Profit_Prediction_XGBoost.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Profit Prediction - XGBoost",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqVywt-Is7hv"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import  StandardScaler,MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy import stats "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VIXMC9avtMTB"
      },
      "source": [
        "index = \"/content/drive/MyDrive/Profit Prediction/Dataset/train.csv\""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKrRCBjKoNo0"
      },
      "source": [
        "index_1 = \"/content/drive/MyDrive/Profit Prediction/Dataset/test.csv\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKZcqGL5Hfut"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "suPeKDshtMQK"
      },
      "source": [
        "df = pd.read_csv(index)  # Import dataset"
      ],
      "execution_count": 1182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNNi_yb6oZ4x"
      },
      "source": [
        "df_test = pd.read_csv(index_1)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVFmwSyptMMm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "a660feb7-a07e-49fa-d858-c80c3e006046"
      },
      "source": [
        "df"
      ],
      "execution_count": 1183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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>id</th>\n",
              "      <th>segment</th>\n",
              "      <th>RID</th>\n",
              "      <th>delivery type</th>\n",
              "      <th>class</th>\n",
              "      <th>sub-class</th>\n",
              "      <th>no of items</th>\n",
              "      <th>location</th>\n",
              "      <th>departure city</th>\n",
              "      <th>departure state</th>\n",
              "      <th>address code</th>\n",
              "      <th>placement date</th>\n",
              "      <th>delivery date</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>7981</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>phone</td>\n",
              "      <td>2</td>\n",
              "      <td>Central</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77095</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>16.448</td>\n",
              "      <td>0.2</td>\n",
              "      <td>5.5512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6334</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>5</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>29.700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.3650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6333</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>shorts</td>\n",
              "      <td>3</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>14.730</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6332</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>television</td>\n",
              "      <td>3</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>43.920</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.7368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6331</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>2</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>66.580</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.9792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6695</th>\n",
              "      <td>6696</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>9389</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>battery</td>\n",
              "      <td>3</td>\n",
              "      <td>East</td>\n",
              "      <td>Burlington</td>\n",
              "      <td>Vermont</td>\n",
              "      <td>5408</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>542.940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>152.0232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6696</th>\n",
              "      <td>6697</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>9597</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>2</td>\n",
              "      <td>West</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>California</td>\n",
              "      <td>95123</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>276.784</td>\n",
              "      <td>0.2</td>\n",
              "      <td>89.9548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6697</th>\n",
              "      <td>6698</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>9390</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>watch</td>\n",
              "      <td>1</td>\n",
              "      <td>East</td>\n",
              "      <td>Burlington</td>\n",
              "      <td>Vermont</td>\n",
              "      <td>5408</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>2.040</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6698</th>\n",
              "      <td>6699</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>440</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>3</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10024</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>207.846</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2.3094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6699</th>\n",
              "      <td>6700</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1659</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>fynota</td>\n",
              "      <td>colddrinks</td>\n",
              "      <td>3</td>\n",
              "      <td>West</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90045</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>160.776</td>\n",
              "      <td>0.2</td>\n",
              "      <td>10.0485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6700 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id    segment   RID  ...    price discount    profit\n",
              "0        1   Consumer  7981  ...   16.448      0.2    5.5512\n",
              "1        2   Consumer  6334  ...   29.700      0.0   13.3650\n",
              "2        3   Consumer  6333  ...   14.730      0.0    4.8609\n",
              "3        4   Consumer  6332  ...   43.920      0.0   12.7368\n",
              "4        5   Consumer  6331  ...   66.580      0.0   15.9792\n",
              "...    ...        ...   ...  ...      ...      ...       ...\n",
              "6695  6696   Consumer  9389  ...  542.940      0.0  152.0232\n",
              "6696  6697  Corporate  9597  ...  276.784      0.2   89.9548\n",
              "6697  6698   Consumer  9390  ...    2.040      0.0    0.9588\n",
              "6698  6699  Corporate   440  ...  207.846      0.1    2.3094\n",
              "6699  6700   Consumer  1659  ...  160.776      0.2   10.0485\n",
              "\n",
              "[6700 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1183
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "APVufZWYxEok"
      },
      "source": [
        "# **Removing outliers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egoK-Gh3xEol"
      },
      "source": [
        "# remove outliers no of items which are greater than 10\n",
        "#df1 = df[df['no of items'] < 10]\n",
        "df1 = df"
      ],
      "execution_count": 1184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oX3oivfJxEol"
      },
      "source": [
        "# remove profit which is less than -4000\n",
        "df1 = df1[df1['profit']>-4000]"
      ],
      "execution_count": 1185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIBdIUjhxEol"
      },
      "source": [
        "# remove price which are greater then 10000\n",
        "df1 = df1[df1['price']<10000]"
      ],
      "execution_count": 1186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "vJ-1kRkXxEoq",
        "outputId": "7b4c0d12-800c-453b-ab1a-a778cf0ef92b"
      },
      "source": [
        "df1.describe()"
      ],
      "execution_count": 1187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>RID</th>\n",
              "      <th>no of items</th>\n",
              "      <th>address code</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3350.188144</td>\n",
              "      <td>4954.018217</td>\n",
              "      <td>3.797820</td>\n",
              "      <td>54725.181425</td>\n",
              "      <td>227.863854</td>\n",
              "      <td>0.155767</td>\n",
              "      <td>29.135746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1933.765264</td>\n",
              "      <td>2921.536490</td>\n",
              "      <td>2.225566</td>\n",
              "      <td>32098.010326</td>\n",
              "      <td>537.949828</td>\n",
              "      <td>0.205809</td>\n",
              "      <td>188.994933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1040.000000</td>\n",
              "      <td>0.836000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3701.892800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1676.000000</td>\n",
              "      <td>2389.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22204.000000</td>\n",
              "      <td>17.340000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.724800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3350.000000</td>\n",
              "      <td>4941.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>55113.000000</td>\n",
              "      <td>54.920000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>8.804400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5024.000000</td>\n",
              "      <td>7507.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>90004.000000</td>\n",
              "      <td>212.640000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>30.023400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6700.000000</td>\n",
              "      <td>9990.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>99301.000000</td>\n",
              "      <td>9892.740000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>4946.370000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                id          RID  ...     discount       profit\n",
              "count  6697.000000  6697.000000  ...  6697.000000  6697.000000\n",
              "mean   3350.188144  4954.018217  ...     0.155767    29.135746\n",
              "std    1933.765264  2921.536490  ...     0.205809   188.994933\n",
              "min       1.000000     1.000000  ...     0.000000 -3701.892800\n",
              "25%    1676.000000  2389.000000  ...     0.000000     1.724800\n",
              "50%    3350.000000  4941.000000  ...     0.200000     8.804400\n",
              "75%    5024.000000  7507.000000  ...     0.200000    30.023400\n",
              "max    6700.000000  9990.000000  ...     0.800000  4946.370000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1187
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EN7Vl2qdwtMu"
      },
      "source": [
        "# **Making new Features**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "CAhxMbixwtM8",
        "outputId": "5ae84893-33c3-497f-ce04-a844caca5fbe"
      },
      "source": [
        "df1"
      ],
      "execution_count": 1188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>segment</th>\n",
              "      <th>RID</th>\n",
              "      <th>delivery type</th>\n",
              "      <th>class</th>\n",
              "      <th>sub-class</th>\n",
              "      <th>no of items</th>\n",
              "      <th>location</th>\n",
              "      <th>departure city</th>\n",
              "      <th>departure state</th>\n",
              "      <th>address code</th>\n",
              "      <th>placement date</th>\n",
              "      <th>delivery date</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>7981</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>phone</td>\n",
              "      <td>2</td>\n",
              "      <td>Central</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77095</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>16.448</td>\n",
              "      <td>0.2</td>\n",
              "      <td>5.5512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6334</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>5</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>29.700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.3650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6333</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>shorts</td>\n",
              "      <td>3</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>14.730</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6332</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>television</td>\n",
              "      <td>3</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>43.920</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.7368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6331</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>2</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>66.580</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.9792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6695</th>\n",
              "      <td>6696</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>9389</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>battery</td>\n",
              "      <td>3</td>\n",
              "      <td>East</td>\n",
              "      <td>Burlington</td>\n",
              "      <td>Vermont</td>\n",
              "      <td>5408</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>542.940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>152.0232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6696</th>\n",
              "      <td>6697</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>9597</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>2</td>\n",
              "      <td>West</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>California</td>\n",
              "      <td>95123</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>276.784</td>\n",
              "      <td>0.2</td>\n",
              "      <td>89.9548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6697</th>\n",
              "      <td>6698</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>9390</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>watch</td>\n",
              "      <td>1</td>\n",
              "      <td>East</td>\n",
              "      <td>Burlington</td>\n",
              "      <td>Vermont</td>\n",
              "      <td>5408</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>2.040</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6698</th>\n",
              "      <td>6699</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>440</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>3</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10024</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>207.846</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2.3094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6699</th>\n",
              "      <td>6700</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1659</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>fynota</td>\n",
              "      <td>colddrinks</td>\n",
              "      <td>3</td>\n",
              "      <td>West</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90045</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>160.776</td>\n",
              "      <td>0.2</td>\n",
              "      <td>10.0485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6697 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id    segment   RID  ...    price discount    profit\n",
              "0        1   Consumer  7981  ...   16.448      0.2    5.5512\n",
              "1        2   Consumer  6334  ...   29.700      0.0   13.3650\n",
              "2        3   Consumer  6333  ...   14.730      0.0    4.8609\n",
              "3        4   Consumer  6332  ...   43.920      0.0   12.7368\n",
              "4        5   Consumer  6331  ...   66.580      0.0   15.9792\n",
              "...    ...        ...   ...  ...      ...      ...       ...\n",
              "6695  6696   Consumer  9389  ...  542.940      0.0  152.0232\n",
              "6696  6697  Corporate  9597  ...  276.784      0.2   89.9548\n",
              "6697  6698   Consumer  9390  ...    2.040      0.0    0.9588\n",
              "6698  6699  Corporate   440  ...  207.846      0.1    2.3094\n",
              "6699  6700   Consumer  1659  ...  160.776      0.2   10.0485\n",
              "\n",
              "[6697 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "9JiTT6dmwtM9",
        "outputId": "03d73a63-55b4-47e6-86aa-19daf1eb871a"
      },
      "source": [
        "df1.describe()"
      ],
      "execution_count": 1189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>RID</th>\n",
              "      <th>no of items</th>\n",
              "      <th>address code</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "      <td>6697.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3350.188144</td>\n",
              "      <td>4954.018217</td>\n",
              "      <td>3.797820</td>\n",
              "      <td>54725.181425</td>\n",
              "      <td>227.863854</td>\n",
              "      <td>0.155767</td>\n",
              "      <td>29.135746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1933.765264</td>\n",
              "      <td>2921.536490</td>\n",
              "      <td>2.225566</td>\n",
              "      <td>32098.010326</td>\n",
              "      <td>537.949828</td>\n",
              "      <td>0.205809</td>\n",
              "      <td>188.994933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1040.000000</td>\n",
              "      <td>0.836000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3701.892800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1676.000000</td>\n",
              "      <td>2389.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22204.000000</td>\n",
              "      <td>17.340000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.724800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3350.000000</td>\n",
              "      <td>4941.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>55113.000000</td>\n",
              "      <td>54.920000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>8.804400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5024.000000</td>\n",
              "      <td>7507.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>90004.000000</td>\n",
              "      <td>212.640000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>30.023400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6700.000000</td>\n",
              "      <td>9990.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>99301.000000</td>\n",
              "      <td>9892.740000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>4946.370000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                id          RID  ...     discount       profit\n",
              "count  6697.000000  6697.000000  ...  6697.000000  6697.000000\n",
              "mean   3350.188144  4954.018217  ...     0.155767    29.135746\n",
              "std    1933.765264  2921.536490  ...     0.205809   188.994933\n",
              "min       1.000000     1.000000  ...     0.000000 -3701.892800\n",
              "25%    1676.000000  2389.000000  ...     0.000000     1.724800\n",
              "50%    3350.000000  4941.000000  ...     0.200000     8.804400\n",
              "75%    5024.000000  7507.000000  ...     0.200000    30.023400\n",
              "max    6700.000000  9990.000000  ...     0.800000  4946.370000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlYoYzLswtM-"
      },
      "source": [
        "df1['price_discount'] = df1['discount'] * df1['price'] # Multiplying price and discount"
      ],
      "execution_count": 1190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVjAMiXccJVE"
      },
      "source": [
        "df1 = df1[df1['price_discount'] < 3000] # remove price_discount greater than 3000"
      ],
      "execution_count": 1191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcnvJx1xwtM-"
      },
      "source": [
        "df1['price_discount_items'] = df1['price_discount'] / df1['no of items'] # Multiplying price, discount and items"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydV7wZAtc1Zy"
      },
      "source": [
        "df1 = df1[df1['price_discount_items'] < 600]  # Remove price_discount_items greater than 600"
      ],
      "execution_count": 1193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNbUcl3swtM-"
      },
      "source": [
        "df1['selling_price'] = df1['price'] - 2 * df1['price_discount'] # Price - Price x Discount"
      ],
      "execution_count": 1194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaNPI-wWkcO_"
      },
      "source": [
        "df1['selling_items'] = (df1['price'] - 2 * df1['price_discount'])/df1['no of items'] # Price - Price x Discount / iitems"
      ],
      "execution_count": 1195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7TNM1gKwtM-"
      },
      "source": [
        "df1['price_items'] = df1['price'] / df1['no of items'] # Dividing price and no of items"
      ],
      "execution_count": 1196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OME9qpuxc_LF"
      },
      "source": [
        "df1 = df1[df1['price_items']<1900] # Remove price_items greater than 1900"
      ],
      "execution_count": 1197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfqZNOoVwtM_"
      },
      "source": [
        "df1['profit_items'] = df1['profit'] / df1['no of items'] # Dividing profit by items"
      ],
      "execution_count": 1198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa4YVdtYZm60"
      },
      "source": [
        "df1['no of items'] = 1 / df1['no of items'] # Reciprocal of no of items"
      ],
      "execution_count": 1199,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xug2xEQVIJoG"
      },
      "source": [
        "**Creating x and y DataFrames**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ke2ZFpljwtNA"
      },
      "source": [
        "x = df1[['price','discount','price_discount','selling_price']]  \n",
        "y = df1[['profit']]"
      ],
      "execution_count": 1220,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3agt_cEwtNB"
      },
      "source": [
        "b = df1[['sub-class']]"
      ],
      "execution_count": 1221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGanZVRjwtNB"
      },
      "source": [
        "a = pd.get_dummies(b) # One Hot Encoding of subclass"
      ],
      "execution_count": 1222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Ehc0qkHwtNC"
      },
      "source": [
        "x = pd.concat([x,a], axis = 1) # Concatenate x and a"
      ],
      "execution_count": 1223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4GZIInxREIg"
      },
      "source": [
        "# Drop Columns as they give zero contribution\n",
        "x = x.drop(x.columns[[7, 12, 15, 17, 18]],axis =1)"
      ],
      "execution_count": 1224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "J6Jabm3mR3TN",
        "outputId": "2007c9d9-b0b8-48cd-a2fa-5df73fc8b031"
      },
      "source": [
        "x"
      ],
      "execution_count": 1225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>price_discount</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>sub-class_battery</th>\n",
              "      <th>sub-class_charger</th>\n",
              "      <th>sub-class_chocolates</th>\n",
              "      <th>sub-class_fastfood</th>\n",
              "      <th>sub-class_headset</th>\n",
              "      <th>sub-class_hoodies</th>\n",
              "      <th>sub-class_laptop</th>\n",
              "      <th>sub-class_pants</th>\n",
              "      <th>sub-class_phone</th>\n",
              "      <th>sub-class_sweets</th>\n",
              "      <th>sub-class_tshirts</th>\n",
              "      <th>sub-class_watch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.448</td>\n",
              "      <td>0.2</td>\n",
              "      <td>3.2896</td>\n",
              "      <td>9.8688</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29.700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>29.7000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.730</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>14.7300</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43.920</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>43.9200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66.580</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>66.5800</td>\n",
              "      <td>0</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</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6695</th>\n",
              "      <td>542.940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>542.9400</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</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6696</th>\n",
              "      <td>276.784</td>\n",
              "      <td>0.2</td>\n",
              "      <td>55.3568</td>\n",
              "      <td>166.0704</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6697</th>\n",
              "      <td>2.040</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2.0400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6698</th>\n",
              "      <td>207.846</td>\n",
              "      <td>0.1</td>\n",
              "      <td>20.7846</td>\n",
              "      <td>166.2768</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6699</th>\n",
              "      <td>160.776</td>\n",
              "      <td>0.2</td>\n",
              "      <td>32.1552</td>\n",
              "      <td>96.4656</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6693 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        price  discount  ...  sub-class_tshirts  sub-class_watch\n",
              "0      16.448       0.2  ...                  0                0\n",
              "1      29.700       0.0  ...                  0                0\n",
              "2      14.730       0.0  ...                  0                0\n",
              "3      43.920       0.0  ...                  0                0\n",
              "4      66.580       0.0  ...                  0                0\n",
              "...       ...       ...  ...                ...              ...\n",
              "6695  542.940       0.0  ...                  0                0\n",
              "6696  276.784       0.2  ...                  0                0\n",
              "6697    2.040       0.0  ...                  0                1\n",
              "6698  207.846       0.1  ...                  1                0\n",
              "6699  160.776       0.2  ...                  0                0\n",
              "\n",
              "[6693 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PszOk07dwtNC"
      },
      "source": [
        "# Converting to numpy Arrays\n",
        "x = x.to_numpy()\n",
        "y = y.to_numpy()"
      ],
      "execution_count": 1226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PukKW7qwtNC",
        "outputId": "5931d541-2056-48eb-982e-db0d40507a13"
      },
      "source": [
        "x.shape"
      ],
      "execution_count": 1227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6693, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqxTiNEblIZ0",
        "outputId": "5e49b64b-7681-4cce-b6c5-ca6a3ae71d89"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 1228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6693, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IA5BWfCCwtND"
      },
      "source": [
        "x[:,0], fitted_lambda = stats.boxcox(x[:,0]) # Price box cox transform\n",
        "x[:,3], fitted_lambda = stats.yeojohnson(x[:,3]) # Profit_price yeo johnson transform\n",
        "#x[:,4], fitted_lambda = stats.boxcox(x[:,4]) # Price / items "
      ],
      "execution_count": 1229,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcTnUoHc20ky"
      },
      "source": [
        "# **Model Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Lg5aqAgtLwZ"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "from sklearn.metrics import r2_score"
      ],
      "execution_count": 1230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-TwPIsb6Wxc"
      },
      "source": [
        "# Standardize the x and y \n",
        "sc_x = StandardScaler()\n",
        "x = sc_x.fit_transform(x)\n",
        "sc_y = StandardScaler()\n",
        "y = sc_y.fit_transform(y)"
      ],
      "execution_count": 1231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CiGOjA4j46gs"
      },
      "source": [
        "# Splitting training and validation set\n",
        "x_train, x_val = x[700:], x[:700]\n",
        "y_train, y_val = y[700:], y[:700]"
      ],
      "execution_count": 1232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAfSpzm5Iu02",
        "outputId": "3cac5796-3e64-49bf-d9c9-59d66c52cab4"
      },
      "source": [
        "y_val.shape"
      ],
      "execution_count": 1233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(700, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g5cqsih-kn_k",
        "outputId": "34c784d0-a8a6-4e11-9225-7219f3e2e240"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 1234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5993, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1234
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5Iw-zIytLsv",
        "outputId": "16772c12-7ce8-4ab3-d24e-a04765eba7c5"
      },
      "source": [
        "# Model Training\n",
        "clf = XGBRegressor(learning_rate=0.1,n_estimators=400,objective =\"reg:squarederror\",gamma=0.05,reg_lambda=0.49)\n",
        "clf.fit(x_train,y_train)"
      ],
      "execution_count": 1235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0.05,\n",
              "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
              "             max_depth=3, min_child_weight=1, missing=None, n_estimators=400,\n",
              "             n_jobs=1, nthread=None, objective='reg:squarederror',\n",
              "             random_state=0, reg_alpha=0, reg_lambda=0.49, scale_pos_weight=1,\n",
              "             seed=None, silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9BMQjOPtLqK",
        "outputId": "d7cb4c00-c9fe-499b-bffe-6b7e2106806e"
      },
      "source": [
        "# Error Score\n",
        "y_pred_xg = clf.predict(x_val)\n",
        "mse = mean_absolute_error(y_val, y_pred_xg)\n",
        "print(\"MAE:\", mse)\n",
        "r_score_xg = r2_score(y_val, y_pred_xg)\n",
        "print(r_score_xg)"
      ],
      "execution_count": 1236,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE: 0.08468512367406661\n",
            "0.8795908666805237\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GOPpEGvOiwS"
      },
      "source": [
        "# **Importance of Features**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCai7e86O7xE"
      },
      "source": [
        "feature_list = ['price','discount','price_discount','selling_price'] # list of columns that you want importance of"
      ],
      "execution_count": 1237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOzDwcZdQYX8",
        "outputId": "1087ee4f-c604-457e-9684-2c3dfc6435b4"
      },
      "source": [
        "clf.feature_importances_"
      ],
      "execution_count": 1238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.0492455 , 0.0680581 , 0.08528708, 0.31143162, 0.00377755,\n",
              "       0.18200152, 0.00409444, 0.07378667, 0.12997521, 0.02347747,\n",
              "       0.0204468 , 0.00896268, 0.00365688, 0.02798402, 0.0062311 ,\n",
              "       0.00158327], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmpJ2xFkOgdG",
        "outputId": "68be5218-c774-4587-e32b-776eacce5f5c"
      },
      "source": [
        "# Get numerical feature importances\n",
        "importances = list(clf.feature_importances_)\n",
        "# List of tuples with variable and importance\n",
        "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
        "# Sort the feature importances by most important first\n",
        "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
        "# Print out the feature and importances \n",
        "[print('Variable: {:96} Importance: {}'.format(*pair)) for pair in feature_importances]"
      ],
      "execution_count": 1239,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variable: selling_price                                                                                    Importance: 0.3100000023841858\n",
            "Variable: price_discount                                                                                   Importance: 0.09000000357627869\n",
            "Variable: discount                                                                                         Importance: 0.07000000029802322\n",
            "Variable: price                                                                                            Importance: 0.05000000074505806\n",
            "Variable: sub-class                                                                                        Importance: 0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, None, None, None, None]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1239
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWKuEOiGy6V_"
      },
      "source": [
        "# **Cross Validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTJVYWjB50GJ"
      },
      "source": [
        "# Custom R square function\n",
        "def r2(y_v, y_p):\n",
        "  r_score_xg = r2_score(y_v, y_p)\n",
        "  return r_score_xg"
      ],
      "execution_count": 1240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXFOrvduy-D-"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import roc_auc_score, make_scorer"
      ],
      "execution_count": 1241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PvMn7hGy_TK",
        "outputId": "0c2ea67f-5388-4a2b-e35a-9850bf2bcf23"
      },
      "source": [
        "# K fold Cross Validation\n",
        "scores = cross_validate(clf, x, y, scoring=make_scorer(r2, greater_is_better=True), cv=10, n_jobs=-1)"
      ],
      "execution_count": 1242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZQnOjV8y-3x"
      },
      "source": [
        "s = pd.DataFrame(scores)"
      ],
      "execution_count": 1243,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "zyRmeTcyAkJe",
        "outputId": "d41a8147-b158-4601-f347-a1b40713d8d3"
      },
      "source": [
        "s"
      ],
      "execution_count": 1244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fit_time</th>\n",
              "      <th>score_time</th>\n",
              "      <th>test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.343928</td>\n",
              "      <td>0.006897</td>\n",
              "      <td>0.889448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.404886</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.886242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.336041</td>\n",
              "      <td>0.007385</td>\n",
              "      <td>0.915898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.374682</td>\n",
              "      <td>0.007484</td>\n",
              "      <td>0.932901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.353662</td>\n",
              "      <td>0.007708</td>\n",
              "      <td>0.912503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.367198</td>\n",
              "      <td>0.007962</td>\n",
              "      <td>0.902577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.341207</td>\n",
              "      <td>0.007833</td>\n",
              "      <td>0.865359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.359004</td>\n",
              "      <td>0.007725</td>\n",
              "      <td>0.846791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.429994</td>\n",
              "      <td>0.007942</td>\n",
              "      <td>0.957824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.374097</td>\n",
              "      <td>0.005494</td>\n",
              "      <td>0.969737</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fit_time  score_time  test_score\n",
              "0  2.343928    0.006897    0.889448\n",
              "1  2.404886    0.007812    0.886242\n",
              "2  2.336041    0.007385    0.915898\n",
              "3  2.374682    0.007484    0.932901\n",
              "4  2.353662    0.007708    0.912503\n",
              "5  2.367198    0.007962    0.902577\n",
              "6  2.341207    0.007833    0.865359\n",
              "7  2.359004    0.007725    0.846791\n",
              "8  2.429994    0.007942    0.957824\n",
              "9  2.374097    0.005494    0.969737"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1244
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1UwAxy0Wy-yO",
        "outputId": "4fab2452-e83c-4582-c2c7-12e2cb9531d7"
      },
      "source": [
        "# R sqaure Value on Overall Dataset\n",
        "s['test_score'].sum()/10"
      ],
      "execution_count": 1245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9079279310505368"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ZPzTS7vovvJ"
      },
      "source": [
        "# **TEST XGBOOST ON TEST Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "vdCslaIxEAP4",
        "outputId": "19f0508e-11e1-486d-c24b-2e054f31d96c"
      },
      "source": [
        "df_test"
      ],
      "execution_count": 964,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>segment</th>\n",
              "      <th>RID</th>\n",
              "      <th>delivery type</th>\n",
              "      <th>class</th>\n",
              "      <th>sub-class</th>\n",
              "      <th>no of items</th>\n",
              "      <th>location</th>\n",
              "      <th>departure city</th>\n",
              "      <th>departure state</th>\n",
              "      <th>address code</th>\n",
              "      <th>placement date</th>\n",
              "      <th>delivery date</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>price_discount</th>\n",
              "      <th>price_discount_items</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>price_items</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6701</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>2408</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>television</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>West</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94122</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>24.200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>24.2000</td>\n",
              "      <td>4.840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6702</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>2409</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>fynota</td>\n",
              "      <td>colddrinks</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>West</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94122</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>359.976</td>\n",
              "      <td>0.2</td>\n",
              "      <td>71.9952</td>\n",
              "      <td>23.9984</td>\n",
              "      <td>215.9856</td>\n",
              "      <td>119.992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6703</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>5425</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>television</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>East</td>\n",
              "      <td>Waterbury</td>\n",
              "      <td>Connecticut</td>\n",
              "      <td>6708</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>3.520</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3.5200</td>\n",
              "      <td>1.760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6704</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>7408</td>\n",
              "      <td>First Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>tablet</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>Central</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>60653</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>11.520</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2.3040</td>\n",
              "      <td>0.4608</td>\n",
              "      <td>6.9120</td>\n",
              "      <td>2.304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6705</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>733</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>West</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Washington</td>\n",
              "      <td>98115</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>242.940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>242.9400</td>\n",
              "      <td>80.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3289</th>\n",
              "      <td>9990</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1171</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10035</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>31.9200</td>\n",
              "      <td>7.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3290</th>\n",
              "      <td>9991</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1170</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>phone</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10035</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>19.440</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>19.4400</td>\n",
              "      <td>6.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3291</th>\n",
              "      <td>9992</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1169</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10035</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>272.940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>272.9400</td>\n",
              "      <td>90.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3292</th>\n",
              "      <td>9993</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>414</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>West</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94110</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>113.568</td>\n",
              "      <td>0.2</td>\n",
              "      <td>22.7136</td>\n",
              "      <td>11.3568</td>\n",
              "      <td>68.1408</td>\n",
              "      <td>56.784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3293</th>\n",
              "      <td>9994</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>5092</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>lighting</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>West</td>\n",
              "      <td>Loveland</td>\n",
              "      <td>Colorado</td>\n",
              "      <td>80538</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-01-2022</td>\n",
              "      <td>3.024</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6048</td>\n",
              "      <td>0.2016</td>\n",
              "      <td>1.8144</td>\n",
              "      <td>1.008</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3294 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id      segment   RID  ... price_discount_items selling_price price_items\n",
              "0     6701     Consumer  2408  ...               0.0000       24.2000       4.840\n",
              "1     6702     Consumer  2409  ...              23.9984      215.9856     119.992\n",
              "2     6703     Consumer  5425  ...               0.0000        3.5200       1.760\n",
              "3     6704     Consumer  7408  ...               0.4608        6.9120       2.304\n",
              "4     6705  Home Office   733  ...               0.0000      242.9400      80.980\n",
              "...    ...          ...   ...  ...                  ...           ...         ...\n",
              "3289  9990     Consumer  1171  ...               0.0000       31.9200       7.980\n",
              "3290  9991     Consumer  1170  ...               0.0000       19.4400       6.480\n",
              "3291  9992     Consumer  1169  ...               0.0000      272.9400      90.980\n",
              "3292  9993     Consumer   414  ...              11.3568       68.1408      56.784\n",
              "3293  9994     Consumer  5092  ...               0.2016        1.8144       1.008\n",
              "\n",
              "[3294 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 964
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIP2N6YBT5ft"
      },
      "source": [
        "df_test.to_csv(\"new_feature_test\")"
      ],
      "execution_count": 965,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkiqTwenEXne"
      },
      "source": [
        "**Preparing Test Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tw6YRlPUkn-9"
      },
      "source": [
        "df_test['price_discount'] = df_test['discount'] * df_test['price'] # Multiplying price and discount"
      ],
      "execution_count": 927,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09qcCuyFkn_L"
      },
      "source": [
        "df_test['price_discount_items'] = df_test['price_discount'] / df_test['no of items'] # Multiplying price, discount and items"
      ],
      "execution_count": 928,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1J-yW2tkn_M"
      },
      "source": [
        "df_test['selling_price'] = df_test['price'] - 2 * df_test['price_discount'] # Price - Price x Discount"
      ],
      "execution_count": 929,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZBqcA1ekn_N"
      },
      "source": [
        "df_test['price_items'] = df_test['price'] / df_test['no of items'] # Dividing price and no of items"
      ],
      "execution_count": 930,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cZLonK7kn_O"
      },
      "source": [
        "df_test['no of items'] = 1 / df_test['no of items'] # Reciprocal of no of items"
      ],
      "execution_count": 931,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-zugGyuEcHB"
      },
      "source": [
        "**X_test for prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mBoD_clkn_P"
      },
      "source": [
        "x_test = df_test[['price','discount','price_discount','selling_price']]"
      ],
      "execution_count": 998,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wj6iiEA8EMnV"
      },
      "source": [
        "a = pd.get_dummies(df_test[['sub-class']]) # One Hot Encoding"
      ],
      "execution_count": 999,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYZa0X_dEVO-"
      },
      "source": [
        "x_test = pd.concat([x_test,a], axis = 1) # Concatenate x_test and a"
      ],
      "execution_count": 1001,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bK03aolfSW1_"
      },
      "source": [
        "x_test = x_test.drop(x_test.columns[[7, 12, 15, 17, 18]],axis =1) # drop columns to get best result"
      ],
      "execution_count": 1002,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "ck0J59qnEb_d",
        "outputId": "2a9ed79a-ca37-40e3-e689-42d56884bb72"
      },
      "source": [
        "x_test"
      ],
      "execution_count": 1003,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>price_discount</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>sub-class_battery</th>\n",
              "      <th>sub-class_charger</th>\n",
              "      <th>sub-class_chocolates</th>\n",
              "      <th>sub-class_fastfood</th>\n",
              "      <th>sub-class_headset</th>\n",
              "      <th>sub-class_hoodies</th>\n",
              "      <th>sub-class_laptop</th>\n",
              "      <th>sub-class_pants</th>\n",
              "      <th>sub-class_phone</th>\n",
              "      <th>sub-class_sweets</th>\n",
              "      <th>sub-class_tshirts</th>\n",
              "      <th>sub-class_watch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.200</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>24.2000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>359.976</td>\n",
              "      <td>0.2</td>\n",
              "      <td>71.9952</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.520</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>3.5200</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.520</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2.3040</td>\n",
              "      <td>6.9120</td>\n",
              "      <td>0</td>\n",
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              "<p>3294 rows × 16 columns</p>\n",
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            ],
            "text/plain": [
              "        price  discount  ...  sub-class_tshirts  sub-class_watch\n",
              "0      24.200       0.0  ...                  0                0\n",
              "1     359.976       0.2  ...                  0                0\n",
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              "3290   19.440       0.0  ...                  0                0\n",
              "3291  272.940       0.0  ...                  0                0\n",
              "3292  113.568       0.2  ...                  1                0\n",
              "3293    3.024       0.2  ...                  0                0\n",
              "\n",
              "[3294 rows x 16 columns]"
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      "outputs": []
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      "outputs": []
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          "data": {
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              "array([ 9.11619  , 47.62829  ,  4.0918427, ..., 39.26441  ,  2.0087872,\n",
              "        1.6656188], dtype=float32)"
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      "execution_count": 1011,
      "outputs": []
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    {
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      "metadata": {
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      "source": [
        "a['id'] = df_test['id'] "
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      "execution_count": 1012,
      "outputs": []
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      "source": [
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              "             id  profit\n",
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              "      <td>39.264408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3292</th>\n",
              "      <td>9993</td>\n",
              "      <td>2.008787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3293</th>\n",
              "      <td>9994</td>\n",
              "      <td>1.665619</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3294 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id     profit\n",
              "0     6701   9.116190\n",
              "1     6702  47.628288\n",
              "2     6703   4.091843\n",
              "3     6704   1.665619\n",
              "4     6705  33.248638\n",
              "...    ...        ...\n",
              "3289  9990   3.972366\n",
              "3290  9991  10.971268\n",
              "3291  9992  39.264408\n",
              "3292  9993   2.008787\n",
              "3293  9994   1.665619\n",
              "\n",
              "[3294 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1017
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuD5Lk_ZPtL7"
      },
      "source": [
        "# saving the dataset\n",
        "b = a.to_csv('test_data14')"
      ],
      "execution_count": 986,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EynGCy6OeCL8"
      },
      "source": [
        "# **Testing Test Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "f45yOI1geGNP",
        "outputId": "3646772c-a41c-41af-fc45-5ee9137e5b66"
      },
      "source": [
        "df_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>segment</th>\n",
              "      <th>RID</th>\n",
              "      <th>delivery type</th>\n",
              "      <th>class</th>\n",
              "      <th>sub-class</th>\n",
              "      <th>no of items</th>\n",
              "      <th>location</th>\n",
              "      <th>departure city</th>\n",
              "      <th>departure state</th>\n",
              "      <th>address code</th>\n",
              "      <th>placement date</th>\n",
              "      <th>delivery date</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6701</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>2408</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>television</td>\n",
              "      <td>5</td>\n",
              "      <td>West</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94122</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>24.200</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6702</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>2409</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>fynota</td>\n",
              "      <td>colddrinks</td>\n",
              "      <td>3</td>\n",
              "      <td>West</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94122</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>359.976</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6703</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>5425</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>television</td>\n",
              "      <td>2</td>\n",
              "      <td>East</td>\n",
              "      <td>Waterbury</td>\n",
              "      <td>Connecticut</td>\n",
              "      <td>6708</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>3.520</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6704</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>7408</td>\n",
              "      <td>First Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>tablet</td>\n",
              "      <td>5</td>\n",
              "      <td>Central</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>60653</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>11.520</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6705</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>733</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>3</td>\n",
              "      <td>West</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Washington</td>\n",
              "      <td>98115</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>242.940</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3289</th>\n",
              "      <td>9990</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1171</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>4</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10035</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3290</th>\n",
              "      <td>9991</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1170</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>phone</td>\n",
              "      <td>3</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10035</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>19.440</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3291</th>\n",
              "      <td>9992</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1169</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>3</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10035</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>272.940</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3292</th>\n",
              "      <td>9993</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>414</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>2</td>\n",
              "      <td>West</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94110</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>113.568</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3293</th>\n",
              "      <td>9994</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>5092</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>lighting</td>\n",
              "      <td>3</td>\n",
              "      <td>West</td>\n",
              "      <td>Loveland</td>\n",
              "      <td>Colorado</td>\n",
              "      <td>80538</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-01-2022</td>\n",
              "      <td>3.024</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3294 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id      segment   RID  ... delivery date    price discount\n",
              "0     6701     Consumer  2408  ...    01-01-2021   24.200      0.0\n",
              "1     6702     Consumer  2409  ...    01-01-2021  359.976      0.2\n",
              "2     6703     Consumer  5425  ...    01-01-2021    3.520      0.0\n",
              "3     6704     Consumer  7408  ...    01-01-2021   11.520      0.2\n",
              "4     6705  Home Office   733  ...    01-01-2021  242.940      0.0\n",
              "...    ...          ...   ...  ...           ...      ...      ...\n",
              "3289  9990     Consumer  1171  ...    01-12-2021   31.920      0.0\n",
              "3290  9991     Consumer  1170  ...    01-12-2021   19.440      0.0\n",
              "3291  9992     Consumer  1169  ...    01-12-2021  272.940      0.0\n",
              "3292  9993     Consumer   414  ...    01-12-2021  113.568      0.2\n",
              "3293  9994     Consumer  5092  ...    01-01-2022    3.024      0.2\n",
              "\n",
              "[3294 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 425
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "nM-Zay3ueIBU",
        "outputId": "e76c7203-fa19-408e-bcc9-a11593ce5fd5"
      },
      "source": [
        "df_test[df_test['discount']>0.4]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>segment</th>\n",
              "      <th>RID</th>\n",
              "      <th>delivery type</th>\n",
              "      <th>class</th>\n",
              "      <th>sub-class</th>\n",
              "      <th>no of items</th>\n",
              "      <th>location</th>\n",
              "      <th>departure city</th>\n",
              "      <th>departure state</th>\n",
              "      <th>address code</th>\n",
              "      <th>placement date</th>\n",
              "      <th>delivery date</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>6711</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>1097</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>7</td>\n",
              "      <td>South</td>\n",
              "      <td>Charlotte</td>\n",
              "      <td>North Carolina</td>\n",
              "      <td>28205</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>35.784</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>6728</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>8268</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>hoodies</td>\n",
              "      <td>1</td>\n",
              "      <td>Central</td>\n",
              "      <td>Aurora</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>60505</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-02-2021</td>\n",
              "      <td>69.375</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>6749</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>3886</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>4</td>\n",
              "      <td>Central</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>60653</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-02-2021</td>\n",
              "      <td>12.128</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>6758</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>8362</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>battery</td>\n",
              "      <td>2</td>\n",
              "      <td>Central</td>\n",
              "      <td>El Paso</td>\n",
              "      <td>Texas</td>\n",
              "      <td>79907</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>5.432</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>6767</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>2121</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>3</td>\n",
              "      <td>South</td>\n",
              "      <td>Ormond Beach</td>\n",
              "      <td>Florida</td>\n",
              "      <td>32174</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>2.808</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3213</th>\n",
              "      <td>9914</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>4359</td>\n",
              "      <td>Same Day</td>\n",
              "      <td>qexty</td>\n",
              "      <td>shorts</td>\n",
              "      <td>1</td>\n",
              "      <td>Central</td>\n",
              "      <td>Aurora</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>60505</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>77.720</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3219</th>\n",
              "      <td>9920</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>9410</td>\n",
              "      <td>First Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>1</td>\n",
              "      <td>East</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>19143</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>3.273</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3247</th>\n",
              "      <td>9948</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>4992</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>5</td>\n",
              "      <td>Central</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>60653</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>1889.990</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3270</th>\n",
              "      <td>9971</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>77</td>\n",
              "      <td>First Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>shorts</td>\n",
              "      <td>3</td>\n",
              "      <td>Central</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77041</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>9.708</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3271</th>\n",
              "      <td>9972</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>76</td>\n",
              "      <td>First Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>3</td>\n",
              "      <td>Central</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77041</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>01-12-2021</td>\n",
              "      <td>1.248</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>306 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id      segment   RID  ... delivery date     price discount\n",
              "10    6711  Home Office  1097  ...    01-01-2021    35.784      0.7\n",
              "27    6728     Consumer  8268  ...    01-02-2021    69.375      0.5\n",
              "48    6749     Consumer  3886  ...    01-02-2021    12.128      0.8\n",
              "57    6758     Consumer  8362  ...    01-01-2021     5.432      0.8\n",
              "66    6767     Consumer  2121  ...    01-01-2021     2.808      0.7\n",
              "...    ...          ...   ...  ...           ...       ...      ...\n",
              "3213  9914     Consumer  4359  ...    01-12-2021    77.720      0.6\n",
              "3219  9920     Consumer  9410  ...    01-12-2021     3.273      0.7\n",
              "3247  9948    Corporate  4992  ...    01-12-2021  1889.990      0.8\n",
              "3270  9971    Corporate    77  ...    01-12-2021     9.708      0.6\n",
              "3271  9972    Corporate    76  ...    01-12-2021     1.248      0.8\n",
              "\n",
              "[306 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uce_kx6NI0hV"
      },
      "source": [
        "# **Box Cox transformation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_e7XoCNitHPP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "f183c2c7-df99-468a-afa1-1d3a98c92618"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>segment</th>\n",
              "      <th>RID</th>\n",
              "      <th>delivery type</th>\n",
              "      <th>class</th>\n",
              "      <th>sub-class</th>\n",
              "      <th>no of items</th>\n",
              "      <th>location</th>\n",
              "      <th>departure city</th>\n",
              "      <th>departure state</th>\n",
              "      <th>address code</th>\n",
              "      <th>placement date</th>\n",
              "      <th>delivery date</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>7981</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>phone</td>\n",
              "      <td>2</td>\n",
              "      <td>Central</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77095</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>16.448</td>\n",
              "      <td>0.2</td>\n",
              "      <td>5.5512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6334</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>5</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>29.700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.3650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6333</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>shorts</td>\n",
              "      <td>3</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>14.730</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6332</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>television</td>\n",
              "      <td>3</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>43.920</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.7368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>6331</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>laptop</td>\n",
              "      <td>2</td>\n",
              "      <td>Central</td>\n",
              "      <td>Westland</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48185</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>66.580</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.9792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6695</th>\n",
              "      <td>6696</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>9389</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>battery</td>\n",
              "      <td>3</td>\n",
              "      <td>East</td>\n",
              "      <td>Burlington</td>\n",
              "      <td>Vermont</td>\n",
              "      <td>5408</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>542.940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>152.0232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6696</th>\n",
              "      <td>6697</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>9597</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>headset</td>\n",
              "      <td>2</td>\n",
              "      <td>West</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>California</td>\n",
              "      <td>95123</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>276.784</td>\n",
              "      <td>0.2</td>\n",
              "      <td>89.9548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6697</th>\n",
              "      <td>6698</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>9390</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>kariox</td>\n",
              "      <td>watch</td>\n",
              "      <td>1</td>\n",
              "      <td>East</td>\n",
              "      <td>Burlington</td>\n",
              "      <td>Vermont</td>\n",
              "      <td>5408</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>2.040</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6698</th>\n",
              "      <td>6699</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>440</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>qexty</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>3</td>\n",
              "      <td>East</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10024</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>207.846</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2.3094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6699</th>\n",
              "      <td>6700</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1659</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>fynota</td>\n",
              "      <td>colddrinks</td>\n",
              "      <td>3</td>\n",
              "      <td>West</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90045</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>01-01-2021</td>\n",
              "      <td>160.776</td>\n",
              "      <td>0.2</td>\n",
              "      <td>10.0485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6700 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id    segment   RID  ...    price discount    profit\n",
              "0        1   Consumer  7981  ...   16.448      0.2    5.5512\n",
              "1        2   Consumer  6334  ...   29.700      0.0   13.3650\n",
              "2        3   Consumer  6333  ...   14.730      0.0    4.8609\n",
              "3        4   Consumer  6332  ...   43.920      0.0   12.7368\n",
              "4        5   Consumer  6331  ...   66.580      0.0   15.9792\n",
              "...    ...        ...   ...  ...      ...      ...       ...\n",
              "6695  6696   Consumer  9389  ...  542.940      0.0  152.0232\n",
              "6696  6697  Corporate  9597  ...  276.784      0.2   89.9548\n",
              "6697  6698   Consumer  9390  ...    2.040      0.0    0.9588\n",
              "6698  6699  Corporate   440  ...  207.846      0.1    2.3094\n",
              "6699  6700   Consumer  1659  ...  160.776      0.2   10.0485\n",
              "\n",
              "[6700 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DfXJdr7MmF4"
      },
      "source": [
        "**Numerical values Transformation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCPZ9WQnMl7T"
      },
      "source": [
        "from scipy import stats "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_vicSkEm6ub"
      },
      "source": [
        "p = df1[['profit_price']]\n",
        "p = p.to_numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "iNInBWqPnPpY",
        "outputId": "1cdc95c7-6359-4406-c36a-1e94938f94e8"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = p  ,  bins = 10 , kde = True , color = 'teal'\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkJJDDqinev1"
      },
      "source": [
        "p = p.reshape((p.shape[0],))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SyI-YCznaU1"
      },
      "source": [
        "p, lmbda = stats.yeojohnson(p)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "inUZvT54nlTD",
        "outputId": "9c205f70-ac9a-45cc-ac1a-9b5336cea6cb"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = p  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_rUpiciM6Ob"
      },
      "source": [
        "discount = df[['discount']]\n",
        "items = df[['no of items']]\n",
        "price = df[['price']]\n",
        "profit = df[['profit']]\n",
        "pd1 = df1[['price_discount']]\n",
        "pdi = df1[['price_discount_items']]\n",
        "sp = df1[['selling_price']]\n",
        "pi = df1[['price_items']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vENIDVg9O5s3"
      },
      "source": [
        "discount = discount.to_numpy()\n",
        "items = items.to_numpy()\n",
        "price = price.to_numpy()\n",
        "profit = profit.to_numpy()\n",
        "pd1 = pd1.to_numpy()\n",
        "pdi = pdi.to_numpy()\n",
        "sp = sp.to_numpy()\n",
        "pi = pi.to_numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7rbOiiHWQL_a"
      },
      "source": [
        "Before Box Cox Transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "oRodl9fRHd8t",
        "outputId": "d47b3d90-93bd-4c45-f697-bdbdafd2c909"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = pd1  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "7ISW1dsZHhHs",
        "outputId": "c26c2b7f-bf01-4802-dd99-46fa31bf7f8a"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = pdi  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "u4a5vez5HmuO",
        "outputId": "eb98c88d-874a-4408-dc8b-c0383615a040"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = sp  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "wi_k8GiUHpty",
        "outputId": "6e8499dc-f913-470f-b8db-5da7259535ca"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = pi  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "SJTRZi13yDOC",
        "outputId": "9056d1bc-f6b5-4415-8990-3f250ebebefb"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = price  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "Hmby7HRMyH0N",
        "outputId": "febd464c-69ac-454f-b9bd-0e0062762484"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = profit  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "kgw_9KnnMl0_",
        "outputId": "e3e72efa-7b32-4def-8a42-a956bf400372"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = discount  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "Fu6aKMrkRij2",
        "outputId": "2270f052-cb29-43ce-dd2c-412df94c6ce0"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = items  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flfTd0ZFQVVj"
      },
      "source": [
        "items = items.reshape((6700,))\n",
        "discount = discount.reshape((6700,))\n",
        "price = price.reshape((6700,))\n",
        "profit = profit.reshape((6700,))\n",
        "pd1 = pd1.reshape((6697,))\n",
        "pdi = pdi.reshape((6697,))\n",
        "sp = sp.reshape((6697,))\n",
        "pi = pi.reshape((6697,))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkLQW8RoRxbM"
      },
      "source": [
        "After Box Cox Transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ag13FepBMlv0"
      },
      "source": [
        "items, fitted_lambda = stats.boxcox(items) \n",
        "price, lambd = stats.boxcox(price)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B97KT78Uyjhf"
      },
      "source": [
        "profit, l = stats.yeojohnson(profit)\n",
        "discount, l = stats.yeojohnson(discount)\n",
        "pd1, l = stats.yeojohnson(pd1)\n",
        "pdi, l = stats.yeojohnson(pdi)\n",
        "sp, l = stats.yeojohnson(sp)\n",
        "pi, l = stats.yeojohnson(pi)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "FxXqJ9ToMlpz",
        "outputId": "dae94b8e-d4d6-4c9d-859c-66a30750318b"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = items  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "NVbFcxRDyyh_",
        "outputId": "746ba948-ddd6-4249-93fd-b7c3c52394a5"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = price  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "A_n81SuOI1Te",
        "outputId": "cd90f4e2-c54d-4e7c-954b-1b6ad8e3cc9e"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = pd1  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "BfU90Hg9I4FH",
        "outputId": "4700a0e8-ac4c-4b86-f279-433f05dd7f84"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = pdi  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "BoO2dY0kJAi0",
        "outputId": "dc25e219-2e2c-49eb-d116-568438600cb4"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = sp  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "bA0YORI4JEix",
        "outputId": "8cbaa355-3467-4d58-87c4-fa3d7f94fa52"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = pi  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "lSPuvA8kzYli",
        "outputId": "d4eba03d-4cf4-4218-9222-26a1f5ac1a00"
      },
      "source": [
        "pd.DataFrame(profit).describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>26.076694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>225.052611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-7205.500905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.702595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.651976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>29.175647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7674.211764</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 0\n",
              "count  6700.000000\n",
              "mean     26.076694\n",
              "std     225.052611\n",
              "min   -7205.500905\n",
              "25%       1.702595\n",
              "50%       8.651976\n",
              "75%      29.175647\n",
              "max    7674.211764"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "wMgBR86ry3zc",
        "outputId": "05092e26-6a93-4a12-e9c7-9626f730c577"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = profit  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "uI4EeXeVy7UK",
        "outputId": "a9d98bf3-6f1e-4108-c32e-9462878565fa"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = discount  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfhIzvAZpX-I"
      },
      "source": [
        "After Yeo johnson transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nAptlafMlmW"
      },
      "source": [
        "discount, lmbda = stats.yeojohnson(discount)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "OP_06J1NppiK",
        "outputId": "043b57da-4f79-43b1-e01b-d45d8f75927a"
      },
      "source": [
        "sns.set_style(\"whitegrid\")  # Setting style(Optional)\n",
        "plt.figure(figsize = (10,5)) #Specify the size of figure we want(Optional)\n",
        "sns.distplot(x = discount  ,  bins = 10 , kde = True , color = 'teal'\\\n",
        "             , kde_kws=dict(linewidth = 4 , color = 'black'))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzwokP82tjRb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "6ec88045-c292-440a-e168-2b2cea330fac"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>RID</th>\n",
              "      <th>no of items</th>\n",
              "      <th>address code</th>\n",
              "      <th>price</th>\n",
              "      <th>discount</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6700.000000</td>\n",
              "      <td>6700.000000</td>\n",
              "      <td>6700.000000</td>\n",
              "      <td>6700.000000</td>\n",
              "      <td>6700.000000</td>\n",
              "      <td>6700.000000</td>\n",
              "      <td>6700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3350.500000</td>\n",
              "      <td>4954.381791</td>\n",
              "      <td>3.798507</td>\n",
              "      <td>54719.073284</td>\n",
              "      <td>234.424275</td>\n",
              "      <td>0.155876</td>\n",
              "      <td>29.121046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1934.267734</td>\n",
              "      <td>2921.304952</td>\n",
              "      <td>2.225327</td>\n",
              "      <td>32092.421012</td>\n",
              "      <td>641.440815</td>\n",
              "      <td>0.205922</td>\n",
              "      <td>230.712951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1040.000000</td>\n",
              "      <td>0.836000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6599.978000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1675.750000</td>\n",
              "      <td>2389.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22204.000000</td>\n",
              "      <td>17.340000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.713750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3350.500000</td>\n",
              "      <td>4941.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>55113.000000</td>\n",
              "      <td>54.960000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>8.803200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5025.250000</td>\n",
              "      <td>7507.250000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>90004.000000</td>\n",
              "      <td>212.940000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>30.023400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6700.000000</td>\n",
              "      <td>9990.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>99301.000000</td>\n",
              "      <td>22638.480000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>8399.976000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                id          RID  ...     discount       profit\n",
              "count  6700.000000  6700.000000  ...  6700.000000  6700.000000\n",
              "mean   3350.500000  4954.381791  ...     0.155876    29.121046\n",
              "std    1934.267734  2921.304952  ...     0.205922   230.712951\n",
              "min       1.000000     1.000000  ...     0.000000 -6599.978000\n",
              "25%    1675.750000  2389.750000  ...     0.000000     1.713750\n",
              "50%    3350.500000  4941.500000  ...     0.200000     8.803200\n",
              "75%    5025.250000  7507.250000  ...     0.200000    30.023400\n",
              "max    6700.000000  9990.000000  ...     0.800000  8399.976000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "j9FL1cKbIz1F",
        "outputId": "7693c533-0917-4e6a-ea5e-a3b31d3397d6"
      },
      "source": [
        "address = df[[\"address code\"]]\n",
        "address = address.to_numpy()\n",
        "x = range(0,6700)\n",
        "plt.figure(figsize=(16, 5), dpi=80)\n",
        "plt.scatter(x, address,color = 'black', label='profit vs location_south')\n",
        "plt.title('Relation')\n",
        "plt.xlabel('South')\n",
        "plt.ylabel('Profit')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1280x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mU8s8y4Izxm",
        "outputId": "ae363a8b-7214-4732-d527-c035d6ea4adc"
      },
      "source": [
        "df[df['address code']>90000].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1729, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wi2iDL2xIzrI",
        "outputId": "62a61864-aa69-413f-a260-1fbd8713586e"
      },
      "source": [
        "df[df['address code']<50000].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3241, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iddxpi78IzoN",
        "outputId": "5da2dba7-d133-42da-fe6f-53b80c223739"
      },
      "source": [
        "df[df['address code']>80000].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2104, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-nf3VPaIzmC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZ5FrMn0IzkC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oi1lJ5pqIzh_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJgng11JIzf3"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiQrviDPIzdk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}