Linear_and_polynomial_Regression.ipynb

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      "cell_type": "markdown",
      "source": [
        "**Linear Regression**"
      ],
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        "id": "3mTKETOZjtuT"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('headbrain.csv',encoding=\"ISO-8859-1\")\n",
        "df"
      ],
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          "height": 423
        },
        "outputId": "fa88e389-9de1-4cc0-d4f1-e64a54f5cead"
      },
      "execution_count": 2,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     Gender  Age Range  Head Size(cm^3)  Brain Weight(grams)\n",
              "0         1          1             4512                 1530\n",
              "1         1          1             3738                 1297\n",
              "2         1          1             4261                 1335\n",
              "3         1          1             3777                 1282\n",
              "4         1          1             4177                 1590\n",
              "..      ...        ...              ...                  ...\n",
              "232       2          2             3214                 1110\n",
              "233       2          2             3394                 1215\n",
              "234       2          2             3233                 1104\n",
              "235       2          2             3352                 1170\n",
              "236       2          2             3391                 1120\n",
              "\n",
              "[237 rows x 4 columns]"
            ],
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              "      <th>Head Size(cm^3)</th>\n",
              "      <th>Brain Weight(grams)</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>1530</td>\n",
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              "      <td>3214</td>\n",
              "      <td>1110</td>\n",
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              "      <th>233</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3394</td>\n",
              "      <td>1215</td>\n",
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              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3233</td>\n",
              "      <td>1104</td>\n",
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              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3352</td>\n",
              "      <td>1170</td>\n",
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              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3391</td>\n",
              "      <td>1120</td>\n",
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              "<p>237 rows × 4 columns</p>\n",
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    {
      "cell_type": "code",
      "source": [
        "a = df['Head Size(cm^3)'].tolist()\n",
        "b = df['Brain Weight(grams)'].tolist()\n",
        "plt.xlabel('Head size (cm^3)')\n",
        "plt.ylabel('Brain weight (grams)');\n",
        "plt.scatter(a, b)"
      ],
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7efef4e742e0>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_x = np.mean(a)\n",
        "mean_y = np.mean(b)\n",
        "print(mean_x)\n",
        "print(mean_y)"
      ],
      "metadata": {
        "id": "LdNEWJPr_5Ny",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b92c197f-0de4-4038-b70c-25c1f6eaac67"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3633.9915611814345\n",
            "1282.873417721519\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculating coefficient\n",
        "n = len(a)\n",
        "numer = 0\n",
        "denom = 0\n",
        "for i in range (n):\n",
        "  numer += (a[i] - mean_x) * (b[i] - mean_y)\n",
        "  denom += (a[i] - mean_x) ** 2\n",
        "b2 = numer /denom\n",
        "b0 = mean_y - (b2 * mean_x)\n",
        "print (\"coefficient: \",b2)\n",
        "print (\"Intercept: \",b0)"
      ],
      "metadata": {
        "id": "H0gLHy4ksruA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9dcab7b7-82ee-4533-b463-2d1f37aaad80"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "coefficient:  0.26342933948939945\n",
            "Intercept:  325.57342104944223\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# flotting value and linear regression line\n",
        "max_x = np.max(a) + 100\n",
        "min_x = np.min(a) -100\n",
        "\n",
        "x = np.linspace(max_x, min_x, 1000)\n",
        "y = b0 + b2 * x\n",
        "\n",
        "plt.plot(x, y, color= 'red', label = \"Regression Line\")\n",
        "plt.scatter(a, b, color= 'green', label = \"Scatter Plot\")\n",
        "\n",
        "plt.xlabel('Head size in cm^3')\n",
        "plt.ylabel('Brain weight in grams')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dWhyJyuelKhh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "74538e01-00d2-40a1-c461-2c0c707fe2ca"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Pf/gD999/P4ceemgleYzoqSkRRDXJnBZXiovh3/+GNm3gH/9wzE6rV8M998ABB1S1dGER3wy0JtG9e3ddtGhRubEvv/ySY4891vM58lfkp2QfXCPyf0sjubR6tFVAc1rLRi1Ze/3a5AuUCrz9tlMR9osvnByJhx5ykutSDBFZrKrdA+1L3TVPFVPjSxobRoLI65tXzmcB1dOcFhdWrHCUxLvvwu9/Dy+/DOeem5BCf4nGzFCGYcSVmmJOi4mffoLhw6FzZ1i4EB55xKnjNHhwtVQUUMtWFqpKnCqiG1VETTSb1kRq7cp8zx54+GG47z7Yu9epBDt2LAQJWa9O1JqVRb169diyZYs9bKoxqsqWLVs8hxEbRtIoLXXqNrVtC7feCqed5qwkHnmkRigKqEUri2bNmrFhwwZqShe92kq9evVo1qxZVYth1GAiDm6ZNw9uvBEWLYJu3SA/30myq2HUGmWRkZFB69atq1oMwzCiIFnRiRElFBYUwC23wCuvQLNmMGUKXHxx1RX6SzA181sZhpGSRFMGJJmdAT0lFG7dCjfcAO3aOVFO99zj5EtcckmNVRRgysIwqpzaUkcp2od+MjPCQyYUFhbCo4867UwfewyGDXNWF2PGODWdajimLAyjCqkO/bTjRbQP/WRmhAcs6aNw1bqmTm+JG26A7t1h6VKnltOhh8ZdhlTFlIVhVCG1qY5StA/9ZHYGrFjqp9tGmDc5jX9P2gSZmTBrFrzzDuTkxP3aqY4pC8OoQmpTHaVoH/rxqNXm1dTnSyjsVXoEz8+ERU9Dj20NYfx4WLYMTj+92ibVxYopC8OoQqpDP+14EctDP6tOVtnr7KzsiDLCR745kiEzh3gz9e3YQe6ML5l//xaGrK4Lo0dTb+33cPXVKV0RNhkkTFmIyEQR+UVEVvqN3SEiG0VkqbsN9Ns3WkQKRGS1iPT3Gx/gjhWIyN8SJa9hVAU1pSy5F6IpA+Lz6WzZs6VsbE/xHs/XzF+Rz/hF41HKJ+NWMvWVlMDTTzsVYfPynLIcq1c7va8PPND7l6zBJKzqrIicBOwEnlfVDu7YHcBOVX2wwrHtgGlAT+BwYDbQ1t39NXAasAFYCFykqiHbxgWqOmsYqYpVOA5OrBVsg30eQBBKby91wl9vvBFWroQTTnDKdfTsGavo1ZIqqTqrqvNEpJXHwwcB01V1H/CdiBTgKA6AAlX9FkBEprvHeusxahjVgFpbR8kDsfp0Qh3Xd/ehjg/i7bfhyCPhpZeqdaG/RFMVPotrRWS5a6Zq7I4dAXzvd8wGdyzYeCVEZLiILBKRRVbSwzCiI9VyPmL16QQ67nc7Yfx/4Z0Hf4JPP3V6S6xaBeedZ4oiBMlWFk8Bvwc6Az8CD8XrxKo6QVW7q2r3gw8+OF6nNYxaQyrmfMTq0/H/fL0i+NuHsOYxuHJpGmnXXuck1f31r1C3btxlr2kkVVmo6s+qWqKqpcDT7Dc1bQSa+x3azB0LNm4YRhzJX5HP0FeGVlnOR7AVTay9MXJzcplwxniuK2jKV/+Cf8yBbSd0I33VlzBuHGRnJ/Jr1SiSGgsmIoep6o/u23MBX6TU68B/RORhHAd3G2ABIEAbEWmNoyQuBC5OpsyGUdPxrShKtCTg/kTnfIQr3heTT+fjj8n967/IXbAZunSBhx+meZ8+cZK8dpHI0NlpwHzgaBHZICJXAPeLyAoRWQ6cAtwAoKpfAC/gOK7fBv7srkCKgWuBd4AvgRfcYw3DiBOBssj9SXTOR0Ky2L/5Bs4/H048ETZuhMmTnRLipiiiJpHRUBcFGH42xPF5QCVDpKrOAmbFUTTDMPwItXJIRs5HXLPYf/3VqQL7+ONOeY677nLCYmtBob9EYxnchpEEUi3KyJ9gK4d0SU9K7+y4ZLEXFTmVYI86yulOd+mlsGaN09LUFEVcCKssROQCETnAfX2riMwUka6JF80wagapGGXkL9vOwp2Vxutn1GfyuZOTkv8RU8STKrz2mlMRdtQo6NoVPv8cnnkGDjssQRLXTrysLMaq6g4RORE4FceU9FRixTKMmkOqVpYNVEoDIq+9FCtRRzwtWQKnnALnnOPUbXrzTScbu1OnpMhd2/Dis/CFSJwBTFDVN0XkngTKZBhJJdHlNlK1smwwx3bDzIZJzyiPKOJpwwan4dCUKU7o65NPwlVX1fpCf4nGy8pio4j8G/gTMEtE6nr8nGGkPMkwEaVqZdlUVWJB2bkTbrsN2raF6dOd/tcFBTBihCmKJODlof9HnNDV/qr6G9AEuDmhUhlGkkiGiShVK8umqhKrREkJPPusUxH27rth0CCnIux990GjRlUtXa0hrLJQ1d3A/4As17F9GLA50YIZRjJIxuw61izkRBFKiaVM9Nbs2Y7T+soroXVrmD8fpk2DVq2qRp5aTNi1m4jcDQwDvoGyovAK/CFxYhlGcmjRqEXAEtbxnl2nYmVZnzwV/TVAyIzqpLBqFdx8s9PGtHVrmDEDLrjACv1VIWH7WYjIaiBHVQuTI1LsWD8LwysVS02AM7tOhZl/VRFrD4mY+OUXuOMOmDABGjaEW2+F666zQn9JIlQ/Cy8+i5XAQfEVyTBSg1Q1EUVDvExHVeL43rsX/vlPxy8xYYLjtC4ogJtuSoqiSBmzWwrjJYTgH8DnbnvUfb5BVT07YVIZRhJJRRNRpIQrxhcJyTLNAU5S3YwZ8Le/wbp1cNZZcP/9cMwx8b9WEOJ572oyXlYWk4F/Avfh9J/wbYZhxJFYZrfxjOoK5PgWhIFtBnr6vOfv8ckncPzxcNFF0LgxzJkDr7+eVEUBqZs0mWp4WVnsVtXHEi6JYdRiQs1uYb8TuklWEwC27tlaLoEwnqaj3JxcPl7/MeMXjUfdmBZFmbxsMie0OCHkbNvTLP3bb52VxIsvOiU5Jk2CIUMgPT1iWeNBtcs3qSK8OLgfxjE/vU55M9SSxIoWPebgNqobwZzK2VnZ7CneE7SEuM8ZP2bOmLg4pX3Z7IHO5eV8IZ3jw5ZCXp5T8K9OHSep7qaboEEDz/Ilgip16KcYoRzcXlYWXdy/vfzGLHTWMOJIsFlsxbpNFfGZS/L65gWM6ook8S9/RT6Xv3Y5hSXBAx/DzbYD7a9TAme9uw7uOQq2boVhw5wy4ocf7lm2RBKPe1cbCKssVPWUZAhiGLWZYE5lL6zftj5ozkQkDtpRb40KqSh8cobbX/Y9FM78Gh54F47ZAj/1PIRDZ8+Gzp09y5QM4nHvagNhzVAAInIG0B6o5xtT1bsSKFdMmBnKqG4Ey/fIqpMVdnWRnZXN5ltiL6ogd4ZOePOSf+L7Hm3X7+ahd+APa+GrbLi5H7zfPosJZz+dsIdwogtC1gZiyrMQkfE4RQSvw+mJfQHQMq4SGkYtJ1i+x7jTx1WKTKrI9n3bE54X4DX/JLdJH5Yv6MbiCdDxZ/jzQMgZCW8cDbuL9yQswiiVe4bUFLw4uJerake/vw2Bt1S1d3JEjBxbWRjJIFkz2fwV+Yx6a1TIFUY8nLFN728a8BqeVi67dsEDDzhbcTEPdC8krzdsyyp/mCCU3l4ak5yBMCd1fIg1g3uv+3e3iBwOFOEUEzSMWksyZ7K5Obk0zGwY8pho/R3+jDt9HBlpGeXGMtIyGHf6uOAfKilxQl/btIE774Qzz+TVV+9jdP/0SooCElfR1sJfE48XZfFfETkIeABYAqwF/hPuQyIyUUR+cTO/K+67UURURJq670VEHhORAhFZ7t+2VUSGisgadxvq9YsZRiJJdiJXOGUgSMyKKjcnl0nnTCpnCpt0zqTgq6U5c6BbN7j8cmjZEj7+mPxbzyb381sp0ZJKhycywqjalFuvxoRUFiKSBsxR1d9U9WUcX8Uxqnqbh3M/BwwIcM7mQD/AX+WfDrRxt+G4bVtFpAlwO3Ac0BO4XUQae7i2YSSUeM9kw2U9p0vohDVFo1ZU/tf2heGW3l7K2uvXBlYUX33llOU49VT47TenEdEnn8D/+39Bu++lS3pCa25Vi3Lr1ZyQykJVS4En/N7vU9VtXk6sqvOArQF2PQLcwv5y5wCDgOfV4VPgIBE5DOgPvKeqW1X1V+A9Aiggw0g28ZzJejFpBZqpVyQaRRWROW3zZrj2WujQAebNcwr/ffUV/OlPZaXDg8lQqqUJjUwKFiAAmOM7TngxQ80RkfNEYi8kLyKDgI2quqzCriOA7/3eb3DHgo0bRpUSz+53XkxaLRuFD0CMRlGFu3b+inzaPtiSW/oJ25v/jtLxT8HVVzsVYW+5BerVK/fZqjQH5ebksvb6teVWRVb3KX54URZXAy8C+0Rku4jsEJHtkV5IROoDfwe8mLAiRkSGi8giEVm0adOmRFzCMMqIZ2lzLyatvL55ZKZnBj1HtIoq1LXzl0/l7Xsv5+289dz/HsxrrnS/NpP8a/4fHHxwwM+lWgtZc3zHDy9tVQ9Q1TRVzVTVA933B0Zxrd8DrYFlIrIWaAYsEZFDgY1Ac79jm7ljwcYDyTlBVburaveDg/yQDSOeBJrJRoOX2XhuTi4HZB4Q8LhY/AHBrj3o10M4ZtCVTJleyI66cOoQOCsXPm+8N+SsPNX6g5jjO354ScrrGmD7vYh4qStVhqquUNXfqWorVW2FY1Lqqqo/4RQpvNSNiuoFbFPVH4F3gH4i0th1bPdzxwyjxuB1Nr51TyAXoOPPGDNnTEQOXJ/Td922dQj7Lcwtf4UXXk7nlXE/cfjmfVx+NnS9Gub8fv9n/WflgZzHwZRoVTiaU22lU53x8sB/EugKrHDf5+B0z2skIiNU9d1AHxKRaUAfoKmIbABuV9Vng1xjFjAQKAB2A5cBqOpWtwf4Qve4u1Q18P8Yw6imeK1NFKx+lCBl414a91QsLaIojfbC6A/h+s8gvU463DaGvvUn8eXe7yt93jcrj6RpUFU1GLK6T/HDSwb3TGCsqn7hvm8H3IUT0TRTVVOrKhiWwW14IxVrCYWSaeSbI8v1mABHUSiV/w+Hylz2z9ROL4Hhi+HOuZC9B9IuHepUhG3WLGx/8kiypi3DunoQa4nytj5FAaCqq0TkGFX9Ng4BUoZRJaRiK81wDZAmL5vsSVFAcAdu/op8R1EoDFwDD74Lx26G/7WCm/rB4n8/V3ZsuFl5sGus27aOVo+2KvcZczRXf7ysLGbg5EtMd4f+BDQFhgAfqWqPhEoYBbayMMKRijPdUDJB4CzudEkPmIMR7Hu0erQVjVav46F34NTvYHU23Hwa/PdoaHlQZN89mLwVlVio6rm2skgtYl1ZDANGAte77z8GbsKpEWW9LoxqSSrOdKORqURLqJ9R31vjnh9/5Lbn1zHsc/g1C647HcZ3h2I3Odyr09e/m15FxRBotbO7aDdZdbK8y2mkJF5CZ/eo6kOqeq67Paiqu1W1VFV3JkNIw4g3qRhSGUqmYPt8oam+UNXsrGyy6mQxZOaQ/RFHu3bBXXdBmzZcshwePh6O+gv867j9iiI7K9uT+c0/4xsopxiys7KDmsW27tmaUiG1RuR4an5U3TAzlBEIf+dxk6wm7CjcUa4znJfmPomWL5hDGai0LzM9kwMyD2Drnq20aNSCgW0GMnnZ5LJjpBSu/CKTRz9sQP1ffoXzz+e1y47n4qVjgzqtwxHM9OQ7j5mbqjexlig3jGpPxRpIW/ZsQVXJzsr2NNOtmCMw8s2RAXMGYsklCJXQVnFfdlY2qu73cGsejV80vkwJ9PkOFk2ACS8X8nW9XfDhh/Diiwwa+NeYZvihTGK+a1teQ83EVhZGrSAWh3agGX9F6mfUZ2inoTyz5BmKSosCXmdgm4HMWjOLddvWlTmmQ42HCuUN9n3abob734NBq2FdI/jbqfBCe3j+/KlxCRMOtbIAx2cxZfCUlAtJNrwRamXhJRqqLXAzTnnyMoe4qv4hnkLGE1MWRkXS7kwLaE/30rkt3APS/1zBbPbREMo8VPH7NNkNt8+FEYtgTx24tzeM6wV7Mxxfwp7iPVGbnvwJpzjN3FS9idUM9SJO06NbcZSGbzOMakMsDm2vEVLxVBQQujqqT+7MYvjrJ1DwGPx5ITzT1XFe/7O3oyh8JqF4VV71mcOys7Ir7TNzU83Gi7IoVtWnVHWBqi72bQmXzHy3JH0AACAASURBVKgSqnOjmPwV+TS9vylypyB3Ck3vb1omfyw1gqoyQiqQospfkc/OfTs47wtY9QQ89C7MbwYdR8DIM2GT24HV548IVlMq2jDh3JxcNt+ymamDp1p0Uy3CixnqDuAX4BVgn288lWs0mRkqOsKVd0hl8lfkc9mrl1XyF2SmZzJx0ERyc3IrldLw+QrC2dbzV+QzZOaQkCuH+hn1SZM0dhbGN5q8olknf0U+E568grw393Hi97D8d07m9XtHlf+cv3ktFRMQA5G/Ip9Rb40qi6bKzspm3OnjUv63V5OI1WfxXYBhVdUj4yFcIjBlER3V5aESiFB+hUDyR6oYQ9Vl8jmjAS5/7fJy4bixUEmedet4bXA7Bi3ZzU8N4NY/wKQuUBrAPpCdlc3mWzZH9V2rAi/K3kg8MfksVLV1gC1lFYURPamY1eyVUDIG2hdpB7Unz3iSKYOnlDO7TBk8Bb1dy8pw5+bkMnHQxHLhrf6huSO6jygr3eHrqR1qvOxhvn07jB4NRx/Nact3c/dJ0OYv8Gy3wIoCYNu+bTS9v2lZX+2hnYamtMlozJwxAaPICksK49rVrjqbWauaoCsLEfmDqr4vIoMD7VfVmQmVLAZsZREdtWllEUt0VNIoLoZnnoHbboNNm2DIEI5v/T6fpgXs/xWSVFtJVCTYvwfE79+kOqywqppoVxYnu3/PCrCdGVcJjZSgujWK8Z8l7izcSVqAn3NmemZA+VOx3EcZqvDWW9CpE4wYAcceCwsXwvPPc+35/6z0b+QF36opVWfWoe57vP5NrB93bARVFqp6u/v3sgDb5ckT0UgWqdYSMxSBMrLrpNehQUaDsmOys7Ir2buDdYiD8ooxkoeq/7FN729aZv6J5GHsO0fHkcK8Y+rDwIFQWAivvAJz50J3Z7IX6N8oUBhrIHwlz333zPc+FRRGXt88MtIyKo0HU/bRUJ3NrKmAZXAb1ZJoTGaBzBAVndS5ObkBndnBzBXhktS8mDnyV+Qz9j9XMfrdPVz+OWyrC//om0HXu57moq5DwzZp8pJhDpGXM082iY6Gqs5m1mQRUzRUdcSURc0nGp+Dl4dFqDDZisf5ynSHI+TDaPduHjz/cK6ZvY3MEvhXT7jnJPi1PmUKzIudvWKRxO37tpdzGFcsD+5PsHuWip0EY8F8FuGJKRpKROp6GTOMZBLK5xDMhOTFDDFmzpiw3ecqlukOx7pt6yqbekpLYcoUOPpobnprG+8cBe3+DDcOcBSF73rh7Oy+7zpk5hAApgyewuZbNnNl1yvLIqvSJb0sGioQge5lRTNfKpmsoqU6mVlTES95FktUtWu4sVTCVhY1n5FvjuSpRU9VGu/bui/zN8wPOHsMthLwn/nLncFbBfuO81oryp9yM9gPPoAbb4TFi6F7dy7o9T0vNf054PXWb1sfdAU1ZfCUgDPloZ2GlitVHm480APTv093oHtg1EyiWlmIyKEi0g3IEpEuItLV3foAYcMxRGSiiPwiIiv9xu4WkeUislRE3hWRw91xEZHHRKTA3d/V7zNDRWSNuw2N4HsbNZhZa2YFHJ+7dm7QmXi4aK/8FfmVnN4+BCk7LhqH6O6i3Tw9/RY491zo0wd++QWmToXPPuOc4Q8FlSvUCirYqmPC4gkBx2etmVVpZj2001DGzBlTbhVW1qc7AOYMrr2EaqvaH6elajPgYb/xHcDfPZz7OeBfwPN+Yw+o6lgAEfkLcBtwDXA60MbdjgOeAo4TkSbA7UB3QIHFIvK6qv7q4fpGDSbYQyuQA9d3vG/2HMwOH8oE5R+p06JRi4hWFo13w20fwJ8X/sDOOq/yjz/AS/3hto6Qm5YWVq5Aq4e8vnllpiev92DdtnVlyYNQ2YbvMzVl1ckK+l1SIrTYqBK8mKHOU9WXozq5SCvgDVXtEGDfaKCFqo4QkX8Dc1V1mrtvNdDHt6nq1e54ueOCYWaomk8wU1AsET+hEsPAURiTzpkEELA0RaXji51KsGM/gEb7nIqwt50Cv7iF/rw6V4M5miO9Bz7Tle96wUxNoZg6eKrZ+GswsZYof0NELhaRv4vIbb4tBmHyROR7IBdnZQFwBPC932Eb3LFg40YtJ5hJaXi34ZXGBWFgm4Fhzxlu1lxUWsSYOWPIzcnlwLoHBj9Q4dxV8MWT8Mg7sOAI6HQNXHPWfkUB3hPCcnNyWXv9WkpvLy0rLQKh70Egc5qi5RzjkSoKr326jZqJF2XxGjAIKAZ2+W1RoapjVLU5kA9cG+15KiIiw0VkkYgs2rRpU7xOa6QQ/lFOFesdZWdlk1Uni/GLxpMm5X/WijJ52eSwkTx5ffOC+ix8rNu2jrQ704I+aLtvhA8mwcwXYF86DMiF04fAF4cEPl8sPoBg0T1PnvFk2IiuUEoqOyubzPTMcmOZ6ZmMO31c1LIa1Z9QPgsfzVR1QAKunQ/MwvFJbASa+1/THduIY4ryH58b6GSqOgGYAI4ZKu7SGlVKIPv65GWTmXDWBKC8XT9QmXDfLD7UzDg3J5dLZl4SVpZAD+Lmv8G9c+CSFfBzAxh+JkzsAiXpoc8Vqw/A3wfhT8tGLQOaqHzXC6Wk/tj+jzyz5JlyYzUxH8uIDC8ri09EJCceFxORNn5vBwFfua9fBy51o6J6AdtU9UfgHaCfiDQWkcZAP3fMqGWEyjcItC8Q67atC9ocyUewXIRgNNwH98yB1f+C876Eh0/Jos118HT38IrCZx5LRL2mcJFfwZRUw8yGzFozq5I/xmeCM2ovoUJnV4jIcuBEYImIrHbDWn3jIRGRacB84GgR2SAiVwD3ichK9/P9gFHu4bOAb4EC4GlgJJQ1WLobWOhud6Vy0yUjMPF4GIZKqIvElONvPtqyZwuXvXpZOXkCPWQDkV4CVy1y2pmO+RDe7tSAt998lJtO3suOet5kUZRnP3+Wy169LO7Jb+ES0PL65lUyNQHsK94XNNLLwmZrN6FKlIecYqlqZFlJScSioVKHeJVYCFWqAwgbyuqrARWIQN3oQpXy6FcAD70DHTbBgtZ16TnjQ+jRI6Sc0eBfryoRBIuGSvUaUkbiiCoaSlXXuQphR4Dth0QIatQ84lUWOpRZJdC+zPTMsmqs6ZIeMiQ21IzZ3+Hd/md4awq8MxXqFcNFF2ey5tVnoEePctVs40Ukq4xoVm/BenOXaEm1KlVvJAcvPoslwCbga2CN+3qtiCxxM7wNIyjxKgsdyqwSaN/EQRMZd/o46mfUD5qk5sPffl+x7pOiHLITxv8Xlo2HXj8If+0Pp/+9BWf+bSK5HS+JuFZUJHhRrNHWcQrmt/DdW6uhZPjjJSnvaeAlVX3Hfd8POA+YBIxT1eMSLmWEmBkqdQiVOFaqpQmtZup1pu+faOb/mXpFcMN8GP2Rs5LIuG4UjB0L2eX7R0SaHOdPZnomqhoywS9cp7hoS29bFVajIrEm5fXyKQoAVX0XOF5VPwWs+qwRkmAO4xItSXg1Uy+rl4qJZuu3rUdK4eLlsPpxuPd9mH0kdBgJPPpoOUURzvQUTlH4VkCTzpkUMgorXHhttKs3q8JqRIKXPIsfReT/gOnu+z8BP4tIOpAizYqNVKVi3aM0Sav0EPWSAxEN4Wo41c+oXynR7LzNv+PmmT/T8wdYfBgMGQzzWlUOqfXScChSR/Hlr11OYUlhubGMtIywvoJg39NLDkewPI1EUNP6Y9Q2vKwsLsZJhnvV3Vq4Y+nAHxMnmlFT8C9XUaqB5xeJCMsMtKrxOawrzaILClh/ak9e/NfPHL4DhpwLPa5yFEUg52643I5gpUeCOYrHzBlTSVEAHFj3wLAP1OrQO70m9seobYRVFqq6WVWvU9Uu7natqm5S1UJVLUiGkEb1pWKUTpOsJgGPC9W0KFr8zSywPyqqZaOWDGwzkFFvjaLx34SHjxcKj2lDk3kLufUUaHsdTO0E6v7v2F20m6GvDGXkmyPLzh1KufmX3fBq5gl2vmARS8G+Z6jS41VJvKLijKojVJ7Fo6p6vYj8FyrHHarq2YkWLlrMwZ0aBDLVZKZnUlxSTKmfBTMjLYMru17puTFPPOTIKIYRi+D2uU5F2IldnIqwPx0Q+lwjuo/gyTOejHs/53ieLxUd19G0wTWST7QO7inu3weBhwJshhGSQLPJwpLCcooCQER44YsXPM08o1l9lJNDYdCXTkXYcW/DosOhy9Uw/OzwigJgwmKnFlUwE5eX6raBCGZKGthmYGzf16WqZ/GhmjgZ1YNQSXmL3b8fAAuAn1T1A9+WLAGN6otXP0RhSaGnzmyR2r0rRit1/QHmPgevzoCiNDg9F/oPgRWHev9OPod1bk4uQzsNLZe057W6bSACmZKOb3Y84xeNj9jOH6/clnhSHfwqRmjC+ixE5CxgKfC2+76ziLyeaMGM1MXr7D4es0Z/H0ckM2Z/xdJsG0yeCYsnwLGb4JozoOMIeLsNhKlIXol02V8dcNaaWZVMK7HM4P0DAfL65vH+d+9Hdf5UnMVbmG71x0vo7B1AT9zS4Kq6VERaJ1AmI4UJ1ooTKPcfP39FfsBS4cHIzspmR+GOShFB2/dtJ39FPrk5uSFnzBXDMncW7kR27uauj+GmT0AU/nGis3kt9BeI4d2Gl7tuMHkgtlDRUC1ew60Q8vrmBW3FWpUkM0zXiD9eQmeLVHVbhTErbl9L8TK79ykUr53YfPkOB2RWdhr4l8YONjNuktWknHnq+1/Xcc5HW1jzOIydB68cA0dfB38/NbiiyM7KZkT3EeVWDhXxObd9hJIn1lDRUAoh3ArBZvFGIvCiLL4QkYuBdBFpIyKPA58kWC4jRfFSvtprfwmANEkre5AFCxP1nTuYU3lv8d6y6536DXw+Hp75L3zbGI67EnLPh/UHBb5+y0Yt0duVzbds5oQWJ3BQvcoH1s+oz9TBU8spCp88gcp8b9+3nVFvjYrJyRxMIQjiaYUQrBWrYUSLF2VxHdAe2AdMA7YB1ydSKCM1yV+RH7TtqP/DzasjNTM9k+fPfb7sQRbO1h7MqbyraBfH/gJvToX3pkDDQjj/AjjxcljQLPj1/bOjg62GsrOyg87Kc3Nyg66GvDjsQxFMMV7T/ZpK5r54N04yjEB4URaHuX2ze6hqd1W9VVX3JlwyI+UIZkevONsN9tDPzsouZxq5ossV5RLHBrYZGLCO1M7CnWUPwYpO5YN3wpNvwPKn4P99Dzf2g2OvhZfbU8l57a9kGmQ0YNI5k8qVIwm0GmqY2TDkrNxL0pw/Xp3MgUxJUwZPKbe6saxoI5l4cXBPFJFmOJ3qPgTmqeqKxIplpCLBZsWKlnugBnOwjjt9XNlxwXpqD+00lBe+eKFSRzufE91nBqtbBNd/Cn//ELKK4ckecNfJsKVBcPn9lUxFpRdtuGm4+lMViSQPI5xDOJT/yMxORrzxUu7jZOBY4HHgIOBNEbHWprWQUHZ0/9msFwdrsAfdrDWzaJjZsNI1dhftZtRboxCFC1c4Pa/vmwP/a+1UhB01MLSiCHQ+f/9BtOGmkUYYzVozK6LjQ5GK+RRGzcVLnsWJwI3AGOAM4A3gzwmWy0hB8vrmBfRZKFrJcRvOwRpNT+2jV29h/jMw7WXYkgWnDIVzLoKvm+4/ZurgqZ56aIOzSvHZ+3cW7iQjLaPcfv9w02C+gdyc3LKOfF6I9EEeyieRivkURs3Fi89iLnAOMAHoo6ojVXVaQqUyUpLcnNyoY/8rEupBV3HfkVvhhRfg44nQbDsMPQe6D4e5FbJ90iU94Kom2MNckDJ7/5Y9WxARsrOyK62GAvkGLnv1Mpre35S0O53/QhUVjZdAgFDkr8in6f1NuWTmJUF9EpYVbSQTL8qiKXAXcDzwtojMFpG7EyuWkWr4ZrjBiHQ266Wn9kF74IF34Mt/wcA1cE/fTNpeB8933l8R1h9fwlzFVY2vvao/glRSfIUlhTTMbFhpNRTIZOaLeAqmaK7pfk3UD/JQeSr+5jPLpzCSSVgHt6r+JiLfAs1x+lr8PyAj9KdARCYCZwK/qGoHd+wB4CygEPgGuExVf3P3jQauAEqAv/i1cR0AjMPpn/GMqt4X6Zc0YsNLox9fxJLXB1XFpkjlMpyLish4Yjx9n/+IxntgUhcYewpsPkgRyYQKWd6+kNKKeRChruUlXyTUWEV8imbzLZvLxk5ocUJUGdzh8lT85bGsaCNZeOnB/S3wFU4k1IfAAlWt3KWl8udOAnYCz/spi37A+6paLCL/BFDV/xORdjg5HD2Bw4HZQFv3VF8DpwEbcCKyLlLVVaGubSXK44vXXtYxl8FWhf/+F26+Gb7+mtmt4cb+sNyv0F+DjAbsLd5LiZaQLukM7zY8qJIIRSQlwb1+/3iV2w5WzttHtGXQDSMcsfbgPkpVB6rqP1T1Iy+KAkBV5wFbK4y9q6rF7ttPcVYqAIOA6aq6T1W/AwpwFEdPoEBVv3WvO9091kgiXv0RsRTRmzXjHua3zYJBgyj47VvOuBhOu7S8ogDYVbSrrPJriZaUq/IaSYJaJPb+YH3EKxIvx3Ko85hPwqgqvITOJqozyeXAW+7rI4Dv/fZtcMeCjVdCRIaLyCIRWbRp06YEiFt7ieQhGHHY5saNfDPoJAZcNJajftjHyIFw7NXFvNVWPFWE9SmoSBPUIrH3Vzw2Oyu7UpmPeD7EgymnUNnkhpFowpqhYjq5SCvgDZ8Zym98DNAdGKyqKiL/Aj5V1anu/mfZr0gGqOqV7vgQ4DhVvTbUdc0MFV8C+SwCOYghAhPJzp3wwAPwwAPsK9zDo73g3t6wPYqKsIIE9UMkymQTS0XZVDi/YQQilBnKSwZ3vIUZhuP47qv7NdVGHAe6j2buGCHGjSQRyEE8sM3AgG1Qw86uS0pg8mS49Vb48Uf405849tAZfNc4evlaNGqR9AS1RDuWzXFtpBpekvLqisjFIvJ3EbnNt0VzMTey6RbgbFX1D/d4HbjQvVZroA1Od76FQBsRaS0imcCF7rFGnAln768YjnpCixPIqpNVtr9BRgOy6mQxZOaQcp/3P2/uiEP4td2RcMUV0LIlfPIJTJ9OaauWUcvtq0uVjAQ1K9pn1Ga8rCxew6k0uxin8qwnRGQa0AdoKiIbgNuB0UBd4D0RAcf0dI2qfiEiLwCrgGLgz6qOF1NErgXewQmdnaiqX3iVwfCG14ZGwY4Hx/G8q2hXuc9/vP5jJi+bTIsfdvP6u3Dmml9Y21hY+cC19L7xMXB+A+T1zeOyVy+jqLSo7HwZaRmUaAmlYVxm/nWpEtnwJ9J7ZBg1DS+hsysr+hxSHfNZREYkYaT5K/IZ+srQsoikUByyO42x/yvl6kWwKxPuOQke7wmHNi1/3vwV+Vz+2uXluuRlpmdyVOOjWLU5ZJQ0sL8pUSLt/JHcI8OorsTqs/hERHKs0mzNJZhdv+LD0Te7Dqco6hbBXz6DMR+W0qAQxneHO/vA5gaBrzdmzphK7VQLSwr5cvOXnuQfv2g8J7Q4IaF2fivaZ9R2vORZnAgsFpHVIrJcRFaIyPJEC2Ykj1DVZEe+ObLMTj/0laGhO+Ap/HElfPkE3D8bPmzpVIS97oz9iqLi9fJX5AdNeAuVmFbxuGjzO7wS7B6lSVrMvgvzhRjVAS/K4nQch3M/nFIdZ7p/jRpCqGqy4xeNL8tdCLWi6PU9fPIszHgJttWFMy6vy6yHR/D94cET33wrlXgQbIYfrwdxsNyHEi2JqeGQNTAyqgtBlYWIHOi+3BFkM2oIoarJhpvdt/oVpr8I85+Flr/BZYNg8C0tuPj6Z3nyjCdDJr6FqoEUrGprMALN/OP5IPYl5qVLeqV9sWSuh2pgZBipRFAHt4i8oapnish3gFI+n1ZV9chkCBgN5uCOHK/1j3w02uN0qRv1GRSnwdN/aMRhdz7En467wvM5QtVAGtF9RKU8DoDMtEwKS8v7N4LVpEqEUzqYzNHWhYr3+QwjFqKqDaWqZ7p/W6vqke5f35ayisKIDJ+ZZt22dZVm84Fm93VK4NoFQsFjcNN82HBmbxp8t4Hr3/4tIkUBwf0ALRu1LFuVVOxFUVhaSEZaRsC+ExVJhFM63vkc1sDIqC548VkgIo1FpKeInOTbEi2YkXj8zTTgmJx8CqJSTwaFM1bDyqeEx2cpTXudQtriJfz+1XlwRMByXWEJV8wvNyc3YIvVotKigH0nKpKIB3G8Gw5ZAyOjuuAlg/tKYB5OYtyd7t87EiuWkQwC2csVLTPT+Gb3p+88jNnPwxvT4LAGh8Drr8OcOdClS0zX91LML5bVQSIexPFuOGQNjIzqgpekvBVAD5xs684icgxwr6oOToaA0WA+C2+EtZf/8INTw+m556BJE7jjDrj6asgI2/sqINEkzQXzO6RLOqVaGvY8VpDPMLwTymfhRVksVNUeIrIUp+LrPhH5QlXbJ0LYeGDKwhvBHsTH1GvOl3uvgPvvh+Ji+MtfYMwYOOigqK8VqESIl2ZJXrr0xdx0yTAMIPbmRxtE5CDgVZyaTq8B3sNmjJSloplGSmH4ikwWP7zTWUWccQZ8+aVTSjwGRQHeQ0Qr5kUA5cw00YauWuKbYcSGl+ZH56rqb6p6BzAWeBY4J9GCGdHj9cHoby//w7ew4tkM/v1yIfVbt4WPPoIXXoAj4xP45sX3ECwvAiireBssMTCUD8MS3wwjdkIqCxFJF5GvfO9V9QNVfd1ra1Uj+UTcMS6zO2vf78ic56F9ncNh2jSYPx9OOCHsdSKZqXuJTAq3+gh1jVARTtGuakyZGMZ+QioLt0z4ahGxoO9qgueM4M2b4brroEMHmDsX7rsPvvoKLrywrHR4MKKZqXuJTAq3+hj11qiQ5w9GLKsaUxiG4eDFZ9EY+EJE5ojI674t0YIZ0RH2wbhvHzz4IBx1FKVPPcmU4+pxyNU7aFX3KfLXvOzpGtGUqAgVIuqb0QfL5vatGrbs2RLy/MGIx6rGMGo7XkqUj024FEbcCNaLusWBzeHFF+H//g+++46NvTtxdtfVLGm80zkggmY+0eY+BCohHi7aKR4Janl988I2RrIS5IYRGi8O7g98G/AFMM99baQggcw9J/9Yl4XP14U//hEaNoR33+WEwb+xpPHecsd5nUnHMzM6VDHBiglqaRL45xps3IeXxDcru2EYoQlVdbaXiMwVkZki0kVEVgIrgZ/dXtpGCuL/YGz1K7z2Wn3m/nsfB/+0A555Bj7/HE47LWGZ0ZE6iYNdT5BKpTyCtVgN13oVKvcQr7jCsbIbhhGaUFOyfwH3AtOA94ErVfVQ4CTgH0mQzYiS3BZnsvanC/lufF3O/kph7FhYswauuALSnTyFWGbSwWbqQFgncUVl0iSriWc5WjZqGfDYYOORYGU3DCM0oUqUL1XVzu7rL1X1WL99n6tqbIWBEkitzeAuLoann4bbbnOinS69FPLyoFmzSodGm1EdilClOSafOxmg0jUz0zNRVYpKi8LKEY3MVu7DMLwTbQa3/9p+T4V9YftdishEEfnFNV/5xi4QkS9EpFREulc4frSIFLjtW/v7jQ9wxwpE5G/hrlsrUYVZs6BjRxg5Etq3h0WLYPLkgIoCop9JhzIzBTMp+brJjXprVCX/RGFJIQfWPdCTHJHKbOGwhhE/Qq0sSoBdOE2PsgDf/3IB6qlqyGpybhnzncDzqtrBHTsWRwn9G7hJVRe54+1wzF09gcOB2UBb91RfA6cBG4CFwEWquirUtWvVymL5crjxRpg9G9q0cUpznH122FyJaAg3s4+0gZKPUI1+YlkZJKL5kWHUZKJtfpSuqgeq6gGqWsd97Xsftuyoqs4DtlYY+1JVVwc4fBAwXVX3qep3QAGO4ugJFKjqt27W+HT3WOPHH+HKK6FzZ1iyBMaNg5UrYdCghCgKCJ+LEKxPdTiC+UliXRlYOKxhxA9PzY+SwBHA937vN7hjwcYrISLDRWSRiCzatGlTwgStcnbvhrvvdlYRzz8PN9wABQVOZdjMzHKHxrt8RbiHb6g+1QDZWdkRRRzFmihn4bCGET9SRVnEjKpOUNXuqtr94IMPrmpx4k9pqaMc2rZ1HNgDBjgVYR96CBo3rnR4Iuz1Xh6+uTm5TD53ckClMO70cRH5HGJdGVg4rGHEDy8Z3MlgI9Dc730zd4wQ47WHuXMdv8SSJdCjB0yfDieeGPIjoWbl0UYDecmEhv0Z4MF8DV6vHzQb3ePKIJwchmF4J1WUxevAf0TkYRwHdxtgAY4zvY2ItMZREhcCF1eZlMnm66/hllvgtdegRQvIz3cK/aWFXxB6nZUHciBD4AdsJA/fQKU9IsWrcgpFPOQwDMNDp7yoTywyDegDNAV+Bm7HcXg/DhwM/AYsVdX+7vFjgMuBYuB6VX3LHR8IPAqkAxNVNeyTIpWioaKK5tmyBe66C558ErKyYPRouP5657VHvEQCBYpuiiTvIVa83BvLkzCM5BFTW9XqSKooi4iTyPbtgyeecBzY27fDVVfBnXfCIYcEPHeoh6iXa0cS6hrvcNNEJAUahhEbsbZVNaLEczSPKrz8MrRr5/gmevVy8ifGjw+qKMI5r70ksEUSQhrvcFMrCW4Y1YtU8VnUSDz5DRYscBTERx85jYjefhv69w/4OR9endfh7PXBHMjBjo0nlgNhGNULW1kkkJChpuvXQ24uHHec48ieMMGpCBtGUUD8HrSBQksz0zPJSCufcxmJU9lrboflQBhG9cKURQIJ9DA+pCSLN1Z2dPIlZs6EMWOcpLqrroI63hZ6wSq1BhsPRiBT1cRBE5l0zqSoqq9GktthORCGUb0wB3eC8TmiN25dx81fNWHsnGKytm6HSy6Be++F5s3Dn6QCTe9vGrDFaHZWNptv2RwPsaMi0lpMFulkGKlFKAe3+SwSTG5OLrkbsx2/xKpV0Ls3PPwwdA/47+GJrXu2RjSeLCI1j1kOhGFUH8wMlUhWrHB8EKefDoWFjtnpuLt8VwAACzlJREFUgw9iUhSQuvb+VJXLMIzYMWWRCH76CYYPdyrCLlwIjzwCX3wB554bl4qwqWrvT1W5DMOIHVMW8WT3bqczXZs28NxzTiXYggIn+7pCRdhYSNUWoKkql2EYsWMO7nhQWgr/+Y9TlmPDBhg8GP75TzjqqOTJYBiGESOWwZ1I5s1zciWGDHGyrT/4wMnGNkVhGEYNwpRFtKxZ46wgTj7Z8VFMmeJkY590UlVLZhiGEXdMWUTK1q1Od7r27eG99+Cee2D1aidvwkPp8FDEu7OdYRhGvDBl4ZXCQnj0Uce89NhjMGyYs7oYMwbqR953uiKJ6Gzn9bqmoAzDCIcpi3CowiuvOCuJG25wOtUtXerUcjr00LhdpiqqsFaVgjIMo/phyiIUixY5PonBg6FuXXjrLXjnHcjJifulqqIKq5UJNwzDK6YsAvH99050U48ejj9i/HhnNTFgQMIuGSzLOdLigJFgZcINw/CKKQt/duyAW291KsK++KKTN7FmDVx9teeKsNGS1zevUmlwgB2FOxJmFrLyHIZheMWUhT87djhO7PPOc3pM3HsvHHhgUi6dm5PLgXUrX6uwpDBhZiErz2EYhles6qw/hx8O33wTsJVpMghWNTZRZiFfGQ4rE24YRjgSpixEZCJwJvCLqnZwx5oAM4BWwFrgj6r6q4gIMA4YCOwGhqnqEvczQ4Fb3dPeo6qTEyUzUGWKAoK3OU2kWcjKhBuG4YVEmqGeAyp6hP8GzFHVNsAc9z3A6UAbdxsOPAVlyuV24DigJ3C7iDROoMxVipmFDMNIVRKmLFR1HlDRrjII8K0MJgPn+I0/rw6fAgeJyGFAf+A9Vd2qqr8C71FZAdUYrGqrYRipSrJ9Foeo6o/u658An83nCOB7v+M2uGPBxishIsNxViW0aFF9o3nMLGQYRipSZdFQ6tRGj1t9dFWdoKrdVbX7wQcfHK/TGoZhGCRfWfzsmpdw//7ijm8Emvsd18wdCzZuGIZhJJFkK4vXgaHu66HAa37jl4pDL2Cba656B+gnIo1dx3Y/d8wwDMNIIokMnZ0G9AGaisgGnKim+4AXROQKYB3wR/fwWThhswU4obOXAajqVhG5G1joHneXqgZORjAMwzAShrVVNQzDMABrq2oYhmHEiCkLwzAMIyymLAzDMIywmLIwDMMwwmLKIkash7VhGLUBK1EeA74e1r7WpL4e1oCV7DAMo0ZhK4sYsB7WhmHUFkxZxID1sDYMo7ZgyiIGrIe1YRi1BVMWMWDNigzDqC2YsogBa1ZkGEZtwWpDGYZhGIDVhjIMwzBixJSFYRiGERZTFoZhGEZYTFkYhmEYYTFlYRiGYYSlRkZDicgmnLathkNTYHNVC5FC2P0oj92PytTWe9JSVQ8OtKNGKgujPCKyKFg4XG3E7kd57H5Uxu5JZcwMZRiGYYTFlIVhGIYRFlMWtYMJVS1AimH3ozx2Pypj96QC5rMwDMMwwmIrC8MwDCMspiwMwzCMsJiyqIaISD0RWSAiy0TkCxG50x1vLSKfiUiBiMwQkUx3vK77vsDd38rvXKPd8dUi0r9qvlFshLgfz4nIdyKy1N06u+MiIo+533u5iHT1O9dQEVnjbkOr6jvFAxFJF5HPReQN932t/H34CHA/avXvI2JU1bZqtgECNHRfZwCfAb2AF4AL3fHxwAj39UhgvPv6QmCG+7odsAyoC7QGvgHSq/r7xfF+PAecH+D4gcBb7ud6AZ+5402Ab92/jd3Xjav6+8VwX/4K/Ad4w31fK38fIe5Hrf59RLrZyqIaog473bcZ7qbAH4CX3PHJwDnu60Hue9z9fUVE3PHpqrpPVb8DCoCeSfgKcSXE/QjGIOB593OfAgeJyGFAf+A9Vd2qqr8C7wEDEil7ohCRZsAZwDPue6GW/j6g8v0IQ43/fUSDKYtqirukXgr8gvOj/Qb4TVWL3UM2AEe4r48Avgdw928Dsv3HA3ymWlHxfqjqZ+6uPNeU8IiI1HXHgn3vGnM/gEeBW4BS9302tfj3QeX74aO2/j4ixpRFNUVVS1S1M9AMZ7Z3TBWLVKVUvB8i0gEYjXNfeuCYDv6vCkVMGiJyJvCLqi6uallSgRD3o1b+PqLFlEU1R1V/A/4HHI+zXK7j7moGbHRfbwSaA7j7GwFb/McDfKZa4nc/Bqjqj64pYR8wif0mlGDfu6bcjxOAs0VkLTAdx/w0jtr7+6h0P0Rkai3+fUSFKYtqiIgcLCIHua+zgNOAL3Eekue7hw0FXnNfv+6+x93/vjoeu9eBC91omNZAG2BBcr5F/AhyP75y7cw+e/05wEr3I68Dl7pRL72Abar6I/AO0E9EGotIY6CfO1atUNXRqtpMVVvhOKzfV9VcaunvI8j9uKS2/j6ipU74Q4wU5DBgsoik4yj8F1T1DRFZBUwXkXuAz4Fn3eOfBaaISAGwFec/DKr6hYi8AKwCioE/q2pJkr9LPAh2P94XkYNxolqWAte4x8/CiXgpAHYDlwGo6lYRuRtY6B53l6puTeL3SDT/R+38fQQj334f3rFyH4ZhGEZYzAxlGIZhhMWUhWEYhhEWUxaGYRhGWExZGIZhGGExZWEYhmGExZSFUW0RkZ0V3g8TkX/F6dxzRaS7x2OvEZFLY7xedxF5LJZzRHnd0SJSJCJDKowPcstgLBWRRSJyYrJlM1ILy7MwjBhR1fFxOMciYFEcxPGMqyAGAMcCL4nIj6o62909B3hdVVVEOuJUrK3VJWVqO7ayMGokblb3yyKy0N1OcMd7ish8t6/BJyJytDueJSLTReRLEXkFyApy3vtEZJU7637QHbtDRG4SkcNlf2+EpSJSIiItg8lS4bx9ZH+fhTtEZKK7uvlWRP4SRJYBIrJEnD4ec/w+O1lEPhSRdSIyWETuF5EVIvK2iGS4x50KXAoMVNUCnGzkO0WkE4Cq7tT9SVgNCF3F16gF2MrCqM5kiVNp1kcTnFIN4NRCekRVPxKRFjhlGY4FvgJ6q2qx+8C8FzgPGAHsVtVj3Zn0kooXE5Fs4FzgGHfGfZD/ftX/3969g0YVRGEc/39KsEksxAdaiQ+QINqIBIsV0U4QQgrBykYbH2CjtVoELAVJIRY2QhCMRXxgowRBOzERRRuxsYg2ViYs5ljMXJws697Erdz9ftXufcw9t1jOnhk4E1+BagOdc8DhiPgi6d5fYulkD3AEGAI+SpqIiGYRyybgNtCIiM+SNhT37sz3DgOvgLGIuJyT4HHgYa4gqiqCiJgn9VAq33cUGAc25/usjzlZ2P/sZ+40C6Q1C6BaZzgGDKe2PwCslzRIapJ3V9Ju0r/lgXy+AdwEiIhZSbNtnvcDWADu5Cpgul1QuXI4A1Tz/G1jKfbgaOdRbnC3KGke2EJqiV0ZAWbyPhO0tJ14EhFNSXPAWuBpPj4HbO/wzGUiYgqYktQAruf3sD7lZGG9ag0wEhEL5cG8AP48IkaVtg99sdIBczVyEDhKarh3ntTRtRx/K6nX0okiGbSNpcZi8fkXq/utLuZ4lyQ1i+mkpVWOQx5nRtIOSRsj4vtq77fe4DUL61XPgAvVF+X9lUmVRdVW+nRx/QxwKl+7F9jXOmBVmUTEY+ASsL/l/ABwH7gSEZ9WEEs3XgMNpW6wtExDdU3SLuVSSGkP6nWktuXWp5wsrFddBA7khej3/OkoegMYl/SG5f+yJ4BBSR+Aa0C7jYOGgOk8RfWStKdz6RBpGuxqsci9rUMs/ywivgFngQeS3gKT3Y7ZYgx4l9eEbgEniwrF+pC7zpqZWS1XFmZmVsvJwszMajlZmJlZLScLMzOr5WRhZma1nCzMzKyWk4WZmdX6DQafQuRml23HAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating Root Mean Squares Error\n",
        "\n",
        "rmse = 0\n",
        "for i in range (n):\n",
        "  y_pred = b0 + b2 * a[i]\n",
        "  rmse += (b[i] - y_pred) ** 2\n",
        "rmse = np.sqrt(rmse/n)\n",
        "print(rmse)"
      ],
      "metadata": {
        "id": "q4eJ2qgRUoxl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3d18845-0f5c-465e-9e24-cf08d7f2404f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "72.1206213783709\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split \n",
        " \n",
        "X_train, X_test, y_train, y_test = train_test_split( a, b, test_size=.1, random_state=1)\n",
        "\n",
        "plt.scatter(X_train, y_train, color='blue')\n",
        "plt.scatter(X_test, y_test, color='red')\n",
        "plt.xlabel('Head size (cm^3)')\n",
        "plt.ylabel('Brain weight (grams)');"
      ],
      "metadata": {
        "id": "cGBpzjllx8vm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "9003e235-7687-4b1b-fcbb-8f7a3386a240"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "..........................................................................................................................."
      ],
      "metadata": {
        "id": "BQRHjFow2ePm"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xtYPdXGx2hTv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Polynomial Regression**"
      ],
      "metadata": {
        "id": "wHmisQyhybSQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Importing the dataset\n",
        "dataset = pd.read_csv('https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/position_salaries.csv')\n",
        "X = dataset.iloc[:, 1:2].values\n",
        "y = dataset.iloc[:, 2].values\n",
        "dataset"
      ],
      "metadata": {
        "id": "Kvpzg0-k0VGo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "3bec3722-44bc-4354-9674-5b57152c4fd5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Position  Level   Salary\n",
              "0   Business Analyst      1    45000\n",
              "1  Junior Consultant      2    50000\n",
              "2  Senior Consultant      3    60000\n",
              "3            Manager      4    80000\n",
              "4    Country Manager      5   110000\n",
              "5     Region Manager      6   150000\n",
              "6            Partner      7   200000\n",
              "7     Senior Partner      8   300000\n",
              "8            C-level      9   500000\n",
              "9                CEO     10  1000000"
            ],
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              "      <td>C-level</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>CEO</td>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae33bdbf-32d8-4fbe-825c-b915a3e108a0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae33bdbf-32d8-4fbe-825c-b915a3e108a0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "from sklearn.model_selection import train_test_split \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "GoGc8D811Mqv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting Polynomial Regression to the dataset\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree=4)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "pol_reg = LinearRegression()\n",
        "pol_reg.fit(X_poly, y)\n",
        "\n",
        "# Visualizing the Polymonial Regression results\n",
        "def viz_polymonial():\n",
        "    plt.scatter(X, y, color='red')\n",
        "    plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue')\n",
        "    plt.title('Truth or Bluff (Polynomial Regression)')\n",
        "    plt.xlabel('Position level')\n",
        "    plt.ylabel('Salary')\n",
        "    plt.show()\n",
        "    return\n",
        "viz_polymonial()"
      ],
      "metadata": {
        "id": "SdKiG98T1Ssl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "5eb14608-a116-42f2-9aab-20b2746910e5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting a new result with Polymonial Regression\n",
        "pol_reg.predict(poly_reg.fit_transform([[5.5]]))\n"
      ],
      "metadata": {
        "id": "XmM6oWoE1fPY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fd93cdb-9787-4016-8dd8-c9129b891711"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([132148.43750002])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    }
  ]
}