Decision_Tree_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Decision Tree Regression",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOtWPwvzez0HBdURmm5Khw0",
      "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/Lakshaypahuja21/Machine-Learning-Basics/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": "markdown",
      "metadata": {
        "id": "3CnrCTNoG0av"
      },
      "source": [
        "# decision tree regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5pvfn-spG1NH"
      },
      "source": [
        "## lib\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-AxSbGLG7U8"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6xwaAc_HHYd"
      },
      "source": [
        "## dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FewyCAVzHIhp"
      },
      "source": [
        "df = pd.read_csv('Position_Salaries.csv')\n",
        "x = df.iloc[:, 1:-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ySKUWzpYHKeY"
      },
      "source": [
        "## training the decision tree model on full data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1VfOSHDHOKL",
        "outputId": "95ee6b0e-fc0e-48c5-9ec2-1a6d62a3b79c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "regressor = DecisionTreeRegressor(random_state = 0)\n",
        "regressor.fit(x, y)"
      ],
      "execution_count": 13,
      "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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sckY6hEpHU1l"
      },
      "source": [
        "## predicting new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-rs4m3OHWm0",
        "outputId": "22cffdf9-5b55-448e-aa02-ac91c20a9831",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regressor.predict([[6.5]])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([150000.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Soo32ta4HYAQ"
      },
      "source": [
        "## visualizing decision tree regression (high scale)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOOolv7wdK5_",
        "outputId": "64efa57d-7850-46c1-8d0d-26fef667dc91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "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(decision tree)')\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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H1zqS0spqEUTEioi4JCLeBnwR+BrwlKSrJO2RaYRmZoNc9+xkUq0jKa3sawSSjpT0S+AHwHeB/wBuougbv5mZbayep6mE8ruGHiPpwz83Iv5UUD5T0n9VPywzs8ZRz9NUQhmJIL1j6MqIOLvU8oj4VNWjMjNrIPXeIuizaygi1gHvG4BYzMwaUr0ngnK7hv4o6QLgemBld2FE3J9JVGZmDWTQdw2l9ktfC7uHAnhXdcMxM2s8DdEiiIh3Zh2ImVmjWrWqMVoESDoC2Ad4Oa/1dAHZzMw26OqCHXaodRQ9K/c5gouBjwCnAQI+DIzLMC4zs4ZR711D5Y419LaIOAF4LiK+DryVZIA4MzPrQ71fLC43EaSDqNIpaRdgDbBzNiGZmTWWRmkR3JxOGHMucD+wALg2q6DMzOpCWxs0NcGQIclrW1u/NlPviaDcu4b+L307S9LNwFYR8UJ2YZmZVd+6dRVU/tnP4OMnQ1cnIFi4GKaeDOsFH/1oRfut966hXhOBpKN6WUZE/KL6IZmZVd+NN8JRR8H69eWu8dH0p0AXcEL6U6ERIypfZ6D01SJ4fy/LAnAiMLNB4b77IAK+/vUyh4OePp3kNFdMcHZld84PHQon9CN5DJReE0FEnDRQgZiZZamjA3bcMT2/l+Oyq2Hhwk3Lx42DrzbWI1R+oMzMcqGjA0aNqmCFGTNg2jTo7NxQNmJEUt5g/ECZmeVCxYmgpQVaW5MWgJS8trYm5Q2m3BbB2yJiX0kPRsTXJX0X+HWWgZmZVVNHB+y9d4UrtbQ05Im/WLnPEaxKX7sfKFuLHygzs0Gk4hZBjpTbIrip6IGyAC7JLCozsypatw6WLXMi6Em5ieARYF1EzJI0Htgf+FV2YZmZVc+zzya3jjoRlFZu19BXI2KFpLeTTEZzKfDj7MIyM6ueZ55JXp0ISis3EXQ/mH0EcElE3AIMzyYkM7Pq6uhIXp0ISis3ESyV9BOSW0jnSNqygnXNzGrKiaB35Z7MjwFuBd4bEc8DOwJfyCwqM7MqciLoXbmjj3ZSMK5QRDwFPJVVUGZm1dSdCHbaqbZx1Ct375hZw+vogO22gy23rHUk9cmJwMwanh8m650TgZk1PCeC3jkRmFnDcyLoXaaJQNJESY9Kmi/pjF7qfUhSSGrOMh4zyycngt5llggkDQUuBA4DxgOT0+EpiuttC3wauCerWMwsvyKSJ4udCHqWZYvgAGB+RDweEauB64BJJer9H/BtNoxwamZWNcuXw5o1TgS9yTIR7AosLvi8JC17maT9gTHpkBU9kjRN0lxJczu6bwg2MyuDHybrW80uFksaAnwP+FxfdSOiNSKaI6J5lP81zawCTgR9yzIRLAXGFHwenZZ12xZ4PXCnpAXAgcBsXzA2s2pyIuhblongXmBPSbtJGg4cC8zuXhgRL0TEyIhoiogm4G7gyIiYm2FMZpYzTgR9yywRRMRa4FSSweoeBm6IiHmSzpZ0ZFb7NTMr5ETQt3JnKOuXiJgDzCkqm95D3YOzjMXM8qmjA0aMSH6sND9ZbGYNraMDRo6sdRT1zYnAzBqanyrumxOBmTU0J4K+ORGYWUPz8BJ9cyIws4bmFkHfnAjMrGF1diY/TgS9cyIws4blZwjK40RgZg3LiaA8TgRm1rCcCMrjRGBmDcuJoDxOBGbWsJwIyuNEYGYNq6MDttgCttuu1pHUNycCM6s/bW3Q1ARDhiSvbW392kz3MwRSVaNrOJmOPmpmVrG2Npg2LXkAAGDhwuQzQEtLRZvyU8XlcSIws0ytWAFf/jKsXFnmCjdsAZ0/2risE/j4FnB7Zfu+5x54/esrWyePnAjMLFO33goXXAA77wzDyjnjrDywh3Lgt5Xte/hwOPzwytbJIycCM8tUe3vSRz9/fpmTwzT9V9IdVGzcOFiwoNrhGb5YbGYZa2+H3XarYIawGTM2rTxiRFJumXAiMLNMPfwwjB9fwQotLdDamrQApOS1tbXiC8VWPncNmVlm1q6FRx+FiRMrXLGlxSf+AeQWgZll5okn4KWXYO+9ax2J9caJwMwy096evFbUNWQDzonAzDLTnQjcIqhvTgRmlpn2dhgzBrbdttaRWG+cCMwsM+3t7hYaDJwIzCwT69fDI4+4W2gwcCIws0wsWpSMG+cWQf1zIjCzTPiOocHDicDMMuE7hgYPJwIzy0R7O7zmNbDjjrWOxPriRGBmmWhvd2tgsHAiMLOqi/Cto4OJE4GZVd2TTyYzkzkRDA6ZJgJJEyU9Kmm+pDNKLD9dUrukByXdLmlclvGYWR+qNGm87xgaXDJLBJKGAhcChwHjgcmSiv8s/go0R8S+wEzgO1nFY2Z96J40fuHCpG+ne9L4fiQDJ4LBJcv5CA4A5kfE4wCSrgMmAe3dFSLijoL6dwPHZRiPWa68+CJMmJB005Tl2YmwfsHGZZ3ACUPgM5Xte+XK5G6hUaMqW89qI8tEsCuwuODzEuAtvdSfAvy61AJJ04BpAGPHjq1WfGYNbc4cePBBmDwZdtihjBUuuh6ITcvXC475RMX7nzAhmWDM6l9dzFAm6TigGTio1PKIaAVaAZqbm0v8pZpZsVmz4FWvgmuugaFDy1jhlu/0PGn8hZUnAhs8srxYvBQYU/B5dFq2EUmHAmcCR0bESxnGY5YbnZ1wyy1w1FFlJgHwpPE5lmUiuBfYU9JukoYDxwKzCytIeiPwE5Ik8HSGsZjlyq23Jv30H/pQBSt50vjcyqxrKCLWSjoVuBUYClweEfMknQ3MjYjZwLnAK4CfK+lMXBQRR2YVk1lezJoFO+0EB5XsbO2FJ43PpUyvEUTEHGBOUdn0gveHZrl/szx66SW46SY4+mjYYotaR2ODgZ8sNmswv/0tLF+eJAKzcjgRmNWDKj3RCzBzJmy/PRxySNWiswZXF7ePmuVa9xO9nZ3J5+4neqHi/vo1a+DGG+HII2H48CrHaQ3LicAsA1ddBfffX2blK1ZD5zc3LusETlkNf6lsv888A889524hq4wTgVmVLV0KU6bAlluW+a18xQd6KAeurnz/++4L73lP5etZfjkRmFXZZZfBunXJ8A67717GCk1v7PmJ3gULqh2e2SZ8sdisitauTZ7Beu97y0wC4Cd6reacCMyq6JZbkq6hU06pYCU/0Ws1pojBNYZbc3NzzJ07t9ZhmJU0cSLMmwdPPAHD3PFqdUTSfRHRXGqZWwRmVbqH/5//TMb4mTrVScAGF/+5Wr5V8R7+1tZkpM8pU6oco1nGnAisoTz/PPzpTxWscPpvoLNoZLbOtHyH8hNBBFx+OUyaBLvuWsH+zeqAE4E1jLVr4R3vgIceqmStK0sXPw0cUXkMn/xk5euY1ZoTgTWMq65KksAPfwhv6W1S1EKTJsG/ntq0/DU7J2M1VGCbbWCffSpaxawuOBFYQ+jshOnTkwRw6qkVzJV73jEbXyOA5B7+8z4NB2QSqlnd8V1D1hDOPx+efBLOPbfCCdN9D7+ZE4HVUJVu23zmGTjnHHj/+5NrBBVraUmGcli/Pnl1ErCccdeQVc2zzybn0rLMnAmnfwm6OoEdYeGLMPVLsGLLiofOPOssePHFJBmYWeWcCGyzRSR3y/z4x5WsdXT6U6ALOCX9qdDHPgbjx1e+npk5EVgVXHBBkgROPBHe9KYyVzrtNKDU8CaCH/2oov1vvTUce2xFq5hZASeCPGprgzPPhEWLYOzYZJTLfvaL3347fPazyV2Yl12WdPeX5bybeh56+dTKEoGZbR5fLM6b7iEVFi5M+nS6h1Tox4Xaxx+HY46B170OrrmmgiQAHnrZrI64RTCQqvhNHJJb32fMSO52XLOmzJWWvx9iadGGgOOHQIVPxa5alTxEdeONsO22la378u9dxeNhZv2Tj0RQ5RNwv2Oo0uBmADffnHSzL1gAH/xg8muV5fwrKNk3H4ITP11RDBIcf3wFE7AUa2nxid+sDjT+fATFJ2BIuiD6+dDQ6tVw113Jt+Dbb0++FZdl8aJkMJxiw4bBmHLP4ol165KcNn48XHQRHHRQ3+u8rKnJ0yKa5VBv8xE0fovgzDOhs5PLOYnv8rmkrBM4aQv4ZuWbW7IEli9P7lR55zthp53KXPGaO0uXrwXefkLFcey/P3ziE2VOjl5oxozSidF982a51fiJYNEiAHZiGeNp31C+Bhi/V8Wbe8c74Igj4JBDNr3W2au7pvf8TfzqyhNBv7lv3syKNH7XUL10hVS5i8rMrBL5nqqyXm5T9OBmZlanGr9rqJ66QnyXjJnVocZPBOATsJlZLxq/a8jMzHqVaSKQNFHSo5LmSzqjxPItJV2fLr9HUlOW8ZiZ2aYySwSShgIXAocB44HJkooHCp4CPBcRewDfB76dVTxmZlZali2CA4D5EfF4RKwGrgMmFdWZBFyVvp8JHCJVNNGgmZltpiwTwa7A4oLPS9KyknUiYi3wArDJs7qSpkmaK2luR0dHRuGameXToLhrKCJagVYASR2SSjwhNqiMBJ6pdRB1xMdjAx+Ljfl4bGxzjse4nhZkmQiWAmMKPo9Oy0rVWSJpGLA9sKy3jUbEqGoGWQuS5vb0hF8e+Xhs4GOxMR+PjWV1PLLsGroX2FPSbpKGA8cCs4vqzAb+O31/NPC7GGxjXpiZDXKZtQgiYq2kU4FbgaHA5RExT9LZwNyImA1cBlwjaT7wLEmyMDOzAZTpNYKImAPMKSqbXvB+FfDhLGOoU621DqDO+Hhs4GOxMR+PjWVyPAbd6KNmZlZdHmLCzCznnAjMzHLOiWAASRoj6Q5J7ZLmSapstvgGJGmopL9KurnWsdSapFdKminpEUkPS3prrWOqJUmfTf+fPCTpWklb1TqmgSLpcklPS3qooGxHSb+R9Fj6ukO19udEMLDWAp+LiPHAgcAnS4y/lDefBh6udRB14nzg/0XE64A3kOPjImlX4FNAc0S8nuTOwzzdVXglMLGo7Azg9ojYE7g9/VwVTgQDKCKeioj70/crSP6jFw+7kRuSRgNHAJfWOpZak7Q98F8kt1QTEasj4vnaRlVzw4Ct04dNRwBP1jieARMRd5HcUl+ocGy2q4APVGt/TgQ1kg65/UbgntpGUlM/AP4XWF/rQOrAbkAHcEXaVXappG1qHVStRMRS4DxgEfAU8EJE3FbbqGru1RHxVPr+X8Crq7VhJ4IakPQKYBbwmYhYXut4akHS+4CnI+K+WsdSJ4YB+wM/jog3AiupYtN/sEn7vyeRJMhdgG0kHVfbqOpHOgJD1e79dyIYYJK2IEkCbRHxi1rHU0MTgCMlLSAZovxdkn5a25BqagmwJCK6W4gzSRJDXh0KPBERHRGxBvgF8LYax1Rr/5a0M0D6+nS1NuxEMIDSuRYuAx6OiO/VOp5aiogvRcToiGgiuQj4u4jI7Te+iPgXsFjSa9OiQ4D2GoZUa4uAAyWNSP/fHEKOL56nCsdm+2/gxmpt2IlgYE0Ajif59vu39OfwWgdldeM0oE3Sg8B+wDdrHE/NpC2jmcD9wN9JzlW5GW5C0rXAn4HXSloiaQpwDvBuSY+RtJjOqdr+PMSEmVm+uUVgZpZzTgRmZjnnRGBmlnNOBGZmOedEYGaWc04EZgUknSzphPT9iZJ2KVh2aTUGCUy3e8HmbifrbVp+ZDpVpdlgExEXF3w8EXiIdLCziPhYLWIyy5pbBNYQJDWl4/i3pWP5z5Q0Il12SDqQ29/Tcd63TMvPSeeGeFDSeWnZWZI+L+looJnkAa+/Sdpa0p2SmtN6k9PtPSTp2wVxvChphqQHJN0tqdeBwSSNkjRL0r3pzwRJQyQtkPTKgnqPSXp1qfrVP5qWN04E1kheC1wUEXsDy4FPpJOZXAl8JCL+k6QVfIqknYAPAvtExL7ANwo3FBEzgblAS0TsFxFd3cvS7qJvA+8ieQL4zZK6hwTeBrg7It4A3AVM7SPm84HvR8SbgQ8Bl0bEepLhAz6Y7u8twMKI+Hep+pUeJLNiTgTWSBZHxB/T9z8F3k6SHJ6IiH+k5VeRjPv/ArAKuEzSUUBnBft5M3BnOiDaWqAt3SbAaqB7trX7gKY+tnUocIGkv5GMJbNdOjrt9cBH0jrHpp97q2/Wb75GYI2keLyUHsdPiYi1kg4gGUBltTcAAAESSURBVMzsaOBUkm/4m2tNbBi3ZR19/x8bAhwYEasKCyX9GdhD0iiSCUi+0Uf9zQ7c8sstAmskYwvm+f0o8AfgUaBJ0h5p+fHA79Nv0dtHxBzgsyRTQxZbAWxbovwvwEGSRkoaCkwGft/PmG8jGWwOAEn7wcvjzf8S+B7JaLXLeqtvtjmcCKyRPEoyD/TDwA4kk7ysAk4Cfi7p7ySzoV1McoK/OR3p8w/A6SW2dyVwcffF4u7CdJaoM4A7gAeA+yKiv0MCfwpoTi9YtwMnFyy7HjiODd1CfdU36xePPmoNIZ368+Z0onMzq4BbBGZmOecWgZlZzrlFYGaWc04EZmY550RgZpZzTgRmZjnnRGBmlnP/H7mLurSvpK00AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NIdwA81eKapK"
      },
      "source": [
        "## not best for 2 variables|"
      ]
    }
  ]
}