Decision_Tree_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Decision Tree Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPcfSvGFX9P3HzDM0MqzRT9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/seaneldrin1/Automated-Manual-Comparison/blob/master/Decision_Tree_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qk158XfF28xc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "data = pd.read_csv(\"/Position_Salaries.csv\")\n",
        "x = data.iloc[:,1:2].values\n",
        "y= data.iloc[:,2].values\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(x,y, test_size= 0.2,  random_state =0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ov2RBtcU4ay0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "614530aa-94ac-4670-e1d0-a297f852da83"
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "regressor =  DecisionTreeRegressor(random_state=0)\n",
        "regressor.fit(x,y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=0, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxpg0obF5oAB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5d41c5e8-235f-4032-f507-8bd76fe8ed5e"
      },
      "source": [
        "y_pred= regressor.predict([[6.5]])\n",
        "y_pred"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([150000.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNvZZlwW5-a-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "f77d004f-7be3-4f3d-9aa4-1abd30f09b81"
      },
      "source": [
        "x_grid = np.arange(min(x), max(x),0.1)\n",
        "x_grid = x_grid.reshape((len(x_grid),1))\n",
        "plt.scatter(x,y,color='red')\n",
        "plt.plot(x_grid, regressor.predict(x_grid), color = 'blue')\n",
        "plt.title(\"Truth or Bluff(Decison Tre Regression\")\n",
        "plt.xlabel(\"Position Level\")\n",
        "plt.ylabel(\"salary\")\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cq4bMzArOVUNmZgXnqiEzs4Lr6nKJwMys0FwiMDMrOCcCM7OCc9WQmVnBuURgZlZwTgRmZgXnqiEzswLbsAE2bnSJwMyssJp5mEpwIjAzy10zD1MJTgRmZrlr5mEqwYnAzCx33VVDLhGYmRWUSwRmZgVX6EQgaaKkxyXNl3ROL8ucLKld0jxJP84zHjOzRmj2qqEReW1Y0nDgcuCdwDLgQUmzIqK9ZJl9gXOBwyLiWUmvzCseM7NGKXKJ4BBgfkQsiIh1wA3ApLJlzgQuj4hnASLiqRzjMTNriCIngrHA0pL3y7JppfYD9pP0G0n3S5pYaUOSpkqaK2luR0dHTuGameWj2auGGt1YPALYFzgKOBW4UtJO5QtFxPSImBARE8aMGTPIIZqZvTxFLhEsB/Yseb9HNq3UMmBWRKyPiIXAn0iJwcysZbREFxNZw2+tHgT2lbS3pJHAKcCssmVuJZUGkDSaVFW0YAD7MjNrWq3SxcQTki6WNL7aDUfEBuAsYA7wGHBTRMyTdIGkE7PF5gBPS2oH7gE+FxFP1xC/mVnTa/aqoWpvH30D6Yr+h5KGAVcDN0TEqr5WiojZwOyyadNKfg/g09mPmVlL6uoCCUaObHQklVVVIoiI1RFxZUQcCnwB+DLwpKQZkl6Ta4RmZkNc9+hkUqMjqazqNgJJJ0r6D+BfgW8BfwPcTtkVv5mZ9dTMw1RC9VVDT5Dq8C+OiPtKpt8s6W31D8vMrHU08zCVUEUiyO4YuiYiLqg0PyL+ue5RmZm1kGYvEfRbNRQRG4HjByEWM7OW1OyJoNqqod9Iugy4EVjTPTEiHs4lKjOzFjLkq4YyB2WvpdVDAfxdfcMxM2s9LVEiiIi35x2ImVmrWru2NUoESDoOeD3wUl7rrQHZzMw26+qCnXdudBS9q/Y5gh8A7wfOBgS8DxiXY1xmZi2j2auGqu1r6NCIOA14NiK+AvwtqYM4MzPrR7M3FlebCLJOVOmUtDuwHtgtn5DMzFpLq5QI7sgGjLkYeBhYBFyfV1BmZk1h5kxoa4Nhw9LrzJkD2kyzJ4Jq7xr6avbrLZLuAF4REc/nF5aZWf1t3FjDwj/+MfzTR6CrExAsXgpnfgQ2CT7wgZr22+xVQ30mAkkn9TGPiPhp/UMyM6u/226Dk06CTZuqXeMD2U+JLuC07KdGo0bVvs5g6a9EcEIf8wJwIjCzIeGhhyACvvKVKruDnjaNdJorJ7igtjvnhw+H0waQPAZLn4kgIs4YrEDMzPLU0QG77JKd36tx1bWwePGW08eNg39prUeo/ECZmRVCRweMGVPDChdeCFOnQmfn5mmjRqXpLcYPlJlZIdScCCZPhunTUwlASq/Tp6fpLabaEsGhEXGgpEcj4iuSvgX8LM/AzMzqqaMD9t+/xpUmT27JE3+5ap8jWJu9dj9QtgE/UGZmQ0jNJYICqbZEcHvZA2UBXJlbVGZmdbRxIzz9tBNBb6pNBH8ENkbELZLGAwcDt+YXlplZ/TzzTLp11Imgsmqrhv4lIlZLOpw0GM0Pge/nF5aZWf2sXJlenQgqqzYRdD+YfRxwZUTcCYzMJyQzs/rq6EivTgSVVZsIlkv6N9ItpLMlbV3DumZmDeVE0LdqT+YnA3OAd0XEc8AuwOdyi8rMrI6cCPpWbe+jnZT0KxQRTwJP5hWUmVk9dSeCXXdtbBzNytU7ZtbyOjpghx1g660bHUlzciIws5bnh8n65kRgZi3PiaBvTgRm1vKcCPqWayKQNFHS45LmSzqnj+XeIykkTcgzHjMrJieCvuWWCCQNBy4HjgHGA6dm3VOUL7c98AnggbxiMbPiikhPFjsR9C7PEsEhwPyIWBAR64AbgEkVlvsq8A0293BqZlY3q1bB+vVOBH3JMxGMBZaWvF+WTXuJpIOBPbMuK3olaaqkuZLmdnTfEGxmVgU/TNa/hjUWSxoGfBv4TH/LRsT0iJgQERPG+K9pZjVwIuhfnolgObBnyfs9smndtgcOAP5T0iLgrcAsNxibWT05EfQvz0TwILCvpL0ljQROAWZ1z4yI5yNidES0RUQbcD9wYkTMzTEmMysYJ4L+5ZYIImIDcBaps7rHgJsiYp6kCySdmNd+zcxKORH0r9oRygYkImYDs8umTetl2aPyjMXMiqmjA0aNSj9WmZ8sNrOW1tEBo0c3Oorm5kRgZi3NTxX3z4nAzFqaE0H/nAjMrKW5e4n+ORGYWUtziaB/TgRm1rI6O9OPE0HfnAjMrGX5GYLqOBGYWctyIqiOE4GZtSwnguo4EZhZy3IiqI4TgZm1LCeC6jgRmFnL6uiArbaCHXZodCTNzYnAzJrPzJnQ1gbDhqXXmTMHtJnuZwikukbXcnLtfdTMrGYzZ8LUqekBAIDFi9N7gMmTa9qUnyqujhOBmeVq9Wr44hdhzZoqV7hpK+j8Xs9pncA/bQV317bvBx6AAw6obZ0iciIws1zNmQOXXQa77QYjqjnjrHlrL9OBu2rb98iRcOyxta1TRE4EZpar9vZURz9/fpWDw7S9LVUHlRs3DhYtqnd4hhuLzSxn7e2w9941jBB24YVbLjxqVJpuuXAiMLNcPfYYjB9fwwqTJ8P06akEIKXX6dNrbii26rlqyMxys2EDPP44TJxY44qTJ/vEP4hcIjCz3CxcCC++CPvv3+hIrC9OBGaWm/b29FpT1ZANOicCM8tNdyJwiaC5ORGYWW7a22HPPWH77RsdifXFicDMctPe7mqhocCJwMxysWkT/PGPrhYaCpwIzCwXS5akfuNcImh+TgRmlgvfMTR0OBGYWS58x9DQ4URgZrlob4dXvxp22aXRkVh/nAjMLBft7S4NDBVOBGZWdxG+dXQocSIws7pbsSKNTOZEMDTkmggkTZT0uKT5ks6pMP/TktolPSrpbknj8ozHzPpRp0HjfcfQ0JJbIpA0HLgcOAYYD5wqqfxr8TtgQkQcCNwMfDOveMysH92Dxi9enOp2ugeNH0AycCIYWvIcj+AQYH5ELACQdAMwCWjvXiAi7ilZ/n7ggznGY1YoL7wAhx2Wqmmq8sxE2LSo57RO4LRh8Mna9r1mTbpbaMyY2tazxsgzEYwFlpa8Xwa8pY/lpwA/qzRD0lRgKsBee+1Vr/jMWtrs2fDoo3DqqbDzzlWscMWNQGw5fZPg5I/VvP/DDksDjFnza4oRyiR9EJgAHFlpfkRMB6YDTJgwocI31czK3XILvPKVcN11MHx4FSvc+c3eB42/vPZEYENHno3Fy4E9S97vkU3rQdI7gPOAEyPixRzjMSuMzk6480446aQqkwB40PgCyzMRPAjsK2lvSSOBU4BZpQtIeiPwb6Qk8FSOsZgVypw5qZ7+Pe+pYSUPGl9YuVUNRcQGSWcBc4DhwNURMU/SBcDciJgFXAxsB/xEqTJxSUScmFdMZkVxyy2w665wZMXK1j540PhCyrWNICJmA7PLpk0r+f0dee7frIhefBFuvx3e+17YaqtGR2NDgZ8sNmsxd90Fq1alRGBWDScCs2ZQpyd6AW6+GXbcEY4+um7RWYtrittHzQqt+4nezs70vvuJXqi5vn79erjtNjjxRBg5ss5xWstyIjDLwYwZ8PDDVS78o3XQ+fWe0zqBj66D39a235Ur4dlnXS1ktXEiMKuz5cthyhTYeusqr8pXv7uX6cC1te//wAPh7/++9vWsuJwIzOrsqqtg48bUvcM++1SxQtsbe3+id9GieodntgU3FpvV0YYN6Rmsd72ryiQAfqLXGs6JwKyO7rwzVQ199KM1rOQneq3BFDG0+nCbMGFCzJ07t9FhmFU0cSLMmwcLF8IIV7xaE5H0UERMqDTPJQKzOt3D/+c/pz5+zjzTScCGFn9drdjqeA//9Ompp88pU+oco1nOnAispTz3HNx3Xw0rfPqX0FnWM1tnNn3n6hNBBFx9NUyaBGPH1rB/sybgRGAtY8MGOOII+MMfalnrmsqTnwKOqz2Gj3+89nXMGs2JwFrGjBkpCVx6Kbylr0FRS02aBH95csvpr94t9dVQg223hde/vqZVzJqCE4G1hM5OmDYtJYCzzqphrNxLTu7ZRgDpHv5LPgGH5BKqWdPxXUPWEr77XVixAi6+uMYB030Pv5kTgTVQnW7bXLkSLroITjghtRHUbPLk1JXDpk3p1UnACsZVQ1Y3zzyTzqVVuflm+PS50NUJ7AKLX4Azz4XVW9fcdeb558MLL6RkYGa1cyKwly0i3S3z/e/XstZ7s58SXcBHs58a/eM/wvjxta9nZk4EVgeXXZaSwOmnw5veVOVKZ58NVOreRPC979W0/222gVNOqWkVMyvhRFBEM2fCeefBkiWw116pl8sB1ovffTd86lPpLsyrrkrV/VW55Pbeu14+q7ZEYGYvjxuLi6a7S4XFi1OdTneXCgNoqF2wAE4+GV73OrjuuhqSALjrZbMm4hLBYKrjlTikW98vvDDd7bh+fZUrrToBYnnZhoAPDYMan4pduzY9RHXbbbD99rWt+9LnruPxMLOBKUYiqPMJeMAx1KlzM4A77kjV7IsWwT/8Q/pYVfnuj6hYNx+C0z9RUwwSfOhDNQzAUm7yZJ/4zZpA649HUH4ChlQFMcCHhtatg3vvTVfBd9+droqrsnRJ6gyn3IgRsGe1Z/Fk48aU08aPhyuugCOP7H+dl7S1eVhEswLqazyC1i8RnHcedHZyNWfwLT6TpnUCZ2wFX699c8uWwapV6U6Vt78ddt21yhWv+8/K0zcAh59WcxwHHwwf+1iVg6OXuvDCyonRdfNmhdX6iWDJEgB25WnG0755+npg/H41b+6II+C44+Doo7ds6+zTvdN6vxK/tvZEMGCumzezMq1fNdQsVSF1rqIyM6tFsYeqbJbbFN25mZk1qdavGmqmqhDfJWNmTaj1EwH4BGxm1ofWrxoyM7M+5ZoIJE2U9Lik+ZLOqTB/a0k3ZvMfkNSWZzxmZral3BKBpOHA5cAxwHjgVEnlHQVPAZ6NiNcA3wG+kVc8ZmZWWZ4lgkOA+RGxICLWATcAk8qWmQTMyH6/GThaqmmgQTMze5nyTARjgaUl75dl0youExEbgOeBLZ7VlTRV0lxJczs6OnIK18ysmIbEXUMRMR2YDiCpQ1KFJ8SGlNHAykYH0UR8PDbzsejJx6Onl3M8xvU2I89EsBzYs+T9Htm0SssskzQC2BF4uq+NRsSYegbZCJLm9vaEXxH5eGzmY9GTj0dPeR2PPKuGHgT2lbS3pJHAKcCssmVmAR/Ofn8v8KsYan1emJkNcbmVCCJig6SzgDnAcODqiJgn6QJgbkTMAq4CrpM0H3iGlCzMzGwQ5dpGEBGzgdll06aV/L4WeF+eMTSp6Y0OoMn4eGzmY9GTj0dPuRyPIdf7qJmZ1Ze7mDAzKzgnAjOzgnMiGESS9pR0j6R2SfMk1TZafAuSNFzS7yTd0ehYGk3STpJulvRHSY9J+ttGx9RIkj6V/Z/8QdL1kl7R6JgGi6SrJT0l6Q8l03aR9EtJT2SvO9drf04Eg2sD8JmIGA+8Ffh4hf6XiuYTwGONDqJJfBf4eUS8DngDBT4uksYC/wxMiIgDSHceFumuwmuAiWXTzgHujoh9gbuz93XhRDCIIuLJiHg4+3016R+9vNuNwpC0B3Ac8MNGx9JoknYE3ka6pZqIWBcRzzU2qoYbAWyTPWw6CljR4HgGTUTcS7qlvlRp32wzgHfXa39OBA2Sdbn9RuCBxkbSUP8KfB7Y1OhAmsDeQAfwo6yq7IeStm10UI0SEcuBS4AlwJPA8xHxi8ZG1XCviogns9//AryqXht2ImgASdsBtwCfjIhVjY6nESQdDzwVEQ81OpYmMQI4GPh+RLwRWEMdi/5DTVb/PYmUIHcHtpX0wcZG1TyyHhjqdu+/E8Egk7QVKQnMjIifNjqeBjoMOFHSIlIX5X8n6d8bG1JDLQOWRUR3CfFmUmIoqncACyOiIyLWAz8FDm1wTI32V0m7AWSvT9Vrw04Egygba+Eq4LGI+Haj42mkiDg3IvaIiDZSI+CvIqKwV3wR8RdgqaTXZpOOBtobGFKjLQHeKmlU9n9zNAVuPM+U9s32YeC2em3YiWBwHQZ8iHT1+0j2c2yjg7KmcTYwU9KjwEHA1xscTwIAEpcAAALTSURBVMNkJaObgYeB/yWdqwrT3YSk64H/AV4raZmkKcBFwDslPUEqMV1Ut/25iwkzs2JzicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzgnAisJUjamN2O+wdJP5E0qsb1d5d0c/b7QaW39Uo6UVJdnvKV9EI9ttPLts+X9Nm8tm+ty4nAWkVXRByU9VS5DvhILStHxIqIeG/29iDg2JJ5syKibvdsmzUbJwJrRb8GXpP1336rpEcl3S/pQABJR5Y80Pc7SdtLastKEyOBC4D3Z/PfL+l0SZdl67ZJ+lW2zbsl7ZVNv0bSpZLuk7RA0nt7ja6MpH0k/VzSQ5J+Lel1knaUtFjSsGyZbSUtlbRVpeXrfgStUJwIrKVkXRYfQ3oa9SvA7yLiQOCLwLXZYp8FPh4RBwFHAF3d60fEOmAacGNWwrixbBffA2Zk25wJXFoybzfgcOB4anvqczpwdkS8KYvtioh4HngEODJb5nhgTtbvzhbL17Avsy2MaHQAZnWyjaRHst9/TerT6QHgPQAR8StJu0raAfgN8G1JM4GfRsSy1J1NVf4WOCn7/TrgmyXzbo2ITUC7pKq6CM56oj0U+ElJDFtnrzcC7wfuIfXHdEU/y5sNiBOBtYqu7Ar/Jb2d3CPiIkl3ktoBfiPpXcDaOsTwYunuq1xnGPBceeyZWcDXJe0CvAn4FbBtH8ubDYirhqyV/RqYDCDpKGBlRKyStE9E/G9EfAN4ECivY18NbN/LNu9j85CJk7N9DFg2HsVCSe/L4pSkN2TzXsji+y5wR0Rs7Gt5s4FyIrBWdj7wpqw3z4vY3IXvJ7OG4UeB9cDPyta7Bxjf3VhcNu9s4Ixs3Q+RxlyuxaisN8nun0+TEsoUSb8H5pEGZOl2I/DB7LVbX8ub1cy9j5qZFZxLBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBfd/Axac/NGIymEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}