Scaling_and_Transformations.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Scaling and Transformations"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yfinance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:25.367Z",
          "iopub.execute_input": "2021-07-18T23:36:25.374Z",
          "iopub.status.idle": "2021-07-18T23:36:25.465Z",
          "shell.execute_reply": "2021-07-18T23:36:25.490Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "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>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:25.475Z",
          "iopub.execute_input": "2021-07-18T23:36:25.480Z",
          "iopub.status.idle": "2021-07-18T23:36:26.192Z",
          "shell.execute_reply": "2021-07-18T23:36:26.256Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "                 Open   High        Low      Close  Adj Close     Volume\nDate                                                                    \n2018-08-20  19.790001  20.08  19.350000  19.980000  19.980000   62983200\n2018-08-21  19.980000  20.42  19.860001  20.400000  20.400000   55629000\n2018-08-22  20.280001  20.92  20.209999  20.900000  20.900000   62002700\n2018-08-23  21.190001  22.32  21.139999  22.290001  22.290001  113444100\n2018-08-24  22.910000  24.00  22.670000  23.980000  23.980000  164328200",
            "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>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-08-20</th>\n      <td>19.790001</td>\n      <td>20.08</td>\n      <td>19.350000</td>\n      <td>19.980000</td>\n      <td>19.980000</td>\n      <td>62983200</td>\n    </tr>\n    <tr>\n      <th>2018-08-21</th>\n      <td>19.980000</td>\n      <td>20.42</td>\n      <td>19.860001</td>\n      <td>20.400000</td>\n      <td>20.400000</td>\n      <td>55629000</td>\n    </tr>\n    <tr>\n      <th>2018-08-22</th>\n      <td>20.280001</td>\n      <td>20.92</td>\n      <td>20.209999</td>\n      <td>20.900000</td>\n      <td>20.900000</td>\n      <td>62002700</td>\n    </tr>\n    <tr>\n      <th>2018-08-23</th>\n      <td>21.190001</td>\n      <td>22.32</td>\n      <td>21.139999</td>\n      <td>22.290001</td>\n      <td>22.290001</td>\n      <td>113444100</td>\n    </tr>\n    <tr>\n      <th>2018-08-24</th>\n      <td>22.910000</td>\n      <td>24.00</td>\n      <td>22.670000</td>\n      <td>23.980000</td>\n      <td>23.980000</td>\n      <td>164328200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.201Z",
          "iopub.execute_input": "2021-07-18T23:36:26.207Z",
          "iopub.status.idle": "2021-07-18T23:36:26.223Z",
          "shell.execute_reply": "2021-07-18T23:36:26.262Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Normalization"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.232Z",
          "iopub.execute_input": "2021-07-18T23:36:26.239Z",
          "iopub.status.idle": "2021-07-18T23:36:26.541Z",
          "shell.execute_reply": "2021-07-18T23:36:26.621Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate MinMaxScaler\n",
        "MM_scaler = MinMaxScaler()\n",
        "\n",
        "# Fit MM_scaler to the data\n",
        "MM_scaler.fit(dataset[['Volume']])\n",
        "\n",
        "# Transform the data using the fitted scaler\n",
        "dataset['Volume_MM'] = MM_scaler.transform(dataset[['Volume']])"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.550Z",
          "iopub.execute_input": "2021-07-18T23:36:26.556Z",
          "iopub.status.idle": "2021-07-18T23:36:26.565Z",
          "shell.execute_reply": "2021-07-18T23:36:26.626Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset[['Volume_MM', 'Volume']].head())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Volume_MM    Volume\n",
            "Date                           \n",
            "2014-01-02   0.076577  20548400\n",
            "2014-01-03   0.085293  22887200\n",
            "2014-01-06   0.158004  42398300\n",
            "2014-01-07   0.159994  42932100\n",
            "2014-01-08   0.114329  30678700\n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.575Z",
          "iopub.execute_input": "2021-07-18T23:36:26.581Z",
          "iopub.status.idle": "2021-07-18T23:36:26.596Z",
          "shell.execute_reply": "2021-07-18T23:36:26.629Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[['Volume_MM', 'Volume']].hist()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 2 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.605Z",
          "iopub.execute_input": "2021-07-18T23:36:26.610Z",
          "iopub.status.idle": "2021-07-18T23:36:26.727Z",
          "shell.execute_reply": "2021-07-18T23:36:26.827Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Standardization"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.738Z",
          "iopub.execute_input": "2021-07-18T23:36:26.743Z",
          "iopub.status.idle": "2021-07-18T23:36:26.751Z",
          "shell.execute_reply": "2021-07-18T23:36:26.833Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate StandardScaler\n",
        "SS_scaler = StandardScaler()\n",
        "\n",
        "# Fit SS_scaler to the data\n",
        "SS_scaler.fit(dataset[['Volume']])\n",
        "\n",
        "# Transform the data using the fitted scaler\n",
        "dataset['Volume_SS'] = SS_scaler.transform(dataset[['Volume']])\n"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.760Z",
          "iopub.execute_input": "2021-07-18T23:36:26.766Z",
          "iopub.status.idle": "2021-07-18T23:36:26.776Z",
          "shell.execute_reply": "2021-07-18T23:36:26.837Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset[['Volume_SS', 'Volume']].head())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Volume_SS    Volume\n",
            "Date                           \n",
            "2014-01-02  -0.509131  20548400\n",
            "2014-01-03  -0.439377  22887200\n",
            "2014-01-06   0.142538  42398300\n",
            "2014-01-07   0.158459  42932100\n",
            "2014-01-08  -0.206997  30678700\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.784Z",
          "iopub.execute_input": "2021-07-18T23:36:26.789Z",
          "iopub.status.idle": "2021-07-18T23:36:26.802Z",
          "shell.execute_reply": "2021-07-18T23:36:26.840Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[['Volume_SS', 'Volume']].hist()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 2 Axes>",
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEVCAYAAAAb/KWvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWKUlEQVR4nO3df7DldX3f8edLVoEiEXXNlQKyNjITiYxKN4g1TW4lSRWTLtOqg0UBh3bH1KZmZMZurYnRMRmcKf6AsdpNcVgs/qAaZEfQyIB3otNAg4qsiD9Ws5RdV1YQkFWTZs27f5zv0uvmLPfce8+553w/PB8zZ+73+/l+zznv7z3v8zrf873nfG+qCklSWx437QIkSeNnuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwnxFJ5pPsnnYdktpguI9Zks8kefuQ8U1Jvpdk3TTqktaCOymzw3Afv23Aq5PkkPHXAFdX1YEp1CQtS0s7KUkuSvL1JA8nuTfJDUmO7ZadmOQTSe5L8lCSrya5cMolj4XhPn6fBJ4K/NODA0meDPwWcFWS9yT5bnd5T5Ijh91IkkryrEXzVyZ5Rzc9n2R3kjcl2Zdkb5Jzkpyd5JtJfpDkzYuu+7gkW5J8O8n9Sa5J8pQJbb/a0MROSpJfA/4YeFVVHQs8G/jYolU+BNwDnMzgefsa4N61rnMSDPcxq6qfANcA5y8afiXwdeBfAWcCzwOeC5wBvGWFd/V04CjgBOAPgD8BXg38YwYvLL+f5Jndur8LnAP8GvAPgQeA963wfvXY8Ena2En5ZeAvqurLAFX1g6raVlUPL1p+ZVX9qKoOVNWXq+rTy/5tzSDDfTK2AS9PclQ3f343dh7w9qraV1XfB97GYE9hJf4W+KOq+lvgo8B64L1V9XBV3Ql8jcELCMDrgP9cVbur6m+AP+zq681ba62thnZSbgX+eZK3JXnRkBehW4D3JTk3yTNWuA0zyXCfgKr6AnAfcE6SX2DQ/B9m0JB3L1r17m5sJe6vqp920z/pfi5+O/kT4Ind9MnAtUkeTPIgcBfwU2Buhfetx4be76RU1eeBfwmcDlwP3J/kXUmO6FZ5BfB54PeBv0pye5JfXuG2zBTDfXKuYvBkeDXwZ1V1L/BdBkF70DO6sWF+DPyDRfNPX0Ut9wAvrarjFl2Oqqo9q7hNNa6VnZSq+nRV/TbwFGATcCHwb7plD1TVlqr6pe52bgc+OeRvDb1juE/OVcCvA/+Wwd4OwEeAtyR5WpL1DN6G/o/DXP924F8nOSLJSxi8FV2pDwB/lORkgO7+N63i9vTY0cxOSlX9XVXdBNwMPGfI8vuA/8Lghar3Hzgw3CekqnYB/ws4BtjeDb8DuA24A9gBfKkbG+YNwG8DDzJ4G/zJVZTz3q6GzyZ5mMFxxhes4vb02NHrnZTuo5vnJnlyBs7oarilW/7OJM9Jsq77eOTvADur6v5V1DkbqsqLFy9eDnsBFhj88fLIbv4o4DJgb3e5DDiqWzYP7F503Y3AncDDDD52+BHgHYdZdx1QwIZFY18AXt1NPw54I/CN7va+DfzxErX/KnATg8NLDwPfBN60aPnlwLeA/cD3gU8Bz57273wcl3QbKElqiIdlJKlBhrukXktyXpL9Qy53Tru2afKwjCQ1aCa+obh+/frasGHD0GU/+tGPOOaYY9a2oDFzG9bGF7/4xfuq6mnTrmMUj9bz49KHxwz6Uees1vhoPT8T4b5hwwZuu+22ocsWFhaYn59f24LGzG1YG0nuXnqt2fBoPT8ufXjMoB91zmqNj9bzHnOXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGzcQ3VB/Njj0PceGW65d1nV2XvGxC1UiTt2GZ/Q72vP4+99wlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0aKdyT7EqyI8ntSW7rxp6S5MYk3+p+PrkbT5LLkuxMckeS0ye5AdIk2PPqu+Xsuf+zqnpeVW3s5rcAN1XVKcBN3TzAS4FTustm4P3jKlZaY/a8ems1h2U2Adu66W3AOYvGr6qBW4Djkhy/ivuRZoU9r94Y9X+oFvDZJAX8t6raCsxV1d5u+feAuW76BOCeRdfd3Y3tXTRGks0M9nKYm5tjYWFh6B3PHQ0Xn3ZgxDIHDndb07J///6Zq2m5WtiGZZpazy+332F4z/flMetDnX2o8VCjhvuvVNWeJD8P3Jjk64sXVlV1T4KRdU+WrQAbN26s+fn5oetdfvV1XLpjef/He9d5w29rWhYWFjjc9vVFC9uwTFPr+eX+Q3gY3vN9ecz6UGcfajzUSIdlqmpP93MfcC1wBnDvwbee3c993ep7gJMWXf3EbkzqDXtefbdkuCc5JsmxB6eB3wS+CmwHLuhWuwC4rpveDpzffYLgTOChRW9lpZlnz6sFoxzvmAOuTXJw/Q9X1WeS/CVwTZKLgLuBV3br3wCcDewEfgy8duxVS5Nlz6v3lgz3qvoO8Nwh4/cDZw0ZL+D1Y6lOmgJ7Xi3wG6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0MjhnuSIJF9O8qlu/plJbk2yM8nHkjyhGz+ym9/ZLd8wodqlibLn1WfL2XN/A3DXovl3Au+uqmcBDwAXdeMXAQ904+/u1pP6yJ5Xb40U7klOBF4G/PduPsCLgY93q2wDzummN3XzdMvP6taXesOeV9+tG3G99wBvAo7t5p8KPFhVB7r53cAJ3fQJwD0AVXUgyUPd+vctvsEkm4HNAHNzcywsLAy947mj4eLTDgxddjiHu61p2b9//8zVtFwtbMMyvYcp9fxy+x2G93xfHrM+1NmHGg+1ZLgn+S1gX1V9Mcn8uO64qrYCWwE2btxY8/PDb/ryq6/j0h2jvgYN7Dpv+G1Ny8LCAofbvr5oYRtGNe2ev3DL9cu+7WE935fHrA919qHGQ42Smi8C/kWSs4GjgJ8D3gscl2RdtydzIrCnW38PcBKwO8k64EnA/WOvXJoce169t+Qx96r6T1V1YlVtAM4Fbq6q84DPAS/vVrsAuK6b3t7N0y2/uapqrFVLE2TPqwWr+Zz7fwTemGQng+OLV3TjVwBP7cbfCGxZXYnSzLDn1RvLOphdVQvAQjf9HeCMIev8NfCKMdQmTZ09r77yG6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDlnei9J7YsJLzYV/ysglUIq2NYT1/8WkHHvXc8PZ829xzl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1aMlwT3JUkv+d5CtJ7kzytm78mUluTbIzyceSPKEbP7Kb39kt3zDhbZDGyp5XC0bZc/8b4MVV9VzgecBLkpwJvBN4d1U9C3gAuKhb/yLggW783d16Up/Y8+q9JcO9BvZ3s4/vLgW8GPh4N74NOKeb3tTN0y0/K0nGVbA0afa8WjDSMfckRyS5HdgH3Ah8G3iwqg50q+wGTuimTwDuAeiWPwQ8dYw1SxNnz6vvRvoH2VX1U+B5SY4DrgV+cbV3nGQzsBlgbm6OhYWFoevNHT34R7+Tdrj7H4f9+/dP9PbXQgvbsBzT7Plx9ftSz51ZeTz70Ft9qPFQI4X7QVX1YJLPAS8EjkuyrttTORHY0622BzgJ2J1kHfAk4P4ht7UV2AqwcePGmp+fH3qfl199HZfuWFaZK7LrvOH3Pw4LCwscbvv6ooVtWIlp9PyFW64fS+0Xn3bgUZ87k+z55ehDb/WhxkON8mmZp3V7LyQ5GvgN4C7gc8DLu9UuAK7rprd383TLb66qGmPN0kTZ82rBKLvExwPbkhzB4MXgmqr6VJKvAR9N8g7gy8AV3fpXAB9KshP4AXDuBOqWJsmeV+8tGe5VdQfw/CHj3wHOGDL+18ArxlKdNAX2vFrgN1QlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhq0ZLgnOSnJ55J8LcmdSd7QjT8lyY1JvtX9fHI3niSXJdmZ5I4kp096I6RxsufVglH23A8AF1fVqcCZwOuTnApsAW6qqlOAm7p5gJcCp3SXzcD7x161NFn2vHpvyXCvqr1V9aVu+mHgLuAEYBOwrVttG3BON70JuKoGbgGOS3L8uAuXJsWeVwvWLWflJBuA5wO3AnNVtbdb9D1grps+Abhn0dV2d2N7F42RZDODvRzm5uZYWFgYep9zR8PFpx1YTpkrcrj7H4f9+/dP9PbXQgvbsBLT6Plx9ftSz51ZeTz70Ft9qPFQI4d7kicCnwB+r6p+mOSRZVVVSWo5d1xVW4GtABs3bqz5+fmh611+9XVcumNZr0Ersuu84fc/DgsLCxxu+/qihW1Yrmn1/IVbrl9pyT/j4tMOPOpzZ5I9vxx96K0+1HiokT4tk+TxDJr86qr602743oNvPbuf+7rxPcBJi65+Yjcm9YY9r74b5dMyAa4A7qqqdy1atB24oJu+ALhu0fj53ScIzgQeWvRWVpp59rxaMMrxjhcBrwF2JLm9G3szcAlwTZKLgLuBV3bLbgDOBnYCPwZeO86CpTVgz6v3lgz3qvoCkMMsPmvI+gW8fpV1SVNjz6sFfkNVkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaNPnTLfbEhhWciW/XJS+bQCXS2lhJz4N93xfuuUtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatCS4Z7kg0n2JfnqorGnJLkxybe6n0/uxpPksiQ7k9yR5PRJFi9Nin2vvhtlz/1K4CWHjG0BbqqqU4CbunmAlwKndJfNwPvHU6a05q7EvlePLRnuVfXnwA8OGd4EbOumtwHnLBq/qgZuAY5LcvyYapXWjH2vvlu3wuvNVdXebvp7wFw3fQJwz6L1dndjezlEks0M9nKYm5tjYWFh+B0dDRefdmCFZU7W4Wo+1P79+0ded1a1sA1jsKq+H7Xnx9Xvk3rujLsP+tBbfajxUCsN90dUVSWpFVxvK7AVYOPGjTU/Pz90vcuvvo5Ld6y6zInYdd78SOstLCxwuO3rixa2YZxW0vej9vyFW65fdX0wCPZJPHdG7ftR9aG3+lDjoVb6aZl7D77t7H7u68b3ACctWu/EbkxqgX2v3lhpuG8HLuimLwCuWzR+fvfpgTOBhxa9jZX6zr5Xbyz5ni3JR4B5YH2S3cBbgUuAa5JcBNwNvLJb/QbgbGAn8GPgtROoWZo4+159t2S4V9WrDrPorCHrFvD61RYlTZt9r77zG6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDZvNE6T2xYcTzbl982oGfOUf3rkteNqmSpIkbte8Xs+fXnnvuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfKskFPgWfX0WPNoPX/oWVMPsudXx3DvCV8Q9Fhjz6+Oh2UkqUHuuTfMPR891qyk56HNvp/InnuSlyT5RpKdSbZM4j6kWWPfa5aMPdyTHAG8D3gpcCrwqiSnjvt+pFli32vWTOKwzBnAzqr6DkCSjwKbgK9N4L40Zod7W3u4TzSstRl++2zf99hSh3Om2f8r7flJhPsJwD2L5ncDLzh0pSSbgc3d7P4k3zjM7a0H7htrhWvsP7gNY5N3Purik9eojGGW7Ptl9PxYzMpjtpQ+1DnNGlfa81P7g2pVbQW2LrVektuqauMalDQxboNg9J4fl748Zn2osw81HmoSf1DdA5y0aP7EbkxqmX2vmTKJcP9L4JQkz0zyBOBcYPsE7keaJfa9ZsrYD8tU1YEk/x74M+AI4INVdecqbnLN3sZOkNvQuAn0/Tj05THrQ519qPFnpKqmXYMkacw8/YAkNchwl6QGzWy4t/BV7iQfTLIvyVenXctKJTkpyeeSfC3JnUneMO2a9LOWeq4kOTLJx7rltybZMIUal+yjJPNJHkpye3f5gynUuSvJju7+bxuyPEku636XdyQ5fa1rHFlVzdyFwR+kvg38I+AJwFeAU6dd1wq241eB04GvTruWVWzD8cDp3fSxwDf7+Fi0ehnluQL8O+AD3fS5wMemUOeSfQTMA5+a8u9zF7D+UZafDXwaCHAmcOu0e+Bwl1ndc3/kq9xV9X+Bg1/l7pWq+nPgB9OuYzWqam9Vfambfhi4i8G3MTUbRnmubAK2ddMfB85KkjWssaU+2gRcVQO3AMclOX7aRQ0zq+E+7KvcfWyEpnRv558P3DrlUvT/jfJceWSdqjoAPAQ8dU2qG2KJPnphkq8k+XSSX1rbygAo4LNJvtidLuJQvckmz+eukSR5IvAJ4Peq6ofTrkf9tEQffQk4uar2Jzkb+CRwyhqX+CtVtSfJzwM3Jvl69w68d2Z1z92vcs+QJI9n8IS8uqr+dNr16GeM8lx5ZJ0k64AnAfevSXWLLNVHVfXDqtrfTd8APD7J+rWssar2dD/3AdcyOOy1WG+yaVbD3a9yz4ju2OwVwF1V9a5p16O/Z5Tnynbggm765cDN1f11cK2M0kdJnn7wbwFJzmCQT2v2IpTkmCTHHpwGfhM49JNu24Hzu0/NnAk8VFV716rG5ZjJwzI1m1/lXrYkH2HwCYD1SXYDb62qK6Zb1bK9CHgNsCPJ7d3Ym7s9K03Z4Z4rSd4O3FZV2xmE6oeS7GTwB/5zp1Dq0D4CngFQVR9g8MLzO0kOAD8Bzl3jF6E54Nru9WUd8OGq+kyS1y2q8QYGn5jZCfwYeO0a1rcsnn5Akho0q4dlJEmrYLhLUoMMd0lqkOEuSQ0y3DV1yznBWpJndCeg+nJ34qaz16JGqW8Md82CK4GXjLjuW4Brqur5DD7S918nVZTUZ4a7pm7YCdaS/EKSz3Tn+Ph8kl88uDrwc930k4DvrmGpUm/M5JeYJAb/s/J1VfWtJC9gsIf+YuAPGZzY6XeBY4Bfn16J0uwy3DVzupNL/RPgfy46M+2R3c9XAVdW1aVJXsjgm5fPqaq/m0Kp0swy3DWLHgc8WFXPG7LsIrrj81X1F0mOAtYD+9auPGn2ecxdM6c7FexfJXkFPPKvzZ7bLf4/wFnd+LOBo4DvT6VQaYZ5bhlN3eITrAH3Am8Fbgbez+Dfsz0e+GhVvT3JqcCfAE9k8MfVN1XVZ6dRtzTLDHdJapCHZSSpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatD/AxET8xbLr1omAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.810Z",
          "iopub.execute_input": "2021-07-18T23:36:26.815Z",
          "iopub.status.idle": "2021-07-18T23:36:26.912Z",
          "shell.execute_reply": "2021-07-18T23:36:26.987Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Log Transformation"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PowerTransformer"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.920Z",
          "iopub.execute_input": "2021-07-18T23:36:26.926Z",
          "iopub.status.idle": "2021-07-18T23:36:26.936Z",
          "shell.execute_reply": "2021-07-18T23:36:26.990Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate PowerTransformer\n",
        "pow_trans = PowerTransformer()\n",
        "\n",
        "# Train the transform on the data\n",
        "pow_trans.fit(dataset[['Volume']])\n",
        "\n",
        "# Apply the power transform to the data\n",
        "dataset['Volume_LG'] = pow_trans.transform(dataset[['Volume']])"
      ],
      "outputs": [],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.944Z",
          "iopub.execute_input": "2021-07-18T23:36:26.950Z",
          "iopub.status.idle": "2021-07-18T23:36:26.957Z",
          "shell.execute_reply": "2021-07-18T23:36:26.993Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset[['Volume_LG', 'Volume']].head())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Volume_LG    Volume\n",
            "Date                           \n",
            "2014-01-02  -0.356839  20548400\n",
            "2014-01-03  -0.238326  22887200\n",
            "2014-01-06   0.485856  42398300\n",
            "2014-01-07   0.501407  42932100\n",
            "2014-01-08   0.095745  30678700\n"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:26.965Z",
          "iopub.execute_input": "2021-07-18T23:36:26.970Z",
          "iopub.status.idle": "2021-07-18T23:36:26.981Z",
          "shell.execute_reply": "2021-07-18T23:36:26.996Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[['Volume', 'Volume_LG']].hist()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-07-18T23:36:27.013Z",
          "iopub.execute_input": "2021-07-18T23:36:27.027Z",
          "iopub.status.idle": "2021-07-18T23:36:27.147Z",
          "shell.execute_reply": "2021-07-18T23:36:27.138Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}