Prediction_of_Delivery_Time_using_Sorting_Time(SLR).ipynb

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        "<a href=\"https://colab.research.google.com/github/manasik29/Prediction-of-Delivery-Time-of-newspaper-using-Sorting-Time-of-newspaper/blob/main/Prediction_of_Delivery_Time_using_Sorting_Time(SLR).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "***1) Delivery_time -> Predict delivery time using sorting time***\n",
        "\n",
        "Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python."
      ],
      "metadata": {
        "id": "0SxFDvL49oM_"
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      "source": [
        "***Load Data***"
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        "import pandas as pd"
      ]
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      "source": [
        "data = pd.read_csv('/content/delivery_time.csv')"
      ],
      "metadata": {
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              "    Delivery Time  Sorting Time\n",
              "0           21.00            10\n",
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        "#Renaming all columns\n",
        "df = data.rename({'Delivery Time':'Delivery_Time', 'Sorting Time':'Sorting_Time'}, axis = 1)"
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              "    Delivery_Time  Sorting_Time\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jt3tUdyS-BDJ",
        "outputId": "90276247-d9a5-4b9b-d763-8546026fc515"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21 entries, 0 to 20\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Delivery_Time  21 non-null     float64\n",
            " 1   Sorting_Time   21 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 464.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***EDA***"
      ],
      "metadata": {
        "id": "PP-iqF8O_atT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssd9pf2dsRB-",
        "outputId": "328f8619-315b-49fe-ccfb-79ea3e201c2d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
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      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Delivery_Time  Sorting_Time\n",
              "0          21.00            10\n",
              "1          13.50             4\n",
              "2          19.75             6\n",
              "3          24.00             9\n",
              "4          29.00            10"
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      },
      "execution_count": 9,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "    Delivery_Time  Sorting_Time\n",
              "16          13.75             6\n",
              "17          18.11             7\n",
              "18           8.00             2\n",
              "19          17.83             7\n",
              "20          21.50             5"
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9fce1dd-e4b3-4c48-926b-c153305d2817 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9fce1dd-e4b3-4c48-926b-c153305d2817');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTSEIEhetYdP",
        "outputId": "e9532ea6-962a-4bae-9a1c-4bb98b39aa0e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Delivery_Time    float64\n",
              "Sorting_Time       int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No requirement of transformation or dtype correction"
      ],
      "metadata": {
        "id": "WxArQpvAwmWp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check for missing values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vPKR5kjvBp5",
        "outputId": "05aa8247-3f5b-4cb7-c496-34dd3d1ef533"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Delivery_Time    0\n",
              "Sorting_Time     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#to genrate heat map\n",
        "import seaborn as sns\n",
        "cols = df.columns\n",
        "colors = ['#000099', '#ffff00']   #define colors, yellow for missing, blue for non missing\n",
        "sns.heatmap(df[cols].isnull(),\n",
        "               cmap= sns.color_palette(colors))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "Dus5sQbjvAx8",
        "outputId": "21e93966-2db3-4fcb-84e9-ad7bc3a6d0f7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9bfe2c2190>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence we can conclude no missing values"
      ],
      "metadata": {
        "id": "gNYzE9gpvazO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#to check for duplicate rows\n",
        "df[df.duplicated()].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHnqMI_ds-DA",
        "outputId": "5c814e04-adc2-4004-8542-694b6c778935"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No duplicate rows"
      ],
      "metadata": {
        "id": "ZETk4u2_wgpR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "QfTRnVAct1dX",
        "outputId": "6c2dcb4a-12d1-43ad-8e96-eef19d113ab8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Delivery_Time  Sorting_Time\n",
              "count      21.000000     21.000000\n",
              "mean       16.790952      6.190476\n",
              "std         5.074901      2.542028\n",
              "min         8.000000      2.000000\n",
              "25%        13.500000      4.000000\n",
              "50%        17.830000      6.000000\n",
              "75%        19.750000      8.000000\n",
              "max        29.000000     10.000000"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Delivery_Time</th>\n",
              "      <th>Sorting_Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16.790952</td>\n",
              "      <td>6.190476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.074901</td>\n",
              "      <td>2.542028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.500000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>17.830000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>19.750000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>10.000000</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Outlier Detection\n",
        "import matplotlib.pyplot as plt\n",
        "plt.boxplot(df['Delivery_Time'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "f16YdGgzuQ94",
        "outputId": "6b9808fe-9095-4ac0-bf0d-6054dcddddcb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7f9bfb4f8f10>,\n",
              "  <matplotlib.lines.Line2D at 0x7f9bfb4fd490>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f9bfb4fd9d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f9bfb4fdf10>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7f9bfb4f89d0>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f9bfb5044d0>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f9bfb504a10>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "As9hkyTIv5v3"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df.Delivery_Time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "qa_2G-s6v-mm",
        "outputId": "a8e474be-2fd6-4beb-e9f0-c67aca3fff90"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9bfb4e8fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(df['Sorting_Time'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "VJ9dt8dYud-n",
        "outputId": "ca650437-5ed1-4140-e76e-caa2785bafba"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7f9bfb3abad0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f9bfb3b2050>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f9bfb3b2590>,\n",
              "  <matplotlib.lines.Line2D at 0x7f9bfb3b2ad0>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7f9bfb3ab550>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f9bfb3b9090>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f9bfb3b95d0>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df.Sorting_Time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "zZQeoa7YwD_Z",
        "outputId": "c008e20f-bc0b-491a-db28-423c8a81216a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9bfb36bc10>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we can coclude there are no outliers."
      ],
      "metadata": {
        "id": "G7ENnZnjwHAn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**- Performing Transformation on each column**"
      ],
      "metadata": {
        "id": "s-zeO2GrinVj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "PCBojP6EkCGp"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax=plt.subplots(2, figsize=(6,4), sharex= False, sharey = False)\n",
        "sns.boxplot(np.log(df.Sorting_Time), ax=ax[0])\n",
        "sns.boxplot(np.log(df.Delivery_Time), ax=ax[1])\n",
        "plt.suptitle(\"Log Transformation on Continuous Variables\", fontsize= 17, y = 1.06)\n",
        "plt.tight_layout(pad=2.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "JjEHKts6it3a",
        "outputId": "df111d66-71f9-4aea-e684-8d7045ebe672"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax=plt.subplots(2, figsize=(6,4), sharex= False, sharey = False)\n",
        "sns.boxplot(np.sqrt(df.Sorting_Time), ax=ax[0])\n",
        "sns.boxplot(np.sqrt(df.Delivery_Time), ax=ax[1])\n",
        "plt.suptitle(\"Sqrt Transformation on Continuous Variables\", fontsize= 17, y = 1.06)\n",
        "plt.tight_layout(pad=2.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "-1qZ7PErkmJA",
        "outputId": "f43c22f0-b03b-4b50-85f4-1a86f654c935"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax=plt.subplots(2, figsize=(6,4), sharex= False, sharey = False)\n",
        "sns.boxplot(np.cbrt(df.Sorting_Time), ax=ax[0])\n",
        "sns.boxplot(np.cbrt(df.Delivery_Time), ax=ax[1])\n",
        "plt.suptitle(\"Cbrt Transformation on Continuous Variables\", fontsize= 17, y = 1.06)\n",
        "plt.tight_layout(pad=2.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "duicO6VMkvYw",
        "outputId": "01ca74aa-5257-4992-fb7f-39ea204cc144"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation**"
      ],
      "metadata": {
        "id": "pMs_uop1k70T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation coef\n",
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "gLXWk-1E_MS-",
        "outputId": "e2c992c8-5807-4fac-aa1f-aa4ec8ee5cd3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Delivery_Time  Sorting_Time\n",
              "Delivery_Time       1.000000      0.825997\n",
              "Sorting_Time        0.825997      1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25a5e8ce-22fd-4479-ae76-094ba0a286ad\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Delivery_Time</th>\n",
              "      <th>Sorting_Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Delivery_Time</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.825997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sorting_Time</th>\n",
              "      <td>0.825997</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25a5e8ce-22fd-4479-ae76-094ba0a286ad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-25a5e8ce-22fd-4479-ae76-094ba0a286ad button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-25a5e8ce-22fd-4479-ae76-094ba0a286ad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "16QxQ123lAFk",
        "outputId": "6045b18a-7b75-4c9e-ec01-7c2d40b827b4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f9bf9bcf350>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Fitting a Regression Equation***"
      ],
      "metadata": {
        "id": "Ad2sxgn3A6yd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "Aqlw0VFFA1hH"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = smf.ols(\"Delivery_Time~Sorting_Time\", data=df).fit()"
      ],
      "metadata": {
        "id": "nfP3LFvdBBvf"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model parameters\n",
        "model.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWu-pQsuBZ7-",
        "outputId": "b91adc92-a288-4b5f-99f9-45d6a378b09e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept       6.582734\n",
              "Sorting_Time    1.649020\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get t-values & p-values\n",
        "print(model.tvalues, '\\n',model.pvalues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_ubAyQ1B5az",
        "outputId": "d654220a-5d5c-42a8-d59e-e1e0a12f3cdf"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept       3.823349\n",
            "Sorting_Time    6.387447\n",
            "dtype: float64 \n",
            " Intercept       0.001147\n",
            "Sorting_Time    0.000004\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#r squared value\n",
        "model.rsquared"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55tlELbpCDZN",
        "outputId": "8a433f2b-55b7-4f53-c490-f7c9dc16146d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6822714748417231"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x = \"Sorting_Time\", y = \"Delivery_Time\", data = df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "ab7LMjbDCa0u",
        "outputId": "17626f0b-dd3a-46c7-fe52-913b7b1a08fd"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9bf47ac650>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "B7awlVkoKOCU",
        "outputId": "526c23ba-01a3-4734-cfc7-ba848c86a3f1"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:          Delivery_Time   R-squared:                       0.682\n",
              "Model:                            OLS   Adj. R-squared:                  0.666\n",
              "Method:                 Least Squares   F-statistic:                     40.80\n",
              "Date:                Fri, 09 Sep 2022   Prob (F-statistic):           3.98e-06\n",
              "Time:                        14:08:46   Log-Likelihood:                -51.357\n",
              "No. Observations:                  21   AIC:                             106.7\n",
              "Df Residuals:                      19   BIC:                             108.8\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "================================================================================\n",
              "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
              "--------------------------------------------------------------------------------\n",
              "Intercept        6.5827      1.722      3.823      0.001       2.979      10.186\n",
              "Sorting_Time     1.6490      0.258      6.387      0.000       1.109       2.189\n",
              "==============================================================================\n",
              "Omnibus:                        3.649   Durbin-Watson:                   1.248\n",
              "Prob(Omnibus):                  0.161   Jarque-Bera (JB):                2.086\n",
              "Skew:                           0.750   Prob(JB):                        0.352\n",
              "Kurtosis:                       3.367   Cond. No.                         18.3\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>Delivery_Time</td>  <th>  R-squared:         </th> <td>   0.682</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.666</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   40.80</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 09 Sep 2022</td> <th>  Prob (F-statistic):</th> <td>3.98e-06</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>14:08:46</td>     <th>  Log-Likelihood:    </th> <td> -51.357</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>   106.7</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   108.8</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>    <td>    6.5827</td> <td>    1.722</td> <td>    3.823</td> <td> 0.001</td> <td>    2.979</td> <td>   10.186</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sorting_Time</th> <td>    1.6490</td> <td>    0.258</td> <td>    6.387</td> <td> 0.000</td> <td>    1.109</td> <td>    2.189</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 3.649</td> <th>  Durbin-Watson:     </th> <td>   1.248</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.161</td> <th>  Jarque-Bera (JB):  </th> <td>   2.086</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.750</td> <th>  Prob(JB):          </th> <td>   0.352</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 3.367</td> <th>  Cond. No.          </th> <td>    18.3</td>\n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Residual Analysis***\n",
        "\n",
        "***Test for Noramlity of Residual through QQ plot***"
      ],
      "metadata": {
        "id": "QEnrWYhpxrDB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "KNh5J0upxEtR"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#q line is the 45 degrees line\n",
        "qqplot = sm.qqplot(model.resid, line= 'q')\n",
        "plt.title('Normal Q-Q Plot of Residual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "39cRtsPhxG-t",
        "outputId": "61fcdf89-8418-4dc8-fd98-796fe56c1bf5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "list(np.where(model.resid>6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBi4o2wtxW1W",
        "outputId": "23b2b2b3-893d-4c07-bf8f-9f29c4254061"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([20])]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Residual plot for Homoscedasticity***"
      ],
      "metadata": {
        "id": "xeGcrrrKx4rD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_standardize_values( vals): \n",
        "  return (vals - vals.mean())/vals.std() # get standardized values of fitted values and residual"
      ],
      "metadata": {
        "id": "hndcNlxRx2aF"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(get_standardize_values(model.fittedvalues), get_standardize_values(model.resid))\n",
        "plt.ylabel('Standardize Residual values')\n",
        "plt.xlabel('Standardize Fitted values')\n",
        "plt.title('Residaul Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "-43SOJ-Mx_Yn",
        "outputId": "463cb5a2-7138-4bd2-cb73-0268f8f1e4c4"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Residual vs Regressors***"
      ],
      "metadata": {
        "id": "8Fp_QKrZyG2D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "fig = sm.graphics.plot_regress_exog(model, \"Sorting_Time\", fig=fig)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "9bJaSZZpyFOW",
        "outputId": "203a5f1d-b8ba-4ac8-b71b-f5176ff5293f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Model Deletion by Cook's Distance and Leverage value***"
      ],
      "metadata": {
        "id": "zX8Vas8YzhJO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_influence = model.get_influence()\n",
        "(c, _) = model_influence.cooks_distance"
      ],
      "metadata": {
        "id": "EmJdvwPjzgif"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the influencers values using stem plot\n",
        "fig = plt.subplots(figsize=(20,8))\n",
        "plt.stem(np.arange(len(df)), np.round(c,3))\n",
        "plt.xlabel('Row Index')\n",
        "plt.ylabel('Cooks Distance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "ko3A1G40zou9",
        "outputId": "e1f26ed3-efbd-4445-8a0f-2ea5edf3370d"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#index and values of influencers where c is more than .5\n",
        "(np.argmax(c), np.max(c))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMZ4dfsJzvJ8",
        "outputId": "b3d96616-ef0f-41f7-a998-202fee70fd18"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 0.462053041265031)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics.regressionplots import influence_plot\n"
      ],
      "metadata": {
        "id": "s4tg4-j0z6gH"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "influence_plot(model)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "c3hImBM6z8wg",
        "outputId": "9bbf09fd-2945-42eb-ef8a-64766ddbbb83"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = df.shape[1]\n",
        "n = df.shape[0]\n",
        "leverage_cutoff = 3*(k+1)/n"
      ],
      "metadata": {
        "id": "ofo7TIDJ0APG"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "leverage_cutoff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IWCEy-4l0DHJ",
        "outputId": "3f10f780-2144-4baf-e987-a748ca56ec65"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42857142857142855"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above plots, as C<1 & leverage_cutoff value is higher than any datapoint, hence we will not delete any datapoint."
      ],
      "metadata": {
        "id": "CS-FZjdszwsg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Prediction***"
      ],
      "metadata": {
        "id": "_Z0ESk3DE_wB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict Delivery Time for Sorting Time = 5, 10\n",
        "newdata = pd.Series([5,10])"
      ],
      "metadata": {
        "id": "hNcGQDK9E0-Q"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred = pd.DataFrame(newdata, columns = ['Sorting_Time'])"
      ],
      "metadata": {
        "id": "1-LVNHsvFQGy"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "sWtGA1ekFb12",
        "outputId": "027a3f29-292b-4926-d249-8f373f11c7ca"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sorting_Time\n",
              "0             5\n",
              "1            10"
            ],
            "text/html": [
              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(data_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQjnXJdsFkxx",
        "outputId": "c3cbc46e-986b-4f50-dd46-fef9a8fadc5b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    14.827833\n",
              "1    23.072933\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    }
  ]
}