4-Desicion Tree Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2286ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fcfee7b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"decision+tree+regression+dataset.csv\",sep=\";\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d27a0b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1\n",
       "0   1  100\n",
       "1   2   80\n",
       "2   3   70\n",
       "3   4   60\n",
       "4   5   50\n",
       "5   6   40\n",
       "6   7   30\n",
       "7   8   20\n",
       "8   9   10\n",
       "9  10    5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "946f3d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,0].values.reshape(-1,1)\n",
    "y = df.iloc[:,1].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76acee51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# desicion tree regression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(x,y)\n",
    "\n",
    "tree_reg.predict([[5]])\n",
    "x_ = np.arange(min(x),max(x),0.01).reshape(-1,1)\n",
    "y_head = tree_reg.predict(x_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54d6229b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,color=\"red\")\n",
    "plt.plot(x_,y_head,color=\"green\")\n",
    "plt.xlabel(\"tribun level\")\n",
    "plt.ylabel(\"ucret\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "930b98d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}