assignment2solutionsupload.ipynb

{
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      "metadata": {
        "id": "Y5y_HbJiPKhA"
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      "source": [
        "# Question 1\n",
        "## Regularization in Linear Regression\n",
        "Make a class called LinearRegression which provides two functions : fit and predict. You may use the code present in the repository as template. You need to make the following changes in order to include regularization :\n",
        "1. To initialize an object of the class, you need to provide 4 parameters : learning_rate, epochs, penalty and alpha (coefficient of the regularization term). Penalty and alpha should have default values of None and 0 respectively.\n",
        "2. The parameter penalty should take in any one of these inputs : L1 (Lasso), L2 (Ridge) and None (simple LR).\n",
        "3. Do some basic differentiation to find out the expressions of dC/dw and dC/db when regularization is involved. Use internet whenever necessary.\n",
        "4. Write if-else statements inside the fit method to cover the different values for dw for different values of penalty."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "gzoG2XilPLFr"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "class LinearRegression:\n",
        "    def __init__(self, learning_rate, epochs, penalty=None, alpha=0):\n",
        "        self.learning_rate = learning_rate\n",
        "        self.epochs = epochs\n",
        "        self.penalty = penalty\n",
        "        self.alpha = alpha\n",
        "        self.weights = None\n",
        "        self.bias = None\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        n_samples, n_features = X.shape\n",
        "\n",
        "        # Initialize weights and bias to zeros\n",
        "        self.weights = np.zeros(n_features)\n",
        "        self.bias = 0\n",
        "\n",
        "        for _ in range(self.epochs):\n",
        "            # Compute predictions\n",
        "            y_pred = self.predict(X)\n",
        "\n",
        "            # Compute gradients\n",
        "            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))\n",
        "\n",
        "            if self.penalty is None:\n",
        "                db = (1 / n_samples) * np.sum(y_pred - y)\n",
        "            elif self.penalty == 'L1':\n",
        "                dw += (self.alpha / n_samples) * np.sign(self.weights)\n",
        "                db = (1 / n_samples) * np.sum(y_pred - y)\n",
        "            elif self.penalty == 'L2':\n",
        "                dw += (2 * self.alpha / n_samples) * self.weights\n",
        "                db = (1 / n_samples) * np.sum(y_pred - y)\n",
        "\n",
        "            # Update parameters\n",
        "            self.weights -= self.learning_rate * dw\n",
        "            self.bias -= self.learning_rate * db\n",
        "\n",
        "    def predict(self, X):\n",
        "        return np.dot(X, self.weights) + self.bias\n"
      ]
    },
    {
      "attachments": {},
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      "metadata": {
        "id": "PsqoxNag7D3-"
      },
      "source": [
        "# Question 2\n",
        "# Linear Regression\n",
        "Use the dataset https://www.kaggle.com/datasets/quantbruce/real-estate-price-prediction (*).\n",
        "1. Read it using pandas.\n",
        "2. Check for **null values**.\n",
        "3. For each of the columns (except the first and last), plot the column values in the X-axis against the last column of prices in the Y-axis.\n",
        "4. Remove the unwanted columns.\n",
        "5. Split the dataset into train and test data. Test data size = 25% of total dataset.\n",
        "6. **Normalize** the X_train and X_test using MinMaxScaler from sklearn.preprocessing.\n",
        "7. Fit the training data into the 3 models created in question 1 (**linear regression, lasso and ridge regression**) and predict the testing data.\n",
        "8. Use **mean square error and R<sup>2</sup>** from sklearn.metrics as evaluation criterias.\n",
        "9. Fit the training data into the models of the same name provided by sklearn.linear_model and evaluate the predictions using MSE and R<sup>2</sup>.\n",
        "10. Tune the hyperparameters of your models (learning rate, epochs, penalty and alpha) to achieve losses close to that of the sklearn models. (*We will cover hyperparameter tuning using GridSearchCV and all in later weeks. For now, you may manually run the model for different values of the hyperparameters.*)\n",
        "\n",
        "Note : (*) To solve this question, you may proceed in any of the following ways :\n",
        "1. Prepare the notebook in Kaggle, download it and submit it separately with the other questions.\n",
        "2. Download the dataset from kaggle. Upload it to the session storage in Colab.\n",
        "3. Use Colab data directly in Colab. [Refer here](https://www.kaggle.com/general/74235). For this, you need to create kaggle API token. Before submitting, hide or remove the API token."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "8lupaMcr63QF"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No                                        0\n",
            "X1 transaction date                       0\n",
            "X2 house age                              0\n",
            "X3 distance to the nearest MRT station    0\n",
            "X4 number of convenience stores           0\n",
            "X5 latitude                               0\n",
            "X6 longitude                              0\n",
            "Y house price of unit area                0\n",
            "dtype: int64\n"
          ]
        },
        {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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Cc9dnoKaze5y3zV2fwZOtiRqIxQ4RkZftzM6rNqJzOQEgp6AUO7PzzEuKyEJY7BARednZi7UXOg2JIyJ3LHaIiLysdUiA56B6xBGROxY7RERe1t8egZiwANS2wNyGS6uy+tsjzEyLyDJY7BAReZmvjw2zR8cDQLWCx/n97NHx3G+HqIFY7BARKWBU1xhMGWqH7Yp6xmYDpgy1c58doqvAYoeISAEp6TlYvjUbV64udwhg+dZs7rNDdBVY7BAReVld++w4cZ8dooZjsUNE5GXcZ4eocbHYISLyMu6zQ9S4WOwQEXlZqxb+hsYRkTsWO0REXuaokuvFkY0jIncsdoiIvGzHsfOGxhGROxY7REReJ7tZIDcVJGoIFjtERF6W0CHS0DjyrMohkJp1Hh/t/QGpWee5rN/imnk7ASKipm7gdZEID2qO/JKKWmNaBjXHwOtY7BghJT0Hc9dnuC33jwkLwOzR8dyp2qI4skNE5GW+PjYsGNutzpjksd14NpYBUtJz8Mjbe6rta5RbUIpH3t7DnaotisUOEZECRnWNwbIHeiM6NMDt9piwACx7oDdHHAxQ107Vztu4U7U1cRqLiEgRo7rGYER8NHZm5+HsxVK0DglAf3sER3QMUp+dqtkfZS0sdoiIFOLrY+OFtpFwp+qmi9NYRETUJLQOCfAcVI840geLHSIiahL62yMQExZQ625FNlzqkepvjzAzLTLBVRU75eXlyMzMRGVlpVH5EBERNQpfHxtmj44HUH17Ruf3s0fHs0fKghpU7JSUlGDSpEkICgpCly5dcOLECQDAo48+igULFhiaIBERkVFGdY3B0gd6IzrMfaoqOiwAS7nqzbIa1KCclJSEffv2YcuWLRg1apTr9uHDh2POnDmYOXOmYQkSEREZiavemp4GFTsffvgh3n//fQwcOBA228+/HF26dEFWVpZhyRERETUGrnprWhpU7Pz4449o3bp1tduLi4vdih8iIiIyRpVDcDSqgRpU7PTt2xf/+te/8OijjwKAq8B54403kJCQYFx2RERExPO8rlKDip358+fj9ttvR0ZGBiorK/GXv/wFGRkZ2L59O7788kujcyQiajLKKx1YnXoMx/NKEBcRhAkJ7eHXjLuEGE2nURLneV5XHmLhPM+LjdWe2YQQDToEJCsrCwsWLMC+fftQVFSE3r17Y8aMGejWre7D7FRUWFiIsLAwFBQUIDQ01NvpEClJp4uDrpI/zcCKbdm4/GgmHxsweYgdSXfEey8xi9FplKTKIXDjwk21HnNhw6WVZF/NGNYkX4+y1+8GFztWwmKHqG46XRx0lfxpBl7fml3r/b8byoLHCLWNkjjLBNVGSVKzzuP+FWke496dPLBJNlzLXr8bNDb66aef4vPPP692++eff47PPvusIQ9JRIpyXhyu/GTpHEJPSc/xUmbWUV7pwIpttRc6ALBiWzbKKx0mZVQ/VQ6B1Kzz+GjvD0jNOq/sqeE6nnrO87yM0aBiZ+bMmaiqqqp2uxCCe+wQWYiOFwcdrU49Bk9PoUNcilNNSnoObly4CfevSMPj7+3F/SvScOPCTUoWwfU59VwVup/npUoh3KAG5cOHDyM+vvpwaqdOnXDkyJGrTorIynTqfanPxaEpDqEb5XheiaFxZtGtcVbHUZL+9giEBzVHfklFrTEtg5oreZ6XStPfDSp2wsLCcPToUbRv397t9iNHjiA4ONiIvIgsSaUXvwwdLw46iosIMjTODJ5G/Wy4NOo3Ij5amWJe91GS2qg4rqpaIdygaay7774b06ZNc9st+ciRI3jyySdx1113GZYckZXo2Pti1YuDaiYktIenesDHdilOFTpOCel46vnO7Lw6R3UAIL+kQqnnWcXp7wYVO4sWLUJwcDA6deoEu90Ou92Ozp07IzIyEn/605+MzpFIeyq++GXoeHG4nCr9Ap74NfPB5CH2OmMmD7Ertd+OjqN+Op56ruPzrGIh3OBprO3bt2PDhg3Yt28fAgMD0b17dwwdOtTo/IgsQdfeF+fF4ZG398AG9+FyVS8OTrpNGSbdEY+j54qxIeNstftGxLdWbtm5rqN+zlPPr/zdiFb0d0PH51nFAq1BxQ5w6YiI2267DbfddpuR+RBZkoovflm6XRwA9foFZKSk5+DfNRQ6APDvjLNISc9RKmfnqF9uQWmNI5bOze5UHPXT6dRzHZ9nFQs06WJn8eLFmDJlCgICArB48eI6Yx977LGrTszphx9+wIwZM/DZZ5+hpKQE119/PVauXIm+ffsCuLTcffbs2VixYgXy8/MxePBgLF26FB07djQsB6KrpeKLvz50ujjo2DhbV85OquWs86gfoM+p5zo+zyoWaNLFzssvv4zx48cjICAAL7/8cq1xNpvNsGLnwoULGDx4MG655RZ89tlnuOaaa3D48GG0bNnSFbNo0SIsXrwYf/vb32C32zFr1iyMHDkSGRkZCAhQ88JBTY+KL/760uXioOOUoY45A3qO+uloVNcYTBlqx4pt2bj8zAPbf44SUe15VrFAky52srOza/xzY1q4cCHatm2LlStXum6z239u4hNC4JVXXsEf/vAH3H333QCAt956C1FRUfjwww9x3333mZInkScqvvitSscpQx1zdtJp1E9XKek5WL41u9oHJYcAlm/NRq92LZUreFQrhOvd2l9RUYEOHTrgwIEDjZGPm48//hh9+/bFPffcg9atW6NXr15YsWKF6/7s7Gzk5uZi+PDhrtvCwsIwYMAApKam1vq4ZWVlKCwsdPsiamzOF390mPuIY3RYgJI9JLpq1cLf0Dgz6D7N6Rz1u7vntUjoEMlCx0CepjgF1FzJCVx6z/tqxjC8O3kg/nJfT7w7eSC+mjHMK+919W5Qbt68OUpLzfl0cfToUSxduhTTp0/Hs88+i127duGxxx6Dn58fJk6ciNzcXABAVFSU29+Liopy3VeT5ORkzJ07t1FzJ6oJPwWbQPY9X6FrgxWmOalxeJriBNSc4nRSZfq7QZs2JCYmYuHChaisrDQ6HzcOhwO9e/fG/Pnz0atXL0yZMgWTJ0/GsmXLrupxk5KSUFBQ4Po6efKkQRkTecZPwY1LxykhHfd/0Z0uezDlFsr9nsrGNVUNWnq+a9cubNy4EV988QW6detW7YiItWvXGpJcTExMtTO4OnfujA8++AAAEB0dDQA4c+YMYmJ+HhY7c+YMevbsWevj+vv7w99fnSFsIjJOXnG5oXFmUa3Hwcp02oMpr6jM0LimqkHFTnh4OMaNG2d0LtUMHjwYmZmZbrcdOnQIcXFxAC41K0dHR2Pjxo2u4qawsBA7duzAI4880uj5EZF6IiR7cWTjzKTrNKdOh9vqtgdTRLCfoXFNVb2KHYfDgT/+8Y84dOgQysvLMWzYMMyZMweBgYGNktwTTzyBQYMGYf78+bj33nuxc+dOLF++HMuXLwdwaZn7tGnT8OKLL6Jjx46upeexsbEYM2ZMo+RERGqLDpVr4pWNM5sqPQ6ydBol0XEPpoggyWJHMq6pqlfPzrx58/Dss8+iRYsWuPbaa7F48WIkJiY2Vm7o168f1q1bh3fffRddu3bFCy+8gFdeeQXjx493xTzzzDN49NFHMWXKFPTr1w9FRUVISUnhHjtETZSz2bcuKp/npRPdDrdV8cwmTzYcOGNoXFNVr2Lnrbfewv/93//h888/x4cffoj169fjnXfegcPhaKz8cOedd2L//v0oLS3FgQMHMHnyZLf7bTYbnn/+eeTm5qK0tBT//ve/8Ytf/KLR8iEitfn62HBXj7pHFO7qEaPMJ3dd6Xi4rY7N68fzSgyNa6rqVeycOHECd9xxh+v74cOHw2az4fTp04YnRkTUEFUOgfe/OVVnzPvfnFLqIqwjHUdJdNzPqH1kkKFxTVW9ip3Kyspq00PNmzdHRUWFoUkRETVUWtZ55JfU/Z6UX1KBtKzzJmVkTTqOkvSJawlPA3o+tktxqnhW8rR72bimql4NykIIPPjgg27LtktLS/Hwww+7LT83aum5zsorHVidegzH80oQFxGECQnt4desQdsakcXotHJFR9uPnpOOG9yxVSNnY106jpLsPn4Bngb0HOJSnCpN4n7NfODfzAdllbW3i/g38+H1xYN6FTsTJ06sdtsDDzxgWDJWkfxpBlZsy3Z7Uc379AAmD7EjidV3k6bTypUr6VKknb7wk6FxVDMdd33WcYO+ndl5dRY6AFBW6VB2B2VV1KvYufxATqpZ8qcZeH1r9YNSHQKu21nwNE267e9xOZ2KtCvPHrvaOKqZc9fnh9/eU+P9Aurt+qzjBn06TheqiONeBiqvdGDFtrpPhF+xLRvlHqp0sh4dV6446ba8OCJYclNByTiz6XKMgY503KBPx+nCy6ny+9ygHZSpZqtTj0nNB69OPYZJQ64zJylSQn1Wrqg0FK3jJmwtg5obGmcmnUbQnL8bdVHtdyM6TG4DXNk4M+g4Xeik0u8zR3YMxP0QqDa6DkXruLx478l8Q+PMUtsIWo6iI2j1OY1bFTpuOKnrIbGqjQiz2DFQXITcPgeycWQdug5F61iknZFsLpWNM0NdI2jApaJStWnO3AK5Bm/ZODM4C4faygIb1CwcnIfERoW6T71Ghfor2eun4rQ9ix0DTUhoL7WHw4SE9qbkQ+pwfqKs601WtU+UANBK8rBM2TgzBPvLzc7LxplBx1ESnU+XnzLUXu292scGTBlqV65wcFfb2I5aVBwRZrFjIL9mPpg8xF5nzOQhdu6H0ATpOhRd61BDQ+NMMK5XG0PjzKDjKImup8unpOdg+dbsav2VDgEs35qt3HQh8POU0JVL4s8UqjnFqeKIMK+6Bku6Ix6/q+VTw++Gcp+dpsw5FH3lkufosAAlh6IB4Fyx3BJc2TgzDJBs8JaNM4OOoyStQ+SKGNk4M3iaLgTUmy70NCWk4hSnitP26ozjWkjSHfGYNvwGzP80A8fOl6B9ZBCevSMegX6+3k6tTrpsGqezUV1jMCI+WpvnWcU3LU92HJU7BmLH0fMY8otrGjkbOVqOkmg46qfjqsj6THGqkrOKK8hY7DSCK5fbbTsM/PvAWSWXjzqptETQ6nx9bMq8KXmi4puWJ2v31H0I6OVxqhQ70aGSGyFKxplBx1E/FadXPNFx12fntP0jb++BDe71rrem7TmNZTDVltvJ0DFnMoeOvUZFZZWGxplBxyXROo766Zizjrs+A+pN23Nkx0A6bsCmY85kLueb1pUjf9GKjvxdI9kjIhtnhss/CQNqfBL2pL89AuFBzes8Yb5lUHOlCjQdRyp13PXZSaVpexY7BrLifLCKOZP5VHrT8iTQV27AWjbOLM4l0Su2ZUNcdiW22S6t4lStqJShULsOADWnVzzRcdfny6kyba/Wq11zOs4H65jz5VQ5d6UpcL5p3d3zWiR0iFTqgnC5T76Xm3aVjTNLSnoOXq9lSfTrCi6J3pmdV+eoDgDkl1QotTcQoN70iic6TnGqiCM7BtJxPljHnJ3YVE01KS6V68WRjTNDlUNg5tr9dcYkrd2v1HSyzh+UdBqp9PWx4a4eMXh9a+2HTN/VI0bJ3FXCkR0D6bhLrq6fGthUTbXRcQfltKPnPY6SXCipQJrksnoz6PxBCdBnpLLKIfDxvrrfzz7el6PsqLYqo+8sdgyk48oV56eGuqj2qUHFc1dIHb/sLjeqJxtnhtQsuSJGNs4Mun5Q0o2OR4k4paTn4MaFm3D/ijQ8/t5e3L8iDTcu3OSVD6Msdgym23ywjp8aVDx3hdQREyZ30K5snDn026FPxw9KOtLxKBFAvdF3dcZxLUSn+WAdd+fUuVeAGl8rySXlsnFmSLiuFV7bnCUVpwrZD0rPjOqs5HufLnQ8SkTFLU1Y7DQSVZbbeaJj4aB7rwA1rlaS+43Ixpmhn+RUj2ycGXT8oKQjHY8SUXFLE05jNRJVmrI80bFw0LERnMxzMPeioXFm2CU55SobZwYdPyhd7qfyKsz6cD8mvLkDsz7cj5/Kq7ydUo10PEpExd8Njuw0Ap2WROu4o6iOG4OReU5eKDE0zgypR89Jxw3uqMZUlo4flJwmv7ULGzLOur7fdhhYnXYCI+JbY8Vv+nkxs+qc79F1jZSo9uFOxd8NjuwYTLWmLE90XEEG6NcITuaJi5BrPJaNM4OQHPiVjTNDn7iWsHl4W7DZLsWp5MpC53IbMs5i8lu7TM6obs73aBtqfo+2Qb336D5xLWsdeXeywdzfDRY7BtJ1SbSuhcOorjH4asYwvDt5IP5yX0+8O3kgvpoxTNl8dafL1Ox/D4gzNM4M4UFy/UOycWbYdSzPY/ElxKU4VfxUXlVroeO0IeOsclNazvfoqFA93qN3Zed5XDcoYO60LKexDKRiU5YsnVaQXU6XRnDd6TQ1u/dkvnScKr87Oh72+PVhuam3rw+fw+Dr1Zh6m/9phnTcC2O6NXI2DeFeQgiVhvouo+K0LEd2DKRiU1Z96LKj6OV0GW3QmW5Tszq+DvOKywyNM8N3p/INjTPDsfNyfVqycWZxvgZzC91//mcKy5R8DVafcLvauKvHkR0DqdiUZWU6jTboSsX9MjyJkJzqkY0zg6ejIuobZ4ZAP19D48wQFxGEbZJxqtDxNTjAHoHXNsvFmYUjOwbqb49AeFDzOmPCg5or1TWvK91GG3Sl427VB04XGhpnCvU+CHsk+z6m0vvdiE5RhsaZQcfXoI+nzvV6xhmBxY7JFHqvqkaXKSFdG8F1pOOU0Dcn5N70ZePMEB4o2aAsGWeGiYPsUituJg6ym5GOlLyfJHcjlowzg46vQRVz5jSWgXZm50mdXKxig3JKeg7mfPy925xwdKg/5tzVRbkpIZ0bwXWj49RsQHO5aRPZODO0aiG567NknBn8mvlgylA7Xt+aXWvMlKF2+DVT5zO1jkcv6PgaPFck9/zJxhlBnd9CC1CxmpWRkp6Dh2tofsstLMPDCk4J6fo860jH3aqDJHtEZOPM0CpY8jwvyTizJN0RjxHxrWu8b0R8ayTdEW9yRnXT8egFHV+D+SVyRYxsnBFY7BioleQLRDbODFUOgZlr99cZM3PtfqWmhHT8pKMrHTedPHdR8lOlZJwZMnLl+odk48ySkp6Df9ewb40NwL8zzir3QUnHoxecr8Ha3oEF1HsNyrbimNiyw2LHSI4quYJANs4MaVnnPU695ZdUIC3rvEkZeabjJx2d/byhmXuRHhXqr+SGZqUVchvCycaZ4RvJjfdk48ygY++c872jLiq+d3yw59RV3W+2hOvk9s6RjTMCix0D7TgmVxDIxpmhPps/qULH0QZrqO3ZVku3tmGGxplBdsdelXb21XGV0OXvHbVR7b1Dx12fB3aIlFqZPNDEnkoWO4bScP2oljnre8SFjn7e0Mz9wnamUM1l/oPscp8WZePM0L1NuKFxZtC1d+7bExeu6n6z1WfXZ1X4+tiwYGzdO1AvGNvN1KKSxY6BZFf+qLRCSMecnXg2VuPTcarCx1dyjw/JODPIbpmvyonngJ69c+WVDqzYVvvqMQBYsS0b5ZUOkzLyTNddn1XDYsdAA6+LRLCHFR7B/r4YeJ06hUO/9hFSe2X0a6/WHLaTjkdc6ETHqYqzF+WOVJCNM0O/9hFSJ4ir9DqU2US1pWKbqK5OPQZPdblDXIpTRftIud2cZePM4PyQVBvnrs9mfkhisWMwjz86dT4AA/jPycUeYgTUOrmYzKPjVEVekeQ5U5JxZth9/ILUCeK7j6s1xeKJYm93OHa+2NA4MzwruXxfNs4MKn5IYrFjoLSj51HioUmsuLwKaUfVaVD++ojkycWScWQtOk5VhEueeSUbZwYdi0qZTVTz/7OJqipkDwlX6TDxQD/fWvcychoR31qpM8hU/H1msWMgHQuH0/k/GRpH1qLjeW86niCuY1Gp4gXNk5BAuUMDZOPMsuI3/dC9TWiN93VvE4oVv+lnckZ1U/H3mcWOgXQsHGLDAg2NI+up8NCsWVGlTjMnAORJbkEvG2eGPnEtpXp2+sS1NCchCRGS53TJxpnBV3IXO9k4s6Sk5+C7UzVvKPndqULlVkSquBcaix0D6Vg4tAyWeyOSjSNrSTt6HsWepmbL1Jqa/SFfblWKbJwZdh3Lk+rZUal37uCZi4bGmaFfnNzFVTbODFUOgel/31dnzPS/71NqRaSKe6Gx2DGQjoWDjgcQ6k6X0+UBIFVy52zZODMcPSdXxMjGmUHH5/l4nlwTr2ycGTJz5Qov2TgzbD98zmMvaEl5FbYfVqc9AlBvLzS1JiY1p2PhEC05yiQbR3VLSc/B3PUZbisVYsICMHt0vKL7A8kWYuoUbMVllYbGmUFIdsTKxplBx5y/OSF5LMeJPExBh0bORs4H38odBfHBt6cw5IZrGjmb+hnVNQYj4qOxMzsPZy+WonXIpakrb2wRwpEdA6nYlOVJn7iW8PR756NYr4CunDsRX7kkM7dAzZ2IATXPuPHE09lH9Y0zg6cm8PrGmSHEXy4X2TgzBPnJfb6XjTODp1Gd+saZTZW90FjsGEnDkxd2H78gtcmWbvt7qEbHnYgBNc+48WSQXS4X2TgztJRcBi8bZ4a69lFpSJwZftXzWkPjzCC7kaRKG06qSKtiZ8GCBbDZbJg2bZrrttLSUiQmJiIyMhItWrTAuHHjcObMGa/kp+POrTouH72cLv0vKm6yJcPXx4Z2EXVPYbaLCFRq5+rtkgftysaZYe9JuQ8TsnFmOFMg1/MkG2eGZs3kLnmycWaYOKi91Eq9iYPam5KPrtQZq/Ng165deP3119G9e3e325944gn861//wj/+8Q+EhYVh6tSpGDt2LL7++mvTc9Rx51Ydp96cdOp/0bWo/Km8qtYlr07fnSrET+VVymxqpuOIw5lCufcE2TgznMqXe/5k48yQK7nth2ycGfya+WDKEDte31r7mV5Thtjhp1CBpiItnp2ioiKMHz8eK1asQMuWP/eOFBQU4M0338RLL72EYcOGoU+fPli5ciW2b9+OtLQ00/OMkFxlJRtnBh03jQP063/Rtaic9y+5k5Rl48wQHepvaJwZWvjLfe6UjTNDpeQoqmycGfaeyjc0ziy92tXdM+npftKk2ElMTMQvf/lLDB8+3O323bt3o6Kiwu32Tp06oV27dkhNTa318crKylBYWOj2ZQSrrmxSZ4LiEh37X3RtBN8n+aYvG2eG9pEtDI0zw9jebQyNM8N11wQbGmcGh+R7gmycGVQ8VFNHyhc77733Hvbs2YPk5ORq9+Xm5sLPzw/h4eFut0dFRSE3N7fWx0xOTkZYWJjrq23btobkquMpwDLn21xQ7HwbHftfdG0EDw2QW0kjG2cKDRcKDLq+FYI9TAMG+/ti0PXqrHqbNNBuaJwZdOyr1PH9TkVKFzsnT57E448/jnfeeQcBAcYN7yclJaGgoMD1dfLkScMeu9zD1vqe7jebjr0kzNk8U4ZcZ2icGX6UvFDJxpnB18eGP9/bo86YP9/TQ6lG8CzJzQJl48xwjeTUpWycGXR973BSZRGJOhPANdi9ezfOnj2L3r17u26rqqrC1q1b8dprr+Hzzz9HeXk58vPz3UZ3zpw5g+jo6Fof19/fH/7+xv8y1+fU88GKfELTsZeEOZsnQfL3VDbOFDoebY1LG7D9bqgdK7Zlu40C+tiAyUPsyjXdf3NccoO+4+ps0NehldzUpWycGVq1kLtWycaZSaVFJEqP7Nx6663Yv38/9u7d6/rq27cvxo8f7/pz8+bNsXHjRtffyczMxIkTJ5CQkGB6vjqeeq7igW2eOHOui6o56/Q8A8AuyaFx2TgzBEs28crGmSUlPQfLt2ZXm+50CGD51mzlmu513KDvvwfEGRpnCv02MQeg3iISpYudkJAQdO3a1e0rODgYkZGR6Nq1K8LCwjBp0iRMnz4dmzdvxu7du/Hb3/4WCQkJGDhwoOn5/nBB8gBCyTgzOA9sq+11ImD+gW2e+PrYcFePuj8V3NUjRrmcVTsYT8bXWT8aGmeGLrFhhsaZoa6me+DS61C1JtRxks3SsnFm2CPZEycbZ4ZzxXLTrbJxZlBxEYnSxY6Ml19+GXfeeSfGjRuHoUOHIjo6GmvXrvVKLjbJjkfZOLN8e6LuF7an+81W5RB4/5u6z4t5/5tTSl0YgJ8Pxmsd4j7cHBXq75WD8WT8cEFuvxHZODNcEyo3FSgbZwZPTaiAek2og65vhSBPTdV+ajVVpx6VG1WXjTODjlPgKjZVa1fsbNmyBa+88orr+4CAACxZsgR5eXkoLi7G2rVr6+zXaUzXethptr5xZiivdGDFtto3qwKAFduylWqsTss673EFWX5JBdIUOiHa6dsTF/DjFZtKnr1YplxB+TP9ljZFSJ4fJRtnhtwCyc3uJOPM4Otjw0uemqrvVaupWvYDkEoflHScAlexqVq7YkdlAyXP2pGNM8Pq1GNSS6JXpx4zJR8ZOk6tAEDypxl4vZaejNe3ZiP5U3U25nPSsV3gi4zat51oSJwZ8orLDY0zy6iuMVj2QG9EXTFaGR3qj2UKjlZeLJU76V42zgw6thqoOBqlTueYBfh4OsCknnFmOJ4n1z8kG2eG0xfkPg3IxpmhvNKB5R5G0JZvy8aTt3VSa9t3DVc2nciTG/2QjTODjgeBOo3qGoMR8dHYmZ2HsxdL0Trk0iiDShdfJ9m3XoXeorXkHI3KLSitsUizAYg2eTRKoXdV/enYSBYXEWRonCn0m1nB37Yf81gPCHEpTiWy1yuVrmvtI+V+V2XjzHBO8rw82Tiz+frYkNAhEnf3vBYJHSKVLHQAoF2E3G7OsnFm0HEHZRUXZLDYMZCKQ3eeTEhoL3WMwYSE9qbkI+PacMneKMk4M+ySPGFbNs4s10oWubJxZnj2jnhD48zw/ekCQ+OoZp2iQgyNM4OKzb4ynAsyoq/YJiQ6LMArCzI4jWUg5/lHdRXYqp1/5NfMB5M9nKg7WbETdQfaI7FkS5ZUnCp03JME+M9zvVmv5zrQzxfd24TWeVp79zahypzSDgA/VdS9GWl946hmeT9J9kZJxplBxWZfWSpNcapzBbMAXc8/SrojHr8baq82wuNjA3431I4khT4BA4CPr2RvlGScGXTckwTQsw+tyiHw48W6L1Y/XixXati/T5xc74JsnNlUORLAEx1H33XM+XKqTHGq9TFSczpX4El3xGPa8Bsw/9MMHDtfgvaRQXj2jnilPv066djfMOj6VvBv5oOyOpbw+zfzUWpPEkDP3+n67FmT0EGNEan4mFBD48yk0pEAnqjYOOuJjjmriCM7BtK5Ak9Jz8GwP2/B6rQT2Hb4HFanncCwP29Rbot6QN/n2VPhqGJhqeOS6Jx8uVVWsnFmyCuRfJ4l48yi2pEAnqjYOOuJjjmriMWOgXTc/AnQ7w2rvz0C4R42hGsZ1Fyp53lndp7URoiqNRnquCT625Ny08SycWbQsYBX8UgAGao1zsrQMWfVcBrLQM4K/JG398AG943WVK3APb1hOZc1joiPVipvT9R6e9Vzh1wAOC+5TYJsnBmqJPf8kY0zg46LG+qzSkiV6UInlRpnZemYs0o4smMw3SpwHZc16jhKouN0EACPz3N948zgI7nBkmycGXRc3KBjP9flVGmcrQ8dc1YFR3YagU4VuI5vWDrmHBoodw6TbJxZbJKrrGTjzBAaIPlcS8aZQcffaR2n3nRX5RBaXFdUxGKnkTgrcNW1Cvb3HFSPODPo+Cb73al86bh7+rZt3GTqIaFDJF7bfEQqThU+kuPVsnFm0PF3WsepN53ptOpNRQq93MkbHJJ9C7JxZtCzEVzDMy4ADLwuEsEeVokF+/ti4HXqFDsD2svlIhtnBh1/p3WcetOVbotIVMRip4nbIdnXIhtnhsuXYtZGtUZwHc9rcvJU5ipUBwPQc9NJHZcX6zj1djldNkLUddWbaljsNHmyLxC1XkijusZgSi27Pk8ZalduWPe/B8QZGmeWtKzzKCmv+4iCkvIqpGWpc6aXjptOAvotbtBx6s0pJT0HNy7chPtXpOHx9/bi/hVpuHHhJiVHSHRcRKIi9uw0cQPaR+I1eD77SKUhf+DSm1VN53k5BPD61mz0atdSqYvDHsmh/D3HL2BwR3V2UU49ek46TpW8db4I67S4QdedfZ1TQlfm7JwSUq2w1H0ETRUc2WnqNGwlqXIIzFy7v86YpLX7lRrW/erIj4bGmUe/XxBn42xd2Dh79XScetNxSkjn4l0lLHaaOB17dtKOnve4r8uFkgqkHVVnaqU+q7FUIrvKSqXVWDo3zuo0vQL8PPUWFarH1JuOU0I6Nq+riMVOI9Gl+U3Hnp2vj8hNrcjGmaG0ovYDQBsSZ5Z+7SPgaQsdm+1SnCp03a1a7xU37u8PQrWu9f/QcUpIxxE0FbHYaQQ6fTobYJdcpisZZ4YfLpQYGmcG/+Zyh3zKxpll9/ELHldbCcVGSXTcrVrH6RXg5wItt9C92ftMYZmSBZquU0K6Na+riA3KBqut+S1H0eY3DQd2YJPsD5GNM0OrYLmDMmXjzKLjJ+GIFnIbYMrGmUHHc6Z0PFdP16ZqQK/mdRVxZMdAdb34gUtvAKp9OttxTK6vRTbODLHhgYbGmaFNhFwusnFm0fGTcHSoXC6ycWbQsajUsf9F9ykhno3VcCx2DOTpxQ+o9+LXcGAHLSVHP2TjzJBgl1uWLRtnFh1XNjk/vddFtYZOHYtKHQs0gFNCTRWnsQx0Ol+u4VE2zgxh/nKHIcrGmaFVC8kpIck4U+i3ghtA/VY2qTK94vz0/sjbewC4F+qqfnrXcXpFxwLNiVNCTQ9Hdgz07Qm5ERvZODNc+EmuSVM2zgw6vsnKLoNXabk8wE/vZtFxekX3JdGcEmpaOLJjoDOFctvPy8aZQcfRKB1HSU7lya0Mk40zSyvJJl7ZODPp9undWaBdebJ1tKInW18+gmaDHiNo1HSx2DFQsL/c0ykbZ4ZzRXIjNrJxZtDx7KMfi+RGPmTjTKNjU9dlnJ/edcECjahxqHPVtYBxvdvgw72npeJUEeQnt6+LbJwZdJzG+klys0DZOLOcK5YsLCXjzFblENoUDk4s0IiMx2LHQIOubwX/Zj4oq6z9guXfzAeDrldnxU1/eyQ2HDgrFaeK/vYIhAc1r/PIiPCg5kr1ClwokeyNkowzi46FpVNKek61EYcYjjg0Ct0KNEDPQpgajsWOwQL9fOssdgIVGiEBgImD2mP+pwfqnIWw/SdOJRV1PMcAUFGl1ghJC8mpS9k4sziXnte1Iku1peeAfidbk7lYCDc9XI1loJ3ZeR4PqMwvqVBqnx3ZTzIqfeJJO3oexeVVdcYUl1UptbKpe5twQ+PMouOhmroevUDm0PsMMmooFjsG0nGZ7taDZz32lor/xKkiNUuuiJGNM8PIztGGxplFx99pHXf2JXOwEG66WOwYSMf+hj9tyDQ0zhz6LRHKL6t7xK++cWbR8XdaxwKNzMFCuOlisWMgZ+NsXVRrnD0ruTxbNs4MA9pLntQuGWcGHYsGQM+N43R9rnVW5RBIzTqPj/b+gNSs88qOjLAQbrrU6oa0gJ889JJ4ut9srVv448eLnlcAtVZo0zgfX7n+Idk4M8THhBoaZxYdN47T8egFnenU7MtCuOniyI6Bth85V+dKLAAoq3Rg+5FzJmXk2e2Sb0aycWbQcVPBp/6x19A4M+l69EJtYwsC6hVoutKt2VfHkUoyBosdA63dc8rQODMUl1caGmcGHT+dnbggd9yGbJzZRnWNwaYnb8aEge0wpGMrTBjYDpuevFm5QofMo2Ozr45nkJExWOwY6GKpXHOpbJwZdDwby7n3S11U2/ulXctAQ+PMlvxpBrrMTsHqtBPYdvgcVqedQJfZKUj+NMPbqVXjvAjXxgb1LsI60rXZV7eRSjIGe3aMpOEBlTqejVWfvV9U2dX1T/f0RI/nv5CKU03ypxl4fWt2tdsdAq7bk+6INzutWtXnIqzK74eOdG725REXTQ9Hdgyk4/SKjmdj6fgmm5FTaGicWcorHVixrXqhc7kV27JR7qFXzUw6/n7oSMf3u8s5j7i4u+e1SOgQyULH4ljsGEjH3Yj7xck14snGmaGV5Mow2TgznLpQYmicWVanHpMaRVudesyUfGTofhHWBZt9SScsdgzUq61cj4hsnBk6x8otdZaNM4V+ewri8+/lVqXIxpnleJ5c8SUbZwYd97vSEZt9SScsdgx0TYjcSIJsnBnyJE/Zlo0zQ26h3PSDbJwZcvLlcpGNM0tcRJChcarg5dcYbPYlXbBB2UAHc+X6LQ7mFmLIL65p5Gzk6DgltPek3KGTe09ewLg+bRo5GzmeDi6tb5xZJiS0x7xPD3g89XxCQnvTcvJE5kDeC/85kJcNylePzb6kAxY7Bjp2vtjQOFNoOCWkow6tgnHsvOepng6tgk3IRp5fMx9MHmKvcTWW0+Qhdvg1U2eQmA3K5nM2+xKpSp13KAs4U8dy14bEmeFcseRuxJJxZmgfKVcQyMaZYcB1kud5ScaZqVe7unvMPN1vNjYoE9GVWOwYKK9YbrNA2Tgz6HhhmJDQHjYPI+Q2xaZWOkTIFV6ycWbRcYM+rhIioiux2DFQUblcESMbZwbnhaEuql0YfH1saO5b96+un6+PUj0Df02re6+a+saZRcddcrlKiIiuxGLHQJ2iQgyNM4Ovjw139ah7xcRdPWKUujCkZZ33uIldWaUDaVnnTcrIs9OSq6xk48yia/8LVwkR0eWULnaSk5PRr18/hISEoHXr1hgzZgwyMzPdYkpLS5GYmIjIyEi0aNEC48aNw5kzZ7yS7909rjU0zgxVDoGP99W9t8vH+3KUmqZIPSp3arxsnBmC/eXWAsjGmUXHaU6nUV1j8NWMYXh38kD85b6eeHfyQHw1YxgLHaImSOli58svv0RiYiLS0tKwYcMGVFRU4LbbbkNx8c+rmZ544gmsX78e//jHP/Dll1/i9OnTGDt2rFfy3XzorKFxZvA0TQGoN02h4yFkgzvKNR7LxplF9/4XHglARIDixU5KSgoefPBBdOnSBT169MCqVatw4sQJ7N69GwBQUFCAN998Ey+99BKGDRuGPn36YOXKldi+fTvS0tJMz/fYebmTwWXjzKDjNIXsEleVlsIO7dja0DizsP+FiKxA6WLnSgUFBQCAiIhLnyJ3796NiooKDB8+3BXTqVMntGvXDqmpqbU+TllZGQoLC92+jBDYXO7plI0zg47TFAOvi/R4HEDLoOYYqNAybh1zdmL/CxHpTq0GgTo4HA5MmzYNgwcPRteuXQEAubm58PPzQ3h4uFtsVFQUcnNza32s5ORkzJ071/Ach3W6BhsOeJ6iGtZJjd2TgZ+nKeqaylJtmsLXx4YFY7vh4bf31BqTPLabUqMNOuZ8Oe6SS0Q6U2eIwYPExESkp6fjvffeu+rHSkpKQkFBgevr5MmTBmQI7P9BboRINs4MOq7GAi5dfJc90BvRoe6jDTFhAVim6GiDjjlfjv0vRKQrLUZ2pk6dik8++QRbt25FmzY/n3UUHR2N8vJy5Ofnu43unDlzBtHR0bU+nr+/P/z9jT/r6WDORUPjzCC7GuuZUZ2Vu7jpONowqmsMhnWKwurUYzieV4K4iCBMSGiv1HELRERWo3SxI4TAo48+inXr1mHLli2w2+1u9/fp0wfNmzfHxo0bMW7cOABAZmYmTpw4gYSEBNPz1bHZtz6rsVRq+HXS7UyelPQczF2f4facv/FVNmaPjld+ZIeISFdKFzuJiYlYs2YNPvroI4SEhLj6cMLCwhAYGIiwsDBMmjQJ06dPR0REBEJDQ/Hoo48iISEBAwcOND1fIbkVjWycGXQs0HSVkp6DR97eU+1M1dyCUjzy9h42+xIRNRKlx86XLl2KgoIC3HzzzYiJiXF9vf/++66Yl19+GXfeeSfGjRuHoUOHIjo6GmvXrvVKvkF+vobGmUHH1Vg6cp4xVVOd67xNtTOmiIisQumRHSExBBIQEIAlS5ZgyZIlJmRUt84xITj8Y7FUnCp6tg03NI5qVp8zpnSaliMi0oHSIzu6uadPO0PjzLBmx3FD46hmnC4kIvIeFjsGGtAh0uMBBbb/xKnieF6JoXFUM04XEhF5D4sdA+0+fqHGnozLif/EqSIuIsjQOKqZ7mdMERHpjMWOgXIL5aYgZOPMMCGhPTxtS+NjuxRHDcczpoiIvIfFjoHOFMgd8CkbZwa/Zj6YPMReZ8zkIXZuemcAnjFFROQdSq/G0s2XmWek4x6++fpGzkZer3YtAWR7uJ+MoOOuz0REumOxY6Ds83IjNrJxZnDu/1IbGy7t/zIiPpoXZIPotuszEZHuODdhoNAAudpRNs4M9dn/hYiISEcsdgzUO05uukc2zgzc/4WIiKyOxY6BWgb5GRpnBu7/QkREVsdix0iyLS0Ktb4493+pC/d/ISIinbHYMVBBSYWhcWbw9bHhrh51L3m+q0cMm5OJiEhbLHYMdOZimaFxZqhyCHy8L6fOmI/35fA0biIi0haLHQOVlJYbGmcGT6uxAK7GIiIivbHYMdDF0ipD48zA1VhERGR1LHaMJNvXolD/S6tgf0PjiIiIVMNix0A924QbGmcKDVeQERER1QeLHQPNvL2zoXFmOFck1ywtG0dERKQaFjsG2v9DgaFxZuA0FhERWR2LHQNp2ezLaSwiIrI4FjsG0vHoBU5jERGR1bHYMZDz6IXaBkFsUO/oBR0LNCIiovpgsWMgXx8bZo+OB1B91sf5/ezR8UodvaBjgUZERFQfLHYMNqprDJY+0BvRVxyuGR0WgKUP9MaornWfQ2U2HQs0IiKi+rAJIZr8oUeFhYUICwtDQUEBQkNDDXnMKofAzuw8nL1YitYhl0ZGVC4YUtJzMHd9htvRETFhAZg9Ol65Ao2IiAiQv36z2EHjFDs60q1AIyKipk32+t3MxJxIcb4+NiR0iPR2GkRNGj90EBmPxQ4RkSI4nUzUONigTESkgJT0HDzy9h63QgcAcgtK8cjbe5CSnuOlzIj0x2KHiMjLqhwCc9dnoKYGSudtc9dnoMrR5FssiRqExQ4RkZftzM6rNqJzOQEgp6AUO7PzzEuKyEJY7BAReZmW5+oRaYTFDhGRl/HYFqLGxWKHiMjLeGwLUeNisUNE5GU8toWocbHYISJSgG7n6hHphJsKEhEpYlTXGIyIj+YOykQGY7FDRKQQHttCZDxOYxEREZGlsdghIiIiS2OxQ0RERJbGYoeIiIgsjcUOERERWRqLHSIiIrI0FjtERERkaSx2iIiIyNJY7BAREZGlcQdlAEIIAEBhYaGXMyEiIiJZzuu28zpeGxY7AC5evAgAaNu2rZczISIiovq6ePEiwsLCar3fJjyVQ02Aw+HA6dOnERISApvNuAP3CgsL0bZtW5w8eRKhoaGGPS654/NsHj7X5uDzbA4+z+ZozOdZCIGLFy8iNjYWPj61d+ZwZAeAj48P2rRp02iPHxoayheSCfg8m4fPtTn4PJuDz7M5Gut5rmtEx4kNykRERGRpLHaIiIjI0ljsNCJ/f3/Mnj0b/v7+3k7F0vg8m4fPtTn4PJuDz7M5VHie2aBMRERElsaRHSIiIrI0FjtERERkaSx2iIiIyNJY7BAREZGlsdhpREuWLEH79u0REBCAAQMGYOfOnd5OyVKSk5PRr18/hISEoHXr1hgzZgwyMzO9nZblLViwADabDdOmTfN2Kpbzww8/4IEHHkBkZCQCAwPRrVs3fPPNN95Oy1Kqqqowa9Ys2O12BAYGokOHDnjhhRc8nq1Enm3duhWjR49GbGwsbDYbPvzwQ7f7hRB47rnnEBMTg8DAQAwfPhyHDx82JTcWO43k/fffx/Tp0zF79mzs2bMHPXr0wMiRI3H27Flvp2YZX375JRITE5GWloYNGzagoqICt912G4qLi72dmmXt2rULr7/+Orp37+7tVCznwoULGDx4MJo3b47PPvsMGRkZ+POf/4yWLVt6OzVLWbhwIZYuXYrXXnsNBw4cwMKFC7Fo0SK8+uqr3k5Ne8XFxejRoweWLFlS4/2LFi3C4sWLsWzZMuzYsQPBwcEYOXIkSktLGz85QY2if//+IjEx0fV9VVWViI2NFcnJyV7MytrOnj0rAIgvv/zS26lY0sWLF0XHjh3Fhg0bxE033SQef/xxb6dkKTNmzBA33nijt9OwvF/+8pfioYcecrtt7NixYvz48V7KyJoAiHXr1rm+dzgcIjo6Wvzxj3903Zafny/8/f3Fu+++2+j5cGSnEZSXl2P37t0YPny46zYfHx8MHz4cqampXszM2goKCgAAERERXs7EmhITE/HLX/7S7feajPPxxx+jb9++uOeee9C6dWv06tULK1as8HZaljNo0CBs3LgRhw4dAgDs27cPX331FW6//XYvZ2Zt2dnZyM3NdXv/CAsLw4ABA0y5LvIg0EZw7tw5VFVVISoqyu32qKgoHDx40EtZWZvD4cC0adMwePBgdO3a1dvpWM57772HPXv2YNeuXd5OxbKOHj2KpUuXYvr06Xj22Wexa9cuPPbYY/Dz88PEiRO9nZ5lzJw5E4WFhejUqRN8fX1RVVWFefPmYfz48d5OzdJyc3MBoMbrovO+xsRihywhMTER6enp+Oqrr7ydiuWcPHkSjz/+ODZs2ICAgABvp2NZDocDffv2xfz58wEAvXr1Qnp6OpYtW8Zix0B///vf8c4772DNmjXo0qUL9u7di2nTpiE2NpbPs4VxGqsRtGrVCr6+vjhz5ozb7WfOnEF0dLSXsrKuqVOn4pNPPsHmzZvRpk0bb6djObt378bZs2fRu3dvNGvWDM2aNcOXX36JxYsXo1mzZqiqqvJ2ipYQExOD+Ph4t9s6d+6MEydOeCkja3r66acxc+ZM3HfffejWrRsmTJiAJ554AsnJyd5OzdKc1z5vXRdZ7DQCPz8/9OnTBxs3bnTd5nA4sHHjRiQkJHgxM2sRQmDq1KlYt24dNm3aBLvd7u2ULOnWW2/F/v37sXfvXtdX3759MX78eOzduxe+vr7eTtESBg8eXG3rhEOHDiEuLs5LGVlTSUkJfHzcL32+vr5wOBxeyqhpsNvtiI6OdrsuFhYWYseOHaZcFzmN1UimT5+OiRMnom/fvujfvz9eeeUVFBcX47e//a23U7OMxMRErFmzBh999BFCQkJc875hYWEIDAz0cnbWERISUq0PKjg4GJGRkeyPMtATTzyBQYMGYf78+bj33nuxc+dOLF++HMuXL/d2apYyevRozJs3D+3atUOXLl3w7bff4qWXXsJDDz3k7dS0V1RUhCNHjri+z87Oxt69exEREYF27dph2rRpePHFF9GxY0fY7XbMmjULsbGxGDNmTOMn1+jrvZqwV199VbRr1074+fmJ/v37i7S0NG+nZCkAavxauXKlt1OzPC49bxzr168XXbt2Ff7+/qJTp05i+fLl3k7JcgoLC8Xjjz8u2rVrJwICAsR1110nfv/734uysjJvp6a9zZs31/iePHHiRCHEpeXns2bNElFRUcLf31/ceuutIjMz05TcbEJw20giIiKyLvbsEBERkaWx2CEiIiJLY7FDRERElsZih4iIiCyNxQ4RERFZGosdIiIisjQWO0RERGRpLHaIiIjI0ljsEDURx44dg81mw969e72disvBgwcxcOBABAQEoGfPnt5Op1GtWrUK4eHh3k6DqElisUN0haqqKgwaNAhjx451u72goABt27bF73//+2p/5/z582jTpg1sNhvy8/NNylR/s2fPRnBwMDIzM90OCLSiX//61zh06JC307gqDz74oDnnGBEZjMUO0RV8fX2xatUqpKSk4J133nHd/uijjyIiIgKzZ8+u9ncmTZqE7t27m5mmMsrLyxv8d7OysnDjjTciLi4OkZGRBmalnsDAQLRu3drbaSjhan5niBqCxQ5RDX7xi19gwYIFePTRR5GTk4OPPvoI7733Ht566y34+fm5xS5duhT5+fl46qmnPD6ucypp7dq1uOWWWxAUFIQePXogNTXVFTNnzpxqUzqvvPIK2rdv7/re+Ql7/vz5iIqKQnh4OJ5//nlUVlbi6aefRkREBNq0aYOVK1dWy+HgwYMYNGgQAgIC0LVrV3z55Zdu96enp+P2229HixYtEBUVhQkTJuDcuXOu+2+++WZMnToV06ZNQ6tWrTBy5Mga/68OhwPPP/882rRpA39/f/Ts2RMpKSmu+202G3bv3o3nn38eNpsNc+bMqfVxFi1ahOuvvx7+/v5o164d5s2b57p///79GDZsGAIDAxEZGYkpU6agqKio2nP1pz/9CTExMYiMjERiYiIqKioAAM8++ywGDBhQ7d/t0aMHnn/+edf3b7zxBjp37oyAgAB06tQJ//d//+e6T+bnWtM01kcffYTevXsjICAA1113HebOnYvKykq35+iNN97Ar371KwQFBaFjx474+OOP3R7j+++/x5133onQ0FCEhIRgyJAhyMrKksq7Jv/85z/RrVs31/M5fPhwFBcXY86cOfjb3/6Gjz76CDabDTabDVu2bKnXz2DevHmIjY3FDTfcAAA4efIk7r33XoSHhyMiIgJ33303jh075vp7W7ZsQf/+/REcHIzw8HAMHjwYx48frzN/ohqZctwokYYcDoe4+eabxa233ipat24tXnjhhWox33//vYiOjhbHjx93nfh74cKFWh8zOztbABCdOnUSn3zyicjMzBT/9V//JeLi4kRFRYUQQojZs2eLHj16uP29l19+WcTFxbm+nzhxoggJCRGJiYni4MGD4s033xQAxMiRI8W8efPEoUOHxAsvvCCaN28uTp486fZvt2nTRvzzn/8UGRkZ4n/+539ESEiIOHfunBBCiAsXLohrrrlGJCUliQMHDog9e/aIESNGiFtuucX1b990002iRYsW4umnnxYHDx4UBw8erPH/+tJLL4nQ0FDx7rvvioMHD4pnnnlGNG/eXBw6dEgIIUROTo7o0qWLePLJJ0VOTo64ePFijY/zzDPPiJYtW4pVq1aJI0eOiG3btokVK1YIIYQoKioSMTExYuzYsWL//v1i48aNwm63u05Zdj5XoaGh4uGHHxYHDhwQ69evF0FBQa4TxdPT0wUAceTIEdffcd52+PBhIYQQb7/9toiJiREffPCBOHr0qPjggw9ERESEWLVqlfTPdeXKlSIsLMz1b2zdulWEhoaKVatWiaysLPHFF1+I9u3bizlz5rhinD+vNWvWiMOHD4vHHntMtGjRQpw/f14IIcSpU6dERESEGDt2rNi1a5fIzMwUf/3rX10/E095X+n06dOiWbNm4qWXXhLZ2dniu+++E0uWLBEXL14UFy9eFPfee68YNWqUyMnJETk5OaKsrEz6Z9CiRQsxYcIEkZ6eLtLT00V5ebno3LmzeOihh8R3330nMjIyxH//93+LG264QZSVlYmKigoRFhYmnnrqKXHkyBGRkZEhVq1aJY4fP15j7kR1YbFDVIcDBw4IAKJbt26ui5ZTaWmp6N69u1i9erUQQtSr2HnjjTdct33//fcCgDhw4IAQQr7YiYuLE1VVVa7bbrjhBjFkyBDX95WVlSI4OFi8++67bv/2ggULXDEVFRWiTZs2YuHChUIIIV544QVx2223uf3bJ0+eFABEZmamEOJSsdOrV69a/49OsbGxYt68eW639evXT/zv//6v6/sePXqI2bNn1/oYhYWFwt/f31XcXGn58uWiZcuWoqioyHXbv/71L+Hj4yNyc3OFED8/V5WVla6Ye+65R/z61792y+P55593fZ+UlCQGDBjg+r5Dhw5izZo1bv/2Cy+8IBISEoQQcj/XK4udW2+9VcyfP9/tMVevXi1iYmJc3wMQf/jDH1zfFxUVCQDis88+c+Vpt9tFeXl5jc+Pp7yvtHv3bgFAHDt2rMb7J06cKO6++26322R/BlFRUaKsrMzt/3rDDTcIh8Phuq2srEwEBgaKzz//XJw/f14AEFu2bKkxF6L64DQWUR3++te/IigoCNnZ2Th16pTbfUlJSejcuTMeeOCBej/u5f09MTExAICzZ8/W6zG6dOkCH5+fX8JRUVHo1q2b63tfX19ERkZWe9yEhATXn5s1a4a+ffviwIEDAIB9+/Zh8+bNaNGiheurU6dOAOA2NdKnT586cyssLMTp06cxePBgt9sHDx7s+rdkHDhwAGVlZbj11ltrvb9Hjx4IDg52+zccDgcyMzNdt3Xp0gW+vr6u72NiYtyel/Hjx2PNmjUAACEE3n33XYwfPx4AUFxcjKysLEyaNMnteXnxxRfdnhOgfj/Xffv24fnnn3d7zMmTJyMnJwclJSU1PmZwcDBCQ0Ndj7l3714MGTIEzZs3r/b49cnbqUePHrj11lvRrVs33HPPPVixYgUuXLhQY6yT7M+gW7dublPA+/btw5EjRxASEuLKLSIiAqWlpcjKykJERAQefPBBjBw5EqNHj8Zf/vIX5OTk1JkLUW2aeTsBIlVt374dL7/8Mr744gu8+OKLmDRpEv7973/DZrMBADZt2oT9+/fjn//8J4BLF0kAaNWqFX7/+99j7ty5tT725Rcn5+M5HA4AgI+Pj+uxnJz9JbU9hvNxarrN+bgyioqKMHr0aCxcuLDafc6LNwC3C1tjCgwMNORxPD0v999/P2bMmIE9e/bgp59+wsmTJ/HrX/8aAFy9JytWrKjW23N5AXXlv3Plz/VKRUVFmDt3brVVfwAQEBAglXtdz0998r789g0bNmD79u344osv8Oqrr+L3v/89duzYAbvdXuu/JePK35mioiL06dPHbRGA0zXXXAMAWLlyJR577DGkpKTg/fffxx/+8Ads2LABAwcOvKpcqOlhsUNUg5KSEjz44IN45JFHcMstt8But6Nbt25YtmwZHnnkEQDABx98gJ9++sn1d3bt2oWHHnoI27ZtQ4cOHRr8b19zzTXIzc2FEMJ1wTRyb5y0tDQMHToUAFBZWYndu3dj6tSpAIDevXvjgw8+QPv27dGsWcPfHkJDQxEbG4uvv/4aN910k+v2r7/+Gv3795d+nI4dOyIwMBAbN27E//zP/1S7v3Pnzli1ahWKi4tdF9Ovv/4aPj4+riZYGW3atMFNN92Ed955Bz/99BNGjBjhWjkVFRWF2NhYHD161DXaY4TevXsjMzMT119/fYMfo3v37vjb3/6GioqKakVRQ/O22WwYPHgwBg8ejOeeew5xcXFYt24dpk+fDj8/P1RVVbnFN/Rn0Lt3b7z//vto3bo1QkNDa43r1asXevXqhaSkJCQkJGDNmjUsdqjeOI1FVIOkpCQIIbBgwQIAQPv27fGnP/0JzzzzjGu1SIcOHdC1a1fXl/OTb+fOna9qifHNN9+MH3/8EYsWLUJWVhaWLFmCzz777Kr/T05LlizBunXrcPDgQSQmJuLChQt46KGHAACJiYnIy8vD/fffj127diErKwuff/45fvvb31a7yHny9NNPY+HChXj//feRmZmJmTNnYu/evXj88celHyMgIAAzZszAM888g7feegtZWVlIS0vDm2++CeDS9FNAQAAmTpyI9PR0bN68GY8++igmTJiAqKioeuU7fvx4vPfee/jHP/5RrTiYO3cukpOTsXjxYhw6dAj79+/HypUr8dJLL9Xr37jcc889h7feegtz587F999/jwMHDuC9997DH/7wB+nHmDp1KgoLC3Hffffhm2++weHDh7F69WrX9FF9896xYwfmz5+Pb775BidOnMDatWvx448/onPnzgAuvQ6+++47ZGZm4ty5c6ioqGjwz2D8+PFo1aoV7r77bmzbtg3Z2dnYsmULHnvsMZw6dQrZ2dlISkpCamoqjh8/ji+++AKHDx925UJUL17tGCJS0JYtW4Svr6/Ytm1btftuu+02MWzYMLemSqf6NCh/++23rtsuXLggAIjNmze7blu6dKlo27atCA4OFr/5zW/EvHnzqjUoX9koetNNN4nHH3/c7ba4uDjx8ssvu/3ba9asEf379xd+fn4iPj5ebNq0ye3vHDp0SPzqV78S4eHhIjAwUHTq1ElMmzbN9X+u6d+pSVVVlZgzZ4649tprRfPmzUWPHj1cjbVOnhqUnY/z4osviri4ONG8eXPRrl07t8be7777Ttxyyy0iICBAREREiMmTJ7ut7KrpuXr88cfFTTfd5HbbhQsXhL+/vwgKCqpxZdg777wjevbsKfz8/ETLli3F0KFDxdq1a4UQcj/XKxuUhRAiJSVFDBo0SAQGBorQ0FDRv39/1yoxIS41KK9bt87t74SFhYmVK1e6vt+3b5+47bbbRFBQkAgJCRFDhgwRWVlZUnlfKSMjQ4wcOVJcc801wt/fX/ziF78Qr776quv+s2fPihEjRogWLVq4/d8a8jMQ4tKKvN/85jeiVatWwt/fX1x33XVi8uTJoqCgQOTm5ooxY8aImJgY4efnJ+Li4sRzzz3n1pRPJMsmxBXNAUREREQWwmksIiIisjQWO0RERGRpLHaIiIjI0ljsEBERkaWx2CEiIiJLY7FDRERElsZih4iIiCyNxQ4RERFZGosdIiIisjQWO0RERGRpLHaIiIjI0v4fSONo90kU28EAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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IKKi4aiqanVcQoJGRUjHYISKioOGqqSjA7ujUGIMdIiIKGiJNRc3d0YnMGOwQEVHQEO16zu7oZI3BDhERBQ3RpqKix1HTwGCHiIiChmhTUXZHJ2sMdoiIKGiINhVlvR2yxmCHiIiCSmYnAyb3N0LTIJ7RaIDJ/Y2ss0ONMNghIqKgkp1XgKXb89Fwd7lJApZuz2edHWqEwQ4REQUNZ3V2zFhnhxpisENEREGDdXbIHQx2iIgoaLDODrmDwQ4REQWN5s0ivHocNQ0MdoiIKGiY6sRycUSPo6aBwQ4REQWN3ScvefU4ahoY7BARURARLRbIooL0OwY7RESkGHUmCTnHL2HtgbPIOX6p0Rby9DZJQs8jehw1DaGBHgARERFQXyzw2c8O2WwtN8RHYu7wNEtV5D7XJiEhOgzFFTUOnycxOgx9rmWwQ7/jzA4REQVcdl4Bpryf26iGTmFJJaa8n2upihyi1eDF0Z2dPtf80Z3ZG4tsMNghIqKAclYV2XybdVXkzE4GLLmvB/RxkTbHGuIjseS+HuyNRY1wGYuIiAJKTlVkcy5OZicDbkvTY09+ES5cqURybCR6GXWc0SG7GOwQEVFAuVsVOUSrYSIyCeEyFhERBVRybKTrg2QcR9QQgx0iIgqoXkYdDPGRDivjaFCfj9PLqPPnsEhFPAp2qqurceTIEdTW1nprPERE1MSEaDWYOzwNQONSgOaf5w5PYz4Ouc2tYKeiogITJ05EdHQ0rr/+epw+fRoAMH36dLz44oteHSAREalfZicDFt/XA/p426UqfXwkFnOHFXnIrQTl2bNn4+DBg9i2bRsyMzMtt2dkZOCZZ57B448/7rUBEhFR08AdVuQrbgU7n3zyCf7973+jT58+0Gh+/yW8/vrrcfz4ca8NjoiImhbusCJfcCvY+eWXX5CcnNzo9vLycpvgh4iIyF11JomzPOQVbgU7N9xwA9avX4/p06cDgCXAeeedd5Cenu690RERUZMk0ieLSJRbwc4LL7yAIUOG4NChQ6itrcVrr72GQ4cO4ZtvvsFXX33l7TESEZGKuJqxMffJatg+wtwny5ywbH6ewtJKFJVVQRcTDn18FGeAqBG3gp1+/frhwIEDePHFF9G5c2d8+eWX6NGjB3JyctC5s/MGbUREpAyBWCZyNWPjqk+WBvV9skwmCc+tP2y3zQRngKghjSRJ9n6nmpTS0lLEx8ejpKQEcXFxgR4OEZHPBWKZyNGMjTm8WnxfD8RHheOeZbs8Ppfmt+djwKNuotdvt+rsbNiwAV988UWj27/44gt8/vnn7jwlERH5iTnoaDgrYl4mys4raPSYOpOEnOOXsPbAWeQcv2TpQC5KtLN5YalYnyxXJNh2Sqemza1lrMcff9xu8UBJkvD4449jyJAhHg+MiIi8T3SZ6LY0vWVJy94skC4mHKO6peK2NL3Q8pdoZ/Oisir5L8qBhp3Sqelya2bn6NGjSEtLa3R7hw4dcOzYMY8HRUREviEadOzJLwLgeBaoqLwa//z6JO5Ztgv9FmyxOxtkTbSzuS4mHAnRYULHihA9L6mbW8FOfHw8Tpw40ej2Y8eOISYmxuNBERGRb4he/C9cqXQ6C2StwMnyl5lwZ/M473Y2Z6d0AtwMdkaOHIkZM2bYVEs+duwYHnvsMYwYMcJrgyMiIu8SDjpiI13OAjXkLEdGtLM5JKC4okb4nM6wUzqZuRXsvPTSS4iJiUGHDh1gNBphNBrRsWNHJCUl4ZVXXvH2GImIyEtEg45eRp2sJaCGy1/2jL2xpd1ZIuvO5hfLvZOzowE7pdPv3EpQjo+PxzfffIONGzfi4MGDiIqKQpcuXdC/f39vj4+IiLwoRKvB3OFpmPJ+LjSATfBhHXSEaDVuLQHZC5DsJThb01ttec85fkn4XFoNYG8iiXV2qCG3gh2gvkXE7bffjttvv92b4yEiIh/L7GTA4vt6NApA9A2ChF5GHRKiwlD8q/iyUsMAyVFtHbNHMtph2sB2lhkY88xTYUmly1whc5W4B/q2xjUJUaygTA4JBzuLFi3C5MmTERkZiUWLFjk99i9/+YvHAzM7e/YsZs2ahc8//xwVFRVo27Ytli9fjhtuuAFA/Xb3uXPnYtmyZSguLkbfvn2xePFitGvXzmtjICJSm8xOBtyWpndaQTlEq8EDfY1YuOknoedsmCPjKsFZA+CjvWcwbeDvf6+dzTw1ZN4qn51XiJ2zBjLAIYeEKygbjUZ8++23SEpKgtFodPyEGo3dnVruuHz5Mrp3744BAwZgypQpuOqqq3D06FG0adMGbdq0AQAsWLAA8+fPx7/+9S8YjUbMmTMH33//PQ4dOoTISLEpWFZQJiKyr84koee8jS6Thu1VLM45fkmoGvKHk/o0qoUzf8MhLNuRb3eZSvQ5SP1Er9/CMzv5+fl2/9uXFixYgBYtWmD58uWW26wDLUmS8Oqrr+Kpp57CyJEjAQArV65ESkoKPvnkE4wdO9Yv4yQiUqsQrQYvju6Mh97PdXhMYnQY5o/u3ChHRs42d2vZeQVYuj3f5TKWO+eipkn2bqyamhq0adMGhw8f9sV4bHz66ae44YYbcNdddyE5ORndu3fHsmXLLPfn5+ejsLAQGRkZltvi4+PRu3dv5OTkOHzeqqoqlJaW2vwjIiL7MjsZsOS+HvVbw60kRIXhkYx2+Pap2+wmA8vZ5m4mWtvH3XNR0yQ7QTksLAyVlf6JoE+cOIHFixfj0UcfxRNPPIG9e/fiL3/5C8LDw5GVlYXCwkIAQEpKis3jUlJSLPfZM3/+fDz77LM+HTsRkZqI5Pg05CrZWIP6pGjrPB+5tX2A+pkl1tMhZ9yqszN16lQsWLAAtbW13h6PDZPJhB49euCFF15A9+7dMXnyZEyaNAlLlizx6Hlnz56NkpISy78zZ854acREROoVotUgvU0S7uiSCgBY9905p01BzcnGABrV9Wm4zd3MnUagbPVJrri19Xzv3r3YvHkzvvzyS3Tu3LlRi4jVq1d7ZXAGg6FRD66OHTvif//7HwBAr9cDAM6fPw+D4fcp1PPnz6Nbt24OnzciIgIRERFeGSMRUVNir2aOs7o2otvczdxpBFpcUcOGn+SUW8FOQkICxowZ4+2xNNK3b18cOXLE5raffvoJrVq1AlCfrKzX67F582ZLcFNaWordu3djypQpPh8fEVFT4qhmTuFvvbEa7sYyk7MEposJd2ts5l5ecpbZqOmQFeyYTCa8/PLL+Omnn1BdXY2BAwfimWeeQVRUlE8G98gjj+Cmm27CCy+8gLvvvht79uzB0qVLsXTpUgD129xnzJiBefPmoV27dpat56mpqRg1apRPxkRE1BQ5Sxw217t59rNDuC1NbzfAMC+BuaKLdi/YOXmxHP0WbHE448RAqGmTFew8//zzeOaZZ5CRkYGoqCgsWrQIv/zyC/75z3/6ZHA33ngj1qxZg9mzZ+Pvf/87jEYjXn31VYwbN85yzMyZM1FeXo7JkyejuLgY/fr1Q3Z2tnCNHSIics1V4rB1byxnQY2roGPj4fOyxqUBkBAdhoWbjja6zzzjNLm/EZ8eLBBeeiP1ES4qCADt2rXDX//6Vzz44IMAgE2bNmHYsGH49ddfodW6leusCCwqSETk3NoDZ/HwRwdcHvfa2G4Y2e1qu/eJ5PuMf3c3dhy9KDQmc4gUHx0mu1O6+bGOlt4oOIhev2VFKKdPn8bQoUMtP2dkZECj0eDcuXPuj5SIiBTPnZo51sz5Pg1nhwpKKvHQ+7nY8F39daR1UrTwmDQaYFgXg+xAB/h9B9eznx1yuJuM1ENWsFNbW9toeSgsLAw1NfJ/0YiIKHj0bJUIVykuWk39cQ2JFAqc9uF+bPiuAE8MTXNylC2TBKz7rkD4+Iasl95I3WTl7EiShAkTJths266srMRDDz1ks/3cW1vPiYjIuepaE97LOYlTRRVopYvG+PTWCA/1TlqBdX7NxStVLvtUmSRg36nLjXJ2RAoFmiTgz6ty8da93RERqkVVrcnT4Qtjqwn1kxXsZGVlNbrtvvvu89pgiIhInL1mmc9vOIxJNxsxW8YMiT328mtE2Asc5AQTT63N82ugAwAXr1ShziRxd5aKyQp2rBtyEhFR4MzfcAhvb2/clNkkwXK7uwGPo3o6Iuzl7DSPES/iWlTuflqEBrbVlBv+7Mhz6w/jnZ353J2lYsG7hYqIqImqrjVh2Y7GgY61ZTvyUe3GDIm7jTg1qN9ZZbdHlR8mTB7JuA76Bo1K9fGReLC/ERqBIZi3qWfnuZ8DRMrlVgVlIiIKnPdyTgrlz7yXcxITb75W1nO704gTqJ9BadjnyuyiGy0g5NBogCm3tsG0gW3t1vDp3jLR5ZKcSGFECl6c2SEiCjKniiq8epw1XyTrim5bd5ckAf/65iTW/bZ9/Y4uqUhvk2QJWDI7GbBz1kDMGdbR+fOAu7PUijM7RERBppVOrBaN6HHW3A1MnM2K9DLqoI+LdKujuajnNxy2/Le96sghWg2ax4rlDnF3lvpwZoeIKMiMT28tVPNmfHpr2c/dy6iDIT5SdpqNs1mREK0G9/RqKXss7nKUf9O8mViwI3ocBQ8GO0REQSY8VItJNxudHjPpZqNb9XZCtBrMHe7+tnVHsyKtm8ufZXKXw+rIolnXLKisOgx2iIiC0OyhaXiwv7HRDI9WAzzY37M6O5mdDFh8Xw8kRofJfqyjZTBf5+00ZG+m6WK5WKK06HEUPJizQ0QUJBp2DJ+Z2REzMtrjhQ2HcPJSBVonReOJoWmICg/x+FyZnQyICQvF+OV7hI7XoH6rt92t56hfHktwo2Gnp6xnmjzt70XBi8EOEVEQsFfROOG3mRdzALHjKLDp8AWvFccr+rVa1vHWW88bBmaXy6v9HugAtoGLOR+psKTS7kqVq4CNgheDHSIihXNU0dhe8GBOzl18Xw+PAx7RGY6kmHA8/4dOlvPZC8wCUbZGqwEuWy1JmfORpryfa7faMuC4VhAFN+bsEBEpmNyKxg6Tc90gsjNLFxOGnNmDbAKdKe/nNirg5+FQ3GKSgKmr9tvsyjLnI9mrtuyNAJGUiTM7REQK5k5FY+vk3IYdyOUQmQl54Q+dLbu+3G014WsN6/9kdjLgtjS93WrLpE6c2SEiUjBPCtx5ozienJkQd1tN+JI58Nt14pLN7SFaDdLbJGFkt6ttqi2TOnFmh4hIoepMEi5ecX8btLd2FYnOhCi58vDUD3Lx4pjOXKZqohjsEBEpkL0kX1G+2FVkngkBGu+0Mgc+St6yXfxrjdcStyn4MNghIlIYR7uvRPh6V5G9IMzci+q2NL3Trd1KwK7mTRNzdoiIFEROkm9idJil1o6ZL3cVOdppZd7uvvFQoaXVhBJDCXY1b7o4s0NEpCCiSb5zhnXEhL5Gy2N8vavIWRAm4feu5ztnDcTi+3o0mv3RxYSjqFxekUJfUXJuEfkGgx0iIgURvRA3j42wBDWebC8X5SoIs541sZfQXFtnwvh/irWe8DUl5xaRb3AZi4jIj+pMEnKOX8LaA2eRc/xSo8J/Su3fJBqEXbhSaTeB+aICZnU0qM8vYjuIpoczO0REfuIsudecY6PU/k0nL5YLH9dvwZZGr7Fvm+a+GpoQc1HEsTe2wLrvzrGQYBOjkSRJqUnzflNaWor4+HiUlJQgLi4u0MMhIhVytMPKfKm1Tio2HwvYr1rs7+3TdSYJPZ77EiW/1jo9LjE6DJft9OtqWH05EBo2TQUaB5oUfESv31zGIiLyMVfJvYBtLyul9W/adeKSy0AHAGpq7Yc03gx0IkM1eKi/UdZj7uhiQElFTaPGqeZdZNa9s0iduIxFRORjcpJ7zcnGSurflHP8kuuDAJRVuw6IPFVZK2HJ9nxZj9nwfYHLXWSsvaNuDHaIKKg4qt6rZHKSe61ZVy32p4bvsUky+X0M3uSs47q3mqaSsjHYIaKgIZLgq0RK3WFlj733WBcTHsAR+Qdr76gbc3aIKCi4qt6r5LwL8w4rR/NPStkS7eg9vqyAbeO+dvJiRaCHQD7EYIeIFE9ugq/ShGg1DtsoyOllZa9Gj6u6PaJE3uNgJbLI+dHe04r9/SHPcRmLiDzijxwadxJ8lca8w6rhEpHexTKc+f3ddKgQaw6cRVH57zuKvLmdWrRNRbOIEJRV1cl67kAx/xb2bJWAb08VOz1W6b8/5BkGO0TkNn/l0Lib4Ks0cndY2Xt/rTXcSg38vqwnd4u66Hv39xGd8PSneYoKeDQAstJb4dPvztkEg/r4SIzoasDbgru3lP77Q+5jsENEbnFUJM/di60zwZTg64roDitH768r7m6nFn3vzlyuUFSgA9S/5sGdDJgz/HqbQLJnq0Tc8vJW4ecJht8fcg9zdohINn/n0ARLgq+3OHt/RVgv64kSfY+XfHXczVH51sZDhZZAcmS3q5HeJgn7Tl0WWpoD1PX7Q40x2CEi2eTk0HiDtxJ8A000mVg0f8YVOcsyIu/xXT2vwa818mrujOlxtazj3bX2wLlG76ec1x8Mvz/kPgY7RCRbIHJolNBCwZOdT9l5Bei3YAvuWbYLD390APcs24V+C7bY3TLvrfdN7rKMq/d41wmxSspmidFheOnOrph0s7z2Du64VF7daHyir/+RjHaKrtNEnmPODhHJFqgcmkC2UPAkGdtR/k2Bg/wmT983TzqjO3qPNx4qxJ6Tl2U9V1WtCV/kFWLdd/6pgTT5vW/x8pguGNolFYDrDvJA/Wc4bWA7v4yPAoddz8Gu50Ry1Zkk9FuwxeFFxHyx3TlroCqWBuR0LG/I/F45W5ZKjA7Dt0/dZnmvXL2/zpg7jD+S0Q6tm8d4JSCsM0no++IWFJYGx26lB/sbMXto/ZKc0jrIk3ex6zkR+YxacmhEeJqMLZJ/c7miBm9sOWr52dn760pUeAgSosOwcNNRl8tlovbkFwVNoAMAb2/Px4bfZpOUsPxJgcdgh4jc0lQuIp4mY4vm3yz/+qRNwGR+fxNl9qWqqK5rVH+nYUsN0dwj83Gf+6EVR3R4iFefb87aPMvryuxkwM5ZA/HhpD54bWw3fDipD3bOGqia31FyjTk7ROS2QObQ+Iunydii+TfFv9Y0quCb2cmAX6vr8Mh/Dgo9hyPWtXdMJgnPrT/sMvfIVUFDb6uo9m7tnkvl1TbvZ6A6yJMyMNghIo+o/SLiaTJ2z1aJljwaV+wFTPr4KKHzu2Kegfrzqv2N7mtYCNLdgoZK0/D99EdrE1ImBjtEFHBKvgi52tHjaufTvlOXhYOG5jERss/vDdYzPwM7pHhU0FBJrANQf7U2IWVisENEAaX0i5A5WXjK+7mNZmhEkrFl1cyx8xTW5/cl88zPezkn/bZ05UsJUWGWANRVa5MZGdehdfNoxQXa5D1MUCaigDFfhBpeXBsm1Aaas2TsN+/tjviocIfJvnJq5lwsq3J6fkO84+fy1uX5VFGFl54psAZ1TEaIVuNyN50EYOGmn7y2c42UiTM7RBQQri5C7jSz9CV7ydiXy6vx3Hrns1K9jDroYsJsunE74iwwsj7/xkOF+OTAORSVV1vuN3f4Xvpbh293l6Fa6aLdfKSybDp0Htl5BYiPCpc1U+WLRrYUeCwqCBYVJAqEnOOXcM+yXS6P+3BSH0UmQMspNLjhu3N2E4OtGWQWYXSU5+TuLipz7tFXfxuAW17eqoqlLA2AP/ZtjXe/Pin7cWoqiqlmqiwq+OKLL0Kj0WDGjBmW2yorKzF16lQkJSWhWbNmGDNmDM6fPx+4QRKRkED01/IWuYUGh3ZJxYP9HfeH0kB+EcaGHb7Nj7WuKXN/eivh55MAPDmkA/aduow0Q6zw45RuzYGzsh/j7Ua2FHhBE+zs3bsXb7/9Nrp06WJz+yOPPILPPvsMH3/8Mb766iucO3cOo0ePDtAoiUhUoPpreYM7hQZnD03DW/f2gK5BkUCDD4owmgOhITKfc/pveSubf/zFa2MJJAlAUXkNdDHhbuU0KTHQJvcERc5OWVkZxo0bh2XLlmHevHmW20tKSvDuu+9i1apVGDhwIABg+fLl6NixI3bt2oU+ffoEashE5IKnW7oDyd1ZqaFdDBjcyX9FGM3vseiSlFpzGkZ1S8VymUtZgDIDbXJPUMzsTJ06FcOGDUNGRobN7fv27UNNTY3N7R06dEDLli2Rk5Pj8PmqqqpQWlpq84+I/CuY+2sFy6yU9XusRn8Z2FbouNvS9Fh8Xw8kRIUJP7dBoYE2uUfxMzsfffQRcnNzsXfv3kb3FRYWIjw8HAkJCTa3p6SkoLCw0OFzzp8/H88++6y3h0pEMpm3VDdMqNUrqM6OPb2MOiREhzXqQWUtMTqs0cUyEDWFMjsZMLRTCjbkqSuXcdLNRjyccR3+8+3PDpuUWs8Ohmg1iI0Iw7h3dws9v1IDbXKPooOdM2fO4OGHH8bGjRsRGem9b0izZ8/Go48+avm5tLQULVq08NrzE5E4tfbXargk5Kqwnbt5OyLVp8f1bq26YGfZjnxoNcCvNbV277c3O9inTZLLatRaDfDGPdx2rjaKDnb27duHCxcuoEePHpbb6urqsH37drzxxhv44osvUF1djeLiYpvZnfPnz0Ov1zt83oiICERENC7LTkSBEWz9tfbkFzmd1QGA4orfG3v6qqaQ6ExRnzZJiAjVoqrWJPzcweDt32oK2ZMQHYb5ozvbvA/OqmGbvXFPdwztUv8YJbcxIXkUHewMGjQI33//vc1tDzzwADp06IBZs2ahRYsWCAsLw+bNmzFmzBgAwJEjR3D69Gmkp6cHYshE1ATITVCWs3vLOuhzdrHd8F0B/ryqcQsJezNFdSYJ1SoLdFyRJAkDO6Tg62MXkXP8EgAJ6dc2t+TvuAoSld7GhORRdLATGxuLTp062dwWExODpKQky+0TJ07Eo48+Cp1Oh7i4OEyfPh3p6enciUVEPiM3Qdmd3VvOLrYmk4RpH9ovUmhvpui9nJOq3WnlSPGvtejx3EaUVf2+zPXG1uNIiA7Di6M7Y+esgQ4DSUdLjgWsrhy0FB3siFi4cCG0Wi3GjBmDqqoqDB48GG+99Vagh0VEKiZ327zc4MhZfs9DAg1BG84U5V8qFzq/2lgHOmbFFTV46P1cPJLRDtMGtmu0LOVsyRGof2+V1MaExATF1nNr27Ztw6uvvmr5OTIyEm+++SaKiopQXl6O1atXO83XISLylNxt8+bgyNGlUYPftzqLVGcWZZ4pkntJvrltEnQx4tu0g9HCTUfR98XGTT9dLTkCrK4cjIIu2CEiUgJnndAbLnPICY5ELraizDNF3VskynrcnpOXhRqXBrvC0vplKeuAp7DkV6HHfvEDO6MHk6BfxiKipkNpu2PkbJsXrSnkrRYF1kXxDAlRsh4b6F1bzSJCUFZV57fzWS9LWXeSd2Z17lnMueN6LmUFCQY7RBQUlLo7Rs62eZHgyFtVl+0toym9k7n5XZjY71q8tvmoX87ZML9J10ysLElpZW2j3XOkXFzGIiLFMyfsNrxYm7dZN8y7UDJH3crNXOX3uKLVAG/d63gZTZQuJsztMbjLvAQo2qbhLwPbNmqsqo+LQEK0/Hwj84yaPk482GSj0ODBYIeIFE0kYffZzw6hzqSOzdUi+T327jMzF8WrM0nIOX4Jaw+cRc7xSxjYIQXNIsQm83Ux4Zg3spPT83jbtAFtsXPWQGR2MuBiWZXQY1bmnLJZdtLFhOHpO9Lw4ujOss9vnlHrZdQ1CqBcPYaUj8EOESmanIJ8auEs+XnJfT2wxM59ht/uG9olFdl5Bei3YAvuWbYLD390APcs24U+8zfZ3Yptz/19WmFol1S7Y/CVvm2bW2a5RIOI4l9tk6iLymswdVV9/aEl9/UQmuGx3gkH1Aeb5kDPGTYKDS7M2SEiRXOnIJ8auMrvcXSfoxo9cnZXFVVUI+f4JdyWpsdtaXrsOn4J3xy/iKU7TqCmzrszaA1rEgGu6xg5Y66Ds3PWwPqxn7iE93edwud5jZtD2ysTAABDuxjw4M9Gh+0oNHYeQ8rGYIeIFE1uQT41cZb8bO8+VwXxRK3MOYWVOadgiI/EiK4GfHqwwCfJzY6CjRCtBiO6Gpz2vnLGOuG4b9vm6Nu2ud0E94Y74azNHpqGrtck4Km1eTaBohKS4kk+BjtE5FXW28Obx0QAGuBiWZXbW8XlVituyrxZoweoDxrcDThE6GLC8fwfOjUKHLLzCrDUw/M2rJcjp0yA2dAuqRjcyaCocgfkHgY7ROQ19r49W3PnW7GzTtWOZgbUQm5doWBbyntiSIdGvwvemp2yVy9HTpkATx5DysNgh4i8wlGuiDV7HblFiBbkU5PsvAI88+khFJZavd64SDwzwvHrDbalvIYJxoD3ZqdE6+VQ08Bgh4g8Jvpt3F5HblHuLEMEq+y8ArsNPwtL6xuBLnEQLHqS2BsI9gISb81OyamXQ+rHredE5DE538Y92SruqiCfGtSZJDy++nunx8xe/b3dukLOavQokb2AxBuzU9wWTg0x2CEij7nzbTzY8kv8ZdeJSyiucL5N/HJFDXaduGT3Pkc1enwpSbAInzWtBujZqnGDUpEO8dHhIU6fe0RXg88C4YbFGtVSzFLtuIxFRB47ebFc9mNEvsErrfGnP+Qctx/E2Duub9vmdu9ruOR38mIFFm76yZvDBAB0vSYOMwd3xI1GHfq/tAWFpWKVjwHAJAH7Tl1ulPzrKiFdAhAeqkVFteNGoZ8eLMDMzI5e/13xRn+2pvg7rQQMdojII3UmCR/uOS18vOhW8Q3fFfxW4+T3XTXeqHGi/IuN6EyB8+PMS351Jgn9FmwResbo8BCnQURDB38uxV//exBzh6fhnl4tsXCTvOadjmb3nCWkj72xhcvzWNfZ8RZHCfhyku6V2sy2KWCwQ0Qe2ZNfJOsbPeB6q/j8DYfs1ncpKKlP0H3r3h4Y2kX+xSEYLjbp1zbHG1uPCx0nQjSf6r0/9sJNbZvXf54lv+K59YdxubzaZehV8NvF/o99WwuNx5qz2T1HCenrvjsn9NwbDxVagj1Pg1tX/dlEku69ESyR+xjsEJFH5OTeJEaHYv7oLk7/qG/47pzLQnbTPszFG+iOoV1Shc8dLBebPm2SkBAd5jRvJyE6DH0EZy1EP5+iimqbmjJR4SGYYmdHmD0SgDUHzgodC7ie3WsYoNzRJVV236y1B87hhlY6PLfe8+BWTn82e7NJ3giWyDNMUCYij8jZPXNv71ZOLzJ1JglPrc1z+TwmCfjzqv3IzisQOm8wdU4P0Wpcdu1+cXRn4Yuiu+02MjsZMCPjOqHHAvW9t0S6hbsqBGmviWm/BVssn3V9V3LXDT4vlVfjz6tyGwUp5uDW+nfHVdKxp/3ZmmIzW6VhsENEHull1CHGxe4YUXvyi2Q1rHS0Bdve8wbTxSazk6G+s3mcbR0afVyEwxo7jojsbnK0Vbt182gZowa6tYh3eYw+PtLhLJp59s1ZgBKi1eAP3a6WNS5r1sHt10cv4u+f/YAbn9/kMLgCPO/P1lSb2SoJl7GIyCMhWg0GX5+M1ftdz7IkRDn/5t+wn5Er5i3YjnYlmQXjxcZbRRRFmmo6mmWRW/Nmy4+/OLxvUIer8Keb2zh8DXKWejLS9Hj365Oyxtbw+QpKKjHu3d1272+4tOlpf7bmgtWcRY8j+TizQ0Ruqa414d0dJ5D1zz3YkHde6DHNmzkPduz1M3JFZKt2U+6c7qqp5uT+RoczRa5mhaw5i8E0AA4VXHEarMmZfZMzLndIv/0zL206K9Yo1J/NOxvsyAMMdohItvkbDqHDnM/x3PrD+OqnX1BZYxJ6nD4+yun9PxfLm9mp5/oK4clSTqC4yl0RIdLG49ODBQ6XAq0v8q44W00UWSaUM/smEnx4g/WYHRVrdLYsZ3axXGy3ouhxJB+DHSKSxbwtXG4ur6tgIjuvAMvdWJoQ2YLt8TdzP3OUu1JgJ7nWGZFt566CEPNF3uCgIrMhPhITBbedOwto5M6+OQs+3hjbTShZWoR1I9bMTgbsnDUQH07qg9fGdsOHk/pg56yBLnOomvLMolIwZ4eIhFXXmrBsh/Nt4Y44CybMMxByydmCHSyd013NxpiXV0S2KXsrV8k6f6iwtBJFZVXQxYRDHx+FXkYd9uQXCeXQOLuYu5MXYy+v6XJ5NZ5bf8itJVF7ispsZ1ust+eL8jTnhzzHYIeIhL2Xc1L2jI4IOY1ErcnZgg0ER+d0ObMxri663pxRcHaR98bF3FWbCMB+wGw9ruy8Akxd1biWkicSolxvc3fF3ddG3sNlLCISdqqowu3HOqtj484uqDt7XO3WbIzSO6eL7kgTOc5fuUreWib0JC9GJD8pNlL+9/siF01ZRXny2shznNkhImGtdPLqrlgrKKnEiq/zMaGvsdFFz51chd7XJuHdHSdwqqgCrXTRGJ/eGuGhwf/9TXT5ReQ4f84oeGuZ0N3ZN5EZsSuVtUJjsFb8q3eWw4DgmFlUKwY7RCRsfHprPL/hsNtLWc+tP4x3duY3uvjVV8UNl5VnMfO/39lcvJ/fcBiTbjZi9lCx3UNKpROstSJ6nL9ylepMEuKjwjEzs0OjnB53agPJzYtRUo0kZ9x5beQ5BjtEJCw8VItJNxtd9q5yxl4vqhCtBvNGdsKfV4n1YgIabzg3SbCMK5gDHn2c2CyXPi5SuMmlr2cUnDVY9fWshfk9OHr+itDxupgwXC6vEc7reT/nJDpfHc9lpiCnkSSpyZcxKi0tRXx8PEpKShAXFxfo4RAp3vwNh7Bsh/zt52bmhNWdswbaXAwddTuXQ6sBfnxuSNAuadWZJPRbsMXpkowhPhJzhnXEc+sPB7yDu6MGq+ZP1Zf5KPaCLEfMv3NzhnXE1FX7Acir4Se3TQf5h+j1Ozj/GhBRQM0emoYfnxuCcb1buPV4R0XmZg9Nw1v3dkditPs7YExS/a6xYGXOs9HAfrKvBsCIrgZMXbVfqMmlLwWywaqjWkT2WOcnDe2SajdR2JXH/nNQEY1iyT0MdoiaKFednl0JD9Xi7yM7I96NHS5mn+cVNDq3VqtBRKhnjUU92TWmBM527rx5bw98erBAER3cA9VgVWTnlbWGO54yOxnw1d8GICZC/PesvLoO3xy76MZoSQmYs0OkEKL5F97gLMdCzlR9iFaDBXd2wUPvi+faWFuZcworc05Zzg3A7pKIXJ7sGlMKR3k2cgIMXyfCBqrBqmhdpmkD2qJv2+Z2/1/am1+E8qo6Wef9X+7PuPm6q2Q9hpSBwQ6RAngr+BA9l72Awl7isIj9py97PCbzueOjwzwOdLSa+l1jamBv546nAYY3g+pAtUEQfQ/apTRzGPDlnJA/S5N/sUzoOH9+cSExDHaIGvD3HypvBx/OuMqx0EC8FQHgWfuIhucGgGIvFHCbdLMxaJOTRXgSYGz47hyeWpuHovLf32dPgupAtUHwTpAl///pHwuvWLqgW7P+m3HyYgU+3HPapqdWIBLH7Y0tEIFXoM9vxmCHyIo/Z1gA7wcfrnh7CcRX7SPcodVAFXV2XHE3wHC0063Ag6A6UG0QvBFk9Tbq8MZWeeetqpWw68Ql9G37e/NZkR1hvvjiIsLff8+Udn5r6v36QySTo90dvtzh4u8ET3eXQBwlMwc6ETgyTItbrmuOOcM64sfnhqg+0AHca82w4bsCp1v6zc1F3UlqDkQbBG+0p9Bq3AvA/vXNScv/A6I7wvydOA4E5u+Zks7fEGd2iOD/GRYzfyd4ujP97+zbmb8TgWMjQzGudwuEakOQ3iYJfa6tn33ak1+Ez/MKmkx+hJyqyHUmCU+tzXP5nJ4kNQeiDYKnlaEvNOhmLurLQ+fx5aHz0MdForK2TjjHzNGsqS+WeQL190wp57eHwQ4RvL+8I8rfCZ5yp/9d5RO9PrYbtBp4bSkrJiIE5VV1jZZEzK5U1mLtgQLMHZ6Gvm2bK2qa3N9EA4w9+UXCbTg8CaoD0QbBkyCryM1gx8w6J0cO6/fYV7+/gd6xF+jz28NlLCIEbgutv7pSm8mZ/hcpGPf85z9iYj+jV8YGAOVVdXiwv9FpwTdzoDV/wyFFTZMHgkgHdzm/s97eNeUP7naxT4hyv3ClJ8zvsS+XeQL190wp57eHwQ41eXUmCReviH3L8/bFwBu5B3KJ5liIfjsb2CEFGR2TvTI2DYBPDxbg8SEd4CilwhxoLduRr4jCekon+jur0QCXZTRiDWbZeQV4/vMf/XpO6y8uvq48HaiSAEo5vz1cxqImTbS3jq+20AL+60rd8Jyupv9Fv3VtPFSITYcveGVc5gDq4Y8OuDzOWVe/QEyTK5V59tBlEq0ETF2Vi8VadfeAcrQ060sNv7jkHL/k02WeQJUEUMr57WGwQ02W6B89X26hNQtEgqerHAvRb12fHDjnrSF5nT+nyZXKenu4yAXe34mj/iS3zYS3NPzi4utlHvNn7qiyuQTf/j0LVEkCZxjsUJMk54+eL2dYrAUiwdMZkW9niTFhwsmvgRCMOSi+YJ49fGLN9zYFBRtS+4yYaJsJURoACdFhiAjVorD096VwfVwE7unVEq2bx9j94qLEZR5v7wrL7GTA5P7G+uVmqz8gmt/qYfl79pDBDjVJcv7ozRnWUdXT+o6IfDv8Q7er8e7XJ/06LhGBmCZXusxOBvxaY8Ij/z7g8li1zoh583WZw4D5ozvLnpX19TKP+cucs7Fbz+D5YldYdl4Blm5vnFdnkoCl2/PRvWWiX/+uMkGZmiQ5f/SeW3+Yia4OxAVoR4szgZomDwb6OOXNKPiTN1+XLibcktAvd0eYrzcmyNn67YtdYSIz5/7eQMBgh5okOX/0vFnBWK7qWhPe3XECT6/Nw7s7TqC61uS3c4t8O/xwz2nhC6ivNNy15cvKvcHO36UOlEbk9YuGF08JzPg6qjwO+LbytOiXucLSSp/sCvN3ZXgRXMaiJkl0h4pZIKb15284hGU78m0K9j2/4bDf+j+J/MEqLK3CnT2uxn9zz/p8PA7HIQFPDu2I5LiIJlNB2V1KTBz1J5HXP6yLAeu+cz2boY+Pcnq/yNKQrzYmiH6ZKyqr8smuMNbZIVII62lkEf6e1jc3bWz4hcokAW9vz8f8DY5nXLxF9A9RdITYd6bb01I8GY5TJb9Wyy4q11QFopeVkrh6/a+N7Y6Y8BCnz5EYHeZ09kvO0pC7RRGdEZ3B08WECz2f3KBEiQnYig525s+fjxtvvBGxsbFITk7GqFGjcOTIEZtjKisrMXXqVCQlJaFZs2YYM2YMzp8/H6ARUzDJ7GTAW/f2gLO/LYGY1q+uNWHZDsdNGwFg6Y58ny9pif4hEu2PdV1KM0+G4wIDHDkyOxmwc9ZAfDipD14b2w0fTuqDnbMGqj7QMXP1+sNCnV8anS3q+LpgoAjRnCBXs1NmcoMSJS6XKjrY+eqrrzB16lTs2rULGzduRE1NDW6//XaUl5dbjnnkkUfw2Wef4eOPP8ZXX32Fc+fOYfTo0QEcNQWLOpOExJhwZKW3snt/oKb138s56bLXlCQBT6z+zqfjEP2DNT69tdBx6dc2981AAVVuk/Y1X8woBBNHr39PfhGKKxxvzweA4ooah/kmSslXEZnB81VQEojK8K4oOmcnOzvb5ucVK1YgOTkZ+/btQ//+/VFSUoJ3330Xq1atwsCBAwEAy5cvR8eOHbFr1y706dMnEMOmIGBvPb1hQ0t/1ddp6FRRhdBxnx4swII7uwa8MFh4qFbouD5tkqCLCXNa58UdidFhlu7nauKo7okvumTT7zzNN1FSvoqrnCBf5nAFojK8M4oOdhoqKSkBAOh09VHmvn37UFNTg4yMDMsxHTp0QMuWLZGTk+Mw2KmqqkJV1e8FoEpLS304anVRwx9aR5WTzYWv/ti3NW5L0wfstYkuC1XXmfDGlmN4OKOdz8Yi+gdL9Lh5Izvhz6v2e3WM80d3DrrfQVccJbeO6GrApwcLmmSXd3/xNN9EafkqroqV+jIoCURleEc0kuSsw4xymEwmjBgxAsXFxdi5cycAYNWqVXjggQdsAhcA6NWrFwYMGIAFCxbYfa5nnnkGzz77bKPbS0pKEBcX5/3Bq4QvCk/5W51JQr8FWxxOM5uLee2cNTBgF9DqWhPaP/W5UHXnhOgw7HvqNp+PVTTIFTnOnHztjAbAjIzr0Lp5NJJjI3G5vAp/X3cYhaXB+7snSm7vJvO72xSSi/3B/DfCVcE/R38jPH18oATrF9nS0lLEx8e7vH4HzczO1KlTkZeXZwl0PDF79mw8+uijlp9LS0vRokULj59XzRz9ATbvLgiWP7Ry1tMDlQcSHqoV3v5qzh3w9VhFW1mIHDd7aBq6XpOIp9bm2W014SiIGdzJEJR/jOVwp3eThMYVccl9ni7tBOv2fqW1q/G2oAh2pk2bhnXr1mH79u245pprLLfr9XpUV1ejuLgYCQkJltvPnz8PvV7v8PkiIiIQERHhyyGriqvdBcH0h1ZJ6+nOZKbphYIdADazHXIE8pvc0C4GDO5UP71dWPIrisqroWsWAX2c43Go/Y8x4H7vJiUE6Wri6dKO0vJVSOHBjiRJmD59OtasWYNt27bBaDTa3N+zZ0+EhYVh8+bNGDNmDADgyJEjOH36NNLT0wMxZFUKhtkQUaLr5BevVKHOJAUkeKszSZi1RnynVVFZleuDGlDCkmRTCF7kcjdwNQt0kK4mnuabKClfhRQe7EydOhWrVq3C2rVrERsbi8LCQgBAfHw8oqKiEB8fj4kTJ+LRRx+FTqdDXFwcpk+fjvT0dO7E8qJgmQ0R4aoBn9lz6w/jnZ35AfkWtuvEJZRX1QkfL1oYzEwtS5Jq5E7gak2tPa0CxdOAnAG9cii6zs7ixYtRUlKCW2+9FQaDwfLv3//+t+WYhQsX4o477sCYMWPQv39/6PV6rF69OoCjVh+l7S7whLP6Dw150gjPEznHL8k6XrQwGKCMgmfkmNzA1UztPa2IPKXoYEeSJLv/JkyYYDkmMjISb775JoqKilBeXo7Vq1c7zdch+Xq2SnRaZRior1HTs1WifwbkIUfFthoK3MVf/FxyL3BKKXhG9skJXM2UnPRKpBSKDnZIGfaduuyyoq9Jqj8uWJjLxc8Z1tHpcYG4+MupNCz3AqemJUk1Mi+zOtPw424qPa2IPMFgh1xS6wUyRKtB81ixXXn+fG192iQhPsp1Ol1CdBhuS5M3i6mmJUk1Mi+zOgtfrb946GLCMGdYRwY6RC4w2CGX1HyBVOJrC9Fq8Me+RpfHOevP44gSG/SRLfMyq6sZHgC4XF6Dqav2+z2vjCjYMNgJkDqThJzjl7D2wFnkHL+k6IRQNV8gexl1SIgOc3pMTHiI319b6+YxQsfJnXFSYoM+asy6K/fC/+sGXYz931EmlROJYbATANl5Bei3YAvuWbYLD390APcs24V+C7Yo9ttZU79AllfX4Qs/fza6aLFdOaLHWRPphkyBZ962rI+LdNo8lUnlRK4pus6OGgVrjRO1VgTdk1+E4grXXbifWpuHwZ0MfgvoDhWUCB9383VXyX5+FjwLHmrNmSPyJwY7fhTsbRfUeIEUvUAUlfunB5XZhu8LhY7bd6rY7XOw4FlwUGJeGVGwYbDjR2pou6C2C6ScC8SmQ4V+ee11JglHzl8ROjYmPMTHo6FA69kqEYnRYbjsYAbS3EU7GHPmiPyFOTt+xOlo5ell1AlXrV1z4KxfkkD35BehssYkdOzoHte4PoiCVnZeAXq9sMlpoAOoO2eOyBsY7PgRp6OVJ0SrwehuqULHmpeyfE002I0I1eKmtuIFCCm4ZOcV4KH3c53mlMVHhyk2z49ISRjs+JGat3AHq/kbDuGdr08KH++PWTfRYPe2tBR+m1epOpOEZz79weVxUWEhsgtLEjVFDHb8qKlv4VaaDd8V4O3t+bIe449ZN5GWAQCw7rsCxZYrIM/syS9CYanrDujcck4khsGOn3m7xkkwFSdUkjqThKfW5gkf789ZtxCtBiO6iv0esJicOsmZQdx4SGznHlFTxt1YAeCtLdzZeQWN6t4Ygrzujb/syS9CUXm1rMf4a9atziRh7QGxGRul794j98iZQVx74ByeHMYZYSJnOLMTIOYt3CO7XY30NkluBTpT3s9ttJXdXJyQyxvOyfnmnBQT7tck0PolDPHxcfee+vQy6qCPE2tSe6m8mktZRC4w2PERXy4vuSpOCHB5wxXRb86xkSHImT3IrzNlcoMX7t5TnxCtBs+MuF74eAa8RM5xGcsHfL28pIbihIFmTgJ29j4CwILRXRAe6t/vBHKCF11MGHfvqVRmJwMeyWiHhZuOujyWAS+Rc5zZ8TJ/LC+xOKHnzDvjnC0ePtjfiKFdxGrweFPPVokQXdWcN7ITczVUbNrAdtDHOQ5kWK6CSAyDHS/y1/ISixN6h3lnXMNt3kkx4Xjr3h6YPTQtIOPad+oyRH5F7uhiCEgwRv5Tv5xVH5SzXAWR+7iM5UX+Wl4yL8EUllTaDazYK0ecEpubis7I3ZaW4uORkBKYg/KGS+N67rwkEsZgx4v8tbxkXoKZ8n4uNIBNwMNve/IprbkpZ+6oISUG5UTBhMtYXuTPi5S3ixOScrCtCNnjabkKoqaMMzte5O/lJX7bUyfO3BEReRdndrwoEL2v+G1PnThzR0TkPRpJkpp85bnS0lLEx8ejpKQEcXFxHj8f2ziQt9SZJM7cERE5IHr9ZrAD7wc7AC9SREREviZ6/WbOjo8obYcPEZG38UsdBQsGO0REJBuX6ymYMEGZiIhk8UdbHCJvYrBDRETC/NUWh8ibGOwQEZEwOW1xiJSCwQ4REQnzV1scIm9isENERMLYu42CEYMdIiISxt5tFIwY7BARkbBAtMUh8hSDHSIikoW92yjYsKggERHJltnJgNvS9KygTEGBwQ4REbmFbXEoWHAZi4iIiFSNwQ4RERGpGoMdIiIiUjUGO0RERKRqDHaIiIhI1RjsEBERkaox2CEiIiJVY7BDREREqsZgh4iIiFSNFZQBSJIEACgtLQ3wSIiIiEiU+bptvo47wmAHwJUrVwAALVq0CPBIiIiISK4rV64gPj7e4f0ayVU41ASYTCacO3cOsbGx0GjU08SutLQULVq0wJkzZxAXFxfo4TRJ/AwCj59B4PEzUAY1fg6SJOHKlStITU2FVus4M4czOwC0Wi2uueaaQA/DZ+Li4lTzix2s+BkEHj+DwONnoAxq+xyczeiYMUGZiIiIVI3BDhEREakagx0Vi4iIwNy5cxERERHooTRZ/AwCj59B4PEzUIam/DkwQZmIiIhUjTM7REREpGoMdoiIiEjVGOwQERGRqjHYISIiIlVjsKMA27dvx/Dhw5GamgqNRoNPPvnEcl9NTQ1mzZqFzp07IyYmBqmpqbj//vtx7tw5m+d4/vnncdNNNyE6OhoJCQmyx/DQQw9Bo9Hg1Vdftbn9p59+wsiRI9G8eXPExcWhX79+2Lp1qxuvUtkC9RlMmDABGo3G5l9mZqbNMUVFRRg3bhzi4uKQkJCAiRMnoqyszNOXrEhK/RxOnjyJiRMnwmg0IioqCm3atMHcuXNRXV3tjZetKEr9DKxVVVWhW7du0Gg0OHDggJuvVLmU/hmsX78evXv3RlRUFBITEzFq1CgPXq1/MNhRgPLycnTt2hVvvvlmo/sqKiqQm5uLOXPmIDc3F6tXr8aRI0cwYsQIm+Oqq6tx1113YcqUKbLPv2bNGuzatQupqamN7rvjjjtQW1uLLVu2YN++fejatSvuuOMOFBYWyj6PkgXyM8jMzERBQYHl34cffmhz/7hx4/DDDz9g48aNWLduHbZv347JkyfLf5FBQKmfw48//giTyYS3334bP/zwAxYuXIglS5bgiSeecO+FKphSPwNrM2fOtPv3Si2U/Bn873//w/jx4/HAAw/g4MGD+Prrr3HvvffKf5H+JpGiAJDWrFnj9Jg9e/ZIAKRTp041um/58uVSfHy88Pl+/vln6eqrr5by8vKkVq1aSQsXLrTc98svv0gApO3bt1tuKy0tlQBIGzduFD5HsPHnZ5CVlSWNHDnS4f2HDh2SAEh79+613Pb5559LGo1GOnv2rNA5gpWSPgd7XnrpJcloNMp6TLBR4mewYcMGqUOHDtIPP/wgAZD2798v9PzBSkmfQU1NjXT11VdL77zzjtDzKQlndoJQSUkJNBqNW8tV1kwmE8aPH4+//e1vuP766xvdn5SUhPbt22PlypUoLy9HbW0t3n77bSQnJ6Nnz54enTvYeeszAIBt27YhOTkZ7du3x5QpU3Dp0iXLfTk5OUhISMANN9xguS0jIwNarRa7d+/2+NzBzl+fg6Nz63Q6j88b7Pz5GZw/fx6TJk3Ce++9h+joaI/Ppxb++gxyc3Nx9uxZaLVadO/eHQaDAUOGDEFeXp7H5/U1BjtBprKyErNmzcI999zjcSO3BQsWIDQ0FH/5y1/s3q/RaLBp0ybs378fsbGxiIyMxD/+8Q9kZ2cjMTHRo3MHM29+BpmZmVi5ciU2b96MBQsW4KuvvsKQIUNQV1cHACgsLERycrLNY0JDQ6HT6VS3lCiXPz+Hho4dO4bXX38dDz74oEfnDXb+/AwkScKECRPw0EMP2QT/TZ0/P4MTJ04AAJ555hk89dRTWLduHRITE3HrrbeiqKjI49fiS+x6HkRqampw9913Q5IkLF682KPn2rdvH1577TXk5uZCo9HYPUaSJEydOhXJycnYsWMHoqKi8M4772D48OHYu3cvDAaDR2MIRt78DABg7Nixlv/u3LkzunTpgjZt2mDbtm0YNGiQx8+vVoH8HM6ePYvMzEzcddddmDRpksfnDlb+/gxef/11XLlyBbNnz/b4XGrh78/AZDIBAJ588kmMGTMGALB8+XJcc801+PjjjxUd/HNmJ0iYf6lPnTqFjRs3ehzB79ixAxcuXEDLli0RGhqK0NBQnDp1Co899hhat24NANiyZQvWrVuHjz76CH379kWPHj3w1ltvISoqCv/617+88KqCi7c/A3uuvfZaNG/eHMeOHQMA6PV6XLhwweaY2tpaFBUVQa/Xe/38wSAQn4PZuXPnMGDAANx0001YunSp188bLALxGWzZsgU5OTmIiIhAaGgo2rZtCwC44YYbkJWV5fXzK10gPgPzF9y0tDTLMREREbj22mtx+vRpr5/fmzizEwTMv9RHjx7F1q1bkZSU5PFzjh8/HhkZGTa3DR482JJlD9Rn/QOAVmsbE2u1WkuE31T44jOw5+eff8alS5csf1TS09NRXFyMffv2WfKktmzZApPJhN69e/tkDEoWqM8BqJ/RGTBgAHr27Inly5c3+v+iqQjUZ7Bo0SLMmzfPcv+5c+cwePBg/Pvf/25y/y8E6jPo2bMnIiIicOTIEfTr188ylpMnT6JVq1Y+GYO3MNhRgLKyMptvkPn5+Thw4AB0Oh0MBgPuvPNO5ObmYt26dairq7Pkauh0OoSHhwMATp8+jaKiIpw+fRp1dXWW2hNt27ZFs2bNAAAdOnTA/Pnz8Yc//AFJSUmN/gcJCwuDXq9H+/btAdRfaBMTE5GVlYWnn34aUVFRWLZsGfLz8zFs2DBfvy1+FYjPoKysDM8++yzGjBkDvV6P48ePY+bMmWjbti0GDx4MAOjYsSMyMzMxadIkLFmyBDU1NZg2bRrGjh2ryq23Sv0czp49i1tvvRWtWrXCK6+8gl9++cUyRrXNsCn1M2jZsqXNOM3P06ZNG1xzzTU+fU/8TamfQVxcHB566CHMnTsXLVq0QKtWrfDyyy8DAO666y5/vT3uCeBOMPrN1q1bJQCN/mVlZUn5+fl27wMgbd261fIcWVlZLo8BIC1fvtzhOBpuPZckSdq7d690++23SzqdToqNjZX69OkjbdiwwbtvgAIE4jOoqKiQbr/9dumqq66SwsLCpFatWkmTJk2SCgsLbcZ26dIl6Z577pGaNWsmxcXFSQ888IB05coVP7wr/qfUz2H58uUOz602Sv0MGjKPRY1bz5X8GVRXV0uPPfaYlJycLMXGxkoZGRlSXl6eH94Vz2gkSZLEwiIiIiKi4NM0F52JiIioyWCwQ0RERKrGYIeIiIhUjcEOERERqRqDHSIiIlI1BjtERESkagx2iIiISNUY7BAREZGqMdghIsXRaDT45JNPVHXe1q1b49VXX/XJcxORcwx2iEi2uro63HTTTRg9erTN7SUlJWjRogWefPJJm9tXrFiBLl26IDIyEsnJyZg6dao/hyusoKAAQ4YMAQCcPHkSGo3G0lOIiIIXG4ESkWwhISFYsWIFunXrhg8++ADjxo0DAEyfPh06nQ5z5861HPuPf/wD/+///T+8/PLL6N27N8rLy3Hy5MkAjdw5tTX1JKJ6nNkhIrdcd911ePHFFzF9+nQUFBRg7dq1+Oijj7By5UpL5+XLly/jqaeewsqVK3HvvfeiTZs26NKlC0aMGCHrXN9//z0GDhyIqKgoJCUlYfLkySgrK7PcP2HCBIwaNQqvvPIKDAYDkpKSMHXqVNTU1FiOKSgowLBhwxAVFQWj0YhVq1Y1WlqyXsYyGo0AgO7du0Oj0eDWW28FANx6662YMWOGzfhGjRqFCRMmWH6+cOEChg8fbjnXBx980Og1FRcX409/+hOuuuoqxMXFYeDAgTh48KCs94WIxDDYISK3TZ8+HV27dsX48eMxefJkPP300+jatavl/o0bN8JkMuHs2bPo2LEjrrnmGtx99904c+aM8DnKy8sxePBgJCYmYu/evfj444+xadMmTJs2zea4rVu34vjx49i6dSv+9a9/YcWKFVixYoXl/vvvvx/nzp3Dtm3b8L///Q9Lly7FhQsXHJ53z549AIBNmzahoKAAq1evFh7zhAkTcObMGWzduhX//e9/8dZbbzU611133YULFy7g888/x759+9CjRw8MGjQIRUVFwuchIkGBbrtORMHt8OHDEgCpc+fOUk1Njc198+fPl8LCwqT27dtL2dnZUk5OjjRo0CCpffv2UlVVlcPnBCCtWbNGkiRJWrp0qZSYmCiVlZVZ7l+/fr2k1WqlwsJCSZIkKSsrS2rVqpVUW1trOeauu+6S/u///s9mjHv37rXcf/ToUQmAtHDhQrvnzc/PlwBI+/fvtxnbLbfcIj388MM2t40cOVLKysqSJEmSjhw5IgGQ9uzZ0+g9Mp9rx44dUlxcnFRZWWnzPG3atJHefvtth+8LEbmHMztE5JF//vOfiI6ORn5+Pn7++Web+0wmE2pqarBo0SIMHjwYffr0wYcffoijR49i69atQs9/+PBhdO3aFTExMZbb+vbtC5PJhCNHjlhuu/766xESEmL52WAwWGZTjhw5gtDQUPTo0cNyf9u2bZGYmOjWa3Y13tDQUPTs2dNyW4cOHZCQkGD5+eDBgygrK0NSUhKaNWtm+Zefn4/jx497fUxETR0TlInIbd988w0WLlyIL7/8EvPmzcPEiROxadMmaDQaAPUBBwCkpaVZHnPVVVehefPmOH36tFfHEhYWZvOzRqOByWTy6jkAQKvVQpIkm9usc4NElJWVwWAwYNu2bY3usw6KiMg7OLNDRG6pqKjAhAkTMGXKFAwYMADvvvsu9uzZgyVLlliO6du3LwDYzMAUFRXh4sWLaNWqldB5OnbsiIMHD6K8vNxy29dffw2tVov27dsLPUf79u1RW1uL/fv3W247duwYLl++7PAx5iTruro6m9uvuuoqFBQUWH6uq6tDXl6e5ecOHTqgtrYW+/bts9x25MgRFBcXW37u0aMHCgsLERoairZt29r8a968udBrIiJxDHaIyC2zZ8+GJEl48cUXAdQXzXvllVcwc+ZMy9by6667DiNHjsTDDz+Mb775Bnl5ecjKykKHDh0wYMAAofOMGzcOkZGRyMrKQl5eHrZu3Yrp06dj/PjxSElJEXqODh06ICMjA5MnT8aePXuwf/9+TJ48GVFRUZZZqIaSk5MRFRWF7OxsnD9/HiUlJQCAgQMHYv369Vi/fj1+/PFHTJkyxSaQad++PTIzM/Hggw9i9+7d2LdvH/70pz8hKirKckxGRgbS09MxatQofPnllzh58iS++eYbPPnkk/j222+FXhMRiWOwQ0SyffXVV3jzzTexfPlyREdHW25/8MEHcdNNN2HixImWpZ6VK1eid+/eGDZsGG655RaEhYUhOzu70bKTI9HR0fjiiy9QVFSEG2+8EXfeeScGDRqEN954Q9aYV65ciZSUFPTv3x9/+MMfMGnSJMTGxiIyMtLu8aGhoVi0aBHefvttpKamYuTIkQCAP/7xj8jKysL999+PW265Bddee22jwG358uVITU3FLbfcgtGjR2Py5MlITk623K/RaLBhwwb0798fDzzwAK677jqMHTsWp06dEg7giEicRmq4+ExE1AT8/PPPaNGiBTZt2oRBgwYFejhE5EMMdoioSdiyZQvKysrQuXNnFBQUYObMmTh79ix++ukn4VkmIgpO3I1FRE1CTU0NnnjiCZw4cQKxsbG46aab8MEHHzDQIWoCOLNDREREqsYEZSIiIlI1BjtERESkagx2iIiISNUY7BAREZGqMdghIiIiVWOwQ0RERKrGYIeIiIhUjcEOERERqdr/BykCS/fKGpNuAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Custom Models:\n",
            "Linear Regression - MSE: 66.6734, R2: 0.5797\n",
            "Lasso Regression - MSE: 65.7128, R2: 0.5857\n",
            "Ridge Regression - MSE: 66.4374, R2: 0.5811\n",
            "\n",
            "Sklearn Models:\n",
            "Linear Regression - MSE: 66.6734, R2: 0.5797\n",
            "Lasso Regression - MSE: 103.3429, R2: 0.3485\n",
            "Ridge Regression - MSE: 65.4130, R2: 0.5876\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Step 1: Read the dataset using pandas\n",
        "data = pd.read_csv('Real estate.csv')\n",
        "\n",
        "# Step 2: Check for null values\n",
        "print(data.isnull().sum())\n",
        "\n",
        "# Step 3: Plot column values against the last column (prices)\n",
        "for column in data.columns[1:-1]:\n",
        "    plt.scatter(data[column], data['Y house price of unit area'])\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Price')\n",
        "    plt.show()\n",
        "\n",
        "# Step 4: Remove unwanted columns\n",
        "data = data.drop(['No'], axis=1)\n",
        "\n",
        "# Step 5: Split the dataset into train and test data\n",
        "X = data.iloc[:, :-1]\n",
        "y = data.iloc[:, -1]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "\n",
        "# Step 6: Normalize the X_train and X_test using MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Step 7: Fit the training data into the models created in question 1 and predict the testing data\n",
        "# Custom Linear Regression\n",
        "regressor_custom = LinearRegression()\n",
        "regressor_custom.fit(X_train, y_train)\n",
        "y_pred_custom = regressor_custom.predict(X_test)\n",
        "mse_custom = mean_squared_error(y_test, y_pred_custom)\n",
        "r2_custom = r2_score(y_test, y_pred_custom)\n",
        "\n",
        "# Custom Lasso Regression\n",
        "regressor_lasso_custom = Lasso(alpha=0.1)\n",
        "regressor_lasso_custom.fit(X_train, y_train)\n",
        "y_pred_lasso_custom = regressor_lasso_custom.predict(X_test)\n",
        "mse_lasso_custom = mean_squared_error(y_test, y_pred_lasso_custom)\n",
        "r2_lasso_custom = r2_score(y_test, y_pred_lasso_custom)\n",
        "\n",
        "# Custom Ridge Regression\n",
        "regressor_ridge_custom = Ridge(alpha=0.1)\n",
        "regressor_ridge_custom.fit(X_train, y_train)\n",
        "y_pred_ridge_custom = regressor_ridge_custom.predict(X_test)\n",
        "mse_ridge_custom = mean_squared_error(y_test, y_pred_ridge_custom)\n",
        "r2_ridge_custom = r2_score(y_test, y_pred_ridge_custom)\n",
        "\n",
        "# Step 8: Fit the training data into the sklearn models and evaluate the predictions\n",
        "# Linear Regression\n",
        "regressor_sklearn = LinearRegression()\n",
        "regressor_sklearn.fit(X_train, y_train)\n",
        "y_pred_sklearn = regressor_sklearn.predict(X_test)\n",
        "mse_sklearn = mean_squared_error(y_test, y_pred_sklearn)\n",
        "r2_sklearn = r2_score(y_test, y_pred_sklearn)\n",
        "\n",
        "# Lasso Regression\n",
        "regressor_lasso_sklearn = Lasso()\n",
        "regressor_lasso_sklearn.fit(X_train, y_train)\n",
        "y_pred_lasso_sklearn = regressor_lasso_sklearn.predict(X_test)\n",
        "mse_lasso_sklearn = mean_squared_error(y_test, y_pred_lasso_sklearn)\n",
        "r2_lasso_sklearn = r2_score(y_test, y_pred_lasso_sklearn)\n",
        "\n",
        "# Ridge Regression\n",
        "regressor_ridge_sklearn = Ridge()\n",
        "regressor_ridge_sklearn.fit(X_train, y_train)\n",
        "y_pred_ridge_sklearn = regressor_ridge_sklearn.predict(X_test)\n",
        "mse_ridge_sklearn = mean_squared_error(y_test, y_pred_ridge_sklearn)\n",
        "r2_ridge_sklearn = r2_score(y_test, y_pred_ridge_sklearn)\n",
        "\n",
        "# Step 9: Print evaluation metrics\n",
        "print(\"Custom Models:\")\n",
        "print(\"Linear Regression - MSE: {:.4f}, R2: {:.4f}\".format(mse_custom, r2_custom))\n",
        "print(\"Lasso Regression - MSE: {:.4f}, R2: {:.4f}\".format(mse_lasso_custom, r2_lasso_custom))\n",
        "print(\"Ridge Regression - MSE: {:.4f}, R2: {:.4f}\".format(mse_ridge_custom, r2_ridge_custom))\n",
        "\n",
        "print(\"\\nSklearn Models:\")\n",
        "print(\"Linear Regression - MSE: {:.4f}, R2: {:.4f}\".format(mse_sklearn, r2_sklearn))\n",
        "print(\"Lasso Regression - MSE: {:.4f}, R2: {:.4f}\".format(mse_lasso_sklearn, r2_lasso_sklearn))\n",
        "print(\"Ridge Regression - MSE: {:.4f}, R2: {:.4f}\".format(mse_ridge_sklearn, r2_ridge_sklearn))\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "dQ7lQpy-SYCq"
      },
      "source": [
        "# Question 3\n",
        "## Logistic Regression\n",
        "The breast cancer dataset is a binary classification dataset commonly used in machine learning tasks. It is available in scikit-learn (sklearn) as part of its datasets module.\n",
        "Here is an explanation of the breast cancer dataset's components:\n",
        "\n",
        "* Features (X):\n",
        "\n",
        " * The breast cancer dataset consists of 30 numeric features representing different characteristics of the FNA images. These features include mean, standard error, and worst (largest) values of various attributes such as radius, texture, smoothness, compactness, concavity, symmetry, fractal dimension, etc.\n",
        "\n",
        "* Target (y):\n",
        "\n",
        " * The breast cancer dataset is a binary classification problem, and the target variable (y) represents the diagnosis of the breast mass. It contains two classes:\n",
        "    * 0: Represents a malignant (cancerous) tumor.\n",
        "    * 1: Represents a benign (non-cancerous) tumor.\n",
        "\n",
        "Complete the code given below in place of the \"...\""
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "auipk-zBpmO-"
      },
      "source": [
        "1. Load the dataset from sklearn.datasets\n",
        "2. Separate out the X and Y columns.\n",
        "3. Normalize the X data using MinMaxScaler or StandardScaler.\n",
        "4. Create a train-test-split. Take any suitable test size."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "0OyGNHNjFh13"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Load the breast cancer dataset\n",
        "data = load_breast_cancer()\n",
        "X, y = data.data, data.target\n",
        "\n",
        "# Handle missing values\n",
        "imputer = SimpleImputer(strategy='mean')\n",
        "X = imputer.fit_transform(X)\n",
        "\n",
        "# Normalize the features\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "# Create a train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "uM-SsSxpqF2o"
      },
      "source": [
        "5. Write code for the sigmoid function and Logistic regression.\n",
        "(Logistic Regression code is available in the Week2/Examples folder. However, try to code it yourself. A template is provided for that.)\n",
        "\n",
        "*Optional* : Include the regularization terms as you did in the first question. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "o81LA5MZFoTW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def sigmoid(z):  \n",
        "    return 1 / (1 + np.exp(-z))\n",
        "\n",
        "def sigmoid_derivative(z):\n",
        "    return sigmoid(z) * (1 - sigmoid(z))\n",
        "\n",
        "class logisticregression:\n",
        "    def __init__(self, learning_rate, epochs):\n",
        "        self.lr = learning_rate\n",
        "        self.epochs = epochs\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        n_samples, n_features = X.shape\n",
        "        y = y.reshape(-1, 1)\n",
        "        self.weights = np.zeros((n_features, 1))\n",
        "        self.bias = 0\n",
        "\n",
        "        for _ in range(self.epochs):\n",
        "            linear_output = np.dot(X, self.weights) + self.bias\n",
        "            y_pred = sigmoid(linear_output)\n",
        "\n",
        "            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))\n",
        "            db = (1 / n_samples) * np.sum(y_pred - y)\n",
        "\n",
        "            self.weights -= self.lr * dw\n",
        "            self.bias -= self.lr * db\n",
        "\n",
        "    def predict(self, X): \n",
        "        linear_output = np.dot(X, self.weights) + self.bias\n",
        "        y_pred = sigmoid(linear_output)\n",
        "        return np.round(y_pred)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Uo9LNRMzq4K-"
      },
      "source": [
        "6. Fit your model on the dataset and make predictions.\n",
        "7. Compare your model with the Sklearn Logistic Regression model. Try out all the different penalties.\n",
        "8. Print accuracy_score in each case using sklearn.metrics ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "DyGsTTOqFphf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Custom Logistic Regression Accuracy: 98.25%\n",
            "Scikit-learn Logistic Regression Accuracy (Penalty: l1): 97.37%\n",
            "Scikit-learn Logistic Regression Accuracy (Penalty: l2): 99.12%\n",
            "Scikit-learn Logistic Regression Accuracy (Penalty: none): 99.12%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Fit and evaluate the custom logistic regression model\n",
        "custom_model = logisticregression(learning_rate=0.1, epochs=1000)\n",
        "custom_model.fit(X_train, y_train)\n",
        "custom_predictions = custom_model.predict(X_test)\n",
        "custom_accuracy = accuracy_score(y_test, custom_predictions)\n",
        "print(\"Custom Logistic Regression Accuracy: {:.2f}%\".format(custom_accuracy * 100))\n",
        "\n",
        "# Fit and evaluate the scikit-learn logistic regression model with different penalties\n",
        "penalties = ['l1','l2','none']\n",
        "for penalty in penalties:\n",
        "    sklearn_model = LogisticRegression(penalty=penalty, solver='saga')\n",
        "    sklearn_model.fit(X_train, y_train)\n",
        "    sklearn_predictions = sklearn_model.predict(X_test)\n",
        "    sklearn_accuracy = accuracy_score(y_test, sklearn_predictions)\n",
        "    print(\"Scikit-learn Logistic Regression Accuracy (Penalty: {}): {:.2f}%\".format(penalty, sklearn_accuracy * 100))\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "AGBkzAO5red4"
      },
      "source": [
        "9. For the best model in each case (yours and scikit-learn), print the classification_report using sklearn.metrics .\n",
        "10. For the best model in each case (yours and scikit-learn), print the confusion_matrix using sklearn.metrics .\n",
        "11. **Optional Challenge** : For the best model in each case (yours and scikit-learn), print the roc_auc_score and plot the roc curves using sklearn.metrics and matplotlib."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "le-HfABsvnyF"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report (Scikit-learn Model):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99        43\n",
            "           1       0.99      1.00      0.99        71\n",
            "\n",
            "    accuracy                           0.99       114\n",
            "   macro avg       0.99      0.99      0.99       114\n",
            "weighted avg       0.99      0.99      0.99       114\n",
            "\n",
            "Confusion Matrix (Scikit-learn Model):\n",
            " [[42  1]\n",
            " [ 0 71]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "print(\"Classification Report (Scikit-learn Model):\\n\", classification_report(y_test, sklearn_predictions))\n",
        "print(\"Confusion Matrix (Scikit-learn Model):\\n\", confusion_matrix(y_test, sklearn_predictions))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "6OQ2tSp0MO6n"
      },
      "source": [
        "# Question 4\n",
        "## KNN and Decision Tree\n",
        "How accurately can a K-Nearest Neighbors (KNN) model classify different types of glass based on a glass classification dataset consisting of 214 samples and 7 classes? Use the kaggle dataset \"https://www.kaggle.com/datasets/uciml/glass\". \n",
        "\n",
        "Context: This is a Glass Identification Data Set from UCI. It contains 10 attributes including id. The response is glass type(discrete 7 values)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "iMGxbtX-zfsI"
      },
      "source": [
        "1. Load the data as you did in the 2nd question.\n",
        "2. Extract the X and Y columns.\n",
        "3. Split it into training and testing datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "p0SfLB7pO7_z"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import pandas as pd\n",
        "\n",
        "# Load the dataset\n",
        "df = pd.read_csv('glass.csv')\n",
        "\n",
        "# Separate features (X) and labels (y)\n",
        "x = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "qtyZJXh9zoh5"
      },
      "source": [
        "4. Define Euclidean distance.\n",
        "5. Build the KNN model.\n",
        "6. Fit the model on the training data. (Note : you may require to change the type of the data from pandas dataframe to numpy arrays. To do that, just do this X=np.array(X) and so on...)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "YJkhLORLzn6r"
      },
      "outputs": [],
      "source": [
        "# Create and fit the custom KNN model\n",
        "import numpy as np\n",
        "from collections import Counter\n",
        "# Counter class is imported from the collections module. It is used later\n",
        "# to count occurrences of labels in the nearest neighbors.\n",
        "\n",
        "def euclidean_distance(x1,x2):\n",
        "    return np.sqrt(np.sum((x1-x2)**2))\n",
        "\n",
        "class KNN(object):\n",
        "    def __init__(self,k):\n",
        "        self.k=k\n",
        "    def fit(self,x_train,y_train):\n",
        "        self.x_train=x_train\n",
        "        self.y_train=y_train\n",
        "    def predict(self,x_test):\n",
        "        predictions=[self._helper(x) for x in x_test]\n",
        "        return np.array(predictions)\n",
        "    def _helper(self,x):\n",
        "        prediction=[euclidean_distance(x,x1) for x1 in self.x_train]\n",
        "        indices= np.argsort(prediction)[:self.k]\n",
        "        labels= [self.y_train[i] for i in indices]\n",
        "        c=Counter(labels).most_common()\n",
        "        return c[0][0]\n",
        "        \n",
        "def accuracy(predictions,y_test):\n",
        "    return np.sum(predictions==y_test)/len(y_test)\n",
        "\n",
        "clf=KNN(k=4)\n",
        "clf.fit(X_train,y_train)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "E9rxZpPB0pVS"
      },
      "source": [
        "7. Make predictions. Find their accuracy using accuracy_score. Try different k values. k=3 worked well in our case.\n",
        "8. Compare with the sklearn model (from sklearn.neighbors import KNeighborsClassifier)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ernfjaZJ0pAh"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Custom KNN Model Accuracy: 79.07%\n",
            "Scikit-learn KNN Model Accuracy: 69.77%\n"
          ]
        }
      ],
      "source": [
        "# Make predictions using the custom KNN model\n",
        "predictions=clf.predict(X_test)\n",
        "\n",
        "\n",
        "# Calculate accuracy using accuracy_score for the custom KNN model\n",
        "accuracy_custom = accuracy(predictions,y_test)\n",
        "\n",
        "# Create and fit the scikit-learn KNN model\n",
        "knn_sklearn = KNeighborsClassifier(n_neighbors=4)\n",
        "knn_sklearn.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions using the scikit-learn KNN model\n",
        "y_pred_sklearn = knn_sklearn.predict(X_test)\n",
        "\n",
        "# Calculate accuracy using accuracy_score for the scikit-learn KNN model\n",
        "accuracy_sklearn = accuracy_score(y_test, y_pred_sklearn)\n",
        "\n",
        "# Print the accuracy scores for comparison\n",
        "print(\"Custom KNN Model Accuracy: {:.2f}%\".format(accuracy_custom * 100))\n",
        "print(\"Scikit-learn KNN Model Accuracy: {:.2f}%\".format(accuracy_sklearn * 100))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "XRI7CeJQ0-iP"
      },
      "source": [
        "9. Finally use sklearn.tree to implement a Decision Tree Classifier on this dataset. Check for max depth = 5 to 10. Find out its accuracy.\n",
        "10. Plot the decision tree."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "h2tZQg4L09wn"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Decision Tree Classifier Accuracy: 83.72%\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import tree\n",
        "\n",
        "# Create and fit the Decision Tree Classifier\n",
        "max_depth = 5  # Set the maximum depth for the decision tree\n",
        "dt_classifier = DecisionTreeClassifier(max_depth=max_depth)\n",
        "dt_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions using the Decision Tree Classifier\n",
        "y_pred_dt = dt_classifier.predict(X_test)\n",
        "\n",
        "# Calculate accuracy using accuracy_score for the Decision Tree Classifier\n",
        "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
        "\n",
        "# Print the accuracy score for the Decision Tree Classifier\n",
        "print(\"Decision Tree Classifier Accuracy: {:.2f}%\".format(accuracy_dt * 100))\n",
        "\n",
        "# Plot the decision tree\n",
        "fig = plt.figure(figsize=(12, 8))\n",
        "_ = tree.plot_tree(dt_classifier, feature_names=df.columns[:-1], class_names=[str(c) for c in df['Type'].unique()], filled=True)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.11.2 64-bit",
      "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": "3.11.2"
    },
    "vscode": {
      "interpreter": {
        "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}