simple-linear-regression-using-ols.ipynb

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   "source": [
    "# **SIMPLE LINEAR REGRESSION USING ORDINARY LEAST SQUARES**"
   ]
  },
  {
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   "source": [
    "# [Kaggle](https://www.kaggle.com/code/avd1729/simple-linear-regression-using-ols)"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "# **Setting up the environment**"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
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   "execution_count": 2,
   "id": "c96f20ac",
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     "status": "completed"
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   "outputs": [],
   "source": [
    "data = pd.read_csv(\"/kaggle/input/salary-data-simple-linear-regression/Salary_Data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b84cf7",
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     "start_time": "2023-04-15T05:47:28.714647",
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    "tags": []
   },
   "source": [
    "# **Building a regression model**"
   ]
  },
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     "name": "stdout",
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     "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": [
    "data.info()"
   ]
  },
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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>const</th>\n",
       "      <th>YearsExperience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   const  YearsExperience\n",
       "0    1.0              1.1\n",
       "1    1.0              1.3\n",
       "2    1.0              1.5\n",
       "3    1.0              2.0\n",
       "4    1.0              2.2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X = sm.add_constant(data['YearsExperience'])\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79b11aea",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-04-15T05:47:31.204126Z",
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     "end_time": "2023-04-15T05:47:31.210655",
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     "status": "completed"
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   "outputs": [],
   "source": [
    "y = data['Salary']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f2e583",
   "metadata": {
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     "start_time": "2023-04-15T05:47:31.218259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Train-Test Split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "644a9852",
   "metadata": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train , X_test , y_train , y_test = train_test_split(X , y ,train_size = 0.7 , random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6fd36f44",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "salary_lm = sm.OLS(y_train , X_train).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc6c3e64",
   "metadata": {
    "papermill": {
     "duration": 0.007265,
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     "start_time": "2023-04-15T05:47:31.450025",
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    "tags": []
   },
   "source": [
    "# **Model Diagnostics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7c3d1b8",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "const              25918.438335\n",
       "YearsExperience     9339.081724\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_lm.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "730ada8f",
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     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>      <td>0.955</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>      <td>Salary</td>             <td>AIC:</td>          <td>424.3780</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2023-04-15 05:47</td>        <td>BIC:</td>          <td>426.4670</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>         <td>21</td>          <td>Log-Likelihood:</td>     <td>-210.19</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>1</td>           <td>F-statistic:</td>        <td>427.6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>           <td>19</td>        <td>Prob (F-statistic):</td>  <td>1.74e-14</td> \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.957</td>            <td>Scale:</td>        <td>3.1967e+07</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>const</th>           <td>25918.4383</td> <td>2507.5217</td> <td>10.3363</td> <td>0.0000</td> <td>20670.1352</td> <td>31166.7415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>YearsExperience</th>  <td>9339.0817</td> <td>451.6530</td>  <td>20.6776</td> <td>0.0000</td>  <td>8393.7612</td> <td>10284.4023</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>0.532</td>  <td>Durbin-Watson:</td>   <td>1.883</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td> <td>0.766</td> <td>Jarque-Bera (JB):</td> <td>0.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>     <td>0.281</td>     <td>Prob(JB):</td>     <td>0.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>   <td>2.368</td>  <td>Condition No.:</td>    <td>12</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                     Results: Ordinary least squares\n",
       "=========================================================================\n",
       "Model:                OLS                Adj. R-squared:       0.955     \n",
       "Dependent Variable:   Salary             AIC:                  424.3780  \n",
       "Date:                 2023-04-15 05:47   BIC:                  426.4670  \n",
       "No. Observations:     21                 Log-Likelihood:       -210.19   \n",
       "Df Model:             1                  F-statistic:          427.6     \n",
       "Df Residuals:         19                 Prob (F-statistic):   1.74e-14  \n",
       "R-squared:            0.957              Scale:                3.1967e+07\n",
       "-------------------------------------------------------------------------\n",
       "                  Coef.     Std.Err.    t    P>|t|    [0.025     0.975]  \n",
       "-------------------------------------------------------------------------\n",
       "const           25918.4383 2507.5217 10.3363 0.0000 20670.1352 31166.7415\n",
       "YearsExperience  9339.0817  451.6530 20.6776 0.0000  8393.7612 10284.4023\n",
       "-------------------------------------------------------------------------\n",
       "Omnibus:                  0.532          Durbin-Watson:             1.883\n",
       "Prob(Omnibus):            0.766          Jarque-Bera (JB):          0.625\n",
       "Skew:                     0.281          Prob(JB):                  0.732\n",
       "Kurtosis:                 2.368          Condition No.:             12   \n",
       "=========================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_lm.summary2()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cb85b1",
   "metadata": {
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     "start_time": "2023-04-15T05:47:31.548622",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Residual analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf5ec3c1",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-04-15T05:47:31.572584Z",
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     "end_time": "2023-04-15T05:47:31.851038",
     "exception": false,
     "start_time": "2023-04-15T05:47:31.563370",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea69ae1",
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     "start_time": "2023-04-15T05:47:31.859389",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# **1.Check for Normal Distribution of Residual**"
   ]
  },
  {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "resid = salary_lm.resid\n",
    "probplot = sm.ProbPlot(np.array(resid))\n",
    "plt.figure(figsize=(10,8))\n",
    "probplot.ppplot(line='45')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32939556",
   "metadata": {
    "papermill": {
     "duration": 0.008268,
     "end_time": "2023-04-15T05:47:32.066859",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.058591",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    " >  We find that the residuals doesn't follow normal distribution , this maybe due to outliers or insufficient data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902e51d7",
   "metadata": {
    "papermill": {
     "duration": 0.008168,
     "end_time": "2023-04-15T05:47:32.084780",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.076612",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **2.Test for Homoscedasticity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e318252b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:32.103666Z",
     "iopub.status.busy": "2023-04-15T05:47:32.103309Z",
     "iopub.status.idle": "2023-04-15T05:47:32.108076Z",
     "shell.execute_reply": "2023-04-15T05:47:32.107248Z"
    },
    "papermill": {
     "duration": 0.016302,
     "end_time": "2023-04-15T05:47:32.109910",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.093608",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_standarized_values(vals):\n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f158a8d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:32.129319Z",
     "iopub.status.busy": "2023-04-15T05:47:32.128380Z",
     "iopub.status.idle": "2023-04-15T05:47:32.294172Z",
     "shell.execute_reply": "2023-04-15T05:47:32.292836Z"
    },
    "papermill": {
     "duration": 0.17771,
     "end_time": "2023-04-15T05:47:32.296222",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.118512",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mBlZ89913kZWVdcsFteTKlSs4ffq0blmj0SAnJweenp7o2rUr4uLicP78eXz00UcAgNmzZ2PNmjWIjY3FrFmzkJGRgU2bNuGzzz4za51ERERkP4x+mqw5UVFRZh+/JysrCyEhIQgJCQEAxMbGIiQkBEuWLAEAFBcX6411pFarsXv3bqSlpaF///549dVXsXr16mYfqyciIiLpMXpusuasWLECa9euxdmzZ03xcTaDc5MRERHZH7POTRYSEqLXgVoIgZKSEvz+++9Yu3at8dUSERERWZHRYWj8+PF6yw4ODujSpQuGDx+O3r17m6ouIiIiIosw2W2y9oq3yYiIiOyPyW+TGTM/FwMDERER2RODwlDnzp1bHGjxz2pqam6pICIiIiJLMigM7du3T/f32bNnsXjxYsycORPh4eEA6iZE3bJlCxITE81TJREREZGZGN1naOTIkXj88cfx8MMP663/9NNPsWHDBqSlpZmyPqtjnyEiIiL7Y8z52+hBFzMyMvRmrq8XGhqKgwcPGvtxRERERFZldBjy9/fH+vXrG61/77334O/vb5KiiIiIiCzF6HGG3nrrLUycOBHffvstBg8eDADIzMxEQUGB2afjICIiIjI1o68MjRkzBqdOncLf/vY3XLx4EWVlZRg3bhxOnTqFMWPGmKNGIiIiIrPhoIutYAdqIiIi+2PyQRdzc3MRHBwMBwcH5Obmtti2b9++hldKREREZGUGhaH+/fujpKQEPj4+6N+/P2QyGZq6oCSTyTjoIhEREdkVg8KQRqNBly5ddH8TERERtRcGhaGAgIAm/yYiIiKyd0Y/TbZlyxbs2rVLt7xo0SJ07twZQ4YMwblz50xaHBEREZG5GR2GXn/9dXTs2BFA3WjUa9aswYoVK+Dt7Y0FCxaYvEAiIiIiczJ60MWioiL06NEDAPDVV1/h73//O5544gn89a9/xfDhw01dHxEREZFZGX1lyM3NDWVlZQCAPXv24P777wcAyOVy/PHHH6atjoiIiMjMjL4yNGrUKDz++OMICQnBqVOnMHbsWADAzz//jMDAQFPXR2SUmlqBg5qLKK2ohI+7HGFqTzg6yKxdFhER2TCjw9C7776Ll156CUVFRUhOToaXlxcA4PDhw3j44YdNXiCRoVKOFyNhZx6KtZW6dSqFHEujgxAZrLJiZUREZMs4HUcrOB2HfUg5XoyYpCNoeDDXXxNaN20AAxERkYQYc/42us8QABw4cADTpk3DkCFDcP78eQDAxx9/jO+//74tH0d0S2pqBRJ25jUKQgB06xJ25qGmlrmfiIgaMzoMJScnY/To0ejYsSOOHDmCqqoqAEBFRQVef/11kxdI1JqDmot6t8YaEgCKtZU4qLlouaKIiMhuGB2Gli1bhvXr12Pjxo1wcnLSrR8yZAiOHDli0uKIDFFa0XwQaks7IiKSFqPDUH5+PoYNG9ZovYeHBy5fvmyKmoiM4uMuN2k7IiKSFqPDkEqlwunTpxut//7779GtWzeTFEVkjDC1J1QKOZp7gF6GuqfKwtSeliyLiIjshNFh6Mknn8S8efPw008/QSaT4bfffsMnn3yChQsX4qmnnjJHje1STa1ARkEZvs45j4yCMnbuvQWODjIsjQ4CgEaBqH55aXQQxxsiIqImGT3O0KJFi6DVajFixAhUVlZi2LBhcHFxwcKFCzFnzhxz1NjucDwc04sMVmHdtAGN9quS+5WIiFrR5nGGrl27hry8PNTW1iIoKAhubm64du0aXF1dTV2jVZl6nCGOh2NeHIGaiIgAC4wzBACurq4IDQ1FWFgYOnTogFWrVrHPUCs4Ho75OTrIEN7dC+P6347w7l4MQkRE1CqDw1B1dTVefPFF3H333RgyZAi++uorAMDmzZvRrVs3rFy5EvPmzTNXne0Cx8MhIiKyPQb3GYqPj8e7776LUaNG4YcffsCkSZPwz3/+E2lpaUhMTMTUqVP1xh2ixjgeDhERke0xOAx98cUX+PDDDzFhwgQcPXoUISEhKC8vx88//4wOHYzuhy1JHA+HiIjI9hh8m6yoqAh33303AKBfv35wdnbG888/zyBkBI6HQ0REZHsMDkPXr1+Hs7OzbtnJyQkKhcIsRbVk7dq1UKvVkMvlGDhwIA4cONBs27S0NMhkskavkydPWrDimzgeDhERke0x6rLOkiVLdI/OV1dXY9myZY0C0apVq0xXXQNbt27F/PnzsXbtWvz1r3/Fe++9h6ioKOTl5aFr167Nbpefn6/3WF2XLl3MVmNrOB4OERGRbTF4nKHhw4dDJmv5ioVMJsPevXtNUlhTBg0ahAEDBmDdunW6dX369MH48eORmJjYqH1aWhpGjBiBS5cuoXPnzm36TlOPM1SP4+EQERGZjzHnb4OvDKWlpd1qXbekuroahw8fxuLFi/XWR0RE4Mcff2xx25CQEFRWViIoKAgvvfQSRowY0WzbqqoqVFVV6ZbLy8tvrfBm1I+HQ0RERNbV5kEXLe3ChQuoqamBr6+v3npfX1+UlJQ0uY1KpcKGDRuQnJyMbdu2oVevXhg5ciT279/f7PckJiZCoVDoXv7+/ib9HURERGRb7O5RsIa36oQQzd6+69WrF3r16qVbDg8PR1FREd58800MGzasyW3i4uIQGxurWy4vL2cgIiIiasfsJgx5e3vD0dGx0VWg0tLSRleLWjJ48GAkJSU1+76LiwtcXFzaXCcREZGlsP+padhNGHJ2dsbAgQORmpqKCRMm6NanpqZi3LhxBn9OdnY2VCo+sUVE9o0nQUo5XtzoyWQVn0xuE7sJQwAQGxuL6dOnIzQ0FOHh4diwYQMKCwsxe/ZsAHW3uM6fP4+PPvoIAPD2228jMDAQd955J6qrq5GUlITk5GQkJydb82cQEd0SngQp5XgxYpKONJr4u0RbiZikI1g3bQCPBSMYFIZyc3MN/sC+ffu2uZjWTJ48GWVlZXjllVdQXFyM4OBg7N69GwEBAQCA4uJiFBYW6tpXV1dj4cKFOH/+PDp27Ig777wTu3btwpgxY8xWIxGROfEkSDW1Agk78xodA0DdhN8yAAk78zAqSMmrhQYyaJwhBwcHyGSyFjsr16upqTFZcbbAXOMMEREZq6ZW4J7le/WuCP2ZDHUDuH7//H08CbZjGQVleHhjZqvtPps1WNJDuBhz/jbo0XqNRoMzZ85Ao9EgOTkZarUaa9euRXZ2NrKzs7F27Vp0796dt5+IiMzooOZis0EIqLsqUKytxEHNRcsVRRZXWtH8MdCWdmTgbbL621AAMGnSJKxevVrvVlPfvn3h7++Pl19+GePHjzd5kURExJMg1fFxl5u0HbVh0MVjx45BrVY3Wq9Wq5GXl2eSooiIqDGeBAkAwtSeUCnkjSb8ridDXYf6MLWnJcuya0aHoT59+mDZsmWorLz5Xx5VVVVYtmwZ+vTpY9LiiIjoJp4ECaibzmlpdBAANDoW6peXRgex35gRjA5D69evx//93//B398f999/P+6//37ccccdSE1Nxfr1681RIxERgSdBuikyWIV10wZAqdC/CqhUyPlEYRsYPGv9n127dg1JSUk4efIkhBAICgrC1KlT0alTJ3PUaFV8moyIbA3HGaJ6HHyzecacv9sUhqSEYYiIbBFPgkQtM/mj9Q19/PHHuOeee+Dn54dz584BAN566y18/fXXbfk4IiIykqODDOHdvTCu/+0I7+7FIER0C4wOQ+vWrUNsbCyioqJw6dIl3SCLt912G95++21T10dERBJRUyuQUVCGr3POI6OgDDW1vHFBlmH03GTvvPMONm7ciPHjx+ONN97QrQ8NDcXChQtNWhwREUkD+0GRNRl9ZUij0SAkJKTRehcXF1y9etUkRRERkXTUz7fWcHTt+vnWUo4XW6kykgqjw5BarUZOTk6j9d988w2CgoJMURMREUlEa5OOAnWTjvKWGZmT0bfJnnvuOTz99NOorKyEEAIHDx7EZ599hsTERLz//vvmqJGIiNopY+Zbk/Kko2ReRoehf/zjH7hx4wYWLVqEa9euYerUqbj99tvx73//G1OmTDFHjURE1E5xvjWyBUaHIQCYNWsWZs2ahQsXLqC2thY+Pj6mrouIiCSA862RLTC6z9Arr7yCvXv3AgC8vb11Qejq1at45ZVXTFsdERG1a5xvjWyB0WEoPj4eUVFRWLVqld76K1euICEhwWSFERFR+8f51sgWtGkE6o8++giJiYmYOXMmqqurTV0TERFJCCcdJWszem4yBwcHlJSUoKKiAtHR0ejcuTO2b98OAPDz89ONSN1ecG4yIiLL4HxrZEpmnZtMJqs7MLt3747MzEx4eHggNDQUWVlZbauWiIgInG+NrMfoMPTnC0keHh7YvXs3JkyYgPHjx5uyLiJJ4txMRESWZ/Sj9Zs3b4ZCodAtOzg4YPXq1QgJCcH+/ftNWhyRlHBuJiIi6zC6z5DUsM8QWUL93EwN/8dYf5OAnUiJiIxjzPnboCtDq1evxhNPPAG5XI7Vq1c3204mk+GZZ54xrloiiWttbiYZ6uZmGhWkZB8KIiIzMOjKkFqtRlZWFry8vKBWq5v/MJkMZ86cMWmB1sYrQ2RuGQVleHhjZqvtPps1mHMzEREZyORXhjQaTZN/E9Gt49xMRETWZdTTZNevX0e3bt2Ql5dnrnqIJIdzMxERWZdRYcjJyQlVVVW6sYaI6NZxbiYiIusyepyhZ555BsuXL8eNGzfMUQ+R5HBuJiIi6zJ6nKGffvoJ3333Hfbs2YO77roLnTp10nt/27ZtJiuOSCrq52ZqOM6QkuMMWRSngyCSJqPDUOfOnTFx4kRz1EIkaZHBKowKUvJkbCUc9JJIujjoYiv4aD1R+8dBL4naH7NO1EpE1J60NuglUDfoJeeJI2q/jL5NBgBffvklvvjiCxQWFqK6ulrvvSNHjpikMCIiSziouah3a6whAaBYW4mDmosc9JKonTL6ytDq1avxj3/8Az4+PsjOzkZYWBi8vLxw5swZREVFmaNGIiKz4aCXRGR0GFq7di02bNiANWvWwNnZGYsWLUJqairmzp0LrVZrjhqJiMyGg14SkdFhqLCwEEOGDAEAdOzYERUVFQCA6dOn47PPPjNtdUREZsZBL4nI6DCkVCpRVlYGAAgICEBmZt0EkxqNBpZ4MG3t2rVQq9WQy+UYOHAgDhw40GL79PR0DBw4EHK5HN26dcP69evNXiMR2Q8OeklERoeh++67Dzt37gQAPPbYY1iwYAFGjRqFyZMnY8KECSYv8M+2bt2K+fPn48UXX0R2djaGDh2KqKgoFBYWNtleo9FgzJgxGDp0KLKzs/HCCy9g7ty5SE5ONmudRGRf6ge9VCr0b4UpFXI+Vk8kAUaPM1RbW4va2lp06FD3INoXX3yB77//Hj169MDs2bPh7OxslkIBYNCgQRgwYADWrVunW9enTx+MHz8eiYmJjdo///zz2LFjB06cOKFbN3v2bBw9ehQZGRlNfkdVVRWqqqp0y+Xl5fD39+c4Q0QSwBGoidoPY8YZMvrRegcHBzg43Lyg9NBDD+Ghhx4yvkojVVdX4/Dhw1i8eLHe+oiICPz4449NbpORkYGIiAi9daNHj8amTZtw/fp1ODk5NdomMTERCQkJpiuciOyGo4OMj88TSZBBYSg3N9fgD+zbt2+bi2nJhQsXUFNTA19fX731vr6+KCkpaXKbkpKSJtvfuHEDFy5cgErV+NJ3XFwcYmNjdcv1V4aIiIiofTIoDPXv3x8ymQxCCMhkLV8yrqmpMUlhzWn4/a3V1FT7ptbXc3FxgYuLyy1WSURERPbCoA7UGo0GZ86cgUajQXJyMtRqNdauXYvs7GxkZ2dj7dq16N69u1k7Jnt7e8PR0bHRVaDS0tJGV3/qKZXKJtt36NABXl68FE5EREQGXhkKCAjQ/T1p0iSsXr0aY8aM0a3r27cv/P398fLLL2P8+PEmLxIAnJ2dMXDgQKSmpuo9tZaamopx48Y1uU14eLjuybd6e/bsQWhoaJP9hYiIiEh6jH60/tixY1Cr1Y3Wq9Vq5OXlmaSo5sTGxuL999/HBx98gBMnTmDBggUoLCzE7NmzAdT193n00Ud17WfPno1z584hNjYWJ06cwAcffIBNmzZh4cKFZq2TiIiI7IfRT5P16dMHy5Ytw6ZNmyCX143JUVVVhWXLlqFPnz4mL/DPJk+ejLKyMrzyyisoLi5GcHAwdu/erbtyVVxcrDfmkFqtxu7du7FgwQK8++678PPzw+rVqzFx4kSz1klERET2w+hxhg4ePIjo6GjU1taiX79+AICjR49CJpPhv//9L8LCwsxSqLUYM04BERER2QZjzt9GhyEAuHbtGpKSknDy5EkIIRAUFISpU6eiU6dObS7aVjEMERER2R+zDroIAK6urnjiiSfaVBwRERGRLWlTGDp16hTS0tJQWlqK2tpavfeWLFliksKIiIiILMHoMLRx40bExMTA29sbSqVSb/BCmUzGMERERER2xegwtGzZMrz22mt4/vnnzVEPERERkUUZPc7QpUuXMGnSJHPUQkRERGRxRoehSZMmYc+ePeaohWxUTa1ARkEZvs45j4yCMtTUGv0AIhERkc0y+jZZjx498PLLLyMzMxN33XVXo2kt5s6da7LiyPpSjhcjYWceirWVunUqhRxLo4MQGayyYmVERESmYfQ4Q01NxaH7MJkMZ86cueWibImUxxlKOV6MmKQjaHiA1HeZXzdtAAMRERHZJLOOM6TRaNpcGNmPmlqBhJ15jYIQAAjUBaKEnXkYFaSEo4OsiVZERET2weg+QyQNBzUX9W6NNSQAFGsrcVBz0XJFERERmUGbBl389ddfsWPHDhQWFqK6ulrvvVWrVpmkMLKu0ormg1Bb2tm6mlqBg5qLKK2ohI+7HGFqT17xIiKSCKPD0HfffYe//e1vUKvVyM/PR3BwMM6ePQshBAYMGGCOGskKfNzlJm1ny9hJnIhI2oy+TRYXF4dnn30Wx48fh1wuR3JyMoqKinDvvfdy/KF2JEztCZVCjuaujchQFxjC1J6WLMvk6juJN7wlWKKtREzSEaQcL7ZSZUREZClGh6ETJ05gxowZAIAOHTrgjz/+gJubG1555RUsX77c5AWSdTg6yLA0OggAGgWi+uWl0UF2fSuptU7iQF0ncY6rRETUvhkdhjp16oSqqioAgJ+fHwoKCnTvXbhwwXSVkdVFBquwbtoAKBX6t8KUCnm7eKyencSJiAhoQ5+hwYMH44cffkBQUBDGjh2LZ599FseOHcO2bdswePBgc9RIVhQZrMKoIGW77FwstU7iRETUNKPD0KpVq3DlyhUAQHx8PK5cuYKtW7eiR48eeOutt0xeIFmfo4MM4d29rF2GyUmpkzgRETXP6DDUrVs33d+urq5Yu3atSQsispT6TuIl2som+w3JUHdL0N47iRMRUcuM7jPUrVs3lJWVNVp/+fJlvaBEZOuk0EmciIhaZ3QYOnv2LGpqahqtr6qqwvnz501SFJGltPdO4kRE1DqDb5Pt2LFD9/e3334LhUKhW66pqcF3332HwMBAkxZHZAntuZM4ERG1zuBZ6x0c6i4iyWQyNNzEyckJgYGBWLlyJR544AHTV2lFUp61noiIyF6ZZdb62tpaAIBarcahQ4fg7e19a1USERER2QCjnybTaDTmqIOIiIgkxJYmyDY4DP3000+4ePEioqKidOs++ugjLF26FFevXsX48ePxzjvvwMXFxSyFUvNs6YAiIiJqja1NkG1wGIqPj8fw4cN1YejYsWN47LHHMHPmTPTp0wf/+te/4Ofnh/j4eHPVSk2wtQOKiIioJfUTZDfssFw/QbY1nuQ1+NH6nJwcjBw5Urf8+eefY9CgQdi4cSNiY2OxevVqfPHFF2YpkprGGdeJiMie2OoE2QaHoUuXLsHX11e3nJ6ejsjISN3y3XffjaKiItNWR82y1QOKiIioObY6QbbBYcjX11fXebq6uhpHjhxBeHi47v2Kigo4OTmZvkJqkq0eUERERM2x1QmyDQ5DkZGRWLx4MQ4cOIC4uDi4urpi6NChuvdzc3PRvXt3sxRJjdnqAUVERNQcW50g2+AO1MuWLcODDz6Ie++9F25ubtiyZQucnZ1173/wwQeIiIgwS5HUmK0eUERERM2x1QmyDQ5DXbp0wYEDB6DVauHm5gZHR0e99//zn//Azc3N5AVS02z1gCIiImpO/QTZMUlHIAP0zl/WnCDb6IlaFQpFoyAEAJ6ennpXisi8OOM6ERHZI1ucINvgucmkytbnJuM4Q0REZI/MPWCwMedvhqFW2HoYAjgCNRERUUPGnL+Nvk1mLZcuXcL06dOhUCigUCgwffp0XL58ucVtZs6cCZlMpvcaPHiwZQq2IEcHGcK7e2Fc/9sR3t2LQYiIiMgIRk/Uai1Tp07Fr7/+ipSUFADAE088genTp2Pnzp0tbhcZGYnNmzfrltmviYiIiP7MLsLQiRMnkJKSgszMTAwaNAgAsHHjRoSHhyM/Px+9evVqdlsXFxcolUpLlUpERER2xi5uk2VkZEChUOiCEAAMHjwYCoUCP/74Y4vbpqWlwcfHBz179sSsWbNQWlraYvuqqiqUl5frvYiIiKj9soswVFJSAh8fn0brfXx8UFJS0ux2UVFR+OSTT7B3716sXLkShw4dwn333Yeqqqpmt0lMTNT1S1IoFPD39zfJbyAiIiLbZNUwFB8f36iDc8NXVlYWAEAma9wpWAjR5Pp6kydPxtixYxEcHIzo6Gh88803OHXqFHbt2tXsNnFxcdBqtboXJ58lIiJq36zaZ2jOnDmYMmVKi20CAwORm5uL//3vf43e+/333+Hr62vw96lUKgQEBOCXX35pto2LiwtcXFwM/kwiIiKyb1YNQ97e3vD29m61XXh4OLRaLQ4ePIiwsDAAwE8//QStVoshQ4YY/H1lZWUoKiqCSsXBCImIiKiOXfQZ6tOnDyIjIzFr1ixkZmYiMzMTs2bNwgMPPKD3JFnv3r2xfft2AMCVK1ewcOFCZGRk4OzZs0hLS0N0dDS8vb0xYcIEa/0UIiIisjF2EYYA4JNPPsFdd92FiIgIREREoG/fvvj444/12uTn50Or1QIAHB0dcezYMYwbNw49e/bEjBkz0LNnT2RkZMDd3d0aP4GIiIhsEKfjaIU9TMdBRERE+trldBxERERE5sAwRERERJLGMERERESSxjBEREREksYwRERERJLGMERERESSxjBEREREksYwRERERJLGMERERESSxjBEREREksYwRERERJLGMERERESSxjBEREREksYwRERERJLGMERERESSxjBEREREktbB2gUQERG1VzW1Agc1F1FaUQkfdznC1J5wdJBZuyxqgGGIiIjIDFKOFyNhZx6KtZW6dSqFHEujgxAZrLJiZdQQb5MRERGZWMrxYsQkHdELQgBQoq1ETNIRpBwvtlJl1BSGISIiIhOqqRVI2JkH0cR79esSduahprapFmQNDENEREQmdFBzsdEVoT8TAIq1lTiouWi5oqhFDENEREQmVFrRfBBqSzsyP4YhIiIiE/Jxl5u0HZkfwxAREZEJhak9oVLI0dwD9DLUPVUWpva0ZFnUAoYhIiIiE3J0kGFpdBAANApE9ctLo4M43pANYRgiIiIyschgFdZNGwClQv9WmFIhx7ppAzjOkI3hoItERERmEBmswqggJUegtgMMQ0RERGbi6CBDeHcva5dBreBtMiIiIpI0hiEiIiKSNIYhIiIikjSGISIiIpI0hiEiIiKSNIYhIiIikjSGISIiIpI0hiEiIiKSNIYhIiIikjS7CUOvvfYahgwZAldXV3Tu3NmgbYQQiI+Ph5+fHzp27Ijhw4fj559/Nm+hREREZFfsJgxVV1dj0qRJiImJMXibFStWYNWqVVizZg0OHToEpVKJUaNGoaKiwoyVEhERkT2RCSGEtYswxocffoj58+fj8uXLLbYTQsDPzw/z58/H888/DwCoqqqCr68vli9fjieffLLJ7aqqqlBVVaVbLi8vh7+/P7RaLTw8PEz2O4iIiMh8ysvLoVAoDDp/282VIWNpNBqUlJQgIiJCt87FxQX33nsvfvzxx2a3S0xMhEKh0L38/f0tUS4RERFZSbsNQyUlJQAAX19fvfW+vr6695oSFxcHrVarexUVFZm1TiIiIrIuq4ah+Ph4yGSyFl9ZWVm39B0ymUxvWQjRaN2fubi4wMPDQ+9FRERE7VcHa375nDlzMGXKlBbbBAYGtumzlUolgLorRCqVSre+tLS00dUiIiIiki6rhiFvb294e3ub5bPVajWUSiVSU1MREhICoO6JtPT0dCxfvtws30lERET2x276DBUWFiInJweFhYWoqalBTk4OcnJycOXKFV2b3r17Y/v27QDqbo/Nnz8fr7/+OrZv347jx49j5syZcHV1xdSpU631M4iIiMjGWPXKkDGWLFmCLVu26Jbrr/bs27cPw4cPBwDk5+dDq9Xq2ixatAh//PEHnnrqKVy6dAmDBg3Cnj174O7ubtHaiYiIyHbZ3ThDlmbMOAVERERkGzjOEBEREZGBGIaIiIhI0hiGiIiISNIYhoiIiEjSGIaIiIhI0hiGiIiISNLsZpwhsm01tQIHNRdRWlEJH3c5wtSecHRofg44IiIiW8EwRLcs5XgxEnbmoVhbqVunUsixNDoIkcGqFrYkIiKyPt4mo1uScrwYMUlH9IIQAJRoKxGTdAQpx4utVBkREZFhGIaozWpqBRJ25qGpIczr1yXszENNLQc5JyIi28UwRG12UHOx0RWhPxMAirWVOKi5aLmiyGg1tQIZBWX4Ouc8MgrKGF6JSHLYZ4jarLSi+SDUlnZkeezvRUTEK0N0C3zc5SZtR5bF/l5ERHUYhqjNwtSeUCnkaO4BehnqrjKEqT0tWRYZgP29iIhuYhiiNnN0kGFpdBAANApE9ctLo4M43pANYn8vIqKbGIbolkQGq7Bu2gAoFfq3wpQKOdZNG8B+J2Zgig7P7O9FRHQTO1DTLYsMVmFUkJIjUFuAqTo8s78XEdFNDENkEo4OMoR397J2Ge1afYfnhteB6js8G3Mlrr6/V4m2ssl+QzLUXd1jfy8ikgLeJiOyA6bu8Mz+XkRENzEMEdkBc3R4Zn8vIqI6vE1GZAfM1eGZ/b2IiBiGiGxeTa3AhYoqg9q2pcMz+3sRkdQxDBHZsKaeHmuKPXV4rqkVvBJFRDaFYYjIRjX39FhD9tThmXOhEZEtYgdqIhvU0tNjDdlLh2fOhUZEtopXhohsUGtPj9V7eWwfzPyr2uavCLU2NIAMdUMDjApS2vxvIaL2h1eGiGyQoU+Febu72EV44FxoRGTLGIaIbFB7my6Dc6ERkS1jGCKyQfXTZTR3zUeGuo7H9vD0GND+wh0RtS8MQ0Q2qL1Nl9Hewh0RtS8MQ0Q2qj1Nl9Hewh0RtS8yIYRhMztKVHl5ORQKBbRaLTw8PKxdDklQexqkkOMMEZGlGHP+ZhhqBcMQkWm1p3BHRLbLmPM3xxkiIoviXGhEZGvYZ4iIiIgkjWGIiIiIJM1uwtBrr72GIUOGwNXVFZ07dzZom5kzZ0Imk+m9Bg8ebN5CiYiIyK7YTRiqrq7GpEmTEBMTY9R2kZGRKC4u1r12795tpgqJiIjIHtlNB+qEhAQAwIcffmjUdi4uLlAqlWaoiIiIiNoDu7ky1FZpaWnw8fFBz549MWvWLJSWlrbYvqqqCuXl5XovIiIiar/adRiKiorCJ598gr1792LlypU4dOgQ7rvvPlRVVTW7TWJiIhQKhe7l7+9vwYqJiIjI0qwahuLj4xt1cG74ysrKavPnT548GWPHjkVwcDCio6PxzTff4NSpU9i1a1ez28TFxUGr1epeRUVFbf5+IiIisn1W7TM0Z84cTJkypcU2gYGBJvs+lUqFgIAA/PLLL822cXFxgYuLi8m+k4iIiGybVcOQt7c3vL29LfZ9ZWVlKCoqgkpl+BxI9bOVsO8QERGR/ag/bxsy65jdPE1WWFiIixcvorCwEDU1NcjJyQEA9OjRA25ubgCA3r17IzExERMmTMCVK1cQHx+PiRMnQqVS4ezZs3jhhRfg7e2NCRMmGPy9ZWVlAMC+Q0RERHaooqICCoWixTZ2E4aWLFmCLVu26JZDQkIAAPv27cPw4cMBAPn5+dBqtQAAR0dHHDt2DB999BEuX74MlUqFESNGYOvWrXB3dzf4ez09PQHUhbHWdmZ7V15eDn9/fxQVFXHSWnB//Bn3xU3cFzdxX9zEfXGTpfaFEAIVFRXw8/NrtS1nrW8FZ62/iftCH/fHTdwXN3Ff3MR9cRP3xU22uC/a9aP1RERERK1hGCIiIiJJYxhqhYuLC5YuXcrH7cF90RD3x03cFzdxX9zEfXET98VNtrgv2GeIiIiIJI1XhoiIiEjSGIaIiIhI0hiGiIiISNIYhoiIiEjSGIaa8Nprr2HIkCFwdXVF586dDdpm5syZkMlkeq/Bgwebt1ALaMu+EEIgPj4efn5+6NixI4YPH46ff/7ZvIVawKVLlzB9+nQoFAooFApMnz4dly9fbnGb9nJcrF27Fmq1GnK5HAMHDsSBAwdabJ+eno6BAwdCLpejW7duWL9+vYUqtQxj9kdaWlqjY0Amk+HkyZMWrNj09u/fj+joaPj5+UEmk+Grr75qdZv2fFwYuz/a63GRmJiIu+++G+7u7vDx8cH48eORn5/f6nbWPjYYhppQXV2NSZMmISYmxqjtIiMjUVxcrHvt3r3bTBVaTlv2xYoVK7Bq1SqsWbMGhw4dglKpxKhRo1BRUWHGSs1v6tSpyMnJQUpKClJSUpCTk4Pp06e3up29Hxdbt27F/Pnz8eKLLyI7OxtDhw5FVFQUCgsLm2yv0WgwZswYDB06FNnZ2XjhhRcwd+5cJCcnW7hy8zB2f9TLz8/XOw7+8pe/WKhi87h69Sr69euHNWvWGNS+vR8Xxu6Peu3tuEhPT8fTTz+NzMxMpKam4saNG4iIiMDVq1eb3cYmjg1Bzdq8ebNQKBQGtZ0xY4YYN26cWeuxJkP3RW1trVAqleKNN97QrausrBQKhUKsX7/ejBWaV15engAgMjMzdesyMjIEAHHy5Mlmt2sPx0VYWJiYPXu23rrevXuLxYsXN9l+0aJFonfv3nrrnnzySTF48GCz1WhJxu6Pffv2CQDi0qVLFqjOOgCI7du3t9imvR8Xf2bI/pDCcSGEEKWlpQKASE9Pb7aNLRwbvDJkQmlpafDx8UHPnj0xa9YslJaWWrski9NoNCgpKUFERIRunYuLC+699178+OOPVqzs1mRkZEChUGDQoEG6dYMHD4ZCoWj1d9nzcVFdXY3Dhw/r/XsCQERERLO/OyMjo1H70aNHIysrC9evXzdbrZbQlv1RLyQkBCqVCiNHjsS+ffvMWaZNas/Hxa1o78dF/eTp9ZOeN8UWjg2GIROJiorCJ598gr1792LlypU4dOgQ7rvvPlRVVVm7NIsqKSkBAPj6+uqt9/X11b1nj0pKSuDj49NovY+PT4u/y96PiwsXLqCmpsaof8+SkpIm29+4cQMXLlwwW62W0Jb9oVKpsGHDBiQnJ2Pbtm3o1asXRo4cif3791uiZJvRno+LtpDCcSGEQGxsLO655x4EBwc3284Wjo0OFvkWGxAfH4+EhIQW2xw6dAihoaFt+vzJkyfr/g4ODkZoaCgCAgKwa9cuPPjgg236THMx974AAJlMprcshGi0zhYYui+Axr8JaP132dNx0RJj/z2bat/UentlzP7o1asXevXqpVsODw9HUVER3nzzTQwbNsysddqa9n5cGEMKx8WcOXOQm5uL77//vtW21j42JBOG5syZgylTprTYJjAw0GTfp1KpEBAQgF9++cVkn2kq5twXSqUSQF3SV6lUuvWlpaWNkr8tMHRf5Obm4n//+1+j937//XejfpctHxdN8fb2hqOjY6OrHi39eyqVyibbd+jQAV5eXmar1RLasj+aMnjwYCQlJZm6PJvWno8LU2lPx8UzzzyDHTt2YP/+/bjjjjtabGsLx4ZkwpC3tze8vb0t9n1lZWUoKirSCwS2wpz7Qq1WQ6lUIjU1FSEhIQDq+lmkp6dj+fLlZvnOW2HovggPD4dWq8XBgwcRFhYGAPjpp5+g1WoxZMgQg7/Plo+Lpjg7O2PgwIFITU3FhAkTdOtTU1Mxbty4JrcJDw/Hzp079dbt2bMHoaGhcHJyMmu95taW/dGU7OxsuzkGTKU9Hxem0h6OCyEEnnnmGWzfvh1paWlQq9WtbmMTx4bFumrbkXPnzons7GyRkJAg3NzcRHZ2tsjOzhYVFRW6Nr169RLbtm0TQghRUVEhnn32WfHjjz8KjUYj9u3bJ8LDw8Xtt98uysvLrfUzTMLYfSGEEG+88YZQKBRi27Zt4tixY+Lhhx8WKpXK7vdFZGSk6Nu3r8jIyBAZGRnirrvuEg888IBem/Z4XHz++efCyclJbNq0SeTl5Yn58+eLTp06ibNnzwohhFi8eLGYPn26rv2ZM2eEq6urWLBggcjLyxObNm0STk5O4ssvv7TWTzApY/fHW2+9JbZv3y5OnToljh8/LhYvXiwAiOTkZGv9BJOoqKjQ/f8BALFq1SqRnZ0tzp07J4SQ3nFh7P5or8dFTEyMUCgUIi0tTRQXF+te165d07WxxWODYagJM2bMEAAavfbt26drA0Bs3rxZCCHEtWvXREREhOjSpYtwcnISXbt2FTNmzBCFhYXW+QEmZOy+EKLu8fqlS5cKpVIpXFxcxLBhw8SxY8csX7yJlZWViUceeUS4u7sLd3d38cgjjzR6LLa9HhfvvvuuCAgIEM7OzmLAgAF6j8nOmDFD3HvvvXrt09LSREhIiHB2dhaBgYFi3bp1Fq7YvIzZH8uXLxfdu3cXcrlc3HbbbeKee+4Ru3btskLVplX/aHjD14wZM4QQ0jsujN0f7fW4aGofNDxH2OKxIRPi//dSIiIiIpIgPlpPREREksYwRERERJLGMERERESSxjBEREREksYwRERERJLGMERERESSxjBEREREksYwRERERJLGMEQkYWfPnoVMJkNOTs4tf1ZgYCDefvvtW/6c5piyVlORyWT46quvAFi3vpkzZ2L8+PFm/Y74+Hj079/frN9BZC0MQ0QWVFpaiieffBJdu3aFi4sLlEolRo8ejYyMDF2bP59g7cmhQ4fwxBNPWLsMq/H390dxcTGCg4MNam+JAENEhpHMrPVEtmDixIm4fv06tmzZgm7duuF///sfvvvuO1y8eNHapbVZdXU1nJ2d0aVLF2uX0ibXr183yczYjo6OUCqVJqiIiCyNV4aILOTy5cv4/vvvsXz5cowYMQIBAQEICwtDXFwcxo4dC6DuVhMATJgwATKZTLdcUFCAcePGwdfXF25ubrj77rvxf//3f3qfHxgYiNdffx3//Oc/4e7ujq5du2LDhg16bQ4ePIiQkBDI5XKEhoYiOztb7/2amho89thjUKvV6NixI3r16oV///vfem3qr2gkJibCz88PPXv21H1//W2yDz/8EDKZrNErPj5e9zmbN29Gnz59IJfL0bt3b6xdu9aoWpsSGBiIV199FVOnToWbmxv8/Pzwzjvv6LWRyWRYv349xo0bh06dOmHZsmUAgJ07d2LgwIGQy+Xo1q0bEhIScOPGDd12v/zyC4YNGwa5XI6goCCkpqbqfW5Tt8l+/vlnjB07Fh4eHnB3d8fQoUNRUFCA+Ph4bNmyBV9//bVu36SlpQEAzp8/j8mTJ+O2226Dl5cXxo0bh7Nnz+r9G8XGxqJz587w8vLCokWL0NIUk1qtFh07dkRKSore+m3btqFTp064cuUKAOD5559Hz5494erqim7duuHll1/G9evXm/3c4cOHY/78+Xrrxo8fj5kzZ+qWq6ursWjRItx+++3o1KkTBg0apPudAHDu3DlER0fjtttuQ6dOnXDnnXdi9+7dzX4nkdlYdFpYIgm7fv26cHNzE/PnzxeVlZVNtiktLdXN8FxcXCxKS0uFEELk5OSI9evXi9zcXHHq1Cnx4osvCrlcLs6dO6fbNiAgQHh6eop3331X/PLLLyIxMVE4ODiIEydOCCGEuHLliujSpYuYPHmyOH78uNi5c6fo1q2bACCys7OFEEJUV1eLJUuWiIMHD4ozZ86IpKQk4erqKrZu3ar7nhkzZgg3Nzcxffp0cfz4cXHs2DHd97/11ltCCCGuXbsmiouLda/PPvtMdOjQQezZs0cIIcSGDRuESqUSycnJ4syZMyI5OVl4enqKDz/80OBamxIQECDc3d1FYmKiyM/PF6tXrxaOjo667xWiblZtHx8fsWnTJlFQUCDOnj0rUlJShIeHh/jwww9FQUGB2LNnjwgMDBTx8fFCCCFqampEcHCwGD58uMjOzhbp6ekiJCREABDbt28XQgih0Wj06vv111+Fp6enePDBB8WhQ4dEfn6++OCDD8TJkydFRUWFeOihh0RkZKRuH1VVVYmrV6+Kv/zlL+Kf//ynyM3NFXl5eWLq1KmiV69eoqqqSghRN9u5QqEQX375pcjLyxOPPfaYcHd3F+PGjWt2v0ycOFFMmzat0bqHH35Yt/zqq6+KH374QWg0GrFjxw7h6+srli9frnt/6dKlol+/frrle++9V8ybN0/vM8eNG6ebpV0IIaZOnSqGDBki9u/fL06fPi3+9a9/CRcXF3Hq1CkhhBBjx44Vo0aNErm5uaKgoEDs3LlTpKenN/s7iMyFYYjIgr788ktx2223CblcLoYMGSLi4uLE0aNH9dr8+QTbkqCgIPHOO+/olgMCAvROeLW1tcLHx0esW7dOCCHEe++9Jzw9PcXVq1d1bdatW9dqwHjqqafExIkTdcszZswQvr6+upPzn7+/Pgz92enTp4WXl5dYsWKFbp2/v7/49NNP9dq9+uqrIjw8/JZqDQgIEJGRkXrrJk+eLKKionTLAMT8+fP12gwdOlS8/vrreus+/vhjoVKphBBCfPvtt8LR0VEUFRXp3v/mm29aDENxcXFCrVaL6urqJmudMWNGowCzadMm0atXL1FbW6tbV1VVJTp27Ci+/fZbIYQQKpVKvPHGG7r3r1+/Lu64444Ww9C2bduEm5ubbn9qtVohl8vFrl27mt1mxYoVYuDAgbplY8PQ6dOnhUwmE+fPn9drM3LkSBEXFyeEEOKuu+7SBU4ia2KfISILmjhxIsaOHYsDBw4gIyMDKSkpWLFiBd5//3292wsNXb16FQkJCfjvf/+L3377DTdu3MAff/yBwsJCvXZ9+/bV/S2TyaBUKlFaWgoAOHHiBPr16wdXV1ddm/Dw8EbftX79erz//vs4d+4c/vjjD1RXVzd6iuiuu+6Cs7Nzq79Xq9XigQceQFRUFJ577jkAwO+//46ioiI89thjmDVrlq7tjRs3oFAojKq1KQ3bhYeHN3rKLTQ0VG/58OHDOHToEF577TXdupqaGlRWVuLatWs4ceIEunbtijvuuMPgenJycjB06FCj+iMdPnwYp0+fhru7u976yspKFBQUQKvVori4WO+7O3TogNDQ0BZvlY0dOxYdOnTAjh07MGXKFCQnJ8Pd3R0RERG6Nl9++SXefvttnD59GleuXMGNGzfg4eFhcO0NHTlyBEII3W3UelVVVfDy8gIAzJ07FzExMdizZw/uv/9+TJw4Ue8YJrIUhiEiC5PL5Rg1ahRGjRqFJUuW4PHHH8fSpUtbDEPPPfccvv32W7z55pvo0aMHOnbsiL///e+orq7Wa9fwxCuTyVBbWwsALZ4s633xxRdYsGABVq5cifDwcLi7u+Nf//oXfvrpJ712nTp1avWzampqMHnyZHh4eGDjxo269fX1bNy4EYMGDdLbxtHR0eBajSGTyfSWG9ZfW1uLhIQEPPjgg422lcvlTdbT8DMb6tixo9F11tbWYuDAgfjkk08avXcrHdSdnZ3x97//HZ9++immTJmCTz/9FJMnT0aHDnWngMzMTEyZMgUJCQkYPXo0FAoFPv/8c6xcubLZz3RwcGi0X/7cx6i2thaOjo44fPiw7t+1npubGwDg8ccfx+jRo7Fr1y7s2bMHiYmJWLlyJZ555pk2/1aitmAYIrKyoKAgvUfpnZycUFNTo9fmwIEDmDlzJiZMmAAAuHLlil6nWkO/5+OPP8Yff/yhO1FnZmY2+p4hQ4bgqaee0q0rKCgw6nvqLViwAMeOHcOhQ4cgl8t16319fXH77bfjzJkzeOSRR9pca3MatsvMzETv3r1b3GbAgAHIz89Hjx49mq2nsLAQv/32G/z8/ABAbziEpvTt2xdbtmxp9mk1Z2fnRv/OAwYMwNatW+Hj49PsVRmVSoXMzEwMGzYMQN0VtcOHD2PAgAEt1vPII48gIiICP//8M/bt24dXX31V994PP/yAgIAAvPjii7p1586da/HzunTpguLiYt1yTU0Njh8/jhEjRgAAQkJCUFNTg9LSUgwdOrTZz/H398fs2bMxe/ZsxMXFYePGjQxDZHF8mozIQsrKynDfffchKSkJubm50Gg0+M9//oMVK1Zg3LhxunaBgYH47rvvUFJSgkuXLgEAevTogW3btiEnJwdHjx7F1KlTdVdYDDV16lQ4ODjgscceQ15eHnbv3o0333xTr02PHj2QlZWFb7/9FqdOncLLL7+MQ4cOGf1bN2/ejLVr12L9+vVwcHBASUkJSkpKdE8uxcfHIzExEf/+979x6tQpHDt2DJs3b8aqVasMrrU5P/zwA1asWIFTp07h3XffxX/+8x/MmzevxW2WLFmCjz76CPHx8fj5559x4sQJbN26FS+99BIA4P7770evXr3w6KOP4ujRozhw4IBecGjKnDlzUF5ejilTpiArKwu//PILPv74Y+Tn5wOo+3fOzc1Ffn4+Lly4gOvXr+ORRx6Bt7c3xo0bhwMHDkCj0SA9PR3z5s3Dr7/+CgCYN28e3njjDWzfvh0nT57EU089hcuXL7e6X+699174+vrikUceQWBgIAYPHqx7r0ePHigsLMTnn3+OgoICrF69Gtu3b2/x8+677z7s2rULu3btarKOnj174pFHHsGjjz6Kbdu2QaPR4NChQ1i+fLnuibH58+fj22+/hUajwZEjR7B371706dOn1d9CZHLW7LBEJCWVlZVi8eLFYsCAAUKhUAhXV1fRq1cv8dJLL4lr167p2u3YsUP06NFDdOjQQQQEBAgh6jrnjhgxQnTs2FH4+/uLNWvWNOrA2lQH5n79+omlS5fqljMyMkS/fv2Es7Oz6N+/v0hOTtbr9FtZWSlmzpwpFAqF6Ny5s4iJiRGLFy/W6zjbVMffht8/Y8YMAaDR68+1fPLJJ6J///7C2dlZ3HbbbWLYsGFi27ZtBtfalICAAJGQkCAeeugh4erqKnx9fcXbb7+t1wbNdFBPSUkRQ4YMER07dhQeHh4iLCxMbNiwQfd+fn6+uOeee4Szs7Po2bOnSElJabEDtRBCHD16VERERAhXV1fh7u4uhg4dKgoKCoQQdU8Ojho1Sri5uQkAYt++fUIIIYqLi8Wjjz4qvL29hYuLi+jWrZuYNWuW0Gq1Qoi6DtPz5s0THh4eonPnziI2NlY8+uijLXagrvfcc88JAGLJkiVNvufl5SXc3NzE5MmTxVtvvSUUCoXu/YYdqKurq0VMTIzw9PQUPj4+IjExsdHTZPVPJwYGBgonJyehVCrFhAkTRG5urhBCiDlz5oju3bsLFxcX0aVLFzF9+nRx4cKFVn8HkanJhDDxzXkiIisJDAzE/PnzG41/Q0TUEt4mIyIiIkljGCIiIiJJ420yIiIikjReGSIiIiJJYxgiIiIiSWMYIiIiIkljGCIiIiJJYxgiIiIiSWMYIiIiIkljGCIiIiJJYxgiIiIiSft/6IEO0em+hmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(get_standarized_values(salary_lm.fittedvalues) , get_standarized_values(resid))\n",
    "plt.xlabel(\"Standarized predicted values\")\n",
    "plt.ylabel(\"Standarized Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a848d499",
   "metadata": {
    "papermill": {
     "duration": 0.01233,
     "end_time": "2023-04-15T05:47:32.318236",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.305906",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    " > It can be observed that the residuals are random and have no funnel shape , which means the residuals have constant variance (homoscedasticity)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9482ca4e",
   "metadata": {
    "papermill": {
     "duration": 0.011371,
     "end_time": "2023-04-15T05:47:32.340961",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.329590",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Outlier Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cb0f82",
   "metadata": {
    "papermill": {
     "duration": 0.010468,
     "end_time": "2023-04-15T05:47:32.362984",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.352516",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **1.Z-Score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67bc30ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:32.382128Z",
     "iopub.status.busy": "2023-04-15T05:47:32.381763Z",
     "iopub.status.idle": "2023-04-15T05:47:32.396427Z",
     "shell.execute_reply": "2023-04-15T05:47:32.395537Z"
    },
    "papermill": {
     "duration": 0.026779,
     "end_time": "2023-04-15T05:47:32.398715",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.371936",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "      <th>z_score_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [YearsExperience, Salary, z_score_salary]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import zscore\n",
    "data['z_score_salary'] = zscore(data.Salary)\n",
    "data[(data.z_score_salary > 3.0) | (data.z_score_salary < -3.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af73ebf",
   "metadata": {
    "papermill": {
     "duration": 0.008666,
     "end_time": "2023-04-15T05:47:32.417779",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.409113",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> There are no observations that are outliers as per Z-Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88acef70",
   "metadata": {
    "papermill": {
     "duration": 0.009113,
     "end_time": "2023-04-15T05:47:32.435782",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.426669",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **2.Cook's Distance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed693860",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:32.456480Z",
     "iopub.status.busy": "2023-04-15T05:47:32.454894Z",
     "iopub.status.idle": "2023-04-15T05:47:32.645287Z",
     "shell.execute_reply": "2023-04-15T05:47:32.644423Z"
    },
    "papermill": {
     "duration": 0.20314,
     "end_time": "2023-04-15T05:47:32.648121",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.444981",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cooks Distance')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_influence = salary_lm.get_influence()\n",
    "(c,p) = data_influence.cooks_distance\n",
    "plt.stem(np.arange(len(X_train)), np.round(c,3) , markerfmt=\",\")\n",
    "plt.xlabel(\"Row index\")\n",
    "plt.ylabel(\"Cooks Distance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960a5c57",
   "metadata": {
    "papermill": {
     "duration": 0.011045,
     "end_time": "2023-04-15T05:47:32.670909",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.659864",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> It can be observed that 2 of the observations' Cook distance exceed 1 and hence they are outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98120ca6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:32.698870Z",
     "iopub.status.busy": "2023-04-15T05:47:32.698218Z",
     "iopub.status.idle": "2023-04-15T05:47:32.704798Z",
     "shell.execute_reply": "2023-04-15T05:47:32.703707Z"
    },
    "papermill": {
     "duration": 0.02569,
     "end_time": "2023-04-15T05:47:32.708199",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.682509",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nX_train = X_train.drop([7,19] , axis=0)\\ny_train = y_train.drop([7,19] , axis=0)\\n'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "X_train = X_train.drop([7,19] , axis=0)\n",
    "y_train = y_train.drop([7,19] , axis=0)\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919ee35e",
   "metadata": {
    "papermill": {
     "duration": 0.009131,
     "end_time": "2023-04-15T05:47:32.726873",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.717742",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **3.Leverage values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "506bf2ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:32.747753Z",
     "iopub.status.busy": "2023-04-15T05:47:32.746903Z",
     "iopub.status.idle": "2023-04-15T05:47:32.963118Z",
     "shell.execute_reply": "2023-04-15T05:47:32.962140Z"
    },
    "papermill": {
     "duration": 0.228999,
     "end_time": "2023-04-15T05:47:32.965371",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.736372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.regressionplots import influence_plot\n",
    "fig ,ax = plt.subplots(figsize=(10,8))\n",
    "influence_plot( salary_lm , ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08300274",
   "metadata": {
    "papermill": {
     "duration": 0.009563,
     "end_time": "2023-04-15T05:47:32.988405",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.978842",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> The size of the circle is proportional to the product of residual and leverage values , larger the circle , the larger\n",
    "the residual and hence larger the influence of the observation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "114a6fa9",
   "metadata": {
    "papermill": {
     "duration": 0.009497,
     "end_time": "2023-04-15T05:47:33.008001",
     "exception": false,
     "start_time": "2023-04-15T05:47:32.998504",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "887ffb54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:33.029750Z",
     "iopub.status.busy": "2023-04-15T05:47:33.029063Z",
     "iopub.status.idle": "2023-04-15T05:47:33.034697Z",
     "shell.execute_reply": "2023-04-15T05:47:33.033148Z"
    },
    "papermill": {
     "duration": 0.020439,
     "end_time": "2023-04-15T05:47:33.038304",
     "exception": false,
     "start_time": "2023-04-15T05:47:33.017865",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = salary_lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "668e9797",
   "metadata": {
    "papermill": {
     "duration": 0.009193,
     "end_time": "2023-04-15T05:47:33.057506",
     "exception": false,
     "start_time": "2023-04-15T05:47:33.048313",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Finding R-Squared and RMSE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d94a34a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:33.079347Z",
     "iopub.status.busy": "2023-04-15T05:47:33.078925Z",
     "iopub.status.idle": "2023-04-15T05:47:33.087667Z",
     "shell.execute_reply": "2023-04-15T05:47:33.086533Z"
    },
    "papermill": {
     "duration": 0.022704,
     "end_time": "2023-04-15T05:47:33.089807",
     "exception": false,
     "start_time": "2023-04-15T05:47:33.067103",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9414466227178215"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score , mean_squared_error\n",
    "r2_score(y_test , y_pred)\n",
    "#r2_score(y_pred , y_test) slightly different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e36bb47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T05:47:33.112106Z",
     "iopub.status.busy": "2023-04-15T05:47:33.111733Z",
     "iopub.status.idle": "2023-04-15T05:47:33.120257Z",
     "shell.execute_reply": "2023-04-15T05:47:33.118627Z"
    },
    "papermill": {
     "duration": 0.022592,
     "end_time": "2023-04-15T05:47:33.122765",
     "exception": false,
     "start_time": "2023-04-15T05:47:33.100173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6146.923007994572"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test , y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 14.177778,
   "end_time": "2023-04-15T05:47:33.854276",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-04-15T05:47:19.676498",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}