.execute_reply.started": "2022-12-21T06:28:29.346625Z"
        },
        "id": "HSZFdQMXj-C1"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:28:10.143995Z",
          "iopub.status.busy": "2022-12-21T06:28:10.143608Z",
          "iopub.status.idle": "2022-12-21T06:28:10.154180Z",
          "shell.execute_reply": "2022-12-21T06:28:10.153126Z",
          "shell.execute_reply.started": "2022-12-21T06:28:10.143963Z"
        },
        "id": "tku1BvCRj-C3"
      },
      "outputs": [],
      "source": [
        "# Separating Label\n",
        "X = df.drop('actual_productivity',axis=1)\n",
        "y = df['actual_productivity']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIS6rjKpj-C4"
      },
      "source": [
        "# Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T05:57:47.720015Z",
          "iopub.status.busy": "2022-12-21T05:57:47.719634Z",
          "iopub.status.idle": "2022-12-21T06:00:42.870867Z",
          "shell.execute_reply": "2022-12-21T06:00:42.869029Z",
          "shell.execute_reply.started": "2022-12-21T05:57:47.719982Z"
        },
        "id": "Y7fh2aQUj-C4"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# TRAINING ERROR PER DEGREE\n",
        "train_rmse_errors = []\n",
        "# TEST ERROR PER DEGREE\n",
        "test_rmse_errors = []\n",
        "\n",
        "for d in range(1,7):\n",
        "    # CREATE POLY DATA SET FOR DEGREE \"d\"\n",
        "    polynomial_converter = PolynomialFeatures(degree=d,include_bias=False)\n",
        "    poly_features = polynomial_converter.fit_transform(X)\n",
        "    \n",
        "    # SPLIT THIS NEW POLY DATA SET\n",
        "    X_train, X_test, y_train, y_test = train_test_split(poly_features, y, test_size=0.3, random_state=101)\n",
        "    \n",
        "    scaler.fit(X_train)\n",
        "    X_train = scaler.transform(X_train)\n",
        "    X_test = scaler.transform(X_test)\n",
        "    \n",
        "    # TRAIN ON THIS NEW POLY SET\n",
        "    model = LinearRegression(fit_intercept=True)\n",
        "    model.fit(X_train,y_train)\n",
        "    \n",
        "    # PREDICT ON BOTH TRAIN AND TEST\n",
        "    train_pred = model.predict(X_train)\n",
        "    test_pred = model.predict(X_test)\n",
        "    \n",
        "    # Calculate Errors\n",
        "    \n",
        "    # Errors on Train Set\n",
        "    train_RMSE = np.sqrt(mean_squared_error(y_train,train_pred))\n",
        "    \n",
        "    # Errors on Test Set\n",
        "    test_RMSE = np.sqrt(mean_squared_error(y_test,test_pred))\n",
        "\n",
        "    # Append errors to lists for plotting later\n",
        "    \n",
        "   \n",
        "    train_rmse_errors.append(train_RMSE)\n",
        "    test_rmse_errors.append(test_RMSE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:00:42.873877Z",
          "iopub.status.busy": "2022-12-21T06:00:42.873008Z",
          "iopub.status.idle": "2022-12-21T06:00:43.130923Z",
          "shell.execute_reply": "2022-12-21T06:00:43.129761Z",
          "shell.execute_reply.started": "2022-12-21T06:00:42.873828Z"
        },
        "id": "RIc-lKvDj-C6",
        "outputId": "02a21be2-677f-43ea-8c20-a3d23035bc59"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f65cc309dd0>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting errors\n",
        "plt.plot(range(1,7),train_rmse_errors,label='TRAIN')\n",
        "plt.plot(range(1,7),test_rmse_errors,label='TEST')\n",
        "plt.xlabel(\"Polynomial Complexity\")\n",
        "plt.ylabel(\"RMSE\")\n",
        "plt.ylim(0,10)\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:00:43.132571Z",
          "iopub.status.busy": "2022-12-21T06:00:43.132224Z",
          "iopub.status.idle": "2022-12-21T06:00:43.371598Z",
          "shell.execute_reply": "2022-12-21T06:00:43.370254Z",
          "shell.execute_reply.started": "2022-12-21T06:00:43.132528Z"
        },
        "id": "vE0Qc0Rnj-C6",
        "outputId": "ac1688f4-b538-4d74-e7a1-99d307f381b0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f65bf724850>"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting errors\n",
        "plt.plot(range(1,7),train_rmse_errors,label='TRAIN')\n",
        "plt.plot(range(1,7),test_rmse_errors,label='TEST')\n",
        "plt.xlabel(\"Polynomial Complexity\")\n",
        "plt.ylabel(\"RMSE\")\n",
        "plt.ylim(0,0.5)\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:00:43.375174Z",
          "iopub.status.busy": "2022-12-21T06:00:43.374732Z",
          "iopub.status.idle": "2022-12-21T06:00:43.382793Z",
          "shell.execute_reply": "2022-12-21T06:00:43.381628Z",
          "shell.execute_reply.started": "2022-12-21T06:00:43.375142Z"
        },
        "id": "nQKgjtVDj-C8",
        "outputId": "175e1153-62b8-4862-a604-0035207df870"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.14658074389524323"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_rmse_errors[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7x1OpiIxj-C8"
      },
      "source": [
        "#### The minimum RMSE error we get from here is from linear regression i.e 0.146"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gOuzyRsRj-C8"
      },
      "source": [
        "# SVM Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:28:34.137403Z",
          "iopub.status.busy": "2022-12-21T06:28:34.136937Z",
          "iopub.status.idle": "2022-12-21T06:29:51.246758Z",
          "shell.execute_reply": "2022-12-21T06:29:51.245529Z",
          "shell.execute_reply.started": "2022-12-21T06:28:34.137365Z"
        },
        "id": "DIAqddDMj-C9",
        "outputId": "9f66bc60-c6b8-4c88-80dd-8093aeb40e49"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVR(),\n",
              "             param_grid={'C': [0.001, 0.01, 0.1, 0.5, 1], 'degree': [2, 3, 4],\n",
              "                         'epsilon': [0, 0.01, 0.1, 0.5, 1, 2],\n",
              "                         'gamma': ['scale', 'auto'],\n",
              "                         'kernel': ['linear', 'rbf', 'poly']})"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# SCALE DATA\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "scaled_X_train = scaler.transform(X_train)\n",
        "scaled_X_test = scaler.transform(X_test)\n",
        "\n",
        "# SVM REGRESSION #\n",
        "from sklearn.svm import SVR,LinearSVR # SVM Regression\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "param_g = {'C':[0.001,0.01,0.1,0.5,1],\n",
        "             'kernel':['linear','rbf','poly'],\n",
        "              'gamma':['scale','auto'],\n",
        "              'degree':[2,3,4],\n",
        "              'epsilon':[0,0.01,0.1,0.5,1,2]}\n",
        "svr = SVR()\n",
        "grid = GridSearchCV(svr,param_grid=param_g)\n",
        "grid.fit(scaled_X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:30:51.922445Z",
          "iopub.status.busy": "2022-12-21T06:30:51.921237Z",
          "iopub.status.idle": "2022-12-21T06:30:51.929054Z",
          "shell.execute_reply": "2022-12-21T06:30:51.928185Z",
          "shell.execute_reply.started": "2022-12-21T06:30:51.922400Z"
        },
        "id": "c0Pjz7fCj-C-",
        "outputId": "46a507e6-f3e7-4444-d700-52ff2dfec77e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'C': 0.5, 'degree': 2, 'epsilon': 0.01, 'gamma': 'scale', 'kernel': 'rbf'}"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grid.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:30:52.908381Z",
          "iopub.status.busy": "2022-12-21T06:30:52.907105Z",
          "iopub.status.idle": "2022-12-21T06:30:52.932126Z",
          "shell.execute_reply": "2022-12-21T06:30:52.930837Z",
          "shell.execute_reply.started": "2022-12-21T06:30:52.908328Z"
        },
        "id": "y4l_WgThj-C_"
      },
      "outputs": [],
      "source": [
        "grid_preds = grid.predict(scaled_X_test)\n",
        "MAE=mean_absolute_error(y_test,grid_preds)\n",
        "MSE=np.sqrt(mean_squared_error(y_test,grid_preds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:30:54.394539Z",
          "iopub.status.busy": "2022-12-21T06:30:54.394088Z",
          "iopub.status.idle": "2022-12-21T06:30:54.401530Z",
          "shell.execute_reply": "2022-12-21T06:30:54.400313Z",
          "shell.execute_reply.started": "2022-12-21T06:30:54.394493Z"
        },
        "id": "IOcsRSvqj-DA",
        "outputId": "07e91944-3f04-45e4-ab8e-55d52f32152c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.1461277025000012"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "MSE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBBKaBmOj-DA"
      },
      "source": [
        "### Inference\n",
        "Very Similar to linear Regression, with RMSE 0.146"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKDioVewj-DB"
      },
      "source": [
        "# Random forest regresssor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:31:00.159896Z",
          "iopub.status.busy": "2022-12-21T06:31:00.159498Z",
          "iopub.status.idle": "2022-12-21T06:31:00.283963Z",
          "shell.execute_reply": "2022-12-21T06:31:00.282542Z",
          "shell.execute_reply.started": "2022-12-21T06:31:00.159863Z"
        },
        "id": "4Vz7Ug1tj-DB"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:31:00.633805Z",
          "iopub.status.busy": "2022-12-21T06:31:00.632745Z",
          "iopub.status.idle": "2022-12-21T06:31:00.641016Z",
          "shell.execute_reply": "2022-12-21T06:31:00.640043Z",
          "shell.execute_reply.started": "2022-12-21T06:31:00.633755Z"
        },
        "id": "h1vkmJr_j-DC"
      },
      "outputs": [],
      "source": [
        "def run_model(model,X_train,y_train,X_test,y_test):\n",
        "    \n",
        "    # Fit Model\n",
        "    model.fit(X_train,y_train)\n",
        "    \n",
        "    # Get Metrics\n",
        "    \n",
        "    preds = model.predict(X_test)\n",
        "    \n",
        "    rmse = np.sqrt(mean_squared_error(y_test,preds))\n",
        "    print(f'RMSE : {rmse}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:31:04.223642Z",
          "iopub.status.busy": "2022-12-21T06:31:04.222570Z",
          "iopub.status.idle": "2022-12-21T06:31:04.890917Z",
          "shell.execute_reply": "2022-12-21T06:31:04.889717Z",
          "shell.execute_reply.started": "2022-12-21T06:31:04.223596Z"
        },
        "id": "enl3c0hbj-DD",
        "outputId": "f2f50fad-a500-49d5-af89-c0073778227c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE : 0.13457555919416928\n",
            "RMSE : 0.1390969106692785\n",
            "RMSE : 0.13700447245624195\n"
          ]
        }
      ],
      "source": [
        "trees = [10,50,100]\n",
        "for n in trees:\n",
        "    \n",
        "    model = RandomForestRegressor(n_estimators=n)\n",
        "    \n",
        "    run_model(model,X_train,y_train,X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3HWudAij-DE"
      },
      "source": [
        "#### Minimum RMSE error is 0.1370"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nx23f6ofj-DE"
      },
      "source": [
        "# Gradient Boost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:31:08.113058Z",
          "iopub.status.busy": "2022-12-21T06:31:08.112229Z",
          "iopub.status.idle": "2022-12-21T06:31:08.119196Z",
          "shell.execute_reply": "2022-12-21T06:31:08.117794Z",
          "shell.execute_reply.started": "2022-12-21T06:31:08.113000Z"
        },
        "id": "wgYq5YBSj-DG"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:31:08.332082Z",
          "iopub.status.busy": "2022-12-21T06:31:08.331693Z",
          "iopub.status.idle": "2022-12-21T06:31:08.462839Z",
          "shell.execute_reply": "2022-12-21T06:31:08.461541Z",
          "shell.execute_reply.started": "2022-12-21T06:31:08.332050Z"
        },
        "id": "oV64kPf4j-DG",
        "outputId": "9d444d1e-67c2-4c04-9e70-66d665d5f060"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE : 0.13601465608856553\n"
          ]
        }
      ],
      "source": [
        "model = GradientBoostingRegressor()\n",
        "\n",
        "run_model(model,X_train,y_train,X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7jZr4_oj-DH"
      },
      "source": [
        "#### By far our most accurate model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2LXVa4ij-DI"
      },
      "source": [
        "# Inference\n",
        "We tried various kind of regression methods, least RMSE we could get was 0.1360, mean actual productivity is 0.7365"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-21T06:31:12.152639Z",
          "iopub.status.busy": "2022-12-21T06:31:12.151631Z",
          "iopub.status.idle": "2022-12-21T06:31:12.159293Z",
          "shell.execute_reply": "2022-12-21T06:31:12.158127Z",
          "shell.execute_reply.started": "2022-12-21T06:31:12.152600Z"
        },
        "id": "2w4fZoO8j-DI",
        "outputId": "b99e7bf4-6663-4def-b30f-62fbb2ae215b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "18.46571622539036"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "0.1360/0.7365*100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYQC1q1Lj-DI"
      },
      "source": [
        "Our error is about 18% off from the average, which is pretty inaccurate.\n",
        "\n",
        "Although our features correlate to the productivity of team in a broad sense (as seen from the heatmap) we still need more data, to train/test our model. For now data seems fairly random and we cannot predict from it accurately."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.15"
    },
    "colab": {
      "provenance": []
    }
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