{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Veri setimiz\n",
    "dataset = pd.read_csv('maas.csv')\n",
    "X = dataset.iloc[:,1:2].values\n",
    "y = dataset.iloc[:,2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state = 0)\n",
    "regressor.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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58qw5MzPrFdwjMjOzXHhorhMkHSZprqT/kPSspFMkDZI0X9Ly9D6w6PhrJK2Q9Jyks4vS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\nmrOqEgGvvFJm5rl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x243f0965e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decision Tree Regression sonuçlarını görselleştirme\n",
    "X_grid = np.arange(min(X), max(X), 0.01)\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('Decision Tree Regression')\n",
    "plt.xlabel('Pozisyon')\n",
    "plt.ylabel('Maas')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grafiktende tahmin edileceği gibi seviyesi 7 olan bir kişinin maası 200000 olacaktır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 200000.]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.predict(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 300000.]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.predict(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 500000.]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.predict(9))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}