Simple Linear-Regression_Deliverytime.ipynb

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   "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')"
   ]
  },
  {
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   "source": [
    "# <font color='Green'>Step-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6a04198-6afb-4384-950a-2ed6baf794d0",
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   "outputs": [
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     "data": {
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       "<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>0</th>\n",
       "      <td>21.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.50</td>\n",
       "      <td>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.00</td>\n",
       "      <td>10</td>\n",
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       "      <th>5</th>\n",
       "      <td>15.35</td>\n",
       "      <td>6</td>\n",
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       "      <th>6</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <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",
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       "    <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": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Immporting Data\n",
    "\n",
    "df = pd.read_csv('delivery_time.csv')\n",
    "df"
   ]
  },
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   "id": "873088d0-528c-4623-a15c-a0a685e99e6d",
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   "source": [
    "# <font color='Green'> Step-2\n",
    "\n",
    "Performing EDA On Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3498af33-7e4a-4c00-8df7-bcf452add0fd",
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   "outputs": [
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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",
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       "      <th>3</th>\n",
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       "      <td>10.75</td>\n",
       "      <td>4</td>\n",
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       "      <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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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.88</td>\n",
       "      <td>4</td>\n",
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       "      <th>16</th>\n",
       "      <td>13.75</td>\n",
       "      <td>6</td>\n",
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       "      <th>17</th>\n",
       "      <td>18.11</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.00</td>\n",
       "      <td>2</td>\n",
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       "    <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"
      ]
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "# Renaming columns\n",
    "\n",
    "df1 = df.rename({'Delivery Time':'Delivery_Time','Sorting Time':'Sorting_Time'}, axis = 1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43626a15-bac0-4208-b9f9-2a1590b7d8d5",
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   "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: 468.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6b72d31-3b3f-436c-9270-d3f9606ba563",
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     "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": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4e82feb-57f3-4313-b4f2-e38834f88fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery_Time    0\n",
       "Sorting_Time     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "05567699-1399-4809-af0c-4004d0b816bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "412d707f-3859-404e-99c4-4a443480a274",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delivery_Time</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Delivery_Time, Sorting_Time]\n",
       "Index: []"
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     "metadata": {},
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   "source": [
    "df1[df1.duplicated()]"
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   "source": [
    "# <font color='Green'>Step-3\n",
    "\n",
    "Plotting the data to check for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e5a2095-e183-47b6-b106-99198d5e3619",
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (6,4))\n",
    "plt.subplot(121)\n",
    "plt.boxplot(df1['Delivery_Time'])\n",
    "plt.title('Delivery Time')\n",
    "plt.subplot(122)\n",
    "plt.boxplot(df1['Sorting_Time'])\n",
    "plt.title('Sorting Time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d83ae87a-a56e-4d85-9d5e-6f9d35a1de1a",
   "metadata": {},
   "source": [
    "**As you can see there are no Outliers in the data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d598a2f-b41b-4b8c-8681-111f2186b257",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-4\n",
    "\n",
    "Checking the Correlation between variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2f60fc8e-91f0-4ca3-a556-cce0208890f7",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b6a9ca-204c-4adc-b8fb-f20e457eebc6",
   "metadata": {},
   "source": [
    "Visualization of Correlation beteen x and y\n",
    "\n",
    "regplot = regression plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d06886f-cdca-4e72-9405-f22748562dd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sorting Time', ylabel='Delivery Time'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=df['Sorting Time'],y=df['Delivery Time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8750dc-af31-451f-8d1d-c9a629d2a00b",
   "metadata": {},
   "source": [
    "**As you can see above,**\n",
    "\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": "dab21953-2e0e-4295-a3ec-fc4d68c82609",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-5\n",
    "\n",
    "Checking for Homoscedasticity or Hetroscedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf8482c7-b177-4453-9f7e-137accdad0dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6,4), facecolor = 'lightgreen')\n",
    "sns.scatterplot(x = df1['Sorting_Time'], y = df1['Delivery_Time'])\n",
    "plt.title('Hetroscedasticity', fontweight = 'bold', fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2db5ada6-53f2-48a7-8ee2-0b3804b01366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery_Time    25.754619\n",
       "Sorting_Time      6.461905\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0945a97e-16d9-4eed-86fb-e83209eafbd4",
   "metadata": {},
   "source": [
    "**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": "08272c2f-828c-42db-bce2-f8d29048f00b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-6\n",
    "\n",
    "Feature Engineering\n",
    "\n",
    "Trying different transformation of data to estimate normal distribution and to remove any skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e6d89229-eb30-4b8d-bc47-4aa923111f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df1['Delivery_Time'], bins = 10, kde = True)\n",
    "plt.title('Before Transformation')\n",
    "sns.displot(np.log(df1['Delivery_Time']), bins = 10, kde = True)\n",
    "plt.title('After Transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d1593709-3eeb-446c-9306-1455150ef832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Before Transformation','After Transformation']\n",
    "sns.distplot(df1['Delivery_Time'], bins = 10, kde = True)\n",
    "sns.distplot(np.log(df1['Delivery_Time']), bins = 10, kde = True)\n",
    "plt.legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45065302-5173-4c8a-9ce9-e45bd246a4c9",
   "metadata": {},
   "source": [
    "**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": 36,
   "id": "0af6add8-5b52-49f1-a74a-c63050fcf343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xENGlNkCmMJM+juhSO9f9ZBRkREREJF+F1Q1kVq+GBPhkvH0U4OPJrF4Nb6uPjBriiYiISL4LqxtI+9oB6uwrIiIizsnVxUJoSLk8PaZuLYmIiIjTUpARERERp6UgIyIiIk5LQUZEREScloKMiIiIOC0FGREREXFaCjIiIiLitBRkRERExGkpyIiIiIjTUpARERERp6UgIyIiIk5LQUZEREScloKMiIiIOC0FGREREXFaCjIiIiLitBRkRERExGkpyIiIiIjTUpARERERp6UgIyIiIk5LQUZEREScloKMiIiIOC0FGREREXFaCjIiIiLitBRkRERExGkpyIiIiIjTUpARERERp6UgIyIiIk5LQUZEREScloKMiIiIOC0FGREREXFaCjIiIiLitBRkRERExGkpyIiIiIjTUpARERERp6UgIyIiIgXnwgV44w24di1PDlciT44iIiIicjOGAfPnwyuvmGHGywtGjrztwyrIiIiISP46cADCw+GXX8xxnTrQokWeHFq3lkRERCR/XL4Mr74K995rhphSpWDSJNi1C+67L0++ha7IiIiISN777jsYPBhOnzbHXbvC9OlQqVKefhsFGREREck7p0/DkCGwdKk5rlwZPvwQunTJl2+nW0siIiJy+65dM28b1aplhpgSJczbSgcO5FuIAV2RERERkdu1caM5mXf/fnPcsiXMmmVO6s1nuiIjIiIiufPXX9Cvnxlc9u+H8uVh3jzYsKFAQgzoioyIiIjYy2o1A8vIkRAXZ2577jl4910oV65AS1GQERERkZzbt8+8jbRpkzm+5x7zNtI//uGQcnRrSURERG4tKcm8AtOggRliSpeGKVNgxw6HhRjQFRkRERG5laVLzZ4wZ86Y40cegQ8+gKAgx9aFgoyIiIhk59QpM8AsW2aOq1SBf/0LHnrIoWXdSLeWREREJKPUVJg40ewJs2wZuLnB6NFmT5hCFGJAV2RERETkRj//bE7mPXjQHLduDTNnQu3ajq0rG7oiIyIiIvDnn9C3rxlcDh6EChXgk09g3bpCG2LAwUFmwoQJNGnSBC8vL/z8/OjWrRtHjhzJsE+bNm2wWCwZvl588UUHVSwiIlLEWK0wdy7UqAHz55vbXngBDh+GZ54Bi8Wh5d2KQ4PMhg0bGDhwIL/99hs//fQT165d48EHH+Ty5csZ9nv++eeJjo62fU2aNMlBFYuIiBQhe/fCfffB88/DxYtQvz5s3gxz5oCvr6OryxGHzpFZsWJFhvH8+fPx8/Njx44dtGrVyra9VKlSBAQEFHR5IiIiRVNSEowbB9OmQVoalCkDb75pPqFUwrmmzxaqOTLx8fEA+P4tBX722WeUL1+eunXrMnr0aK5cuZLtMVJSUkhISMjwJSIiIoBhwOLF5tNI771nhpgePeDQIXjpJacLMVCInlqyWq0MGzaMf/zjH9StW9e2/cknn6Ry5cpUrFiRvXv38uqrr3LkyBEWLVqU5XEmTJhAZGRkQZUtIiLiHKKizCsuy5eb4+BgsydMp06Ores2WQzDMBxdBEB4eDg//vgjGzdu5K677sp2v7Vr19K2bVuOHTtGSEhIptdTUlJISUmxjRMSEggKCiI+Ph5vb+98qV1ERKTQSk01r7689RZcvWr2hBk5El57DUqVcnR12UpISMDHx+eWv78LxRWZQYMG8f333/Pzzz/fNMQANGvWDCDbIOPh4YGHh0e+1CkiIuJUNmwwe8IcOmSO27QxF3isWdOhZeUlh86RMQyDQYMGsXjxYtauXUtwcPAt37N7924AAgMD87k6ERERJxUbC717m8Hl0CHw84MFC2Dt2iIVYsDBV2QGDhzI559/ztKlS/Hy8iImJgYAHx8fSpYsyfHjx/n888/p1KkT5cqVY+/evbz00ku0atWKevXqObJ0ERGRwie9J8yoUebj1BYL9O8P77wDd9zh6OryhUPnyFiyabIzb948+vTpw5kzZ+jVqxf79+/n8uXLBAUF8cgjjzBmzJgcz3fJ6T02ERERp7ZnD7z4Ivz2mzlu0MC8jfT/UzKcjVPMkblVhgoKCmLDhg0FVI2IiIgTSkyEiAiYPt18nNrLy5zYO3CgUz5Oba+i/wlFRESKIsOARYtg6FA4e9bc9s9/wvvvw513Ora2AqQgIyIi4mxOnIBBg+DHH81xSIjZEyYszLF1OUCh6uwrIiIiN5GSAuPHQ506Zohxd4c33oB9+4pliAFdkRERESn00qwGh7/4jkpvvIxX1DFzY9u2MGOGuWp1MaYgIyIiUoitXb+XlJdG0HH3agD+LF2Wfz0UTuiYQYTVqOjg6hxPQUZERKQwslo5MG4yjSa9hU/KZaxYWNigE1NaPU2iZxk+/WwXs3pZCKtbvBvEKsiIiIgUNrt2Ybz4InW2bgVgn38Ir3cYyN7A6rZdLEDksoO0rx2Aq0vWfdmKAwUZERGRwiIhAcaOhQ8/xGK1kuhekimtnmFBg05YXVwz7GoA0fHJbI2KIzSknGPqLQQUZERERBzNMOCbb2DYMDh3DoA/HnyYR6t1J9br5iElNjG5AAosvPT4tYiIiCMdPw6dOsFjj5khplo1WLWKMzP/c8sQA+Dn5VkARRZeCjIiIiKOkJJiLiVQty6sWGH2hImIMHvCtG9P02BfAn08yW72iwUI9PGkabBvQVZd6CjIiIiIFLS1a6FePXM+THIytGtnBphx48DTvMLi6mIhokttgExhJn0c0aV2sZ7oCwoyIiIiBef8eejVy2xm9/vvEBAAX3wBq1ZB9eqZdg+rG8isXg0J8Ml4+yjAx5NZvRoW+0evQZN9RURE8l9aGsyZA6+9BvHxYLGYq1O//Tb4+Nz0rWF1A2lfO4CtUXHEJibj52XeTiruV2LSKciIiIjkp5074cUXYds2c9yoEcyeDY0b5/gQri6WYv2I9c3o1pKIiEh+SEiAoUOhSRMzxHh7mytUb9liV4iRm9MVGRERkbxkGPDVV/DSSxAdbW7r2RPeew8CNaclrynIiIiI5JVjx8y5L6tWmeO774aZM82nkiRf6NaSiIjI7UpOhshIsyfMqlXg4WGO9+5ViMlnuiIjIiJyO1avhgED4OhRc/zggzBjhtmhV/KdrsiIiIjkRkwMPPkktG9vhpjAQPjvf80uvQoxBUZBRkRExB5paebTRzVqmM3sXFxgyBA4fNhcL8mi/i4FSbeWREREcmr7drMnzI4d5rhJE7MnTMOGjq2rGNMVGRERkVuJj4fBg6FpUzPE+PiYTyNt3qwQ42C6IiMiIpIdw4Avv4Thw805MQBPPQVTppjrJInDKciIiIhk5ehR82mk1avNcfXq5lWYtm0dW5dkoFtLIiIiN0pOhogIsyfM6tVmT5i33jJ7wijEFDq6IiMiIpJu1SqzM++xY+Y4LMx8QikkxLF1SbZ0RUZEROTcOXjiCejQwQwxFSvC11/DDz8oxBRyCjIiIlJ8paXB9OlQs6bZzM7FBYYNg0OHoEcP9YRxArq1JCIixdO2bWZPmJ07zXGzZjBrFjRo4Ni6xC66IiMiIsXLpUvmPJhmzcwQU7as2dTu118VYpyQrsiIiEjxYBjw+ecwYgScP29ue/ppmDwZ/P0dW5vkmoKMiIgUfUeOmD1h1q41xzVrmj1h7r/fsXXJbdOtJRERKbquXoU33oB69cwQ4+kJ48fDnj0KMUWErsiIiEjRtGKFORfmxAlz3KkTfPghVK3q2LokT+mKjIiIFC1nz8Jjj0HHjmaIufNO+PZb+P57hZgiSEFGRESKhuvX4YMPzPkvX38Nrq7mYo+HDsGjj6onTBGlW0siIuL8tmwxe8Ls3m2Omzc3H6muX9+hZUn+s/uKzM6dO9m3b59tvHTpUrp168Zrr71GampqnhYnIiJyUxcvQng4hIaaIeaOO+Cjj2DTJoWYYsLuINO/f39+//13AE6cOMETTzxBqVKl+Prrrxk5cmSeFygiIpKJYcCCBeZtpNmzzXHv3uZj1s8/by41IMWC3X/Tv//+O/feey8AX3/9Na1ateLzzz9n/vz5fPvtt3ldn4iISEaHD8MDD8Azz0BsLNSqBevXw/z5UKGCo6uTAmb3HBnDMLBarQCsXr2azp07AxAUFMRff/2Vt9WJiIiku3rV7AEzaRJcuwYlS8LYseaEXnf3fPu2aVaDrVFxxCYm4+flSdNgX1xdNHG4sLA7yDRu3Ji3336bdu3asWHDBmbNmgVAVFQU/mrxLCIi+eGHH2DQIIiKMsedO5s9YapUyddvu2J/NJHLDhIdn2zbFujjSUSX2oTVDczX7y05Y/etpWnTprFz504GDRrE66+/TrVq1QD45ptvaNGiRZ4XKCIixdgff0CPHvDQQ2aIuesuWLwYvvuuQEJM+MKdGUIMQEx8MuELd7Jif3S+fn/JGYthGEZeHCg5ORlXV1fc3Nzy4nB5JiEhAR8fH+Lj4/H29nZ0OSIikhPXr8P06RARAUlJZk+Yl14yx2XK5Pu3T7Ma3DdxbaYQk84CBPh4svHVB3SbKZ/k9Pd3rqZ1X7p0iblz5zJ69Gji4uIAOHjwILGxsbmrVkREJN3mzdC4sblKdVIStGgBO3eaq1QXQIgB2BoVl22IATCA6PhktkbFFUg9kj2758js3buXtm3bUrZsWU6ePMnzzz+Pr68vixYt4vTp03z66af5UaeIiBR1cXEwerTZBwbA19ec2Nu3b4E/Th2bmH2Iyc1+kn/s/skYPnw4ffv25ejRo3h6etq2d+rUiZ9//jlPixMRkWLAMOCTT8yeMOkhpm9f8zHrfv0c0hPGz8vz1jvZsZ/kH7uvyGzbto05c+Zk2n7nnXcSExOTJ0WJiEgxcfAgDBgAGzaY4zp1YNYsaNnSoWU1DfYl0MeTmPhksppImj5Hpmmwb0GXJn9jd8z18PAgISEh0/bff/+dCmpEJCIiOXHlinkbqX59M8SULAnvvmvOhclBiEmzGmw+foGlu8+y+fgF0qx58tyKjauLhYgutQEztNwofRzRpbYm+hYCdl+Refjhh3nzzTf56quvALBYLJw+fZpXX32V7t2753mBIiJSxHz/PQweDCdPmuOHHzafUKpcOUdvL6jeLmF1A5nVq2Gm7xWgPjKFit2PX8fHx9OjRw+2b99OYmIiFStWJCYmhtDQUH744QdKly6dX7Xmih6/FhEpJE6fhqFDYckScxwUZDa169o1x4dI7+3y919c6ddFZvVqmOcBQ519HSOnv79z3Udm48aN7N27l6SkJBo2bEi7du1yXWx+UpAREXGwa9fggw9g3Di4fBlKlDB7wowda9fj1OrtUrzk9Pe33beW0t13333cd999uX27iIgUB7/+Ci++CPv2meN//MOczHvPPXYfyp7eLqEh5XJZsDibHAWZ6dOn5/iAQ4YMyXUxIiJSRFy4AK++Ch9/bI7LlTN7wvTpk+vHqdXbRbKSoyDz/vvv5+hgFotFQUZEpDgzDJg/H155xQwzAM8+CxMnQvnyt3Vo9XaRrOQoyESlrzYqIiKSnf37ITwcNm40x3XrmreR8mgagnq7SFYKvl2iiIgULZcvm7eRGjQwQ0ypUuZtpJ078yzEgHq7SNZydEVm+PDhvPXWW5QuXZrhw4ffdN+pU6fmSWEiIuIEvvvO7Alz+rQ57trV7AlTqVK+fDv1dpG/y1GQ2bVrF9euXbP9WUREirnTp2HIEFi61BxXrmz2hOnSJd+/dVjdQNrXDlBvFwFuo4+Ms1AfGRGRPHTtGrz/PkRGmssMlCgBI0bAG29AIWuIKs4tp7+/7Z4j8+yzz5KYmJhp++XLl3n22WftPZyIiDiLjRuhYUNzPsyVK+aaSLt3m2skKcSIg9gdZD755BOuXr2aafvVq1f59NNP86QoEREpRP76y3yEumVL88mk8uVh3jxzscc6dRxdnRRzOe7sm5CQgGEYGIZBYmIinp7/e04/LS2NH374AT8/v3wpUkREHMBqNQPLyJEQF2due+458wpMOXXOlcIhx0GmbNmyWCwWLBYL1atXz/S6xWIhMjIyT4sTEREH2bfP7AmzaZM5vucemD0bWrRwbF0if5PjILNu3ToMw+CBBx7g22+/xdf3fw2H3N3dqVy5MhUrVrTrm0+YMIFFixZx+PBhSpYsSYsWLZg4cSI1atSw7ZOcnMyIESP48ssvSUlJoUOHDsycORN/f3+7vpeIiORAUhK8+SZMnQppaebcl8hI8wklNzdHVyeSid1PLZ06dYqgoCBccrlWxo3CwsJ44oknaNKkCdevX+e1115j//79HDx4kNL/P3EsPDyc5cuXM3/+fHx8fBg0aBAuLi5sSv+/hFvQU0siIjm0dKnZE+bMGXP86KMwbRoEBTm0LCmecvr7O1ePX1+6dImtW7cSGxuL1WrN8Nozzzxjf7X/788//8TPz48NGzbQqlUr4uPjqVChAp9//jk9evQA4PDhw9SqVYvNmzfTvHnzWx5TQUZE5BZOnjSvuCxbZo6rVIF//Qseeui2DptmNdTrRXItp7+/c3xrKd2yZct46qmnSEpKwtvbG4vlfz+UFovltoJMfHw8gO221Y4dO7h27Rrt2rWz7VOzZk0qVaqU4yAjIiLZSE39X0+Yq1cx3Nw4228gu58ZSDm/O2hqNXIdPFbsj87UfTdQ3XclH9gdZEaMGMGzzz7LO++8Q6lSpfKsEKvVyrBhw/jHP/5B3bp1AYiJicHd3Z2yZctm2Nff35+YmJgsj5OSkkJKSoptnJCQkGc1iogUGT//bE7mPXgQgLjGoQy47zl+8/CHpUeA3AePFfujCV+4M9PCjjHxyYQv3MmsXg0VZiTP2D3R5ezZswwZMiRPQwzAwIED2b9/P19++eVtHWfChAn4+PjYvoJ0b1dE5H/+/BP69oXWrc0QU6ECe8d/QKMHXjNDzA3Sg8eK/dE5Pnya1SBy2cEsV6dO3xa57CBp1iLdVF4KkN1BpkOHDmzfvj1Pixg0aBDff/8969at46677rJtDwgIIDU1lUuXLmXY//z58wQEBGR5rNGjRxMfH2/7OpM+aU1EpDizWmHuXKhRA+bPN7f170/awUP0t9TBsGS+hZSb4LE1Ki7D7aSsjhkdn8zWqDj76hfJht23lh566CFeeeUVDh48yD333IPb3x7He/jhh3N8LMMwGDx4MIsXL2b9+vUEBwdneL1Ro0a4ubmxZs0aunfvDsCRI0c4ffo0oaGhWR7Tw8MDDw8POz+ViEgRtncvvPgibN5sjuvXN3vCNG/O1uMXchw8QkNu3QQvNjH7Y+VmP5FbsTvIPP/88wC8+eabmV6zWCykpaXl+FgDBw7k888/Z+nSpXh5ednmvfj4+FCyZEl8fHzo168fw4cPx9fXF29vbwYPHkxoaKgm+oqI3EpSEowbZz5CnZYGZcqYPWIGDzYXeyTvg4efl+etd7JjP5FbsTvI/P1x69sxa9YsANq0aZNh+7x58+jTpw8A77//Pi4uLnTv3j1DQzwREcmGYcCSJeYj1X/8YW7r0cN8QumG2/eQ98GjabAvgT6exMQnZzlPxgIE+JiPYovkhVz1kXEm6iMjIsVKVJR5xWX5cnMcHGz2hOnUKcvd06wG901ce8vgsfHVB3L8KHb6U0tAhmOmv1tPLUlO5FsfGYDLly+zYcMGTp8+TWpqaobXhgwZkptDiojI7UhNhffeg7fegqtXzeUERo6E116Dmzxl6upiIaJLbcIX7sRC1sEjokttu/rJhNUNZFavhpn6yASoj4zkA7uvyOzatYtOnTpx5coVLl++jK+vL3/99RelSpXCz8+PEydO5FetuaIrMiJS5K1fDwMGwKFD5rhNG5g1C2rWzPEh8qOBnTr7yu3ItyUK2rRpQ/Xq1Zk9ezY+Pj7s2bMHNzc3evXqxdChQ3n00Udvu/i8pCAjIkVWbCy88gp8+qk59vMzr8o89RRk8Tj1rSh4SGGSb7eWdu/ezZw5c3BxccHV1ZWUlBSqVq3KpEmT6N27d6ELMiIiRU56T5hRo+DiRTO09O8P77wDd9yR68O6ulhy9Ii1SGFid5Bxc3OzrXzt5+fH6dOnqVWrFj4+Pmo+JyKS3/bsMXvC/PabOb73XtJmzmKr393EnrqCX5xVV1KkWLE7yDRo0IBt27Zx991307p1a8aOHctff/3FggULbGskiYhIHktMhIgImD7d7Anj5QVvvcXK1o8y7sffiY7/zbarFmeU4sTuJQreeecdAgPN/zjGjx/PHXfcQXh4OH/++ScfffRRnhcoIlKsGQZ8+y3UqmX2gUlLg3/+Ew4dYkXbx3jxy72ZOvPmZo0kEWelPjIiIoXViRMwaBD8+KM5rloVZsyAsDBb/5fslhfITf8XkcIkp7+/7b4iIyIieSPNarD5+AWW7j7L5uMX/rcwY0oKjB8PdeqYIcbdHd54A/bvh7AwQIsziqSze45McHAwlps81lfY+siIiBRG2fVt+cD/Ik0nvwGHD5sbH3gAZs40V62+gRZnFDHZHWSGDRuWYXzt2jV27drFihUreOWVV/KqLhGRIiu9hf+N9/XLX77IyO//Q9MD68wNfn7mnJiePbPsCaPFGUVMdgeZoUOHZrl9xowZbN++/bYLEhEpytKsBpHLDtpCjMWw0nPPSl5dPx+flMtYsbC4WRe6/TAfV9/se8JocUYRU57NkenYsSPffvttXh1ORKRIunFuS+3zJ1i04BXeWTkDn5TL7PMPodsz7zGizQtsvWi96XHS10iC/62JlC63aySJOKNcLRqZlW+++QZfXyV/EZGbiU1MpnTKFYZv/Iw+O5bhalhJdC/JlFbPsKBBJ6wurrb9bkWLM4rksiHejZN9DcMgJiaGP//8k5kzZ+ZpcSIiRYphUHPjKtbMfZmAJPNpomU1W/LWA88R65VxaYCczm0JqxtI+9oBWiNJii27g0y3bt0yjF1cXKhQoQJt2rShph0rrYqIFCvHj8OgQdRYsQKAk2UDeePBcH4Jbphht9zMbdEaSVKc2R1kIiIi8qMOEZGiKSUFJk82+8IkJ4O7O8f6DeKhUveRWsI9w66a2yJiP7uDzNmzZ/n222/5/fffcXd3p0aNGjz22GPccRsrroqIFElr18KAAXDkiDlu1w5mzKBa9ep8kEUfGc1tEbGfXUsUzJw5k+HDh5OammprF5yQkEDJkiWZO3cuPXv2xDAMdu/eTYMGDfKtaHtoiQIRyQ9pViP7eSnnz8OIEfDZZ+Y4IMDsCfP44xl6wtz0GCLFXE5/f+f4iszy5csZMmQIw4YNY8SIEbaFI6Ojo5k8eTK9e/cmKCiImTNnUrNmzUITZERE8lp2XXkjOtUgbNN38NprEB9vhpaBA+Htt8HHJ9NxNLdF5PblOMhMnjyZUaNG8fbbb2fYHhgYyNSpUylVqhTt27cnICCACRMm5HmhIiKFQVZdeQHKHdlP4IfhEH3U3NCoEcyeDY0bF3iNIsVJjm8teXt7s23bNmr8bb2PdEeOHKFWrVqcPHmSSpUq5WmRt0O3lkQkK7m5rZPVitNeKZcZ/stCntm5HFfDSpJHKUpNmYhLeDi4uub3xxApsvL81lJaWhpubm7Zvu7m5kbJkiULVYgRkaLrduaXZHtr6BYTbTOsOG0YdD78C2+snYv///eEWVqrNW8/0I/pHTsRqhAjUiByHGTq1KnD0qVLeemll7J8fcmSJdSpUyfPChMRyU5ug0j6e7O6NRQTn0z4wp3M6tUw22Okd9utfPEcb62aRauTuwA4cUdF3nhwAJuq3JthPxHJfzkOMgMHDiQ8PBwPDw9eeOEFSpQw33r9+nXmzJnDmDFj1NlXRPLd7QSRvy/YeCMDs49L5LKDtK8dkOXVHX93GLrxcwb89jUeaddIcXVjZvN/Mrt5D1Ju6AmjFadFCk6Og0zv3r3Zt28fgwYNYvTo0YSEhGAYBidOnCApKYkhQ4bQp0+ffCxVRIq72w0iGW4NZXOM6PhktkbFZX6aaPVqmg0YQPOj5mTen6s0YGz7Fznpe6dtF604LVLw7GqIN2XKFHr06MEXX3zB0f//j7lVq1b07NmT5s2b50uBIiLpbiuIkPNbPhn2i46G4cPhyy+xAMkV/Hk5tDfLa7bEuKEnjLryijiG3Z19mzdvrtAiIg6RqyByg5ze8vHz8oS0NJg1C15/HRISwMUFBg3C88036XzmCjvUlVekULA7yIiIOIpdQSQLTYN9CfTxJCY+OcvbU7ZbQxdOwOPhsGOH+UKTJmZPmIbmAo9hPj5acVqkkHBxdAEiIjmVHkSyiwsWzKeXspuj4upiIaJLbdu+f3+vV8plvty7ENfmzcwQ4+MDM2bA5s22EHPjsUJDytH13jsJDSmnECPiIAoyIuI0bhVE4NZzVMLqBjKrV0MCfG64amMYPH3yV7YuHEzlL+eDYcBTT8Hhw+aij+oJI1Jo2bVopDNSZ1+Roud2+sikS2+od3n/QRpPfoOymzaYL1SvDjNnQtu2+VG6iORQnnf2vdH169dZv349x48f58knn8TLy4tz587h7e1NmTJlcl20iEhOhNUNvO05Kq6pKYR+Oh3efRdSU8HDA8aMgVdeMf8sIk7B7iBz6tQpwsLCOH36NCkpKbRv3x4vLy8mTpxISkoKs2fPzo86RUQyuK2Vo1etMlelPnbMHIeFwb/+BSEheVegiBQIu+fIDB06lMaNG3Px4kVKlixp2/7II4+wZs2aPC1ORCRPnTsHjz8OHTqYIaZiRfjqK/jhB4UYESdl9xWZX375hV9//RV3d/cM26tUqcLZs2fzrDARkTyTlmY+fTRmDCQmmj1hhgyByEjQ3DkRp2Z3kLFaraSlpWXa/scff+Dl5ZUnRYmI5Jlt2+DFF2HnTnPcrJnZ6K5BA8fWJSJ5wu5bSw8++CDTpk2zjS0WC0lJSURERNCpU6e8rE1EJPcuXTIfnW7WzAwxZcuaTe1+/VUhRqQIsfvx6z/++IMOHTpgGAZHjx6lcePGHD16lPLly/Pzzz/j5+eXX7Xmih6/FilmDAM+/xxGjIDz581tTz8NkyeDv79jaxORHMvp7+9c9ZG5fv06X375JXv37iUpKYmGDRvy1FNPZZj8W1goyIgUI0eOmFdh1q41xzVrmj1h7r/fsXWJiN3ytY9MiRIl6NWrV66LExFJb0iXJ2sVXb0K77wDkyaZPWE8PeGNN+Dll+FvDyaISNGSoyDz3Xff5fiADz/8cK6LEZHiIS868/7vYCvMnjAnTpjjTp3gww+hatU8rFhECqsc3VpyccnZnGCLxZLlE02OpFtLIoXLiv3RhC/cmWn16fRrMbN6NcxZmDl7FoYNg2++Mcd33gnTp8Mjj4BFCziKOLuc/v7OUUKxWq05+ipsIUZECpc0q0HksoOZQgxg2xa57CBp1pv8/9X16zBtmjn/5ZtvzAUdhw+HQ4fg0UcVYkSKGa1+LSIFZmtUXIbbSX9nANHxyWyNist6hy1boEkTeOklSEqC5s1hxw547z1QHyuRYilXQWbNmjV07tyZkJAQQkJC6Ny5M6tXr87r2kSkiIlNzD7E3HS/ixfNpnahobB7N9xxB3z0EWzaBPXr532hIuI07A4yM2fOJCwsDC8vL4YOHcrQoUPx9vamU6dOzJgxIz9qFJEiws/L0779DAMWLIAaNWDOHHPcuzccPgzPP28uNSAixZrdfWTuuusuRo0axaBBgzJsnzFjBu+8806hW29Jk31FCo80q8F9E9cSE5+c5TwZCxDg48nGVx/A9fcjEB4O69ebL9aqZS4t0Lp1AVYsIo6Sp5N9b3Tp0iXCwsIybX/wwQeJj4+393AiUoy4uliI6FIb+N9TSunSx5HtquD6xhioV88MMSVLwoQJ5i0lhRgR+Ru7g8zDDz/M4sWLM21funQpnTt3zpOiRKToCqsbyKxeDQnwyXibKcDHk68rXeTBx9ubze2uXYPOneHAARg1So3tRCRLdnf2rV27NuPHj2f9+vWEhoYC8Ntvv7Fp0yZGjBjB9OnTbfsOGTIk7yoVkSIjrG4g7WsH2Dr73pUUR8Op47AsXmTucNddZlO7rl31OLWI3JTdc2SCg4NzdmCLhRPpnTYdSHNkRAqx69fNJnYREebj1K6u5qPVERFQpoyjqxMRB8q3tZaioqJuqzAREQA2bzYn8+7ZY45btDAn89ar59i6RMSp6NlFESlYcXHwwgtmcNmzB3x9Ye5c+OUXhRgRsZvdV2QMw+Cbb75h3bp1xMbGYrVaM7y+aNGiPCtORIoQw4BPPzVXpP7rL3Nb374wcSJUqODY2kTEadkdZIYNG8acOXO4//778ff3x6KJeCJyKwcPwoABsGGDOa5Tx7yN1LKlY+sSEadnd5BZsGABixYtolOnTvlRj4gUJVeuwFtvwZQp5sTekiXNibwvvaTHqUUkT9gdZHx8fKhatWp+1CIiRcn338PgwXDypDl++GHzCaXKlR1alogULXZP9h03bhyRkZFcvXo1P+oREWd3+jQ88gh06WKGmKAgWLIEli5ViBGRPGf3FZnHHnuML774Aj8/P6pUqYKbm1uG13fu3JlnxYmIE7l2DT74AMaNg8uXoUQJ8xbS2LHqCSMi+cbuINO7d2927NhBr169NNlXREy//govvgj79pnj++4zJ/PWrevYukSkyLM7yCxfvpyVK1dy33335Uc9IuIE0qwGW6PiuHgmmkYz38X/68/MF8qVg0mToE8fcFGbKhHJf3YHmaCgILX6FynGVuyPJvK7A9y38XtGr5+H79UEAP545Anu+uhDKF/ewRWKSHFi9/8yvffee4wcOZKT6U8iiEixsWJ/NO+/v4gPZg1l8o8f4Hs1gcPlK/PPpybSsnovVsRcc3SJIlLM2L1o5B133MGVK1e4fv06pUqVyjTZNy4uLk8LvF1aNFIkb6QlJvF55+d4YuM3uFnTuOLmwbR/PMl/GnflumsJLECAjycbX30AVxfNnROR25Nvi0ZOmzbtduoSEWf03XdcDx/I0+f+AGDV3c0Z1+4Fznn72XYxgOj4ZLZGxREaUs5BhYpIcZOrp5ZEpJg4dQqGDIHvvsMD+MPbj4j2/VlTrVm2b4lNTC64+kSk2LutxwqSk5NJSEjI8GWPn3/+mS5dulCxYkUsFgtLlizJ8HqfPn2wWCwZvsLCwm6nZBHJiWvXzKePateG776DEiU4+8IQ2vebedMQA+Dn5VlARYqI5CLIXL58mUGDBuHn50fp0qW54447MnzZe6z69eszY8aMbPcJCwsjOjra9vXFF1/YW7KI/E2a1WDz8Qss3X2WzccvkGa9YarcL79Agwbw6qvmWkktW8Lu3QTMmkbZCmXJbvaLBQj08aRpsG9BfAQRESAXt5ZGjhzJunXrmDVrFk8//TQzZszg7NmzzJkzh3fffdeuY3Xs2JGOHTvedB8PDw8CAgLsLVNEsrFifzSRyw4SHf+/W0CBPp68fZ8/bedNhXnzzI3ly8PkydC7N1gsuAIRXWoTvnAnFsw5MenSw01El9qa6CsiBcruKzLLli1j5syZdO/enRIlStCyZUvGjBnDO++8w2effZbnBa5fvx4/Pz9q1KhBeHg4Fy5cuOn+KSkpt3W7S6QoW7E/mvCFOzOEGIthpdXP39HwwRb/CzHPPQeHD5uN7W7o3h1WN5BZvRoS4JPx9lGAjyezejUkrG5gQXwMEREbu6/IxMXF2Va/9vb2tj1ufd999xEeHp6nxYWFhfHoo48SHBzM8ePHee211+jYsSObN2/G1dU1y/dMmDCByMjIPK1DpChIsxpELjuY4UpKjT9PMn7lDBqfPQTAsYBggr/6FNeW2XfuDqsbSPvaAWyNiiM2MRk/L/N2kq7EiIgj2B1kqlatSlRUFJUqVaJmzZp89dVXNG3alGXLllG2bNk8Le6JJ56w/fmee+6hXr16hISEsH79etq2bZvle0aPHs3w4cNt44SEBIKCgvK0LhFntDUqznYlplTqVYZu+oJ+25ZQwrBy2c2T9+97kvmNHmZBxVqE3uJYri4WPWItIoWC3UGmb9++7Nmzh9atWzNq1Ci6dOnCv/71L65du8bUqVPzo0abqlWrUr58eY4dO5ZtkPHw8MDDwyNf6xBxRrGJyWAYPHj0NyJWf8SdiX8CsKJ6KJFtXyDau8L/9hMRcRJ2B5mXXnrJ9ud27dpx6NAhdu7cSbVq1ahXr16eFvd3f/zxBxcuXCAwUPfhRewVFB/L3G/fpN3xbQCc8fEnol1/1lZrmmE/PT4tIs7E7iDzd1WqVKFKlSq5em9SUhLHjh2zjaOioti9eze+vr74+voSGRlJ9+7dCQgI4Pjx44wcOZJq1arRoUOH2y1bpPhITYX336dBZCSWq1dJdSnBv5s+woctHifZ7X+hJX2JAT0+LSLOJMdPLW3evJnvv/8+w7ZPP/2U4OBg/Pz8eOGFF0hJSbHrm2/fvp0GDRrQoEEDAIYPH06DBg0YO3Ysrq6u7N27l4cffpjq1avTr18/GjVqxC+//KJbRyI59fPPZk+YUaOwXL1KXONQHuo7nSmte2cKMaDHp0XE+eR40ciOHTvSpk0bXn31VQD27dtHw4YN6dOnD7Vq1WLy5Mn079+fcePG5We9dtOikVIs/fknjBwJ8+eb4woVYMoUePppVhyIybKPTESX2np8WkQKjZz+/s5xkAkMDGTZsmU0btwYgNdff50NGzawceNGAL7++msiIiI4ePBgHpSfdxRkpFixWuE//zFDzMWL5rYXXoAJE8D3f7eM0qyGHp8WkUItz1e/vnjxIv7+/rbxhg0bMnTlbdKkCWfOnMlluSJy2/buhRdfhM2bzXH9+jB7NjRvnmlXPT4tIkVFjufI+Pv7ExUVBUBqaio7d+6k+Q3/QCYmJuLm5pb3FYrIzSUlwcsvQ8OGZogpUwamToXt27MMMSIiRUmOr8h06tSJUaNGMXHiRJYsWUKpUqVo2bKl7fW9e/cSEhKSL0WKSBYMA5YsgSFD4I8/zG09esD778Nddzm0NBGRgpLjIPPWW2/x6KOP0rp1a8qUKcMnn3yCu7u77fX//Oc/PPjgg/lSpIj8TVQUDB4My5eb4+BgmDEDbrEIq4hIUZPjyb7p4uPjKVOmTKa1juLi4ihTpkyGcFMYaLKvFCmpqfDee/DWW3D1Kri5wauvwmuvQcmSjq5ORCTP5Plk33Q+Pj5Zbvf1VRMtkXy1YQOEh8Mhc4FH7r8fZs6EmjUdW5eIiAPddmdfEclnsbHwyivw6acApJarwL6XxpD6+JM0rVqOrNeBFxEpHhRkRAorqxXmzoVRo+DiRQyLhcVNHmJcaC8SEsvA3C1qZCcixZ6CjEhhtGeP2RPmt98AiK9Vlz6N+7KrYo0Mu8XEJxO+cCezejVUmBGRYklBRqQwSUyEiAiYPh3S0sDLC+ubb/JQUm3+SLqWaXcDc52kyGUHaV87QN15RaTYyXFDPBHJvTSrwebjF1i6+yybj18gzfq3hwUNA779FmrVMvvApKXBP/8Jhw6xpcvTWYYY21uB6PhktkbF5e+HEBEphHRFRiSfrdgfffNFGk+cgEGD4McfzRdDQuBf/4KwMABid5/N0feJTUy+9U4iIkWMgoxIPlqxP5rwhTv5e7OmmPhkhs7fwrLU36j+7w8gORnc3c2eMKNHZ+gJ4+flmaPvldP9RESKEgUZkXySZjWIXHYwU4gBaH5qL2+vmklI3P8vLdC2rdmZt0aNTPs2DfYl0MeTmPjkLI9lAQJ8zBWsRUSKG82REcknW6PiMtxOAih/+SJTv3+PL758jZC4P/izdFmOTp0NP/2UZYgBc6XqiC61ATO03Ch9HNGltib6ikixpCAjkk9unLNiMaw8tesH1vz7RR49sA4rFj5t8BBtn5vNwfs7g+XmISSsbiCzejUkwCfj7aMAH089ei0ixZpuLYnkk/Q5K3XOH2f8yhncG/07APv8Q3i9w0D2BlbPsN+thNUNpH3tALZGxRGbmIyfl3k7SVdiRKQ4U5ARySdNy5Vg4i//ocfmJbgaVhLdSzKl1TMsaNAJq4trrua2uLpYCA0pl39Fi4g4GQUZkbxmGPDNN7gOG8bj584BsKxmS9564DlivcwQorktIiJ5Q0FGJC8dPw4DB8LKlea4WjW2vfIm71zwI/aGib8BWiNJRCRPKMiI5IWUFJg0CcaPN//s7m72gxk1iiaenmy0GprbIiKSDxRkRG7XmjUwYAD8bk7mpV07sydM9eq2XTS3RUQkf+jxa5HciomBp54yg8vvv0NAAHzxBaxalSHEiIhI/lGQEbFXWpp5xaVmTfj8c7MHzKBBcPgwPPHELXvCiIhI3tGtJRF77NgBL74I27eb40aNYPZsaNzYsXWJiBRTuiIjkhPx8TBkCDRtaoYYb29zheotWxRiREQcSFdkRG7GMOCrr2DYMHNODEDPnvDeexCoR6dFRBxNQUYkO0ePmj1hfvrJHN99N8ycaU7uFRGRQkG3lkT+LjkZIiPhnnvMEOPhAePGwd69CjEiIoWMrsiI3Oinn8yeMMeOmeMHHzTnwtx9t2PrEhGRLOmKjAhAdLQ59+XBB80QExgIX34JK1YoxIiIFGIKMlK8paWZV1xq1jSDi4uL+XTSoUPw+OPqCSMiUsjp1pIUX9u3mz1hduwwx02amD1hGjZ0bF0iIpJjCjJS/Fy6BGPGmE8gGQb4+MA770D//uDqSpoWeBQRcRoKMlJ8GIZ5++ill+D8eXPbU0/BlCnmOknAiv3RRC47SHR8su1tgT6eRHSpTVhd9Y0RESlsNEdGiofff4f27eHJJ80QU706rF4NCxdmCDHhC3dmCDEAMfHJhC/cyYr90Y6oXEREbkJBRoq25GSIiDB7wqxZY/aEefNNsydM27a23dKsBpHLDmJkcYj0bZHLDpJmzWoPERFxFN1akqJr5UqzM+/x4+Y4LMx8QikkJNOuW6PiMl2JuZEBRMcnszUqjtCQcvlUsIiI2EtXZKToOXfOfHQ6LMwMMRUrmusl/fBDliEGIDYx+xCTm/1ERKRgKMhI0XH9OkyfbvaE+eorsyfMsGFmT5h//vOmPWH8vDxz9C1yup+IiBQM3VqSomHrVrMnzK5d5rhpU7MnTIMGOXp702BfAn08iYlPznKejAUI8DEfxRYRkcJDV2TEaaVZDbbuOkHUY70xmjc3Q0zZsjBrFvz6a45DDICri4WILrUBM7TcKH0c0aW2+smIiBQyCjLilFbsO8ebPV8nuGUjgr/+FIth8GODdqxdvMG8MuPqavcxw+oGMqtXQwJ8Mt4+CvDxZFavhuojIyJSCOnWkjidX5b9gtfgQUSe2gvAcd+7GPPgAH6rXA9WnGFW+Qq5Dh1hdQNpXztAnX1FRJyEgow4j6tXsY4fT7N3J+Gedo3kEu5Mb/EE/276CNdc3QDzNlDksoO0rx2Q6/Dh6mLRI9YiIk5CQUacw4oVMHAgLidO4A6sq9qIse3DOVM2IMNu6vciIlK8KMhI4Xb2rPkI9TffAHDVL4CXWvRlRfUWN32cWv1eRESKB032lcLp+nWYNs3sCfPNN+bk3eHD2btqMytq/OOmIQbU70VEpLjQFRkpfLZsMZ882r3bHDdvbvaEqV+fxlaDwB+Oq9+LiIgAuiIjhcnFi2aACQ01Q8wdd8BHH8GmTVC/PqB+LyIikpGCjDieYcCCBVCjBsyZY45794bDh+H5582lBm6gfi8iIpJOt5bEsQ4fhvBwWL/eHNeqZXbmbd36pm9TvxcREQEFGXGUK1dg/HiYPBmuXYOSJWHsWBg+HNzdc3QI9XsREREFGclTaVbj1ldJfvgBBg2CqChz3LmzuWp1cHDBFywiIk5NQUbyzIr90UQuO0h0/P96uAT6eBLRpbY5b+WPP2DoUFi0yHzxrrvgww+ha9dbPk4tIiKSFQUZyRMr9kcTvnBnpkeiY+KTGfTpNr43dlJz9nuQlGT2hHnpJYiIgDJlHFKviIgUDQoyctvSrAaRyw5m2delwdlDjF85g5p/njQ3tGhhTuatV68gSxQRkSJKQUZu29aouAy3kwB8riby6ob5PLlnJQAXPb2IG/sWIa8OzvQ4tYiISG4pyMhty7CukWHQff9aXlv3MeWuJgDw1T3tmNCmL+M6tiZEIUZERPKQgozctvR1jar9dZrxq2bS7Mx+AI6Ur8SYBwewLahuhv1ERETyioKM3Lam/p68+dtCev7yNW7WNK64eTC9RU8+btKVa65uWv9IRETyjYKM3J7vv8d10CCeOXUKgJ+qNWNcu/6c9fEDtP6RiIjkLwUZyZ3Tp82eMEuWmONKldg5YhxjL1fKMPE34MY+MiIiInlMQUbsc+0afPABjBsHly9DiRLmsgJjx9KwdGk25qSzr4iISB5RkJGc27TJXOBx3z5zfN99Zk+YunVtu2j9IxERKUgOfRb2559/pkuXLlSsWBGLxcKS9NsU/88wDMaOHUtgYCAlS5akXbt2HD161DHFFmcXLsBzz5nBZd8+KFcOPv4YNmzIEGJEREQKmkODzOXLl6lfvz4zZszI8vVJkyYxffp0Zs+ezZYtWyhdujQdOnQgOTk5y/0ljxkGzJsHNWqYwQWgXz84cgSefVaN7URExOEcemupY8eOdOzYMcvXDMNg2rRpjBkzhq5duwLw6aef4u/vz5IlS3jiiScKstTiZ/9+8zbSxo3muG5dmD0b/vEPx9YlIiJyg0L7v9RRUVHExMTQrl072zYfHx+aNWvG5s2bHVhZEXf5Mrz6KjRoYIaYUqVg8mTYuVMhRkRECp1CO9k3JiYGAH9//wzb/f39ba9lJSUlhZSUFNs4ISEhfwosir77DgYPNh+tBujWzXxCqVIlh5YlIiKSnUJ7RSa3JkyYgI+Pj+0rKCjI0SUVfqdPm6Gla1fzz5Urm6Fm8WKFGBERKdQKbZAJCAgA4Pz58xm2nz9/3vZaVkaPHk18fLzt68yZM/lap1O7dg0mTYJatWDpUrMnzKhRcOAAdOni6OpERERuqdDeWgoODiYgIIA1a9Zw7733AuZtoi1bthAeHp7t+zw8PPDw8CigKguXNHua0W3caE7m3W8u8EirVjBzJtSpU3AFi4iI3CaHBpmkpCSOHTtmG0dFRbF79258fX2pVKkSw4YN4+233+buu+8mODiYN954g4oVK9KtWzfHFV1IrdgfTeSygxmWBwjManmAv/4yJ/P+5z/muHx5mDIFnnkGLOrAKyIizsViGIbhqG++fv167r///kzbe/fuzfz58zEMg4iICD766CMuXbrEfffdx8yZM6levXqOv0dCQgI+Pj7Ex8fj7e2dl+UXGiv2RxO+cCd//4tMjyWzejUkrLa/2RNm5EiIizNfeP55mDDBbHAnIiJSiOT097dDg0xBKOpBJs1qcN/EtRmuxNzIAvzj6jkW7PgEy6ZN5sZ69cylBVq0KLhCRURE7JDT39+Fdo6M5MzWqLhsQ0yp1KsM3fQF/bYtwWJYoXRpePNNGDLEnNgrIiLi5PTbzMnFJmYdYtof/Y1xP83hzsQ/ATj3QEcqzp8DehxdRESKEAUZJ+fn5ZlhfFf8eSJWz6H9sa0AnPHxZ2z7F3nh3cFUDNJcGBERKVoUZJxc02BfAn08uRCXyLPbljJ00xeUvJ5CqksJPmr2KDNCH6Ns+bI0DfZ1dKkiIiJ5TkHGybm6WJgWmMAd7w2j+gVzaYHfgury+oMDOVHevI0U0aV29v1kREREnJiCjDP7808YOZJm8+cDEFe6LG+1eZbFde4HiyXrPjIiIiJFiIKMM7JazYZ2I0fCxYtmI7sXXsDn7fE8Fg9tctLZV0REpAhQkHE2e/fCiy/C5s3muH59mD0bmjfHFQgt79DqREREClShXTRS/iYpCV5+GRo2NENMmTLw/vuwfTs0b+7o6kRERBxCV2QKO8OAJUvMJnZ//GFu69HDDDF33eXQ0kRERBxNQaYwi4qCwYNh+XJzHBwMM2ZAx46OrUtERKSQ0K2lwig11VzMsU4dM8S4ucGYMXDggEKMiIjIDXRFprDZsAHCw+HQIXN8//0wcybUrOnYukRERAohXZEpLGJjoXdvaNPGDDF+frBwIaxZoxAjIiKSDQUZR7Na4aOPzLDy6admT5jwcDh8GJ56yhyLiIhIlnRryZH27DF7wvz2mzlu0MDsCdO0qWPrEhERcRK6IuMIiYkwfDg0amSGGC8vmDYNtm5ViBEREbGDrsgUJMOARYtg6FA4e9bc9thjMHUq3HmnY2sTERFxQgoyBeXECRg0CH780RyHhJg9YTp0cGxdIiIiTky3lvJbSgqMH2/2hPnxR3B3h7FjYd8+hRgREZHbVOyvyKRZDbZGxRGbHytGr1tnPoF05Ig5btvW7AlTvXreHF9ERKSYK9ZBZsX+aCKXHSQ6Ptm2LdDHk4gutQmrG5j7A58/by7wuHChOfb3N+fB9Oypx6lFRETyULG9tbRifzThC3dmCDEAMfHJhC/cyYr90fYf1Go1H5+uWdMMMRYLDBxo9oR58kmFGBERkTxWLINMmtUgctlBjCxeS98Wuewgadas9sjGrl0QGmreSrp0CRo2hC1b4F//grJlb79oERERyaRYBpmtUXGZrsTcyACi45PZGhV364MlJMCwYdC4sdkHxssLpk83/9ykSZ7VLCIiIpkVyzkysYnZh5gc72cY8M03Zog5d87c9vjj5lyYihVvv0gRERG5pWIZZPy8PG9vv+PHzbkvK1ea42rVzKeR2rfPowpFREQkJ4rlraWmwb4E+niS3dRbC+bTS02DfTO+kJICb71l9oRZudLsCRMRYfaEUYgREREpcMUyyLi6WIjoUhsgU5hJH0d0qZ2xn8zatVCvntnMLiXFDC7798O4ceCZsys8IiIikreKZZABCKsbyKxeDQnwyRhCAnw8mdWr4f/6yMTEwFNPmc3sfv8dAgLgiy/MKzJ33+2AykVERCRdsZwjky6sbiDtawdk3dk3LQ3mzIHXXoP4eHBxgQED4O23wcfH0aWLiIgIxTzIgHmbKTSkXMaNO3fCiy/Ctm3muHFjs9Fdo0YFX6CIiIhkq9jeWspSfDwMGWL2f9m2Dby9zYZ2v/2mECMiIlIIFfsrMoDZE+arr+CllyD6/5cm6NkT3nsPAm9jzSURERHJVwoyYDay+/pr88933232hGnXzrE1iYiIyC3p1hLAffeBhwdERsLevQoxIiIiTkJXZMB8GqlzZ6ha1dGViIiIiB10RQagRAmFGBERESekICMiIiJOS0FGREREnJaCjIiIiDgtBRkRERFxWgoyIiIi4rQUZERERMRpKciIiIiI01KQEREREaelICMiIiJOS0FGREREnJaCjIiIiDgtBRkRERFxWgoyIiIi4rRKOLqA/GYYBgAJCQkOrkRERERyKv33dvrv8ewU+SCTmJgIQFBQkIMrEREREXslJibi4+OT7esW41ZRx8lZrVbOnTuHl5cXFovF0eWQkJBAUFAQZ86cwdvb29HlFCo6N1nTecmezk3WdF6yp3OTtcJ4XgzDIDExkYoVK+Likv1MmCJ/RcbFxYW77rrL0WVk4u3tXWh+WAobnZus6bxkT+cmazov2dO5yVphOy83uxKTTpN9RURExGkpyIiIiIjTUpApYB4eHkRERODh4eHoUgodnZus6bxkT+cmazov2dO5yZozn5ciP9lXREREii5dkRERERGnpSAjIiIiTktBRkRERJyWgoyIiIg4LQWZAjB+/HhatGhBqVKlKFu2bI7e06dPHywWS4avsLCw/C3UAXJzbgzDYOzYsQQGBlKyZEnatWvH0aNH87fQAhYXF8dTTz2Ft7c3ZcuWpV+/fiQlJd30PW3atMn0M/Piiy8WUMX5Z8aMGVSpUgVPT0+aNWvG1q1bb7r/119/Tc2aNfH09OSee+7hhx9+KKBKC5Y952X+/PmZfjY8PT0LsNqC8/PPP9OlSxcqVqyIxWJhyZIlt3zP+vXradiwIR4eHlSrVo358+fne50Fzd7zsn79+kw/MxaLhZiYmIIp2A4KMgUgNTWVf/7zn4SHh9v1vrCwMKKjo21fX3zxRT5V6Di5OTeTJk1i+vTpzJ49my1btlC6dGk6dOhAcnJyPlZasJ566ikOHDjATz/9xPfff8/PP//MCy+8cMv3Pf/88xl+ZiZNmlQA1eaf//73vwwfPpyIiAh27txJ/fr16dChA7GxsVnu/+uvv9KzZ0/69evHrl276NatG926dWP//v0FXHn+sve8gNmx9cafjVOnThVgxQXn8uXL1K9fnxkzZuRo/6ioKB566CHuv/9+du/ezbBhw3juuedYuXJlPldasOw9L+mOHDmS4efGz88vnyq8DYYUmHnz5hk+Pj452rd3795G165d87WewiSn58ZqtRoBAQHG5MmTbdsuXbpkeHh4GF988UU+VlhwDh48aADGtm3bbNt+/PFHw2KxGGfPns32fa1btzaGDh1aABUWnKZNmxoDBw60jdPS0oyKFSsaEyZMyHL/xx57zHjooYcybGvWrJnRv3//fK2zoNl7Xuz5t6coAYzFixffdJ+RI0caderUybDt8ccfNzp06JCPlTlWTs7LunXrDMC4ePFigdR0O3RFphBbv349fn5+1KhRg/DwcC5cuODokhwuKiqKmJgY2rVrZ9vm4+NDs2bN2Lx5swMryzubN2+mbNmyNG7c2LatXbt2uLi4sGXLlpu+97PPPqN8+fLUrVuX0aNHc+XKlfwuN9+kpqayY8eODH/XLi4utGvXLtu/682bN2fYH6BDhw5F5mcDcndeAJKSkqhcuTJBQUF07dqVAwcOFES5hV5x+Jm5Hffeey+BgYG0b9+eTZs2ObqcLBX5RSOdVVhYGI8++ijBwcEcP36c1157jY4dO7J582ZcXV0dXZ7DpN+f9ff3z7Dd39+/UN67zY2YmJhMl29LlCiBr6/vTT/jk08+SeXKlalYsSJ79+7l1Vdf5ciRIyxatCi/S84Xf/31F2lpaVn+XR8+fDjL98TExBTpnw3I3XmpUaMG//nPf6hXrx7x8fFMmTKFFi1acODAgUK5qG5Byu5nJiEhgatXr1KyZEkHVeZYgYGBzJ49m8aNG5OSksLcuXNp06YNW7ZsoWHDho4uLwMFmVwaNWoUEydOvOk+hw4dombNmrk6/hNPPGH78z333EO9evUICQlh/fr1tG3bNlfHLCj5fW6cVU7PS27dOIfmnnvuITAwkLZt23L8+HFCQkJyfVxxfqGhoYSGhtrGLVq0oFatWsyZM4e33nrLgZVJYVWjRg1q1KhhG7do0YLjx4/z/vvvs2DBAgdWlpmCTC6NGDGCPn363HSfqlWr5tn3q1q1KuXLl+fYsWOFPsjk57kJCAgA4Pz58wQGBtq2nz9/nnvvvTdXxywoOT0vAQEBmSZtXr9+nbi4ONvnz4lmzZoBcOzYMacMMuXLl8fV1ZXz589n2H7+/Plsz0NAQIBd+zuj3JyXv3Nzc6NBgwYcO3YsP0p0Ktn9zHh7exfbqzHZadq0KRs3bnR0GZkoyORShQoVqFChQoF9vz/++IMLFy5k+OVdWOXnuQkODiYgIIA1a9bYgktCQgJbtmyx+6mwgpbT8xIaGsqlS5fYsWMHjRo1AmDt2rVYrVZbOMmJ3bt3AzjFz0xW3N3dadSoEWvWrKFbt24AWK1W1qxZw6BBg7J8T2hoKGvWrGHYsGG2bT/99FOGqxHOLjfn5e/S0tLYt28fnTp1ysdKnUNoaGimR/SL2s9MXtm9e3fh/PfE0bONi4NTp04Zu3btMiIjI40yZcoYu3btMnbt2mUkJiba9qlRo4axaNEiwzAMIzEx0Xj55ZeNzZs3G1FRUcbq1auNhg0bGnfffbeRnJzsqI+RL+w9N4ZhGO+++65RtmxZY+nSpcbevXuNrl27GsHBwcbVq1cd8RHyRVhYmNGgQQNjy5YtxsaNG427777b6Nmzp+31P/74w6hRo4axZcsWwzAM49ixY8abb75pbN++3YiKijKWLl1qVK1a1WjVqpWjPkKe+PLLLw0PDw9j/vz5xsGDB40XXnjBKFu2rBETE2MYhmE8/fTTxqhRo2z7b9q0yShRooQxZcoU49ChQ0ZERITh5uZm7Nu3z1EfIV/Ye14iIyONlStXGsePHzd27NhhPPHEE4anp6dx4MABR32EfJOYmGj7dwQwpk6dauzatcs4deqUYRiGMWrUKOPpp5+27X/ixAmjVKlSxiuvvGIcOnTImDFjhuHq6mqsWLHCUR8hX9h7Xt5//31jyZIlxtGjR419+/YZQ4cONVxcXIzVq1c76iNkS0GmAPTu3dsAMn2tW7fOtg9gzJs3zzAMw7hy5Yrx4IMPGhUqVDDc3NyMypUrG88//7ztH6mixN5zYxjmI9hvvPGG4e/vb3h4eBht27Y1jhw5UvDF56MLFy4YPXv2NMqUKWN4e3sbffv2zRDuoqKiMpyn06dPG61atTJ8fX0NDw8Po1q1asYrr7xixMfHO+gT5J0PP/zQqFSpkuHu7m40bdrU+O2332yvtW7d2ujdu3eG/b/66iujevXqhru7u1GnTh1j+fLlBVxxwbDnvAwbNsy2r7+/v9GpUydj586dDqg6/6U/Nvz3r/Tz0bt3b6N169aZ3nPvvfca7u7uRtWqVTP8e1NU2HteJk6caISEhBienp6Gr6+v0aZNG2Pt2rWOKf4WLIZhGAV2+UdEREQkD6mPjIiIiDgtBRkRERFxWgoyIiIi4rQUZERERMRpKciIiIiI01KQEREREaelICMiIiJOS0FGREREnJaCjIgTW79+PRaLhUuXLjm6FLtYLBaWLFmSZ8erUqUK06ZNy7PjOcrJkyexWCy2dbKc9e9XpCApyIgUUhaL5aZf48aNc3SJtzRu3LgsVyWPjo6mY8eOBVpLXFwcw4YNo3Llyri7u1OxYkWeffZZTp8+XaB1pOvTp49t0cd0QUFBREdHU7duXYfUJOKMtPq1SCEVHR1t+/N///tfxo4dy5EjR2zbypQpw/bt2x1RGqmpqbi7u+f6/QEBAXlYza3FxcXRvHlz3N3dmT17NnXq1OHkyZOMGTOGJk2asHnzZqpWrVqgNWXF1dW1wM+NiLPTFRmRQiogIMD25ePjg8ViybCtTJkytn137NhB48aNKVWqFC1atMgQeACWLl1Kw4YN8fT0pGrVqkRGRnL9+nXb66dPn6Zr166UKVMGb29vHnvsMc6fP297Pf3Kyty5cwkODsbT0xOAS5cu8dxzz1GhQgW8vb154IEH2LNnDwDz588nMjKSPXv22K4izZ8/H8h8a+mPP/6gZ8+e+Pr6Urp0aRo3bsyWLVsAOH78OF27dsXf358yZcrQpEkTVq9ebde5fP311zl37hyrV6+mY8eOVKpUiVatWrFy5Urc3NwYOHCgbd+sblPde++9Ga6ATZ06lXvuuYfSpUsTFBTEgAEDSEpKsr0+f/58ypYty8qVK6lVqxZlypQhLCzMFk7HjRvHJ598wtKlS23nZv369ZluLWVl48aNtGzZkpIlSxIUFMSQIUO4fPmy7fWZM2dy99134+npib+/Pz169LDrXIk4GwUZkSLg9ddf57333mP79u2UKFGCZ5991vbaL7/8wjPPPMPQoUM5ePAgc+bMYf78+YwfPx4Aq9VK165diYuLY8OGDfz000+cOHGCxx9/PMP3OHbsGN9++y2LFi2y/aL95z//SWxsLD/++CM7duygYcOGtG3blri4OB5//HFGjBhBnTp1iI6OJjo6OtMxAZKSkmjdujVnz57lu+++Y8+ePYwcORKr1Wp7vVOnTqxZs4Zdu3YRFhZGly5dcnxLyGq18uWXX/LUU09lutpRsmRJBgwYwMqVK4mLi8vx+XZxcWH69OkcOHCATz75hLVr1zJy5MgM+1y5coUpU6awYMECfv75Z06fPs3LL78MwMsvv8xjjz1mCzfR0dG0aNHilt/3+PHjhIWF0b17d/bu3ct///tfNm7cyKBBgwDYvn07Q4YM4c033+TIkSOsWLGCVq1a5fhziTglRy+/LSK3Nm/ePMPHxyfT9nXr1hmAsXr1atu25cuXG4Bx9epVwzAMo23btsY777yT4X0LFiwwAgMDDcMwjFWrVhmurq7G6dOnba8fOHDAAIytW7cahmEYERERhpubmxEbG2vb55dffjG8vb2N5OTkDMcOCQkx5syZY3tf/fr1M9UNGIsXLzYMwzDmzJljeHl5GRcuXMjh2TCMOnXqGB9++KFtXLlyZeP999/Pct+YmBgDyPb1RYsWGYCxZcuWbI9Vv359IyIiItt6vv76a6NcuXK28bx58wzAOHbsmG3bjBkzDH9/f9u4d+/eRteuXTMcJyoqygCMXbt2GYbxv7/fixcvGoZhGP369TNeeOGFDO/55ZdfDBcXF+Pq1avGt99+a3h7exsJCQnZ1ipS1GiOjEgRUK9ePdufAwMDAYiNjaVSpUrs2bOHTZs22a7AAKSlpZGcnMyVK1c4dOgQQUFBBAUF2V6vXbs2ZcuW5dChQzRp0gSAypUrU6FCBds+e/bsISkpiXLlymWo5erVqxw/fjzHte/evZsGDRrg6+ub5etJSUmMGzeO5cuXEx0dzfXr17l69ardk3QNw7jp6/bM+Vm9ejUTJkzg8OHDJCQkcP36ddv5LFWqFAClSpUiJCTE9p7AwEBiY2Ptqvnv9uzZw969e/nss89s2wzDwGq1EhUVRfv27alcuTJVq1YlLCyMsLAwHnnkEVtNIkWRgoxIEeDm5mb7s8ViAchwayYyMpJHH3000/vS57rkROnSpTOMk5KSCAwMZP369Zn2LVu2bI6PW7JkyZu+/vLLL/PTTz8xZcoUqlWrRsmSJenRowepqak5On6FChVsoSwrhw4dokSJEgQHBwPmbaO/h55r167Z/nzy5Ek6d+5MeHg448ePx9fXl40bN9KvXz9SU1NtoeHGvxMw/15uFaZuJSkpif79+zNkyJBMr1WqVAl3d3d27tzJ+vXrWbVqFWPHjmXcuHFs27bNrr8TEWeiICNSxDVs2JAjR45QrVq1LF+vVasWZ86c4cyZM7arMgcPHuTSpUvUrl37pseNiYmhRIkSVKlSJct93N3dSUtLu2l99erVY+7cucTFxWV5VWbTpk306dOHRx55BDB/mZ88efKmx7yRi4sLjz32GJ999hlvvvlmhnkyV69eZebMmTzyyCP4+PgAZvC58YmxhIQEoqKibOMdO3ZgtVp57733cHExpxl+9dVXOa4nXU7Ozd81bNiQgwcPZvt3CVCiRAnatWtHu3btiIiIoGzZsqxduzbLICtSFGiyr0gRN3bsWD799FMiIyM5cOAAhw4d4ssvv2TMmDEAtGvXjnvuuYennnqKnTt3snXrVp555hlat25N48aNsz1uu3btCA0NpVu3bqxatYqTJ0/y66+/8vrrr9seC69SpQpRUVHs3r2bv/76i5SUlEzH6dmzJwEBAXTr1o1NmzZx4sQJvv32WzZv3gzA3XffbZtgvGfPHp588knb1aacGj9+PAEBAbRv354ff/yRM2fO8PPPP9OhQwdcXFz44IMPbPs+8MADLFiwgF9++YV9+/bRu3dvXF1dba9Xq1aNa9eu8eGHH3LixAkWLFjA7Nmz7aon/dzs3buXI0eO8Ndff2W46pOdV199lV9//ZVBgwaxe/dujh49ytKlS22Tfb///numT5/O7t27OXXqFJ9++ilWq5UaNWrYXZ+Is1CQESniOnTowPfff8+qVato0qQJzZs35/3336dy5cqAectj6dKl3HHHHbRq1Yp27dpRtWpV/vvf/970uBaLhR9++IFWrVrRt29fqlevzhNPPMGpU6fw9/cHoHv37oSFhXH//fdToUIFvvjii0zHcXd3Z9WqVfj5+dGpUyfuuece3n33XVt4mDp1KnfccQctWrSgS5cudOjQgYYNG9p1DsqXL89vv/3G/fffT//+/QkODqZ169akpaWxe/du27wigNGjR9O6dWs6d+7MQw89RLdu3TLMdalfvz5Tp05l4sSJ1K1bl88++4wJEybYVQ/A888/T40aNWjcuDEVKlRg06ZNt3xPvXr12LBhA7///jstW7akQYMGjB07looVKwLmLb1FixbxwAMPUKtWLWbPns0XX3xBnTp17K5PxFlYjNu9aSsi4oQ+/vhjBgwYwH//+99MHXZFxHnoioyIFEv9+vXjyy+/5NChQ1y9etXR5YhILumKjIiIiDgtXZERERERp6UgIyIiIk5LQUZEREScloKMiIiIOC0FGREREXFaCjIiIiLitBRkRERExGkpyIiIiIjTUpARERERp/V/CdrmMJCQ6f0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WwsXFRYSGhqps4ieEtPHbRx99JBwdHV+5iZ8Q0tLmli1bChsbG7Wb+KmL+fbt2+Kdd94Rjo6OQi6Xi/fff1/8/fff+T7fl++RsylfYmKiEEKIPXv2iK5duwp3d3dhaWkp3N3dRc+ePcWlS5dUroOapeBC/LeJn6Ojo7C2thaNGzfOdxO/tWvX5rk+ZxO/lwUEBIjatWvnOe/p6Sk6d+6sPM7KyhLDhw8Xbm5uwsbGRjRv3lzExMSIgIAAERAQoHLtxo0bha+vryhTpswrN/HLyMgQX3zxhXB3dxcWFhaiWrVqBW7ipy5Odcv8ifRBJgRneBEREZHp4JwbIiIiMilMboiIiMikMLkhIiIik8LkhoiIiEwKkxsiIiIyKUxuiIiIyKSUuk38FAoF/v77b9jb2xt1DR0iIiL6jxACGRkZcHd3f+WGmKUuufn777/h4eFh6DCIiIioEG7duoVKlSoV2KbUJTc5Rd9u3boFBwcHA0dDREREmkhPT4eHh4dK8db8lLrkJmcoysHBgckNERFRCaPJlBJOKCYiIiKTwuSGiIiITAqTGyIiIjIpTG6IiIjIpDC5ISIiIpPC5IaIiIhMCpMbIiIiMilMboiIiMikMLkhIiIik1LqdigmIiIi45GtEDiemIqUjCw421ujsbcTzM2KVtiayQ0REREZxPZzSYjcFI+ktCzlOTe5NSKCfBHo51bo+3JYioiIiIrd9nNJCF0Rq5LYAEByWhZCV8Ri+7mkQt+byQ0REREVq2yFQOSmeAg1r+Wci9wUj2yFuhavxuSGiIiIitXxxNQ8PTa5CQBJaVk4nphaqPszuSEiIqJilZKRf2JTmHYvY3JDRERExcrZ3lqn7V7G5IaIiIiKVWNvJ7jJrZHfgm8ZpFVTjb2dCnV/JjdERERUrMzNZIgI8gWAPAlOznFEkG+h97thckNERETFLtDPDQt6N4CrXHXoyVVujQW9GxRpnxtu4kdEREQGEejnhna+rtyhmIiIiEyHuZkM/j7ldXpPDksRERGRSWFyQ0RERCaFyQ0RERGZFCY3REREZFKY3BAREZFJYXJDREREhnXxIpCUpLPbMbkhIiIiw3j+HJg8GahXDwgP19ltuc8NERERFb+TJ4FPPwXOnJGOMzOlLxubIt+aPTdERERUfJ48AUaOBBo3lhKb8uWB5cuBLVt0ktgA7LkhIiKi4hIdDfTvD1y5Ih1/+CHw/feAs7NO34Y9N0RERKRfaWnAwIFA69ZSYlOxIvC//wGrVuk8sQEMnNwsWLAAdevWhYODAxwcHODv749t27YVeM3atWtRs2ZNWFtbo06dOti6dWsxRUtERERa+9//AF9fYPFi6XjgQOD8eSAoSG9vadDkplKlSvj2229x8uRJnDhxAm+99Ra6du2K8+fPq21/5MgR9OzZEyEhITh16hS6deuGbt264dy5c8UcORERERUoJUUaduraFfj7b6BqVWlYauFCQC7X61vLhBBCr++gJScnJ0ybNg0hISF5Xvvggw/w+PFjbN68WXmuadOmqF+/PhYuXKjR/dPT0yGXy5GWlgYHBwedxU1EREQAhABWrACGDgVSUwFzc2D4cGDixCJNGNbm77fRzLnJzs7G6tWr8fjxY/j7+6ttExMTg7Zt26qc69ChA2JiYoojRCIiIirIzZtAp05Anz5SYlOvHnDsGDB1qs5WQmnC4Kulzp49C39/f2RlZcHOzg7r16+Hr6+v2rbJyclwcXFROefi4oLk5OR87//06VM8ffpUeZyenq6bwImIiEiiUAALFgCjRwOPHgFWVsCECcCIEYCFRbGHY/Cemxo1aiAuLg7Hjh1DaGgogoODER8fr7P7R0VFQS6XK788PDx0dm8iIqJS7+JFoGVLaYfhR4+A5s2BuDhg7FiDJDaAESQ3lpaWqFq1Kt544w1ERUWhXr16+P7779W2dXV1xd27d1XO3b17F66urvnef8yYMUhLS1N+3bp1S6fxExERlUrPnwNTpkhDT4cPA3Z2wLx5wIEDQM2aBg3N4MnNyxQKhcowUm7+/v7Ys2ePyrldu3blO0cHAKysrJRLzXO+iIiIqAhOngQaNQLGjQOePQM6dpSWdw8aBJgZPrUw6JybMWPGoGPHjqhcuTIyMjKwcuVKREdHY8eOHQCAPn36oGLFioiKigIADBkyBAEBAZgxYwY6d+6M1atX48SJE1ics3aeiIiI9CczU1r1NH26NM+mfHlg9mygVy9AJjN0dEoGTW5SUlLQp08fJCUlQS6Xo27dutixYwfatWsHALh58ybMcmWAzZo1w8qVKzF+/HiMHTsW1apVw4YNG+Dn52eoj0BERFQ6FFPpBF0wun1u9I373BAREWkhLU0qdJkzSlKxorQySo87DKtTIve5ISIiIiOzaVOxl07QBYPvc0NERERGJiUFGDwYWLNGOq5aFViyBAgIMGxcGmLPDREREUmEAJYvB2rVkhIbc3NpSOrMmRKT2ADsuSEiIiJAKp0wcCCwfbt0XK8e8NNPwBtvGDauQmDPDRERUWmmUEib79WuLSU2VlbA5MnAX3+VyMQGYM8NERFR6XXxItCvn7TDMCCVTliyxOA7DBcVe26IiIhKG3WlE374wShKJ+gCe26IiIhKk5MngZAQ4PRp6bhjR2DhQqByZcPGpUPsuSEiIioNMjOBUaOAxo2lxKZ8eWll1JYtJpXYAOy5ISIiMn0lqHSCLrDnhoiIyFSlpQGffQa0bi0lNhUrAv/7H7BqlckmNgCTGyIiItO0aZO0vHvRIum4hJRO0AUOSxEREZmSEl46QRfYc0NERGQKhABWrPivdIKZWYksnaAL7LkhIiIq6W7elObWbNsmHZfg0gm6wJ4bIiKikip36YRt20yidIIusOeGiIioJLp4UVrefeiQdGwipRN0gT03REREJUnu0gmHDplc6QRdYM8NERFRSVEKSifoAntuiIiIjF1O6YQmTaTExsnJZEsn6AJ7boiIiIzZ/v1Av36lpnSCLrDnhoiIyBjllE5o1UpKbNzdgY0bTb50gi4wuSEiIjI26konxMcDXboYNq4SgsNSRERExiIlBRgyBFi9WjouhaUTdIE9N0RERIYmBPDbb4Cvr5TYlOLSCbrAnhsiIiJDKmTphGyFwPHEVKRkZMHZ3hqNvZ1gbiYrhoCNH5MbIiIiQ1AogAULgNGjgUePpNIJEyYAI0YAFhYFXrr9XBIiN8UjKS1Lec5Nbo2IIF8E+rnpO3Kjx2EpIiKi4paQIA03hYdLiU3z5kBcHDB2rEaJTeiKWJXEBgCS07IQuiIW288l6THwkoHJDRERUXEpYumEbIVA5KZ4CDWv5ZyL3BSPbIW6FqUHh6WIiIiKQ2ws8OmnRSqdcDwxNU+PTW4CQFJaFo4npsLfp3wRAy652HNDRESkTzmlExo3lhKb8uULXTohJSP/xKYw7UwVe26IiIj0Zf9+oH9/4PJl6biIpROc7a112s5UseeGiIhI19LTgdBQqXTC5cs6K53Q2NsJbnJr5LfgWwZp1VRjb6dCv4cpYHJDRESkS5s3S5vxLVwoHeuwdIK5mQwRQb4AkCfByTmOCPIt9fvdMLkhIiLShXv3gI8+AoKCgDt3pNIJ0dFSkiOX6+xtAv3csKB3A7jKVYeeXOXWWNC7Afe5AefcEBERFY0QwMqVUk2oBw+k0glffglMnAjY2OjlLQP93NDO15U7FOeDyQ0REVFh3bollU7YulU61rB0gi6Ym8lK9XLvgnBYioiISFsKBTB/vjS3ZutWqXTC5MnAX38VS2JDBWPPDRERkTYSEoB+/aQdhgGpdMKSJRrtMEzFgz03REREmnj+HIiKKnTpBCo+7LkhIiJ6ldhYICREKm4JFKp0AhUf9twQERHlJ3fphLi4IpVOoOLDnhsiIiJ1dFw6gYoPe26IiIhy01PpBCo+TG6IiIhybNqkt9IJVHw4LEVERHTvnrTD8KpV0nHVqsCPP0q9N1TisOeGiIhKLyGA334DatWSEhszM2DkSODMGSY2JRh7boiIqHS6eVMqnbBtm3Rct65UOqFhQ8PGRUXGnhsiIipdckon1K4tJTaWlsA33wAnTjCxMRHsuSEiotKDpRNKBfbcEBGR6Xv+HJgyhaUTSgn23BARkWmLjQU+/RQ4fVo6DgyUlnp7eho2LtIbg/bcREVFoVGjRrC3t4ezszO6deuGhISEV143e/Zs1KhRAzY2NvDw8MAXX3yBrKysYoiYiIhKjNylE06fBpycpNIJW7cysTFxBu252b9/P8LCwtCoUSO8ePECY8eORfv27REfHw9bW1u116xcuRKjR4/Gzz//jGbNmuHSpUvo27cvZDIZZs6cWcyfgIiIjNLLpRM++ACYM4c7DJcSBk1utm/frnK8bNkyODs74+TJk2jZsqXaa44cOYLmzZvjo48+AgB4eXmhZ8+eOHbsmN7jJSIiI5eWJvXWLFokHbu7AwsWcIfhUsaoJhSnpaUBAJycnPJt06xZM5w8eRLHjx8HAFy7dg1bt25Fp06d1LZ/+vQp0tPTVb6IiMgEbd4sLe/OSWwGDGDphFLKaCYUKxQKDB06FM2bN4efn1++7T766CPcv38fb775JoQQePHiBT777DOMHTtWbfuoqChERkbqK2wiIjK0lBSpdMLq1dKxj4+0vJs7DJdaRtNzExYWhnPnzmF1zi9nPqKjozFlyhTMnz8fsbGxWLduHbZs2YKvv/5abfsxY8YgLS1N+XXr1i19hE9ERMUtp3SCr6+U2JiZAV9+qffSCdkKgZirD7Ax7g5irj5AtkLo7b2ocGRCCIP/VMLDw7Fx40YcOHAA3t7eBbZt0aIFmjZtimnTpinPrVixAgMGDMCjR49gZlZwvpaeng65XI60tDQ4ODjoJH4iIipmBiqdsP1cEiI3xSMp7b8Vum5ya0QE+SLQz02v713aafP326A9N0IIhIeHY/369di7d+8rExsAePLkSZ4ExtzcXHk/IiIyYQYsnbD9XBJCV8SqJDYAkJyWhdAVsdh+Lkmv70+aM+icm7CwMKxcuRIbN26Evb09kpOTAQByuRw2NjYAgD59+qBixYqIiooCAAQFBWHmzJl4/fXX0aRJE1y5cgVfffUVgoKClEkOERGZoJdLJzRrJs2tqVVL72+drRCI3BQPdf+EFgBkACI3xaOdryvMzWR6j4cKZtDkZsGCBQCAVi+NjS5duhR9+/YFANy8eVOlp2b8+PGQyWQYP3487ty5gwoVKiAoKAiTJ08urrCJiKg4PX8OTJ8OREYCT59KpROiooBBg6R5NsXgeGJqnh6b3ASApLQsHE9Mhb9P+WKJifJn0ORGk2Gk6OholeMyZcogIiICEREReoqKiIiMRmwsEBICxMVJxwYqnZCSodku+Jq2I/0ymtVSRERESrlLJ8TFSaUTfv3VYKUTnO2tddqO9Mto9rkhIiICUKjSCdkKgeOJqUjJyIKzvTUaezvpdO5LY28nuMmtkZyWpXbejQyAq1x6XzI8JjdERGQc0tOl3pqFC6VjDUsnFMfybHMzGSKCfBG6IhYyQCXByUmhIoJ8OZnYSHBYioiIDG/zZmkzvpzERsPSCcW5PDvQzw0LejeAq1x16MlVbo0FvRtwnxsjwp4bIiIynHv3pNIJq1ZJx1WrAj/+qNEOw4ZYnh3o54Z2vq56HQKjomNyQ0RExU8IYOVKKbF58EBa0j18ODBxIlC2rEa3MNTybHMzGZd7GzkmN0REVLxu3ZJKJ2zdKh0XsnQCl2dTfjjnhoiIikfu0glbtxa5dAKXZ1N+2HNDRET6p650wk8/ATVrFvqWXJ5N+WHPDRER6c/z51KphHr1pMTGzg6YOxc4eLBIiQ3w3/Js4L/l2Dm4PLt0Y3JDRET6ERsr7TA8dqxUEyowEDh3DggP11lNKC7PJnU4LEVERLqVmSkVuZw+HcjOlkonzJ4N9O4NyHTfi8Ll2fQyJjdERKQ7Bw5Ic2u0KJ2gC1yeTblxWIqIiIouPR0IDQUCAqTExt0d2LgRWL1a74kN0cvYc0NEREWzebO0b82dO9LxgAHAd98Bcrlh46JSi8kNEREVzsulE3x8gCVLNCqdQKRPWg9LxcbG4uzZs8rjjRs3olu3bhg7diyePXum0+CIiMgICQH89htQq5aU2JiZASNGAGfOMLEho6B1cjNw4EBcunQJAHDt2jV8+OGHKFu2LNauXYuRI0fqPEAiIjIit24Bb78trXx68EAqnXDsmDQMpUFNqGyFQMzVB9gYdwcxVx8gW6Fu+z2iotF6WOrSpUuoX78+AGDt2rVo2bIlVq5cicOHD+PDDz/E7NmzdRwiEREZnEIBLFwIjBoFPHoklU6YMAEYORKwsNDoFtvPJSFyU7xKsUs3uTUigny5Hw3plNY9N0IIKBQKAMDu3bvRqVMnAICHhwfu37+v2+iIiMigshUCp3YdxYOG/kBYmJTYNGsGxMUB48ZpldiErojNU8U7OS0LoStisf1ckh6ip9JK6+SmYcOG+Oabb7B8+XLs378fnTt3BgAkJibCxcVF5wESEZFh7Dh1E4s79oNvx5Yof+o4HltYY0ZQOLYv+F2ab6OhbIVA5KZ4tfWfcs5FbornEBXpjNbDUrNnz0avXr2wYcMGjBs3DlWrVgUA/PHHH2jWrJnOAyQiouJ35PcdqPT5IHRIuQYA2O/dAGM7hONvuTOwMg4LzMw0Hko6npiap8cmNwEgKS0LxxNTuREf6YTWyU3dunVVVkvlmDZtGszNzXUSFBERGUhmJhQTJ6Lx9Okoo1DgH2t7TGrTH+trt1aWTpBB6mlp5+uqUYmDlIz8E5vCtCN6lULtUPzw4UMsWbIEY8aMQWpqKgAgPj4eKSkpOg2OiIiK0YEDQL16MPvuO5RRKLCpZgu06zcf6/3eUqkJlbunRRPO9tavbqRFO6JX0brn5syZM2jTpg0cHR1x/fp19O/fH05OTli3bh1u3ryJX3/9VR9xEhGRvqSnS6ugFi4EAGRWcMHgFv2xq1rTAi/TtKelsbcT3OTWSE7LUjvvRgapindjbyctAydST+uem2HDhuGTTz7B5cuXYW39X5bdqVMnHDhwQKfBERGRnm3eDNSurUxs0L8/zu448srEBtC8p8XcTIaIIF8AUiKTW85xRJAvq3iTzmid3Pz1118YOHBgnvMVK1ZEcnKyToIiIiI9u3cP+OgjICgIuH1bKp2wdy+weDHeqOcNN7l1nkQkhwzS/jTa9LQE+rlhQe8GcJWrJkSucmss6N2A+9yQTmk9LGVlZYX09PQ85y9duoQKFSroJCgiItITIYCVK6WaUA8eSKUThg0DIiOVOwzn9LSEroiFDFAZSipKT0ugnxva+brieGIqUjKy4GwvJUjssSFd07rnpkuXLpg0aRKeP38OAJDJZLh58yZGjRqF9957T+cBEhGRjrxcOqFOHal0wrRpeUon6KunxdxMBn+f8uhavyL8fcozsSG9kAkhtNo1KS0tDd27d8eJEyeQkZEBd3d3JCcnw9/fH1u3boWtra2+YtWJ9PR0yOVypKWlwcHBwdDhEBHpXLZCqPaOeDrC/MfF0qThjAypdMJXX0mlEywttbsXe1rIQLT5+611cpPj0KFDOHPmDB49eoQGDRqgbdu2hQq2uDG5ISJT9nL9pioPbmPG7nl4/fq/+5P5+wM//aTVDsNExqBYkpuSiskNEZmqnPpNAkCZ7Bfo/9d6DD20ElbZz/HYwho3ho+D7zdjAG64SiWQNn+/NZpQPGfOHI3ffPDgwRq3JSIi3chdv6l28hV8t20OaucqnTCuQziyHSrjkMwMTG3I1GmU3MyaNUujm8lkMiY3RESFVJT5LccTU5F6Pw0jj6zCgGPrUEYo8NDaDpPa9Me62v/uMMz6TVRKaJTcJCYm6jsOIqJS7eW5MoC0l0xEkK9GK5Oyo6OxbelgVPnnbwDA5potMLHtANy3LafSjvWbqDTQep8bIiLSrdxzZXJLTstC6IrYgpde/1s64c1/dxi+a+eEr9qFYmd1f7XNWb+JSgONkpthw4bh66+/hq2tLYYNG1Zg25kzZ+okMCKi0iD3XJmXCbyiAveWLcBnn0k7DAPY0KgTJjTrg3Rruzz3Yv0mKk00Sm5OnTql3LTv1KlTeg2IiKg0OZ6YqjIU9bLcFbiVc2Xu3ZN2GF61Sjr28QF+/BHWFWoiQ8e7ChOVRBolN/v27VP7PRERFY2mc2BSMrJeWTohEMCC3g3yzN1x1WLuDpEp0HrOzaefforvv/8e9vb2KucfP36Mzz//HD///LPOgiMiMnWazoGplPEAeHsAsHWrdKJOHWkzvkaNVNqxfhNRITbxMzc3R1JSEpydnVXO379/H66urnjx4oVOA9Q1buJHRMYkWyHw5tS9SE7LUjvvxkwo8NnF3RixbylkWpZOIDIlOt/EL+emQggIIZCRkQFr6//+tZGdnY2tW7fmSXiIiKhgBVXg9nlwG1Hb56Lx7fPSCZZOINKIxsmNo6MjZDIZZDIZqlevnud1mUyGyMhInQZHRFQa5FTgzpkroyydcHglrF48B2xtgagoYNAglk4g0oDGyc2+ffsghMBbb72FP//8E05O/y0ntLS0hKenJ9zd3fUSJBFRSVCUHYZz5sqc2xwNz5GfwzHh396aDh2ARYsAT089Rk5kWjRObgICAgBIuxV7eHjAzMxMb0EREZU0Rd1hGJmZMI+MRL3p04HsbMDJCZg1C/j4Y6l0AhFprFBVwR8+fIjjx48jJSUFCoVC5bU+ffroLDh94IRiItK1/HYYzklJCtxhGAAOHAD69QMuX5aOe/QA5swBXFz0ES5RiaSXCcU5Nm3ahF69euHRo0dwcHCALNe/KGQymdEnN0RE6hR2SKlIOwz/WzoB/5ZOgLs7MH8+0LVrUT8OUammdXIzfPhwfPrpp5gyZQrKli2rj5iIiLRWlPkuRRlSKtQOw0Ce0gno3x/47jvA0VGjmIkof1onN3fu3MHgwYOZ2BCR0ShKclKkopXQcodhIN/SCWjdWqP7ENGraT0ruEOHDjhx4oQ+YiEi0lpOcvJy70lOcrL9XFK+175qSAmQhpSyFflPTdR0h2FnOyupdEKtWlJiY2YGfPklcOYMExsiHdO656Zz584YMWIE4uPjUadOHVhYWKi83qVLF50FR0RUkCLNd0ERhpRyaeztBDe5db47DMsA1BXpaDqkL7B1i3SyTh3g55+Bhg0L/oBEVChaJzf9+/cHAEyaNCnPazKZDNnZ2UWPiohIA0VNTrQeUlKjoB2GzYQCH8Vtx8TDv0L2+BFLJxAVE62HpRQKRb5f2iY2UVFRaNSoEezt7eHs7Ixu3bohISHhldc9fPgQYWFhcHNzg5WVFapXr46tOcXkiKjUKGpyovGQ0iva5eww7Cr/r5136h38+fs4fLNzPso8fgQ0awbExQHjxzOxIdIzrXtudGn//v0ICwtDo0aN8OLFC4wdOxbt27dHfHw8bG1t1V7z7NkztGvXDs7Ozvjjjz9QsWJF3LhxA45cYUBU6hQ1OdFkSMlVLq28epWcHYb/unQXDvO/R81fZsHs2VOpdMK330qlE7j5KVGxKFRy8/jxY+zfvx83b97Es2fPVF4bPHiwxvfZvn27yvGyZcvg7OyMkydPomXLlmqv+fnnn5GamoojR44o5/t4eXlp9wGIyCQUNTkpaEgpZ4ZORJCvxkvKzU/HoWlICHDqlHSCpROIDELrHYpPnTqFTp064cmTJ3j8+DGcnJxw//59lC1bFs7Ozrh27Vqhg7ly5QqqVauGs2fPws/PT22bTp06wcnJCWXLlsXGjRtRoUIFfPTRRxg1ahTM1RSUe/r0KZ4+fao8Tk9Ph4eHB3coJjIROaulAPXJySt3B4ZuSidg0iRg2rT/SifMng307s3SCUQ6os0OxVonN61atUL16tWxcOFCyOVynD59GhYWFujduzeGDBmCd999t1BBKxQKdOnSBQ8fPsShQ4fybVezZk1cv34dvXr1wqBBg3DlyhUMGjQIgwcPRkRERJ72EydOVFutnMkNkekocnKCImwCePCgVDrh0iXpmKUTiPRCr8mNo6Mjjh07hho1asDR0RExMTGoVasWjh07huDgYFy8eLFQQYeGhmLbtm04dOgQKlWqlG+76tWrIysrC4mJicqempkzZ2LatGlISsq7nwV7bohKh6LsUFwo6enA6NHAggXSMUsnEOmVXmtLWVhYKCuCOzs74+bNm6hVqxbkcjlu3bpVqIDDw8OxefNmHDhwoMDEBgDc3NxgYWGhMgRVq1YtJCcn49mzZ7B8aRWClZUVrKysChUXEZUc5mayfPei0TmWTiAyalonN6+//jr++usvVKtWDQEBAZgwYQLu37+P5cuX5ztPJj9CCHz++edYv349oqOj4e3t/cprmjdvjpUrV0KhUCiTrEuXLsHNzS1PYkNEpFP37gFDh0o7DQMsnUBkpLRelzhlyhS4uUlj2JMnT0a5cuUQGhqKe/fuYfHixVrdKywsDCtWrMDKlSthb2+P5ORkJCcnIzMzU9mmT58+GDNmjPI4NDQUqampGDJkCC5duoQtW7ZgypQpCAsL0/ajEBFpRggpofH1lf6XpROIjJrWc250+ub5rCJYunQp+vbtC0CawOzl5YVly5YpX4+JicEXX3yBuLg4VKxYESEhIfmulnqZNmN2RES4dQsIDZWGogCpdMJPPwGNGhk2LqJSRq8Tiks6JjdEpBGFQtqjZtQoICODpROIDEyvE4q9vb3z7XEBUKR9boiIjMKlS9Ly7oMHpWN/f2DJEmlYioiMntbJzdChQ1WOnz9/jlOnTmH79u0YMWKEruIiIip+z58DM2YAEycCT/8tnRAVJZVO0GDYm4iMg9bJzZAhQ9SenzdvHk6cOFHkgIiodCr2fWpeduoU8OmnUnFLgKUTiEownc25uXbtGurXr4/09HRd3E5vOOeGyPjoYofhQnu5dEK5clLphI8/ZukEIiOizd9vnZWo/eOPP+Dk9OrKuUREueXUhsqd2ABAcloWQlfEYvu5vDuP68yBA0C9elLV7uxsqXTChQtAnz5MbIhKsEJt4pd7QrEQAsnJybh37x7mz5+v0+CIyLRlKwQiN8WrregtIBW/jNwUj3a+rrodonq5dIKbm1Q6oVs33b0HERmM1slNt5f+z29mZoYKFSqgVatWqFmzpq7iIqJS4Hhiap4em9wEgKS0LBxPTNVdaQWWTiAyeVonN+oqbxMRFUZKRv6JTWHaFejl0glVqkilE956q+j3JiKjonVyc+fOHfz555+4dOkSLC0tUaNGDfTo0QPlypXTR3xEZMKc7a112k4tIYBVq4AhQ4D796XSCcOGAZGRQNmyhb8vERktrZKb+fPnY9iwYXj27JlypnJ6ejqGDRuGJUuWoGfPnhBCIC4uDq+//rpeAiYi09HY2wlucmskp2WpnXcjA+Aql5aFFwpLJxCVShqvltqyZQsGDx6M8PBw3LlzBw8fPsTDhw9x584dDBw4EMHBwTh06BB69eqFTZs26TNmIjIR5mYyRARJu/6+PF045zgiyFf7ycQKhTRZuHZtKbGxtJSWe584wcSGqBTQeJ+bVq1a4c0338Q333yj9vXx48djxowZcHV1RXR0NDyNdOMr7nNDZHx0us8NSycQmSS9FM50cHDAX3/9hRo1aqh9PSEhAbVq1cL169dRuXJl7aMuJkxuiIxTkXcoZukEIpOml8KZ2dnZsLCwyPd1CwsL2NjYGHViQ0TGy9xMVvjl3qdOASEh0v8CQPv2UukELy+dxUdEJYfGc25q166NjRs35vv6hg0bULt2bZ0ERUSkkcxMYMwYaR7NqVNS6YRly4Dt25nYEJViGvfchIWFITQ0FFZWVhgwYADKlJEuffHiBRYtWoTx48dzh2IiKj4HD0pzay5dko7ffx+YOxdwcTFsXERkcBonN8HBwTh79izCw8MxZswY+Pj4QAiBa9eu4dGjRxg8eDD69u2rx1CJiMDSCUT0SlpXBT969ChWrVqFy5cvAwCqVauGnj17omnTpnoJUNc4oZioBGPpBKJSSy8TinM0bdq0xCQyRGQiWDqBiLSg8YRiIqJiJ4SU0Pj6Sv9rZgYMHw6cPcvEhojypXXPDRFRsWDpBCIqJPbcEJFxYekEIioi9twQkfFg6QQi0oFC9dy8ePECu3fvxqJFi5CRkQEA+Pvvv/Ho0SOdBkdEpcTz58C33wJ160qJja0t8P330vdMbIhIS1r33Ny4cQOBgYG4efMmnj59inbt2sHe3h5Tp07F06dPsXDhQn3ESURGrtC1oVg6gYh0TOvkZsiQIWjYsCFOnz6N8uX/qwPzzjvvoH///joNjohKhkJV9c7MlObSTJsGZGdLpRNmzQL69AFkWhTMJCJ6idbJzcGDB3HkyBFYWlqqnPfy8sKdO3d0FhgRlQzbzyUhdEUsXt4NNDktC6ErYrGgd4O8CQ5LJxCRHmk950ahUCA7OzvP+du3b8Pe3l4nQRFRyZCtEIjcFJ8nsQGgPBe5KR7Zin+P0tOBQYOAli2lxMbNDVi/Hvj9dyY2RKQzWic37du3x+zZs5XHMpkMjx49QkREBDp16qTL2IjIyB1PTFUZinqZAJCUloXjianSsu7atf+rCdWvHxAfz5pQRKRzWg9LzZgxAx06dICvry+ysrLw0Ucf4fLly3jttdewatUqfcRIREYqJSP/xCaH05M0VPq8H7Btg3SCpROISM+0Tm4qVaqE06dPY/Xq1Thz5gwePXqEkJAQ9OrVCzY2NvqIkYiMlLO9df4vCoEuFw4gYvcilM9Ml0onfPGFNIm4bNniC5KISp1CbeJXpkwZ9O7dW9exEFEJ09jbCW5yaySnZanMu3FLv4dvds5Hm6t/AQBEnTqQsXQCERUTjZKb//3vfxrfsEuXLoUOhogMo7B71JibyRAR5IvQFbGQAYBQ4KO47RgdvRT2zzLxzKwMboR+gWozv5HKKBARFQOZEELdQgcVZmaazTuWyWRqV1IZk/T0dMjlcqSlpcHBwcHQ4RAZXKH2qFFzj5+X7sTwtdPR5NY5AMBZj1p4OHcBWnQN0EvcRFS6aPP3W6PkxpQwuSH6T3571OT02ajdo+ZlL14AM2ZARERA9vQpXljb4NaIr1D5qxEwt2D5OiLSDW3+fvO/PESl1Kv2qJFB2qOmna9r/kNUuUonyACgXTuUWbQI3t7eeoubiOhVClU4c8+ePXj77bfh4+MDHx8fvP3229i9e7euYyMiPdJqj5qXZWYCY8ZIE4RPnZJKJyxbBuzYATCxISID0zq5mT9/PgIDA2Fvb48hQ4ZgyJAhcHBwQKdOnTBv3jx9xEhEeqDJHjVq2x08CNSvL1Xxzs6WSifExwPBwawJRURGQethqSlTpmDWrFkIDw9Xnhs8eDCaN2+OKVOmICwsTKcBEpF+FLhHjbp26elSb838+dKxm5v0PXcYJiIjo3XPzcOHDxEYGJjnfPv27ZGWlqaToIhI/3L2qMmvr0UGadVUY2+n/0on5CQ2LJ1AREZM6+SmS5cuWL9+fZ7zGzduxNtvv62ToIhI/3L2qAGQJ8HJOf6muQvMP+4NvP02cPu2VDph926pfIKjY3GGS0SkMa2HpXx9fTF58mRER0fD398fAHD06FEcPnwYw4cPx5w5c5RtBw8erLtIiUjnAv3csKB3gzz73Lg6WGFhmUuo915f4P59lk4gohJF631uNF3iKZPJcO3atUIFpU/c54Yor9w7FFfKeIAG346FbOsW6cU6dQCWTiAiA9PrPjeJiYmFDoyIjJO5mQz+3uWAxYuBkSOBjAzAwgL46itg1CiWTiCiEoWb+BERcOkS0L8/cOCAdOzvDyxZAvj6GjYuIqJC0Dq5EULgjz/+wL59+5CSkgKFQqHy+rp163QWHBHp2b+lExARATx9Ks2niYoCwsIAc3NDR0dEVChaJzdDhw7FokWL0Lp1a7i4uEDGTbuISqZcpRMAAO3bA4sWAV5eBg2LiKiotE5uli9fjnXr1qFTp076iIeI9C0rS1r19N130g7D5coBs2YBffpwh2EiMglaJzdyuRxVqlTRRyxEpG8HD0ob8F26JB2//z4wdy7g4mLYuIiIdEjrTfwmTpyIyMhIZGZm6iMeItKH9HRpHk3LllJi4+YGrF8P/P47ExsiMjla99z06NEDq1atgrOzM7y8vGBhYaHyemxsrM6CIyId2LoV+Owz4NYt6bhfP2DaNO4wTEQmS+vkJjg4GCdPnkTv3r2LPKE4KioK69atw8WLF2FjY4NmzZph6tSpqFGjhkbXr169Gj179kTXrl2xYcOGQsdBZJLu3weGDgV++006rlJFKpvw1lsGDYuISN+0Tm62bNmCHTt24M033yzym+/fvx9hYWFo1KgRXrx4gbFjx6J9+/aIj4+Hra1tgddev34dX375JVq0aFHkOIhKsty7CzvbW6OxVzmY/74GGDyYpROIqFTSOrnx8PDQWdmC7du3qxwvW7YMzs7OOHnyJFq2bJnvddnZ2ejVqxciIyNx8OBBPHz4UCfxEJU0288lqdSFck2/j+n7FuLNi0elBiydQESlkNYTimfMmIGRI0fi+vXrOg8mLS0NAODk5FRgu0mTJsHZ2RkhISE6j4GopNh+LgmhK2KRlJYFmVCg16mt2PVTKN68eBTPzMrgctgI4MQJJjZEVOpo3XPTu3dvPHnyBD4+PihbtmyeCcWpqamFCkShUGDo0KFo3rw5/Pz88m136NAh/PTTT4iLi9Povk+fPsXTp0+Vx+np6YWKj8iYZCsEIjfFQwDwTr2DqO1z0fTWOQDASfeaGN1xMB5VrI5DZSzAfYaJqLTROrmZPXu2HsIAwsLCcO7cORw6dCjfNhkZGfj444/x448/4rXXXtPovlFRUYiMjNRVmERG4XhiKlL+eYyBf63HF4dWwvrFMzyxsMJ3LYPxa4POUJiZA2lZOJ6YCn+f8oYOl4ioWMmEEMLQQYSHh2Pjxo04cOAAvL29820XFxeH119/Hea5at7k1LYyMzNDQkICfHx8VK5R13Pj4eGhUcl0In3JMwnY2wnmZpqvPNy3ZideGzIIde5eBQAc8HodYwPDcVuuumfN9x/WR9f6FXUaOxGRIaSnp0Mul2v097tIVcGzsrLw7NkzlXPaJAxCCHz++edYv349oqOjC0xsAKBmzZo4e/asyrnx48cjIyMD33//PTw8PPJcY2VlBSsrK41jItK3lycBA4Cb3BoRQb4I9HMr+OKsLODrr9Fq6lTIsrPx0NoOX7/VH3/6vaW2dIKzvbWuwyciMnpaJzePHz/GqFGj8Pvvv+PBgwd5Xs/Oztb4XmFhYVi5ciU2btwIe3t7JCcnA5BKPNjY2AAA+vTpg4oVKyIqKgrW1tZ55uM4/rsRWUHzdIiMRc4k4Je7S5PTshC6IhYLejfIP8E5dEjagC8hATIAe/xaYnSr/rhnWy5PUxkAV7nUI0REVNpovVpq5MiR2Lt3LxYsWAArKyssWbIEkZGRcHd3x6+//qrVvRYsWIC0tDS0atUKbm5uyq81a9Yo29y8eRNJSUnahklkdHJPAn5ZzrnITfHIVrzUIiMDCA8HWrQAEhKUpROer1qN+7bl8HJ/Tc5xRJCvVkNdRESmQus5N5UrV8avv/6KVq1awcHBAbGxsahatSqWL1+OVatWYevWrfqKVSe0GbMj0qWYqw/Q88ejr2y3qn/T/yYBv6J0QpGGuIiIShC9zrlJTU1VVgV3cHBQLv1+8803ERoaWohwiUqHlIysVzfKaaeudMLixUCbNiptA/3c0M7XtUiTk4mITI3Ww1JVqlRBYmIiAGmC7++//w4A2LRpk3L+CxHlpdHkXiFQa/9WoFYtKbExMwOGDwfOns2T2OQwN5PB36c8utavCH+f8kxsiKjU07rn5pNPPsHp06cREBCA0aNHIygoCD/88AOeP3+OmTNn6iNGIpPQ2NsJbnJrJKdlqZ1345Z+H9P2LUT1nNIJfn5S6YTGjYs1TiKikq7I+9xcv35dOe+mbt26uopLbzjnhgwpZ7UU8N8kYplQoOfpHRiz72fYP8sELCyAr74CRo0CLC0NFywRkRHR5u+3UWziV5yY3JCh5Z4E7JV6B9/mKp2Apk2BJUuA2rUNGyQRkZHR5u+3xnNuYmJisHnzZpVzv/76K7y9veHs7IwBAwao7ARMROoF+rnh0PCW2C+OY8+vg9H01jmIsmWB77+X9rJhYkNEVCQaJzeTJk3C+fPnlcdnz55FSEgI2rZti9GjR2PTpk2IiorSS5BEJiUuDubN/OH53SSYP30KtGsH2blzwODBgDnLXBIRFZXGyU1cXBza5FqtsXr1ajRp0gQ//vgjhg0bhjlz5ihXThGRGllZwLhxQMOGwMmTQLlywLJlwI4dwCtKjxARkeY0Xi31zz//wMXlv6J8+/fvR8eOHZXHjRo1wq2cjcaISFWu0gkAgO7dgblzAVdXw8ZFRGSCNO65cXFxUe5v8+zZM8TGxqJp06bK1zMyMmBhYaH7CIlKspdLJ7i6AuvWAWvXMrEhItITjZObTp06YfTo0Th48CDGjBmDsmXLokWLFsrXz5w5Ax8fH70ESVQibd0qTQ6eN086DgkB4uOBd94xbFxERCZO42Gpr7/+Gu+++y4CAgJgZ2eHX375BZa59uD4+eef0b59e70ESVSiaFg6gYiI9EPrfW7S0tJgZ2cH85dWdaSmpsLOzk4l4TFG3OeG9EYIYM0a4PPPpQTHzExKciZNAmxtDR0dEVGJptfCmXK5XO15JycnbW9FZDpu3wZCQ4GcvaBYOoGIyGC0LpxJRLkoFMDChYCvr5TYWFgAkZHSUm8mNkREBqF1zw0R/evyZWl594ED0jFLJxARGQX23BBp68UL4LvvgLp1pcSmbFlg9myWTiAiMhLsuSHSRlyctKQ7VqrsjXbtgEWLuMMwEZERYc8NkSZyl06IjQUcHYGlS1k6gYjICLHnhuhVXi6d8N57wA8/cIdhIiIjxZ4bovykpwNhYXlLJ/zxBxMbIiIjxp4bInW2bgU++wzIKQYbEgJMmyZV8iYiIqPG5IYoN5ZOICIq8ZjcEAGvLJ2QrRA4npiKlIwsONtbo7G3E8zNZIaOmoiI1GByQ/SK0gnbzyUhclM8ktKylJe4ya0REeSLQD83Q0RMREQF4IRiKr0UCmmPmgJKJ2w/l4TQFbEqiQ0AJKdlIXRFLLafSzJE5EREVAAmN1Q6Xb4MvPWWNGk4I0MqnXDqFDBhAvBvZftshUDkpngINZfnnIvcFI9shboWRERkKExuqHTJXTph//4CSyccT0zN02OTmwCQlJaF44mp+o2ZiIi0wjk3VHpoWTohJSP/xKYw7YiIqHiw54ZMXyFLJzjbW2t0e03bERFR8WDPDZm2l0sndO8OzJ2r0Q7Djb2d4Ca3RnJaltp5NzIArnJpWTgRERkP9tyQacrIAMLD85ZOWLtW49IJ5mYyRAT5ApASmdxyjiOCfLnfDRGRkWFyQ6Zn61ZpcvC8edJxSAgQHw+8847Wtwr0c8OC3g3gKlcdenKVW2NB7wbc54aIyAhxWIpMRnbKPaQOCEOFjWsBAMLbG7Iffyxy6YRAPze083XlDsVERCUEkxsq+YRA3PSFqBw5FhUeP0S2zAw/N+yClZ1DMMrFF4E6eAtzMxn8fcrr4E5ERKRvTG6oZLt9Gykff4r60bsAABdf88SojoNx2r0GZJlA6IpYDh8REZUynHNDJdO/pRNE7dpwjt6FZ2ZlMPPNXgjqOxun3WsA4C7CRESlFXtuqOS5fBno3x/Yvx8yALHuNTAqcDAuV/DM0zT3LsIcViIiKh2Y3FDJ8eIFMHMmEBEhbcxXtizOho1Ed7wOhZl5gZdyF2EiotKDw1JUMsTFAU2aAKNGSYlNu3bAuXN4NDD8lYkNwF2EiYhKEyY3ZNxeUTohZxfh/BZlywC4cRdhIqJShckNGa9Dh4D69YEpU4DsbKl0woULQN++gExKZ7iLMBERvYzJDRkfdaUT/vwz39IJ3EWYiIhy44RiMi7btgEDBwK3bknHISHAtGlAuXIFXsZdhImIKAeTGzIO9+8DX3wBrFghHXt7A1qWTuAuwkREBHBYigxNCGD1asDXV0pszMyAYcOAs2eLXBOKiIhKJ/bckOHcvg0MGgRs2iQd164N/PSTtOSbiIiokNhzQ8Xv39IJqF1bSmwsLICJE6Wl3kxsiIioiNhzQ8UrV+kEAEDTpsCSJVKiQ0REpAPsuSG9y1YIxCTcxfmh46CoU1dKbMqWBWbPlvayYWJDREQ6xJ4b0qvt55KwctH/MGLtNNS+exUAcLxqA2TOnY+AQA5BERGR7jG5Ib3ZeTIRVz8fjZ+P/oEyQoE0K1t83aY//vRrA0Tfx4JKSdxgj4iIdM6gw1JRUVFo1KgR7O3t4ezsjG7duiEhIaHAa3788Ue0aNEC5cqVQ7ly5dC2bVscP368mCImTWUfOIgagQEIi/kdZYQCW6s3Q9t+C/FHnbYQ/5ZOiNwUj2yFMHCkRERkagya3Ozfvx9hYWE4evQodu3ahefPn6N9+/Z4/PhxvtdER0ejZ8+e2LdvH2JiYuDh4YH27dvjzp07xRg55evf0gnmAS3hef8WUmzLYWC3sRj0zljcs/tvl2EBICktC8cTUw0XKxERmSSZEMJo/ul87949ODs7Y//+/WjZsqVG12RnZ6NcuXL44Ycf0KdPn1e2T09Ph1wuR1paGhwcHIoaMuW2dSvw2WfK0gmr67bHlNafIt3aLt9Lvv+wPrrWr1hcERIRUQmlzd9vo5pzk5aWBgBwcnLS+JonT57g+fPn+V7z9OlTPH36VHmcnp5etCApLzWlE+InTsfoeKtXXupsb/3KNkRERNowmqXgCoUCQ4cORfPmzeHn56fxdaNGjYK7uzvatm2r9vWoqCjI5XLll4eHh65CpgJKJ9To/Q7c5NbIr2ylDICbXCpuSUREpEtGk9yEhYXh3LlzWL16tcbXfPvtt1i9ejXWr18Pa2v1PQBjxoxBWlqa8utWTrVpKprbt4GuXYGePYF796S9ao4cAWbMAGxtYW4mQ0SQLwDkSXByjiOCfFm1m4iIdM4okpvw8HBs3rwZ+/btQ6VKlTS6Zvr06fj222+xc+dO1K1bN992VlZWcHBwUPmiIlBXOiEyUm3phEA/Nyzo3QCuctXE01VujQW9G3AZOBER6YVB59wIIfD5559j/fr1iI6Ohre3t0bXfffdd5g8eTJ27NiBhg0b6jlKUipE6YRAPze083XF8cRUpGRkwdleGopijw0REemLQZObsLAwrFy5Ehs3boS9vT2Sk5MBAHK5HDY2NgCAPn36oGLFioiKigIATJ06FRMmTMDKlSvh5eWlvMbOzg52dvmvyqEiePECmDkTiIgAsrKk0glTpgDh4YC5+SsvNzeTwd+nfDEESkREZOCl4DKZ+n+9L126FH379gUAtGrVCl5eXli2bBkAwMvLCzdu3MhzTUREBCZOnPjK9+RScC3FxQEhIdKwEwC0bQssXgxo2MtGRESkCyVmKbgmeVV0dLTK8fXr1/UTDKnKygK+/hqYOhXIzgYcHYFZs4DgYCCfpJSIiMgYGNU+N2QkDh0C+vUDckphvPce8MMPgKurYeMiIiLSgFGsliIj8W/pBLRoISU2rq7An38Cf/zBxIaIiEoM9tyQZNs2YOBAZekEhIQA06YB5coVfB0REZGRYXJT2qkpnYDFi6WJw0RERCUQh6VKKyGANWvUlk5gYkNERCUZe25Ko9u3gUGDpB2GAWkTvp9+yrPDMBERUUnEnpvSRKGQhpw0KJ1ARERUUrHnprR4uXRCkyZSb00BpROIiIhKIvbcmLoXL6RVT3XrSolN2bLSZnyHDzOxISIik8SeG1N2+rS0pPvkSemYpROIiKgUYM+NKcrKAsaNAxo2lBIbR0dg6VJg504mNkREZPLYc2NqDh+Wemtyl06YOxdwczNsXERERMWEPTemIiMD+Pxz9aUTmNgQEVEpwp4bU8DSCUREREpMbkqyBw+k0gnLl0vHL5VOyFYIHE9MRUpGFpztrdHY2wnmZjIDBkxERKR/TG5KIiGA33+XhqHu3ZNKJwwdCkyaBNjaAgC2n0tC5KZ4JKVlKS9zk1sjIsgXgX4cpiIiItPFOTclzZ07QLduwIcfSolN7drAkSPAjBkqiU3oiliVxAYAktOyELoiFtvPJRkgcCIiouLB5KakyCmd4OsL/O9/+ZZOyFYIRG6Kh1Bzi5xzkZvika1Q14KIiKjk47BUSXDlilQ6ITpaOi6gdMLxxNQ8PTa5CQBJaVk4npgKf5/y+omXiIjIgNhzY8xySifUqSMlNhqUTkjJyD+xKUw7IiKikoY9N8aqkKUTnO2tNbq9pu2IiIhKGvbcGJusLGD8eNXSCT//rHHphMbeTnCTWyO/Bd8ySKumGns76TJqIiIio8HkxpgcPgy8/jowebI0JPXee8CFC8AnnwAyzfanMTeTISLIFwDyJDg5xxFBvtzvhoiITBaTG2OQu3TCxYuqpRNcXbW+XaCfGxb0bgBXuerQk6vcGgt6N+A+N0REZNI458bQtm+XSifcvCkdf/opMH16kUsnBPq5oZ2vK3coJiKiUofJjaG8onSCLpibybjcm4iISh0OSxU3IYA1a4BataTExsxMSnLOntVpYkNERFRaseemON25AwwaJO0wDEh71fz0k8oOw0RERFQ07LkpDupKJ0ycmKd0AhERERUde270TYvSCURERFR07LnRl0KUTiAiIqKiY8+NPhSydAIREREVHXtudKmIpROIiIio6NhzoyuHDwP9+kk7DANS6YQffijUDsNERERUeOy5KSodl04gIiKiomHPTVHoqXQCERERFR6Tm8IohtIJREREVDgcltKGEMDvv7N0AhERkRFjz42mWDqBiIioRGDPzauwdAIREVGJwp6bgrB0AhERUYnDnht1XryQVj2xdAIREVGJw54bdX7+GRgxQvqepROIiIhKFPbcqPPJJ0CrViydQEREVAKx50YdCwtg715AJjN0JERERKQl9tzkh4kNERFRicTkhoiIiEwKkxsiIiIyKUxuiIiIyKQwuSEiIiKTwuSGiIiITAqXgquRrRA4npiKlIwsONtbo7G3E8zNuHqKiIioJDBoz01UVBQaNWoEe3t7ODs7o1u3bkhISHjldWvXrkXNmjVhbW2NOnXqYOvWrTqLafu5JLw5dS96/ngUQ1bHoeePR/Hm1L3Yfi5JZ+9BRERE+mPQ5Gb//v0ICwvD0aNHsWvXLjx//hzt27fH48eP873myJEj6NmzJ0JCQnDq1Cl069YN3bp1w7lz54ocz/ZzSQhdEYuktCyV88lpWQhdEcsEh4iIqASQCSGEoYPIce/ePTg7O2P//v1o2bKl2jYffPABHj9+jM2bNyvPNW3aFPXr18fChQtf+R7p6emQy+VIS0uDg4OD8ny2QuDNqXvzJDY5ZABc5dY4NOotDlEREREVs/z+fqtjVBOK09LSAABOTk75tomJiUHbtm1VznXo0AExMTFq2z99+hTp6ekqX+ocT0zNN7EBAAEgKS0LxxNTX/EpiIiIyJCMJrlRKBQYOnQomjdvDj8/v3zbJScnw8XFReWci4sLkpOT1baPioqCXC5Xfnl4eKhtl5KRf2JTmHZERERkGEaT3ISFheHcuXNYvXq1Tu87ZswYpKWlKb9u3bqltp2zvbVG99O0HRERERmGUSwFDw8Px+bNm3HgwAFUqlSpwLaurq64e/euyrm7d+/C1dVVbXsrKytYWVm9MobG3k5wk1sjOS0L6iYh5cy5aeyd/5AZERERGZ5Be26EEAgPD8f69euxd+9eeHt7v/Iaf39/7NmzR+Xcrl274O/vX6RYzM1kiAjyBSAlMrnlHEcE+XIyMRERkZEzaHITFhaGFStWYOXKlbC3t0dycjKSk5ORmZmpbNOnTx+MGTNGeTxkyBBs374dM2bMwMWLFzFx4kScOHEC4eHhRY4n0M8NC3o3gKtcdejJVW6NBb0bINDPrcjvQURERPpl0KXgMpn6XpClS5eib9++AIBWrVrBy8sLy5YtU76+du1ajB8/HtevX0e1atXw3XffoVOnThq9pyZLybhDMRERkXHRZim4Ue1zUxy0eThERERkHErsPjdERERERcXkhoiIiEwKkxsiIiIyKUxuiIiIyKQwuSEiIiKTwuSGiIiITAqTGyIiIjIpTG6IiIjIpDC5ISIiIpNiFFXBi1POhszp6ekGjoSIiIg0lfN3W5PCCqUuucnIyAAAeHh4GDgSIiIi0lZGRgbkcnmBbUpdbSmFQoG///4b9vb2+RbuLG7p6enw8PDArVu3WO8qFz6X/PHZqMfnkj8+G/X4XPJnbM9GCIGMjAy4u7vDzKzgWTWlrufGzMwMlSpVMnQYajk4OBjFL5Cx4XPJH5+Nenwu+eOzUY/PJX/G9Gxe1WOTgxOKiYiIyKQwuSEiIiKTwuTGCFhZWSEiIgJWVlaGDsWo8Lnkj89GPT6X/PHZqMfnkr+S/GxK3YRiIiIiMm3suSEiIiKTwuSGiIiITAqTGyIiIjIpTG6IiIjIpDC5MYDJkyejWbNmKFu2LBwdHTW6pm/fvpDJZCpfgYGB+g3UAArzbIQQmDBhAtzc3GBjY4O2bdvi8uXL+g20mKWmpqJXr15wcHCAo6MjQkJC8OjRowKvadWqVZ7fmc8++6yYItafefPmwcvLC9bW1mjSpAmOHz9eYPu1a9eiZs2asLa2Rp06dbB169ZiirT4afNsli1bluf3w9rauhijLR4HDhxAUFAQ3N3dIZPJsGHDhldeEx0djQYNGsDKygpVq1bFsmXL9B5ncdP2uURHR+f5fZHJZEhOTi6egLXE5MYAnj17hvfffx+hoaFaXRcYGIikpCTl16pVq/QUoeEU5tl89913mDNnDhYuXIhjx47B1tYWHTp0QFZWlh4jLV69evXC+fPnsWvXLmzevBkHDhzAgAEDXnld//79VX5nvvvuu2KIVn/WrFmDYcOGISIiArGxsahXrx46dOiAlJQUte2PHDmCnj17IiQkBKdOnUK3bt3QrVs3nDt3rpgj1z9tnw0g7Tyb+/fjxo0bxRhx8Xj8+DHq1auHefPmadQ+MTERnTt3RuvWrREXF4ehQ4eiX79+2LFjh54jLV7aPpccCQkJKr8zzs7OeoqwiAQZzNKlS4VcLteobXBwsOjatate4zEmmj4bhUIhXF1dxbRp05TnHj58KKysrMSqVav0GGHxiY+PFwDEX3/9pTy3bds2IZPJxJ07d/K9LiAgQAwZMqQYIiw+jRs3FmFhYcrj7Oxs4e7uLqKiotS279Gjh+jcubPKuSZNmoiBAwfqNU5D0PbZaPPfH1MBQKxfv77ANiNHjhS1a9dWOffBBx+IDh066DEyw9Lkuezbt08AEP/880+xxFRU7LkpQaKjo+Hs7IwaNWogNDQUDx48MHRIBpeYmIjk5GS0bdtWeU4ul6NJkyaIiYkxYGS6ExMTA0dHRzRs2FB5rm3btjAzM8OxY8cKvPa3337Da6+9Bj8/P4wZMwZPnjzRd7h68+zZM5w8eVLlZ21mZoa2bdvm+7OOiYlRaQ8AHTp0MJnfjRyFeTYA8OjRI3h6esLDwwNdu3bF+fPniyNco1ZafmcKq379+nBzc0O7du1w+PBhQ4eTr1JXOLOkCgwMxLvvvgtvb29cvXoVY8eORceOHRETEwNzc3NDh2cwOeO9Li4uKuddXFyMdixYW8nJyXm6fsuUKQMnJ6cCP+NHH30ET09PuLu748yZMxg1ahQSEhKwbt06fYesF/fv30d2drban/XFixfVXpOcnGzSvxs5CvNsatSogZ9//hl169ZFWloapk+fjmbNmuH8+fNGW1y4OOT3O5Oeno7MzEzY2NgYKDLDcnNzw8KFC9GwYUM8ffoUS5YsQatWrXDs2DE0aNDA0OHlweRGR0aPHo2pU6cW2ObChQuoWbNmoe7/4YcfKr+vU6cO6tatCx8fH0RHR6NNmzaFumdx0fezKak0fS6FlXtOTp06deDm5oY2bdrg6tWr8PHxKfR9yTT4+/vD399fedysWTPUqlULixYtwtdff23AyMgY1ahRAzVq1FAeN2vWDFevXsWsWbOwfPlyA0amHpMbHRk+fDj69u1bYJsqVaro7P2qVKmC1157DVeuXDH65Eafz8bV1RUAcPfuXbi5uSnP3717F/Xr1y/UPYuLps/F1dU1z6TQFy9eIDU1Vfn5NdGkSRMAwJUrV0pkcvPaa6/B3Nwcd+/eVTl/9+7dfJ+Dq6urVu1LqsI8m5dZWFjg9ddfx5UrV/QRYomR3++Mg4NDqe21yU/jxo1x6NAhQ4ehFpMbHalQoQIqVKhQbO93+/ZtPHjwQOUPurHS57Px9vaGq6sr9uzZo0xm0tPTcezYMa1XoxU3TZ+Lv78/Hj58iJMnT+KNN94AAOzduxcKhUKZsGgiLi4OAErE74w6lpaWeOONN7Bnzx5069YNAKBQKLBnzx6Eh4ervcbf3x979uzB0KFDled27dql0mNhCgrzbF6WnZ2Ns2fPolOnTnqM1Pj5+/vn2S7AFH9ndCEuLs54/3ti6BnNpdGNGzfEqVOnRGRkpLCzsxOnTp0Sp06dEhkZGco2NWrUEOvWrRNCCJGRkSG+/PJLERMTIxITE8Xu3btFgwYNRLVq1URWVpahPoZeaPtshBDi22+/FY6OjmLjxo3izJkzomvXrsLb21tkZmYa4iPoRWBgoHj99dfFsWPHxKFDh0S1atVEz549la/fvn1b1KhRQxw7dkwIIcSVK1fEpEmTxIkTJ0RiYqLYuHGjqFKlimjZsqWhPoJOrF69WlhZWYlly5aJ+Ph4MWDAAOHo6CiSk5OFEEJ8/PHHYvTo0cr2hw8fFmXKlBHTp08XFy5cEBEREcLCwkKcPXvWUB9Bb7R9NpGRkWLHjh3i6tWr4uTJk+LDDz8U1tbW4vz584b6CHqRkZGh/O8IADFz5kxx6tQpcePGDSGEEKNHjxYff/yxsv21a9dE2bJlxYgRI8SFCxfEvHnzhLm5udi+fbuhPoJeaPtcZs2aJTZs2CAuX74szp49K4YMGSLMzMzE7t27DfURCsTkxgCCg4MFgDxf+/btU7YBIJYuXSqEEOLJkyeiffv2okKFCsLCwkJ4enqK/v37K/+jZUq0fTZCSMvBv/rqK+Hi4iKsrKxEmzZtREJCQvEHr0cPHjwQPXv2FHZ2dsLBwUF88sknKglfYmKiynO6efOmaNmypXBychJWVlaiatWqYsSIESItLc1An0B35s6dKypXriwsLS1F48aNxdGjR5WvBQQEiODgYJX2v//+u6hevbqwtLQUtWvXFlu2bCnmiIuPNs9m6NChyrYuLi6iU6dOIjY21gBR61fOEuaXv3KeRXBwsAgICMhzTf369YWlpaWoUqWKyn9vTIW2z2Xq1KnCx8dHWFtbCycnJ9GqVSuxd+9ewwSvAZkQQhRbNxERERGRnnGfGyIiIjIpTG6IiIjIpDC5ISIiIpPC5IaIiIhMCpMbIiIiMilMboiIiMikMLkhIiIik8LkhoiIiEwKkxsiExIdHQ2ZTIaHDx8aOhStyGQybNiwQWf38/LywuzZs3V2P0O5fv06ZDKZsi5YSf35EhU3JjdEJYRMJivwa+LEiYYO8ZUmTpyotlp7UlISOnbsWKyxpKamYujQofD09ISlpSXc3d3x6aef4ubNm8UaR46+ffsqi17m8PDwQFJSEvz8/AwSE1FJxargRCVEUlKS8vs1a9ZgwoQJSEhIUJ6zs7PDiRMnDBEanj17BktLy0Jf7+rqqsNoXi01NRVNmzaFpaUlFi5ciNq1a+P69esYP348GjVqhJiYGFSpUqVYY1LH3Ny82J8NkSlgzw1RCeHq6qr8ksvlkMlkKufs7OyUbU+ePImGDRuibNmyaNasmUoSBAAbN25EgwYNYG1tjSpVqiAyMhIvXrxQvn7z5k107doVdnZ2cHBwQI8ePXD37l3l6zk9MEuWLIG3tzesra0BAA8fPkS/fv1QoUIFODg44K233sLp06cBAMuWLUNkZCROnz6t7G1atmwZgLzDUrdv30bPnj3h5OQEW1tbNGzYEMeOHQMAXL16FV27doWLiwvs7OzQqFEj7N69W6tnOW7cOPz999/YvXs3OnbsiMqVK6Nly5bYsWMHLCwsEBYWpmyrboirfv36Kj1lM2fORJ06dWBrawsPDw8MGjQIjx49Ur6+bNkyODo6YseOHahVqxbs7OwQGBioTFgnTpyIX375BRs3blQ+m+jo6DzDUuocOnQILVq0gI2NDTw8PDB48GA8fvxY+fr8+fNRrVo1WFtbw8XFBd27d9fqWRGVRExuiEzQuHHjMGPGDJw4cQJlypTBp59+qnzt4MGD6NOnD4YMGYL4+HgsWrQIy5Ytw+TJkwEACoUCXbt2RWpqKvbv349du3bh2rVr+OCDD1Te48qVK/jzzz+xbt065R/f999/HykpKdi2bRtOnjyJBg0aoE2bNkhNTcUHH3yA4cOHo3bt2khKSkJSUlKeewLAo0ePEBAQgDt37uB///sfTp8+jZEjR0KhUChf79SpE/bs2YNTp04hMDAQQUFBGg8nKRQKrF69Gr169crTK2JjY4NBgwZhx44dSE1N1fh5m5mZYc6cOTh//jx++eUX7N27FyNHjlRp8+TJE0yfPh3Lly/HgQMHcPPmTXz55ZcAgC+//BI9evRQJjxJSUlo1qzZK9/36tWrCAwMxHvvvYczZ85gzZo1OHToEMLDwwEAJ06cwODBgzFp0iQkJCRg+/btaNmypcafi6jEMnRZciLS3tKlS4VcLs9zft++fQKA2L17t/Lcli1bBACRmZkphBCiTZs2YsqUKSrXLV++XLi5uQkhhNi5c6cwNzcXN2/eVL5+/vx5AUAcP35cCCFERESEsLCwECkpKco2Bw8eFA4ODiIrK0vl3j4+PmLRokXK6+rVq5cnbgBi/fr1QgghFi1aJOzt7cWDBw80fBpC1K5dW8ydO1d57OnpKWbNmqW2bXJysgCQ7+vr1q0TAMSxY8fyvVe9evVEREREvvGsXbtWlC9fXnm8dOlSAUBcuXJFeW7evHnCxcVFeRwcHCy6du2qcp/ExEQBQJw6dUoI8d/P959//hFCCBESEiIGDBigcs3BgweFmZmZyMzMFH/++adwcHAQ6enp+cZKZIo454bIBNWtW1f5vZubGwAgJSUFlStXxunTp3H48GFlTw0AZGdnIysrC0+ePMGFCxfg4eEBDw8P5eu+vr5wdHTEhQsX0KhRIwCAp6cnKlSooGxz+vRpPHr0COXLl1eJJTMzE1evXtU49ri4OLz++utwcnJS+/qjR48wceJEbNmyBUlJSXjx4gUyMzO1nggshCjwdW3mEO3evRtRUVG4ePEi0tPT8eLFC+XzLFu2LACgbNmy8PHxUV7j5uaGlJQUrWJ+2enTp3HmzBn89ttvynNCCCgUCiQmJqJdu3bw9PRElSpVEBgYiMDAQLzzzjvKmIhMFZMbIhNkYWGh/F4mkwGAyrBOZGQk3n333TzX5cyd0YStra3K8aNHj+Dm5obo6Og8bR0dHTW+r42NTYGvf/nll9i1axemT5+OqlWrwsbGBt27d8ezZ880un+FChWUiZo6Fy5cQJkyZeDt7Q1AGnJ6ORF6/vy58vvr16/j7bffRmhoKCZPngwnJyccOnQIISEhePbsmTKRyP0zAaSfy6sSrFd59OgRBg4ciMGDB+d5rXLlyrC0tERsbCyio6Oxc+dOTJgwARMnTsRff/2l1c+EqKRhckNUyjRo0AAJCQmoWrWq2tdr1aqFW7du4datW8rem/j4eDx8+BC+vr4F3jc5ORllypSBl5eX2jaWlpbIzs4uML66detiyZIlSE1NVdt7c/jwYfTt2xfvvPMOAOkP/PXr1wu8Z25mZmbo0aMHfvvtN0yaNEll3k1mZibmz5+Pd955B3K5HICUDOVeqZaeno7ExETl8cmTJ6FQKDBjxgyYmUnTGH///XeN48mhybN5WYMGDRAfH5/vzxIAypQpg7Zt26Jt27aIiIiAo6Mj9u7dqza5JTIVnFBMVMpMmDABv/76KyIjI3H+/HlcuHABq1evxvjx4wEAbdu2RZ06ddCrVy/Exsbi+PHj6NOnDwICAtCwYcN879u2bVv4+/ujW7du2LlzJ65fv44jR45g3LhxyiXqXl5eSExMRFxcHO7fv4+nT5/muU/Pnj3h6uqKbt264fDhw7h27Rr+/PNPxMTEAACqVaumnMR8+vRpfPTRR8peKU1NnjwZrq6uaNeuHbZt24Zbt27hwIED6NChA8zMzPD9998r27711ltYvnw5Dh48iLNnzyI4OBjm5ubK16tWrYrnz59j7ty5uHbtGpYvX46FCxdqFU/Oszlz5gwSEhJw//59ld6h/IwaNQpHjhxBeHg44uLicPnyZWzcuFE5oXjz5s2YM2cO4uLicOPGDfz6669QKBSoUaOG1vERlSRMbohKmQ4dOmDz5s3YuXMnGjVqhKZNm2LWrFnw9PQEIA2XbNy4EeXKlUPLli3Rtm1bVKlSBWvWrCnwvjKZDFu3bkXLli3xySefoHr16vjwww9x48YNuLi4AADee+89BAYGonXr1qhQoQJWrVqV5z6WlpbYuXMnnJ2d0alTJ9SpUwfffvutMqGYOXMmypUrh2bNmiEoKAgdOnRAgwYNtHoGr732Go4ePYrWrVtj4MCB8Pb2RkBAALKzsxEXF6ecpwQAY8aMQUBAAN5++2107twZ3bp1U5k7U69ePcycORNTp06Fn58ffvvtN0RFRWkVDwD0798fNWrUQMOGDVGhQgUcPnz4ldfUrVsX+/fvx6VLl9CiRQu8/vrrmDBhAtzd3QFIw4Hr1q3DW2+9hVq1amHhwoVYtWoVateurXV8RCWJTBR10JeIyAT89NNPGDRoENasWZNnp2AiKlnYc0NEBCAkJASrV6/GhQsXkJmZaehwiKgI2HNDREREJoU9N0RERGRSmNwQERGRSWFyQ0RERCaFyQ0RERGZFCY3REREZFKY3BAREZFJYXJDREREJoXJDREREZkUJjdERERkUv4PovGjPKdFlxIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smf.qqplot(df1['Delivery_Time'], line = 'r')\n",
    "plt.title('No transformation')\n",
    "smf.qqplot(np.log(df1['Delivery_Time']), line = 'r')\n",
    "plt.title('Log transformation')\n",
    "smf.qqplot(np.sqrt(df1['Delivery_Time']), line = 'r')\n",
    "plt.title('Square root transformation')\n",
    "smf.qqplot(np.cbrt(df1['Delivery_Time']), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b42bb2df-f365-470b-bfab-231f234019a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Before Transformation','After Transformation']\n",
    "sns.distplot(df1['Sorting_Time'], bins = 10, kde = True)\n",
    "sns.distplot(np.log(df1['Sorting_Time']), bins = 10, kde = True)\n",
    "plt.legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0b30adcc-7be2-43d5-a3c8-74eea93eb872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smf.qqplot(df1['Sorting_Time'], line = 'r')\n",
    "plt.title('No transformation')\n",
    "smf.qqplot(np.log(df1['Sorting_Time']), line = 'r')\n",
    "plt.title('Log transformation')\n",
    "smf.qqplot(np.sqrt(df1['Sorting_Time']), line = 'r')\n",
    "plt.title('square root transformation')\n",
    "smf.qqplot(np.cbrt(df1['Sorting_Time']), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5076c64d-9d4c-4383-b1aa-c5bbe904991c",
   "metadata": {},
   "source": [
    "Important Note:\n",
    "+ We only Perform any data transformation when the data is skewed or not normal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd34863b-5689-45ec-851b-101ad06c24a9",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7bafc5f-1326-4175-b0e6-973675a3726b",
   "metadata": {},
   "source": [
    "Fitting a Linear Regression Model\n",
    "\n",
    "Using Ordinary least squares (OLS) regression\n",
    "\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": 41,
   "id": "6e5fd0f5-0810-41f6-ae7b-a0eedc0378fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sm.ols('Delivery_Time~Sorting_Time', data = df1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "19b16090-28e9-46be-bca6-9fc7a3b69e06",
   "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>Tue, 05 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>3.98e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:54:13</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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &  Delivery\\_Time  & \\textbf{  R-squared:         } &     0.682   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.666   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     40.80   \\\\\n",
       "\\textbf{Date:}             & Tue, 05 Mar 2024 & \\textbf{  Prob (F-statistic):} &  3.98e-06   \\\\\n",
       "\\textbf{Time:}             &     20:54:13     & \\textbf{  Log-Likelihood:    } &   -51.357   \\\\\n",
       "\\textbf{No. Observations:} &          21      & \\textbf{  AIC:               } &     106.7   \\\\\n",
       "\\textbf{Df Residuals:}     &          19      & \\textbf{  BIC:               } &     108.8   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                       & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}     &       6.5827  &        1.722     &     3.823  &         0.001        &        2.979    &       10.186     \\\\\n",
       "\\textbf{Sorting\\_Time} &       1.6490  &        0.258     &     6.387  &         0.000        &        1.109    &        2.189     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  3.649 & \\textbf{  Durbin-Watson:     } &    1.248  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.161 & \\textbf{  Jarque-Bera (JB):  } &    2.086  \\\\\n",
       "\\textbf{Skew:}          &  0.750 & \\textbf{  Prob(JB):          } &    0.352  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.367 & \\textbf{  Cond. No.          } &     18.3  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [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:                Tue, 05 Mar 2024   Prob (F-statistic):           3.98e-06\n",
       "Time:                        20:54:13   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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607ad4ef-dca0-4824-b1ff-05698ad73bce",
   "metadata": {},
   "source": [
    "**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.\n",
    "\n",
    "**Square Root transformation on data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ed199fe4-ba76-4f7a-a71a-31183a14f19e",
   "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>Tue, 05 Mar 2024</td>    <th>  Prob (F-statistic):</th> <td>8.48e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                    <td>20:56:51</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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}         & np.sqrt(Delivery\\_Time) & \\textbf{  R-squared:         } &     0.729   \\\\\n",
       "\\textbf{Model:}                 &           OLS           & \\textbf{  Adj. R-squared:    } &     0.715   \\\\\n",
       "\\textbf{Method:}                &      Least Squares      & \\textbf{  F-statistic:       } &     51.16   \\\\\n",
       "\\textbf{Date:}                  &     Tue, 05 Mar 2024    & \\textbf{  Prob (F-statistic):} &  8.48e-07   \\\\\n",
       "\\textbf{Time:}                  &         20:56:51        & \\textbf{  Log-Likelihood:    } &   -5.7320   \\\\\n",
       "\\textbf{No. Observations:}      &              21         & \\textbf{  AIC:               } &     15.46   \\\\\n",
       "\\textbf{Df Residuals:}          &              19         & \\textbf{  BIC:               } &     17.55   \\\\\n",
       "\\textbf{Df Model:}              &               1         & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}       &        nonrobust        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}              &       1.6135  &        0.349     &     4.628  &         0.000        &        0.884    &        2.343     \\\\\n",
       "\\textbf{np.sqrt(Sorting\\_Time)} &       1.0022  &        0.140     &     7.153  &         0.000        &        0.709    &        1.295     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  2.869 & \\textbf{  Durbin-Watson:     } &    1.279  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.238 & \\textbf{  Jarque-Bera (JB):  } &    1.685  \\\\\n",
       "\\textbf{Skew:}          &  0.690 & \\textbf{  Prob(JB):          } &    0.431  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.150 & \\textbf{  Cond. No.          } &     13.7  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [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:                    Tue, 05 Mar 2024   Prob (F-statistic):           8.48e-07\n",
       "Time:                            20:56:51   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": 43,
     "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": "e6df3dbd-004f-4502-b629-657d3a27ce65",
   "metadata": {},
   "source": [
    "**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.\n",
    "\n",
    "**Cube Root transformation on Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "68150e79-5b7e-484c-80ce-ef7d602a3a61",
   "metadata": {},
   "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>Tue, 05 Mar 2024</td>    <th>  Prob (F-statistic):</th> <td>4.90e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                    <td>21:01:55</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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}         & np.cbrt(Delivery\\_Time) & \\textbf{  R-squared:         } &     0.744   \\\\\n",
       "\\textbf{Model:}                 &           OLS           & \\textbf{  Adj. R-squared:    } &     0.731   \\\\\n",
       "\\textbf{Method:}                &      Least Squares      & \\textbf{  F-statistic:       } &     55.25   \\\\\n",
       "\\textbf{Date:}                  &     Tue, 05 Mar 2024    & \\textbf{  Prob (F-statistic):} &  4.90e-07   \\\\\n",
       "\\textbf{Time:}                  &         21:01:55        & \\textbf{  Log-Likelihood:    } &    13.035   \\\\\n",
       "\\textbf{No. Observations:}      &              21         & \\textbf{  AIC:               } &    -22.07   \\\\\n",
       "\\textbf{Df Residuals:}          &              19         & \\textbf{  BIC:               } &    -19.98   \\\\\n",
       "\\textbf{Df Model:}              &               1         & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}       &        nonrobust        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}              &       1.0136  &        0.207     &     4.900  &         0.000        &        0.581    &        1.447     \\\\\n",
       "\\textbf{np.cbrt(Sorting\\_Time)} &       0.8456  &        0.114     &     7.433  &         0.000        &        0.607    &        1.084     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  2.570 & \\textbf{  Durbin-Watson:     } &    1.292  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.277 & \\textbf{  Jarque-Bera (JB):  } &    1.532  \\\\\n",
       "\\textbf{Skew:}          &  0.661 & \\textbf{  Prob(JB):          } &    0.465  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.075 & \\textbf{  Cond. No.          } &     16.4  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [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:                    Tue, 05 Mar 2024   Prob (F-statistic):           4.90e-07\n",
       "Time:                            21:01:55   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": 44,
     "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": "97b10388-bc9e-4be0-b61b-a98eed3ab6b1",
   "metadata": {},
   "source": [
    "**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.\n",
    "\n",
    "**Log transformation on Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ae101776-ede9-4b20-830e-64883134f34f",
   "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>Tue, 05 Mar 2024</td>    <th>  Prob (F-statistic):</th> <td>1.60e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>21:06:33</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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}        & np.log(Delivery\\_Time) & \\textbf{  R-squared:         } &     0.772   \\\\\n",
       "\\textbf{Model:}                &          OLS           & \\textbf{  Adj. R-squared:    } &     0.760   \\\\\n",
       "\\textbf{Method:}               &     Least Squares      & \\textbf{  F-statistic:       } &     64.39   \\\\\n",
       "\\textbf{Date:}                 &    Tue, 05 Mar 2024    & \\textbf{  Prob (F-statistic):} &  1.60e-07   \\\\\n",
       "\\textbf{Time:}                 &        21:06:33        & \\textbf{  Log-Likelihood:    } &    10.291   \\\\\n",
       "\\textbf{No. Observations:}     &             21         & \\textbf{  AIC:               } &    -16.58   \\\\\n",
       "\\textbf{Df Residuals:}         &             19         & \\textbf{  BIC:               } &    -14.49   \\\\\n",
       "\\textbf{Df Model:}             &              1         & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}      &       nonrobust        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}             &       1.7420  &        0.133     &    13.086  &         0.000        &        1.463    &        2.021     \\\\\n",
       "\\textbf{np.log(Sorting\\_Time)} &       0.5975  &        0.074     &     8.024  &         0.000        &        0.442    &        0.753     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  1.871 & \\textbf{  Durbin-Watson:     } &    1.322  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.392 & \\textbf{  Jarque-Bera (JB):  } &    1.170  \\\\\n",
       "\\textbf{Skew:}          &  0.577 & \\textbf{  Prob(JB):          } &    0.557  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.916 & \\textbf{  Cond. No.          } &     9.08  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [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:                   Tue, 05 Mar 2024   Prob (F-statistic):           1.60e-07\n",
       "Time:                           21:06:33   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": 45,
     "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": "a908307a-498a-4d6e-8281-351186a37835",
   "metadata": {},
   "source": [
    "**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.\n",
    "\n",
    "Model Testing\n",
    "As Y = Beta0 + Beta1*(X)\n",
    "Finding Coefficient Parameters (Beta0 and Beta1 values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2254d097-0d95-4d85-a7a6-c72e821bf7ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept       6.582734\n",
       "Sorting_Time    1.649020\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9752ab-5545-4863-bc5d-ed57f4c3368a",
   "metadata": {},
   "source": [
    "Here, (Intercept) Beta0 value = 6.58 & (Sorting Time) Beta1 value = 1.64\n",
    "\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",
    "\n",
    "Null Hypothesis as Beta1=0 (No Slope) and Alternate Hypthesis as Beta1≠0 (Some or significant Slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b918c261-064a-4200-bc6f-f4e9dcf34b17",
   "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": "495afd34-9e94-4055-9b28-4950d355e72c",
   "metadata": {},
   "source": [
    "(Intercept) Beta0: tvalue=3.82 , pvalue=0.001147\n",
    "\n",
    "(daily)     Beta1: tvalue=6.38, pvalue=0.000004\n",
    "\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",
    "\n",
    "R-squared measures the strength of the relationship between your model and the dependent variable on a 0 – 100% scale.\n",
    "\n",
    "Measure goodness-of-fit by finding rsquared values (percentage of variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1bad2d53-5fcb-47a2-9bd9-16f1536a0d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6822714748417231, 0.6655489208860244)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rsquared,model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a0bf141-0a95-4efc-8736-8c82c97d8c12",
   "metadata": {},
   "source": [
    "**Determination Coefficient = rsquared value = 0.68 ; very good fit >= 85%**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a181ca35-07ec-4f62-b74d-248414585a22",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-8\n",
    "\n",
    "Residual Analysis\n",
    "\n",
    "Test for Normality of Residuals (Q-Q Plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a44c94d4-d58c-45b9-b4fa-c079f4fb048a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 51,
   "id": "01855f07-9a58-4cc4-ad1a-bebf7412b29e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "7eaebfab-b33f-45ea-aadd-7886e96c42e8",
   "metadata": {},
   "source": [
    "**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\n",
    "\n",
    "**Residual Plot to check Homoscedasticity or Hetroscedasticity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "55bad756-83ca-4c1e-93e9-df578bc4e691",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardized_values( vals ):\n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "db96e77f-1212-4b24-865e-a5c71c8d3963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 54,
   "id": "079932ce-d667-4a84-bff8-351e4d7557ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "c10f56c0-3688-4d61-a20c-6fe166ea2e70",
   "metadata": {},
   "source": [
    "**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": "227d3bb6-1152-4b4c-9f16-1b2297acfb6f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-9\n",
    "\n",
    "Model Validation\n",
    "\n",
    "Comparing different models with respect to their Root Mean Squared Errors\n",
    "\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": 61,
   "id": "a6e2eb9b-1a8a-432a-8863-a86bc1692a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "046c7018-76ca-44bb-838f-8e303965296f",
   "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": 63,
   "id": "994ca331-74dc-4a8b-9c4b-d56f617c074b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model= 2.934903768890139 \n",
      "model1= 2.762294279320867 \n",
      "model2= 2.7555843098935733 \n",
      "model3= 2.745828897614548\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": 64,
   "id": "a09c1246-e489-4368-bffd-c10500a9ab9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'model2'"
      ]
     },
     "execution_count": 64,
     "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": "d043421f-20df-4f96-9376-bf9407b5524c",
   "metadata": {},
   "source": [
    "**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": "cda83605-872c-40be-aba6-77ea93f5d6a5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='Green'>Step-10\n",
    "\n",
    "Predicting values from Model with Log Transformation on the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0820f67f-3af1-4832-8301-f1d92215f93d",
   "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>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>17.035997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>13.50</td>\n",
       "      <td>10.547128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>19.75</td>\n",
       "      <td>12.808396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>24.00</td>\n",
       "      <td>15.997918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>29.00</td>\n",
       "      <td>17.035997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15.35</td>\n",
       "      <td>12.808396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>19.00</td>\n",
       "      <td>13.889274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.328887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>17.90</td>\n",
       "      <td>17.035997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>18.75</td>\n",
       "      <td>15.997918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>19.83</td>\n",
       "      <td>14.950443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>10.75</td>\n",
       "      <td>10.547128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>16.68</td>\n",
       "      <td>13.889274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>11.50</td>\n",
       "      <td>9.328887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>12.03</td>\n",
       "      <td>9.328887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>14.88</td>\n",
       "      <td>10.547128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>13.75</td>\n",
       "      <td>12.808396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>18.11</td>\n",
       "      <td>13.889274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.996000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7</td>\n",
       "      <td>17.83</td>\n",
       "      <td>13.889274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>21.50</td>\n",
       "      <td>11.698973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sorting_Time  Delivery_Time  Predicted_Delivery_Time\n",
       "0             10          21.00                17.035997\n",
       "1              4          13.50                10.547128\n",
       "2              6          19.75                12.808396\n",
       "3              9          24.00                15.997918\n",
       "4             10          29.00                17.035997\n",
       "5              6          15.35                12.808396\n",
       "6              7          19.00                13.889274\n",
       "7              3           9.50                 9.328887\n",
       "8             10          17.90                17.035997\n",
       "9              9          18.75                15.997918\n",
       "10             8          19.83                14.950443\n",
       "11             4          10.75                10.547128\n",
       "12             7          16.68                13.889274\n",
       "13             3          11.50                 9.328887\n",
       "14             3          12.03                 9.328887\n",
       "15             4          14.88                10.547128\n",
       "16             6          13.75                12.808396\n",
       "17             7          18.11                13.889274\n",
       "18             2           8.00                 7.996000\n",
       "19             7          17.83                13.889274\n",
       "20             5          21.50                11.698973"
      ]
     },
     "execution_count": 65,
     "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": "cbb0acac-b883-4b9c-9861-9f93169e3769",
   "metadata": {},
   "source": [
    "Predicitng from Original Model without any data transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d8bb8e5d-356c-41f3-a41a-61a1be1ab803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 66,
     "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"
   ]
  }
 ],
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