09: Statistical_Thinking_Normality_Test.ipynb

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    "colab": {
      "name": "09: Statistical Thinking - Normality Test.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/Statistics/blob/main/09%3A%20Statistical_Thinking_Normality_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0ODxCuq3cqF"
      },
      "source": [
        "Video Walkthrough of the code : https://www.youtube.com/watch?v=8vOsOST-FPA&list=PL3N9eeOlCrP6IjkyExZW9oZFwt-A1r0qB&index=8"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9bxlsmdUbGs"
      },
      "source": [
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from scipy import stats\n",
        "import statsmodels.api as sm\n",
        "\n",
        "%matplotlib inline\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tf-qQhw_UxRY"
      },
      "source": [
        "marketing_df = pd.read_csv('https://raw.githubusercontent.com/gulabpatel/Statistics/master/Dataset/marketing_cva_f.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kReN7s4U0Mm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "3ae6106e-f2c8-43e3-eae1-c144684a9614"
      },
      "source": [
        "marketing_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
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              "    .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>Customer</th>\n",
              "      <th>State</th>\n",
              "      <th>CLV</th>\n",
              "      <th>Coverage</th>\n",
              "      <th>Income</th>\n",
              "      <th>loc_type</th>\n",
              "      <th>monthly_premium</th>\n",
              "      <th>months_last_claim</th>\n",
              "      <th>Months_Since_Policy_Inception</th>\n",
              "      <th>Total_Claim_Amount</th>\n",
              "      <th>Vehicle_Class</th>\n",
              "      <th>avg_vehicle_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BU79786</td>\n",
              "      <td>Washington</td>\n",
              "      <td>2763.519279</td>\n",
              "      <td>Basic</td>\n",
              "      <td>56274</td>\n",
              "      <td>Suburban</td>\n",
              "      <td>69</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>384.811147</td>\n",
              "      <td>Two-Door Car</td>\n",
              "      <td>40.696695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AI49188</td>\n",
              "      <td>Nevada</td>\n",
              "      <td>12887.431650</td>\n",
              "      <td>Premium</td>\n",
              "      <td>48767</td>\n",
              "      <td>Suburban</td>\n",
              "      <td>108</td>\n",
              "      <td>18</td>\n",
              "      <td>38</td>\n",
              "      <td>566.472247</td>\n",
              "      <td>Two-Door Car</td>\n",
              "      <td>48.755298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HB64268</td>\n",
              "      <td>Washington</td>\n",
              "      <td>2813.692575</td>\n",
              "      <td>Basic</td>\n",
              "      <td>43836</td>\n",
              "      <td>Rural</td>\n",
              "      <td>73</td>\n",
              "      <td>12</td>\n",
              "      <td>44</td>\n",
              "      <td>138.130879</td>\n",
              "      <td>Four-Door Car</td>\n",
              "      <td>70.394474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OC83172</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>8256.297800</td>\n",
              "      <td>Basic</td>\n",
              "      <td>62902</td>\n",
              "      <td>Rural</td>\n",
              "      <td>69</td>\n",
              "      <td>14</td>\n",
              "      <td>94</td>\n",
              "      <td>159.383042</td>\n",
              "      <td>Two-Door Car</td>\n",
              "      <td>53.460212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XZ87318</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>5380.898636</td>\n",
              "      <td>Basic</td>\n",
              "      <td>55350</td>\n",
              "      <td>Suburban</td>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>321.600000</td>\n",
              "      <td>Four-Door Car</td>\n",
              "      <td>32.811507</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Customer       State  ...  Vehicle_Class avg_vehicle_age\n",
              "0  BU79786  Washington  ...   Two-Door Car       40.696695\n",
              "1  AI49188      Nevada  ...   Two-Door Car       48.755298\n",
              "2  HB64268  Washington  ...  Four-Door Car       70.394474\n",
              "3  OC83172      Oregon  ...   Two-Door Car       53.460212\n",
              "4  XZ87318      Oregon  ...  Four-Door Car       32.811507\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6Y8VC-FU2in",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "142729eb-8a9e-4d77-fb5e-8c1279be4699"
      },
      "source": [
        "marketing_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6817 entries, 0 to 6816\n",
            "Data columns (total 12 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   Customer                       6817 non-null   object \n",
            " 1   State                          6817 non-null   object \n",
            " 2   CLV                            6817 non-null   float64\n",
            " 3   Coverage                       6817 non-null   object \n",
            " 4   Income                         6817 non-null   int64  \n",
            " 5   loc_type                       6817 non-null   object \n",
            " 6   monthly_premium                6817 non-null   int64  \n",
            " 7   months_last_claim              6817 non-null   int64  \n",
            " 8   Months_Since_Policy_Inception  6817 non-null   int64  \n",
            " 9   Total_Claim_Amount             6817 non-null   float64\n",
            " 10  Vehicle_Class                  6817 non-null   object \n",
            " 11  avg_vehicle_age                6817 non-null   float64\n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 639.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-PlW4EqU7-8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91ce6667-74e6-4240-fce1-eecec4c72582"
      },
      "source": [
        "print (\"Rows     : \" ,marketing_df.shape[0])\n",
        "print (\"Columns  : \" ,marketing_df.shape[1])\n",
        "print (\"\\nFeatures : \\n\" ,marketing_df.columns.tolist())\n",
        "print (\"\\nMissing values :  \", marketing_df.isnull().any())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows     :  6817\n",
            "Columns  :  12\n",
            "\n",
            "Features : \n",
            " ['Customer', 'State', 'CLV', 'Coverage', 'Income', 'loc_type', 'monthly_premium', 'months_last_claim', 'Months_Since_Policy_Inception', 'Total_Claim_Amount', 'Vehicle_Class', 'avg_vehicle_age']\n",
            "\n",
            "Missing values :   Customer                         False\n",
            "State                            False\n",
            "CLV                              False\n",
            "Coverage                         False\n",
            "Income                           False\n",
            "loc_type                         False\n",
            "monthly_premium                  False\n",
            "months_last_claim                False\n",
            "Months_Since_Policy_Inception    False\n",
            "Total_Claim_Amount               False\n",
            "Vehicle_Class                    False\n",
            "avg_vehicle_age                  False\n",
            "dtype: bool\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5oJZBDNXT_i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "ca6623b9-88f4-4b4d-de00-074bdb68d891"
      },
      "source": [
        "marketing_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>CLV</th>\n",
              "      <th>Income</th>\n",
              "      <th>monthly_premium</th>\n",
              "      <th>months_last_claim</th>\n",
              "      <th>Months_Since_Policy_Inception</th>\n",
              "      <th>Total_Claim_Amount</th>\n",
              "      <th>avg_vehicle_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6817.000000</td>\n",
              "      <td>6817.000000</td>\n",
              "      <td>6817.000000</td>\n",
              "      <td>6817.000000</td>\n",
              "      <td>6817.000000</td>\n",
              "      <td>6817.000000</td>\n",
              "      <td>6817.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8130.229340</td>\n",
              "      <td>50456.580461</td>\n",
              "      <td>92.963034</td>\n",
              "      <td>15.046941</td>\n",
              "      <td>47.832624</td>\n",
              "      <td>377.840789</td>\n",
              "      <td>50.126257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6949.067972</td>\n",
              "      <td>24305.956566</td>\n",
              "      <td>34.615160</td>\n",
              "      <td>10.064762</td>\n",
              "      <td>27.895350</td>\n",
              "      <td>252.648952</td>\n",
              "      <td>9.919767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2119.894333</td>\n",
              "      <td>10037.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.099007</td>\n",
              "      <td>11.875974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4090.750678</td>\n",
              "      <td>28432.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>210.100003</td>\n",
              "      <td>43.449925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5810.630529</td>\n",
              "      <td>48081.000000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>340.800000</td>\n",
              "      <td>50.100924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9075.768214</td>\n",
              "      <td>70187.000000</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>498.612632</td>\n",
              "      <td>56.877234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83325.381190</td>\n",
              "      <td>99981.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>2893.239678</td>\n",
              "      <td>83.387485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                CLV        Income  ...  Total_Claim_Amount  avg_vehicle_age\n",
              "count   6817.000000   6817.000000  ...         6817.000000      6817.000000\n",
              "mean    8130.229340  50456.580461  ...          377.840789        50.126257\n",
              "std     6949.067972  24305.956566  ...          252.648952         9.919767\n",
              "min     2119.894333  10037.000000  ...            0.099007        11.875974\n",
              "25%     4090.750678  28432.000000  ...          210.100003        43.449925\n",
              "50%     5810.630529  48081.000000  ...          340.800000        50.100924\n",
              "75%     9075.768214  70187.000000  ...          498.612632        56.877234\n",
              "max    83325.381190  99981.000000  ...         2893.239678        83.387485\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Akc4MajX9MN9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f65054a1-2a14-4fa9-ab8d-7a2e84e45966"
      },
      "source": [
        "sns.distplot(marketing_df['avg_vehicle_age'], hist=True, hist_kws=dict(ec=\"k\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f81be710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "FdFI2MfeToX-",
        "outputId": "99934a33-c6c9-4a50-a1a9-3396a6198a89"
      },
      "source": [
        "plot=sm.qqplot(marketing_df['avg_vehicle_age'], line = \"45\", fit = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1URHPsXQ_s84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "ee6b1a28-9574-4e57-9f1c-641c37e0b85a"
      },
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.boxplot(x=\"avg_vehicle_age\", data=marketing_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f4948b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqrSieJMHq68"
      },
      "source": [
        "null hypothesis - data came from a normal distribution\n",
        "\n",
        "Alternate hypothesis - unlikely that the data came from a normal distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YBjo2G4HO3y",
        "outputId": "4189939b-9e69-43b5-9877-abcabf175ca8"
      },
      "source": [
        "chi2, p = stats.normaltest(marketing_df['avg_vehicle_age'])\n",
        "print('Chi2 = %.2f, pvalue = %.3f' % (chi2, p))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Chi2 = 1.04, pvalue = 0.593\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYUzSNUQIu2h"
      },
      "source": [
        "Null hypothesis - Sample is drawn from a population that follows a particular distribution\n",
        "\n",
        "Alternate Hypothesis - data did not come from the chosen distribution "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzHjxLWpb6Ax",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7075feb9-d916-4e07-d791-8a76e245fd09"
      },
      "source": [
        "stats.anderson(marketing_df['avg_vehicle_age'],dist='norm')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AndersonResult(statistic=0.38327277704684093, critical_values=array([0.576, 0.656, 0.787, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJvOP5oZ_ywn"
      },
      "source": [
        "from scipy import stats\n",
        "_ , l = stats.boxcox(marketing_df['avg_vehicle_age'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yk3dHQ6Q2b-N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd94997c-d021-4d67-a6b5-c9c21f9bd592"
      },
      "source": [
        "print (\"lamda     : \" ,l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lamda     :  1.0401662851027633\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4X2aLwINNV5"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1CuNRfJ2b5G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "0fff042e-3f38-4dca-b85e-b377cc8c05b8"
      },
      "source": [
        "plot=sm.qqplot(marketing_df['Income'], line = \"45\", fit = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ftF2OJxKyex",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "93d13f8b-6c68-458d-81c8-82f9160da909"
      },
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.boxplot(x=\"Income\", data=marketing_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f4888950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UZFuSMcb4qSmK/rGqXjY9ziumxzoiySzJn4wxTquqC5Ocl+T9SS5N8ttjjFuq6nVJLk5y5qq9UgAOCcIKgEW4Zoxxe5JU1XVJNifZneSOMca1STLGuH+6/Q1Jtk9jN1XVbUn2hNWVY4wHkjxQVbuT/MM0fn2Sn66qFyT5uSSfmt4US5LDD/JrA+AQJKwAWISHV/z8WJ7976OVj/P4iuuPT495WJL7xhivepaPDwD7xTlWACyLm5O8pKpekyTT+VUbk3wpybnT2MuS/Nh07DOa3vW6tap+bbp/VdUrD8bkATi0CSsAlsIY45Ekb0+yvaq+kuTyzM+dujjJYVV1febnYL1zjPHwvh/pSc5NcsH0mF9Lcs5zO3MASGqMseg5AAAArGnesQIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAEDT/wNgmTv2FPU5xwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOGLPUZnyyZR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "f8726877-e6a2-4e13-bdf6-be4bc8b63392"
      },
      "source": [
        "sns.distplot(marketing_df['Income'], hist=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f477c2d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ud2t33BG7qsa",
        "outputId": "b7a09cdd-d4e8-4424-dae3-58c568a17657"
      },
      "source": [
        "stats.anderson(marketing_df['Income'],dist='norm')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AndersonResult(statistic=94.67577589421035, critical_values=array([0.576, 0.656, 0.787, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBQjWECZofZh"
      },
      "source": [
        "_, l = stats.boxcox(marketing_df['Income'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpN7jJvQDk5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0f4f276-bdc3-4d97-9aa0-86966becfe6e"
      },
      "source": [
        "print (\"lamda     : \" ,l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lamda     :  0.44437538279540056\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvZUkhXOJaGA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "f1eda6ae-c86d-42c3-a128-4649bf6d8225"
      },
      "source": [
        "sns.distplot(marketing_df['Income'].apply(lambda x: np.power(x, l)), hist=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f469ab50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxJo21CMLyZD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9dc729af-097c-41ed-9303-0bd19901d987"
      },
      "source": [
        "stats.boxcox(marketing_df['Income'].apply(lambda x: np.power(x, l)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([128.09819109, 120.14015208, 114.53556654, ..., 142.98627724,\n",
              "         83.36466029,  83.94694497]), 1.0000001881629772)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKp_2-PrLyOe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "22c48574-83c3-468c-9db0-8ce4e7195748"
      },
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.boxplot(x=\"CLV\", data=marketing_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f45f06d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6VhGvNIMg7p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ebd2a4ec-6f3d-4d0d-847a-489b6c27848b"
      },
      "source": [
        "sns.distplot(marketing_df['CLV'], hist=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f45ceb50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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on+KwfcCKnNfLQ1rePGZWQdTNdWSaY6dKPwI0hzImn+s64EEAd38cqAbaZr7q4phoiZzJAAGIutEqEsbLWm5ARMpQoS2br7j7Wnf/c3c/ANFIMgB375zimCeBNWGUWBXRzfnNk/JsBq4N21cDj7q7h/SNYbTaSmANsHWqMsMxj4UyCGV+N2y/Alwe6vxGomBTvH6yGfSPjAFQfQYDBAAqkgmWtdSoZSMiZanQb7g/y5P2+HQHhPsnNwKPAM8TjQjbbma3mdlVIdu9QKuZdQE3AbeEY7cTtUZ2AD8AbnD37FRlhrJuBm4KZbWGsgH+CPgDM/tX4JvAJ0JwKgtz1Y0G0NFax75jw4xmtNyAiJSXaZcYMLNziW6015jZ24CJaYkbibrUpuXuDxPdlM9N+3zO9gjwwSmOvR24vZAyQ/ouotFqk9N3AJfNVNdSmasBAgDnL6rlXxy6jw2xqm2q3k0RkeKbaT2bK4gGBSwH/ionfQD445jqtKAMhG60uWjZrFgUxf/uo8MKNiJSVqYNNu5+H3Cfmf2eu3+7SHVaUE4MEDjDezYAdakKWmor6e7VIAERKS8zdaN91N3/J9BhZjdN3u/uf5XnMJmFgZEMFQmjIjE3Mwctb6llr4KNiJSZmbrR6sJv9cnEZCCdmZMutAnLW2p4dl+fHu4UkbIyUzfa34bf/7U41Vl4BkYyczI4YMLylnDfRq0bESkjhT7U+Zdm1mhmlWb2YzPrMbOPxl25hWBgZOyM50XLtbQ5Wia6u1czQItI+Sj0T+r3uXs/8NtEc6OtBv5jXJVaSAZHMmc04/NkqYokixtTatmISFkp9Ftuorvtt4BvuXtfTPVZcKJutLlr2UC0cudBLTcgImWk0GDzPTP7FfB24Mdm1g7o22wODKYzczLsOdfihhT9I5kTU+GIiJRaoUsM3AL8GtDp7mPAcU5dm0ZOQ//I2JyORgNY3BgtE911eHBOyxUROV0zDX3O9Qai521yj/n6HNdnQXH3qGUzx91o7Q0pIAo2F53XMqdli4icjoKCjZl9A7gA+CWQDcmOgs0ZOT6axX1uZg/I1VJbRTJhvKSWjYiUiUJbNp3A2nKaLflsMDEJZ2qOWzbJhNFWX6VuNBEpG4X+Sf0ccG6cFVmIBtMTk3DObcsGoL2hmq4eBRsRKQ+FtmzagB1mthVITyS6+1VTHyIzGUxHPZJzOYPAhMUNKXbs72NkLDunD42KiJyOQoPNF+KsxEI10Y1WNcfdaBAFm3GH3a8e541LGue8fBGR2Sh06PO/EM0cUBm2nwR+EWO9FoS5XF5gstwRaSIipVbo3Gh/ADwE/G1IWgb8U1yVWigmgs1cDxAAWFRXBcArRzVtjYiUXqF/Ut9AtLRyP4C7vwgsjqtSC8Xx9EQ32ty3bFIVSVrrqjRHmoiUhUK/5dLuPjrxIjzYqWHQZ+hEN1oMwQaiZaLVshGRclDot9y/mNkfAzVm9pvAt4D/FV+1FobBdIbKpFGRjC/Y7D2qpQZEpPQK/Za7BegBngX+PfAw8J/jqtRCMTiSoT41mxmDZue8RTXsOzZMJjse2zlERApR6Gi0caIBAZ9296vd/SuFzCZgZuvNbKeZdZnZLXn2p8zsgbB/i5l15Oy7NaTvNLMrZirTzFaGMrpCmVU5+37fzHaY2XYzu7+Qay6G4+kMdTEGmxUttWTHnQNabkBESmzaYGORL5jZq8BOYGdYpfPzMxVsZkngLuBKYC1wjZmtnZTtOqDX3VcDdwJ3hGPXAhuBdcB64G4zS85Q5h3AnaGs3lA2ZrYGuBW4zN3XAX84U92LZSAdd8smWiJ6rwYJiEiJzdSy+RzRKLR3uPsid18EXAJcZmafm+HYi4Eud98VBhds4tRlCTYA94Xth4DLzcxC+iZ3T7v7bqArlJe3zHDMe0IZhDI/ELb/ALjL3XsB3P3wDPUumuMxB5sVE8FGgwREpMRmCjYfA64JX/gAuPsu4KPAx2c4dhmwN+d1d0jLm8fdM0Af0DrNsVOltwLHQhmTz/U64HVm9jMze8LM1uerrJldb2bbzGxbT0/PDJc2NwbTGeqr4ws2S5qqSSZMgwREpORmCjaV7v7q5ER37wEq46nSnKsA1gDvBq4BvmJmzZMzufs97t7p7p3t7e1FqdhgzPdsKpIJljZXa/iziJTcTMFm9DT3AewDVuS8Xh7S8uYJz+40AUemOXaq9CNAc87Cbrnn6gY2u/tYaKG9QBR8Sm5wJENDjMEGokECumcjIqU2U7B5i5n15/kZAN40w7FPAmvCKLEqohv+myfl2QxcG7avBh4No9w2AxvDaLWVRMFh61RlhmMeC2UQyvxu2P4nolYNZtZG1K22a4a6F0Xco9EgBBu1bESkxKb9pnP30560y90zZnYj8AiQBP7O3beb2W3ANnffDNwLfMPMuoCjRMGDkO9BYAeQAW5w9yxAvjLDKW8GNpnZnwFPh7IJed9nZjuIVhn9j+5+5HSva66MjzvHR7OxDhAAWLGohlcHR7XUgIiUVKzfdO7+MNEDoLlpn8/ZHgE+OMWxtwO3F1JmSN9FNFptcroDN4WfsnF8NBrLEHewWdZSA0B37zCrF9fHei4RkanEM0+KzGhiXrQ4R6MBLG+Jhj/vO6YRaSJSOgo2JTIx43Pc92yWn2jZ6L6NiJSOgk2JDIRVOuMejba4oZrKpNHdq5aNiJSOgk2JHE9ngfhbNsmEsaSpRsFGREpKwaZEBtNjANSl4h8htrylhn3qRhORElKwKZHB0LJpSMU/EcPyFrVsRKS0FGxKZHCkmC2bWg4PpBkZy8Z+LhGRfBRsSuT4aPTFH/fQZzg5Im2/hj+LSIko2JTIwEi0JHSqIv6WzbLmKNjoWRsRKZX4/6yWvOJeywbg/i2vAHBsKJoz9TtP7WPv0WE+fMl5sZ5XRGQytWxKJO7lBXI1VFeSMOgdmmmibhGReCjYlMjAyBgN1cVZEiiZMJpqKhVsRKRkFGxKpH8kQ0MRBgdMaKmt4tjQWNHOJyKSS8GmRIqxcFqu5toqtWxEpGQUbEpkID1W5JZNJQMjGTLZ8aKdU0RkgoJNiQyMZIp2zwaibjQH+obVlSYixadgUwLuHnWjFbFl01wXBbZe3bcRkRJQsCmBkbFxMuNelNkDJrTUVAEnn7kRESkmBZsSGAjzohWzG62xRs/aiEjpKNiUwEBYpbOxiC2bZMJorKlUN5qIlISCTQmcWKWziMEGokECatmISCnEGmzMbL2Z7TSzLjO7Jc/+lJk9EPZvMbOOnH23hvSdZnbFTGWa2cpQRlcos2rSuX7PzNzMOuO52sJNdKPVF2Etm1wttZV6sFNESiK2YGNmSeAu4EpgLXCNma2dlO06oNfdVwN3AneEY9cCG4F1wHrgbjNLzlDmHcCdoazeUPZEXRqA/wBsieNaZ6tkLZu6KvqHx7SujYgUXZwtm4uBLnff5e6jwCZgw6Q8G4D7wvZDwOVmZiF9k7un3X030BXKy1tmOOY9oQxCmR/IOc+fEgWjkbm+yNMxWKJg016fwoE9R44X9bwiInEGm2XA3pzX3SEtbx53zwB9QOs0x06V3gocC2W85lxmdhGwwt3/ebrKmtn1ZrbNzLb19PQUeo2npX9iNFqRu9Ha6lMA7O5RsBGR4jqrBwiYWQL4K+CPZsrr7ve4e6e7d7a3t8dar4lutGI+ZwPQWh/dxtr1qoKNiBRXnMFmH7Ai5/XykJY3j5lVAE3AkWmOnSr9CNAcyshNbwAuBH5iZnuAS4HNpR4kMJjOUFeVJJmwop43VZGksbqCXWrZiEiRxRlsngTWhFFiVUQ3/DdPyrMZuDZsXw086u4e0jeG0WorgTXA1qnKDMc8FsoglPldd+9z9zZ373D3DuAJ4Cp33xbXRReimGvZTNZWn2LXq4MlObeILFyx9eO4e8bMbgQeAZLA37n7djO7Ddjm7puBe4FvmFkXcJQoeBDyPQjsADLADe6eBchXZjjlzcAmM/sz4OlQdlkaGMkUvQttQlt9ihcOD5Tk3CKycMX6jefuDwMPT0r7fM72CPDBKY69Hbi9kDJD+i6i0WrT1efdhdQ7boPp4k7CmautIcXWPUfpPT5KS13VzAeIiMyBs3qAQLnqL/LyArnaTgwSUFeaiBSPgk0JRPdsSteNBmiQgIgUlYJNCQwUeUnoXC21VVRVJHjhkO7biEjxKNiUQLEXTsuVTBhvOLeB7fv7S3J+EVmYFGyKbCw7zvBYtmT3bADWLmlkx4F+ohHjIiLxU7Apsol50epL1I0GsG5pI8eGxtjfVxZTxYnIAqBgU2SD6dJMwplr7dImALbv6ytZHURkYVGwKbK+4WgSzqaa0nWjvXFJA2bovo2IFI2CTZFNrJRZygcqa6sqWNVWp2AjIkWjYFNkvWGlzOYStmwA1i1tYsd+daOJSHEo2BTZsdCyaa4t7VQxb17exP6+EQ5qkICIFIGCTZH1Hg8tm9rStmwuXdUKwOO7Xi1pPURkYVCwKbLeoVEaUhVUJkv71q9d0khTTSU/7zpS0nqIyMKgYFNkx4ZGaa4rbasGIJEwLl21iMd3KdiISPwUbIrs2PAYLSW+XzPh1y5oo7t3mL1Hh0pdFRE5yynYFFnv0FjJBwdMeOcF0X2bn7+k+zYiEq/SPca+QB0bGqWjtbakdbh/yysAuDtNNZX8/c/28KF3nFfSOonI2U0tmyLrPT5a8mdsJpgZb17WxAuHBug9Plrq6ojIWUzBpogy2XH6RzJl040G8JYVzYw7/POzB0pdFRE5iynYFNHEvGgtJX7GJteSpmraG1Js/uX+UldFRM5iCjZFNDFVTSnnRZvMzHjbima27jnKzoNavVNE4qFgU0TlMlXNZBd3LKKuKskXH32x1FURkbNUrMHGzNab2U4z6zKzW/LsT5nZA2H/FjPryNl3a0jfaWZXzFSmma0MZXSFMqtC+k1mtsPMnjGzH5vZ+XFe83SODZVfNxpAbaqCT1zWwcPPHuCFQ2rdiMjciy3YmFkSuAu4ElgLXGNmaydluw7odffVwJ3AHeHYtcBGYB2wHrjbzJIzlHkHcGcoqzeUDfA00OnubwYeAv4yjustxInlBcqsZQPwqXetorYyyZ9+b4eWixaRORdny+ZioMvdd7n7KLAJ2DApzwbgvrD9EHC5mVlI3+TuaXffDXSF8vKWGY55TyiDUOYHANz9MXefeET+CWB5DNdakImWTakn4cynpa6Km698Az998VXu3/pKqasjImeZOIPNMmBvzuvukJY3j7tngD6gdZpjp0pvBY6FMqY6F0Stne/nq6yZXW9m28xsW09Pz4wXdzp6h0apSBj1qfJ8lvajl5zPu1a3cfs/P8+vDmphNRGZOwtmgICZfRToBP6/fPvd/R5373T3zvb29ljqEE1VU0nUECsv9295hU1P7uWy1W1UJIwP/e0TfPknL5W6WiJylogz2OwDVuS8Xh7S8uYxswqgCTgyzbFTpR8BmkMZp5zLzN4L/Alwlbunz+iqzsCxodGyG4k2WVNNJR97ZwfDo1m+9vM99IWuPxGRMxFnsHkSWBNGiVUR3fDfPCnPZuDasH018KhHd6c3AxvDaLWVwBpg61RlhmMeC2UQyvwugJm9DfhbokBzOKZrLcih/hHOaUyVsgoFWdZcw0cuOY+ewTTX/v1WBtOZmQ8SEZlGbMEm3D+5EXgEeB540N23m9ltZnZVyHYv0GpmXcBNwC3h2O3Ag8AO4AfADe6enarMUNbNwE2hrNZQNkTdZvXAt8zsl2Y2OeAVzYG+Ec5trCnV6WdlzTkNbHzHCp7d18cf3LeNkbFsqaskIvOYaZjrqTo7O33btm1zWmYmO87r/8sP+PS7L+CP3vf61+ybmIW5HNVUJfjcA//K5W9YzJc/9vaSrzAqIuXLzJ5y9858+/TNUSQ9g2my4865TdWlrsqs/O7blvOnH7iQH//qMJ974Jdkx/XHiYjMXnmOwT0LHegbAaKJL+ebj116PsfTGf7i+7+ipbaK2zasK8sRdSJSvhRsiuTgiWAzP+7ZTJjo4musruTfrG7jG0+8zOKGFJ+5fE2JayYi84mCTZHsPzYMzM+WzYQrLjyXwXSG//bDF2hvSLHxYq3uKSKFUbApkg/856UAAA17SURBVIN9I9RUJmkqk1U6T0fCjH970XIaayr54398lkV1Vbxv3bmlrpaIzAMKNkVyoG+EJU3V8/5eRzJhvPv17bzUM8in/+EXXPeulZzfWgfAhy9RS0dE8tNotCI50Dc870aiTSVVkeTj7+ygqaaS+x7fw6H+kVJXSUTKnIJNkRzsG5l3gwOmU5+q4JOXraQykeBrP99zYmE4EZF8FGyKIDvuHBpIz+vBAfksqqviE5d1MDKW5e8VcERkGgo2RdAzMD8f6CzEkqYaPnbp+Rw9Psq/+9qTDI9qWhsROZWCTRF090Zrty1tPvuCDcCq9np+v3MFT+89xo33/4JMdrzUVRKRMqNgUwQ7DkQLkb3h3MYS1yQ+b1rWxG1XrePHvzrMp//hF5q4U0ReQ8GmCLbv62dRXdVZd89mso+9s4Mv/M5afvj8Ia75yhMnWnQiIgo2RfDc/j7WLW2c98/YFOITl63krg9fxIuHBrnyr3/K1362m3RGrRyRhU4PdcZsNDPOC4cGuO5dq0pdldjlLpXwf//6BXznF9184X/t4G8ee4nffvMSLlvdxpuXN3FO49ndwhORUynYxOyFQwOMZZ0Ll52992vyWVRXxXXvWslLPcfZd2yI+7e+wtd+vgeA9oYUq9rqWNJUzTlN1VRXJMmMjzOWdZ7b10d23MmOO5XJBM21lXzi1zp4y4pmqiuTpb0oETltCjYx27E/GhywbmlTiWtSfGbG6sX1rF5czztXtXGgb5h9x4bZ1zuMOzz1Si+H+tKMZsdJJoyKhGEGSTOSCSOdGSedGef7zx2kKpngzcub6OxYxMUrW3j7eYtoqp2/88yJLDQKNjF7bn8f9akKzl9UW+qqlFRVRYLzW+tOzKM2wd1xokk+8xlKZ+hoq2PrnqNs3X2Ur/50F1/+l2gBt5baShY3VLO4IcXixtSJ7U++a2XclyMis6RgEyN35/GXjvCmZU0kEmf/4IDTYWZM987Upio4PJCmo7WOjtY6PvDWZXQfG+KVI0Mc7B+hZyDNSz2DZHJWEP3WU91c/sbFvOcNi3nL8ma99yJlQMEmRr94pZcXDw9ynf7SnjNVFQlWtdWzqq3+RNq4O0ePj3K4P83B/mH6hzPc9VgX/+PRLhY3pFh/4bmsv/BcLu5YREVSAzBFSkHBJkbf3LqXuqokv/OWpaWuylktYUZbfYq2+hRrl0YDMd637hxeODTA9v39fHPrK3z98ZdpravivW88h3de0MqFy5o4b1EtVRVR8BkZy9I/PMbRoVFeHRjlyPE0/SMZcKeptirqqmtIsaylhlSFBiqIzJaCTUz6hsf43jP7+d23Lacupbe52GqrKnjrihbeuqKF0cw4ixtTfP+5gzz87AEe2Lb3RL6ayiTj7qQzhU2xkzBY3lLLqvY6VrXVs7KtlvaGatpDMGpvSGnUnEgesX4Lmtl64K+BJPBVd/+LSftTwNeBtwNHgA+5+56w71bgOiALfNbdH5muTDNbCWwCWoGngI+5++h054jLaGacz3zzaUYz43xEC4qVXFVFgmNDY7xzVSuXrFzEwb4RDvWPcHRolPTYOAbUVCWprkxSW5WkvrqC+lQFNSFoDI1mGRjJcOGyRvYcGWJXzyC7eo6zZddRhvNMy1NdmWBpUw2t9VW01qWi3/Up2uqrWFQXpTVUV1CZTFCRjO5ZZcadsew4mayfGAY+8XosO46Z0VJbSXNtVEZTTSVJ3YuSeSS2YGNmSeAu4DeBbuBJM9vs7jtysl0H9Lr7ajPbCNwBfMjM1gIbgXXAUuBHZva6cMxUZd4B3Onum8zsy6HsL011jjiuOZMd5yc7e/jKT3exZfdR7vi9N3HhsoU35LmcJcxY2lzD0ubC1xZqqK7knEYYGRvn3MZqzm2s5tcuaGPcncF0hsGRDAMjGQbTYwyMZBgIaa8OjvLykSGOpzMMjWbxmU9VMDNoqqmkpbaKltrwuy4KRLVVSZJmJMJw8mTCSJhRkYx+J0NaMnc7cXJfwqLyjWgoulmUVplMUFWRoCqZIFURtsPrqooElckE4+6MZZ1MdpzMuDOaiX5nslEAzYxHr7PjTiYb/R53J1WRIFWZpLoyQari5O+KpFGZiIJyNDS+8ADr7rhH9/QccAcnSmPS69x/G+Pk9U+81+RJi/LZa44hJ01eK86WzcVAl7vvAjCzTcAGIDfYbAC+ELYfAv7Gon+pDcAmd08Du82sK5RHvjLN7HngPcCHQ577Qrlfmuoc7j6X//cB+PYvurn528/SVp/iz//tm/jQO9SqOZslzGisrqSxeubnfcbdGRrNMpjOcDydIT2WJevRWkfgJ4NACBK5X/5JMxxneDTL8dEsQ6NR8JoIYn3DYxzoGzmRljsy72wzETwhBIgQMMb95DD6uf+ffeZOBCJOjsA8mRZFrslpEwGt2K5710puet/r57zcOIPNMmBvzutu4JKp8rh7xsz6iLrBlgFPTDp2WdjOV2YrcMzdM3nyT3WOV3MrYmbXA9eHl4NmtrPgK53kZeDD/+Vk5APaJp9P9J7koffkVHpPThXre/JH4ec0nT/VDt25Dtz9HuCeOMo2s23u3hlH2fOV3pNT6T05ld6TU83X9yTOhw72AStyXi8PaXnzmFkF0ER0E3+qY6dKPwI0hzImn2uqc4iISJHEGWyeBNaY2UozqyK64b95Up7NwLVh+2rg0XAvZTOw0cxSYZTZGmDrVGWGYx4LZRDK/O4M5xARkSKJrRst3B+5EXiEaJjy37n7djO7Ddjm7puBe4FvhAEAR4mCByHfg0SDCTLADe6eBchXZjjlzcAmM/sz4OlQNlOdo8hi6Z6b5/SenErvyan0npxqXr4npj/yRUQkbpooSkREYqdgIyIisVOwiZmZrTeznWbWZWa3lLo+c8nMVpjZY2a2w8y2m9l/COmLzOyHZvZi+N0S0s3Mvhjei2fM7KKcsq4N+V80s2tz0t9uZs+GY75o8+TxbDNLmtnTZva98HqlmW0J1/FAGOBCGATzQEjfYmYdOWXcGtJ3mtkVOenz7jNlZs1m9pCZ/crMnjezdy70z4mZfS78v3nOzL5pZtVn9eckmtJBP3H8EA1ieAlYBVQB/wqsLXW95vD6lgAXhe0G4AVgLfCXwC0h/RbgjrD9fuD7RA9GXwpsCemLgF3hd0vYbgn7toa8Fo69stTXXeB7cxNwP/C98PpBYGPY/jLw/4TtTwNfDtsbgQfC9trweUkBK8PnKDlfP1NEs3p8KmxXAc0L+XNC9LD5bqAm5/PxibP5c6KWTbxOTNnj7qNEE4VuKHGd5oy7H3D3X4TtAeB5ov9EG4i+XAi/PxC2NwBf98gTRM9GLQGuAH7o7kfdvRf4IbA+7Gt09yc8+p/19ZyyypaZLQd+C/hqeG1E0yk9FLJMfk8m3quHgMtD/hNTNrn7bmBiyqZ595kysybg/yKMEHX3UXc/xgL/nBCNBq6x6Pm/WuAAZ/HnRMEmXvmm7Fk2Rd55LTTr3wZsAc5x9wNh10HgnLA91fsxXXp3nvRy99+B/wRMrFtQ8HRKQO6UTbN5r8rZSqAH+PvQtfhVM6tjAX9O3H0f8P8DrxAFmT6i2erP2s+Jgo2cMTOrB74N/KG79+fuC39pLpjx9Wb228Bhd3+q1HUpIxXARcCX3P1twHGibrMTFuDnpIWopbGSaGb7OmB9SSsVMwWbeBUyZc+8ZmaVRIHmH9z9OyH5UOjaIPw+HNJnOw3RvrA9Ob2cXQZcZWZ7iLou3kO0/tJsp1Oa7XtVzrqBbnffEl4/RBR8FvLn5L3Abnfvcfcx4DtEn52z9nOiYBOvQqbsmbdCn/G9wPPu/lc5u3KnCJo8ddDHw2ijS4G+0I3yCPA+M2sJf/G9D3gk7Os3s0vDuT6eU1ZZcvdb3X25u3cQ/Xs/6u4fYfbTKc1qyqYiXNppc/eDwF4zm5i3/nKi2UEW7OeEqPvsUjOrDXWeeE/O3s9JKUcnLIQfopE1LxCNDPmTUtdnjq/tXURdH88Avww/7yfqS/4x8CLwI2BRyG9Ei9+9BDwLdOaU9e+Ibm52AZ/MSe8EngvH/A1h1ov58AO8m5Oj0VYRfQl0Ad8CUiG9OrzuCvtX5Rz/J+G6d5Izumo+fqaAtwLbwmfln4hGky3ozwnwX4FfhXp/g2hE2Vn7OdF0NSIiEjt1o4mISOwUbEREJHYKNiIiEjsFGxERiZ2CjYiIxE7BRqTMmNm5ZrbJzF4ys6fM7GEze52ZPZeT5/81sz+fdNxbzez54tdYZGYKNiJlJDzg94/AT9z9And/O3ArJ+cNm/BN4EOT0jaGdJGyo2AjUl5+Axhz9y9PJLj7v/LaSRVx9xeAXjO7JCf591GwkTKlYCNSXi4kmv23EN8kas0QpnU56u4vxlUxkTOhYCMyfz0AXG1mCdSFJmVOwUakvGwH3l5IRnffS7Ta468Dv0cUfETKkoKNSHl5FEiZ2fUTCWb2Zl47XXyubwJ3ArvcvXuKPCIlp2AjUkY8mhn3d4H3hqHP24E/J1rJ8vVm1p3z80GimYDXoS40KXOa9VlERGKnlo2IiMROwUZERGKnYCMiIrFTsBERkdgp2IiISOwUbEREJHYKNiIiErv/A+rXadk2Mn5YAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4g9smn4Mgkb"
      },
      "source": [
        "_, l = stats.boxcox(marketing_df['CLV'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hHZfPxFMgYD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c31a52b0-a835-4e43-b5b4-ecc936ec5cf5"
      },
      "source": [
        "print (\"lamda     : \" ,l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lamda     :  -0.3628046005612987\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwT4Qaj0M4vV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "0ddc8544-da07-413c-e7c6-488249650dc4"
      },
      "source": [
        "sns.distplot(marketing_df['CLV'].apply(lambda x: 1/np.power(x,np.abs(l))), hist=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f45f0210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kRodZevyx1uQk0NjZT11Hn1ePq/yTu338zwFLgF8Cf2SMqXU99bSIFFsVnFKeVlzeSnRYCEszYi0/1p1r51Hd2sN33jzPy67pmw9ft8Brg7qjrc52FoE7VtVGZrw1YxtWm6wfX02Pu1fu/sQY8+roDSISbozpN8YUWhCXUpY4WN7C+rxE7CHWT2gTEb56wyKuW5LG0ao2NuYnscQLXzjjWZYZR2iIcKy6nVtWWn/tgPJv7ib+bwCvjtn2Ac6uHqUCQluPc+GV21d7N/GtzIq3pOzydESEhrA0I45jVXoFr5oi8YtIBpAFRIrIOmBkGkQcMLNFSpXykYOu+fubCjy/2lUgWJMTzwtHLuFwGGwWlIFWgWOqFv/NOAd0s4F/H7W9E3jUopiUssSBsmbC7LYP+7uDzersBH61v5LSpm4WpsX4OhzlQ5MmfmPMDmCHiHzKGPM7L8WklCUOlLWwNifB44uaB4q1Oc6Vxo5VtWniD3KTjnCJyH2uu/ki8vWxNy/Ep5RHdPUPcfJSx6wXVg9kC1JjiA4L0UqdasqunmjXT20eqIB2uKKVYYdhUxAn/hCbsDIrnmNasyfoTdXV82PXz3/2TjhKWeNAWQshNmF9rrVX7Pq7tTkJ/Oy9cvqHhgm3B2eXl3Lzyl0R+baIxIlIqIjsFpHGUd1ASvm9A2UtrMyKJzo8uBedW5uTwMCwg5JL41cOVcHB3atYbjLGdAC346zVsxD4a6uCUsqT+gaHOVrVFtT9+yNGSlWMlK5QwcndxD/STLoN+K0xRjsJVcA4VtXGwLCDTRbX5wkEqbHhFKREf3hNgwpO7v7d+7KInAF6gT8VkVRg0mpPIhIB7AXCXcd5xhjzjyJSADwFJAOHgPuNMbo0kLLMgbIWRKCiuUfrvQCFeYm8eboeY4xlpamVf3Mr8RtjHhGRbwPtxphhEekG7pziZf3ADcaYLlfp5ndF5PfA14HvGGOeEpEfAV8EfjiLc1BqUkVlzoXVvVX/3h2+/ALamJ/Ebw9Vc7FRL+QKVtOpVLUUuEdEHgA+Ddw02c7Gqcv1MNR1M8ANwDOu7TuAu6YVsVLT0Dc4zIHyFrYsTPF1KH5jZJH5g9rPH7TcLcv8S2ABcBQYdm02wC+meF0Izu6chcD3gYtAmzFmyLVLNc5aQOO99iHgIYDcXF06Tc3MwfIWBoYcXL0ohdo2rUUPUJASTXJ0GAfLW/jjTYH9u3W6toN3zjbQ1T/EwrRYbl2VodNU3eBuH38hsNxMc/keY8wwsFZEEoDncP7V4O5rHwMeAygsLNRlg9SMvHu+ibAQG5sLknj+yCVfh+MXRITC/MQPF50PVO+eb+T3J+tIiQ0nMz6S4vIWypu6eeia+UE/bXcq7nb1nAQyZnoQY0wb8DZwJZAgIiOfSjZQM9P3VWoq+843sT4vgagwTQSjbcxPorKlh/oAXZGrtLGLV0/WsXxeHF+9fiH3XZHH57cU0NI9wCsnaqd+gyDnbuJPAU6JyGsi8uLIbbIXiEiqq6WPiEQCNwKncX4BfNq123bghZmFrtTkmrv6OVXbwdZFqb4Oxe/8YT5/4LX6B4YcPHukhqToMD6zIYdQ16I6C9NiuGZxKker2jhX3+njKP2bu82gf5rBe2firOwZgvML5jfGmJdF5BTwlIh8A+favU/M4L2VmtLe840AbF2kA7tjZxENOwyhIcKviiq4zcsL08zWvvONtHQP8KWtBYTZP9p2vX5JKidq2nitpI5FaTGIyKQzqO7dHNhjHDPl7nTOPSKSBywyxrwpIlHApCMoxpjjwLpxtpcCm2YSrFLT8ebpBlJjw1k5Lzjr708mxCbkJEZR0dzt61CmpX9wmPcvNrM8M475KZdPRbWH2Ni6MJXnjtZQ3txDQUr0OO+i3K3V82WcUzB/7NqUBTxvVVBKzdbgsIO9Zxu5YUmarjY1gbzkaGrb+ujsG/R1KG47UN5C7+Aw1y6euPtuTU4CEaE2Piht9mJkgcXdPv4/A7YAHQDGmPNAmlVBKTVbB8tb6OwfYtsy/W86kYKUaAyB088/7DC8d6GJ+SnR5CRNvPJrmN1GYV4Spy6109EbOF9q3uRu4u8fXVbBNStHp1gqv7X7dANhdpteuDWJvOQo7DbhvQtNvg7FLWfrOujoG3LrM92Yn4TDwMlLWlZsPO4O7u4RkUdxLrp+I/Aw8JJ1YSk1MzuLKjHG8OzhavKTo3jhqM7dn0hoiI3c5CjeDZDEX1zRSmy4ncXpsVPumxobTlpsOCWXOrhqgX75j+Vui/8RoBE4AfwJ8Crw91YFpdRs1LT10tozyKosHdSdysLUGM7UddLU1e/rUCZV39HH2bpO1uUmEuLmmM2KefGUN3XT1T809c5Bxq3Eb4xx4BzMfdgY82ljzE+mexWvUt5yorqdEBGWZ2rin8qCVOfMmPcv+vdA6LOHazD8oc6QO1ZmxWGA07rozGWmWmxdROSfRKQJOAucda2+9Q/eCU+p6THGcKKmnYVpMX5VjdNfZSVGEhth513XNQ/+6sVjl8hJjCQlJtzt12TERZAUHUZJrfbzjzVVi/+/45zNs9EYk2SMSQI2A1tE5L9bHp1S01TV0kNb7yCrsrW17w6bCFcvTGHvuSb89Y/4Cw2dnK7tYE1OwrReJyIsTo+lrKmbIYfDougC01SJ/37gj40xZSMbXBdg3Qc8YGVgSs3EocpWQkOE5Zlxvg4lYFy/NI26jj7O1PlnmYOXjtUiwowuxFuYGsPgsKGqpdeCyALXVIk/1Bhz2ZC/MaYRZ319pfxGd/8Qx6rbWZWVQESodvO46zrXxVBvnWnwcSSXM8bw8vFLbC5IIi5y+imnICUaAS40dE25bzCZKvFPtiSiLpeo/MorJ2oZGHKwcRoDgArS4iJYmRXHO2f9L/Gfq+/iYmM3t6+eN6PXR4aFkJ0YycVGTfyjTZX414hIxzi3TmCVNwJUyl1PHagkJSac3Emu6lTju35JGocqWmnr8a/23K6TdYjATSvSZ/weC9JiqG7toW9weOqdg8Skid8YE2KMiRvnFmuM0a4e5TcOVbRyuLKNK+Yn6QLiM3DD0jQcBt4561+ze3aV1LEhN5G02IgZv8eC1BgcBsqbAqsgnZWms+auUn7r8X2lxEXY2ZCn3TwzsSY7gfS4cH5/0n8WMals7uF0bQe3rJzxGlAA5CRGYROoaOnxUGSBTxO/CnjlTd3sKqnjvivydL3VGbLZhFtWZLDnXCM9A/5xpetrJXUA3Lxidok/zG5jXkJkwJWgtpImfhXw/vOt84SF2HjwqnxfhxLQbl6ZQd+gg73n/KO7Z1dJHSvmxU1aidNd+cnRVLf2MjSs8/lBE78KcGfrOnnuSA0PXpVPWtzM+4EVbMpPIjEqlN+frPN1KDR09HGoonXWrf0RuUlRDDkMl9p0Pj9o4lcB7tu7zhATbudPr1vg61ACnj3Exi0rM3jjVL3Pu3teO1UPMOv+/RF5yc6/GrSf30kTvwpYb5yqZ/eZBr56/UISosJ8Hc6ccOfaLHoGhnnDlXh95fWSOuanRLMo7fLlFWciNiKU5Ogwyps18YP79fiV8itd/UP8wwsnWZoRyxeuLvB1OHPGpvwk5sVH8PyRGu5cm+X14+8sqqRnYIj3LjSxdVEqTx6o8th75yZFcaGhC2NM0E/51Ra/Ckj/+6VT1HX08S+fWEVoiP439hSbTbhjbRZ7zzf5rEb/qUsdOAysmOfZektZiZF09g/R0ecfs5Z8SX9jVMB54WgNTxdX8fB1C3TevgU+uT6LYYfh+SM1Pjn+iZp2kqLDyEqI9Oj7Zrver6ZVu3s08auAcrq2g0efPcGGvES+9rHFvg5nTlqcHsv63AR2Hqj0eqnm7v4hLjZ2sSor3uPdMZkJkdgEqlt1Zo/28auA0dDZx5d2FBMTYef79673+y6enUWVvg5hxu7dnMdf/fYYRWUtXDE/2WvHHenmsWLZzNAQG+lxEdTolE5t8avA0NYzwANPHKCle4Antm8kI17n7FvptlWZxEbYvf7ldaKmneToMDIt+nyzEiKpbu3120VnvEUTv/J77b2DbP/ZQUobu/nJA4Ws1EXULRcZFsKnN2Tz6olaatu900Ju7up3dvNke76bZ0R2YhS9g8O09gxa8v6BwrLELyI5IvK2iJwSkRIR+QvX9iQReUNEzrt+6uicmlBr9wCfe3w/py618/3PrefqRSm+DilofGFLAQ5j+Pn75V453q6SOgzWdPOMyEp0DvBWB/kAr5Ut/iHgL40xy4ErgD8TkeXAI8BuY8wiYLfrsVIfsbOoksf2lvLx7+7jTG0n927KpbGzP6D7zQNNTlIUH1+Zyc6iSrr6rZ8C+fKxWlJiwsmwsPRGelw4dpsE/QCvZYnfGFNrjDnsut8JnAaygDuBHa7ddgB3WRWDClydfYP8ZF8pzd39PHBlPksydA1dX/jyNfPp7BviV/srLD1OQ2cfRWXNlszmGc1us5ERrwO8XunjF5F8YB1QBKQbY0aKftcB4y6tIyIPiUixiBQ3NvpHtUDlHQ2dfTz+bhltPQNsvyqfhR66bF9N39qcBK5ZnMqP91y0tNX/7OEaHAbW5Fg/fpOdGElNWy+OIB7gtXw6p4jEAL8DvmaM6Rj9bW6MMSIy7r++MeYx4DGAwsLC4P2E5rDxum06+wY/TPoPXlVAQUq0DyILLpN1n927OZev37iYu77/HjveL+fPrl/o8eMbY/jNwSo25s9upS13ZSVEsb+0hcZO31yZ7A8sbfGLSCjOpP9rY8yzrs31IpLpej4T8L8VnpVPdPcPadL3Q2tzEti2NI0f7blIswVlHA6Wt1La1M3dhTkef+/xZLsGeIO5u8eyFr84m/ZPAKeNMf8+6qkXge3AN10/X7AqBhU4Bocd/HJ/Ba3dAzy4JX/SpK8DvN73yMeXcst39/Hvb5zjXz6xyqPv/dSBSmLC7dy2OpPnj1zy6HuPJzU2nLAQW1AP8FrZ4t8C3A/cICJHXbdbcSb8G0XkPPAx12MVxBzG8PTBKqpaeri7MIf5Kdqn728Wpcdy/xV5PHmgkpJL7R573/qOPl46folPrc8iKsw7hQRsIsxLiAjqmj2W/UsbY94FJhqe32bVcVVgMcbwyolaTtV2cNuqTL04y8+M/usqJzGKyDA7D/3iEF+5dgH3X5k36/f/+fvlDDsMX7x6/qzfazqyEiIpKmthaNiB3c9Lf1gh+M5Y+ZVDFa18cLGZLQuS2bJQL87yZ5FhIdyxZh41bb3sOz/7mXZd/UP8en8FH1+ZSW7y7NfVnY6sxEiGHIbzDV1ePa6/0MSvfKauvY8Xj11iQWo0H1+V6etwlBtWZcWzMiue3acbOFLZOqv3+sneUjr6hnjoGu+29gHmuUo0n6jxXLdVINHqnMpSEw3E9g8Os/NAJZGhIdxdmIMtyFdECiR3rZ1HdWsPX915hJf//GoSo6e/7GVDRx+P7S3lttWZrMlJsCDKyaXEhBNmt3Giut1rs4n8ibb4ldcZY3j+aA3NXf3cszGH2IhQX4ekpiEqzP5hCY0/+eUh+gaHp/0e337tLEMOB39781ILIpyaTYR58ZFB2+LXxK+8rri8lWPV7Wxbls78VJ3BE4iyE6P4v3ev4UB5C1976iiDww63X7vrZB3PHKrmy1vne71vf7SshAhO13YwNI3Y5wpN/Mqratt7een4JRamxXDdklRfh6Nm4Y418/iH25ezq6TO7ZZ/VUsPjzx7nNXZ8T5fQS0rMZL+IUdQDvBq4lde0zc4zM6iSqLCtF9/rvjC1QV8466VvH22gU/84H0uTJJEK5t7uOfHH2AMfPez6wiz+zb9BPMAryZ+5RXGGJ47UkNL9wD3bMwlJlznFcwV912Rx0+3b6SuvZdb/3Mf//LKqY+UQ+gfGubJA5Xc8f136Rkc5tdf2uwX5ThSYsKJDgvhZBAmfv3tU15xoLyFEzXt3LQ83S9+6ZVnXb80jV1fu4Zv7zrLE++W8ZN9ZeQlRxEdZqe0qYu+QQebC5L410+uYoGfjOvYRFiRFR+ULX5N/Mpyl9p6eeV4LYvTY7hmsfbrzxXjTdXdkJdIfnIU4aE2jlW10zc4zKaCJK5bksq1i1MtrbU/E6uy4vnV/oqguyBpmXQAABAdSURBVIJXE7+yVE//EDsPOPv1P7NB+/WDQXJMOPduzvV1GG5ZlRX/4QDvsszgWewneL7ilNcNDTt48mAl7b2D3Ls5j2jt11d+ZqQ2VLB192jiV5b511fPcLGxm7vWZpGb5Lv52kpNZH5KdFAO8GriV5b4bXEVP32vjC0LktmQl+jrcJQal80WnAO8mviVx719poG/e/YEWxYmc8tKLb6m/NuqrPigu4JXE7/yqPcvNPGVXx1iaWYsP7xvAyE2HcxV/m1VVjx9gw4uNAbPFbw62qY85rWSOv78ySPkJ0ex4/ObiNPia2oc/rZ05sgA7/HqdpZmBMfMHk38ataMMfzgnYv839fPsiY7gZ9/fiMJUdMv1avmDn9L7pMZPcAbLCWaNfGrj5joF3aiedk1bb088rvj7DvfxB1r5vHNT63y2tqpSnmCzSasmBdcA7z6G6pmpHdgmCfeLeWH71zEAP/7rpXctznX767MVModq7Lj+XVR8FzBq4lfTUtDZx+/eL+CXxVV0NYzyM0r0vn725aTo/P0VQAbPcAbDP38mviVW87Vd/L4vlKeP3KJQYeDG5el8yfXzmdDXpKvQ1Nq1oJtgFcTv5pUWVM3e8418OhzJ4gItXHPxhy+cHWBVthUc8r8lGhiw+0cq2oLigFeTfxqXA0dfbxWUsfpuk5iwu385Y2Lue+KvBktrK2Uv7PZhLW5CRypbPN1KF6hiV99RP/QMK+X1LO/tJkwu42bl6dz5YIUHtyS7+vQlLLUupwEvvf2Bbr7h+Z8QcG5fXZqXBNN2axu7eGpg1W0dg+wqSCJbcvSdaUsFTTW5SbiMM5+/isXJPs6HEtZNm9JRH4qIg0icnLUtiQReUNEzrt+avUuP3GwrIUf7y3F4TB8aet87lybpUlfBZW1OQkAHKlq9XEk1rPyN/vnwPeAX4za9giw2xjzTRF5xPX4by2MQU3BGMMbp+p551wji9NjuHtDDlHjJPxAuhJTqZlIjA5jfko0hyvmfj+/ZS1+Y8xeoGXM5juBHa77O4C7rDq+mtqww/Ds4RreOddIYV4i91+RP27SVypYrM1N4GhVK8YYX4diKW9fopZujKl13a8D0ifaUUQeEpFiESlubGz0TnRBZNhh2FlUwaHKVm5YmsYn1mVpJU0V9ArzkmjqGqC8ucfXoVjKZ9cmG+dX6oRfq8aYx4wxhcaYwtRUXaDbkxzG8LvD1Zyu6+SP1szjY8vStdSCUsCmAucFiQfKmn0cibW8nfjrRSQTwPWzwcvHD3rGGF45XsvRqjZuWp7OlfPn9uwFpaZjQWo0ydFhHCib2wO83k78LwLbXfe3Ay94+fhB7z93X+CD0mauXpjCtYv1LymlRhMRNuYncaBcW/wzIiJPAh8AS0SkWkS+CHwTuFFEzgMfcz1WXvKLD8r5zpvnWJ+byMdXZmj3jlLj2FiQRFVLL7Xtvb4OxTKWTeEwxvzxBE9ts+qYamIvHK3hH18s4WPL0rl2caomfaUmsPnDfv4W7lyb5eNorDH3C08r3j7bwF/+5hib8pP43r3rdPaOUpNYlhlHbISdDy7O3e4eTfxz3KGKFv7Utfj549sLiQgN8XVISvm1EJtw5fxk9p1vmrPz+fVqnTlqZ1Elde19PLbvItFhdu5Yk8VLx2qnfqFSiq2LUnj9VD3lzT1zsgS5tvjnqJbuAX72XhlhITa+cHWB1t1RahquXuSc8fbu+bl58agm/jmoobOPn75XxpDD8PktBSRGaQ19paYjPzmK7MRI9p5v8nUoltDEP8e09w7ywBMH6Oob4sGr8kmPi/B1SEoFHBFh66IU9l9sZnDY4etwPE4T/xzSOzDMF39+kIuNXXzuilxdAF2pWbhuSRqd/UMcLBtbazLwaeKfIwaHHTz860McqmzlP+5Zx6K0WF+HpFRA27oohXC7jddP1fs6FI/TxD8HOByGv/7tMd4+28i/3LWK21Zn+jokpQJeVJidrYtSeb2kbs5N69SpHgHOGMM/v1TC80cv8dc3L+Hezbm+DkmpgDHZAkP3bs7lphXpvHm6npJLHazMivdiZNbSFn8AM8bwrV1n2fFBBV/eWsDD1y3wdUhKzSnblqZhE3itpM7XoXiUJv4A9r23LvCjPRf53OZcHr11mdbfUcrDkmPCuWpBCs8frZlT3T3a1ROgHt9Xyv974xzrchJYlhnHkweqfB2SUnPSJ9dn8fXfHKO4opWN+Um+DscjtMUfgHYWVfKNV06zcl4cn1yfjU1b+kpZ5uYVGUSFhfDs4Wpfh+IxmvgDzFMHKvkfz5/g+iWp3L0xRyttKmWx6HA7t6zM4OVjtfQODPs6HI/QxB9AfvFBOY88e4JrFqXyw/s2YLfpx6eUN3x2Yy6d/UM8e2RutPo1cwSIx/eV8g8vlHDj8nQee2CDlldWyos25ieyMiuOn75bhsMR+IO8Orjr5361v4I3TtWz51wjK7PiuWZRKr87VOPrsJQKKiLCl66ez9eePsqe841cvyTN1yHNirb4/Vj/0DC/Ka5iz7lGNuUncU+h9ukr5Su3rsokIy6C/9p9PuCndmri91Mt3QPc/8QBjle3c/OKDO5cO0+TvlI+FGa38RcfW8ThyjZeKwns+j2a+P1QcXkLt353H0cr27inMEcXR1fKT3xmQzYLUqP59q4zAV2uWRO/H3E4DD/ac5F7HttPeKiNZx++ijU5Cb4OSynlYg+x8eityyht6uZ7b13wdTgzponfT5yr7+TuH3/AN39/hptXpPPSn189p4pCKTVXbFuWzifXZfG9ty9wrKrN1+HMiCZ+H+vuH+Jbu85w63f3cbGxi//z6dV8/971xEWE+jo0pdQE/vGOFaTFhvPwrw9T39Hn63CmTRO/j3T0DfJfu8+z5Vtv8cN3LvKJdVns/svr+ExhjvbnK+Xn4iND+ckDhbT1DPDgzw7S1jPg65CmRefxe1nJpXaeOVTNM4eq6ewbYtvSNL56w0LW5Sb6OjSl1DSszIrnB/dt4Ms7ivnkD97npw9uJD8l2tdhuUUCYT5qYWGhKS4u9nUYMzI07OB4TTv7zjXx+5O1nKnrJCzExo0r0vnTaxewMit+0sUglFL+Z/SCRwfLW/jyL4oZGHLwNzcv4XNX5BEa4h+dKSJyyBhTeNl2XyR+EbkF+C4QAjxujPnmZPsHSuLvHxqmprWX8w1dnKxp52RNO4cqWunoG0KA7MRI1uUmsjo7nqgw/WNLqUA1dqW76tYeHn3uJHvPNZKdGMn2K/O5bXUm8xIifRShk98kfhEJAc4BNwLVwEHgj40xpyZ6jVWJ3xiDw4DDGBzGYAwY1+OBIQe9g8P0DQ67fjroHxymo2+Q1p5BWnsGaO0eoLl7gOrWXqpaeqjr6GPknzPEJixKi2FNdgJbF6dQ19ZHVLgme6XmgvGWODXG8NaZBn74zkWKK1oByEmKZFVWPCuz4slKiCQ1JpyU2HCiw+2E222E221EhIZgt4klY3sTJX5fZKJNwAVjTCmAiDwF3AlMmPhn6iu/PMSec43OpM5HE70nvu/C7TaSo8PIToziygXJtPUMkhQdRkpMOBlxEYTZnX/udfQOadJXao4TEbYtS2fbsnTKmrp5vaSO49XtnKhp59UTUy/daBOwiSDifC+bgCC8/N+uZkFqjEdj9UU2ygJGLxdVDWweu5OIPAQ85HrYJSJnJ3i/FKDJoxH6j7l6bnP1vEDPLVBN69w+Z2EgYy38xqxenjfeRr9thhpjHgMem2o/ESke70+ZuWCunttcPS/QcwtUc/ncxuOLoecaIGfU42zXNqWUUl7gi8R/EFgkIgUiEgZ8FnjRB3EopVRQ8npXjzFmSES+CryGczrnT40xJbN4yym7gwLYXD23uXpeoOcWqObyuV0mIC7gUkop5Tn+cXmZUkopr9HEr5RSQcZvE7+I3CIiZ0Xkgog8Ms7z4SLytOv5IhHJd21PFpG3RaRLRL7n7bjdMYtzu1FEDonICdfPG7wd+1RmcW6bROSo63ZMRD7h7dinMtNzG/V8ruv/5V95K2Z3zeJzyxeR3lGf3Y+8HftUZvO5ichqEflAREpcv3cR3ozdMsYYv7vhHPS9CMwHwoBjwPIx+zwM/Mh1/7PA06770cDVwFeA7/n6XDx8buuAea77K4EaX5+PB88tCrC77mcCDSOP/eE2m3Mb9fwzwG+Bv/L1+Xjwc8sHTvr6HCw6NztwHFjjepwMhPj6nDxx89cW/4dlHYwxA8BIWYfR7gR2uO4/A2wTETHGdBtj3gX8dXWE2ZzbEWPMJdf2EiBSRMK9ErV7ZnNuPcaYIdf2CMDfZh3M+NwAROQuoAzn5+ZvZnVufm4253YTcNwYcwzAGNNsjBn2UtyW8tfEP15Zh6yJ9nEljHac38j+zlPn9ingsDGm36I4Z2JW5yYim0WkBDgBfGXUF4E/mPG5iUgM8LfAP3shzpmY7f/JAhE5IiJ7RGSr1cFO02zObTFgROQ1ETksIn/jhXi9wm9LNqiJicgK4Fs4WyRzhjGmCFghIsuAHSLye2OMv/7lNh3/BHzHGNMVGI3kaakFco0xzSKyAXheRFYYYzp8HZgH2HF2G28EeoDd4qx2udu3Yc2ev7b43Snr8OE+ImIH4oFmr0Q3O7M6NxHJBp4DHjDGXLQ82unxyOdmjDkNdOEcx/AXszm3zcC3RaQc+BrwqOsiRn8x43MzxvQbY5oBjDGHcPanL7Y8YvfN5nOrBvYaY5qMMT3Aq8B6yyP2An9N/O6UdXgR2O66/2ngLeMagfFzMz43EUkAXgEeMca857WI3Tebcytw/dIhInnAUqDcO2G7ZcbnZozZaozJN8bkA/8B/Ksxxp9mnM3mc0sV5xobiMh8YBFQ6qW43TGbXPIasEpEolz/N6/FgvLxPuHr0eWJbsCtOBdsuQj8D9e2/wXc4bofgXOGxAXgADB/1GvLgRacrcZqxozi+/o203MD/h7oBo6OuqX5+nw8dG734xz4PAocBu7y9bl48v/kqPf4J/xsVs8sP7dPjfnc/sjX5+LJzw24z3V+J4Fv+/pcPHXTkg1KKRVk/LWrRymllEU08SulVJDRxK+UUkFGE79SSgUZTfxKKRVkNPErNQURyRCRp0Tkojiror4qIotF5OSoff5RRP5tzOvWishp70es1OQ08Ss1CVexrueAd4wxC4wxG4C/A9LH7PokcM+YbZ91bVfKr2jiV2py1wODxpgP68wbZ7XG0YW/MMacA1pFZPOozXejiV/5IU38Sk1uJXDIzX2fxNnKR0SuAFqMMeetCkypmdLEr5TnPA18WkRsaDeP8mOa+JWaXAmwwZ0djTFVOBdbuRZnDZunLYxLqRnTxK/U5N4CwkXkoZENIrKaj5b6He1J4DtAqTGm2gvxKTVtmviVmoRxVjH8BPAx13TOEuDfgDpgiYhUj7p9BmeVxxVoN4/yY1qdUymlgoy2+JVSKsho4ldKqSCjiV8ppYKMJn6llAoymviVUirIaOJXSqkgo4lfKaWCzP8HPlm1jHCCIF4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SU7Ihy5nPO8z",
        "outputId": "00c4ed55-2fd5-4cfa-b5d2-e16098e55635"
      },
      "source": [
        "stats.boxcox(marketing_df['CLV'].apply(lambda x: 1/np.power(x,np.abs(l))))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.94358158, -0.96772907, -0.94394867, ..., -0.97401118,\n",
              "        -0.94586294, -0.96077198]), 0.9999998972585702)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86-k4RzZzGxy"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Av1iLOtS3Ih7"
      },
      "source": [
        "--------------------------------"
      ]
    }
  ]
}