Simple Linear Regression assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyforest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1Q  Delivery_time -> Predict delivery time using sorting time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=pd.read_csv(\"delivery_time.csv\")\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Performing EDA on data i.e renaming column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_eda=data1.rename({\"Delivery Time\":\"Delivery_Time\",\"Sorting Time\":\"Sorting_Time\"},axis=1)\n",
    "data_eda.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we can see there are no NaN values in our data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21 entries, 0 to 20\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Delivery_Time  21 non-null     float64\n",
      " 1   Sorting_Time   21 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 464.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data_eda.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are no outliers in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_eda.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our dataset is linear in +ve direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x223ee6e4c88>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data_eda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Python\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Delivery_Time', ylabel='Density'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_eda['Delivery_Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Python\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sorting_Time', ylabel='Density'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_eda['Sorting_Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# r value is high therefore correlation is high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_eda.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bulding model by ols method for prediction of delicery time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Sat, 08 May 2021</td> <th>  Prob (F-statistic):</th> <td>3.98e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>18:46:58</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/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          Delivery_Time   R-squared:                       0.682\n",
       "Model:                            OLS   Adj. R-squared:                  0.666\n",
       "Method:                 Least Squares   F-statistic:                     40.80\n",
       "Date:                Sat, 08 May 2021   Prob (F-statistic):           3.98e-06\n",
       "Time:                        18:46:58   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",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "model=smf.ols(\"Delivery_Time~Sorting_Time\",data = data_eda).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Delivery_Time', ylabel='Sorting_Time'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"Delivery_Time\", y=\"Sorting_Time\", data=data_eda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pvalues "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept       0.001147\n",
       "Sorting_Time    0.000004\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.pvalues  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept       6.582734\n",
       "Sorting_Time    1.649020\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "\n",
    "#  Creating a random dataset of sorting time \n",
    "#  For this random dataset of sorting time we are predicting delivery time by using the model we\n",
    "   build above.\n",
    "   \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "randdata=pd.Series([10,15,12,6,8,10,4,7,5,5,12,20,10,6,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sorting_Time\n",
       "0            10\n",
       "1            15\n",
       "2            12\n",
       "3             6\n",
       "4             8"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_data=pd.DataFrame(randdata,columns=['Sorting_Time'])\n",
    "predict_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting delivery time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     23.072933\n",
       "1     31.318032\n",
       "2     26.370973\n",
       "3     16.476853\n",
       "4     19.774893\n",
       "5     23.072933\n",
       "6     13.178814\n",
       "7     18.125873\n",
       "8     14.827833\n",
       "9     14.827833\n",
       "10    26.370973\n",
       "11    39.563132\n",
       "12    23.072933\n",
       "13    16.476853\n",
       "14    13.178814\n",
       "15    14.827833\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_model=model.predict(predict_data)\n",
    "predict_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2Q  Salary_hike -> Build a prediction model for Salary_hike\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
       "3              2.0  43525.0\n",
       "4              2.2  39891.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=pd.read_csv(\"Salary_Data.csv\")\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we can see there are no NaN values in our data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 608.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are no outliers in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our dataset is linear in +ve direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x223effe5988>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YBzRumGXWhiqt0KunzoVX9rW3vEfK3yaro3wl8FrgOeA/U9t5OfbLLFeDXaHnlX3tL9cLfRHxXbJNUieQFQ+6lmwF3tNkI2izYWmwK/S8sq/9FWH2xfNk5TpHkwXn0s2FhGzYqmeFXm9vsHzDNu58dCPLN2yjtze8sq8D5J1TngN8BVgIzIyIZ/Psj1lRVFuhJ8TyDdvo2X8cP/rNuj3SFK+ctLdX9rW5vEfK5wAfjIizHZDNXlJpq6dPHTeDT1+7hBPm38Evlm+qmKYY0YW3iGpzuY6UI+LoPN/frKjKp8A9vmk79618hivufJw1m7M0xOLHn6qYpli7ZYenzrW5vGdfmFkVpRV667bsYP6ty3Z7rjeomqYovc5bRLWnvNMXZtaPSkusf/ir1Zz//tc7TdGBPFI2K7hKS6zPmvNqjn/1JF43eV+nKTqMg7JZwdVaYu00RedxUDZrAw7Aw4dzymZmBeKgbGZWIA7KZmYF4pyyWYO4ZKY1goOyWQO4ZKY1SkemLyRNlfQTSQ9KWirpU3n3yTqbS2Zao3TqSPlF4G8i4l5JewO/lHRLRDyYd8esM1Urmbluy45dzzulYfXoyKAcEWuANen+Vkm/ASYDDsrWFNVKbb6wMzhh/h1OaVjdOjJ9UU7SNOANwKKcu2IdrFKpzfPf/3o+f8P9TmnYgHTkSLlE0gTge8CnI2JLhefnAnMBenp6Wtw7axf1fE8qLYXetP05Ht/0u92OK+0C4pV5Vk3HjpQljSILyFdFxPcrHRMRCyJiVkTM6u7ubm0HrW3U+z0pLYWePf1ApndPYOL40XtUd/MuINafjgzKkgRcAvwmIr6Sd39seKqU0nB5TetPp6Yv3gJ8GLhf0pLU9ncRcWN+XbLhplZ1N7NqOjIoR8TPAX/zren6W8Xn6m42UB0ZlM1awav4rBk6Mqds1gpexWfN4KBsNkjVVvGt37qj6mt6e4PlG7Zx56MbWb5hG7290exuWptx+sJskKqt4qs25c3pDquHR8pmgzTQKW9Od1g9PFI2G6TyKW/rtuxg3F4jeH5nLys2ba849a1WusOzM6zEQdlsCLq6xLSJ4/nt2q187LJ7aqYlBprusOHJ6QuzIao3LeEVflYPj5TNhqjetIRX+Fk9HJTNhmggaQmv8LP+OH1hNkROS1gjeaRsNkROS1gjOSibNYDTEtYoTl+YmRWIg7KZWYEowgVRACRtAB7Pux9NciCwMe9O5GCgn3tjRMypdUCTvydF+jsVpS9F6Qe81Jd+vydD4aA8DEhaHBGz8u5Hq7Xb5y5Sf4vSl6L0A1rXF6cvzMwKxEHZzKxAHJSHhwV5dyAn7fa5i9TfovSlKP2AFvXFOWUzswLxSNnMrEAclM3MCsRBOZkzZ04Avg3vW7/8PfGNJnNQTjZuLMr8dCsyf0+s2RyUzcwKxEHZzKxAXLrTOl5vb7Bi03bWbdnBpH1c69iKzUHZOlpvb3DT0rW7NjY9dOJYzjvpdYwaIQdoKyQHZeto5TtNH7zvGE6Z1cPcKxez44XeXds2zTn8ZQ7MVhhNyylLulTSekkPlLVdIOm3kn4t6QeS9it77nOSlkl6SNI7y9rnpLZlks4uaz9M0qLUfq2kvVL76PR4WXp+WrM+oxVf+U7TfzxzCvNve2TX4x0v9PKZ65awYtP2PLu4h8lTe5DUsNvkqT15fyQbgGaOlC8Dvg5cUdZ2C/C5iHhR0vnA54CzJL0GOBU4HDgE+LGkV6TXfAN4B7AKuEfSwoh4EDgf+LeIuEbShcAZwDfTz6cj4uWSTk3HndLEz2kFVr7TtMRuO05D9nj91h2F2sbpyVUrOeVbv2jY+a79+Jsbdi5rvqaNlCPiZ8BTfdp+FBEvpod3AVPS/ZOAayLiuYh4DFgGHJluyyJieUQ8D1wDnCRJwLHA9en1lwPvKzvX5en+9cBx6XgbhirtNF1uzKguDtp7TB5dM6sozylxfwb8T7o/GVhZ9tyq1FatfSLwTFmAL7Xvdq70/OZ0vA1DpZ2mb5x3NMe84kDOf//rdwvQXzn5CKZNHJ9zL81eksuFPknnAC8CV+Xx/mX9mAvMBejpcd6tU5XvND2zN3jd5H1Zv3UHB+1d3+wLf0+slVo+Upb0UeBE4EPxUt3Q1cDUssOmpLZq7ZuA/SSN7NO+27nS8/um4/cQEQsiYlZEzOru7h7iJ7N2UArQs6cfyPTuCXXNuvD3xFqppUFZ0hzgs8B7I+LZsqcWAqemmROHATOAu4F7gBlppsVeZBcDF6Zg/hPgA+n1pwM3lJ3r9HT/A8Bt4aLRHam3N1i+YRt3PrqR5Ru20dvrP7O1v6alLyRdDRwDHChpFXAu2WyL0cAt6drbXRHxFxGxVNJ1wINkaY1PRMTOdJ5PAjcDI4BLI2JpeouzgGskfRG4D7gktV8CXClpGdmFxlOb9RktP30XhXjOsXWKpgXliDitQvMlFdpKx38J+FKF9huBGyu0LyebndG3fQfwwQF11tpO+aIQeGnO8avmHV2o6W1mA+WCRNaWyheFlJTmHJu1Mwdla0ulRSHlPOfYOoGDsuVmKBfqKi0K8Zxj6wQuSGS5GOqFutKikFfNO3pAc47Nis4jZctFtQt1AykO1NUlpk0cz0F7j2Hdlh2s2LTd0+Ks7XmkbLmodaGu3tkTnhZnncgjZctFIy7UNWK0bVY0DsqWi0ZcqPO0OOtETl9YLhpxoa68VnKJp8VZu/NI2XJTrThQvVPlPC3OOpFHylYoA7l452lx1ok8UrZCGejFu8GU4jQrMgdlKxRfvLPhzkHZCsU1LWy4c1C2QvHFOxvufKHPCqXSxbue/cexYtN21m3ZwaR9fDHPOpuDshVO+UanXkptw43TF1ZoXkptw42DshWaZ2PYcOOgbIXm2Rg23DgoW6F5NoYNN77QZ4XmpdQ23DgoW8v09sagpraVz8Yw63QOytYSntpmVp+m5ZQlXSppvaQHytoOkHSLpEfSz/1TuyTNl7RM0q8lzSx7zenp+EcknV7W/kZJ96fXzJekWu9h+fLUNrP6NPNC32XAnD5tZwO3RsQM4Nb0GOBdwIx0mwt8E7IAC5wLHAUcCZxbFmS/Cfx52evm9PMeliNPbTOrT9OCckT8DHiqT/NJwOXp/uXA+8rar4jMXcB+kg4G3gncEhFPRcTTwC3AnPTcPhFxV0QEcEWfc1V6D8uRp7aZ1afVU+ImRcSadH8tMCndnwysLDtuVWqr1b6qQnut97AmqWenEE9tM6tPbhf6IiIkVd7np0XvIWkuWbqEnp6eZnalY9V7Aa+dp7b5e2Kt1OqR8rqUeiD9XJ/aVwNTy46bktpqtU+p0F7rPfYQEQsiYlZEzOru7h70hxrOBnIBr113CfH3xFqp1UF5IVCaQXE6cENZ+0fSLIzZwOaUgrgZOF7S/ukC3/HAzem5LZJmp1kXH+lzrkrvYU3gC3hmjdW09IWkq4FjgAMlrSKbRfFl4DpJZwCPAyenw28ETgCWAc8CHwOIiKcknQfck477p4goXTz8K7IZHmOB/0k3aryHNUHpAl55YPYFPLPBa1pQjojTqjx1XIVjA/hElfNcClxaoX0x8NoK7ZsqvYc1R+kCXt+csi/gmQ2OV/TZkLTzBTyzInJQtl1cm8Isfw7KBrg2hVlRuJ6yAa5NYVYUDsoGeGqbWVE4fWHAwKa2DTb3bGb980jZgPprU5RyzyfMv4PTLlrECfPv4KalayvWuzCzgfNI2YD6p7ZVyz2/at7Rnn1h1gAOyrZLPVPbauWeHZTNhs7pCxsQ10U2ay4HZRsQ10U2ay6nL2xAvKzarLkclG3AvKzarHnqSl9IGtHsjpiZWf055UckXSDpNU3tjZnZMFdvUP594GHgYkl3SZoraZ8m9svMbFiqKyhHxNaIuCgi3gycRbaLyBpJl0t6eVN7aGZWp8lTe5DUsNvkqa3fKLeuC30pp/xusm2apgH/ClwFHE22ldMrmtQ/M7O6PblqJad86xcNO9+1H39zw85Vr3pnXzwC/AS4ICLKP/H1kt7W+G6ZmQ1P/QblNEq+LCL+qdLzETGv4b2yhnNlN7P20G9Qjoidkk4EKgZlKz7vKmLWPuqdffF/JH1d0tGSZpZuTe2ZNYx3FTFrH/XmlI9IP8tHywEc29DeWFPUU9nN6Q2zYqgrKEfE2xv5ppL+GvhfZIH9frJZHQcD1wATgV8CH46I5yWNBq4A3ghsAk6JiBXpPJ8DzgB2AvMi4ubUPgf4KjACuDgivtzI/reb/nYVcXrDrDjqrhIn6d2SPivpH0q3wbyhpMnAPGBWRLyWLHCeCpwP/FtEvBx4mizYkn4+ndr/LR1HWl14KnA4MAf4D0kj0oXJbwDvAl4DnDbcVyL2V9nN6Q2z4qh3nvKFwDjg7cDFwAeAu4f4vmMlvZDOu4YsFfIn6fnLgS8A3wROSvcBrge+Lkmp/ZqIeA54TNIy4Mh03LKIWJ76fk069sEh9Let9VfZzYXrzYqj3pHymyPiI2Qj1n8E/oBBLhiJiNXA/waeIAvGm8nSFc9ExIvpsFXA5HR/MrAyvfbFdPzE8vY+r6nWPqyVKrvNnn4g07sn7JaWcOF6s+KoNyj/Lv18VtIhwAtkOeABk7Q/2cj1MOAQYDxZ+qHlUg2PxZIWb9iwIY8uFIIL19fm74m1Ur2zL/5L0n7ABcC9ZBfoLh7ke/4h8FhEbACQ9H3gLcB+kkam0fAUYHU6fjUwFVglaSSwL9kFv1J7SflrqrXvJiIWAAsAZs2aNWy3Y3bh+tr8PbFWqrcg0XkR8UxEfA84FHhVRHx+kO/5BDBb0riUGz6OLN/7E7JcNcDpwA3p/sL0mPT8bRERqf1USaMlHQbMIMtz3wPMkHSYpL3ILgYuHGRfh41a6Q0za52aI2VJf1zjOSLi+wN9w4hYJOl6shH3i8B9ZKOQ/waukfTF1HZJesklwJXpQt5TZEGWiFgq6TqygP4i8ImI2Jn69kngZrKZHZdGxNKB9tPMLA/9pS/eU+O5AAYclAEi4lyy8p/llvPS7InyY3cAH6xyni8BX6rQfiNZ9Tqzlpg8tYcnV63s/0CzftQMyhHxsVZ1xKydNbJkZB7lIq046t44VdK7yRZq7JonVa1ynJmZDU69G6deCJwCnAmILJ1waBP7ZWY2LLV88YiZmVU32MUjLzLIxSNmZlbdQBeP/AvZkmgY/OIRMzOror95ym8CVkbEeenxBLJSm78lq9hmZmYN1F/64lvA8wBpg9Qvp7bNpGWnZmbWOP2lL0ZExFPp/inAgrTU+nuSljS1Z2Zmw1B/I+URqQgQZDUqbit7ru45zmZmVp/+AuvVwE8lbSSbgXEHgKSXk6UwzMysgfpbZv0lSbeSTX/7UarOBtkI+8xmd87MbLjpNwUREXdVaHu4Od2xRvDO1Gbty3nhDuOdqa3ZGlkR75ApU1m98omGnKtTOCh3mGo7U79q3tHeBNUawhXxmstBucP03Zn64H3H8Mczp/Dwuq0ATmWYFZyDcocp7Uy944VeDt53DB+efSjzb3vEqQyzNlFvQSJrkt7eYPmGbdz56EaWb9hGb+/Q9uUs35n6j2dO2RWQ4aVUxopN2xvRdTNrAo+Uc9SMi3LlO1M/vG7rbqkMyALz+q07nF82KyiPlHNU7aJc35HsQEfTpZ2pXzFpb8aM2v1PPGZUFwftPabKK80sbw7KOep7UQ5eGsmWlEbTJ8y/g9MuWsQJ8+/gpqVr60pzlKcygF0j8WkTxzf2g5hZwzh9kaPyi3IlfUeyQ5niVp7KWL91Bwft7YUkZkXnkXKO6hnJ1jOarqWUypg9/UCmd09wQDYrOI+Uc1TPSLa/0bSXVJt1llxGypL2k3S9pN9K+o2kP5B0gKRbJD2Sfu6fjpWk+ZKWSfq1pJll5zk9Hf+IpNPL2t8o6f70mvmSChelShfvFj22CYAjp02sOJKtNZoeSr7ZzIopr5HyV4GbIuIDkvYCxgF/B9waEV+WdDZwNnAW8C5gRrodBXwTOErSAcC5wCwggF9KWhgRT6dj/hxYBNwIzAH+p5UfsJaBTIWrNZpevmGbl1SbdZiWj5Ql7Qu8DbgEICKej4hngJOAy9NhlwPvS/dPAq6IzF3AfpIOBt4J3BIRT6VAfAswJz23T0TclUqNXlF2rkKodypcSbW88FDzzWZWPHmkLw4DNgDflnSfpIsljQcmRcSadMxaYFK6PxkoL0m1KrXVal9Vob0wGhVMS/nmcp6HbG2laySSGnbrBHmkL0YCM4EzI2KRpK+SpSp2iYiQ1PTEqKS5wFyAnp6eZr/dLvVMhatHKd/cNw3ieciNldf3ZFjofbFhFeegM6rO5TFSXgWsiohF6fH1ZEF6XUo9kH6uT8+vBqaWvX5KaqvVPqVC+x4iYkFEzIqIWd3d3UP6UANRz1S4Sqv4+rYBzDn8Zdw472iumXsUN8472sWGmiCv74kNTy0fKUfEWkkrJb0yIh4i25D1wXQ7Hfhy+nlDeslC4JOSriG70Lc5ItZIuhn459IsDeB44HMR8ZSkLZJmk13o+wjwtZZ9wH6UprB1770X186dzbPP79xjKlulC4Hf+vBMnnn2Rc763q/3uDg4vXuCL+yZdYi8Zl+cCVyVZl4sBz5GNmq/TtIZwOPAyenYG4ETgGXAs+lYUvA9D7gnHfdPEfFUuv9XwGXAWLJZF4WYeVFt1sVRh03cbXTb90Lg/uP24nfP9+4KyOCZFmadKpegHBFLyKay9XVchWMD+ESV81wKXFqhfTHw2qH1svHqXTLd90Lgh47q4cE1W1zxzWwY8DLrFqp31kXfWRXdE0bTG3imhdkw4KDcQpWmsB06cSxjR43Y7YJe3wuB48eM5Ie/Ws28Y2fsdnHwn//odZ5pYdZhXPuihfpOYTt04ljOPHYGpyy4a4+Ld+Wr+CaMHsmpb+rhmnue4Iy3TmdEF7z6ZfvwmkP29kwLsw7joNxCfZdMjx01YldAhj1zzKVbb2+wdssOTjpiMqWyFqNGiqn7e5Rs1mkclFustGR6evcE7nx0Y10X77q6xLGvnMT0Aye4LrJZh3NQztFAVvaVB3Mz61y+0Jcjb9dkZn15pJwjb9dkZn05KOfMaQkzK+egbNbpUnlMaw8OymadzuUx24ov9JmZFYiDsplZgTgom5kViHPK/SgVpV+3ZccexejNzBrNQbmGakXpveWSmTWL0xc1VCtKv2LT9px7ZmadykG5hnqL0puZNYrTFzXUWzDIeWczaxSPlGuop2BQKe98wvw7OO2iRZww/w5uWrqW3lLhYzOzAfBIuYZ6CgbVuxmqmVk9HJT70V/BoFp5ZwdlMxsopy+GqNJmqN5l2swGK7egLGmEpPsk/Vd6fJikRZKWSbpW0l6pfXR6vCw9P63sHJ9L7Q9JemdZ+5zUtkzS2c38HC5Ub2aNlGf64lPAb4B90uPzgX+LiGskXQicAXwz/Xw6Il4u6dR03CmSXgOcChwOHAL8WNIr0rm+AbwDWAXcI2lhRDzYjA/RN+/8sn3GsLMXFj22yTMxzGzAchkpS5oCvBu4OD0WcCxwfTrkcuB96f5J6THp+ePS8ScB10TEcxHxGLAMODLdlkXE8oh4HrgmHTsgvb3B8g3buPPRjSzfsK3mbIpS3vnIaRN5cM1W3v01z8Qws8HJK33x78BngdIVsonAMxHxYnq8Cpic7k8GVgKk5zen43e193lNtfa6DXaam1cAmtlQtTwoSzoRWB8Rv2z1e1foy1xJiyUt3rBhw672wQZXrwDsTNW+J2bNkMdI+S3AeyWtIEstHAt8FdhPUinHPQVYne6vBqYCpOf3BTaVt/d5TbX2PUTEgoiYFRGzuru7d7VXC65PbX+uZkrDMzE6U7XviVkztDwoR8TnImJKREwju1B3W0R8CPgJ8IF02OnADen+wvSY9PxtERGp/dQ0O+MwYAZwN3APMCPN5tgrvcfCgfSxUnA9dOJYVj+zo2ZKwzMxzGyoirR45CzgGklfBO4DLkntlwBXSloGPEUWZImIpZKuAx4EXgQ+ERE7ASR9ErgZGAFcGhFLB9KRUnAtL9l53kmvY+6Vi2uu3KtnBaCZWS25BuWIuB24Pd1fTjZzou8xO4APVnn9l4AvVWi/EbhxsP2qFFzrXbnX3wpAM7NaijRSLpRKwbWeinFmZkPhZdZ1cr7YzFrBI+U6OV9sZq3goDwAzhebWbM5fWFmViAOymZmBaJsHYZJ2gA8nnc/muRAYGPencjBQD/3xoiYU+uAJn9PivR3KkpfitIPeKkv/X5PhsJBeRiQtDgiZuXdj1Zrt89dpP4WpS9F6Qe0ri9OX5iZFYiDsplZgTgoDw8L8u5ATtrtcxepv0XpS1H6AS3qi3PKZmYF4pGymVmBOCh3MEkrJN0vaYmkxXn3p5kkXSppvaQHytoOkHSLpEfSz/1b1Jc9fu/V+qLM/LTz+q8lzSw7z+np+EcknV7W/sZ0/mXptRXX+kt6ZepD6bZF0qclfUHS6rL2E8peM6Ad4qvtQp+eq/tv0sjfQ4X3+E6Fflwg6bfpvX4gab/UPk3S78p+NxcO4v36/Uw1RYRvHXoDVgAH5t2PFn3WtwEzgQfK2v4FODvdPxs4P6/fe7W+ACcA/wMImA0sSu0HAMvTz/3T/f3Tc3enY5Ve+646+jQCWAscCnwB+NsKx7wG+BUwGjgMeDS9bkS6Px3YKx3zmvSa64BT0/0Lgb8czN+kkb+HCu9xVYV+HA+MTPfPL+vHtPLj+vx+6n2/mp+pv5tHytYRIuJnZJsglCvfCb18h/Q8VOvLScAVkbmLbFu0g4F3ArdExFMR8TRwCzAnPbdPRNwV2X/5V1Df5zoOeDQiai18GdAO8WmkWG0X+oH+TRr5e+j7HrP69iMifhQvbdR8F9m2cVUN8P36+0w1OSh3tgB+JOmXkubm3ZkcTIqINen+WmBSi9630u+9Wl8Guiv75HS/b3t/TgWuLnv8yfRP6kv1UlpnoH2ptQt9Na34PQz07/5nZCPaksMk3Sfpp5KOLuvfQN+vWt9rclDubG+NiJnAu4BPSHpb3h3KSxrdtGqqUc3fe4v7Qsrzvhf4bmr6JvB7wBHAGuBfW9WXcq34PfT3HpLOIdtO7qrUtAboiYg3AJ8B/lPSPo16v3o4KHewiFidfq4HfkCF7bY63LrSPxfTz/WteNMqv/dqfRnoruyr2f2f2lV3ay/zLuDeiFiX+rUuInZGRC9wES99Lwbal01U34W+mlb8Hur6u0v6KHAi8KEUTEmpm03p/i/JcumvGOT7Vet7TQ7KHUrSeEl7l+6TXdh4oParOk75TujlO6Q3TY3fe7W+LAQ+kq7UzwY2p38K3wwcL2n/lF44Hrg5PbdF0uyU0/1IHZ/rNMpSF33ymn/ES9+LAe0QnwJZtV3oq2nF76Hfv7ukOcBngfdGxLNl7d2SRqT709PvYPkg36/aZ6qtnquBvrXfjexK+a/SbSlwTt59avLnvZrsn54vkOXuziDLed4KPAL8GDggr997tb6QXZn/BtmI7H5gVtm5/ozsYtsy4GNl7bPIAumjwNdJi8Cq9Gc82Yh237K2K9N7/ToFjoPLnjsnnfchymZ1kM0keDg9d06fz3t36uN3gdGD+Zs08vdQ4T2+V6Efy8jyvUvS7cL02venv9sS4F7gPYN4v34/U62bV/SZmRWI0xdmZgXioGxmViAOymZmBeKgbGZWIA7KZmYF4qDcImmu4s8lvaus7YOSbmrCe92urKJXqdLV9f2/akjvd0iz38MaT9I5kpam5dZLJB1V49jLJH2g2vPWOCP7P8QaISJC0l8A35X0E7Lf/T8Dg9oVV9LIeKnmQCUfioiml+tM/XiSlxYQWBuQ9Adkq9lmRsRzkg4kqwDXqPP39/20KjxSbqGIeAD4IXAW8A/Ad4BzJN2dCqCcBLtqut4h6d50e3NqPya1LwQeTKvH/lvSryQ9IOmUWu8v6QZJH0n3Py7pqnT/dklfTaOlByQdmdrHp4I1ffv3UUkLJd0G3Jr6+0B6boSyWrX3pBHYx8v6fruk65XVsb0qrYxC0psk/SJ9jrsl7V3tPNYwBwMbI+I5gIjYGBFPSvqH9Dt/QNKC0t+oXLVj0t/335XVkD5H0mOSRqXn9il/bDXkvRJruN3IVlg9RLbC5/8D/jS170e2Ymo8MA4Yk9pnAIvT/WOA7cBhZauPLio7977p5+3pPZak2wWpfRLZSqaj03sdUHb8Ren+20j1ZMlG8pX691GylVGl108re81c4O/T/dHAYrLavMcAm8nW/3cBdwJvJRudLQfelF6zD9m/IiqeJ++/X6fcgAnpu/Ew8B/A/5PaDyg75krSijbgMuAD/RxzO/AfZc99G3hf2ffiX/P+3O1wc/qixSJiu6RrgW3AycB7JP1tenoM0AM8CXxd0hHATrKCKCV3R1brFrLA/q+Szgf+KyLuKDtuj/RFRKyT9A9k9Qr+KCLKa8xenY75WRrV7EdWZ+C9FfoHqcZthY94PPD6svzjvmT/Y3k+9X0VgKQlZMF8M7AmIu5J778lPV/tPKXPbkMQEdskvZHsf9BvB65VtqPIVkmfJRsYHEC25PiHfV7+9hrHXFt23MVk9SX+f+BjwJ8359N0FgflfPSmm4D3R8RD5U9K+gKwDvh9slHljrKnt5fuRMTDyraYOQH4oqRbI+Kf+nnv15HVQjikT3vf9fZRo39HlfejDwFnRsTNfV5zDPBcWdNOan//Kp7HGicidpKNbm+XdD/wceD1ZDUaVqbv4Zjy10gaQzayrnZM+ffz/6TU1jHAiMjSd9YP55TzdTNwZllO7g2pfV+y0WMv8GGy7Xj2IOkQ4NmI+A5wAdmWN1WlXPG7gDcAf6usEljJKemYt5JVs9pco3/9faa/LMslvkJZtbRqHgIOlvSmdPzeykpBDvQ8NgDK9u+bUdZ0BNnfAmCjpAlUvng7po5jyl0B/CdZKsPq4JFyvs4D/h34taQusn+an0g2Evleuih3E9VHpa8DLpDUS1YB6y/LnrtK0u/S/Y3Au8lq534ssgs6fwNcKunYdMwOSfcBo8iqctXqXy0Xk6Ul7k3BfAM1tiuKiOfTBcqvSRoL/A74w4GexwZsAtnvfD+yIu/LyPK+z5BVQltLVrJzNxHxjKSLah3Tx1XAF9l91xOrwVXiDEm3k22k2dE7XlvrpWsCJ0XEh/PuS7vwSNnMmkLS18jSZSfk3Zd24pGymVmB+EKfmVmBOCibmRWIg7KZWYE4KJuZFYiDsplZgTgom5kVyP8F53yFEP/3xHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Python\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Density'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data2['YearsExperience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Python\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Salary', ylabel='Density'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data2['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# r value is high therefore correlation is high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearsExperience</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.978242</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 YearsExperience    Salary\n",
       "YearsExperience         1.000000  0.978242\n",
       "Salary                  0.978242  1.000000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bulding model by ols method for prediction of salary by years of experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Salary</td>      <th>  R-squared:         </th> <td>   0.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   622.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 08 May 2021</td> <th>  Prob (F-statistic):</th> <td>1.14e-20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:40:11</td>     <th>  Log-Likelihood:    </th> <td> -301.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   606.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   609.7</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> 2.579e+04</td> <td> 2273.053</td> <td>   11.347</td> <td> 0.000</td> <td> 2.11e+04</td> <td> 3.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>YearsExperience</th> <td> 9449.9623</td> <td>  378.755</td> <td>   24.950</td> <td> 0.000</td> <td> 8674.119</td> <td> 1.02e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.140</td> <th>  Durbin-Watson:     </th> <td>   1.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.343</td> <th>  Jarque-Bera (JB):  </th> <td>   1.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.363</td> <th>  Prob(JB):          </th> <td>   0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.147</td> <th>  Cond. No.          </th> <td>    13.2</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Salary   R-squared:                       0.957\n",
       "Model:                            OLS   Adj. R-squared:                  0.955\n",
       "Method:                 Least Squares   F-statistic:                     622.5\n",
       "Date:                Sat, 08 May 2021   Prob (F-statistic):           1.14e-20\n",
       "Time:                        19:40:11   Log-Likelihood:                -301.44\n",
       "No. Observations:                  30   AIC:                             606.9\n",
       "Df Residuals:                      28   BIC:                             609.7\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept        2.579e+04   2273.053     11.347      0.000    2.11e+04    3.04e+04\n",
       "YearsExperience  9449.9623    378.755     24.950      0.000    8674.119    1.02e+04\n",
       "==============================================================================\n",
       "Omnibus:                        2.140   Durbin-Watson:                   1.648\n",
       "Prob(Omnibus):                  0.343   Jarque-Bera (JB):                1.569\n",
       "Skew:                           0.363   Prob(JB):                        0.456\n",
       "Kurtosis:                       2.147   Cond. No.                         13.2\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2=smf.ols(\"Salary~YearsExperience\",data=data2).fit()\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Salary'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uTXnOze7x+0Xk5pTx94jI6+5z/k3c/zOyvYcxZurriSc42hbmREekb8/KsY4w+X4PiaTS1NnDweYQIXfpcWV5Af/wsQv5l09dnFZYPCKUFgSoKC2gON9vhWUMjOVs1QPAtQPG/gJ4RlWXA8+4nwN8EFju/lkHfA+cQgH8NXAZsAr465Ri8T3gzpTnXTvMexhjpqhEUjnZ2cOR1jCRAcFd82fl09TZQ31zN23hGAp4RagoDXLv51ZweXV5X/EQd1lxRVkBpYWBEe9XMaM3ZsVFVWuBlgHD1wMPuh8/CHw0ZfwhdewASkRkIfBHwNOq2qKqrcDTwLXuY8WqukNVFXhowGtleg9jzBSjqrSHYjS0hOiMpO9ZSSSVX75+jIPN3bSH4yTVadcyO+hj7qwAX1x7dtoS4qI8H0tKg8wpypt2efWT0XjPucxX1WPux8eB+e7Hi4GGlOMa3bGhxhszjA/1HoOIyDqcKyWWLl062q/FGJNFLtrMh6JxmrsGt2xRVXbUtXDvtjoONjs9urweoawggKAsKilIWy020xIgJ4sJm9BXVRWRzN3jxuk9VHUjsBFgxYoVY3ouxswUZ9pmfqhW+G8d62BDbR2vNbb3jb3vD+Zx6+pKFs4Oph1ry4on1ngXlxMislBVj7m3tprc8SNARcpxS9yxI8DaAeNb3fElGY4f6j2MMePgdNvMJ5JKWyhKRySODuha3Nga4v7nDvK7d072jb1nWSnr1lSxfP6stGMDPmdZcWqzSDP+xvu7vwW4GfiW+/cTKeNfEpHNOJP37W5xeAr4h5RJ/A8A31DVFhHpEJHLgReBzwH/Psx7GGPGwem0mXdy6we3bMkULXz2vCLWraliRWVZ2rFD5aqY8Tdm/xVE5Ec4Vx1zRKQRZ9XXt4DHROR24BDwKffwXwAfAg4AIeBWALeIfBPY5R53t6r2LhL4Hzgr0oLAL90/DPEexphxMJo28+Go07Jl4LxKOJrgx3saeHRXI2F3ddiC4nxuu6qSqwdEC/fmqthelclFBl5+zlQrVqzQ3bt3T/RpGDPlpc65pLaZv/sjF/TdFss2rxJPJPnFG8d58IWDw0YLjyZXxYwdEdmjqisGjtv1ozEmp4ZqM59tXkVV2XbgFPdt648WDvg8fDJDtPDp5KqY8WfFxRiTcwPbzKs6RaUtFBsUMfyaGy38Zkq08B9dsIBbBkQLizi9w0rOsAW+GR9WXIwxY6ozEqO1O0Y8mT6vcrC5m/u21fPC75v7xi6vLuPONdVUzemPFu7NVSkJ+tPyVszkZsXFmCkoF5sUx1oklqC5O0rPgHYtJzt7eHD7QX71Rn+08B+40cIXp0QLiwhFeT5KC6yoTEVWXIyZYs50k+JYiyWcyfrunvTJ+q6eOI/uauDxPY30pEQL335VFTUDooWL8pzbXxbWNXVZcTFmijndTYpjLdmbWz9gsj4aT/Kz147y8Pb0aOHPXbGMD1+0MO2qpDDPR0mBtWqZDqy4GDPFnM4mxbGkqnRE4oM2QSZV+e2+k2x6vp5j7enRwn+yYknaPpiCgFNUrFXL9GHFxZgpZjSbFMdatuaSLx1qZUNtHfuHiRa2/l/TlxUXY6aY9TXV3LVlL6FoPG2T4vqa6jF939RFBItLgty4ooKLl5akHfP7pi42bqtj18Gho4Wt/9f0Z/9ljZlihtqkOFZ6FxH4PFAU8HK0Lcw//fptvnr1clZVl3G8I8Km5+p55q0mem+MXbR4Nutrqjl/UX8CpPX/mjnsv7AxU9DATYpjbUNtHR4Bn9dDUum7YvrBjkPsOdzKf79yhFjCKSvLygu4c00VV6QkQPq9HkoK/MzK9w/1NmYaseJijBlSJJagvrmLWXk+ei9LkqqEonEaW8O8cawDgPKiALdeWckfXbCgbwe9z+NhdoGf4nzr/zXTWHExxmQUd/erdPXEWTArSHN3D/k+Dx2ROM3dUeLuyrDCgJdPr1rKxy9d3Dcx7xGhpMDp/2VFZWay4mKMSaOqbr5Kfx+wG1Ys4Z9+/TbH2+PEUpYbrz6rnK994FxmFzi3u3pbtZRa/68Zz4qLMaZPV0+c1u70pcVvHetg8+6Gvhb44GyCvPOqaq69aEHfWGGeU1RsV70BKy7GGKAnnqClO0o42t8H7EhrmPueqx82WjjP76Xc9qqYAay4GDODxRJJWt15lV6toSgPbT/Ek6+lRAvPLWJdTXq0sC0rNkOx/yuMmYHiiSStoRhdPf19wDJFC88vzuO21VVc8wf90cK9scLFtqzYDMGKizEzSCLpTNa3h2N9RSVbtPBnL1/G9SnRwhYrbEbDiosxM0CmFWDZooU/celiPr1yKUX5zo8HcWOFSyxW2IzCiIqLiHhVNTH8kcaYySbTCrDXG9vZUFvHm+4GyGzRwkX5PsoKAhbWZUZtpFcu+0XkJ8D3VfXNsTwhY0xuZEqCPNTczb0jiBYuCPgoK7Rlxeb0jbS4XAzcCNwnIh5gE7BZVTvG7MyMMaclnkjSEorSFelfAXaqq4cHXkiPFj5vwSzW11RzcUVJ33G2rNjkyoiKi6p2AvcC94rIe4EfAt8RkceBb6rqgTE8R2PMCCSTStuAyfpM0cKLS4LcsSY9WtiWFZtcG/GcC/Bh4FagEvhn4BFgDfAL4JwxOj9jzDBUlY5wnLZwfxJkLJFky6vp0cIlQT83X5keLez1CCUFAWssaXJuxHMuwG+Bb6vqCynjj4tITe5Py5ipLTVYq2IM81a6e+K0pEzWZ4sW/tSKCj6VEi3scVeAzbYVYGaMDFtc3KuWB1T17kyPq+pXcn5WxkxhvcFafq9QEvTT1Bnhri17uRtyVmB64gmau6JEUibrXzrUysZtdbxzwokW9ghc965FfC4lWlhEKMrzUVrgtxVgZkwNW1xUNSEi1wEZi4sxJt2G2jr8Xum7SigI+AhF42yorTvj4pJpsj5TtPCa5U60cEVZf7SwNZY042mkt8WeF5H/AB4FunsHVfWlMTkrY6awhtYQJcH01ihBv5fG1tBpv6aq0hZyJut7N0Ee74jw/ecP8ps3TwwZLZzv91JmK8DMOBtpcbnE/Tv16kWBq3N6NsZMAxWlBTR1RvquXADCsQRLSguGeFZ2HZEYbd0xXjhwis27GjjSHgIVWkL9E/jZooXLCgMU2gowMwFGuhT5D8f6RIyZLtbXVHPXlr2EovG+rPlYQllfUz2q1wlFncn6aDzJzroW/vWZdwjHEnRG4n17VYrzfayrqR4ULVxaaHn1ZmKN+FcaEfkwcAGQ3zuWbZLfmJls7XnzuBtn7qWxNcSSUa4WG5itkkgq3916gJOdURLuLTGPwKw8H8vKCvnQRQvdMaG0wBpLmslhpPtc/hMoAP4QuA/4JLDzdN9URP4XcAfOrbXXcfbPLAQ2A+XAHuAmVY2KSB7wEPAeoBm4QVUPuq/zDeB2IAF8RVWfcsevBe4BvMB9qvqt0z1XY07H2vPmjXryPpZI0poyWa+qvFjfwn3b6mlwG0uCs1+lvDCAxwNNXRGLFjaT0kivXK5U1XeJyGuq+rci8s/AL0/nDUVkMfAV4HxVDYvIYzitZT4EfEdVN7vF7Hbge+7frap6tojcCPwjcIOInO8+7wJgEfAbEendzPld4P1AI7BLRLZYTzQzWSWSSmsoSmekP1tl3/EONtbW80pDW99xBQEv82fl4XeXEIdjCRaXFLC4JGgrwMykM9Li0vtrU0hEFuFcQSw8w/cNikgM54roGM7igM+4jz8I/A1Ocbne/RjgceA/xLnmvx6nv1kPUC8iB4BV7nEHVLUOQEQ2u8dacTGTSjIlW6V3BdiR1jD3P1fP1tRo4aUlXHnWHB5/qZF4UvF5lZ54EgW+fPXZVljMpDTS4vKkiJQA3wZewrmddd/pvKGqHhGR/wMcxilav8a5Ddamqr2L9xuBxe7Hi4EG97lxEWnHuXW2GNiR8tKpz2kYMH5ZpnMRkXXAOoClS5eezpdjzKhlatfSGory8PZD/CwlWvisuYWsq6lmpRstvLgkyKO7G2jqjLC0rHDMdv0bkwsjXS32TffDn4jIk0C+qrafzhuKSCnOlUQV0Ab8GLj2dF7rTKnqRmAjwIoVK3SYw405Y50RJ7Crt11LOJbg8d2NbN7VMGS0sEeEay9awA2rKmyy3kwJQxYXEfn4EI+hqj89jfd8H1Cvqifd1/kpsBooERGfe/WyBDjiHn8EqAAaRcQHzMa5Ldc73iv1OdnGjZkQqcuKwZln+cXrx3hw+yFauqMAzMr38aeXLeX6Sxb33eqyyXozVQ135fLHQzymwOkUl8PA5SJSgHNb7BpgN05jzE/irBi7GXjCPX6L+/l29/FnVVVFZAvwQxH5F5wJ/eU4K9gEWC4iVThF5Ub653KMGVeRmLOsuLcHmKry3IFm7ttW17cCzO8VPnHpEj6zqj9aGCywy0xtQxYXVb0112+oqi+6OTAvAXHgZZxbUz8HNovI37lj97tPuR942J2wb8EpFqjqXnel2Zvu63yxN4pZRL4EPIWzFHmTqu7N9ddhzFB64glau2OEov09wN444kQL7z3qZOwJvdHCy5hX3Ld9jIDPQ3lhHsGAtWsxU5f0Ln0c9sBpvolyxYoVunv37ok+DTPFZWoseai5m/u21fN8SrTwZVVlrKtJjxb2ez2UFNjOejO1iMgeVV0xcHxCNlEaM91kSoE81dXDgy8c4pdvHOtr13KuGy18SUq0sO2sN9PRuG+iNGY6ybRXpbsnzuYM0cK3X1XFe8/pjxYWEYrzfZTYZL2Zhk53E2ULZ7aJ0pgpLVu08M9ePcrDOw7THo4BTquWz12xjOvetTAtnMsm6810N9pNlP+Es+ERTnMTpTFTmarSEYnTHooRT/ZHC299+yT3P5cSLexzo4VXLklrvW+T9WamGG6fy0qgoXcTpYgU4TSa3Ad8Z+xPz5jJo6snTmtKXj3AS4db2VibHi384Xct5OYrKvuihcEm683MM9yVywacTY+ISA3wLeDLOOFhG3Em9o2Z1iKxBM3dUXpS8up/f7KLe2vr2DkgWvj2q6pYmhItbJP1ZqYarrh4VbXF/fgGYKOq/gSnDcwrY3pmxkywTHtVTrjRwk+nRQsXs66mmgsWze47zibrzUw3bHFJaclyDW6TxxE+15gpKZZI0todpaunv6h0hGP8cOdh/uvlI8QSbrRwWQF3rKniyrPK065KCvOcyXq/1ybrzcw1XIH4EfA7ETmFs2JsG4CInA2cVuNKYyareCJJWziWlqsSjSf56ctH+OGLh/uKTXlRgFuuqOTaCxekXZXk+b2UFwbI99tkvTHDtX/5exF5BmfZ8a+1fzu/B2fuxZgpr3cDZEfKXpVEUvnNWyf4/vMHaersAaAw4OXGVRV84tIlaQXE7/VQWhigKM8u5o3pNey/BlXdkWHsnbE5HWPGT6a9KqrKzoMt3FtbT92pbgB8HuEjlyzipsuWMbugf7WXR4SSAj+zg36brDdmAPtVy8xInZEYrd39e1UA3j7eyYbaurRo4avPm8dtqytZVBLsG7M2+MYMz4qLmVG6e5xcldS9Kkfawmx6rp7fvt0fLfzupSWsr6nmnPmz0p5vO+uNGRkrLmZGCEXjtIZiaXtV2kJRHt5xmJ+9epS4e1usem4h69ZUs7KyNO1Wl+2sN2Z0rLiYaS0cTdAa6g/rgv5o4Ud3NxCKOuPzZuVx2+pKrvmD+Wm3unweD6WFtrPemNGy4mKmpUjMKSrhaH9RSSSVX75xjAdeSI8W/uxlS/loSrQwOPMqexvb+cGLh2hsC1NRWsD6mmrWnjdv3L8WY6YiKy5mWonGk7SF0jdAqiov/L6Ze7fVc7glBDjRwh9/92I+c9nSQVclRfk+Xm9o559+/TZ+r1AS9NPUGeGuLXu5G6zAGDMCVlzMtBBPJGkNxeiMxNLG3zjSzsbaOt5IiRb+wAXzueXKSuanRAsDBANeygoD5Pm83PfcK/i90tfRuCDgIxSNs6G2zoqLMSNgxcVMaYmk0haK0pGyqx7gcHOIe5+r4/kD6dHCd66ponpuUdpr+L0eyosCaa3xG1pDlATTr2iCfi+NraEx+kqMmV6suJgpKVMCJEBzVw8Pbj/EL15PiRaeP4v1702PFgbweoSSggDF+YM7FleUFtDUGUkrOOFYgiWlTsfjrfua2FBbR0NryOZjjMnAiouZUjLtqgdn/8qjuxt4fHcjETdaeFFJPndcVcV7z5mbVjxEhNlBPyVBP54smyDX11Rz15a9hKJxgn4v4ViCWEJZX1PN1n1N3LVlr83HGDMEKy5mSsiUAAm90cLHeHjHobRo4ZvcaOGBnYmL8n2UFQTSIoczWXvePO4GNtTW0dgaYknK1cmnN+6w+RhjhmHFxUx6nZEYbaFY2q567Y0Wfr6eo2390cJ/smIJn1pRQeGAJpKpk/Ujtfa8eRmLhc3HGDM8Ky5mxMZ7niFTqxaAVxra2PC7Ot4+0Qm40cIXLeRzVyyjvCgv7Vi/10NZYWBQsTkTw83HGGOsuJgRGs95hlDUKSrReHpRqTvZxcZt9eysb+kbu+rsOdxxVRVLy9N/sA81WX+mhpqPMcY4rLiYEdlQWzfm8wzhaIJfvHaUh3cc5lhHmIXFQW5cWUHlnAK+/8JBfr23P1r4wkVOtPCFi2envcZIJuvP1FDzMcYYhxUXMyJjOc8QiSVo6Y5S+/ZJ7nl2Pz6Pkz9/sjPCN3/+Jj3xZF9jyaVlBdyZIVoYRj5ZnwvZ5mOMMQ4rLmZExmKeIRJL0BaKEYo6rVo272rA5xHyfB5aQzFauqN9e1XKCwPcfGUlHxwQLQyQ73cm6y1e2JjJw4qLGZFczjP0xJ2i0p3S/wvgaHsIAY61R/quVDwCBQEvD92+iuCA4jEWk/XGmNywf5VmRHIxz5CpqST0Rwt3ROJEYv2T+CVBPwUBL/Nm5acVFq9HKAkGKA6ObLLedtMbM/6suJgRO915hlgiSWsoSlckPuixd0440cIvH27rGwv6vcybFSCpEE8qN66sAJzJ+uJ8HyWjiBe23fTGTAwrLiZNLn/Lz9ZUEuBoW5j7M0QLr64u57kDzRzvCLPAXS22qrqMwjwnXnjgjvvhjMcqN2PMYBNSXESkBLgPuBBQ4DbgbeBRoBI4CHxKVVvFue9xD/AhIATcoqovua9zM/BX7sv+nao+6I6/B3gACAK/AL6qA3+6mUFy9Vu+qtNUsi2U3lQSnGjhH+w4zJYhooU//p4lfcefabyw7aY3ZmJM1JXLPcCvVPWTIhIACoD/F3hGVb8lIn8B/AXwdeCDwHL3z2XA94DLRKQM+GtgBU6B2iMiW1S11T3mTuBFnOJyLfDL8fwCp6Jc/JbfEYnR1p3e/wuclWU/2dPI5l3DRwuDM69SWhig+AzjhW03vTETY9yLi4jMBmqAWwBUNQpEReR6YK172IPAVpzicj3wkHvlsUNESkRkoXvs06ra4r7u08C1IrIVKFbVHe74Q8BHseIyrDP5LT9bqxYnWvg4D75wkGY3Wrgoz4kW/ti706OFIfebIG03vTETYyKuXKqAk8D3ReRiYA/wVWC+qh5zjzkOzHc/Xgw0pDy/0R0barwxw/ggIrIOWAewdOnS0/+KponT+S0/HE3QEorSE0ukjY82WhicolN6GvMqQ7Hd9MZMjIkoLj7gUuDLqvqiiNyDcwusj6qqiIz5HImqbgQ2AqxYsWLGz8mM5rf8SCxBayhKOJoY9Njeo+1s+N3IooUB8vxeysdwE6Ttpjdm/E1EcWkEGlX1Rffzx3GKywkRWaiqx9zbXk3u40eAipTnL3HHjtB/G613fKs7viTD8WYYI/ktvyeeoLW7f1d9qsMtIe7bVs9zB071ja2qLOXOmmrOSokW3lnXwuZdDRzvCFNRVsAX157NYvvhb8y0Mu7FRVWPi0iDiJyrqm8D1wBvun9uBr7l/v2E+5QtwJdEZDPOhH67W4CeAv5BRErd4z4AfENVW0SkQ0Qux5nQ/xzw7+P2BU5x2X7LH2qvSnNXDw9tP8TPU6KFz5lfxLqaai5dWpp27M66Fu55dj95Pg/lhQFauqNZV6QNXBZ9RXUZ2+tabDOkMVPARK0W+zLwiLtSrA64FfAAj4nI7cAh4FPusb/AWYZ8AGcp8q0AbhH5JrDLPe7u3sl94H/QvxT5l9hkflbD7WsZaq9KpmjhhbPdaOFz5+LJsHv+x3saCfo9FOY5cy4FAU/GFWkDl0XXn+pi58EW5s0KUF6YZ5shjZnkJqS4qOorOEuIB7omw7EKfDHL62wCNmUY342zh8YMYah9LTXnzKUtHKMjPHivSiyR5MnXjvHw9kO0udHCs4N+brp8GX988eBoYXCaS5YXBTjRGRnRirQNtXXEEgmau+JEE0kSSUUEOsJx5hTl22ZIYyY526E/g2Xa19LdE+M/fnuAyjmFg4pKpmjhPDda+IYM0cIwuLnkSFek7W/qpD0Uw+MRvB4hllBQ6EkJELPNkMZMXlZcZrDUfS2qSlLB6/HQ2BoaVFhePtzKxtr6tGjhD7nRwnMGRAtD9iTIka5Ii8aTIPTdWvMIJJW087LNkMZMXlZcZrDeq4g8n5dEUlFVIrEEC4qDfcdkihbO83moKi/kqrPmDCosw22CHOm+E79XCMcg6d4O8wBJnGXNqmqbIY2Z5Ky4zGC3XLGMv33yTaLxJPl+D5FYsq8LcVNHZFC0sN8rlBb4mR300x2Nc8+z+/kqy1lVXQYw4uaSI9l3cs78YupPddEZceZc8vxeirxCUqE9HLPNkMZMclZcZqDeWOHlC2bxlauX9+05WVAc5PqLF/FKYxt3/Wyvc2sKJ1rYK0IskeibK+m9pbV5VwNrzp2b802QvbfPFsz2pd0+u/sjF1hBMWYKsOIyg2TaALmquoxV1WVE40n++5Uj/Msz79Dp7mUpKwxwixst/Kf3v0hxfvr/LkG/l5NdERaXBMk1a9tizNRmxWUGGGoDZFKV37zVxKbn6mnq7AGcWOEbVlbwyfcs6UuAXFgcpLm7x/lcnAn7nliCpWWFY3be1rbFmKnLiss0Fk8kaQ3F6OoZvAFSVdl9qJWNtXX8/mQ34BSMj1y8iJsuX0pJQSDt+BtXVnDPs/vpSSQpCniJxJPEk9iEujEmIysu09BQu+rBiRbeWFvHSynRwn947lxuu6oq6y2umnPnUl4UYNPzB+02lTFmWFZcppFkUrPuqgc41h7m/ucO8uy+pr6xSypKWFdTxXkLijO+pt/robQwQFGej0UlQd5/wYIxO39jzPRhxWUaSCaVjkiM9nCMRHJwUWkPxXj4xUNseSUlWnhOIXfWVLGqsixtk2Mvjwgl7rLjTI8bY8xQrLhMYapKRyROWyiasahEYgl+8lIjm3c20J0SLXzr6krelyFauNesfD9lhYGsjxtjzHCsuExBqkpnTzxjVj04cy6/euM4D2w/SHNXf7TwZy5bysczRAv3Kgg4myCzPW6MMSNlxWWK6eqJ05ohqx76o4Xv21bPoZRo4Y+9ezGfWbWU4uDgaGHnGA/lRYG0ZpLGGHMm7KfJFNHdE6c1FO3bNT/Q3qPtbKyt4/Uj/dHC7z9/PresrmRBhmhhcJYelxYGKM6QZ2+MMWfCisskF44maAlF6YkNzqoHJ1r4/ufq2bZ/QLTwmmrOmleU8TnDNZc0xpgzZcVlkorEErSGooSjmYtKS3eUB7cf5OevDR8tnKooz0fpCJpLGmPMmbDiMslk6v+VKhSN89iuRh7b00Ak1h8tfPtVVazNEi0MkOf35ry5pDHGZGPFZZKIxpO0haJ09WQuKnE3WvihQdHCS/njixdlvRLxeoSywgCzbF7FGDOOrLgMYeu+JjbU1tHQGqJijNqdDNX/C5wVYL975xT3P1fPkbYw4IR1ffI9S7hxZeZoYXDmVYrzfZQWBGxexRgz7qy4ZLF1XxN3bdmL3yuUBP00dUa4a8te7oacFJh4IklbOEZnlv5fAK82tLGhto59x/ujhT944UJuvjJztHCvkYZ2GWPMWLHiksWG2jr8Xunb+1EQ8BGKxtlQW3dGxWW4ppIA9ae6uXdbHTvq+qOFV59Vzh1rqlhWnr3Fvc2rGGMmCysuWTS0higZsOkw6PfS2Bo6rddLJpX2sNP/K1NTSYCTnT18//mD/PrN430rwM5fWMz6mmouWjI762v7PB5KC/02r2KMmTSsuGRRUVpAU2ckbdd6OJZgSWnBqF5HVekIx2kLZ+7/BdAVifPDnYf56ctH+jZJVpQGuWNNNVedXZ61caQ1lzTGTFZWXLLozXAPReNpGe4jDccarv8XOCvE/u2Z/Ty19wQJ92pmVp6PO2uq+OCFC7M2jhQRZrmT9dZc0hgzGVlxyeJMMtyH6v8F/dHC/7n1933LikWcwhIMeJlXlJ+1aFhzSWPMVGDFZQijzXAPReO0dGfv/wWw62BLWrQwQEnQT1mhH5/HQziWYPOuBlZVl6U9z5pLGmOmEvtJlQPD9f8CJ1r43to69qREC+f5PCwoziPP17+6K9/v4XhHuO9zr0coKQhQnO+zeRVjzJRhxeUMDNf/C7JFC89mXU01G39XT3N3z4DXTLKgOGibII0xU5oVl9MQSyRp7c7eqgUyRwtXzSnkzjVVXFblRAvfuLKCe57dTziWIN/vIRJLEk8qN1+5jCWlQdsEaYyZsqy4jMJIdtVnihaeW5THLasr+cD56dHCq6rL+CrL2byrgeMdYRaVBPl8zVl84MIF4/L1GGPMWJmw4iIiXmA3cERVrxORKmAzUA7sAW5S1aiI5AEPAe8BmoEbVPWg+xrfAG4HEsBXVPUpd/xa4B7AC9ynqt86k3Mdya76TNHChXlePrtqKR9792LysuyaX1VdxpVnz7FNkMaYaWUir1y+CrwFFLuf/yPwHVXdLCL/iVM0vuf+3aqqZ4vIje5xN4jI+cCNwAXAIuA3InKO+1rfBd4PNAK7RGSLqr452hMcya56VWV7XTP3bqvnUHN/tPBHL1nMZy/LHi0Mzn6VkqCfkoLMmyDHo3GmMcaMhQm5qS8iS4APA/e5nwtwNfC4e8iDwEfdj693P8d9/Br3+OuBzarao6r1wAFglfvngKrWqWoU52ro+uHOad/xTj69cQdb9zWRdK9UGlpDtIaiWQvLW8c6+J+Pvspf/fdeDjWH+qKFH7xtFV9Ye9aQhaUoz0dFaZDSwkDWwnLXlr00dUbSGmduTVkYYIwxk9VEXbn8K/DnwCz383KgTVV7Z8gbgcXux4uBBgBVjYtIu3v8YmBHymumPqdhwPhlw52QzyOc6AjzV//9Bl+5+mxWVJVlPbbBjRauTYkWXulGC5+dJVq410ibS45V40xjjBkP415cROQ6oElV94jI2vF+/wHnsg5YB5BfOh+/10ssEeeHOxsyFpeW7igPbT/Ek68d7WssuXxeEetrqrl0WfZoYRh9c8lcN840xpjxNBFXLquBj4jIh4B8nDmXe4ASEfG5Vy9LgCPu8UeACqBRRHzAbJyJ/d7xXqnPyTaeRlU3AhsBiivOVVUdtIkR3Gjh3Y08trs/WriswCkU7eEoP9hxmHhCB+2qh+HnVbLJVeNMY4yZCOM+56Kq31DVJapaiTMh/6yqfhb4LfBJ97CbgSfcj7e4n+M+/qw6S7a2ADeKSJ670mw5sBPYBSwXkSoRCbjvsWWk59e7iRGcpcdPvHKEm+7fyUPbDxGJJSnO93HdRQsJ+DwkVZkd9NPc3cM9z+5nZ0r+CkBR/tDzKkNZX1NNLKGEos4KtVA0PqrGmcYYM5Em0z6XrwObReTvgJeB+93x+4GHReQA0IJTLFDVvSLyGPAmEAe+qKoJABH5EvAUzlLkTaq6dyQnEI4liCeVG1Ys4XfvnOT+5+ppbE2PFr5hZQV3/fde/F4PQXfepLdrcm9PsHy/l7IzDO06k8aZxhgz0STbvo2ZpmjxOfq+b3yfVVVlbDtwkreO9UcLX3vhAm6+opK5s5xo4U/fu8Pp9UX/1YiidEXi/ObP1mbNtTfGmOlGRPao6oqB4/ZT0LW4NEh+wMPGbXV9Y1e60cKVA6KFFxYHae7u6btyQSAWV5aVF1phMcYYrLj0OdgcosedMzl/4SzW15yVNVo4tSdYYcBLNJEkqfD59541nqdsjDGTlhWXFEtKg9yxpoo1Z88ZcgJ+VXUZf+4/l827GjjaFrb5EGOMGcDmXFwV51yoz9S+gG+YTsQBn4fywjyCgdOfrDfGmOnC5lyGUVIQGLKweD1CaWGAYmsuaYwxw7LiMgwL7TLGmNGz4jKEgoCPssIAAZ+FdhljzGhYccnA7/VQXhRIa71ijDFm5OynZwqPCKUFAYqDvlG3azHGGNPPiovL6xEqygrSYoiNMcacHptMcPk8YoXFGGNyxIqLMcaYnLPiYowxJuesuBhjjMk5Ky7GGGNyzoqLMcaYnLPiYowxJuesuBhjjMk5Ky7GGGNyzoqLMcaYnLOwMJeInAQOTeApzAFOTeD7T2b2vcnOvjdDs+9Pdrn63ixT1bkDB624TBIisjtTmpux781Q7HszNPv+ZDfW3xu7LWaMMSbnrLgYY4zJOSsuk8fGiT6BScy+N9nZ92Zo9v3Jbky/NzbnYowxJufsysUYY0zOWXExxhiTc1ZcJpiIVIjIb0XkTRHZKyJfnehzmmxExCsiL4vIkxN9LpOJiJSIyOMisk9E3hKRKyb6nCYLEflf7r+nN0TkRyKSP9HnNJFEZJOINInIGyljZSLytIjsd/8uzeV7WnGZeHHgz1T1fOBy4Isicv4En9Nk81XgrYk+iUnoHuBXqnoecDH2PQJARBYDXwFWqOqFgBe4cWLPasI9AFw7YOwvgGdUdTnwjPt5zlhxmWCqekxVX3I/7sT5AbF4Ys9q8hCRJcCHgfsm+lwmExGZDdQA9wOoalRV2yb0pCYXHxAUER9QAByd4POZUKpaC7QMGL4eeND9+EHgo7l8Tysuk4iIVALvBl6c4FOZTP4V+HMgOcHnMdlUASeB77u3DO8TkcKJPqnJQFWPAP8HOAwcA9pV9dcTe1aT0nxVPeZ+fByYn8sXt+IySYhIEfAT4H+qasdEn89kICLXAU2qumeiz2US8gGXAt9T1XcD3eT4tsZU5c4dXI9TgBcBhSLypxN7VpObOntScrovxYrLJCAifpzC8oiq/nSiz2cSWQ18REQOApuBq0XkBxN7SpNGI9Coqr1XuY/jFBsD7wPqVfWkqsaAnwJXTvA5TUYnRGQhgPt3Uy5f3IrLBBMRwblv/paq/stEn89koqrfUNUlqlqJMyH7rKrab6CAqh4HGkTkXHfoGuDNCTylyeQwcLmIFLj/vq7BFjtksgW42f34ZuCJXL64FZeJtxq4Cee38lfcPx+a6JMyU8KXgUdE5DXgEuAfJvZ0Jgf3au5x4CXgdZyfczO6DYyI/AjYDpwrIo0icjvwLeD9IrIf52rvWzl9T2v/YowxJtfsysUYY0zOWXExxhiTc1ZcjDHG5JwVF2OMMTlnxcUYY0zOWXExM5o4nhORD6aM/YmI/GoM3muriLydsuT88Vy/x4D3WzTW72FMNrYU2cx4InIh8GOcvm4+4GXgWlX9/Wm8lk9V41ke2wp8TVV3n8HpnvF5GDMe7MrFzHiq+gbwM+DrwF3AD4C/FJGdblPI68FpLCoi20TkJffPle74Wnd8C/CmiBSKyM9F5FU3T+SGod5fRJ4Qkc+5H68XkUfcj7eKyD3uVc4bIrLKHS908zkGnt8tIrJFRJ4FnnHP9w33Ma+IfFtEdonIayKyPuXct6bkwjzi7mpHRFaKyAvu17FTRGZlex1jBvJN9AkYM0n8Lc6O7ijwJE6rmdtEpATYKSK/wem99H5VjYjIcuBHwAr3+ZcCF6pqvYh8Ajiqqh+Gvvb4vR4RkbD78dOq+r+BdcDzIlIP/BlOrk+vAlW9RERqgE3AhcBfZjm/3vN4l6q2uF22e92O0x14pYjkue/X2yn43cAFOG3pnwdWi8hO4FHgBlXdJSLFQDjb66hq/ai+22bas+JiDKCq3SLyKNAFfAr4YxH5mvtwPrAU54fvf4jIJUACOCflJXam/IB9HfhnEflH4ElV3ZZy3GcH3hZT1RMichfwW+Bjqpqau/Ej95haESl2i8kHcBp6Djw/cArWwNwO3Oe8S0Q+6X4+G1iOU0x3qmojgIi8AlQC7cAxVd3lvn+H+3i217HiYtJYcTGmX9L9I8AnVPXt1AdF5G+AEzipjx4gkvJwd+8HqvqOiFwKfAj4OxF5RlXvHua9LwKacVrEpxo4KapDnN9lqecxgABfVtWnBjxnLdCTMpRg6J8LGV/HmIFszsWYwZ4Cvpwy9/Bud3w2zm/zSZxmo95MTxaRRUBIVX8AfJthWuG7cykfxLk99TURqUp5+Ab3mKtwbke1D3F+w31NXxAn3gEROUeGDhd7G1goIivd42eJk+o42tcxM5RduRgz2DdxEjBfExEPzi2f64D/C/zEnXz/FdmvEi4Cvi0iSSAGfCHlsdQ5l1M4Ec73Areq6lER+TNgk4hc7R4TEZGXAT9w2zDnN5T7cG53veQWpZMMEWurqlF3IcK/i0gQZ77lfaN9HTNz2VJkYyYpGcely8bkmt0WM8YYk3N25WKMMSbn7MrFGGNMzllxMcYYk3NWXIwxxuScFRdjjDE5Z8XFGGNMzv3/tah4246LeHoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"YearsExperience\", y=\"Salary\", data=data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          25792.200199\n",
       "YearsExperience     9449.962321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          5.511950e-12\n",
       "YearsExperience    1.143068e-20\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "\n",
    "#  Creating a random dataset of YearsExperience \n",
    "#  For this random dataset of YearsExperience we are predicting salary by using the model we\n",
    "   build above.\n",
    "   \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "randdata=pd.Series([1,2.2,1.5,2,3,4,1.3,2.4,0.9,0.5,9,3.3,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>YearsExperience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience\n",
       "0              1.0\n",
       "1              2.2\n",
       "2              1.5\n",
       "3              2.0\n",
       "4              3.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_data2=pd.DataFrame(randdata,columns=[\"YearsExperience\"])\n",
    "pred_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction of salary using yearsexperience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      35242.162520\n",
       "1      46582.117306\n",
       "2      39967.143681\n",
       "3      44692.124842\n",
       "4      54142.087163\n",
       "5      63592.049484\n",
       "6      38077.151217\n",
       "7      48472.109770\n",
       "8      34297.166288\n",
       "9      30517.181359\n",
       "10    110841.861092\n",
       "11     56977.075859\n",
       "12     54142.087163\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_model2=model2.predict(pred_data2)\n",
    "pred_model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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