decision_tree_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1]\n",
      " [ 2]\n",
      " [ 3]\n",
      " [ 4]\n",
      " [ 5]\n",
      " [ 6]\n",
      " [ 7]\n",
      " [ 8]\n",
      " [ 9]\n",
      " [10]]\n",
      "[[  45000]\n",
      " [  50000]\n",
      " [  60000]\n",
      " [  80000]\n",
      " [ 110000]\n",
      " [ 150000]\n",
      " [ 200000]\n",
      " [ 300000]\n",
      " [ 500000]\n",
      " [1000000]]\n",
      "            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\n",
      "prediction\n",
      "[150000.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'decision tree regression model')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "dataset = pd.read_csv('Position_Salaries.csv')\n",
    "x = dataset.iloc[:, 1:2].values\n",
    "y = dataset.iloc[:, 2:].values\n",
    "print(x)\n",
    "print(y)\n",
    "print(dataset)\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "regressor.fit(x,y)\n",
    "regressor.predict(6.5)\n",
    "print('prediction')\n",
    "print(regressor.predict(6.5))\n",
    "\n",
    "\n",
    "x_grid = np.arange(min(x), max(x),0.01)\n",
    "x_grid = x_grid.reshape((len(x_grid), 1))\n",
    "\n",
    "plt.scatter(x,y,color='red')\n",
    "plt.plot(x_grid,regressor.predict(x_grid), color='blue')\n",
    "\n",
    "plt.xlabel('position')\n",
    "plt.ylabel('salary')\n",
    "plt.title('decision tree regression model')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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