Assignment_Simple_linear_regression_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "655dc76c",
   "metadata": {},
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e74a161e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as smf\n",
    "import statsmodels.formula.api as sm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91c639f",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57298a1c",
   "metadata": {},
   "source": [
    "### Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc37e052",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.00</td>\n",
       "      <td>10</td>\n",
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       "      <th>1</th>\n",
       "      <td>13.50</td>\n",
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       "      <td>6</td>\n",
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       "      <th>3</th>\n",
       "      <td>24.00</td>\n",
       "      <td>9</td>\n",
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       "      <th>4</th>\n",
       "      <td>29.00</td>\n",
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       "      <th>6</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.90</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.75</td>\n",
       "      <td>9</td>\n",
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       "      <th>10</th>\n",
       "      <td>19.83</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.68</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.83</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Delivery Time  Sorting Time\n",
       "0           21.00            10\n",
       "1           13.50             4\n",
       "2           19.75             6\n",
       "3           24.00             9\n",
       "4           29.00            10\n",
       "5           15.35             6\n",
       "6           19.00             7\n",
       "7            9.50             3\n",
       "8           17.90            10\n",
       "9           18.75             9\n",
       "10          19.83             8\n",
       "11          10.75             4\n",
       "12          16.68             7\n",
       "13          11.50             3\n",
       "14          12.03             3\n",
       "15          14.88             4\n",
       "16          13.75             6\n",
       "17          18.11             7\n",
       "18           8.00             2\n",
       "19          17.83             7\n",
       "20          21.50             5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('delivery_time.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870b4838",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c8247c5",
   "metadata": {},
   "source": [
    "### Performing EDA On Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3055b55d",
   "metadata": {},
   "source": [
    "#### Renaming columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a21d9ee3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Delivery_Time</th>\n",
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       "      <td>8</td>\n",
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       "      <th>11</th>\n",
       "      <td>10.75</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.68</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.03</td>\n",
       "      <td>3</td>\n",
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       "      <th>15</th>\n",
       "      <td>14.88</td>\n",
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       "      <td>13.75</td>\n",
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       "      <td>7</td>\n",
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       "      <th>18</th>\n",
       "      <td>8.00</td>\n",
       "      <td>2</td>\n",
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       "      <th>19</th>\n",
       "      <td>17.83</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Delivery_Time  Sorting_Time\n",
       "0           21.00            10\n",
       "1           13.50             4\n",
       "2           19.75             6\n",
       "3           24.00             9\n",
       "4           29.00            10\n",
       "5           15.35             6\n",
       "6           19.00             7\n",
       "7            9.50             3\n",
       "8           17.90            10\n",
       "9           18.75             9\n",
       "10          19.83             8\n",
       "11          10.75             4\n",
       "12          16.68             7\n",
       "13          11.50             3\n",
       "14          12.03             3\n",
       "15          14.88             4\n",
       "16          13.75             6\n",
       "17          18.11             7\n",
       "18           8.00             2\n",
       "19          17.83             7\n",
       "20          21.50             5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.rename({'Delivery Time':'Delivery_Time','Sorting Time':'Sorting_Time'}, axis = 1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5075935",
   "metadata": {},
   "source": [
    "### Checking Datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "895de998",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21 entries, 0 to 20\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Delivery Time  21 non-null     float64\n",
      " 1   Sorting Time   21 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 464.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "04c83b95",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.790952</td>\n",
       "      <td>6.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.074901</td>\n",
       "      <td>2.542028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.830000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.750000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       Delivery Time  Sorting Time\n",
       "count      21.000000     21.000000\n",
       "mean       16.790952      6.190476\n",
       "std         5.074901      2.542028\n",
       "min         8.000000      2.000000\n",
       "25%        13.500000      4.000000\n",
       "50%        17.830000      6.000000\n",
       "75%        19.750000      8.000000\n",
       "max        29.000000     10.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770a7a99",
   "metadata": {},
   "source": [
    "### Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9a66bc6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery Time    0\n",
       "Sorting Time     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b9d278",
   "metadata": {},
   "source": [
    "### Checking for Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1e999587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "07149e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
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       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Delivery Time, Sorting Time]\n",
       "Index: []"
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     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51dd78ac",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a14662",
   "metadata": {},
   "source": [
    "### Plotting the data to check for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8e8656a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (9,6))\n",
    "plt.subplot(121)\n",
    "plt.boxplot(df['Delivery Time'])\n",
    "plt.title('Delivery Time')\n",
    "plt.subplot(122)\n",
    "plt.boxplot(df['Sorting Time'])\n",
    "plt.title('Sorting Time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d7f23b",
   "metadata": {},
   "source": [
    "## <font color='red'> As you can see there are no Outliers in the data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932fa248",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2da28981",
   "metadata": {},
   "source": [
    "### Checking the Correlation between variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "85790a32",
   "metadata": {},
   "outputs": [
    {
     "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>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Delivery Time</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.825997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sorting Time</th>\n",
       "      <td>0.825997</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Delivery Time  Sorting Time\n",
       "Delivery Time       1.000000      0.825997\n",
       "Sorting Time        0.825997      1.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a239f995",
   "metadata": {},
   "source": [
    "### Visualization of Correlation beteen x and y\n",
    "### regplot = regression plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1e729a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sorting Time', ylabel='Delivery Time'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=df['Sorting Time'],y=df['Delivery Time'])  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d5554b3",
   "metadata": {},
   "source": [
    "## <font color='red'>As you can see above \n",
    "+ There is good correlation between the two variable.\n",
    "+ The score is more than 0.8 which is a good sign"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576d6762",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d7304f8",
   "metadata": {},
   "source": [
    "### Checking for Homoscedasticity or Hetroscedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ef24601b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6), facecolor = 'lightgreen')\n",
    "sns.scatterplot(x = df['Sorting Time'], y = df['Delivery Time'])\n",
    "plt.title('Hetroscedasticity', fontweight = 'bold', fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8c2c5900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery Time    25.754619\n",
       "Sorting Time      6.461905\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2dc96be",
   "metadata": {},
   "source": [
    "## <font color='red'>As you can see in above graph \n",
    "+ It shows as the Sorting Time Increases Delivery Time also increases with much variation along the way \n",
    "+ The data doesn't have any specific pattern in the variation, but we can't say the variation is homoscedasticity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f6b8b2",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0295379d",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb83fcb",
   "metadata": {},
   "source": [
    "### Trying different transformation of data to estimate normal distribution and to remove any skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "77d29527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Delivery Time'], bins = 10, kde = True)\n",
    "plt.title('Before Transformation')\n",
    "sns.displot(np.log(df['Delivery Time']), bins = 10, kde = True)\n",
    "plt.title('After Transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0005ade8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Before Transformation','After Transformation']\n",
    "sns.distplot(df['Delivery Time'], bins = 10, kde = True)\n",
    "sns.distplot(np.log(df['Delivery Time']), bins = 10, kde = True)\n",
    "plt.legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d060f79b",
   "metadata": {},
   "source": [
    "## <font color='red'> As you can see \n",
    "+ How log transformation affects the data and it scales the values down.\n",
    "+ Before prediction it is necessary to reverse scaled the values, even for calculating RMSE for the models.(Errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8d55392",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smf.qqplot(df['Delivery Time'], line = 'r')\n",
    "plt.title('No transformation')\n",
    "smf.qqplot(np.log(df['Delivery Time']), line = 'r')\n",
    "plt.title('Log transformation')\n",
    "smf.qqplot(np.sqrt(df['Delivery Time']), line = 'r')\n",
    "plt.title('Square root transformation')\n",
    "smf.qqplot(np.cbrt(df['Delivery Time']), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d409e91a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Before Transformation','After Transformation']\n",
    "sns.distplot(df['Sorting Time'], bins = 10, kde = True)\n",
    "sns.distplot(np.log(df['Sorting Time']), bins = 10, kde = True)\n",
    "plt.legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b07bd21f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7OP102G47+OCDMOa1J4dCpXPK0e0kfSZpiqTpknrGqSNJT0qaLekLSYfFrGsnaVa0rlu64nTOZUZJ5ilIadv168MUn02bwvvvQ+/eYaC9448vjfCrhkTjgJd0AQTUil7XACYArQvUOQUYEdVtDUyIyqsB3wD7AtsCU4CmhR3T54NwrmIoyXwJKW377rtm++8fVp59ttmCBWn7LBUdSeaDSFsLIjr2muhtjWgpOK5He2BAVHc8sJOkPYFWwGwzm2NmG4BBUV3nXCXQvTusXbtl2dq1obxE2377bZj/uV27MPjQe+/B669D/fqlFntVktZrEJKqScoDlgCjzGxCgSp7A9/GvF8YlSUqj3eMTpJyJeUuXbq01GJ3zqVPSeYpiFenBhs4f35vaNIEhg8PXUtTp4Yno12xpTVBmNkmM2sB1ANaSTqoQBXF2yxJebxj9DOzbDPLrlu3bonidc6VjZLMU1CwznGMJo8WPEA3aNsWvvwyNCdq1ix5oFVcmdzFZGY/AWOAdgVWLQRi2371gEVJyp1zlUBJ5inI33ZPFpHDhYzmBLbXOsZ0/S+8/Xa4au1KRTrvYqoraafo9fZAW2BmgWpDgYuju5laAyvN7HtgInCApH0kbQucH9V1zlUCJZmnoON5Gxnb4TFmqQl/ZQhP7NiDCc9P57iHT0t/4FVMOh+U2xN4SVI1QiIabGbDJF0JYGZ9geGEO5lmA2uBv0frNkq6FniPcEdTfzObnsZYnXNlrGPHYkygM24cXH01LadODRein3qKzvvvn5b4XBoThJl9ARwap7xvzGsDrkmw/XBCAnHOVXU//AC33AIDBoQ7kt54A848MzQ/XNr4k9TOufJr06Yw/HbjxvDqq2FY7hkzoEMHTw5lwMdics6VT+PHw9VXw+efw5//HOaCbtIk01FVKd6CcM6VL8uWhak+27QJXUuDBsGoUZ4cMsAThHOufNi8GZ59NnQnvfACdO0apgE97zzvTsoQ72JyzmXepEmhO+mzz+CYY8J1h4MKPlfrypq3IJxzmfPjj3DNNXD44TBvXrhLacwYTw7lhCcI51zZ27w5TPHZuDH07QvXXguzZsFFF3l3UjniXUzOubL1xRehO+njj6F16zDi6qFbPTLlygFvQTjnSiylCXxWrYIbboDDDgutheefD0nCk0O5VWgLQtJ+wEIzWy/pOOAQwhwOP6U3NOdcRZCTA506/T5Hw/z54T1EQ2mYhYfcunYNt6126gT33Qe77JKxmF1qUmlBDAE2SdofeB7YB3glrVE55yqMpBP4fPlleMitY0fYe2+YMCFcc/DkUCGkkiA2m9lGoAPwuJndQBiIzznn4k7g8wfWcM38W6B58zAP9L/+FZLD4YeXeXyu+FJJEL9KugC4BBgWldVIX0jOuYpkywl8jL/yH2bwR27moXBX0qxZcOWVUK1apkJ0xZRKgvg70AboZWZzJe0DDExvWM65iiJ/Ap8D+Ip3acd/OIcftSsje4yD/v3BZ3qssApNEGb2JXArMDl6P9fMHkh3YM65iqFjh7VMOPEOpnIwrRnPXTs/wbQXczmp51GZDs2VUKEJQtLpQB7wbvS+haRCZ3eTVF/SaEkzJE2X1DlOnZsl5UXLNEmbJO0SrZsnaWq0LrfIn8w5l35Dh0LTphz0di9qdjyHHRfN5K4V13Phxf6IVWWQym/xLqAVYU5pzCwv6mYqzEagq5lNllQbmCRpVNQiIdrXQ8BD8FsiusHMVsTs43gzW5bSJ3HOlZ05c6BzZxg2DJo1C8NjHHtspqNypSyVaxAbzWxlgTIrbCMz+97M8rulVgMzgL2TbHIB8GoK8TjnMmXdOrj77t+TwsMPh/kaPDlUSqkkiGmSLgSqSTpA0lPAJ0U5iKRGhOlHJyRYnwW0Izxzkc+AkZImSeqUZN+dJOVKyl26dGlRwnLOFcWIEWEQvTvvhDPOCENxd+0KNfymxsoqlQRxHdAMWE/4C38V0CXVA0iqRfji72JmqxJUOx34uED30lFmdhhwMnCNpGPibWhm/cws28yy6/rdEs6VvgUL4Kyz4JRToHr1MHnPa6+FB99cpVboNQgzWwt0j5YikVSDkBxyzOyNJFXPp0D3kpktin4ukfQm4TrI2KLG4Jwrpg0b4JFH4J57wvv77oMbb4SaNTMblyszCROEpP+S5FqDmZ2RbMeSRBiaY4aZPZqk3o7AscDfYsr+AGxjZquj1ycBdyc7nnOuFL3/fpinYdYs6NABHn+84BNxrgpI1oJ4uIT7Pgq4CJgqKS8qux1oAGBmfaOyDsBIM/s5ZtvdgTdDjqE68IqZvVvCeJxzhfnuu9BKGDwY9tsPhg+Hk0/OdFQuQxImCDP7sCQ7NrNxQKEzf5jZi8CLBcrmAM1LcnznXBH8+is8+STcdRds3Ag9e8Itt8B222U6MpdBybqYBpvZuZKmEqerycwOSWtkzrmyMXZsmMBn+nQ49dSQKPbdN9NRuXIgWRdT/pPPp5VFIM65MrZ4Mdx8MwwcCA0bwttvh9tXnYskvM3VzL6PXl5tZvNjF+DqsgnPuaojpVnZSqHuKwM2wlNPhfmgBw+GO+4I8zZEyaEo+3aVnJklXYDJccq+KGy7TCwtW7Y05yqigQPNsrLMwvRrYcnKCuWlWbc1n1ieWoQ3J51kNmtWsfftKgcg1xJ8pyqs35qkqwgthX2Bb2JW1SY81Pa3uBtmUHZ2tuXm+rh+ruJp1ChM1VlQw4Ywb17J6+7KMnpzK5fTn2+px311HuNfS/4KUtz6qezbVQ6SJplZdtx1SRLEjsDOwP1At5hVq23LJ57LDU8QrqLaZpvw93pBEmzeXPy61bWJy3mO+7mN2qzmUW7kHv7JWtXaqm5R9+0qh2QJItk1iJVmNs/MLgAWAr8S7maqJcmfmHGuFCV6Bi1eecp1c3PJ3bYN/+ZKptCc5kyhG735mVpFOl6ycle5pTIfxLXAD8Ao4J1oGZZ0I+dckeTPyhYrKyuUF7nuihVw1VXQqhUHZi3k0m1f4QQ+YAZNk+63qHG4KiDRxYn8BZgN7FpYvfKw+EVqV5ENHGjWsKGZFH4muzAct+6mTWbPP29Wp45ZtWpmXbqYrVxZpP0WNQ5X8VGci9T5JI0GTjSzjelPVyXj1yBclZWXF8ZO+uQTOPpoePppOMSfZXWFS3YNIpUZ5eYAYyS9QxjyGwBLMgCfc66MrFwJ//xnSAi77govvggXX7zV3UnOFUcqCWJBtGwbLc65TDMLT7DddBMsXRquOdxzD+y8c6Yjc5VIKvNB9CyLQJxzKZo2LXQnjR0LrVqFEVcPOyzTUblKqNAEIakucAthVrnfhnY0sxPSGJdzrqDVq8Moq48/DjvuCP36weWXh4cXnEuDVP5l5QAzgX2AnsA8YGIaY3LOxTILU3w2aRJmeLvsMvjqK7jiCk8OLq1S+de1q5k9D/xqZh+a2WVA6zTH5ZwDmDkTTjoJzj8f9tgDxo8PLYddd810ZK4KSCVB/Br9/F7SqZIOBeoVtpGk+pJGS5ohabqkznHqHCdppaS8aOkRs66dpFmSZkvqVnBb5yq1n3+G224Lt6pOnBjuUvrsMzjiiExH5qqQVO5iujcal6kr8BSwA3BDCtttBLqa2WRJtYFJkkaZ2ZcF6n1kZlvMOSGpGvA0cCJhmI+JkobG2da5ysUM3noLunSBBQvgkkvgwQdht90yHZmrglK5iyl/WI2VwPGp7tjCfBLfR69XS5oB7A2k8iXfCphtYepRJA0C2qe4rXMV0+zZcP31MGIEHHxwuEvpT3/KdFSuCkvlLqYXiD/l6GWpHkRSI+BQYEKc1W0kTQEWATeZ2XRCIvk2ps5CIG7bWlInoBNAAx9RzFVEv/wCDzwAvXvDttvCo4/CdddB9VQa+M6lTyr/AmMH5tsO6ED4Mk+JpFrAEKCLma0qsHoy0NDM1kg6BXgLOACI9xho3DFBzKwf0A/CUBupxuVcuTBsWGg1zJ0LF1wADz8Me+2V6aicA1K4SG1mQ2KWHOBc4KBUdi6pBiE55JjZG3H2vcrM1kSvhwM1JNUhtBjqx1StRxGSknNFVVbTff5Wd+5caN8eTj8dataE99+HV17x5ODKl0Sj+CVagMaE6wOF1RMwAHg8SZ09+H3SolaEIT1EaNnMITx7sS0wBWhW2DF9NFdXHGU13SeY7bT9Oss7+x6z7bYLK3v3Nlu/Pv0f0rkEKOForqsJ3TuKfi4GbjOzIYVsdzTwETAVyJ+L6nagQZSY+kZzTVxFuOPpF+BGM/sk2v4U4HGgGtDfzAodkd5Hc3XFke7pPvOdyEj6cC0H8jWcfXa41lC/Ps5lUrGmHK2IPEG44kjXdJ/5devxLY9xA2czhK84gOt5inftL6X3AZwrgWJNORptuL2kf0h6NFoulOQjurpKJS3TfQL71d/AzTzIDP7IqbzDHdzDwUxlZkNPDq5iSJggJB0MzAD+RBh/aT7wF+BjSTtJurdMInQuzUp1us98o0eTu7EFD3Ir7/NnmvIlvbiD6lk1ffpOV3EkujgB5M8kV7C8LeEuo/cSbZupxS9Su+Iq8XSf+RYtMrvggnBFep99bHTX//r0na5cozgXqSXNNLMmCdbNJdxVtDYdSau4/BqEy5iNG6FPH+jRA9avh27dwrL99pmOzLmkijvl6DaSaprZ+thCSdsRRnYtV8nBuYwZNw6uvhqmToV27eCpp2D//TMdlXMlluwi9QBgSDRMBvDbkBmDgZfTG5ZzFcAPP8Cll4bxkn76Cd54I8zu5snBVRIJE4SZ3Qu8C4yVtEzSMuBDYJSZ3VNWATpX7mzaFIbfbtw4PP3crRvMmAEdOoT7XZ2rJJKOxWRmfYA+0XDdmNnqMonKufJqwoTQnTR5MpxwQkgUTeJeqnOuwktpvkIzW+3JwVVpy5eHKT5bt4bFi2HQIPjf/zw5uErNJ7R1LpnNm+HZZ+HAA+GFF6Br1zAN6HnneXeSq/R8wHnnEpk0KXQnffYZHHNM6E46KKWBjJ2rFAptQUjKkvRPSc9G7w+QdFph2zlXYf34I1xzDRx+eBiBb8AAGDPGk4OrclLpYnoBWA+0id4vBHyYDVf5mMFLL4W7k/r2hWuvhVmz4KKLvDvJVUmpdDHtZ2bnSboAwMx+kfx/i6tkpk4N3UnjxkGbNjByJLRokemonMuoVFoQGyRtTzTlp6T9CC0K5yq+Vavghhvg0EPDxefnnw9JwpODcykliDsJD8zVl5QDvA/cUthGkupLGi1phqTpkjrHqdNR0hfR8omk5jHr5kmaKilPkg+w5EqXWXjIrXFjeOIJ+Mc/QnfSZZeFiRycc4V3MZnZKEmTgdaEWeU6m9myFPa9EehqZpOjB+0mSRplZl/G1JkLHGtmP0o6GegHHBGz/vgUj+Vc6r78MlxfGD0asrNh6NBwQdo5t4Vk80Eclr8ADYHvgUVAg6gsKTP73swmR69XE+aW2LtAnU/M7Mfo7XigXvE+hnNby8kJ035us034+drza+DWW6F5c8jLg3/9C8aP9+TgXALJWhCPJFlnwAmpHiQa5O9QYEKSapcDIwocY6QkA/5tZv1SPZ5zOTnQqROsXQtgZM8fwlFX3AC2EP7+d+jdG+rWzXSYzpVrCROEmR1fGgeQVAsYAnQxs1UJ6hxPSBBHxxQfZWaLJO0GjIrmpxgbZ9tOQCeABonmg3RVTvfuITnsz9f04Vr+wkjyrDmddx/EkP5HZTo85yqEVB6U207SjZLekDREUpdoTohCSapBSA45ZvZGgjqHAM8B7c1seX65mS2Kfi4B3gRaxdvezPqZWbaZZdf1vwhdZOn8tdzDHUzjIFoznut5gmxyeXOJJwfnUpXKcxADgNXAU9H7CwjzQZyTbKPoWYnngRlm9miCOg2AN4CLzOyrmPI/ANuY2ero9UnA3SnE6hwMHcrMap2pv2keL/M3buFBFrMnAA29kelcylJJEI3NrHnM+9GSpqSw3VHARcBUSXlR2e1AAwAz6wv0AHYFnomevdsYTX23O/BmVFYdeMXM3k3hmK4qmzMHOneGYcOovXdT/rJsDCPXH/vb6qws6NUrg/E5V8GkkiA+l9TazMYDSDoC+LiwjcxsHOG22GR1/gH8I075HKD51ls4F8e6dfDgg3D//VCtGjz0EDt17szFg2swqzssWAANGoTk0LFjpoN1ruKQmSWvIM0AGgMLoqIGhFtWNwNmZoekNcIiyM7Ottxcf6auShkxAq67Dr75Bs49Fx55BOr53dLOpUrSpKjnZiuptCDalXI8zpXcggXQpQu8+WaYq2HUKGjbNtNROVeppPIk9XxJOwP1Y+vnPwTnXJnasAEefRTuuScMl3HffXDjjVCzZqYjc67SKTRBSLoHuBT4hmjAPor4oJxzpeL998M8DbNmQYcO8Nhj0LBhpqNyrtJKpYvpXMKQ3xvSHYxzcX33XZjq87XXYL/94J134JRTMh2Vc5VeKsNWTgN2SnMczm3t11/DRecmTeCtt6BnT5g2zZODc2UklRbE/YRbXacRMw+EmZ2RtqicGzs2dCdNmwanngpPPgn77pvpqJyrUlJJEC8BvYGphFtbnUufH36Am2+Gl18O1xfefhtOP92n/HQuA1JJEMvM7Mm0R+Kqto0bw/Dbd9wRHnzr3h1uvz08/uycy4hUEsQkSfcDQ9myi8lvc3Wl49NPw3zQeXlw4onQp094tsE5l1GpJIhDo5+tY8r8NldXcsuWQbduYR7ovfeG11+Hv/7Vu5OcKydSeVCuVOaFcO43mzbBc8/BbbfB6tXhmkOPHlCrVqYjc87FSKUFgaRTgWbAb/NAmJkPv+2KLjc3dCdNnAjHHQdPPw1Nm2Y6KudcHKlMGNQXOA+4jjA66zmEOaqdS92KFXDVVdCqFXz7bZgT9IMPPDk4V46l8qDckWZ2MfCjmfUE2hDGZXKucJs3Q//+0LgxPPtsmK9h1iy48EK/1uBcOZdKgvgl+rlW0l7Ar8A+6QvJVRp5efCnP8Hll4cEMWlSGD9phx0yHZlzLgWpJIhhknYCHgImA/OAVwvbSFJ9SaMlzZA0XVLnOHUk6UlJsyV9IemwmHXtJM2K1nVL+RO5MpeTA40awTbbhJ+Dn10J118PLVvC11/DCy+EJ6ObN0+4TU5O0Y+TyjbOuRIws5QXoCawY4p19wQOi17XBr4CmhaocwowgnBtozUwISqvRhg9dl9gW2BKwW3jLS1btjRXtgYONMvKMgtjb2+2vzHAFrO7bZbMrr7abMWKQrYJS1ZWKE/tOKlt45wrHJBrCb5TE7YgJB0uaY+Y9xcDg4F7JO2SQuL53qKH6cxsNWEWur0LVGsPDIjiHA/sJGlPoBUw28zmWBhFdlBU15Uz3bvD2rXQjGmM4The5mLm0ZDTd58Y7lDaeeeE28RauzaUF3acomzjnCuZZF1M/wY2AEg6BngAGACsBPoV5SCSGhEeuJtQYNXewLcx7xdGZYnK4+27k6RcSblLly4tSliuFKyYv5qH6UoeLTiIaVxBP9rwKcN/aJlwmwULilZe3G2ccyWTLEFUM7MV0evzgH5mNsTM/gnsn+oBJNUChgBdzGxVwdVxNrEk5VsXmvUzs2wzy65bt26qYbmSMoPXXuOrak3oyqO8wN9pzCye4wqMbWjQIPGmidaV9jbOuZJJmiAk5T9I92fgg5h1qT5gV4OQHHLM7I04VRay5S2z9YBFScpdeTBzZhgz6fzz2bbe7hxX81M68SzLqQOE8fV69Uq8ea9eW4/Bl45tnHMlkyxBvAp8KOltwq2uHwFI2p/QzZSUJAHPAzPM7NEE1YYCF0d3M7UGVprZ98BE4ABJ+0jaFjg/qusy6eefw/AYhxwSnoju04ddvpnIFc+3pmHD8FhDw4bQrx907Jh4Nx07hjrp3sY5VzIKF7ETrAxf2nsCI83s56jsQKCWFTKaq6SjCUkldh6J24EGAGbWN0oifYB2wFrg72aWG21/CvA44Y6m/mZW6N+K2dnZlpubW1g1V1RmYUa3Ll1Cp/8ll0Dv3rD77pmOzDlXQpImmVl2vHVJu4qiO4sKln2VykHNbBzxryXE1jHgmgTrhgPDUzmWS6PZs8MzDSNGwMEHh+cZ/vSnTEflnCsDqTwo56qiX36BO++Egw6CcePg0UfDk9CeHJyrMlK62OyqmGHDQqth7ly44AJ4+GHYa69MR+WcK2PegnC/mzcP2rcPc0DXrAnvvw+vvOLJwbkqyhOEg/Xrw/2iTZvC//4HDzwAU6bACT5poHNVmXcxVXUjR8K114ZB9c46K4y26k+fOefwFkTVtXAhnHMO/OUv4f2778KQIZ4cnHO/8QRR1WzYAA8+CE2ahIvR99wDU6f+niiccy7iXUxVyejRcM01MGMGnHEGPP447ONzPznn4vMWRFXw/fdhis8TToB16+C//4W33/bk4JxLyhNEZbZxY2glNG4cri/06AHTp8Npp2U6MudcBeBdTJXVxx/D1VfDF19Au3bw1FOwf8qjtDvnnLcgKp0lS+DSS+Hoo+HHH+GNN2D4cE8Ozrki8wRRWWzaBM88E7qTXnkFunULF6M7dAjjYzvnXBF5F1NlMGFC6E6aPDlciH766XAbq3POlYC3ICqy5cuhUydo0wYWL4ZBg8JQGZ4cnHOlwBNERbR5Mzz3XOhO6t8fbrghTAN63nneneScKzVp62KS1B84DVhiZgfFWX8zkD9hZHXgj0BdM1shaR6wGtgEbEw021GVNHly6E6aMAGOOSZ0Jx201el1zrkSS2cL4kXCVKJxmdlDZtbCzFoAtwEfmtmKmCrHR+s9OUC4I+maayA7O8zTMGAAjBnjycE5lzZpSxBmNhZYUWjF4ALg1XTFkmk5OdCoEWyzTfiZk1OEjc3gpZdCd1LfvmHk1Vmz4KKLQCrZvksjPudc5WVmaVuARsC0QupkERLJLjFlc4HJwCSgUyHbdwJygdwGDRpYeTNwoFlWlln4pg9LVlYoL9SUKWZHHx02at3abPLk0tt3Ke7DOVdxAbmW4PtVYX16SGoEDLM41yBi6pwH/M3MTo8p28vMFknaDRgFXGehRZJUdna25ebmlkLkpadRI5g/f+vyhg3DBG5xrVoV5oN+6inYeWfo3Ts8/LbNlg2+Yu27NOJzzlUakiZZgq788nAX0/kU6F4ys0XRzyXAm0CrDMRVKhYsKEK5WXjIrXFjeOIJ+Mc/QnfSZZdtlRyKvO/SiM85V6VkNEFI2hE4Fng7puwPkmrnvwZOAqZlJsKSSzT/zlblX34Jf/4zdOwI9eqFu5T69oVddin5vksjPudclZO2BCHpVeBToLGkhZIul3SlpCtjqnUARprZzzFluwPjJE0BPgPeMbN30xVnuvXqBVlZW5ZlZYVyANasgVtugebNIS8P/vUvGD8eDj+85Psujficc1VXoosTFXFp2bJlCS/XpMfAgWYNG5pJ4efAgWa2ebPZ66+b1asXrgxfdpnZkiWls+8M7MM5VzGRqYvUZa08XqSO66uv4LrrYOTI0HJ45hk48shMR+Wcq4LK+0XqqmPtWrjjDjj44NCN9MQTkJvrycE5Vy75aK5lwSxM83n99eGe0r/9DR56CPbYI9OROedcQt6CSLc5c+D006F9e6hVKwyP8fLLnhycc+WeJ4h0WbcO7r4bmjULSeGhh+Dzz+HYYzMdmXPOpcS7mNJhxIhwEfqbb+Dcc+GRR8KzDc45V4F4C6I0LVgAZ50Fp5wC1avDqFHw2mueHJxzFZIniNKwYQPcf3+Yye3dd+G++2DKFGjbNtOROedcsXkXU0m9/36Yp2HWLOjQAR57LIx055xzFZy3IIrru+/CFJ9t28LGjTB8OLzxhicH51yl4QmiqH79NVx0btIE3n4bevaEadPg5JMzHZlzzpUq72IqirFjQ3fStGlw6qnw5JOw776Zjso559LCWxCpWLw4TPF57LGwejW89VZ4MtqTg3OuEvMEkczGjWFWt8aNYfBg6N49zNvQvj1ImY7OOefSyruYEvn0U7j66jBHw4knQp8+cOCBmY7KOefKTDonDOovaYmkuLPBSTpO0kpJedHSI2ZdO0mzJM2W1C1dMca1dClcfnkYYXXpUnj9dXjvPU8OzrkqJ51dTC8C7Qqp85GZtYiWuwEkVQOeBk4GmgIXSGqaxjiDTZvg3/8O3UkDBoRZ3mbOhLPP9u4k51yVlLYEYWZjgRXF2LQVMNvM5pjZBmAQ0L5UgysoNxfatIErrwwT+EyZAr17h9FXnXOuisr0Reo2kqZIGiGpWVS2N/BtTJ2FUVlckjpJypWUu3Tp0qIdfcUKuOoqaNUKvv0WcnLggw+gafobLM45V95lMkFMBhqaWXPgKeCtqDxef07CeVHNrJ+ZZZtZdt26dVM78ubN8MILoTvp2Wehc+cwVMaFF3p3knPORTKWIMxslZmtiV4PB2pIqkNoMdSPqVoPWFRqB87Lgz/9CS67LCSIyZPD+Ek77FBqh3DOucogYwlC0h5S+HNdUqsoluXAROAASftI2hY4Hxha4gOuXBlaCi1bwtdfw4svhiejDzmkxLt2zrnKKG3PQUh6FTgOqCNpIXAnUAPAzPoCZwNXSdoI/AKcb2YGbJR0LfAeUA3ob2bTix2IWbi2cNNNsGRJuOZw772w884l+XjOOVfppS1BmNkFhazvA/RJsG44MLwUgoAzzoBhw8KF6HfeCS0I55xzhcr0XUxplfOKuP2T0+hEP/ZZ/Ck5Mz05OOdcqirtUBs5OdCpE6xd+3+hYEF4D9CxY+bics65iqLStiC6d4e1a7csW7s2lDvnnCtcpU0QCxYUrdw559yWKm2CaNCgaOXOOee2VGkTRK9ekJW1ZVlWVih3zjlXuEqbIDp2hH79oGHDMHpGw4bhvV+gds651FTau5ggJANPCM45VzyVtgXhnHOuZDxBOOeci8sThHPOubg8QTjnnIvLE4Rzzrm4FEbYrhwkLQXmp/EQdYBladx/ulXk+Cty7ODxZ1JFjh3SH39DM4s7HWelShDpJinXzLIzHUdxVeT4K3Ls4PFnUkWOHTIbv3cxOeeci8sThHPOubg8QRRNv0wHUEIVOf6KHDt4/JlUkWOHDMbv1yCcc87F5S0I55xzcXmCcM45F5cniCQknSNpuqTNkhLeZiZpnqSpkvIk5ZZljMkUIf52kmZJmi2pW1nGmIikXSSNkvR19HPnBPXK1bkv7FwqeDJa/4WkwzIRZzwpxH6cpJXRuc6T1CMTccYjqb+kJZKmJVhfbs87pBR/Zs69mfmSYAH+CDQGxgDZSerNA+pkOt7ixA9UA74B9gW2BaYATctB7A8C3aLX3YDe5f3cp3IugVOAEYCA1sCETMddhNiPA4ZlOtYE8R8DHAZMS7C+XJ73IsSfkXPvLYgkzGyGmc3KdBzFlWL8rYDZZjbHzDYAg4D26Y+uUO2Bl6LXLwFnZi6UlKVyLtsDAywYD+wkac+yDjSO8vrvICVmNhZYkaRKeT3vQErxZ4QniNJhwEhJkyR1ynQwRbQ38G3M+4VRWabtbmbfA0Q/d0tQrzyd+1TOZXk936nG1UbSFEkjJDUrm9BKRXk970VR5ue+Us8olwpJ/wP2iLOqu5m9neJujjKzRZJ2A0ZJmhn9RZB2pRC/4pSVyb3PyWIvwm4ydu7jSOVcZux8FyKVuCYTxu1ZI+kU4C3ggHQHVkrK63lPVUbOfZVPEGbWthT2sSj6uUTSm4Tmepl8SZVC/AuB+jHv6wGLSrjPlCSLXdIPkvY0s++jroAlCfaRsXMfRyrnMmPnuxCFxmVmq2JeD5f0jKQ6ZlYRBsIrr+c9JZk6997FVEKS/iCpdv5r4CQg7p0I5dRE4ABJ+0jaFjgfGJrhmCDEcEn0+hJgq9ZQOTz3qZzLocDF0V01rYGV+V1pGVZo7JL2kKTodSvC98fyMo+0eMrreU9Jxs59pq/el+cF6ED4y2M98APwXlS+FzA8er0v4Y6PKcB0QtdOxmNPNf7o/SnAV4S7WMpF/MCuwPvA19HPXSrCuY93LoErgSuj1wKejtZPJcndceUw9muj8zwFGA8cmemYY2J/Ffge+DX6N395RTnvKcafkXPvQ20455yLy7uYnHPOxeUJwjnnXFyeIJxzzsXlCcI551xcniCcc87F5QnClTuSdo0ZtXKxpO+i1z9J+rKMYzlTUtOY93dLKvLDiZIaJRmps5mkDyR9JekbST0llfr/zWSfRdIYJRnx11VNniBcuWNmy82shZm1APoCj0WvWwCbS/t4kpKNKHAm8NuXqpn1MLP/leKxtyc8xPWAmR0IHEx4GrxzaR0jxpmk8bO4yscThKtoqkl6VmGei5HRFyyS9pP0bjRo30eSmkTlDSW9H80B8L6kBlH5i5IelTQa6B1ve0lHAmcAD0UtmP2i7c6O9nG4pE+iAdQ+k1Q7ail8JGlytBxZyOe5EPjYzEYCmNlawkNRN0fHuEvSTfmVJU2T1Ch6/VYU73TFDFQoaY2kXlFc4yXtXthniSXpJEmfRvG/LqlWVP6ApC+jc/lw0X91rqLxBOEqmgOAp82sGfAT8NeovB9wnZm1BG4CnonK+xCGeT4EyAGejNnXgUBbM+sab3sz+4Tw1/3NUYvmm/wNo+EoXgM6m1lzoC3wC2HMqBPN7DDgvALHi6cZMCm2IDrO9pJ2KmTby6J4s4HrJe0alf8BGB/FNRa4ItlniSWpDnBHdF4OA3KBGyXtQngyv1l0Lu8tJDZXCVT5wfpchTPXzPKi15OARtFfuEcCr0fD1QDUjH62Ac6KXr9MmIgo3+tmtqmQ7RNpDHxvZhPh98HUFMaE6iOpBbCJkISSEfFHFY03+mhB10vqEL2uT0iey4ENwLCofBJwYgr7ytea0A31cXQutgU+BVYB64DnJL0Ts39XiXmCcBXN+pjXm4DtCS3hn6LrFIWJ/TL+OfpZlO3zJfpiv4Ew7lXzaL/rCtnPdMJsYr/vWNoXWGZmP0nayJYt/e2iOscRWi1tzGytpDH564Bf7fcxdDZRtP/nAkaZ2QVbrQiDxP2ZMJDftcAJRdivq4C8i8lVeNFf73MlnQO/zT/cPFr9CeELDaAjMK6I268Gasc57ExgL0mHR9vUji5270hoWWwGLiJM5ZlMDnB0zN1E2xO6pe6M1s8jTEWJwjzK+0TlOwI/RsmhCeEv/8Ik+iyxxgNHSdo/OmaWpAOjVtaOZjYc6EK4YcBVcp4gXGXREbhcUv7IrvnTZV4P/F3SF4Qv7ER3ByXafhBws6TPJe2XX9nCtJznAU9F24wi/AX/DHCJpPGE7qWfScLMfiFcPO4u6StgGeGidU5UZQiwi6Q84CrCaKsA7wLVo891D+GLvTBxP0uBeJYClwKvRvseDzQhJJZhUdmHhJaSq+R8NFfnyhFJZwKPAseb2fwMh+OqOE8Qzjnn4vIuJuecc3F5gnDOOReXJwjnnHNxeYJwzjkXlycI55xzcXmCcM45F9f/A6/TGA5Mf+8kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smf.qqplot(df['Sorting Time'], line = 'r')\n",
    "plt.title('No transformation')\n",
    "smf.qqplot(np.log(df['Sorting Time']), line = 'r')\n",
    "plt.title('Log transformation')\n",
    "smf.qqplot(np.sqrt(df['Sorting Time']), line = 'r')\n",
    "plt.title('square root transformation')\n",
    "smf.qqplot(np.cbrt(df['Sorting Time']), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de98710",
   "metadata": {},
   "source": [
    "# <font color='red'>Important Note:</font> \n",
    "+ We only Perform any data transformation when the data is skewed or not normal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e90207",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e520ba37",
   "metadata": {},
   "source": [
    "### Fitting a Linear Regression Model \n",
    "#### Using Ordinary least squares (OLS) regression \n",
    "#### It is a statistical method of analysis that estimates the relationship between one or more independent variables and a dependent variable; the method estimates the relationship by minimizing the sum of the squares in the difference between the observed and predicted values of the dependent variable configured as a straight line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47654304",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sm.ols('Delivery_Time~Sorting_Time', data = df1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97c65fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>Delivery_Time</td>  <th>  R-squared:         </th> <td>   0.682</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   40.80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 21 Mar 2022</td> <th>  Prob (F-statistic):</th> <td>3.98e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:56:31</td>     <th>  Log-Likelihood:    </th> <td> -51.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>   106.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   108.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>    6.5827</td> <td>    1.722</td> <td>    3.823</td> <td> 0.001</td> <td>    2.979</td> <td>   10.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sorting_Time</th> <td>    1.6490</td> <td>    0.258</td> <td>    6.387</td> <td> 0.000</td> <td>    1.109</td> <td>    2.189</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.649</td> <th>  Durbin-Watson:     </th> <td>   1.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.161</td> <th>  Jarque-Bera (JB):  </th> <td>   2.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.750</td> <th>  Prob(JB):          </th> <td>   0.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.367</td> <th>  Cond. No.          </th> <td>    18.3</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          Delivery_Time   R-squared:                       0.682\n",
       "Model:                            OLS   Adj. R-squared:                  0.666\n",
       "Method:                 Least Squares   F-statistic:                     40.80\n",
       "Date:                Mon, 21 Mar 2022   Prob (F-statistic):           3.98e-06\n",
       "Time:                        20:56:31   Log-Likelihood:                -51.357\n",
       "No. Observations:                  21   AIC:                             106.7\n",
       "Df Residuals:                      19   BIC:                             108.8\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Intercept        6.5827      1.722      3.823      0.001       2.979      10.186\n",
       "Sorting_Time     1.6490      0.258      6.387      0.000       1.109       2.189\n",
       "==============================================================================\n",
       "Omnibus:                        3.649   Durbin-Watson:                   1.248\n",
       "Prob(Omnibus):                  0.161   Jarque-Bera (JB):                2.086\n",
       "Skew:                           0.750   Prob(JB):                        0.352\n",
       "Kurtosis:                       3.367   Cond. No.                         18.3\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d46bf6",
   "metadata": {},
   "source": [
    "## <font color='red'> As you can notice in the above model \n",
    "+ The R-squared and Adjusted R-squared scores are still below 0.85.\n",
    "+ (It is a thumb rule to consider Adjusted R-squared to be greater than 0.8 for a good model for prediction)\n",
    "+ Lets Try some data transformation to check whether these scores can get any higher than this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342caa6c",
   "metadata": {},
   "source": [
    "### Square Root transformation on data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97e8be44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>np.sqrt(Delivery_Time)</td> <th>  R-squared:         </th> <td>   0.729</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                      <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.715</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   51.16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                <td>Mon, 21 Mar 2022</td>    <th>  Prob (F-statistic):</th> <td>8.48e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                    <td>20:56:35</td>        <th>  Log-Likelihood:    </th> <td> -5.7320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>         <td>    21</td>         <th>  AIC:               </th> <td>   15.46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>             <td>    19</td>         <th>  BIC:               </th> <td>   17.55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                 <td>     1</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>nonrobust</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>             <td>    1.6135</td> <td>    0.349</td> <td>    4.628</td> <td> 0.000</td> <td>    0.884</td> <td>    2.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.sqrt(Sorting_Time)</th> <td>    1.0022</td> <td>    0.140</td> <td>    7.153</td> <td> 0.000</td> <td>    0.709</td> <td>    1.295</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.869</td> <th>  Durbin-Watson:     </th> <td>   1.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.238</td> <th>  Jarque-Bera (JB):  </th> <td>   1.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.690</td> <th>  Prob(JB):          </th> <td>   0.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.150</td> <th>  Cond. No.          </th> <td>    13.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              OLS Regression Results                              \n",
       "==================================================================================\n",
       "Dep. Variable:     np.sqrt(Delivery_Time)   R-squared:                       0.729\n",
       "Model:                                OLS   Adj. R-squared:                  0.715\n",
       "Method:                     Least Squares   F-statistic:                     51.16\n",
       "Date:                    Mon, 21 Mar 2022   Prob (F-statistic):           8.48e-07\n",
       "Time:                            20:56:35   Log-Likelihood:                -5.7320\n",
       "No. Observations:                      21   AIC:                             15.46\n",
       "Df Residuals:                          19   BIC:                             17.55\n",
       "Df Model:                               1                                         \n",
       "Covariance Type:                nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "Intercept                 1.6135      0.349      4.628      0.000       0.884       2.343\n",
       "np.sqrt(Sorting_Time)     1.0022      0.140      7.153      0.000       0.709       1.295\n",
       "==============================================================================\n",
       "Omnibus:                        2.869   Durbin-Watson:                   1.279\n",
       "Prob(Omnibus):                  0.238   Jarque-Bera (JB):                1.685\n",
       "Skew:                           0.690   Prob(JB):                        0.431\n",
       "Kurtosis:                       3.150   Cond. No.                         13.7\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = sm.ols('np.sqrt(Delivery_Time)~np.sqrt(Sorting_Time)', data = df1).fit()\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa04fb25",
   "metadata": {},
   "source": [
    "## <font color='red'> As you can notice in the above model\n",
    "+ After Square Root transformation on the Data, R-squared and Adjusted R-squared scores have increased but they are still below 0.85 which is a thumb rule we consider for a good model for prediction.\n",
    "+ Lets Try other data transformation to check whether these scores can get any higher than this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edda52d9",
   "metadata": {},
   "source": [
    "### Cube Root transformation on Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c16790a5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>np.cbrt(Delivery_Time)</td> <th>  R-squared:         </th> <td>   0.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                      <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.731</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   55.25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                <td>Mon, 21 Mar 2022</td>    <th>  Prob (F-statistic):</th> <td>4.90e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                    <td>20:56:41</td>        <th>  Log-Likelihood:    </th> <td>  13.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>         <td>    21</td>         <th>  AIC:               </th> <td>  -22.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>             <td>    19</td>         <th>  BIC:               </th> <td>  -19.98</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                 <td>     1</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>nonrobust</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>             <td>    1.0136</td> <td>    0.207</td> <td>    4.900</td> <td> 0.000</td> <td>    0.581</td> <td>    1.447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.cbrt(Sorting_Time)</th> <td>    0.8456</td> <td>    0.114</td> <td>    7.433</td> <td> 0.000</td> <td>    0.607</td> <td>    1.084</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.570</td> <th>  Durbin-Watson:     </th> <td>   1.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.277</td> <th>  Jarque-Bera (JB):  </th> <td>   1.532</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.661</td> <th>  Prob(JB):          </th> <td>   0.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.075</td> <th>  Cond. No.          </th> <td>    16.4</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              OLS Regression Results                              \n",
       "==================================================================================\n",
       "Dep. Variable:     np.cbrt(Delivery_Time)   R-squared:                       0.744\n",
       "Model:                                OLS   Adj. R-squared:                  0.731\n",
       "Method:                     Least Squares   F-statistic:                     55.25\n",
       "Date:                    Mon, 21 Mar 2022   Prob (F-statistic):           4.90e-07\n",
       "Time:                            20:56:41   Log-Likelihood:                 13.035\n",
       "No. Observations:                      21   AIC:                            -22.07\n",
       "Df Residuals:                          19   BIC:                            -19.98\n",
       "Df Model:                               1                                         \n",
       "Covariance Type:                nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "Intercept                 1.0136      0.207      4.900      0.000       0.581       1.447\n",
       "np.cbrt(Sorting_Time)     0.8456      0.114      7.433      0.000       0.607       1.084\n",
       "==============================================================================\n",
       "Omnibus:                        2.570   Durbin-Watson:                   1.292\n",
       "Prob(Omnibus):                  0.277   Jarque-Bera (JB):                1.532\n",
       "Skew:                           0.661   Prob(JB):                        0.465\n",
       "Kurtosis:                       3.075   Cond. No.                         16.4\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = sm.ols('np.cbrt(Delivery_Time)~np.cbrt(Sorting_Time)', data = df1).fit()\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b7ad41f",
   "metadata": {},
   "source": [
    "## <font color='red'> As you can notice in the above model\n",
    "+ After Cueb root transformation on the Data, R-squared and Adjusted R-squared scores have increased but they are still below 0.85 which is a thumb rule we consider for a good model for prediction.\n",
    "+ Lets Try other data transformation to check whether these scores can get any higher than this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934cd407",
   "metadata": {},
   "source": [
    "### Log transformation on Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb12d165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>np.log(Delivery_Time)</td> <th>  R-squared:         </th> <td>   0.772</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   64.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Mon, 21 Mar 2022</td>    <th>  Prob (F-statistic):</th> <td>1.60e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>20:56:43</td>        <th>  Log-Likelihood:    </th> <td>  10.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td>    21</td>         <th>  AIC:               </th> <td>  -16.58</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td>    19</td>         <th>  BIC:               </th> <td>  -14.49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>     1</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>    1.7420</td> <td>    0.133</td> <td>   13.086</td> <td> 0.000</td> <td>    1.463</td> <td>    2.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Sorting_Time)</th> <td>    0.5975</td> <td>    0.074</td> <td>    8.024</td> <td> 0.000</td> <td>    0.442</td> <td>    0.753</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.871</td> <th>  Durbin-Watson:     </th> <td>   1.322</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.392</td> <th>  Jarque-Bera (JB):  </th> <td>   1.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.577</td> <th>  Prob(JB):          </th> <td>   0.557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.916</td> <th>  Cond. No.          </th> <td>    9.08</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              OLS Regression Results                             \n",
       "=================================================================================\n",
       "Dep. Variable:     np.log(Delivery_Time)   R-squared:                       0.772\n",
       "Model:                               OLS   Adj. R-squared:                  0.760\n",
       "Method:                    Least Squares   F-statistic:                     64.39\n",
       "Date:                   Mon, 21 Mar 2022   Prob (F-statistic):           1.60e-07\n",
       "Time:                           20:56:43   Log-Likelihood:                 10.291\n",
       "No. Observations:                     21   AIC:                            -16.58\n",
       "Df Residuals:                         19   BIC:                            -14.49\n",
       "Df Model:                              1                                         \n",
       "Covariance Type:               nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept                1.7420      0.133     13.086      0.000       1.463       2.021\n",
       "np.log(Sorting_Time)     0.5975      0.074      8.024      0.000       0.442       0.753\n",
       "==============================================================================\n",
       "Omnibus:                        1.871   Durbin-Watson:                   1.322\n",
       "Prob(Omnibus):                  0.392   Jarque-Bera (JB):                1.170\n",
       "Skew:                           0.577   Prob(JB):                        0.557\n",
       "Kurtosis:                       2.916   Cond. No.                         9.08\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = sm.ols('np.log(Delivery_Time)~np.log(Sorting_Time)', data = df1).fit()\n",
    "model3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c1e52a",
   "metadata": {},
   "source": [
    "## <font color='red'> As you can notice in the above model\n",
    "+ After log transformation on the Data, This Model has scored the highest R-squared and Adjusted R-squared scores than the previous model\n",
    "+ Yet both Adjusted R-squared and R-squared scores are still below 0.85 which is a thumb rule we consider for a good model for prediction. \n",
    "+ Though it is now close to 0.8 which for a single feature/predictor variable or single independent variable is expected to be low. Hence , we can stop here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdf555f",
   "metadata": {},
   "source": [
    "### Model Testing\n",
    "#### As Y = Beta0 + Beta1*(X)\n",
    "#### Finding Coefficient Parameters (Beta0 and Beta1 values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6eca2b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept       6.582734\n",
       "Sorting_Time    1.649020\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426654b6",
   "metadata": {},
   "source": [
    "#### Here, (Intercept) Beta0 value = 6.58 & (Sorting Time) Beta1 value = 1.64\n",
    "#### Hypothesis testing of X variable by finding test_statistics and P_values for Beta1 i.e if (P_value < α=0.05 ; Reject Null)\n",
    "#### Null Hypothesis as Beta1=0 (No Slope) and Alternate Hypthesis as Beta1≠0 (Some or significant Slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d0a3866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept       3.823349\n",
      "Sorting_Time    6.387447\n",
      "dtype: float64 \n",
      " Intercept       0.001147\n",
      "Sorting_Time    0.000004\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(model.tvalues,'\\n',model.pvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3091cb",
   "metadata": {},
   "source": [
    "#### (Intercept) Beta0: tvalue=3.82 , pvalue=0.001147\n",
    "#### (daily)     Beta1: tvalue=6.38, pvalue=0.000004\n",
    "#### As (pvalue=0)<(α=0.05); Reject Null hyp. Thus, X(Sorting Time) variable has good slope and variance w.r.t Y(Delivery Time) variable. \n",
    "#### R-squared measures the strength of the relationship between your model and the dependent variable on a 0 – 100% scale.\n",
    "#### Measure goodness-of-fit by finding rsquared values (percentage of variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ee82d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6822714748417231, 0.6655489208860244)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rsquared,model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2df8f6",
   "metadata": {},
   "source": [
    "#### Determination Coefficient = rsquared value = 0.68 ; very good fit >= 85%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39662b6f",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a66ded",
   "metadata": {},
   "source": [
    "### Residual Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532c5367",
   "metadata": {},
   "source": [
    "### Test for Normality of Residuals (Q-Q Plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d98ee15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sm.qqplot(model.resid, line = 'q')\n",
    "plt.title('Normal Q-Q plot of residuals of Model without any data transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f163bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(model2.resid, line = 'q')\n",
    "plt.title('Normal Q-Q plot of residuals of Model with Log transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8f1e8a",
   "metadata": {},
   "source": [
    "## <font color='red'> As you can notice in the above plots\n",
    "+ Both The Model have slightly different plots\n",
    "+ The first model is right skewed and doesn't follow normal distribution\n",
    "+ The second model after log-transformation follows normal distributon with less skewness than first model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86d227d",
   "metadata": {},
   "source": [
    "### Residual Plot to check Homoscedasticity or Hetroscedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3db0f448",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardized_values( vals ):\n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "62e6a988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(get_standardized_values(model.fittedvalues), get_standardized_values(model.resid))\n",
    "plt.title('Residual Plot for Model without any data transformation')\n",
    "plt.xlabel('Standardized Fitted Values')\n",
    "plt.ylabel('Standardized Residual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d7accef2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(get_standardized_values(model2.fittedvalues), get_standardized_values(model2.resid))\n",
    "plt.title('Residual Plot for Model with Log transformation')\n",
    "plt.xlabel('Standardized Features Values')\n",
    "plt.ylabel('Standardized Residual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ceb6d8",
   "metadata": {},
   "source": [
    "## <font color='red'> As you can notice in the above plots\n",
    "+ Both The Model have Homoscedasciticity. \n",
    "+ The Residual(i.e Residual  = Actual Value - Predicted Value) and the Fitted values do not share any Pattern. \n",
    "+ Hence, there is no relation between the Residual and the Fitted Value. It is Randomly distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a573cf6",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04042498",
   "metadata": {},
   "source": [
    "### Model Validation\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f14f1d",
   "metadata": {},
   "source": [
    "### Comparing different models with respect to their Root Mean Squared Errors\n",
    "### We will analyze Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) — AKA the average distance (squared to get rid of negative numbers) between the model’s predicted target value and the actual target value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4bc2ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "755b059a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1_pred_y =np.square(model1.predict(df1['Sorting_Time']))\n",
    "model2_pred_y =pow(model2.predict(df1['Sorting_Time']),3)\n",
    "model3_pred_y =np.exp(model3.predict(df1['Sorting_Time']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52544243",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model= 2.9349037688901394 \n",
      "model1= 2.762294279320867 \n",
      "model2= 2.755584309893575 \n",
      "model3= 2.7458288976145497\n"
     ]
    }
   ],
   "source": [
    "model1_rmse =np.sqrt(mean_squared_error(df1['Delivery_Time'], model1_pred_y))\n",
    "model2_rmse =np.sqrt(mean_squared_error(df1['Delivery_Time'], model2_pred_y))\n",
    "model3_rmse =np.sqrt(mean_squared_error(df1['Delivery_Time'], model3_pred_y))\n",
    "print('model=', np.sqrt(model.mse_resid),'\\n' 'model1=', model1_rmse,'\\n' 'model2=', model2_rmse,'\\n' 'model3=', model3_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2f4281e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'model2'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'model': np.sqrt(model.mse_resid), 'model1': model1_rmse, 'model2': model3_rmse, 'model3' : model3_rmse}\n",
    "min(data, key=data.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80bbf31e",
   "metadata": {},
   "source": [
    "## <font color='red'>As model2 has the minimum RMSE and highest Adjusted R-squared score. Hence, we are going to use model2 to predict our values \n",
    "+ Model2 is the model where we did log transformation on both dependent variable as well as on independent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec32f89",
   "metadata": {},
   "source": [
    "# <font color = 'red'> Step 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7cfa73",
   "metadata": {},
   "source": [
    "###  Predicting values from Model with Log Transformation on the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "00413f4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Sorting_Time</th>\n",
       "      <th>Delivery_Time</th>\n",
       "      <th>Predicted_Delivery_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>21.00</td>\n",
       "      <td>22.597360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.070123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>19.75</td>\n",
       "      <td>16.653216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>24.00</td>\n",
       "      <td>21.218593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>29.00</td>\n",
       "      <td>22.597360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15.35</td>\n",
       "      <td>16.653216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>19.00</td>\n",
       "      <td>18.259981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>9.50</td>\n",
       "      <td>11.005911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>17.90</td>\n",
       "      <td>22.597360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>18.75</td>\n",
       "      <td>21.218593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>19.83</td>\n",
       "      <td>19.776608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>10.75</td>\n",
       "      <td>13.070123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>16.68</td>\n",
       "      <td>18.259981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>11.50</td>\n",
       "      <td>11.005911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>12.03</td>\n",
       "      <td>11.005911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>14.88</td>\n",
       "      <td>13.070123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>13.75</td>\n",
       "      <td>16.653216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>18.11</td>\n",
       "      <td>18.259981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.637887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7</td>\n",
       "      <td>17.83</td>\n",
       "      <td>18.259981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>21.50</td>\n",
       "      <td>14.934324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sorting_Time  Delivery_Time  Predicted_Delivery_Time\n",
       "0             10          21.00                22.597360\n",
       "1              4          13.50                13.070123\n",
       "2              6          19.75                16.653216\n",
       "3              9          24.00                21.218593\n",
       "4             10          29.00                22.597360\n",
       "5              6          15.35                16.653216\n",
       "6              7          19.00                18.259981\n",
       "7              3           9.50                11.005911\n",
       "8             10          17.90                22.597360\n",
       "9              9          18.75                21.218593\n",
       "10             8          19.83                19.776608\n",
       "11             4          10.75                13.070123\n",
       "12             7          16.68                18.259981\n",
       "13             3          11.50                11.005911\n",
       "14             3          12.03                11.005911\n",
       "15             4          14.88                13.070123\n",
       "16             6          13.75                16.653216\n",
       "17             7          18.11                18.259981\n",
       "18             2           8.00                 8.637887\n",
       "19             7          17.83                18.259981\n",
       "20             5          21.50                14.934324"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = pd.DataFrame()\n",
    "predicted['Sorting_Time'] = df1.Sorting_Time\n",
    "predicted['Delivery_Time'] = df1.Delivery_Time\n",
    "predicted['Predicted_Delivery_Time'] = pd.DataFrame(np.exp(model2.predict(predicted.Sorting_Time)))\n",
    "predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39be0209",
   "metadata": {},
   "source": [
    "### Predicitng from Original Model without any data transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bfb4793e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>Sorting_Time</th>\n",
       "      <th>Delivery_Time</th>\n",
       "      <th>Predicted_Delivery_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>21.00</td>\n",
       "      <td>23.072933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.178814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>19.75</td>\n",
       "      <td>16.476853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>24.00</td>\n",
       "      <td>21.423913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>29.00</td>\n",
       "      <td>23.072933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15.35</td>\n",
       "      <td>16.476853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>19.00</td>\n",
       "      <td>18.125873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>9.50</td>\n",
       "      <td>11.529794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>17.90</td>\n",
       "      <td>23.072933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>18.75</td>\n",
       "      <td>21.423913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>19.83</td>\n",
       "      <td>19.774893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>10.75</td>\n",
       "      <td>13.178814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>16.68</td>\n",
       "      <td>18.125873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>11.50</td>\n",
       "      <td>11.529794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>12.03</td>\n",
       "      <td>11.529794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>14.88</td>\n",
       "      <td>13.178814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>13.75</td>\n",
       "      <td>16.476853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>18.11</td>\n",
       "      <td>18.125873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.880774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7</td>\n",
       "      <td>17.83</td>\n",
       "      <td>18.125873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>21.50</td>\n",
       "      <td>14.827833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sorting_Time  Delivery_Time  Predicted_Delivery_Time\n",
       "0             10          21.00                23.072933\n",
       "1              4          13.50                13.178814\n",
       "2              6          19.75                16.476853\n",
       "3              9          24.00                21.423913\n",
       "4             10          29.00                23.072933\n",
       "5              6          15.35                16.476853\n",
       "6              7          19.00                18.125873\n",
       "7              3           9.50                11.529794\n",
       "8             10          17.90                23.072933\n",
       "9              9          18.75                21.423913\n",
       "10             8          19.83                19.774893\n",
       "11             4          10.75                13.178814\n",
       "12             7          16.68                18.125873\n",
       "13             3          11.50                11.529794\n",
       "14             3          12.03                11.529794\n",
       "15             4          14.88                13.178814\n",
       "16             6          13.75                16.476853\n",
       "17             7          18.11                18.125873\n",
       "18             2           8.00                 9.880774\n",
       "19             7          17.83                18.125873\n",
       "20             5          21.50                14.827833"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted1 = pd.DataFrame()\n",
    "predicted1['Sorting_Time'] = df1.Sorting_Time\n",
    "predicted1['Delivery_Time'] = df1.Delivery_Time\n",
    "predicted1['Predicted_Delivery_Time'] = pd.DataFrame(model.predict(predicted1.Sorting_Time))\n",
    "predicted1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8117b2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=612x436 at 0x1F992DD8310>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import ImageGrab\n",
    "ImageGrab.grabclipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f77984",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}