polynomial_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": " polynomial_regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vN99YjPTDena"
      },
      "source": [
        "# Polynomial Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIx_naXnDyHd"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgABwayOe8Wn"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c8YExmOD5x5"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tkdpl_IfFVz"
      },
      "source": [
        "dataset = pd.read_csv(\"Position_Salaries.csv\")\n",
        "x = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Le8SEL-YEOLb"
      },
      "source": [
        "## Training the Linear Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OvHq7CI-f7Or",
        "outputId": "7f8fbc46-c320-4737-e812-68a207d5ee41"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "linear_regressor = LinearRegression()\n",
        "linear_regressor.fit(x, y)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rb5nWuSHEfBV"
      },
      "source": [
        "## Training the Polynomial Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TerkMBZwipA5",
        "outputId": "32fb01d5-08d9-4fd5-f86b-3f19298c906f"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_r = PolynomialFeatures(degree = 4)\n",
        "x_poly = poly_r.fit_transform(x)\n",
        "lin_reg2 = LinearRegression()\n",
        "lin_reg2.fit(x_poly, y)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0O8R0tzbEpvy"
      },
      "source": [
        "## Visualising the Linear Regression results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "U2mnouHykN9i",
        "outputId": "b9f3f119-56fe-42c3-b8cc-73fd761f1579"
      },
      "source": [
        "plt.scatter(x, y, color = \"red\")\n",
        "plt.plot(x, linear_regressor.predict(x), color = \"blue\")\n",
        "plt.title('position level vs salary')\n",
        "plt.xlabel('position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XcSIwMyNV/pwwId39O3Nmqvz5zW/mX/mzGjgRmFmpvfBC6u657rpUB6irK39Wgw6NcWeTzJiZ1YUVK+Cee+Cww6B//9QNNGAA/OEPMGMGfPGLWRKYODE1B7p1S48TJxYceT462iJ4TtIdwPWVM4yZmdWSN9+E8eNTC+C559qp/DlxIgwdCkuXptdNTek1wJAhXRl27jp61eungWeBcZIekzRU0mY5xmVm1mlmz06XevbqBV/7Gmy5ZfqenzMHvv/9Nso/jxixMgk0W7o0La8zHUoEEfH3iLgmIvYCvg18D1gg6QZJH8s1QjOztbB8ebrq58ADYccdU7//scfC1Knw6KMweHA75Z/nzFmz5TWsQ11D2RjBF4DTgAbgJ8BE4N+AKWRTTpqZFa1l5c8+feCSS9ai8mffvmkHrS2vMx0eIwAeAH4UEY9ULL9d0uc7PywzszXz+OPp0s/myp/77QeXXw5HHLGWlT8vvnjVMQJIFeUuvrjTYq4W7X48WWtgfERc2Nr6iPhKp0dlZtYBbVX+HDYMdtppHXfePCA8YkTqDurbNyWBOhsohg5OTCPpfyNi9y6Ip12emMbMWlb+/OhH02DwaafBFlsUHV116oyJaR6WNAq4FfhH88KImNEJ8ZmZtautyp/jxqXHWq38WQ06mgh2zh4ru4cC2L9zwzEzW1XLyp+bbZamfTznnHQzmK27DiWCiNgv70DMzCq1rPy5ww7pSqCTT4ZNNik6uvrS4bF0SV8AdgQ2bF7W1gCymdnaaK3y5xFHpMqf++1Xn5U/q0FH7yO4CugB7AeMA44F/jfHuMysRFqr/Pmtb6XKn/36FR1d/etoi2CviPhXSU9GxPcl/QS4O8/AzKz+taz8ucsu6fmgQeWp/FkNOpoI3soel0raFlgMfDifkMysnq1YAb/9ber+uftu6N49lX4YPhw++1l3/xSho4ngLklbAD8CZpCuGBqXW1RmVndaVv7cZhs4//xU+fPDPq0sVEevGvpB9vQOSXcBG0bEm/mFZWb1Yvbs1Pd/441pLOCzn00VP485pp2ib9ZlVpsIJB29mnVExJ2dH5KZ1brly+HXv07dP/ffDxtskGb8GjYMdtut6OispfZaBIevZl0Aq00EkgYAVwDdgXERcWkb2x0D3A58JiJcP8KsRi1enO70HTMmlefp0wd++MNU+bNnz6Kjs7asNhFExGlru+OsWN1o4CBgHjBV0uSWM5xJ2hT4KvDHtT2WmRXr8cfT2f/NN6+s/Pmzn8Hhh69l5U/rUnneULY78HxEvJi9/xZgINByqssfAJcB3+poLGZWvPfegzvuSAngkUdS5c9TT03dPzvuWHR0tiY6Onn9VcAJwHBAwHFAe7d59ALmVryely2r3O+uQJ+I+E07xx8qaZqkaQsXLuxIyGaWk1dfTYO9/fqlfv/XXoOf/hTmz08lIZwEak9hN5RJ6gZcDpza3rYRMRYYC6kM9boc18zWXAQ89lg6+7/99tQaOOSQNBPYv/+7K3/Wuo4mgrezx+Ybyl6n/RvK5gN9Kl73zpY12xTYCXhQ6Q6SbYDJko7wgLFZdXj7bbjllnT5Z3Plz3POSdU/XfmzfnQ0Efy6lRvKrmnnPVOB/pK2IyWAQcDg5pXZfQjvX0cg6UHgm04CZsWbM2dl5c/Fi1PlzyuvhJNOcuXPetTRRPBnYHlE3CFpB2BX4Jere0NELJM0DLiXdPnodRExS9KFwLSImLwugZtZ54qABx9MZ//NlT8HDkylH/bd16Uf6llHE8F3I+I2SZ8jTUbzY+BKYI/VvSkipgBTWiw7v41t9+1gLGbWiZYsWVn5c9Ys2HJLV/4sm44mguXZ4xeAayLiN5IuyikmM+sCzz+f6v5cf32qA7Trrun5CSe48mfZdDQRzJd0NenmsMskbUAHLz01s+qxYgXce286+2+u/Hnccenaf1f+LK+OJoLjgQHAjyPiDUkfxjeAmdWMN99MZ/ujR6eWwDbbwPe+B0OHuvKndbz66FIq6gpFxAJgQV5BmVnnmDUrnf3fdFOq/LnXXnDhha78aatyFRCzOrNsWar8OWrUysqfgwen7p9ddy06OqtGTgRmdWLRolT588orXfnT1owTgVmNmzEjnf1PmgTvvOPKn7bm/M/ErAa9+26q/Dlq1MrKn6ed5sqftnZ8CahZDVmwAC64IN3oNXhwHVf+nDgRGhpSNbuGhvTacuMWgVmVi4BHH01n/7fdlgaDDzkklX6oy8qfEyem61qXLk2vm5rSa4AhQ4qLq44poraqOjc2Nsa0aa5LZ/XvrbdWVv6cMSNV/vziF1P1z499rOjoctTQkL78W+rXD15+uaujqRuSpkdEY2vr3CIwqzJNTambZ9y4VPlzxx1LVvlzzpw1W27rzInArApEwAMPpLP/X/0qLStt5c++fVtvEfTt2/WxlES99S6a1ZQlS9LZ/k47wQEHwEMPwX/+J7z4Itx5Z7oUtFRJAODii9NlUJV69EjLLRduEZgV4LnnYMwYV/5sVfOA8IgRqTuob9+UBDxQnBsnArMusmIF3HPPysqf662XKn8OHw577lnCM//VGTLEX/xdyInALGdvvAHjx69a+fOCC1z506qHE4FZTlqr/PmDH8DRR7vyp1UXJwKzTtRc+XPkyHQVkCt/Wi1wIjDrBC0rf/btC5deCqef7sqfVv2cCMzWwYwZ6ez/5ptT5c/993flT6s9/qdqtoaaK3+OHJlqAG288crSD3VT9M1KxYnArIMWLICrr04/r76a6v387GdwyimwxRZFR2e29pwIzFajtcqfhx6aBn/rsvKnlZITgVkrWlb+3HzzdOPX2WfXeeVPKyUnArMKc+akK3+uuWZl5c+rrko3uZai8qeVUq4NW0kDJD0j6XlJ57Wy/lxJsyU9Kek+Sf3yjMesNc2VP48+GrbbDv77v2GffeD+++Gpp+DLXy5REvDMYKWUW4tAUndgNHAQMA+YKmlyRMyu2OxxoDEilko6C/hv4IS8YjKrtGQJTJiQun9mzYItt4RvfxvOPLOkFY89M1hp5dki2B14PiJejIh3gVuAgZUbRMQDEZH9q+MxoHeO8ZgBqfLn178OvXvDWWelu3+vvx7mzYNLLilpEoBU7bM5CTRbujQtt7qW5xhBL2Buxet5wB6r2f504O7WVkgaCgwF6Fva/6W2LlpW/lx//VT5c9gwV/58n2cGK62qGCyWdBLQCOzT2vqIGAuMhTRncReGZjXOlT/XgGcGK608u4bmA30qXvfOlq1C0oHACOCIiHgnx3isRGbOTN0+vXunbqCtt05lIJqa4HvfcxJolWcGK608WwRTgf6StiMlgEHA4MoNJO0CXA0MiIjXcozFSmDZMpg8OXX/uPLnWvDMYKWVWyKIiGWShgH3At2B6yJilqQLgWkRMRn4EbAJcJtSJ+2ciDgir5isPrnyZyfyzGCllOsYQURMAaa0WHZ+xfMD8zy+1bfp09PZf2XlzyuuSJU/u3cvOjqz2uFKKVZT3n0XJk1Ks301Nqb6P1/8YroP4L774MgjazQJ+EYuK1BVXDVk1p5XXoGxY/+58uepp6Y6QDXNN3JZwRRRW1djNjY2xrRp04oOw7pABDzySOr+uf12WL4cDjkkFX87+OA6qvzZ0ND6ZZv9+sHLL3d1NFanJE2PiMbW1rlFYFWnufLnyJHw+OMlqPzpG7msYE4EVjWamtKVP+PGlazyp2/ksoLVS+PaalREGuQ96ij4yEfgRz8qYeVP38hlBXOLwAqxZAncdFPq/589u+SVP30jlxXMLQLrUs89B1/7GvTqlfr8N9ywCip/VsOlm0OGpIHhFSvSo5OAdSG3CCx3zZU/R45Mj1VV+dOXbpr58lHLzxtvpLP90aPhhRdSobczz4Qzzqiiom++dNNKwpePWpeaOTP1/d90UzrR3ntvuOiiNBXkBz5QdHQt+NJNMycC6xzNlT9HjoQHH0x9/4MHwznnVHnlT1+6aebBYls3ixbBD3+YLv085hh46SW47LI0+Hvtte0kgWoYpPWlm2ZuEdjamT49nf3fckuq/HnAAfDzn69B5c9qGaT1pZtmHiy2jnv33VTzZ9QoePRR2HhjOOWU1P2zww5ruDMP0pp1KQ8W2zp55ZVU9fPqq+Evf4H+/Tuh8qcHac2qhscIyqgDffMR8PDDcOKJ6ST9Bz9I9f/vvhv+/Gf46lfXsfxzW4OxHqQ163JOBGXT3Dff1JS+7Zv75rNk8NZb6dr/3XaDz30uffEPHw7PPgt33QUDBnRS+WcP0ppVDSeCrlQNV8mMGLFygLbZ0qU0fXsM550HffqkGb/eey9V/pw/Hy6/PIfyz0OGpJlm+vVLtxb365dee5DWrOtFRE397LbbbrHGJkyI6NcvQkqPEyas+T7W1YQJET16RKTz8PTTo0fXxyK9f/wVEPexXxzJndGNZdGtW8TRR0c88EDEihVdG5aZ5QuYFm18r9b/VUMtL1OE1AXR1Wef1XKVTEMDS5oWcRMnM4phzGZHerKQMzb7BWc+dY676M3q1OquGqr/rqE2ukIYMaJr46iCq2Seew6+9ol76MV8zuZKNuItxnMKczf6OJeM2cJJwKyk6j8RVMEXMFDYVTIrVsCUKWmu3+23hzH3f4LD9nqdR7c5iqnszin9fs+G14x037xZidX/fQTVUkvm4otb76LK6SqZ1ip/fv/7KYRtttkO+J9cjmtmtaf+WwTVcpliF10lM3NmKvXcqxecey5ss00qA/Hyy3D++em1mVml+m8RVFMtmSFDcjluW5U/hw2DXXbp9MOZWZ3JNRFIGgBcAXQHxkXEpS3WbwDcCOwGLAZOiIiXOz2QnL6Ai7ZoEVxzDVx5JcydmxoZl10Gp5+e5gA2M+uI3BKBpO7AaOAgYB4wVdLkiJhdsdnpwF8j4mOSBgGXASfkFVO9aK3y58iRcNhhHaz8aWZWIc8Wwe7A8xHxIoCkW4CBQGUiGAhckD2/HRglSVFrNzd0gebKnyNHwmOPpcqfp5++lpU/zcwq5JkIegFzK17PA/Zoa5uIWCbpTWBLYFGOcdWU1ip/XnFFKv+8TkXfzMwyNTFYLGkoMBSgbwnueoqARx5JZ/933AHLl8Ohh6bB34MP7qSib2ZmmTwTwXygT8Xr3tmy1raZJ2k9YHPSoPEqImIsMBZSiYlcoq0Cb70FN9+cJn55/PF0xv+Vr8BZZ+VQ9M3MLJNnIpgK9Je0HekLfxAwuMU2k4FTgEeBY4H7yzg+0NQEY8bAuHHw+uuw006p8udJJ6WxADOzPOWWCLI+/2HAvaTLR6+LiFmSLiRVwZsMXAvcJOl54HVSsiiFCLj//tT98+tfp3vMjjwydf/ss096bWbWFXIdI4iIKcCUFsvOr3j+NnBcnjFUmyVL4MYbU/fP009Dz57w7W+n7p8+fdp/v5lZZ6uJweJ68Oyzqe7P+PHwt7+lGcDGj4cTTkh3ApuZFcWJIEcrVqSpHkeNgnvugfXXh+OOS1M/7rGHu3/MrDo4EeRg9ZU/i47OzGxVTgSdaObMdPZ/002p2vTee6f6dkcfnVoDZmbVyIlgHS1bBr/6VUoArvxpZrXIiWAtLVyYrvt35U8zq3VOBGto2rR09u/Kn2ZWL5wIOqCtyp/DhsEnP1l0dGZm68aJYDVc+dPMysCJoIW2Kn8OHw4HHeTKn2ZWf5wIMs2VP0eOhCeegC22SJU/zz4bPvrRoqMzM8tP6RNBa5U/r746TXHsyp9mVgalTARtVf4cPhw+/3mXfjCzcilVImit8ud558GZZ7ryp5mVV2kSwbXXwrnnpsqfjY1www1w/PGu/GlmVppE0K8fHH546v7ZfXd3/5iZNStNIjjwwPRjZmar8lXxZmYl50RgZlZyTgRmZiXnRGBmVnJOBGZmJedEYGZWck4EZmYl50RgZlZyioiiY1gjkhYCTUXHsY56AouKDqKK+PNYyZ/Fqvx5rGpdPo9+EbFVaytqLhHUA0nTIqKx6DiqhT+PlfxZrMqfx6ry+jzcNWRmVnJOBGZmJedEUIyxRQdQZfx5rOTPYlX+PFaVy+fhMQIzs5Jzi8DMrOScCMzMSs6JoAtJ6iPpAUmzJc2S9NWiYyqapO6SHpd0V9GxFE3SFrtZeTgAAATiSURBVJJul/RnSU9L+mzRMRVJ0tez/yczJd0sqTQTy0q6TtJrkmZWLPuQpN9Jei57/GBnHc+JoGstA74RETsAewLnSNqh4JiK9lXg6aKDqBJXAPdExCeAT1Piz0VSL+ArQGNE7AR0BwYVG1WXGg8MaLHsPOC+iOgP3Je97hROBF0oIhZExIzs+d9J/9F7FRtVcST1Br4AjCs6lqJJ2hz4PHAtQES8GxFvFBtV4dYDNpK0HtADeKXgeLpMRDwEvN5i8UDghuz5DcCRnXU8J4KCSGoAdgH+WGwkhfoZ8J/AiqIDqQLbAQuB67OusnGSNi46qKJExHzgx8AcYAHwZkT8ttioCrd1RCzInr8KbN1ZO3YiKICkTYA7gK9FxN+KjqcIkg4DXouI6UXHUiXWA3YFroyIXYB/0IlN/1qT9X8PJCXIbYGNJZ1UbFTVI9J1/5127b8TQReTtD4pCUyMiDuLjqdAewNHSHoZuAXYX9KEYkMq1DxgXkQ0txBvJyWGsjoQeCkiFkbEe8CdwF4Fx1S0v0j6MED2+Fpn7diJoAtJEqkP+OmIuLzoeIoUEd+JiN4R0UAaBLw/Ikp7xhcRrwJzJX08W3QAMLvAkIo2B9hTUo/s/80BlHjwPDMZOCV7fgrwq87asRNB19obOJl09vtE9nNo0UFZ1RgOTJT0JLAzcEnB8RQmaxndDswAniJ9V5Wm3ISkm4FHgY9LmifpdOBS4CBJz5FaTJd22vFcYsLMrNzcIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwKzCpLOlPQf2fNTJW1bsW5cZxQJzPY7al33k/c+rTzWKzoAs2oSEVdVvDwVmElW7CwivlRETGZ5c4vA6oKkhqyO/8Sslv/tknpk6w7ICrk9ldV53yBbfmk2N8STkn6cLbtA0jclHQs0km7wekLSRpIelNSYbXditr+Zki6riGOJpIsl/UnSY5JWWxhM0laS7pA0NfvZW1I3SS9L2qJiu+ckbd3a9p3/aVrZOBFYPfk4MCYiPgn8DTg7m8xkPHBCRHyK1Ao+S9KWwFHAjhHxr8BFlTuKiNuBacCQiNg5It5qXpd1F10G7E+6A/gzkppLAm8MPBYRnwYeAs5oJ+YrgJ9GxGeAY4BxEbGCVD7gqOx4ewBNEfGX1rZf0w/JrCUnAqsncyPi4ez5BOBzpOTwUkQ8my2/gVT3/03gbeBaSUcDS9fgOJ8BHswKoi0DJmb7BHgXaJ5tbTrQ0M6+DgRGSXqCVEtms6w67a3ACdk2g7LXq9vebK15jMDqSct6KW3WT4mIZZJ2JxUzOxYYRjrDX1fvxcq6Lctp//9YN2DPiHi7cqGkR4GPSdqKNAHJRe1sv86BW3m5RWD1pG/FPL+DgT8AzwANkj6WLT8Z+H12Fr15REwBvk6aGrKlvwObtrL8f4F9JPWU1B04Efj9Wsb8W1KxOQAk7Qzv15v/H+ByUrXaxavb3mxdOBFYPXmGNA/008AHSZO8vA2cBtwm6SnSbGhXkb7g78oqff4BOLeV/Y0HrmoeLG5emM0SdR7wAPAnYHpErG1J4K8AjdmA9WzgzIp1twInsbJbqL3tzdaKq49aXcim/rwrm+jczNaAWwRmZiXnFoGZWcm5RWBmVnJOBGZmJedEYGZWck4EZmYl50RgZlZy/x/TsEJbAp6HEwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stOnSo74E52m"
      },
      "source": [
        "## Visualising the Polynomial Regression results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "b3yaFlqFmNQp",
        "outputId": "d472f317-9dd8-4a73-c906-a4366b83149b"
      },
      "source": [
        "plt.scatter(x, y, color = \"red\")\n",
        "plt.plot(x, lin_reg2.predict(x_poly), color = \"blue\")\n",
        "plt.title('position level vs salary')\n",
        "plt.xlabel('position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aOjXdMdQWu4XAicDMbKvV1ECPHnDSSUVHsmWcCMzMtsLKlTB5Mpx6KnTtWnQ0W8aJwMxsK9x6a5pfqK12C4ETgZnZVhk/HnbbDQ4/vOhItpwTgZnZFnrhBbj9djj9dOjYsehotpwTgZnZFpo0CdaubdvdQuBEYGa2xWpq4IADYNCgoiPZOk4EZmZb4Nln4R//aPutAXAiMDPbIrW1aarp008vOpKtl2sikDRM0tOSZks6byNlRkiaKelJSdflGY+ZWTlEpG6hI4+E3r2LjmbrdcrrwJI6ApcAHwQWAg9LmhIRM0vK7A18A3h/RLwk6R15xWNmVi4PPQTPPAPf+EbRkZRHni2CQ4DZETEnIlYDE4HhjcqcBVwSES8BRMSSHOMxMyuLmhro0gVOOaXoSMojz0SwG7Cg5P3CbFupfYB9JP1N0jRJw5o6kKQxkuok1S1dujSncM3MmrdmDUycmOYV2n77oqMpj6IvFncC9gaOAE4DrpC0Q+NCETEuIgZHxOBebXGOVzNrN+68Mw0kaw93C9XLMxEsAvqUvO+dbSu1EJgSEWsiYi7wL1JiMDOrSDU1sNNOae2B9iLPRPAwsLek/pK2AUYCUxqV+SOpNYCknqSuojk5xmRmtsVefRX++EcYORI6dy46mvJpUSLI7gDaLBGxFjgHuAOYBUyKiCcl/UBS/azddwDLJM0E7gW+GhHLNvdcZmat4aabYNWq9tUtBKCIaL6QNAe4Abi69PbPIgwePDjq6uqKDMHMqtQxx8C8eenWUanoaDaPpOkRMbipfS3tGno3qf/+yuzunjGStitbhGZmFW7RIrjnntQaaGtJoDktSgQRsTwiroiIQ4GvA98FFku6VtJeuUZoZlYBJkxII4pHjSo6kvJr8TUCSSdJugn4FfB/wB7ALcBtOcZnZlYRampgyBDYux3e19jSKSaeIV3M/VlE/L1k+2RJh5U/LDOzyvH44/DYY3DxxUVHko9mE0F2x9A1EfGDpvZHxBfKHpWZWQWprYVOnWDEiKIjyUezXUMRsQ44oRViMTOrOOvXp0QwbBi014kNWto19DdJFwPXA6/Xb4yIR3KJysysQkydCgsXws9/XnQk+WlpIjgwey7tHgrgqPKGY2ZWWWpqoEcPOPHEoiPJT4sSQUQcmXcgZmaV5pln0m2jI0dCt25FR5OfFi9MI+nDwLuALvXbNnYB2cysrVu7Fs48M6078MMfFh1NvlqUCCRdBnQDjgSuBE4FHsoxLjOzQv34x/Dgg3D99bDrrkVHk6+WTjFxaER8AngpIr4PvI80U6iZWbtTVwff/z6MOnQuI77WDzp0gH790u1D7VBLu4ZWZs8rJO0KLAN2ySckM7PirFiRuoR22f51Lv7n+2Hl4rRj/nwYMya9bmfzTLS0RXBrtnLYz4BHgHnAhLyCMjMrynnnwVNPwTWdzmKH+iRQb8UKOP/8YgLLUYumoW7wA9K2QJeIeCWfkDbN01CbWV7uugs+9CE491z45UUd0ixzjUlplFkbs6lpqDfZNSTplE3sIyJu3NrgzMwqwYsvwujRMGAA/OhHwE19U3dQY337tnZouWvuGsGmhlAE4ERgZu3C2WfDkiVwyy3QtSswdmy6JrBixYZC3bql7e3MJhNBRHyqtQIxMyvKhAkwcWKq4w8+ONtYf0H4/PPhuedSS2Ds2HZ3oRg24xpBpQwo8zUCMyunhQvhgANSl9D996dZRtujrV6qMhtQ9nHg84CAjwG7ly1CM7MCrF+frgusWQO//337TQLN8YAyM6taF18Md98Nv/wl7FXFi+62NBGsyp7rB5StxQPKzKwNmzULvv51OOEE+Mxnio6mWC1tCN3SaEBZAFfkFpWZWY5Wr4YzzoDu3eGKK9LQgGrW0kTwFLAuIm6QNBA4GPhjfmGZmeXnggvgkUfgxhvhne8sOpritbRr6NsRsVzSB0iL0VwJ/Da/sMzM8jFtWhowNno0nHxy0dFUhpYmgnXZ84eBKyLiT8A2+YRkZpaP115LE8r16QMXXVR0NJWjpV1DiyRdDnwQuDCbb6ilScTMrCJ85Svw7LNw332w3XZFR1M5WlqZjwDuAI6NiJeBHYGv5haVmVmZ3XYbXH55SgaHHVZ0NJVls2cfLZpHFpvZ5nrhBdh/f3jHO+Dhh2HbbYuOqPVt8eyjZmZtXQT8z//ASy/BnXdWZxJojhOBmbVr48en20R/+lMYNKjoaCqTL/iaWbs1bx6cc066JvClLxUdTeXKNRFIGibpaUmzJZ23iXIflRSSmuy/MjPbXOvWwSc/mV5fey107FhsPJUst0QgqSNwCXAcMBA4LRuV3LhcD+CLwIN5xWJm1eeXv0zTSv/619CvX9HRVLY8WwSHALMjYk5ErAYmAsObKHcBcCEbJrYzM9sqjz+e1pP5yEc2tAps4/JMBLsBC0reL8y2vUnSwUCfbKTyRkkaI6lOUt3SpUvLH6mZtRtvvJEmlNthBxg3zhPKtURhF4sldQB+AXy5ubIRMS4iBkfE4F69euUfnJm1Wd/5DsyYAb/7Hbi6aJk8E8EioE/J+97Ztno9gP2B+yTNA4YCU3zB2My21AMPwM9+BmedldYZsJbJMxE8DOwtqb+kbYCRwJT6nRHxSkT0jIh+EdEPmAacFBEeNmxmm+3VV+ETn4D+/eEXvyg6mrYltwFlEbFW0jmkOYo6AldFxJOSfgDURcSUTR/BzKzlzj0XnnsutQq6dy86mrYl15HFEXEbcFujbd/ZSNkj8ozFzNqvP/4Rrr4avvlNOPTQoqNpezyy2MzatP/8J10TOOgg+O53i46mbXIiMLM2KyItPL98OdTUwDZeLmuLeNI5M2uzfvc7uPXWNIp44FvmLbCWcovAzNqkZ59NF4iPOgq+8IWio2nbnAjMrM1Zty7dKtqpE1xzDXRwTbZV3DVkZm3OT38Kf/97ui7Qp0/z5W3TnEfNrE355z/TNBIjRsDppxcdTfvgRGBmbcaqVWlCuV694Le/9YRy5eKuITNrM775TZg5E/78Z9hxx6KjaT/cIjCzNuHuu9NtomefDcceW3Q07YsTgZlVvJdfhtGjYZ990oViKy8nAjOrPLW1aX3JDh2gXz8+f8JcFi+G8eOhW7eig2t/fI3AzCpLbS2MGQMrVgAwaf4h1Mzvz/dOmcEhhwwqOLj2yS0CM6ss55//ZhJ4nl34LJfxXh7im3WnFBxY++VEYGaV5bnnAAjgv7mKVXRhPGfSecGcYuNqx5wIzKyy9O1LABfyde5gGD/jq+zLv6Bv36Ija7d8jcDMKsrcL/6K//lKD+5afzTD+SP/y6XpCvHYsUWH1m65RWBmFWHt2rTW8P7f+gj/2OYwLt7x29zIR9Huu8O4cTBqVNEhtltuEZhZ4R59NC0wM306nHACXHppZ/r0uQC4oOjQqoJbBGZWmJUr4RvfgMGDYcECuP56mDLFM4q2NrcIzKwQ996bhgvMng2f+hT8/OeeP6gobhGYWat66aW02PxRR8H69fCXv8BVVzkJFMmJwMxaRQRMngwDBsDVV8PXvgaPPw5HH110ZOauITPL3aJFadbQm2+Ggw6C226Dgw8uOiqr5xaBmeVm/Xq47DIYOBDuvDPNHPrQQ04ClcYtAjPLxVNPpWsBf/1r6v65/HLYc8+io7KmuEVgZmW1ejVccAG8+93w5JPpesBddzkJVDK3CMysbKZNSwPDnnwSRo6EX/0Kdt656KisOW4RmNlWW74cvvhFOPRQeOUVuOUWmDDBSaCtcIvAzLbKbbfB5z6XRgaffTb86EfQo0fRUdnmcIvAzLbIkiVw+unw4Q9D9+7wt7/Bb37jJNAW5ZoIJA2T9LSk2ZLOa2L/lyTNlDRD0t2Sds8zHjPbehFw7bVpYNgNN8D3vw+PPALve1/RkdmWyi0RSOoIXAIcBwwETpM0sFGxfwKDI2IQMBn4aV7xmFkLNFo0ntraBrvnzIEPfQhGj06J4NFH4TvfgW23LSJYK5c8WwSHALMjYk5ErAYmAsNLC0TEvRGxIns7DeidYzxmtin1i8bPn5++9s+fn97X1rJ2Lfzf/8H++8ODD8Kll8L996dkYG1fnolgN2BByfuF2baN+TRwe47xmNmmlCwa/6YVK3j0q7UMHQpf+QoccwzMnJkuDnfwFcZ2oyL+lJLOAAYDP9vI/jGS6iTVLV26tHWDM6sW2aLx9VbShfP4MYMXT2HhQpg0Kc0V1Nvt9nYnz0SwCChdXqJ3tq0BSccA5wMnRcQbTR0oIsZFxOCIGNyrV69cgjWreiWLw9/LEQxiBhdyHqO7T2bWLPjYx0AqMD7LTZ6J4GFgb0n9JW0DjASmlBaQdBBwOSkJLMkxFjPbhNdegzs+fhXf7PRT3s9fOYp7CcTd2x7PlZet4+1vLzpCy1NuA8oiYq2kc4A7gI7AVRHxpKQfAHURMYXUFdQd+IPSV43nIuKkvGIys+SVV9JkcFOnpsf06bBu3VF06ngEg7d5lAtWf5sv95lE1x9/x4vGVwFFRNExbJbBgwdHXV1d0WGYtSkvvggPPLCh4n/00TRFdOfOMGQIHH54erzvfWlwmLU/kqZHxOCm9nmKCbN2aMmSdHtnfcX/+ONpe5cuMHQofPvbqeIfOhS6di02ViueE4FZO7B48YZKf+pUmDUrbe/WLU0EN2JEqvgPOcSDv+ytnAjMKkFtbbqP/7nn0t07Y8dusm9+wYKGFf8zz6Tt3bvDBz4An/hEqvjf8x7YZptW+h2szXIiMCta/Yje+sFc9SN6AUaNIgLmzm1Y8c+bl3Zvvz3813+l4ocfntYD7uT/1baZ/E/GrGiNRvQG8MyK3Zj6hceZenuq+BcuTPt22gkOOwzOPTdV/AccAB07FhO2tR9OBGYFWb8+9e3Pnd+HORzGXPozk4E8wH+xmF3hRXjHXRvu6Dn88LQIvKd2sHJzIjDL0csvpxk7585Nj9LX8+bBG28APPBm+b7M5wju43Cmcvius9l34d0ezWu5cyIw2wqrVqUu/Y1V9i+/3LD8DjvAHnukWTxPPDG97j/3Hvr/5kvsvuopupDNstKtG/x0HDgJWCtwIjDbhPXrYdGipiv5OXPg+ecblt922zSN/x57pMFZ/ftnlX3/9Nhhh6bOchS8+6ubddeQWTl5ZLFVvddfh6efhmeffWuFP38+rF69oayUZt+sr9hLK/k99oB3vtN9+FaZPLLYjDS/zqxZaT790sf8+Q3L7dj9DfbYb1sOPBBOPrlhZd+3rwdkWfvjRGDtzrJlb63sZ85s2I3TpQvstx8cuus8PvP8tQxYM4O9mE1/5rLd+nVw7jh3zVjVcCKwNikC/vOfhhV9/bf9JSUTmr/tbemWy2OOSc/1j379svvv+x0Baxo1CVaQ+uudCKxKOBFYRYtIg6kaV/YzZ8JLL20ot/32qYI/8cSGFX7v3s302TdalavZ7WbtkBOBVYT169N99Y378GfNguXLN5Tr1SstmP7xjzes8N/5zi1cPatv37deJKjfblYlnAis1a1ZA489Bg8+mB5PPAFPPQUrV24os8suqYIfPXpDZT9gQEoEZTV2bMN5fiDdwz92bJlPZFa5nAgsV/VdO9OmpceDD6bVsFatSvvf2XEJB66bzpE9FjLwMwcw8L+HMmDAxu63z0H9dQDfw29VzOMIrKxefx3q6lKFX1/5L16c9nXpAgcfnBZDGbrmAYZc8Rn6rPrXhsGz3brBON+tY5YHjyOwXKxfnwZilVb6TzwB69al/XvtBUcfnSr+IUNg0KCSufH7nQmrGt+ts8J365gVwInAWmzZsoaV/kMPpUFakO7aGTIETjopVfyHHAI9e27iYL5bx6xiOBFUoxashrV6NcyYsaFff9o0mD077evQIX27Hzlyw7f9fffdzKkVfLeOWcVwIqg2TayGFWeNYcEL3Zi2y8lvVvrTp9dPkZzu4Bk6FM46Kz2/5z1poNZW8d06ZhXDiaA1bea6tHmIb57P4hXbM5P3MZ33MI2hTFs5lH+fuwuQLui+5z1wzjnpm/7QoWlQVtnnxPfdOmYVozruGqqtZf7XL+X5RUH/3mvY+cfnojNaucJp/E0ccr1LZv361PNSOkBr1iyYOe0VXmX7N8vtzb8YyjSG8BBD6wVuRbkAAAhFSURBVC5m0CDo3Lns4ZhZwTZ111D7TwRZBXzhinM4jwsB6MoK+u+2mv4H7vCWaYT794cePXIIvF+/pvvEd999w0rkW2DNmjR9cn2FX//ceIDWzjtnA7PqrmXA8ocYyEwGMYOdeLEscZhZZavu20ezhcHPZDwH8Dhz6c8c9mDui+9i7qJhPPAAvPpqwx/p2fOtyaH+uW/fLfzGvJV3yaxaBf/6V8PKftastG3Nmg3l+vZNFf4RR2wYjTtgAOy4Y1agthOMucZ982b2pvafCLKKdlcWsyuLN2xfJfjneiLS5GVNLTX4yCNw000NK9oOHaBPn40vTLLzzhvpT2/hXTLLl6dv840r/DlzUndPfQx77pkq+BNPTM8DB6Zplbt3b+bzcN+8mTXS/ruGtrJLZt26NI99U4lizpwNo2brde26ITE0aFHM+hP9L/hveqxMcyS/yNuZue3BzDrjh8zsMfTNSn/Bgg3H6tw53ZZZX9HXP++9d7qoa2bWUr5GkONF2pUrGy5e3jhhNO522qnDi3Rcv4Yl7NwgnP32a1jZDxiQvvV3av9tNjNrBdV9jSDnrpCuXVMlvt9+b91X3+3UsBWxI2vXNqzw+/b1OrdmVpz23yIwM7NNtgj8PdTMrMrlmggkDZP0tKTZks5rYv+2kq7P9j8oqV+e8ZiZ2VvllggkdQQuAY4DBgKnSRrYqNingZciYi/gl5CN+DIzs1aTZ4vgEGB2RMyJiNXARGB4ozLDgWuz15OBo6Wyz2pjZmabkGci2A0ouSuehdm2JstExFrgFWCnxgeSNEZSnaS6pUuX5hSumVl1ahMXiyNiXEQMjojBvcq+ermZWXXLMxEsAvqUvO+dbWuyjKROwPbAshxjMjOzRvJMBA8De0vqL2kbYCQwpVGZKcAns9enAvdEWxvYYGbWxuU6oEzS8cCvgI7AVRExVtIPgLqImCKpCzAeOAh4ERgZEXOaOeZSoInJg9qUnsALRQdRQfx5bODPoiF/Hg1tzeexe0Q02bfe5kYWtweS6jY2wq8a+fPYwJ9FQ/48Gsrr82gTF4vNzCw/TgRmZlXOiaAY44oOoML489jAn0VD/jwayuXz8DUCM7Mq5xaBmVmVcyIwM6tyTgStSFIfSfdKminpSUlfLDqmoknqKOmfkm4tOpaiSdpB0mRJT0maJel9RcdUJEn/L/t/8oSkCdm4o6og6SpJSyQ9UbJtR0l3SXome357uc7nRNC61gJfjoiBwFDg7Cam5q42XwRmFR1EhbgI+HNE7Ae8myr+XCTtBnwBGBwR+5MGpY4sNqpWdQ0wrNG284C7I2Jv4O7sfVk4EbSiiFgcEY9kr5eT/qM3npG1akjqDXwYuLLoWIomaXvgMOB3ABGxOiJeLjaqwnUCumbzkHUDni84nlYTEfeTZlsoVTpt/7XAR8p1PieCgmSrsR0EPFhsJIX6FfA1YH3RgVSA/sBS4Oqsq+xKSW8rOqiiRMQi4OfAc8Bi4JWIuLPYqAq3c0Qszl7/G9i5XAd2IiiApO7ADcC5EfFq0fEUQdIJwJKImF50LBWiE3Aw8NuIOAh4nTI2/duarP97OClB7gq8TdIZxUZVObLJOct2778TQSuT1JmUBGoj4sai4ynQ+4GTJM0jrV53lKSaYkMq1EJgYUTUtxAnkxJDtToGmBsRSyNiDXAjcGjBMRXtP5J2Aciel5TrwE4ErShbhvN3wKyI+EXR8RQpIr4REb0joh/pIuA9EVG13/gi4t/AAkn7ZpuOBmYWGFLRngOGSuqW/b85miq+eJ4pnbb/k8DN5TqwE0Hrej9wJunb76PZ4/iig7KK8XmgVtIM4EDgRwXHU5isZTQZeAR4nFRXVc10E5ImAP8A9pW0UNKngZ8AH5T0DKnF9JOync9TTJiZVTe3CMzMqpwTgZlZlXMiMDOrck4EZmZVzonAzKzKORGYlZD0WUmfyF6PlrRryb4ryzFJYHbci7f2OHkf06pHp6IDMKskEXFZydvRwBNkk51FxGeKiMksb24RWLsgqV82j39tNpf/ZEndsn1HZxO5PZ7N875ttv0n2doQMyT9PNv2PUlfkXQqMJg0wOtRSV0l3SdpcFbutOx4T0i6sCSO1ySNlfSYpGmSNjkxmKRekm6Q9HD2eL+kDpLmSdqhpNwzknZuqnz5P02rNk4E1p7sC1waEQOAV4H/zRYzuQb4eEQcQGoFf07STsDJwLsiYhDww9IDRcRkoA4YFREHRsTK+n1Zd9GFwFGkEcDvlVQ/JfDbgGkR8W7gfuCsZmK+CPhlRLwX+ChwZUSsJ00fcHJ2viHA/Ij4T1PlN/dDMmvMicDakwUR8bfsdQ3wAVJymBsR/8q2X0ua9/8VYBXwO0mnACs24zzvBe7LJkRbC9RmxwRYDdSvtjYd6NfMsY4BLpb0KGkume2y2WmvBz6elRmZvd9UebMt5msE1p40ni9lo/OnRMRaSYeQJjM7FTiH9A1/a62JDfO2rKP5/2MdgKERsap0o6R/AHtJ6kVagOSHzZTf6sCterlFYO1J35J1fk8H/go8DfSTtFe2/UxgavYtevuIuA34f6SlIRtbDvRoYvtDwOGSekrqCJwGTN3CmO8kTTYHgKQD4c355m8CfkGarXbZpsqbbQ0nAmtPniatAz0LeDtpkZdVwKeAP0h6nLQa2mWkCv7WbKbPvwJfauJ41wCX1V8srt+YrRJ1HnAv8BgwPSK2dErgLwCDswvWM4HPluy7HjiDDd1CzZU32yKefdTahWzpz1uzhc7NbDO4RWBmVuXcIjAzq3JuEZiZVTknAjOzKudEYGZW5ZwIzMyqnBOBmVmV+/9MBRmt1DBXvQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "diyJFZHhFFeK"
      },
      "source": [
        "## Predicting a new result with Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0d3GcPOZoAaX",
        "outputId": "d7009b91-d678-4705-df6f-41989fbc35b2"
      },
      "source": [
        "linear_regressor.predict([[6.5]])\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([330378.78787879])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DW7I7ZVDFNkk"
      },
      "source": [
        "## Predicting a new result with Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ck1O4y-opNDI",
        "outputId": "ce937226-53bf-44c7-fc7d-c5240afa13d7"
      },
      "source": [
        "\n",
        "lin_reg2.predict(poly_r.fit_transform([[6.5]]))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([158862.45265155])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}