Model Evaluation and Refinement.ipynb

{
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>...</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
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       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
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       "      <td>11.190476</td>\n",
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       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
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       "      <td>2.68</td>\n",
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       "      <td>52.4</td>\n",
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       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
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       "      <td>12.368421</td>\n",
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       "      <td>54.3</td>\n",
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       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
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       "   Unnamed: 0  Unnamed: 0.1  symboling  normalized-losses  wheel-base  \\\n",
       "0           0             0          3                122        88.6   \n",
       "1           1             1          3                122        88.6   \n",
       "2           2             2          1                122        94.5   \n",
       "3           3             3          2                164        99.8   \n",
       "4           4             4          2                164        99.4   \n",
       "\n",
       "     length     width  height  curb-weight  engine-size  ...  stroke  \\\n",
       "0  0.811148  0.890278    48.8         2548          130  ...    2.68   \n",
       "1  0.811148  0.890278    48.8         2548          130  ...    2.68   \n",
       "2  0.822681  0.909722    52.4         2823          152  ...    3.47   \n",
       "3  0.848630  0.919444    54.3         2337          109  ...    3.40   \n",
       "4  0.848630  0.922222    54.3         2824          136  ...    3.40   \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg    price  \\\n",
       "0                9.0       111.0    5000.0        21           27  13495.0   \n",
       "1                9.0       111.0    5000.0        21           27  16500.0   \n",
       "2                9.0       154.0    5000.0        19           26  16500.0   \n",
       "3               10.0       102.0    5500.0        24           30  13950.0   \n",
       "4                8.0       115.0    5500.0        18           22  17450.0   \n",
       "\n",
       "   city-L/100km  diesel  gas  \n",
       "0     11.190476       0    1  \n",
       "1     11.190476       0    1  \n",
       "2     12.368421       0    1  \n",
       "3      9.791667       0    1  \n",
       "4     13.055556       0    1  \n",
       "\n",
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      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Import clean data \n",
    "path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/module_5_auto.csv'\n",
    "df = pd.read_csv(path)\n",
    "df.to_csv('module_5_auto.csv')\n",
    "df = df._get_numeric_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2833fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries for plotting\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "\n",
    "def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "\n",
    "    ax1 = sns.distplot(RedFunction, hist=False, color=\"r\", label=RedName)\n",
    "    ax2 = sns.distplot(BlueFunction, hist=False, color=\"b\", label=BlueName, ax=ax1)\n",
    "\n",
    "    plt.title(Title)\n",
    "    plt.xlabel('Price (in dollars)')\n",
    "    plt.ylabel('Proportion of Cars')\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "def PollyPlot(xtrain, xtest, y_train, y_test, lr,poly_transform):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "    \n",
    "    \n",
    "    #training data \n",
    "    #testing data \n",
    "    # lr:  linear regression object \n",
    "    #poly_transform:  polynomial transformation object \n",
    " \n",
    "    xmax=max([xtrain.values.max(), xtest.values.max()])\n",
    "\n",
    "    xmin=min([xtrain.values.min(), xtest.values.min()])\n",
    "\n",
    "    x=np.arange(xmin, xmax, 0.1)\n",
    "\n",
    "\n",
    "    plt.plot(xtrain, y_train, 'ro', label='Training Data')\n",
    "    plt.plot(xtest, y_test, 'go', label='Test Data')\n",
    "    plt.plot(x, lr.predict(poly_transform.fit_transform(x.reshape(-1, 1))), label='Predicted Function')\n",
    "    plt.ylim([-10000, 60000])\n",
    "    plt.ylabel('Price')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51725b3e",
   "metadata": {},
   "source": [
    "# Part 1: Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1f3f559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples : 21\n",
      "number of training samples: 180\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y_data = df['price']\n",
    "x_data = df.drop('price', axis=1)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.10, random_state=1)\n",
    "\n",
    "\n",
    "print(\"number of test samples :\", x_test.shape[0])\n",
    "print(\"number of training samples:\",x_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5759aa9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples : 81\n",
      "number of training samples: 120\n"
     ]
    }
   ],
   "source": [
    "# 40% for testing\n",
    "# Write your code below and press Shift+Enter to execute \n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=1)\n",
    "\n",
    "\n",
    "print(\"number of test samples :\", x_test1.shape[0])\n",
    "print(\"number of training samples:\",x_train1.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "58eabb80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x_train[['horsepower']], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7cda4cdb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3635875575078824, 0.6619724197515103)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test[['horsepower']], y_test), model.score(x_train[['horsepower']], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65e3feaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(LinearRegression(), 0.6111827529454426, 0.6755325771980133)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R^2 on the test data using 40% of the dataset for testing\n",
    "\n",
    "model.fit(x_train1[['horsepower']], y_train1), model.score(x_test1[['horsepower']], y_test1), model.score(x_train1[['horsepower']], y_train1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b0c033",
   "metadata": {},
   "source": [
    "# Cross-Validation Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d1247a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.522009915042119"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "\n",
    "Rcross = cross_val_score(model, x_data[['horsepower']], y_data, cv=4)\n",
    "np.mean(Rcross)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "940b5963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23525298.54198599"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(-1 * cross_val_score(model,x_data[['horsepower']], y_data,cv=4,scoring='neg_mean_squared_error'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cc7fb4ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44319612775502903"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rcross = cross_val_score(model, x_data[['horsepower']], y_data, cv=2)\n",
    "\n",
    "Rcross[1] # R^2 for the second fold utilizing the \"horsepower\" feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69bf677c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([14141.63807508, 14141.63807508, 20814.29423473, 12745.03562306,\n",
       "        14762.35027598]),\n",
       " 0    13495.0\n",
       " 1    16500.0\n",
       " 2    16500.0\n",
       " 3    13950.0\n",
       " 4    17450.0\n",
       " Name: price, dtype: float64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using the function 'cross_val_predict' to predict the output. The function splits up the data into the specified number of folds,\n",
    "#with one fold for testing and the other folds are used for training.\n",
    "yhat = cross_val_predict(model,x_data[['horsepower']], y_data,cv=4)\n",
    "yhat[0:5], y_data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cec97e1",
   "metadata": {},
   "source": [
    "# Part 2: Overfitting, Underfitting and Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "748c28b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7426.6731551 , 28323.75090803, 14213.38819709,  4052.34146983,\n",
       "       34500.19124244])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_train)\n",
    "\n",
    "yhat_train = lr.predict(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "yhat_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ab8d9bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11349.35089149,  5884.11059106, 11208.6928275 ,  6641.07786278,\n",
       "       15565.79920282])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_test = lr.predict(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "yhat_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc8b4ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87aef3f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\georg\\anaconda3\\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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\georg\\anaconda3\\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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Title = 'Distribution  Plot of  Predicted Value Using Training Data vs Training Data Distribution'\n",
    "DistributionPlot(y_train, yhat_train, \"Actual Values (Train)\", \"Predicted Values (Train)\", Title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0723be7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\georg\\anaconda3\\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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\georg\\anaconda3\\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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
    "DistributionPlot(y_test,yhat_test,\"Actual Values (Test)\",\"Predicted Values (Test)\",Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d87a41",
   "metadata": {},
   "source": [
    "<p>Comparing Figure 1 and Figure 2, it is evident that the distribution of the test data in Figure 1 is much better at fitting the data. This difference in Figure 2 is apparent in the range of 5000 to 15,000. This is where the shape of the distribution is extremely different. Let's see if polynomial regression also exhibits a drop in the prediction accuracy when analysing the test dataset.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dbe1d13e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4934a677",
   "metadata": {},
   "source": [
    "## Overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4361e6a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.45, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8f757a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures(degree=5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr = PolynomialFeatures(degree=5)\n",
    "x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
    "pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2393a35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = LinearRegression()\n",
    "poly.fit(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "22df5fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6728.71508499,  7308.03837696, 12213.79222245, 18893.17259916,\n",
       "       19995.86710176])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = poly.predict(x_test_pr)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "84f3c593",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values: [ 6728.71508499  7308.03837696 12213.79222245 18893.17259916]\n",
      "True values: [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted values:\", yhat[0:4])\n",
    "print(\"True values:\", y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a0f06bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4saZNm6aRI0dKkt588019+eWXdfPNS0pKtGbNGi1atEiXXXaZoqKiNGzYMH3rW9865Poff/yxTjrppLprJSUlhezH22+/3WAO+549e1RaWqpFixbpn//8pyTpnHPOUf/+/cM+l9ZMd5k5c6YSExMlSRMmTFBBQYF27drVor7WKikp0e7du3XyySdLkq644gpdfPHFdY9feOGFkqSjjz5a+fn5LepXcwjpAAAAXailI94drf6c9Pp69+5d9721Vn/84x81a9asBse89tprMsY0eX1rbbPHSFJNTY0++ugjxcfHH/JYS84PJTo6WjU1NZKk/fv3N3gsLi6u7vuoqChVVVW1uK8tVfszaq/fEZiTDgAAAEnSrFmzNG/ePFVWVkqSVq9erb179+qkk07Sc889p+rqam3ZskXvvvvuIecef/zxeu+997RhwwZJ0s6dOyVJffr0UWlpad1xZ5xxhh5++OG6+7VvHE466ST5/X5J0uuvv14357sl0tPTtWTJEkmq+xSgKS3ta63ExET179+/br75s88+Wzeq3lkI6QAAAJAUKKc4YcIEHXXUUZo4caKuueYaVVVV6YILLtCYMWM0adIkzZkzJ2RAHThwoHJycnThhRfqyCOP1He/+11J0uzZs/Xyyy/XLcZ86KGHlJubq8mTJ2vChAl69NFHJUl33XWXFi1apKOOOkpvvvmmUlNTW9zvW2+9VfPmzdP06dNDlm1sa1/re/rpp3Xbbbdp8uTJWrZsmX7xi1+0uH9tYVqzcjYSZGRk2NrVwQAAAB1h5cqVGj9+vNPdgINCvQaMMUustRmhjmckHQAAAHAZQjoAAADgMoR0AACALsAU48jVlt89IR0AAKCT9erVS8XFxQT1CGStVXFxsXr16tWq86iTDgAA0MmGDx+uoqIibd++3emuwAG9evXS8OHDW3UOIR0AAKCTxcTE1O1uCbQE010AAAAAlyGkAwAAAC5DSAcAAABchpAOAAAAuAwhHQAAAHAZQjoAAADgMoR0AAAAwGUI6QAAAIDLENIBAAAAlyGkAwAAAC5DSAcAAABchpAOAAAAuAwhHQAAAHAZQjoAAADgMoR0AAAAwGUI6QAAAIDLENIBAAAAlyGkAwAAAC5DSAcAAABchpAOAAAAuIyjId0Yk2+MyTPGLDPG5Abbkowxbxlj1gRv+9c7/k5jzFpjzCpjzKx67UcHr7PWGPOQMcYE2+OMMc8H2z8xxqR3+ZMEAAAAWskNI+mnWmunWGszgvfvkLTAWjtG0oLgfRljJki6VNIRks6U9IgxJip4zjxJmZLGBL/ODLZfLWmXtfYwSQ9Iur8Lng8AAADQLm4I6Y2dJ+np4PdPSzq/Xvtz1toD1toNktZKmmaMGSqpr7X2I2utlfRMo3Nqr/WipJm1o+wAAACAWzkd0q2kN40xS4wxmcG2wdbaLZIUvB0UbE+RtLHeuUXBtpTg943bG5xjra2SVCIpuXEnjDGZxphcY0zu9u3bO+SJAQAAAG0V7fDPP8Fau9kYM0jSW8aYr5s4NtQIuG2ivalzGjZYmyMpR5IyMjIOeRwAAADoSo6OpFtrNwdvt0l6WdI0SVuDU1gUvN0WPLxI0oh6pw+XtDnYPjxEe4NzjDHRkhIl7eyM5wIAAAB0FMdCujGmtzGmT+33ks6QtFzSq5KuCB52haRXgt+/KunSYMWWkQosEP00OCWm1BhzXHC++eWNzqm91kWS3gnOWwcAAABcy8npLoMlvRxcxxkt6W/W2v8aYz6T9IIx5mpJhZIuliRr7QpjzAuSvpJUJel6a2118FpzJD0lKV7S68EvSXpC0rPGmLUKjKBf2hVPDAAAAGgPw8ByQxkZGTY3N9fpbgAAAKCHM8YsqVeGvAGnq7sAAAAAaISQDgAAALgMIR0AAABwGUI6AAAA4DKEdAAAAMBlCOkAAACAyxDSAQAAAJchpAMAAAAuQ0gHAAAAXIaQDgAAALgMIR0AAABwGUI6AAAA4DKEdAAAAMBlCOkAAACAyxDSAQAAAJchpAMAAAAuQ0gHAAAAXIaQDgAAALgMIR0AAABwGUI6AAAA4DKEdAAAAMBlCOkAAACAyxDSAQAAAJchpAMAAAAuQ0gHAAAAXIaQDgAAALgMIR0AAABwGUI6AAAu4M/zK31uujz3eJQ+N13+PL/TXQLgoGinOwAAQKTz5/mVOT9T5ZXlkqSCkgJlzs+UJPkm+ZzsGgCHMJIOAIDDshZk1QX0WuWV5cpakOVQjwA4jZAOAIDDCksKW9UOoOcjpAMA4LDUxNRWtQPo+QjpAAA4LHtmtrwx3gZt3hivsmdmO9QjAE4jpAMA4DDfJJ9yZucoLTFNRkZpiWnKmZ3DolEgghlrrdN9cJWMjAybm5vrdDcAAADQwxljllhrM0I9xkg6AAAA4DKEdAAAAMBlCOkAAACAyxDSAQAAAJchpAMAAAAuQ0gHAAAAXIaQDgAAALgMIR0AAABwGUI6AAAA4DKEdAAAAMBlCOkAAACAyxDSAQAAAJchpAMAAAAuQ0gHAAAAXIaQDgAAALgMIR0AAABwGUI6AAAA4DKEdAAAAMBlCOkAAACAyxDSAQAAAJchpAMAAAAuQ0gHAAAAXIaQDgAAALgMIR0AgK7k90vp6ZLHE7j1+53uEQAXina6AwAARAy/X8rMlMrLA/cLCgL3Jcnnc65fAFyHkXQAALpKVtbBgF6rvDzQDgD1ENIBAOgqhYXh25kGA6AeQjoAAF0lNTV0e1JSYNpLQYFk7cFpMAR1IGIR0gEA6CrZ2ZLX27Ct9j7TYADUQ0gHAKCr+HxSTo6UliYZE7jNyZF27gx9fLjpMQB6PEI6AABdyeeT8vOlmprArc8XfhpMuHYAPR4hHQAAp4WbBpOd7Ux/ADiOkA4AgNPCTYOhdjoQsdjMCAAAN/D5COUA6jg+km6MiTLGLDXG/Dt4P8kY85YxZk3wtn+9Y+80xqw1xqwyxsyq1360MSYv+NhDxhgTbI8zxjwfbP/EGJPe5U8QAAAAaCXHQ7qkmyStrHf/DkkLrLVjJC0I3pcxZoKkSyUdIelMSY8YY6KC58yTlClpTPDrzGD71ZJ2WWsPk/SApPs796kAAAAA7edoSDfGDJd0jqTH6zWfJ+np4PdPSzq/Xvtz1toD1toNktZKmmaMGSqpr7X2I2utlfRMo3Nqr/WipJm1o+wAAACAWzk9kj5X0s8k1dRrG2yt3SJJwdtBwfYUSRvrHVcUbEsJft+4vcE51toqSSWSkht3whiTaYzJNcbkbt++vZ1PCQAAAGgfx0K6MebbkrZZa5e09JQQbbaJ9qbOadhgbY61NsNamzFw4MAWdgcAAADoHE5WdzlB0rnGmLMl9ZLU1xjzV0lbjTFDrbVbglNZtgWPL5I0ot75wyVtDrYPD9Fe/5wiY0y0pERJYbZ1AwAAANzBsZF0a+2d1trh1tp0BRaEvmOt/b6kVyVdETzsCkmvBL9/VdKlwYotIxVYIPppcEpMqTHmuOB888sbnVN7rYuCP+OQkXQAAADATdxYJ/0+SS8YY66WVCjpYkmy1q4wxrwg6StJVZKut9ZWB8+ZI+kpSfGSXg9+SdITkp41xqxVYAT90q56EgAAAEBbGQaWG8rIyLC5ublOdwMAAAA9nDFmibU2I9RjTld3AQAAANAIIR0AAABwGUI6AAAA4DKEdAAAAMBlCOkAAACAyxDSAQAAAJchpAMAAAAuQ0gHAAAAXIaQDgAAALgMIR0AAABwGUI6AAAA4DKEdAAAAMBlCOkAAACAyxDSAQAAAJchpAMAAFfw5/mVPjddnns8Sp+bLn+e3+kuAY4hpAMA0IUIoqH58/zKnJ+pgpICWVkVlBQoc34m/z6IWIR0AAC6CEE0vKwFWSqvLG/QVl5ZrqwFWQ71CHAWIR0AgC5CEA2vsKSwVe1AT0dIBwCgixBEw0tNTG1VO9DTEdIBAOgiBNHwsmdmyxvjbdDmjfEqe2a2Qz0CnEVIBwCgixBEw/NN8ilndo7SEtNkZJSWmKac2TnyTfI53TXAEcZa63QfXCUjI8Pm5uY63Q0AQA/lz/Mra0GWCksKlZqYquyZ2QRRIEIZY5ZYazNCPkZIb4iQDgAAEBmcftPcVEiP7rJeAAAAAC5RWxK1tuJSbUlUSa74dIs56QAAAIg4bi+JSkgHAKCF2C0U6DncXhKVkA4AQAuwWyjQs7i9JCohHQCAFnD7R+MAWsftJVEJ6QAAtIDbPxoH0Dpur81PSAcAtJ3fL6WnSx5P4Nbfc6d+uP2jcQCt55vkU/7N+aq5q0b5N+e7JqBLhHQAQFv5/VJmplRQIFkbuM3M7LFB3e0fjQPoWQjpAIC2ycqSyhvO0VZ5eaC9B3L7R+MAehZ2HG2EHUcBoIU8nsAIemPGSDU1Xd8fAOhmmtpxlJF0AEDbpIaZix2uHQDQYoR0AEDbZGdL3oZztOX1BtoBAO1CSAcAtI3PJ+XkSGlpgSkuaWmB+z7maANAexHSAQAAAJchpAMA2ibCSjCi8/nz/Eqfmy7PPR6lz02XP4/XEiIXIR0A0DYRVoIRncuf51fm/EwVlBTIyqqgpECZ8zMJ6ohYhHQAQNsUFrauHWhC1oIslVc2fNNXXlmurAW86UNkIqQDANqGEozoQIUlod/chWsHejpCOgCgbSjBiA6Umhj6zV24dqCnI6QDANqGEozoQNkzs+WNafimzxvjVfZM3vQhMkU73QEAQDfm8xHK0SF8kwKvo6wFWSosKVRqYqqyZ2bXtQORxlhrne6Dq2RkZNjc3FynuwEAAIAezhizxFqbEeoxprsAAAAALkNIBwAAAFyGkA4AAAC4DCEdAAAAcBlCOgAAcAV/nl/pc9Plucej9Lnp8uf5ne4S4BhKMAIAAMf58/zKnJ+p8spySVJBSYEy52dKEmUYEZEYSQcAoIUY6e08WQuy6gJ6rfLKcmUtyHKoR4CzGEkHAKAFGOntXIUlha1qB3o6RtIBAGgBRno7V2piaqvagZ6OkA4AQAsw0tu5smdmyxvjbdDmjfEqe2a2Qz0CnEVIBwCgBRjp7Vy+ST7lzM5RWmKajIzSEtOUMzuHqUSIWIR0AD2P3y+lp0seT+DWz+I+tB8jvZ3PN8mn/JvzVXNXjfJvziegI6IR0gH0LH6/lJkpFRRI1gZuMzMJ6mg3RnoBdCVjrXW6D66SkZFhc3Nzne4GgLZKTw8E88bS0qT8/K7uDQAAYRljllhrM0I9xkg6gJ6lMMwivnDtAAC4ECEdQM+SGmYRX7h2AABciJAOoGfJzpa8DRf3yesNtAMA0E0Q0gH0LD6flJMTmINuTOA2JyfQDriAP8+v9Lnp8tzjUfrcdPnzWNQM4FDRTncAADqcz0cohyv58/zKnJ9Zt3NpQUmBMudnShJVYgA0wEg6AABdJGtBVl1Ar1VeWa6sBVkO9QiAWxHSAQDoIoUloasMhWsHELkI6QAAdJHUxNBVhsK1A4hchHQAALpI9sxseWMaVh/yxniVPZPqQwAaciykG2N6GWM+NcZ8YYxZYYy5J9ieZIx5yxizJnjbv945dxpj1hpjVhljZtVrP9oYkxd87CFjjAm2xxljng+2f2KMSe/yJwoAQJBvkk85s3OUlpgmI6O0xDTlzM5h0SiAQxhrrTM/OBCke1try4wxMZI+kHSTpAsl7bTW3meMuUNSf2vt7caYCZL+LmmapGGS3pY01lpbbYz5NHjux5Jek/SQtfZ1Y8x1kiZba681xlwq6QJr7Xeb6ldGRobNzc3tpGcNAAAABBhjllhrM0I95thIug0oC96NCX5ZSedJejrY/rSk84PfnyfpOWvtAWvtBklrJU0zxgyV1Nda+5ENvON4ptE5tdd6UdLM2lF2AAAAwK0cnZNujIkyxiyTtE3SW9baTyQNttZukaTg7aDg4SmSNtY7vSjYlhL8vnF7g3OstVWSSiQlh+hHpjEm1xiTu3379g56dgAAAEDbOBrSrbXV1topkoYrMCo+sYnDQ42A2ybamzqncT9yrLUZ1tqMgQMHNtNrAAAAoHO5orqLtXa3pIWSzpS0NTiFRcHbbcHDiiSNqHfacEmbg+3DQ7Q3OMcYEy0pUdLOzngOAAAAQEdxsrrLQGNMv+D38ZJOk/S1pFclXRE87ApJrwS/f1XSpcGKLSMljZH0aXBKTKkx5rjgfPPLG51Te62LJL1jnVopCwAAALRQtIM/e6ikp40xUQq8WXjBWvtvY8xHkl4wxlwtqVDSxZJkrV1hjHlB0leSqiRdb62tDl5rjqSnJMVLej34JUlPSHrWGLNWgRH0S7vkmQEAAADt4FgJRreiBCMAAAC6QlMlGJ0cSQcAAG1QXWO1q7xCO/dWqHR/pSSjKI9RkjdWg/rGqVdMlNNdBNBOhHQAAFzMWqt12/fqw7U7tLRwl77+plTrt+9VRXVN2HOGJfbShGGJmpraT6cePkjjh/YR24QA3QshHQAAl6mpsfq8cJdeWbZZb321Vd/s2S9JGtK3l8YN7aOTxw7UsH7xSuodq77xMZKkquoaFe+t0LY9+7V6a5lWbC7R2yu36rdvrFJKv3hddPRwffeYERrWL97JpwaghQjpAAC4RHHZAT332Ub97ZNCbdq9T71iPDr18EE6aexAnTB6gFKTva263rY9+7Vw1Xb9O2+LHnpnjf74zhqde+Qw3XTaWI0c0LuTngWAjsDC0UZYOAoA6GrrtpfpsffW6V/LNquiqkYnHJasi44ertMnDFFCXMeMp23cWa5nPy7QMx/lq7La6rJpI3TbrHFKDI7EA+h6TS0cJaQ3QkgHuj//vOuUtT5Hhb2rlbo3StmjMuWb84jT3QIOsXFnuR5csEb//LxIsdEeXTB1uK48IV1jB/fptJ+5vfSA/vTuWj3zUb6SE+L0y3OP0FmThnbazwMQHiG9FQjpQPfmn3edMjfNU3m9wUFvpZSTMoegDtfYXV6hP7y1Wn/7pFAej9EPjkvTnFNGa0BCXJf1Ia+oRHe+/KWWb9oj37Gp+r9vT6AqDNDFCOmtQEgHurf026JVkFB9SHtaWZTyf1vlQI+Ag6prrJ77rFC/e2OV9uyv0mXTRuiGU8doSGIvR/pTWV2j3725So+9t15HDOurP1+ewcJSoAtRJx1AxCjsfWhAb6od6CrLN5Xozn/mKW9TiY4dmaR7zjtC44b0dbRPMVEe3XnWeE1LT9LNzy/TBY98qKeunKbxQ53tFwDJ43QHAKAjpe4N/XF9uHagsx2oqtbv31yl8/70ob7Zs18PXTZVz2Ue53hAr2/m+MH6x7XHy8jo4kc/0kfrip3uEhDxCOkAepTsUZnyVjZs81YG2oGulldUonP/+KH++M5anTdlmN6+5WSde+QwV24sNG5IX718/XQNSeylq576TJ9u2Ol0l4CIRkgH0KP45jyinJQ5SiuLkrGBuegsGkVXq6mxevS9dbrgkQ+1e1+Fnvxhhv5wyRQlet1d7nBoYrz+9uNjNbRfL135l0+1pICgDjiFhaONsHAUANAe20sP6KcvLNP7a3borIlDdN+Fk10fzhvbume/Ls35WDv3Vuif103X6IEJTncJ6JGaWjjKSDoAAB3k/TXbddaD7+vTDTuVfcFEPeI7qtsFdEka3LeXnrlqmqI9Rlf+5TMVlx1wuktAxCGkAwDQTjU1Vg8tWKPLn/xU/bwxeuWGE+Q7Ns2Vc89bakSSV3++IkNb9+xX5rNLVFFV43SXgIhCSAcAoB1K91fq2r8u0R/eWq3zp6Ro/g0zXFW5pT2OSu2v319ypJYU7NKvXlvpdHeAiEKddAAA2mjd9jJlPpOr/OJy/eLbE3TlCendevQ8lG9PHqbPC3bryQ836Jj0JJ0zeajTXQIiAiEdAIA2ePurrbrl+WWKifbor1cfq+NHJzvdpU5zx1njtHTjLt3+0pc6YlhfpQ/o7XSXgB6P6S4AALSCtVY5i9bpx8/mKm2AV/NvnNGjA7okxUZ79KfvHSWPkf7nH1+ouobKcEBnI6QDANBCVdU1yvrXcv3qta919sShevHa6UrpF+90t7rEsH7x+uV5E7WkYJf+/P56p7sD9HiEdAAAWqB0f6WufjpXf/ukUHNOGa0/XjZVvWKinO5WlzpvyjCdNXGI/vDmaq36ptTp7gA9GiEdAIBmbN69Txc/+pE+WLtD9104SbefOU4eT89aINoSxhj9v/Mnqk+vaN3+0peqYdoL0GkI6QAANGH5phKd/6cPtWnXPj115TG6dFqq011yVHJCnLLOGa9lG3fruc82Ot0doMcipAMAEMbH64v13cc+UkyURy9dN10njhnodJdc4YKpKTpuVJLu/+/X2sFupECnaFFIN8aMNcYsMMYsD96fbIz5387tGgAAznn362264slPNbRfvF6aM11jB/dxukuuUTvtpbyiSve9/rXT3QF6pJaOpP9Z0p2SKiXJWvulpEs7q1MAADjp319u1o+fydXYwX30wjXHa0hiL6e75DqHDeqjq2eM0otLipRXVOJ0d4Aep6Uh3Wut/bRRW1VHdwYAOoTfL6WnSx5P4Nbvd7pH6EZe+GyjfvL3pZqa2k/+Hx+rpN6xTnfJta4/dbSSe8cq+7WvZC2LSIGO1NKQvsMYM1qSlSRjzEWStnRarwCgrfx+KTNTKiiQrA3cZmYS1NEiT3ywQT976UvNGDNQz1x1rPr2inG6S67Wp1eMbj5tjD5ev1MLVm5zujtAj9LSkH69pMckjTPGbJJ0s6Q5ndUpAGizrCypvLxhW3l5oB0Iw1qrhxas0b3//kpnTRyiP19+tOJjI6sGeltdOi1Vowb21q9eX6nK6hqnuwP0GC0K6dba9dba0yQNlDTOWjvDWpvfqT0DgLYoLGxdOyKetVa/em2l/vDWan3nqOH642VTFRdNQG+pmCiPbj9znNZv36uXl25yujtAj9HS6i6/Msb0s9butdaWGmP6G2P+X2d3DgBaLTVMDetw7Yho1TVWP385T39+f4N+OD1dv71osqKjqE7cWmdMGKyJKX318DtrGU0HOkhL/xKdZa3dXXvHWrtL0tmd0iMAaI/sbMnrbdjm9QbagXoqq2t08/PL9PdPN+qGUw/TXbMnROQuoh3BGKObZ45V4c5yvfw5o+lAR2hpSI8yxsTV3jHGxEuKa+J4AHCGzyfl5EhpaZIxgducnEA7ELS/slrXPrtE87/YrDvOGqdbZx0uYwjo7TFz/CBNSknUH99dw2g60AFaGtL/KmmBMeZqY8xVkt6S9HTndQsAWihUuUWfT8rPl2pqArcEdNRTdqBKV/7lM72zapvuPX+irj15tNNd6hGMMbr5tDHauHOf/vl5kdPdAbq96JYcZK39jTEmT9JMSUbSvdbaNzq1ZwDQnNpyi7XVXGrLLUoEc4S0u7xCP/zLZ8rbVKIHLpmi86emON2lHuVb4wZp8vBEzVu4ThcdPUJRTB8C2qzFq2Osta9ba2+11v4PAR2AK1BuEa2wvfSALs35WF9t3qN5vqMI6J3AGKNrThqt/OJyvfXVVqe7A3RrTYZ0Y8wHwdtSY8yeel+lxpg9XdNFAAiDcotooU279+mSxz5SQXG5nvzhMTrjiCFOd6nHmnXEYI1IilfOonVOdwXo1poM6dbaGcHbPtbavvW++lhr+3ZNFwEgDMotogXWby/TxfMWa0fZAf31R9M0Y8wAp7vUo0VHefSjGaP0eeFuLSnY6XR3gG6r2ekuxhiPMWZ5V3QGAFqFcotoxsote3TJYx/pQFWNnss8TkenJTndpYhwccZwJcbHKGfReqe7AnRbzYZ0a22NpC+MMQxNAXCXHlxu0Z/nV/rcdHnu8Sh9brr8eX6nu9TtfF64S9997CPFRHn0/DXH64hhiU53KWJ4Y6P1g+PS9OZXW7Vhx16nuwN0Sy1dODpU0gpjzAJjzKu1X53ZMQBokR5YbtGf51fm/EwVlBTIyqqgpECZ8zMJ6q2weO0Off/xT9S/d6xeuOZ4HTYowekuRZzLp6cpyhj99eMCp7sCdEstKsEo6Z5O7QUAoE7WgiyVVzasWlNeWa6sBVnyTer+b0I629tfbdV1f/tc6cle/fXqYzWoby+nuxSRBvXppTMnDtGLS4p06xmHKz42yukuAd1Kc9VdehljbpZ0saRxkj601r5X+9UVHQSASFNYEro6Tbh2HPTqF5t17V+XaPyQPno+83gCusN+cFyaSvZVav6Xm53uCtDtNDfd5WlJGZLyJJ0l6fed3iMAiHCpiaGXAIVrR8DfPinUTc8t1VFp/fXXHx2r/r1jne5SxJs2MkljBycw5QVog+ZC+gRr7fettY9JukjSiV3QJwCIaNkzs+WNaVi1xhvjVfZMqtaE8+dF6/Xzl/N08tiBevrKaerTK8bpLkGBzY2+f1yaviwq0RcbdzvdHaBbaS6kV9Z+Y62t6uS+AAAk+Sb5lDM7R2mJaTIySktMU87sHOajh2Ct1R/eWq3s11bqnElDlfODDOY+u8wFU1PkjY3SMx8xmg60hrHWhn/QmGpJtbWTjKR4SeXB721P3NAoIyPD5ubmOt0NAEAzrLW6998r9eSHG3RJxnD9+sLJivIYp7uFEO78Z55eXlqkz7JO41MOoB5jzBJrbUaox5rbcTSq0S6j0ew4CgBwWnWN1e0vfaknP9ygK09I130EdFe7OGO49lfW6D9fbnG6K0C30dI66QDQMfx+KT1d8ngCt35qf6N1Kqpq9JO/L9ULuUX6ycwx+sW3J8hDQHe1qSP6afTA3npxSZHTXQG6DUI6gK7j90uZmVJBgWRt4DYzk6COFttXUa3MZ3P1n7wt+vnZ4/TT08fKGAK62xljdHHGCOUW7NL67WVOdwfoFgjpAA7VWaPdWVlSecNNelReHmgHmlGyr1I/eOITvbd6u+67cJIyTxrtdJfQChdMTZHHSC99zmg60BKEdAANdeZod2GYzXjCtQNBO8oO6LKcj/VF0W49fNlRunQaNeO7m8F9e+nksQP10pJNqq4JX7QCQAAhHUBDnTnanRomWIVrByRt2r1Plzz6kdbvKNOfL8/QOZOHOt0ltNHFGSP0zZ79+mDtDqe7ArgeIR1AQ5052p2dLXkbbtIjrzfQjlbx5/mVPjddnns8Sp+bLn9ez5zXv257mS6et1jbyw7or1cfq1MOH+R0l9AOM8cPUp9e0Xpl2SanuwK4HiEdQEOdOdrt80k5OVJammRM4DYnJ9COFvPn+ZU5P1MFJQWysiooKVDm/MweF9SXbyrRJY9+pIrqGj2XeZwy0pOc7hLaKS46SmceMURvrtiq/ZXVTncHcDVCOoCGOnu02+eT8vOlmprALQG91bIWZKm8suGUpPLKcmUt6DkLcD/dsFOX5XysXjFReuGa43XEsESnu4QOcu6UYSo7UKWFq7Y53RXA1QjpABpitNv1CktCTz0K197dvPv1Nv3giU80qG+c/nHt8Ro1MMHpLqEDHT8qWQMSYvXqF5ud7grgaoR0AIditLtdOnu+eGpi6KlH4dq7kxdyN+pHz+RqzOAEvXDN8RrWL97pLqGDRUd5dPakoVqwcpvKDlQ53R3AtQjpANCBumK+ePbMbHljGk5J8sZ4lT2z+y7Atdbq4XfW6Gcvfqnpo5P1XObxSk6Ic7pb6CTnHjlMB6pq9NZX3zjdFcC1COkA0IG6Yr64b5JPObNzlJaYJiOjtMQ05czOkW9S9/zEo7rG6hevrNDv3lytC6am6IkrjlFCXLTT3UInOiq1v1L6xevVZUx5AcLhryCAbs2f51fWgiwVlhQqNTFV2TOzHQ2rXTVf3DfJ121DeX37K6t183PL9N8V3+iak0fp9lnj5PEYp7uFTubxGH178lA98cEGlZRXKtEb43SXANdhJB1At+XP8yvz5asaTi15+SpHSxH25PniHa2kvFI/eOITvfHVN7pr9gTdedZ4AnoEOXPiEFXVWL2zaqvTXQFciZAOoNvKevUmlduKBm3ltkJZr97kUI965nzxzrB59z5d9OhifbGxRH+8bKquPGGk011CFztyeD8N7hunN5YT0oFQCOkAuq3CyuJWtXeFnjZfvDPkFZXo/D99qG9K9uvpq6bp25OHOd0lOMDjMZp1xBAtXL1N+yrY2AhojDnpALqt1BKpoF/odif1lPnineGNFd/o5ueWKal3rJ6dc6wOH9LH6S7BQbOOGKJnPirQe6u368yJQ5zuDuAqjKQD6LaylyXL23C2i7wVgXa4i7VWf160Xtf+dYkOH9JH/7r+BAI6NG1kkvp5Y/TmCkoxAo0R0gF0W74fPaicN2KUtlsyVkrbLeW8ESPfjx50umuop7K6Rj9/ebmyX1upsycO1XOZx2lgH2qgQ4qJ8ui08YP19sqtqqiqcbo7gKsw3QVA9+XzySfJl5UlFRZKqalSdjY7pLpIyb5KXe//XB+s3aHrTx2t/zn9cCq4oIFZRwzRi0uK9PH6Yp00dqDT3QFcg5AOoHvz+QjlLrVhx179+JlcFRTv1W8vmqyLM0Y43SW40IljBig+JkpvfbWVkA7U49h0F2PMCGPMu8aYlcaYFcaYm4LtScaYt4wxa4K3/eudc6cxZq0xZpUxZla99qONMXnBxx4yxphge5wx5vlg+yfGmPQuf6IAEIHeXbVN5z78gYrLDuiZq44loCOsXjFROuGwAXrn622y1jrdHcA1nJyTXiXpf6y14yUdJ+l6Y8wESXdIWmCtHSNpQfC+go9dKukISWdKesQYExW81jxJmZLGBL/ODLZfLWmXtfYwSQ9Iur8rnhgARCprrR5ZuFZXPfWZRvT36tUbZuj40SzkRdO+NW6QNu3epzXbypzuCuAajoV0a+0Wa+3nwe9LJa2UlCLpPElPBw97WtL5we/Pk/SctfaAtXaDpLWSphljhkrqa639yAbegj/T6Jzaa70oaWbtKDsAoGOVV1Tphr8v1W/+u0rnTBqql+ZM14gkb/Mnovvy+6X0dMnjCdz627bb76njAtNc3vl6W8f1DejmXFHdJTgNZaqkTyQNttZukQJBXtKg4GEpkjbWO60o2JYS/L5xe4NzrLVVkkokHTKkY4zJNMbkGmNyt2/f3kHPCgAix8ad5brwkcV6LW+L7jhrnP542VTFx0Y1fyK6L79fysyUCgokawO3mZltCupDE+M1fmhfQjpQj+Mh3RiTIOklSTdba/c0dWiINttEe1PnNGywNsdam2GtzRg4kEUrANAa73y9VbMf/kCbd+/TX354jK49ebT40DICZGVJ5eUN28rLA+1t8K1xA7WkYJdKyis7oHNA9+doSDfGxCgQ0P3W2n8Gm7cGp7AoeFv7trpIUv2VR8MlbQ62Dw/R3uAcY0y0pERJOzv+mQBA5KmqrtH9//1aVz2Vq6GJ8Xrlhhk65fBBzZ+InqGwsHXtzfjWuEGqrrFatIZPtAHJ2eouRtITklZaa/9Q76FXJV0R/P4KSa/Ua780WLFlpAILRD8NTokpNcYcF7zm5Y3Oqb3WRZLesSwdB4B227pnv773+Ceat3CdLps2Qi9fN10jB/R2ulvoSqmprWtvxpQR/dXfG6N3mfICSHJ2JP0EST+Q9C1jzLLg19mS7pN0ujFmjaTTg/dlrV0h6QVJX0n6r6TrrbXVwWvNkfS4AotJ10l6Pdj+hKRkY8xaST9VsFIMAKDtPlizQ2c/+L7yikr0h0uO1K8vnKxeMa2cf95BCw7hoOxsydtoYbDXG2hvgyiP0cljB2rh6u2qrmE8DXBsMyNr7QcKPWdckmaGOSdb0iH/9VtrcyVNDNG+X9LF7egmgGb48/zKWpClwpJCpSamKntmtnyT2FyoJ6qqrtFDC9boj++u1eiBCXou8yiNGdyn9ReqXXBYO5+5dsFhLXaQ7R5qfy8d+Ps6ddwg/WvZZn1RtFtHpfZv/gSgBzPM/mgoIyPD5ubmOt0NoFvw5/mVOT9T5ZUHF495Y7zKmZ1DUO9hCor36qbnlmnZxt36zlHDde/5R8gb28ZxnvT0QDBvLDlZ2rev4WJEr1fKySGoR4jd5RWaeu9bumnmGN182linuwN0OmPMEmttRqjHHK/uAqD7ylqQ1SCgS1J5ZbmyFrStugPcx1qrf+Ru1NkPvq/128v0x8um6veXHNn2gC6FX1hYXNyh1ULQ/fTzxmpySqI+WLPD6a4AjiOkA2izwpLQYStcO7qX3eUVuv5vn+u2F7/UxJRE/ffmkzT7yGHtv3BrFxa2sVoIuqcZYwZo6cbdKt1PKUZENkI6gDZLTQwdtsK1o/t49+ttOnPu+3pzxVbdfuY4/e3Hx2lYv/iOufjZZ4duT0gI3d7GaiHonmYcNlDVNVYfr6diMiIbIR2u48/zK31uujz3eJQ+N13+PKo+uFX2zGx5YxpWd/DGeJU9s23VHVqK10jn2V1eoZ++sExXPvWZ+sZH6+XrTtCcU0YrytOBmxO98ELo9oqK0O3hQj16pKPS+ik+JkofUC8dEY6QDlepXYhYUFIgK6uCkgJlzs8khLmUb5JPObNzlJaYJiOjtMS0Tl80GtGvkU4uW/jmim90+gOL9MqyzbrxW4dp/o0zNGl4Yof+DEmBueehhAvpr73W8X2Aa8VFR2nayCS9v5Z56YhsVHdphOouzkqfm66CkkOrPqQlpin/5vyu7xBcJ2JfI43LFkodVvmkuOyA7pn/lV79YrPGD+2r3140WRNTOiGc1zKtHJU3Rqqp6Zy+wJUef3+9/t9/VmrxHd/quGlWgAtR3QXdBgsRD2JKR2gR+xrJyurwyic1NVZ/+6RQ3/r9e3p9+Rb99PSxevWGEzo3oEuBTwJagznpEWfGmAGSRJUXRDRCOlyFhYgBET2loxkR+xoJV+GkjZVPlm8q0QXzFuvnL+dp/NA+eu0nJ+onM8coJqoL/rfQ1Kh4B+5gie7r8MF9NLBPHFNeENEI6XAVpxYiuk23qj/eAfOkW/OpQcS+RsKNJrdylHnP/krd/eoKnfvwB9q0q1wPfPdI/f3Hx7Vt59C2SksL356TE7g15uB9NjKKOMYYzThsgD5cu0M1NUzLRWQipMNVnFiI6EbdZkpH7TzpggLJ2oPbu7ciqLf2U4OIfY1kZ7drlLmquiYwteV3C/X0R/n6/nFpWvA/p+iCqcNlWjtHvL2aei4+n5SfHxhtz88noEewGYcN0M69FVr5zR6nuwI4goWjjbBwFG7QbRZHhtvePS0tELCa4M/zK2tBVsjnKbnwubqB3x+Yg15YGBhBrw21zXhv9XZl/+crrd5apmPS++v/vj1Bk4f36/z+NqWNzwWRY/PufZp+3zv6v29P0NUzRjrdHaBTNLVwlJDeCCEdblA7ulx/yos3xuu+EWOPJzCC3lgz1ThCPb9DLiGjmruo6NEeq7eWKvs/K/Xe6u1KS/bqjjPH6cyJQ7p+5Bxoo5N+867GDemjnMtDZhig26O6C9DNdJspHW2cJx1qzv0hl+jpC0E7Uf6Ovbrl+WWaNXeRlhbu0v+eM15v3nKSzpo01DUBnepFaIljRybp0/ydzEtHRIp2ugMAurHs7NC1u5uZJ93c3PqIWAjaCYp2leuPC9bqxc+LFBNllHniKF178mj17x3rdNcaaPxJSu06BEnueyMKRx03Kln/WFKkVVtLNX5oX6e7A3QpRtIBF/Ln+XXlv65ssJjyyn9d2frRxk7eoVI+X6urcfjz/PKY8H96XPupgYsV7SrX//1ruU793UK9vHSTfnBcmhb97FTdefZ41wV0qZtVL4Kjjh2VJEn6eH2YXWqBHow56Y0wJx1uMOA3A1S879D/KSXHJ2vHz1pYN7gTd6hsq6bmortyzr3Lff3NHj323nq9+sVmeYx00dEjdOO3DnP9Do2eezyyOvT/PaxDQCgz7n9HE4cl6tEfHO10V4AOx5x0oJsJFdAlqbi8uOUj452wQ2V7hZuLHmWiCOit8Fn+Tl311Gc6c+77emPFN7pyeroW/exU/frCSa4P6FIEb0iFNjl2ZLI+2VDMvHREHEI64EZN/b+opTXJO3iHyhZpZnpNuLnoNbaGgN6M/ZXVemlJkc57+ANd/OhHWrZxt/7n9LFafMe39L/fnqChiV0Xztu76DPkhlRVRtlPFnTOtKz26uxpY2jScaOStKu8Umu2lTndFaBLsXAUcKHkcqm4d+j2BmpHxkNNX0lNDV3DvJU7VLZY4+k1tW8ipLr+pUYnqaDq0E8JUqOTOqdPPUDRrnL5PynU859t1M69FTpsUILuPe8IXXT0CMXHRnV5fzpi0WftcVkLslRYUqDUEqPst618eZJ06OvGUS14XaNzHTcqWVJgXvrhQ7pwZ1zAYcxJb4Q56XAD/6kDdNWMYlXUexsdWyU9+YqCQaaecDXJr7tOmjfv0PY5c6RHHunQ/kpq0cZG/lMHKHN6scrrrWX0Vkg5i5Ple7eFc+0jQGV1jd75epv+kVukd77eKkk6fcJgXXF8uo4fnexoGcUO32irHRtidQm39y8CWGs14/53deSIRD3iY146epam5qQzkg64kO9HD0oPXKmsEytVmCillkjZC0IEdCn8yPhrr7Wuvb1aML3G995OqVjKmqmGz2v5zs7pUzezcsse/SO3SK8s26TivRUa2CdO1548Wr7j0pTikrnm4aYsNVdWM/wFHZiW1Rpu718EMMbo2JFJWrh6u6y1rqn1D3Q2QjrgRj6ffJJ89bdNP/tsad3TLa9J3tXhoiXTa1JT5csrOPTNRlrkLhjcuLNcr+Vt0atfbNaKzXsUG+XRaRMG6eKjR+jEMQMUHeWupUOpiakhR9LbvOizq6dltZbb+xchpo1M0j+XbtK67Xt12KAEp7sDdAl3/fUHcJDPF/g4vaYmcPvII62rSd7G3UDbLDs78KahvsZvIs4+O/S54dp7qKJd5cpZtE7nPfyBTvzNu/r1618r2mN0z7lH6JOfz9QjvqN16rhBrgvoUphFn+3ZfMrtr4mWvK7R6TLS+0uSPi/Y5XBPgK7DSDrQnfh8LV+s1sbdQNvVNymwkLV29D87u2F/u3oKjkvU1Fit2LxH73y9Te98vVVfFJVIkiYPT9SdZ43T2ZOGakSSt5mruEPDRZ+FSk1MVfbMbPkm+eSfd52y1ueosHe1UvdGKXtUpnxzmln/4PbXREte1+h0owYkqJ83RrkFO3XJMSOc7g7QJVg42ggLR+EW/jx/yCDUuov43RUuPJ5A+cjGwi1+7cbKDlTpVwv+qb99tkxV+8YpWskysjpyRH/NOmKIzpk0VKnJ3SOYt4R/3nXK3DRP5TEH27yVUk7KnKaDegS9JtA+Vz/1mTYU79U7/3OK010BOgwLR4FupiPK3AUObsXIexu16s1ED57fu7+yWp8X7tJH64r10bpifV64UzW2t2o0RQeiPtduz2dS3Fe6+KTfyTfpBKe72+Gy1ueovNFU4fKYQLtPTYT0HvyaQMc6Kq2/Fny9TTv3Viipd2zzJwDdHCEdcKFQO3OWV5Yra0GWqzb9afWbia6egtOJ9h6o0hcbd2tJwS59tL5YuQW7VFFVI4+RJg3vpxrvW/qm8l0d8HwlmerASdVy3e+woxT2rm5Ve50e9JpA58pIOzgv/bQJgx3uDdD5COmAC3V4mbtO0uo3E910fq+1Vht27NXSwt36vHCXPi/crVXf7FHtLuXjh/bVD45L0/TRyTpmZJL69oqR554TZaMOncbhtt9hR0ndG6WChEMDeereZjZc6qavCXS9I0f0U0yUUS4hHRGCkA64UIeXueskofrYVLukLpmC0x7VNYFAvmJzib7askdfbd6j5ZtKtKu8UpLUJy5aU1L76fRTD9PUtP6aOqKf+nkP/ei9u/wOO0r2qMyQc9KzR2U2f7LLXxNwh14xUTpiWKKWFLCvAiIDIR1woeyZ2Q2mkUjtLHPXSTzGoxp76OI+j3Ff6cBQ9uyv1NptZVr1TWkglG/eo5VbSrWvMjAiHBvl0dghCTp9wmBNTe2vo1L767BBCYryNL+ZSnf5HXYU35xHpHlqfXUXoBWOTuuvZz8uUEVVjWKju8ffGaCtCOmACzVV5s5NQgX0ptqdsru8Qmu2lWnN1jKt2VaqtcHvv9mzv+6YPnHRGj+sry6dNkIThvbVEcMSddighDYHAad+hx1SFaiNfHMeaXqRKNBOGWn99cQHG7R8c4mOSu3vdHeATkVIB1zKN8nnulDuZtZabSs9oHXby7R2W1ldEF+zrUw7yg7UHeeNjdJhgxI0/bBkjRnUR4cNStDYwQka0d8rTwtGyFujq3+HHVYVCHCpo4ObGi3J30VIR49HSAfQZsnxySreVxyyvbMcqKpWQXG51m0r07rtZVq3fa/WB2/LDlTVHdcnLlqHDU7Qt8YNDITxwQkaMyhBwxLjOzyMu0V3qQoEtNWgPr2UmuTVkoJd+rHTnQE6GSEdQJs9eNaDuuqVq1RRXVHXFhsVqwfPerBd17XWaufeinoBPBDC120v08ad5XVVVSRpWGIvjR6UoO8claLRgxI0emDga3DfOBnTM8N4OJ1SFchtG2Ih4h2d1l8frN0ha23E/TeOyEJIB9BmHTHvend5hVZ9U6rVW0u1amupVn9TptXbSrU7WE1FkuKiPRo5oLcmpiTqvCkpGj2wt0YPTNDIAb3VO44/Y7VSo5NUUHXoJxup0Ultu6Df37CGeUFB4L5EUIdjpozop5eXbtKWkv0a1i/e6e4AncbYUNsxR7CMjAybm5vrdDeAHqeiqkart5bqqy17tPqbYCDfWqqtew7OF+/TK1qHD+6jMYMDc8Vrw3hKv547RaUj+U8doMzpxSqvVxHSWyHlLE6W790drb9genro3UDT0qT8/LZ2E2iXLzbu1nl/+lCP+I7S2ZOGOt0doF2MMUustRmhHmMICkCHq6iq0apvSpW3qUR5m0q0YnOJvt5SqorqQNWXuGiPxgxO0AmHDdC4IX00dnAfHT6kj4b07cXH1+3ge2+nVCxlzZQKE6XUEil7geRb3sa60oVhpsmEawe6wLihfRQb5dEXG3cT0tGjEdKBSNDJ84o37d6nJQW7tCR/pz4v3K2vv9mjyurAp3R9ekVr4rBE/fCEdE1MSdTEYX2Vlty7RbXG0UqpqfLlFciX16g9rY0bKKWmhh5JT+2ZGzKhe4iLjtKEYX21bONup7sCdCpCOroGi89arcPqXXfwvOLqGquVW/bos/ydgWBesEtbSgL1xr2xUTpyeD9dNWOkJqUkalJKolKTvIyOd5Xs7Ia/a0nyegPtbrge0EGmjOinF3I3qrrG8oYfPRYhHZ2PxWet1lH1rv15fmUtu0KFt1UfnPqQp8DvIiurRf/+1lpt2LFXH64r1uK1O7R4XbFK9gUWdQ5N7KWj0/orI62/MtKTNG5IH0VHsQugY2p/nx31hrijrwd0kCNHJOqpxflas61U44b0dbo7QKdg4WgjLBztBCw+a7X0uekqKDn03ywtMU35N+e36BqNg74UXEQ4PxjUjZFqQu8MWrKvUotWb9d7q7dr8dod2hwcKR+W2EsnHDZAJxw2QNNGJh1aWYFPTHoc/7zrlLU+R4W9q5W6N0rZozLlm8OuonDWhh17dervFur+70zSd49h+hW6LxaOwlksPmu1jqh3HXJjm9jAokJfng6ZV7xhx14tWLlVC1Zu02f5O1VVY9XPG6Ppo5N13ehAME9PbmLqSks/MSHIdxv+edcpc9M8lScE7hckVCtz0zxpngjqcFR6sld9e0Vr2cbdhHT0WIR0dD4Wn7VaamJqyJH01MSW/5uFDfqJkrxe2f+Xrbyi3frPl1v01ldbtX7HXknS4YP7KPOkUZo5frCmjOjX7HzPurnzuwuUmllvSo106LQapj51K1nrc+oCeq3ymEC7T4R0OMcYoyNH9NOyjSVOdwXoNIR0dD4Wn7Va9sxsZb58lcrtwZ08vSZW2TNb/m8WMuhbKXXvGN33y1/rP5v7a+PDHyraY3T86GRdMT1d3xo3SCOSvC3+GQ2m1BipoJ+UOTvwWF1Qr/+JSVZWw9eB1Kr58ehahb2rW9UOdKWpI/rpTwvXqbyiSt5Y4gx6HlZ4ofP5fFJOTmAOujGB25wcQlkTfF9KOa9ape2WjJXSdgfu+75s+TWyZ2bLGxMI3NE1Q5VY+X2lVPxZinpAfy6O18gBCfrNRZOV+7+n6dmrj9UV09NbFdClpqfU1Kn/iQlTnzqf3x9YB+LxBG79/jZfKnVvVKvaga505Ih+qq6xWr5pj9NdAToFbz3RNXw+QnlrZGXJV1Ap35L6jZWtGnE+b+x39dzgQuWui1JczRGyqtaAfjt127cmadYRQ9S/d2zzF2lGk1NqpEM/MWHqU+fq4OlE2aMyA3PSYw62eSsD7YDTjhzRT1JgB9JpI5Oc7QzQCRhJB9yojSPO1lp9vL5YP31+mab+8r/KWzNZHttXu6Kf0qZeV+nrmutVHf9+hwR0Kfwc+dQShf7E5OyzQ18oXDtap6npRG3gm/OIclLmKK0sKvCJTlmUclLmsGgUrjAgIU7D+8ezqRF6LEowNkIJRrhCK8tWlh2o0sufF+nZjwu0emuZ+sRFq9SzUFtqXlaFWSXVW/vZmjKOzQlZ5jHGq5zZOaHruVOOs3M1tWkUf+vRA13nX6Llm/Zo0c9OdborQJtQghHoblq42Hb11lI9+1GB/vl5kfZWVGtSSqJ+c9FkzZ48TL1/fZas59Bg1poyjs2pDeIt3hmVOemdy5jQYZwdX9FDTUxJ1Gt536ikvFKJ3pjmTwC6EUI64EZhdnr0T5ayHkjXtl0DNEiXSRXjFBvt0bcnD9Xlx6drSnCOptQxZRxb1NVJvpbvgsqc9M4VbrScUXT0UBOHBRbArNhcoumHDXC4N0DHYk460EH8eX6lz02X5x6P0uemy5/X9qoakgJBPT8/sCtofr6ePkK68aW/qmLbrRpUcY+qKgaoLM6vW8/bpT9cMqVBQJcaVnep5Y3xtqqMY4fLzg58IlAf5TgBtNHElEBIz9tEvXT0PIR0oAPUzs0uKCmQlVVBSYEy52e2P6hLKt1fqT8vWq+7XrDqu/8GSUY7Yv6gTb1+pGLP3/WrD38e8jzfJJ9yZucoLTFNRkZpiWnh54q3Q6venFCOs3MlJ7euHejmknrHKqVfvJZvpgwjeh4WjjbCwlG0Rfrc9JBTS9qzSHPP/ko99WG+Hn9/vfbsr9J+z5cqiX5J+z1LGiwENTKquaumjT1vn1YvHEXn8vulq66SKg5ugqXYWOnJJ3kjhB7rmmdztXprmd699RSnuwK0WlMLRxlJBzpA2HrhbVikuWd/pR5asEYz7ntHf3hrtaaNTNIr15+gXoMe0/6ohgFdamaOeQdubBNKyM2MKsuVtaBtJf/QTj5fIJDX/6SCgI4ebuKwRG3YsVel+yud7grQoQjpQAcIWy+8FYs0Dw3nyZp/www9fsUxOnJEP2XHnS1vo/8HeSul7LgwNcZrN7YpKAgsHKzd2KYDg3phiE8PJKmgpKBj5uWj9RqtZSCgo6ebOLx28ShTXtCzENKBDhBukebZY85udr72/spq5SxapxPvf1d/eGu1jh2VrH/fOEOPX5GhScMT647z3f+acl6V0nYrsLHMbinn1UB7SB28sU0oqWXht4dv1bz8Th7xB9Bz1VZ4Wc7iUfQwzElvhDnpaCt/nr9BvfCzx5ytp794Oux87eoaq39+XqQH3lqtzSX7dcrhA3XrGYfXVSs4hMcTvgZ2TYg56a09vg38k40yZ0vlTWxg2uy8/MZb2UuBii8sKAXQQsf9aoGOG5WkuZdOdborQKs0NSedkN4IIR0dJexi0r5pevKsT3T/66u0amupjhyeqNvPGqfpo5up8dva3Tq7YnfP9HT5+xYoa6ZUkKhD5stLLVjYyi6kANrpR0/nKr94r97+6clOdwVoFRaOAm3Q3rrnoRaNxtaM0f7tc3TVU7k6sHad/vTKffrX776v6R+/0fwFW1tjvCtqkmdny7fOq/y5UlqYT5qbnZfPLqQA2mliSl+t216mvQeqnO4K0GEI6UAIHVH3vH449dh+Sq64SUMPPKBedoTuXfi43nr0xzrn6w9kWrqgs7U1xruiJnm9n3H2KkkhPpg7e0yYha21kpJa1w4AjUxKSZS10ldbWDyKnoOQDoTQEaUFzx5ztmSj1bfyO0rZn6Pe1aeoJPofOn3DTfrBJ/9STE11vYu3cEGnGyt3BPv02hlpIae7vLYmzMJWAOggtWt5WDyKnoSQjjZp71QQt2tv3XNrrV7L26hhBx5R/6ortd/zpTbHXafdMU/rraE7wvzQDp7e0QUlGOtr87/Zzp2taweARgb37aUBCXFavomRdPQchHS0WkdMBWnNz3LizUB76p6v3Vamy5/8VNp9rayqtDX2/7Q97v+pyrNFklQYpnhLh0/v6IISjPW1+d8sNczj4doBIIQJw/pqJdNd0IMQ0tFqXbXLZFe+GWgse2a2vKZhXUGviVX2zPCLLvdXVut3b6zSWQ8u0hcbd0t9/qEtcTdqf9TSBsellnbRf3ZdvCAzXK34pv7NAid2wQJXAD3e+KF9tHZbmSqrO6bELOA0Qjparb1TQVrKyS3nfV9KOa/aRhsHWfm+DH38u6u26YwHFunhd9dq9uRheufWU5R97unyxsY1OM4b41X2W2H+B9LR0zu6eITaN8mnnNk5SktMk5FRWmJaXU34pk/sggWuAHq8CUP7qqK6Ruu2lzndFaBDRDvdAXQ/qYmpIet/t2QqSGt01ZuBkLKy5CuolG9J/cbKg1NFsrKkwkJ9c/gk/fIHd+u1PbEaPbC3/vbjY+vqndeG0/obHGXPzJYvJ0tSiLrgHRWe/f5A/woKAqG3/l4InTxC7Zvkaz6UhzzRRygH0C7jh/aVJK3cskfjhvR1uDdA+zGSjlZr87SGVmrPvPB2KyyUf5KUfrPkuStw65+kusWXVYUb9cTRszXzzP/Vgp3SbYP36fWbTjpkQyLfJJ/yb85XzV01yr85PxBgm5je0e45+PUXi0qBgG6CJVcYoQbQg40a0Fux0R6t3FLqdFeADuFoSDfGPGmM2WaMWV6vLckY85YxZk3wtn+9x+40xqw1xqwyxsyq1360MSYv+NhDxgRSiTEmzhjzfLD9E2NMepc+wVbqLru/tnlaQyt11ZuBUPwnJylztlTQT7ImcJs5W/JPNvqy7zCde8UDundmpo4p+kpvPT5H1z94q2KjW/ifU5jpHf7Jav8c/FCLRa09uHsnAR1ADxUd5dHYwQksHkWP4fRI+lOSzmzUdoekBdbaMZIWBO/LGDNB0qWSjgie84gxJip4zjxJmZLGBL9qr3m1pF3W2sMkPSDp/k57Ju1QUVWjOX9dooffWet0V1os5AhxJ/yMrngzEErWaVJ5w3Wj2hcTp6zTf6jzf/B7FXv7ad7Lv9JfXrxbqSVbwy/G9PsD2957PIHbJsofdsgc/HCfADS1WLQVfew0110nRUcH3rRERwfuA0ArjR9ChRf0HI7OSbfWLgoxun2epFOC3z8taaGk24Ptz1lrD0jaYIxZK2maMSZfUl9r7UeSZIx5RtL5kl4PnnN38FovSnrYGGOsy4asY6M9qq6xemzRen3v2FQlJ8Q1f1KEaPMc53YqrGq4iDOuepKSK2+Uoofpu8v+qzvffVJ9K+oF6lDzyWunntSObNfWKf/wQ+nppw9pL/hZ+aHXkELO/w/Hf3KSMqcX173BqP0EQMlJCvmvGK6PUteNul93nTRv3sH71dUH7z/ySNf0AUCPMH5oX/1jSZG2le7XoD69nO4O0C5Oj6SHMthau0WSgreDgu0pkjbWO64o2JYS/L5xe4NzrLVVkkokJTf+gcaYTGNMrjEmd/v27R34VFruZ2cervKKKj38bvcZTe/Jaue9G+tVUsX1GlLxa0lGir9Pv36/UUAPtxgzXJ3ynJyQ7VFhir5E1X1g1LxQnwCUxwbaQ5/QtbXUQ8rJaV07AIRxcPEo89LR/bkxpIcTYsNx2SbamzqnYYO1OdbaDGttxsCBA9vRxbY7bFAfXZIxQn/9uEAbd4YeUe3OutsOpdkzs9VPJ2jY/keUUH2GSqL/qd0Jtyn7wmtbXi4w3BST6urQzaFerZKq7cHjm/t3bPwJQHPtnVpLvaXTaML8e4RtB4AwJtSr8AJ0d24M6VuNMUMlKXi7LdheJGlEveOGS9ocbB8eor3BOcaYaEmJkly71/jNp41VlMfo92+ucrorHcrJTYnaorjsgD5dMUGJ++5UdPQBbY27TYkDFijn3IcDU298vsAizJqaphdjhiupGBV6ZDxtb5j2xDRJLft3bHVFnM6qpV6/yoy1B6fRODHfHUDESPTGaFhiL0I6egQ3hvRXJV0R/P4KSa/Ua780WLFlpAILRD8NTokpNcYcF6zqcnmjc2qvdZGkd9w2H72+IYm9dNUJI/WvZZv1ZdFup7vTYcIuiHz1JucXLNby+2XT0/XKEafo9P99Wa99sUk3nzZGq+/+kfbfs6pti2PDlVrMzAzZnj0qs8lqNi1ZWNrqijjZ2VJso/kxsbHtr6Xuhmk0ACLS+KEsHkXP4HQJxr9L+kjS4caYImPM1ZLuk3S6MWaNpNOD92WtXSHpBUlfSfqvpOutrZsHMEfS45LWSlqnwKJRSXpCUnJwkelPFawU42bXnjJaAxLi9ItXVqimxrXvJ1ol7KZElcXuGGn1+7Xlljv0o4wrdNPs2zRi52b9+6+36uatn7W8rGIo4XbSfOSRkO2+OY80Wc2mJZs7takiTuP3rR3xPrYgzGLXUO29e4c+Nlw7ADRh/NC+Wrd9r/ZXMmUO3Ztx8cCyIzIyMmxubq6jfXhpSZH+5x9f6DffmaxLjhnR/Akulz43PWSFkrTdUv7cxo3Bet5dxFqrv5/2ff168nmq9ETp1kXP6sol8xVla8L2xZ/nP3QX0S6oQBP23zExTfk3H9rPll00PXRwbu/vITo69JzyqCipqurQtpoQq2Y9HualA2i1/3y5Rdf/7XPNv2GGJg1PdLo7QJOMMUustRmhHnPjdJeId+FRKcpI66/7/vu1Ssorne5Ou4WcglEhZS8IcXBHLFhsocLicn3vz5/o58d8TxO/Was3nrxBP8p9JRDQJfn7FhyySNOf51fmy1c1nBf+8lWBx+Zdp/TbouW52yj9tmj55zVR67sNtcmzZ2YrxhPToC3GE9O+zZ06a+FoaxaDhgroTbUDQBPGD+0jicWj6P4I6S5kjNE95x2h3eUV+p0bFpG2c7ObkFMwFifLlxfi4PYuWGyB6hqrJz/YoFlzFylvU4l+/alff3suS2m7v6k7xj9JyjzPHLJI86aXr1G5rWhwvXJboZtevFqZm+apIKE6sENpQrUyN80LHdTbsagyuJlu2Put1lkLR9PSWt4eZiFt2HYAaEJacm/1ivFo9VbKMKJ7I6S71BHDEnX58el69uMCfbrBwYI0HVSl45AdSn/0YOhFle1dsNiMtdvKdMljH+mX//5Kx45K0pu3nKTLrj5HplFfsk4zKo9uOBWsvLJcxTV7Q1632BxQecNBbpXHSFnrQ9T6buOiyqwFWaqobvgGoaK6onU7kjYWbnFre38Prblu7eZJLW0HgCZEeYwOG5SgVYR0dHOEdBf72ZmHKzXJq5+9+IX2VTg0N7ezqnSEW1TZSbtcVlXXaN7CdTr7ofe1dluZ/nDJkfrLD4/RsH7xIftSmNgxazUKe4f4vbVxiklLFo6GE7a+emf9Hlpz3UcekebMOThyHhUVuM9uowDaaOzgPoyko9sjpLuYNzZa939nsvKLy52b9tKZm910ka+/2aMLHlms+//7tU49fKDe+ulJuvCo4Q2nijSqfZ4afcjGtAEhZpd4K6TkMPtPpZaG+E+sjVNMWl0DPajZ+uotrfveWq24rn/OCUr/3XB57jZK/91w+eec0DF9ABCRDh/cR1v3HOgR67oQuQjpLnf86GT94Lg0PfnhBmemvXTGnGW/XxowQP77v6/0Cwrk+YVV+gUF8j9wZYeWYKyoqtHct1dr9h8/0Obd+/Sn7x2lR79/tAb16dXsudlvB8J3k2ygQk3OGzF68J3YQ473VkjZi+NDXLxtU0xaXQM9qCX11Z3U3Ta7AuB+YwcHFo+u3sZoOrovQno3cMdZ45SW5NVNzy3Vrr3NJccO1tFzloNz3P3DipU5Wyrop8BCy35S5qxK+R+/qb09liTlFZXo3Ic/0Ny31+icSUP11k9P1jmTh7Z4oaXvvZ3KmR8I4Qoz88VIyn85Tb5b/iLf55W6YqkUVR04PqpaumKp5PskxBB7G6eYtKkGuto3TaYruP1NBIDuZ+yQQEhf9Q0hHd0XddIbcUOd9FCWbyrRhY8s1sghB7Sq6mYV7unCGt1+f2AOemFhYAQ9O7vtUyKCdbnTbw4E88bSdkv5D7T9NVleUaUH316jxz/YoAEJsco+f5JOmzC42fMOqX3+Spl8C4slSQNuk4pD7KuTHJ+sHT/bETj/1AHKnF6s8nqbd3orFKhi8+6ONj+fjtAp9dU7kOcej2yId0JGRjV3UYYRQOtZazXp7jd14VEp+uV5E53uDhAWddJ7gIkpiTp9SplWbYrRzp1TWjwtIOyCwVbwT5bSb5Y8dwVu/ZPb/jxqN84pCLO/RLj2lli4apvOeGCRHlu0XhcfPVxv3nJyiwP6IdMtTi2V/+hAuZYD0aHPO7C7uK4kZdZpahDQpcD9rNPa/nw6ytk1hx36aYANtrtAW+faA0A4xhiNHZzA4lF0a4T0buQ/Rbep3POR+ldepV7VgaTc1LSApjbeaakOny8cFSX/pCYeNq1/SW4vPaAb/75UP/zLZ4qL9uj5zON033cmKzE+pvmTFWa6ha1Q1gV9pbQ0lcWGPq8sVnUlKQurikMeU1jlYPnMoNe2LDx0wasJtrtAW+faA0BTxg7uo1XflIoZA+iuCOndSOGeAu2I/YMqTZEGVNyp6JphgfYQUxkkKevVm0JuvJP1asvnfXfYfOHrrpOio+WfUK3M2QpZJUWSqtVwekNTnwTU1Fj9/dNCzfz9Qr2x/BvdctpYvXbTiTp2VJjKLGGEmgoiSQVVxYGqJM1NYy8vV2pZ6I133DAaHLIMZBPtXa2tc+0BoCljB/fRrvJK7Sjr4rVcQAcJ80E+3Ci1LEoFCfu0PfaXGnLgDxpU8X/aEnerUvfuD3l8YWVxyIBZWBl61DfkNTpi0eF110nz5kmSsmYeOi2kvrR6Ybd2FL/2TULtKL4kTRt0rn7+cp4+y9+l40YlKfuCSRo9MKHlfaonykSp2h4aWKNMoC/J8ckq3nfov1n9sovZb1Qr81Jvgzc0bhkNTi0JPf8/taTLuxKWb5KPUA6gQx0eXDy6emupBvaJc7g3QOsxkt6NZL9RLW+FVOXZqu2xv1K0HaKh+3+hu94K/V4rXAhrTTjrkPnCOTnyTwrMZ29qzrm3IvAca4Uaxd9XIWW9/LnOfPB9rdlWpt9cNFl/X/asRg/tF6iUEh0deFPQCqECev32B896UDGehlNnYqqkB/978L5vT5prR4OzFxxaTtJbEWgHgJ5qzODAwA3z0tFdEdK7Ed+etLqygBWeFTKVv1OsHad3jrxXB6oODZrZy5JDh7NlLZ8O0hHzhWunuBT0U/ipIzZYsnBPWl1Tg9F6a9S76jSl7H9MtvwUXZIxQu/8zym65Mlfy8ybJ1UHn391dWDUvhVBPS3MVJXadt8kn/5y/l+CAVxKKzH6yyuSLy94YLAkpW+ST/k356vmrhrl35zvioAuNXzdmNra7vMb/lsDQE8zMCFO/b0xhHR0W4T07iQ7W751XuXPlWrukfL/8KHuX5Cj94dO0E/+vlQVVcH53H6/lJ4u38LiQ8PZGzHy/ejBFv/Ijpgv3NwUF0mSkZ4+SvLffnZdU+1ofWzNGA058FsNqLxZlZ4tMsm/0a8vnKSk3rENRunrqs9MUqDueAtlv1Gt2KqGbbFVDUf1DwZwq/yjnw0E3FbUOHdU49fNXMm3rh217gGgGwhUeOlDrXR0W4T07iTEJjiX3HKZ7p49QW+s2Kqrn/5M5c8GNguqLXXoy5PyHzSBcBbceKerA2VhC8sqlsdIWQdeq7t/a9R3NHj/TzXkwO8VZQdpR8zvVWp+puyEg+Vh6o/S122KNDvQ3mLJyaEqFErJYT5xCLfdffDNkTyeutKMrtDGzZMAoLs7fEgfrdlaRoUXdEssHO1ufL5DwtUPJXljo3XHP7+Ub/tOPVkTpf71D7A2EMzy81v945pavNnS0fTUfmlhK6g0VlBSoF17K/Twu2v1bOGJSlCNquxL2tL7BY3Ys0/ZCyTfntekOYHjQ43Sl8cG2n3p6S3agCnrNKmy0Uh6ZXSgvcUxNriTqsqDc+iDpRkluSMMh3jdAEBPN2ZwH5UeqNKWkv0a1i/e6e4ArcJIeg9xyTEj9IjvaK3oN0LnX/57fT2g0XzjwrZtAd8RJRiz486WpwUbRxrbS/2qLtFJv3lXf/lwgy5Y8a7efyxT+b9/WtW/3BeYppGnBs+lyU2RCgoCb1BqA3OYke1wtcxbVeM8K+tgQK9VXh5oBwA44vDBgQovq5iXjm6IkN6DnDlxiP7+7oPaFx2nC37we/173IyDD6a2rV53R5Rg/MtnOappota4sV71rbxEKfufUGLl5Tp2VJL+e/NJuv+rVzS0NES5yHrPJcoTetFnVOM3BU0E5g6pYBPuTVAb3xwBANpvbG2FF+aloxsipPcwR//0R/r3C3dqwrb1uuG8O3Tr2TeppN+AhosEWzF3uiMC7ILU6pBVXTy2rxIrv6eU/U+qf9XlOuBZpfK+9+jxK47R2MF9An32NqwsU1tJpVbY8omhXtlhAnOH7HgZ7k1QG98cAQDar583VgP7xGnNtjKnuwK0GiG9p/H5NGjub/T3xY/qxsXP6eUjvqVZ1z+pt6aeFlg446+3sLQFU0Gy484OXcYx7uyQx7dETE26kipuVMr+v6hf1fd0wJOnLXE3aXvcL1UTvb7Bc2luwWPY8omhasGHCcwdsuNlC95QAAC63mEDE7RuOyEd3Y9hxXNDGRkZNjc31+ludJgvi3br1n98odVby3T8qGRlPXKrJn7x4aEHhltYOmCA/MOKlTUzUKUltSSwCY5vc7K0Y0eL+mDuNjKKU3z1sepTfaZ61UxWjfZrb9S7Ko2er0pPwxFue1fLX5P+yUaZsxsuHvVWBOuA59U70Ovt/Iomfn9gSk0LFqsCALrG//4rT68s26wv7zpDxjQx9xJwgDFmibU2I+RjhPSGelpIl6TK6hr97ZNCzX17tXaVV2rm2k/140//qWM3Lj84C8WYQEnBxpr6g9bMa6equkaf5e/SD/70S1XEzpBHXlWZrSqNek1lUW+oxnPoyEaUiVLVL6pCXC2M9HT5+xaEfhORkEBgBoAI99SHG3T3/K/06c9nalDfXk53B2igqZBOCcYIEBPl0RXT03X+1BQ9efFNenbUDF36vfs0btsGnffVQs1euUjD+3ubv1AL7NpbocXrirVg5Va9s2qbdpdXSjEzVB71ocqi3tEBz/LAzkphhJtjHlZ2tnyZmfLl1aus4vVKOQ8SygEAOmxQoMLL2u1lhHR0K4T0CJIYH6NbLjtBc+Zcp5dGHa8XJ52m+0+5UvefcqXGxFXr+FeWKyM9SWMHJ2jkgN6Ki44KbOhTfGiFFZucrB2lB7Rue5nWbivT8k0lyi3YpbXBxTn9vTH61uGDNHP8YJ370kjVeA60qI9pia3cqr42iDPNBAAQwuhBvSVJ67aVafroAQ73Bmg5prs00hOnuxyi3tzpwvFT9dqP79TivqnKzd+p8orASLbHSEm945RcWa4+61fLBKfC7I+O1c7eiSpOHKj99uBUmL69opWRnqSj0/rr2JFJmpraX1GewOPptxgV9Du0G8YGdgmt5Y3xtn7BJgAATbDWatLdb+o7R6XonvMmOt0doAGmu6ChertPpkq6NvhVWV2jNVvLtDY4Or69dL927q1QaXSNtH69tH+/eltpTGo/JU0cpWH94nXYoAQdNihBQ/r2kscTev569vuxypxV0WBxp7GSVWAOerWtVlpimrJnZhPQAQAdyhij0QN7a932vU53BWgVQjrqxDz3d03IytKE+tNGfuCTFPINXov5+p0ozV+grJmBnUCNDo6gV9vquprkBHQAQGcYPTBBi9eF2BwPcDHqpCOgifrp/jy/0uemy3OPR+lz0+XPC7/5UUjvvitfnpQ/N1C/3DYacC+vLFfWgtC7gQIA0F6jByXomz37Vbq/0umuAC1GSHeDVuwA2mmysqTy8oZt5eXyP36TMudnqqCkQFZWBSUFypyf2bqgXq+0Y2Fi6EMKS0LvBgoAQHsdNihBkrSeKS/oRgjpTmvlDqCdpjB0SM6aUqzyyobhvT0j30nlYdrjk9p0PQAAmjN6YCCk11YgA7oDQrrTwoxgK6uLp3+kpoZs7pCRb3Z4AwA4KC3Zq2iP0drthHR0H4R0p4UZwQ7b3lmyswObANXn9So1Jjnk4amJoUN9SPXKfO4Ms2fSzn07W349AABaISbKo7Rkr9Yxko5uhJDutDAj2GHbO4vPJ+XkSGlpgZHvtDQpJ0fZ5z4ob0zDZF1bjaXF0g5uUJRaEvqQVoV+AABa6bBBCYyko1shpDstzAi2slsRgjuKzyfl5wcWeubnSz6ffJN8ypmdo7TENBkZpSWmtX7DoXrPMXuB5K1o+HCrQz8AAK102KAEFRSXq6KqpvmDARegTrrTusG29r5JvvbVMK/3HH3LC6XkJGWdJhVW7VRqYio10gEAnW70wARV11gV7tyrwwb1cbo7QLMI6W5QbwfQHqvec/QFvwAA6Cq1ZRjXbisjpKNbYLoLAADo8SjDiO6GkA4AAHq83nHRGprYi5COboOQDgAAIsKogb21oTjMrnqAyxDSAQBARBg5oLc2bC+Trbd/B+BWhHQAABARRg5I0J79Vdq5t6L5gwGHEdIBAEBEGDWgtyRpw469DvcEaB4hHQAARISRhHR0I4R0AAAQEYb3j1e0xxDS0S0Q0gEAQESIjvIoNclLSEe3QEgHAAARY+SA3oR0dAuEdAAAEDFqQ3pNDWUY4W6EdAAAEDFGDuytA1U1+mbPfqe7AjSJkA4AACIGFV7QXRDSAQBAxKgN6esJ6XA5QjoAAIgYg/v0UnxMlDZsJ6TD3QjpAAAgYng8RukDeiu/mJAOdyOkAwCAiDKKMozoBgjpAAAgoowc0FuFO8tVWV3jdFeAsAjpAAAgoqQP6K3qGquNO8ud7goQFiEdAABEFMowojsgpAMAgIgyipCOboCQDgAAIkr/3rHq540hpMPVCOkAACDijBzQW+uplQ4XI6QDAICIk54cqPACuBUhHQAARJzUJK82l+zTgapqp7sChERIBwAAESd9gFfWSht37nO6K0BIhHQAABBxUpMCFV4KdzIvHe5ESAcAABEnLdkrSSooZl463ImQDgAAIk5y71j1jo0ipMO1COkAACDiGGOUltxbBcVMd4E7EdIBAEBESkv2qoAyjHApQjoAAIhIqcleFe3cp+oa63RXgENEREg3xpxpjFlljFlrjLnD6f4AAADnpSf3VkV1jbaUUIYR7tPjQ7oxJkrSnySdJWmCpMuMMROc7RUAAHBaWlKgwkshi0fhQj0+pEuaJmmttXa9tbZC0nOSznO4TwAAwGGptWUYmZcOF4qEkJ4iaWO9+0XBtjrGmExjTK4xJnf79u1d2jkAAOCMoYnxiokylGGEK0VCSDch2hqsELHW5lhrM6y1GQMHDuyibgEAACdFeYxGJHnZdRSuFAkhvUjSiHr3h0va7FBfAACAi6QleZW/g5F0uE8khPTPJI0xxow0xsRKulTSqw73CQAAuEBacm8V7iyXtZRhhLv0+JBura2SdIOkNyStlPSCtXaFs70CAABukJrkVdmBKu3cW+F0V4AGop3uQFew1r4m6TWn+wEAANwlfcDBCi/JCXEO9wY4qMePpAMAAISTmtRbklRQzOJRuAshHQAARKwRSfEyRpRhhOsQ0gEAQMSKi47S0L692HUUrkNIBwAAES0tuTe7jsJ1COkAACCipSV7mZMO1yGkAwCAiDYiyasdZRUqr6hyuitAHUI6AACIaMP7x0uSinbtc7gnwEGEdAAAENFGJAVqpW9kXjpchJAOAAAi2oj+hHS4DyEdAABEtAEJsYqPiWK6C1yFkA4AACKaMUbD+8dr4y5G0uEehHQAABDxRiR5tXEnI+lwD0I6AACIeCMYSYfLENIBAEDEG5HkVen+KpWUVzrdFUASIR0AAKCuVjqj6XALQjoAAIh4wynDCJchpAMAgIhXt6ERI+lwCUI6AACIeInxMerbK5oKL3ANQjoAAICCZRgZSYdLENIBAAAkjejvZU46XIOQDgAAIGlEUryKdu2TtdbprgCEdAAAACkw3eVAVY22lx5wuisAIR0AAEAKTHeRpI27WDwK5xHSAQAAFJjuIklFLB6FCxDSAQAAJKX0Y0MjuAchHQAAQFJ8bJQGJMRRKx2uQEgHAAAIGpEUT610uAIhHQAAIGhEfzY0gjsQ0gEAAIJGJMVr8+79qqqucboriHCEdAAAgKDh/b2qrrH6Zs9+p7uCCEdIBwAACErpFyjDuHk3IR3OIqQDAAAEpfQPhPRNu5mXDmcR0gEAAIJqR9I3sesoHEZIBwAACOoVE6Xk3rHatJuQDmcR0gEAAOpJ6R+vIkbS4TBCOgAAQD0p/eIZSYfjCOkAAAD1pPSL1+bd+2StdboriGCEdAAAgHpS+sdrf2WNdu6tcLoriGCEdAAAgHrqKrww5QUOIqQDAADUM4wyjHABQjoAAEA9w/szkg7nEdIBAADqSYyPUe/YKMowwlGEdAAAgHqMMUrpTxlGOIuQDgAA0EhtGUbAKYR0AACARoaxoREcRkgHAABoJKV/vHaXV2rvgSqnu4IIRUgHAABohFrpcBohHQAAoJG6MoxUeIFDCOkAAACNpPTzSpKKGEmHQwjpAAAAjQzqE6eYKMNIOhxDSAcAAGjE4zEaktiLMoxwDCEdAAAghBTKMMJBhHQAAIAQUvp5me4CxxDSAQAAQkjpH6+tpftVUVXjdFcQgQjpAAAAIQzvFy9rpW9K9jvdFUQgQjoAAEAIw9jQCA4ipAMAAIQwtF8vSdI3ewjp6HqEdAAAgBCGJgZC+ubdTHdB1yOkAwAAhOCNjVZifIy2lDCSjq5HSAcAAAhjaGIvbWEkHQ4gpAMAAIQxrF+8NlPdBQ4gpAMAAIQxNLEX013gCEI6AABAGMP6xWt3eaX2VVQ73RVEGEI6AABAGHUVXhhNRxcjpAMAAIQxNDGwoRG7jqKrEdIBAADCOFgrnZF0dC1COgAAQBhDgiF9CyPp6GKEdAAAgDB6xUQpuXcsFV7Q5QjpAAAATRjar5c2s6ERupgjId0Yc7ExZoUxpsYYk9HosTuNMWuNMauMMbPqtR9tjMkLPvaQMcYE2+OMMc8H2z8xxqTXO+cKY8ya4NcVXfYEAQBAjzE0MZ6RdHQ5p0bSl0u6UNKi+o3GmAmSLpV0hKQzJT1ijIkKPjxPUqakMcGvM4PtV0vaZa09TNIDku4PXitJ0l2SjpU0TdJdxpj+nficAABADzQssZe2MJKOLuZISLfWrrTWrgrx0HmSnrPWHrDWbpC0VtI0Y8xQSX2ttR9Za62kZySdX++cp4PfvyhpZnCUfZakt6y1O621uyS9pYPBHgAAoEWG9otX6YEqle6vdLoriCDRTnegkRRJH9e7XxRsqwx+37i99pyNkmStrTLGlEhKrt8e4pwGjDGZCozSS1KZMSbUGwgnDZC0w+lOoNvhdYO24HWDtoiI103f+53uQY8TEa+bZqSFe6DTQrox5m1JQ0I8lGWtfSXcaSHabBPtbT2nYaO1OZJywvTJccaYXGttRvNHAgfxukFb8LpBW/C6QVvwumlap4V0a+1pbTitSNKIeveHS9ocbB8eor3+OUXGmGhJiZJ2BttPaXTOwjb0CQAAAOhSbivB+KqkS4MVW0YqsED0U2vtFkmlxpjjgvPNL5f0Sr1zaiu3XCTpneC89TcknWGM6R9cMHpGsA0AAABwNUfmpBtjLpD0R0kDJf3HGLPMWjvLWrvCGPOCpK8kVUm63lpbHTxtjqSnJMVLej34JUlPSHrWGLNWgRH0SyXJWrvTGHOvpM+Cx/3SWruz859dp3DtVBy4Gq8btAWvG7QFrxu0Ba+bJpjAoDMAAAAAt3DbdBcAAAAg4hHSAQAAAJchpLuMMSbfGJNnjFlmjMkNtiUZY94yxqwJ3rJzaoQzxjxpjNlmjFlery3s68QYc6cxZq0xZpUxZpYzvYYbhHnt3G2M2RT8u7PMGHN2vcd47UQ4Y8wIY8y7xpiVxpgVxpibgu38zUGTmnjt8DenBZiT7jLGmHxJGdbaHfXafiNpp7X2PmPMHZL6W2tvd6qPcJ4x5iRJZZKesdZODLaFfJ0YYyZI+rukaZKGSXpb0th6i7IRQcK8du6WVGat/V2jY3ntQMFdv4daaz83xvSRtESBXb9/KP7moAlNvHYuEX9zmsVIevdwnqSng98/rcALHBHMWrtIgWpG9YV7nZwn6Tlr7QFr7QZJaxX4A4gIFOa1Ew6vHchau8Va+3nw+1JJKxXYwZu/OWhSE6+dcHjt1ENIdx8r6U1jzBJjTGawbXCwVryCt4Mc6x3cLNzrJEXSxnrHFanpP5KITDcYY74MToepnbbAawcNGGPSJU2V9In4m4NWaPTakfib0yxCuvucYK09StJZkq4PfjQNtIcJ0cY8N9Q3T9JoSVMkbZH0+2A7rx3UMcYkSHpJ0s3W2j1NHRqijddNBAvx2uFvTgsQ0l3GWrs5eLtN0ssKfMyzNTivq3Z+1zbneggXC/c6KZI0ot5xwyVt7uK+wcWstVuttdXW2hpJf9bBj5d57UCSZIyJUSBk+a21/ww28zcHzQr12uFvTssQ0l3EGNM7uLBCxpjeks6QtFzSq5KuCB52haRXnOkhXC7c6+RVSZcaY+KMMSMljZH0qQP9g0vVBq2gCxT4uyPx2oEkY4xRYHfvldbaP9R7iL85aFK41w5/c1om2ukOoIHBkl4OvKYVLelv1tr/GmM+k/SCMeZqSYWSLnawj3ABY8zfJZ0iaYAxpkjSXZLuU4jXibV2hTHmBUlfSaqSdH2krpRH2NfOKcaYKQp8rJwv6RqJ1w7qnCDpB5LyjDHLgm0/F39z0Lxwr53L+JvTPEowAgAAAC7DdBcAAADAZQjpAAAAgMsQ0gEAAACXIaQDAAAALkNIBwAAAFyGkA4AAAC4DCEdAAAAcJn/D6P8djIvzezlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train, y_test, poly,pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c1f9ec59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5567716902601785"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R^2 of the training data\n",
    "poly.score(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d81cb8c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-29.871479278233295"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R^2 of the test data\n",
    "poly.score(x_test_pr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d77a90c",
   "metadata": {},
   "source": [
    "A negative R^2 is a sign of overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f942046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3, 0.75, 'Maximum R^2 ')"
      ]
     },
     "execution_count": 26,
     "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": [
    "Rsqu_test = []\n",
    "\n",
    "order = [1, 2, 3, 4]\n",
    "for n in order:\n",
    "    pr = PolynomialFeatures(degree=n)\n",
    "    \n",
    "    x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "    \n",
    "    x_test_pr = pr.fit_transform(x_test[['horsepower']])    \n",
    "    \n",
    "    lr.fit(x_train_pr, y_train)\n",
    "    \n",
    "    Rsqu_test.append(lr.score(x_test_pr, y_test))\n",
    "\n",
    "plt.plot(order, Rsqu_test)\n",
    "plt.xlabel('order')\n",
    "plt.ylabel('R^2')\n",
    "plt.title('R^2 Using Test Data')\n",
    "plt.text(3, 0.75, 'Maximum R^2 ')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5bf59935",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(order, test_data):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=test_data, random_state=0)\n",
    "    pr = PolynomialFeatures(degree=order)\n",
    "    x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "    x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
    "    poly = LinearRegression()\n",
    "    poly.fit(x_train_pr,y_train)\n",
    "    PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train,y_test, poly, pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fb375a4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0464ec0020524b569e008dc403fa041a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3, description='order', max=6), FloatSlider(value=0.45, description='tes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(order, test_data)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(f, order=(0, 6, 1), test_data=(0.05, 0.95, 0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f555995a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr1 = PolynomialFeatures(degree=2)\n",
    "pr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49508f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_pr1 = pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "x_test_pr1 = pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3cb84b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110, 70)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_pr1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d339b5d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly1 = LinearRegression()\n",
    "poly1.fit(x_train_pr1, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "905f2f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1567.59548786,  10535.71470682, -43342.84355523,  -9458.21047162,\n",
       "        20849.19317199])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat1 = poly1.predict(x_test_pr1)\n",
    "yhat1[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c215ea2d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\georg\\anaconda3\\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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\georg\\anaconda3\\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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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66HHFl5IcURt/K/zVSf5rVX3f/M95pX/fpbtQ4656VZLHVtUdq+qacw0f7O4z5vFbvWZ7cx2OSfJ784k6VXVoVT1oHve6JEdX1b1r6m/73Gx83nhlkvtW1cOqav+qusEcWpLvXP8PJbmgqv7fqrr23Hp3+/lNXTIFmd+squ+pqpsl+ZUF1uNlSX42U6vi6qtvHJjkK919SVXdNVPQ2cjJmbrOHFBVR+aKbgzJ1DL6wKr6D3O916rpUm7r9gnP9GW+366qm1XVNWq6FNsDM72mWx1ji3r//Jwnza/5gzLeBeDNSW5dVY+a1/uAqrpLVf1gVX1XTdfrPWj+SH+lzmTapjeoBS9JV1XXrKo7ZwqqX03yknnUgZm/AFVTP/NfW/PUtfvOgUm+kalV8bszHSsbmo+h12Vq8b9+pv7X2WLdNluP/ebj9IWZPu14zoJ1rV2P62QKP+fM831sppbFLc3b6AcznTdulKnby9ppblNV95nPR5dk+uLc6m232Tl1IzdM8uR5+Q/N1Gf9+Hncydn42DknU9evjc6Bx2faB39u3o9/NsltM+2bo7Y6F++KDc+RVfVjVfVDc264IFPD0C79r6yq762qJ2XqX/6b3f3tbL2ffCnJzebz84qRcx676CoRoOfAcc8kr62qk5P8r8wfq1bVf6rp2qNrb38/P33/TJ3zn56pX+b3Z/oy11XR31XVhZneTf9WppPuYzeY9lZJ3pHpH9v7k/xZX3Fd2t/PFAzOq6p1v8ixgVdk6n7zb5m+ufzkJOnu85P8cqa+up/P1JKx+vqyr53/nltV6/VRPG6e93syXc3hkiwWuLZNT/2LfydT39EvZmqJffiqSZ6d5GXza/awdWaxN9fhTzO1Vrxt3j8+kOnLQunuUzNdeeRVmdbrq7nytrlcd/9rpo/Dn5bpE6KTc0Vf3Rdn6ot5XlW9cX7j9MBM/Rg/m+mTiL/I1HKXTKHkc/O4t2V6bbbynkzf4P98d3941fBfTvLced2emc1bGX8n07b76lzD6tbKMzO1MD4j0z+zMzOFvY3Oo8/NdMWO983z+8Mkj+zule9ubHaMLWRuZf9Pma6Ac16m1ts3Zwpyi87jwiQ/mWl//UKm4/MPcsWb2EclOaOmbgmPn5eR7v5EpgD3mXm7bvRR/q/Pr/1XMnWXOzHTFRa+No9/TqYriZyfqdvNG9Y8f+355uWZ9o3PZ7oiwQcWWM1XZWoZfu2aN6XrrtsG7lFVF2UKSe9Kcr1MX9BbaYDYqq61x8DHk/xxpm3/pSQ/lOkqE5v52bmG8zIds+cmuXOv/wXOa2a6XOmXM23TG2bad5Otz6kb+WCm/fbLma4y8ZDuXunStdmx8/V5+v87r//dV890nsfRmc4d52a6utPRPX0JeMgC5+JdseE5MtMbmNdl2i9OS/LuTG+2V573kKr6alVt9j2O86rqa0k+mukc+tDuPm5en632k3/MdGWef6uqlddr5JzHLqre8nthO1NNXwh4c3ffvqqul+ST3b0rP3Bx9yTP6+6j5sePSnL37n7idtYLsCdU1QeTHNPdL9lyYgB2yVWiBXr+xutn54+UVi7dtehVFj6c5HvqiktR3SdT6wHAjldV/76qbjR/9P3zSX44yVv3dl0AV2X7ZICuqldn+jjjNlV1VlX9YqZvjf9iVZ2S6eOMB202jxXzR8lPT/IPVfXRTB3t//dyKgfYdrfJdInF8zN9BP6Q7v7i3i0J4Kptn+3CAQAAe8M+2QINAAB7y+78ytleccghh/QRRxyxt8sAAOAq7sQTT/xydx+6dvg+F6CPOOKInHDCCXu7DAAAruKqat1fwtWFAwAABgjQAAAwQIAGAIABAjQAAAwQoAEAYMDSAnRVXauqPlRVp1TVqVX1nHWmOaqqzq+qk+fbM5dVDwAAbIdlXsbuG0nu090XVdUBSd5XVW/p7g+sme693X30EusAAIBts7QA3dNvhF80PzxgvvndcAAA9mlL7QNdVftV1clJzk7y9u7+4DqT3WPu5vGWqrrdBvN5XFWdUFUnnHPOOcssGQAANrXUAN3dl3X3HZPcLMldq+r2ayY5Kcnh3X2HJC9M8sYN5nNsdx/Z3Uceeuh3/JoiAADsMXvkKhzdfV6SdyW535rhF3T3RfP945McUFWH7ImaAABgVyzzKhyHVtXB8/1rJ7lvkk+smeZGVVXz/bvO9Zy7rJoAAGB3LfMqHDdO8rKq2i9TMP7r7n5zVT0+Sbr7mCQPSfKEqro0ycVJHj5/+RAAAHakZV6F4yNJ7rTO8GNW3X9RkhctqwYAANhufokQAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQ2+wnfmK6AQBw1bS0n/K+unrHO/Z2BQAALJMWaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADlhagq+paVfWhqjqlqk6tquesM01V1Quq6vSq+khV/ciy6gEAgO2w/xLn/Y0k9+nui6rqgCTvq6q3dPcHVk1z/yS3mm93S/Ln818AANiRltYC3ZOL5ocHzLdeM9mDkrx8nvYDSQ6uqhsvqyYAANhdS+0DXVX7VdXJSc5O8vbu/uCaSW6a5MxVj8+ah62dz+Oq6oSqOuGcc85ZWr0AALCVpQbo7r6su++Y5GZJ7lpVt18zSa33tHXmc2x3H9ndRx566KFLqBQAABazR67C0d3nJXlXkvutGXVWksNWPb5Zki/siZoAAGBXLPMqHIdW1cHz/WsnuW+ST6yZ7E1JHj1fjePuSc7v7i8uqyYAANhdy7wKx42TvKyq9ssU1P+6u99cVY9Pku4+JsnxSR6Q5PQkX0/y2CXWAwAAu21pAbq7P5LkTusMP2bV/U7yxGXVAAAA280vEQIAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBgaQG6qg6rqndW1WlVdWpVPWWdaY6qqvOr6uT59sxl1QMAANth/yXO+9IkT+vuk6rqwCQnVtXbu/vja6Z7b3cfvcQ6AABg2yytBbq7v9jdJ833L0xyWpKbLmt5AACwJ+yRPtBVdUSSOyX54Dqj71FVp1TVW6rqdhs8/3FVdUJVnXDOOecss1QAANjU0gN0VV03yeuTPLW7L1gz+qQkh3f3HZK8MMkb15tHdx/b3Ud295GHHnroUusFAIDNLDVAV9UBmcLzK7v7DWvHd/cF3X3RfP/4JAdU1SHLrAkAAHbHMq/CUUlenOS07n7+BtPcaJ4uVXXXuZ5zl1UTAADsrmVeheNeSR6V5KNVdfI87BlJbp4k3X1MkockeUJVXZrk4iQP7+5eYk0AALBblhagu/t9SWqLaV6U5EXLqgEAALabXyIEAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAgC0DdFX9YVVdr6oOqKp/qKovV9V/3hPFAQDATrNIC/RPdvcFSY5OclaSWyf5taVWBQAAO9QiAfqA+e8Dkry6u7+yxHoAAGBH23+Bad5UVZ9IcnGSX66qQ5NcstyyAABgZ9q0BbqqrpHk75LcI8mR3f2tJF9P8qA9UBsAAOw4mwbo7v52kj/u7q9292XzsK9197/tkeoAAGCHWaQP9Nuq6meqqkZmXFWHVdU7q+q0qjq1qp6yzjRVVS+oqtOr6iNV9SMjywAAgD1tkT7Qv5rkOkkurapLklSS7u7rbfG8S5M8rbtPqqoDk5xYVW/v7o+vmub+SW413+6W5M/nvwAAsCNtGaC7+8BdmXF3fzHJF+f7F1bVaUlummR1gH5Qkpd3dyf5QFUdXFU3np8LAAA7ziIt0Kmq78nUSnytlWHd/Z5FF1JVRyS5U5IPrhl10yRnrnp81jzsSgG6qh6X5HFJcvOb33zRxQIAwLbbMkBX1S8leUqSmyU5Ocndk7w/yX0WWUBVXTfJ65M8df5BliuNXucp/R0Duo9NcmySHHnkkd8xHgAA9pRFvkT4lCR3SfK57v6xTC3J5ywy86o6IFN4fmV3v2GdSc5KctiqxzdL8oVF5g0AAHvDIgH6ku6+JEmq6prd/Ykkt9nqSfNVO16c5LTufv4Gk70pyaPnq3HcPcn5+j8DALCTLdIH+qyqOjjJG5O8vaq+msVaie+V5FFJPlpVJ8/DnpHk5knS3cckOT7TT4SfnukHWh47UDsAAOxxi1yF48Hz3WdX1TuTHJTkrQs8731Zv4/z6mk6yRMXqBMAAHaEDbtwVNVdqur+q4d197vnuz+01KoAAGCH2qwP9B8lOW2d4R+fxwEAwNXOZgH6Bt19xtqB3X16khssrSIAANjBNgvQ195k3HW2uxAAANgXbBag31FVvzdfju5yVfWcJP+43LIAAGBn2uwqHE9L8hdJTl91Gbo7JDkhyS8tuS4AANiRNgzQ3f21JI+oqu9Pcrt58Knd/Zk9UhkAAOxAi1wH+jNJhGYAAMhiP+UNAADMNvshle/bk4UAAMC+YLMW6NclSVX9wx6qBQAAdrzN+kBfo6qeleTWVfWra0d29/OXVxYAAOxMm7VAPzzJJZlC9oHr3AAA4Gpns8vYfTLJH1TVR7r7LXuwJgAA2LEWuQrHP1XV86vqhPn2x1V10NIrAwCAHWiRAH1ckguTPGy+XZDkJcssCgAAdqotf0glyS26+2dWPX7Oqp/2BgCAq5VFWqAvrqp7rzyoqnsluXh5JQEAwM61SAv045O8fFW/568m+fnllQQAADvXlgG6u09Jcoequt78+IKlVwUAADvUIi3QSQRnAABIFusDDQAAzARoAAAYsFAXjqq6Z5IjVk/f3S9fUk0AALBjbRmgq+oVSW6R5OQkl82DO4kADQDA1c4iLdBHJrltd/eyiwEAgJ1ukT7QH0tyo2UXAgAA+4JFWqAPSfLxqvpQkm+sDOzu/7i0qgAAYIdaJEA/e9lFAADAvmKRXyJ8d1V9b5K7zIM+1N1nL7csAADYmbbsA11VD0vyoSQPTfKwJB+sqocsuzAAANiJFunC8VtJ7rLS6lxVhyZ5R5LXLbMwAADYiRa5Csc11nTZOHfB5wEAwFXOIi3Qb62qv0/y6vnxzyY5fnklAQDAzrXIlwh/rap+Jsm9klSSY7v7b5ZeGQAA7ECLtECnu1+f5PVLrgUAAHa8DQN0Vb2vu+9dVRcmWf0z3pWku/t6S68OAAB2mA0DdHffe/574J4rBwAAdrZFrgP9ikWGAQDA1cEil6O73eoHVbV/kjsvpxwAANjZNgzQVfWbc//nH66qC+bbhUm+lORv91iFAACwg2wYoLv795MclOTl3X29+XZgd9+gu39zz5UIAAA7x6ZdOLr720nusIdqAQCAHW+RPtAfqKq7LL0SAADYByzyQyo/luS/VNXnknwtV1wH+oeXWhkAAOxAiwTo+y+9CgAA2Eds2YWjuz+X5OAkD5xvB8/DAADgameRH1J5SpJXJrnhfPvLqvqVZRcGAAA70SJdOH4xyd26+2tJUlV/kOT9SV64zMIAAGAnWuQqHJXkslWPL5uHAQDA1c4iLdAvSfLBqvqbTMH5QUlevNSqAABgh9oyQHf386vqXUnuPQ96bHf/81KrAgCAHWqRLhwrKklH9w0AAK7GFrkKxzOTvCzJ9yQ5JMlLquq3l10YAADsRIv0gX5Ekjt19yVJUlXPS3JSkt9dZmEAALATLdKF44wk11r1+JpJPr2UagAAYIdbpAX6G0lOraq3Z+oD/RNJ3ldVL0iS7n7yEusDAIAdZZEA/TfzbcW7llMKAADsfItcxu5lVfVdSW49D/pkd39ruWUBAMDOtGWArqqjMl2F44xMl7A7rKp+vrvfs9TKAABgB1qkC8cfJ/nJ7v5kklTVrZO8Osmdl1kYAADsRItcheOAlfCcJN39qSQHLK8kAADYuRZpgT6xql6c5BXz40cmOXF5JQEAwM61SIB+fJInJnlypj7Q70nyZ8ssCgAAdqpNA3RVXSPJid19+yTP3zMlAQDAzrVpH+ju/naSU6rq5nuoHgAA2NEW6cJx40y/RPihJF9bGdjd/3FpVQEAwA61SIB+zq7MuKqOS3J0krPnLiBrxx+V5G+TfHYe9Ibufu6uLAsAAPaUDQN0VV0r0xcIb5nko0le3N2XDsz7pUlelOTlm0zz3u4+emCeAACwV23WB/plSY7MFJ7vn+kHVRY2/1LhV3a9NAAA2Hk268Jx2+7+oSSZrwP9oSUs/x5VdUqSLyR5enefut5EVfW4JI9Lkpvf3PcZAQDYezZrgf7Wyp3BrhuLOinJ4d19hyQvTPLGjSbs7mO7+8juPvLQQw9dQikAALCYzQL0Harqgvl2YZIfXrlfVRfs7oK7+4Luvmi+f3ySA6rqkN2dLwAALNOGXTi6e79lLriqbpTkS93dVXXXTGH+3GUuEwAAdtcil7HbJVX16iRHJTmkqs5K8qwkByRJdx+T5CFJnlBVlya5OMnDu7uXVQ8AAGyHpQXo7n7EFuNflOkydwAAsM/Y9Ke8AQCAKxOgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAOWFqCr6riqOruqPrbB+KqqF1TV6VX1kar6kWXVAgAA22WZLdAvTXK/TcbfP8mt5tvjkvz5EmsBAIBtsbQA3d3vSfKVTSZ5UJKX9+QDSQ6uqhsvqx4AANgOe7MP9E2TnLnq8VnzsO9QVY+rqhOq6oRzzjlnjxQHAADr2ZsButYZ1utN2N3HdveR3X3koYceuuSyAABgY3szQJ+V5LBVj2+W5At7qRYAAFjI3gzQb0ry6PlqHHdPcn53f3Ev1gMAAFvaf1kzrqpXJzkqySFVdVaSZyU5IEm6+5gkxyd5QJLTk3w9yWOXVQsAAGyXpQXo7n7EFuM7yROXtXwAAFgGv0QIAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAgKUG6Kq6X1V9sqpOr6rfWGf8UVV1flWdPN+eucx6AABgd+2/rBlX1X5J/meSn0hyVpIPV9WbuvvjayZ9b3cfvaw6AABgOy2zBfquSU7v7s909zeTvCbJg5a4PAAAWLplBuibJjlz1eOz5mFr3aOqTqmqt1TV7dabUVU9rqpOqKoTzjnnnGXUCgAAC1lmgK51hvWaxyclOby775DkhUneuN6MuvvY7j6yu4889NBDt7dKAAAYsMwAfVaSw1Y9vlmSL6yeoLsv6O6L5vvHJzmgqg5ZYk0AALBblhmgP5zkVlX1fVX1XUkenuRNqyeoqhtVVc337zrXc+4SawIAgN2ytKtwdPelVfWkJH+fZL8kx3X3qVX1+Hn8MUkekuQJVXVpkouTPLy713bzAACAHaP2tbx65JFH9gknnLC3y9hQzT2/97GXFQCANarqxO4+cu1wv0QIAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGCNAAADBAgAYAgAECNAAADBCgAQBggAANAAADBGgAABggQAMAwAABGgAABgjQAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAAwRoAAAYIEADAMAAARoAAAYI0AAAMECABgCAAQI0AAAMEKABAGCAAA0AAAMEaAAAGCBAAwDAAAEaAAAGLDVAV9X9quqTVXV6Vf3GOuOrql4wj/9IVf3IMusBAIDdtbQAXVX7JfmfSe6f5LZJHlFVt10z2f2T3Gq+PS7Jny+rHgAA2A77L3Hed01yend/Jkmq6jVJHpTk46umeVCSl3d3J/lAVR1cVTfu7i8usa5d89SnJiefvMCE75r+HHXU0koBALjauOMdkz/5k71dxZUsM0DfNMmZqx6fleRuC0xz0yRXCtBV9bhMLdRJclFVfXJ7S91OlSSH1Lvz5b1dCQs7JLG99hG21b7F9tp32Fb7lqvX9nr3u5M//dO9tfTD1xu4zABd6wzrXZgm3X1skmO3o6g9oapO6O4j93YdLMb22nfYVvsW22vfYVvtW2yvvW+ZXyI8K8lhqx7fLMkXdmEaAADYMZYZoD+c5FZV9X1V9V1JHp7kTWumeVOSR89X47h7kvN3ZP9nAACYLa0LR3dfWlVPSvL3SfZLclx3n1pVj5/HH5Pk+CQPSHJ6kq8neeyy6tnD9pnuJiSxvfYlttW+xfbad9hW+xbbay+r6QIYAADAIvwSIQAADBCgAQBggAC9G/xU+b5jgW11VFWdX1Unz7dn7o06SarquKo6u6o+tsF4x9UOssD2cmztEFV1WFW9s6pOq6pTq+op60zj+NohFtxejq+9ZJnXgb5KW/VT5T+R6XJ8H66qN3X36l9aXP1T5XfL9FPla39MhiVbcFslyXu7++g9XiBrvTTJi5K8fIPxjqud5aXZfHsljq2d4tIkT+vuk6rqwCQnVtXb/d/asRbZXonja6/QAr3rLv+p8u7+ZpKVnypf7fKfKu/uDyQ5uKpuvKcLZaFtxQ7R3e9J8pVNJnFc7SALbC92iO7+YnefNN+/MMlpmX79dzXH1w6x4PZiLxGgd91GP0M+Og3Lt+h2uEdVnVJVb6mq2+2Z0tgFjqt9j2Nrh6mqI5LcKckH14xyfO1Am2yvxPG1V+jCseu27afKWbpFtsNJSQ7v7ouq6gFJ3pjpI0x2HsfVvsWxtcNU1XWTvD7JU7v7grWj13mK42sv2mJ7Ob72Ei3Qu85Ple87ttwO3X1Bd1803z8+yQFVdcieK5EBjqt9iGNrZ6mqAzKFsVd29xvWmcTxtYNstb0cX3uPAL3r/FT5vmPLbVVVN6qqmu/fNdOxce4er5RFOK72IY6tnWPeDi9Oclp3P3+DyRxfO8Qi28vxtffowrGLruY/Vb5PWXBbPSTJE6rq0iQXJ3l4+5nOvaKqXp3kqCSHVNVZSZ6V5IDEcbUTLbC9HFs7x72SPCrJR6vq5HnYM5LcPHF87UCLbC/H117ip7wBAGCALhwAADBAgAYAgAECNAAADBCgAQBggAANAMA+qaqOq6qzq+pjC0z776rqpKq6tKoess7461XV56vqRVvNS4AG2AZVdVlVnVxVH6uq11bVd28w3T9t0/J+uqqeOd9/fFU9ejfm9dL1/pmsmeZdVXXkfP+M7fixhqp6UlW5TBqwO16a5H4LTvuvSR6T5FUbjP9vSd69yIwEaIDtcXF337G7b5/km0kev3pkVe2XJN19z21a3q8n+bN5nsd098u3ab7bbmXd13FckifvyVqAq5bufk+Sr6weVlW3qKq3VtWJVfXeqvqBedozuvsjSb69dj5Vdeck35vkbYssV4AG2H7vTXLLqjqqqt5ZVa9K8tEkqaqLViaqql+vqo9W1SlV9bx52Lon/tWq6tZJvtHdX54fP7uqnj7ff1dV/UFVfaiqPlVVP7rO86uqXlRVH6+q/5PkhqvG/XhV/fNc13FVdc3NVrSq3jjXempVPW7V8Iuq6rlV9cEk96iq583L+0hV/Y8k6e6vJzlj/gU1gO1ybJJf6e47J3l65saGjVTVNZL8cZJfW3QBfokQYBtV1f5J7p/krfOguya5fXd/ds1090/y00nu1t1fr6rrz6OOTfL47v6XqrpbphP/fdYs5l5JTtqkjP27+65V9YBMvwx43zXjH5zkNkl+KFOLy8eTHFdV18r0ceiPd/enqurlSZ6Q5E82WdYvdPdXquraST5cVa/v7nOTXCfJx7r7mfO6vTjJD3R3V9XBq55/QpIfTfKhTZYBsJCqum6SeyZ57fwr50myaUNAkl9Ocnx3n7nqOZsSoAG2x7VX/dzuezMFxnsm+dDa8Dy7b5KXzK2wmUPooif+Gyc5Z5Na3jD/PTHJEeuM/3dJXt3dlyX5QlX94zz8Nkk+292fmh+/LMkTs3mAfnJVPXi+f1iSWyU5N8llSV4/D78gySVJ/mJu8X7zquefneQ7WtkBdtE1kpzX3XcceM49kvxoVf1ykusm+a6quqi7f2OjJwjQANvj4rUn7DkEf22D6StJrxm26In/4iQHbTL+G/Pfy7LxeX7tsldqWlhVHZXpjcA95lb0dyW51jz6kjmgp7svnbtp/HiShyd5Uq5oVb9WpvUB2G3dfUFVfbaqHtrdr63pRPzD3X3KJs955Mr9qnpMkiM3C8+JPtAAe8vbkvzCytU6qur63X1Bks9W1UPnYVVVd1jnuaclueVuLPs9SR5eVftV1Y2T/Ng8/BNJjqiqlXk/Kpt/I/2gJF+dw/MPJLn7ehPNLesHdffxSZ6a5I6rRt86yZaXnwJYT1W9Osn7k9ymqs6qql9M8sgkv1hVpyQ5NcmD5mnvUlVnJXlokv9VVafu6nK1QAPsBd391qq6Y5ITquqbSY5P8oxMJ/4/r6rfTnJAktckWdty8p4kf1xV1d3rtSRv5W8ytQB/NMmnMofk7r5kvqzca+e+3B9Ocswm83lrksdX1UeSfDLJBzaY7sAkfzv3sa4k/3XVuHslec4urANAuvsRG4z6jkvbdfeHk9xsi/m9NNN3QTZVu3buBWBvqqo/TfJ33f2OvV3LrqqqOyX51e5+1N6uBWCELhwA+6b/nmTdH2vZhxyS5Hf2dhEAo7RAAwDAAC3QAAAwQIAGAIABAjQAAAwQoAEAYIAADQAAA/5/tKX+696sHkwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
    "DistributionPlot(x_test_pr1,yhat1,\"Actual Values (Test)\",\"Predicted Values (Test)\",Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20325c16",
   "metadata": {},
   "source": [
    "The predicted value is higher than actual value for cars where the price $10,000 range, conversely the predicted price is lower than the price cost in the $30,000 to $40,000 range. As such the model is not as accurate in these ranges."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b6c572",
   "metadata": {},
   "source": [
    "# Part 3: Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bf4fee87",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr=PolynomialFeatures(degree=2)\n",
    "x_train_pr=pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])\n",
    "x_test_pr=pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8703bb7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e4579089",
   "metadata": {},
   "outputs": [],
   "source": [
    "RigeModel=Ridge(alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d1e29798",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: [ 6570.82441941  9636.24891471 20949.92322737 19403.60313255]\n",
      "test set : [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "RigeModel.fit(x_train_pr, y_train)\n",
    "yhat = RigeModel.predict(x_test_pr)\n",
    "print('predicted:', yhat[0:4])\n",
    "print('test set :', y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c0331203",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████| 1000/1000 [00:02<00:00, 483.56it/s, Test Score=0.564, Train Score=0.859]\n"
     ]
    }
   ],
   "source": [
    "#select the value of alpha that minimizes the test error. \n",
    "from tqdm import tqdm\n",
    "\n",
    "Rsqu_test = []\n",
    "Rsqu_train = []\n",
    "dummy1 = []\n",
    "Alpha = 10 * np.array(range(0,1000))\n",
    "pbar = tqdm(Alpha)\n",
    "\n",
    "for alpha in pbar:\n",
    "    RigeModel = Ridge(alpha=alpha) \n",
    "    RigeModel.fit(x_train_pr, y_train)\n",
    "    test_score, train_score = RigeModel.score(x_test_pr, y_test), RigeModel.score(x_train_pr, y_train)\n",
    "    \n",
    "    pbar.set_postfix({\"Test Score\": test_score, \"Train Score\": train_score})\n",
    "\n",
    "    Rsqu_test.append(test_score)\n",
    "    Rsqu_train.append(train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "85a9ee49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18c78aeb340>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot out the value of R^2 for different alphas\n",
    "\n",
    "width = 12\n",
    "height = 10\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "plt.plot(Alpha,Rsqu_test, label='validation data  ')\n",
    "plt.plot(Alpha,Rsqu_train, 'r', label='training Data ')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('R^2')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae25077b",
   "metadata": {},
   "source": [
    "the model is built and tested on the same data, so the training and test data are the same.\n",
    "\n",
    "The red line in the Figure represents the R^2 of the training data. As alpha increases the R^2 decreases. Therefore, as alpha increases, the model performs worse on the training data\n",
    "\n",
    "The blue line represents the R^2 on the validation data. As the value for alpha increases, the R^2 increases and converges at a point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "648147b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: [ 6472.05406775  9537.15585237 21078.08955884 19750.77444841]\n",
      "test set : [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "RigeModel1=Ridge(alpha=10)\n",
    "RigeModel1.fit(x_train_pr, y_train)\n",
    "yhat = RigeModel1.predict(x_test_pr)\n",
    "print('predicted:', yhat[0:4])\n",
    "print('test set :', y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3b6d1f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5418576440207341"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RigeModel1.score(x_test_pr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a061100f",
   "metadata": {},
   "source": [
    "# Part 4: Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "40461c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=10000)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters1= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000]}]\n",
    "RR=Ridge()\n",
    "Grid1 = GridSearchCV(RR, parameters1,cv=4)\n",
    "Grid1.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)\n",
    "BestRR=Grid1.best_estimator_\n",
    "BestRR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c915e5d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8411649831036149"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BestRR.score(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "73f46415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1, normalize=True)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grid search for the alpha parameter and the normalization parameter\n",
    "parameters2= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000], 'normalize' : [True, False]}]\n",
    "RR2=Ridge()\n",
    "Grid2 = GridSearchCV(RR2, parameters2,cv=4)\n",
    "Grid2.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)\n",
    "BestRR2=Grid2.best_estimator_\n",
    "BestRR2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6a795194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8067357870903056"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BestRR2.score(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)\n"
   ]
  }
 ],
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