random_forest_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu007/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction\n",
      "[160600.]\n"
     ]
    },
    {
     "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.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators=500,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('random forest trees regression model')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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