machinelearning1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2db80f51",
   "metadata": {},
   "source": [
    "# 1. Regression\n",
    "- Basit Doğrusal Regresyon\n",
    "- Çoklu Doğrusal Regresyon\n",
    "- Polinomal Regresyon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0cd17d",
   "metadata": {},
   "source": [
    "# 1. Basit Doğrusal Regresyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18a7a430",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a268d060",
   "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>deneyim</th>\n",
       "      <th>maas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deneyim  maas\n",
       "0      0.5  2500\n",
       "1      0.0  2250\n",
       "2      1.0  2750\n",
       "3      5.0  8000\n",
       "4      8.0  9000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dogrusal_regresyon_veriseti_20220209.csv\", sep= \";\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc6b8583",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(df.deneyim,df.maas)\n",
    "plt.xlabel(\"Deneyim (Yıl)\")\n",
    "plt.ylabel(\"Maaş(TL)\")\n",
    "plt.title(\"Deneyim Maaş İlişkisi\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "919db273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineer_reg = LinearRegression()\n",
    "\n",
    "x = df.deneyim.values.reshape(-1,1)\n",
    "y = df.maas.values.reshape(-1,1)\n",
    "\n",
    "lineer_reg.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3431fec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B0 : [[1663.89519747]]\n",
      "b0_ : [1663.89519747]\n",
      "b1 : [[1138.34819698]]\n"
     ]
    }
   ],
   "source": [
    "y_ekseni_kesisim = np.array([0]).reshape(-1,1)\n",
    "b0 = lineer_reg.predict(y_ekseni_kesisim)\n",
    "\n",
    "print(\"B0 :\", b0)\n",
    "\n",
    "b0_ = lineer_reg.intercept_\n",
    "print(\"b0_ :\", b0_)\n",
    "\n",
    "b1 = lineer_reg.coef_\n",
    "print(\"b1 :\", b1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46f19a32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14211\n",
      "'lı Yıllık Deneyime sahip birinin maaşı : [14185.72536421]\n"
     ]
    }
   ],
   "source": [
    "deneyim = 11\n",
    "\n",
    "maas_yeni = 1693+1138*deneyim\n",
    "print(maas_yeni)\n",
    "\n",
    "sonuc = lineer_reg.predict(np.array([deneyim]).reshape(-1,1))\n",
    "print(\"'lı Yıllık Deneyime sahip birinin maaşı : {}\".format(sonuc[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "db7c7e0b",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "array = np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]).reshape(-1,1)\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(x,y)\n",
    "\n",
    "y_head = lineer_reg.predict(array)\n",
    "\n",
    "plt.plot(array, y_head, color =\"r\")\n",
    "plt.xlabel(\"Deneyim (YIL)\")\n",
    "plt.ylabel(\"Maaş (TL)\")\n",
    "plt.title(\"Deneyim Maaş İlişkisi\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc186ae3",
   "metadata": {},
   "source": [
    "# 2. Çoklu Doğrusal Regresyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8126e482",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd243889",
   "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>deneyim</th>\n",
       "      <th>maas</th>\n",
       "      <th>yas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2500</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2250</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2750</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9000</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deneyim  maas  yas\n",
       "0      0.5  2500   22\n",
       "1      0.0  2250   21\n",
       "2      1.0  2750   23\n",
       "3      5.0  8000   25\n",
       "4      8.0  9000   28"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"coklu_dogrusal_regresyon_veriseti_20220209.csv\",sep=\";\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77fc6ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.5, 22. ],\n",
       "        [ 0. , 21. ],\n",
       "        [ 1. , 23. ],\n",
       "        [ 5. , 25. ],\n",
       "        [ 8. , 28. ],\n",
       "        [ 4. , 23. ],\n",
       "        [15. , 35. ],\n",
       "        [ 7. , 29. ],\n",
       "        [ 3. , 22. ],\n",
       "        [ 2. , 23. ],\n",
       "        [12. , 32. ],\n",
       "        [10. , 30. ],\n",
       "        [14. , 34. ],\n",
       "        [ 6. , 27. ]]),\n",
       " array([[ 2500],\n",
       "        [ 2250],\n",
       "        [ 2750],\n",
       "        [ 8000],\n",
       "        [ 9000],\n",
       "        [ 6900],\n",
       "        [20000],\n",
       "        [ 8500],\n",
       "        [ 6000],\n",
       "        [ 3500],\n",
       "        [15000],\n",
       "        [13000],\n",
       "        [18000],\n",
       "        [ 7500]], dtype=int64))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.iloc[:,[0,2]].values\n",
    "y = df.maas.values.reshape(-1,1)\n",
    "x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4213af5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coklu_dogrusal_regresyon = LinearRegression()\n",
    "\n",
    "coklu_dogrusal_regresyon.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f484fb5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 Yıllık Deneyim ve 35 yaş sonucu çıkan maaş : [[11046.35815877]] TL\n"
     ]
    }
   ],
   "source": [
    "test_verisi1 = np.array([[10,35]])\n",
    "test_sonucu1 = coklu_dogrusal_regresyon.predict(test_verisi1)\n",
    "print(\"10 Yıllık Deneyim ve 35 yaş sonucu çıkan maaş : {} TL\".format(test_sonucu1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf8b7e46",
   "metadata": {},
   "source": [
    "# 3. Polinomsal Regresyon "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f396ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28112b1a",
   "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>araba_fiyat</th>\n",
       "      <th>araba_max_hiz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   araba_fiyat  araba_max_hiz\n",
       "0           60            180\n",
       "1           70            180\n",
       "2           80            200\n",
       "3          100            200\n",
       "4          120            200"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"polinomsal_regresyon_veriseti_20220209.csv\", sep = \";\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ac6e856",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.araba_max_hiz.values.reshape(-1,1)\n",
    "x = df.araba_fiyat.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd1eae6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HS9xnGlkNhNbW1rEdHR0l4+nq6mLQoEG1ht8rHGN9OMb6cIz104g4l3W+VHbf6La9e3StiRMnLomIcd3Lq84NJenzwDeAjbw1OiqAP63lxpIGATcCfx8RL1fobii1IyqUb1sYcTlwOcC4ceNiwoQJJW+0aNEiyu3LC8dYH46xPhxj/TQiznNnL6Bz/cZtytsGt/DFU+pzr1qaob4KHBwRwyNiRHrUmigGkiWK6yLiplT8XGpaIj0/n8pXA8OKTh8KrEnlQ0uUm5kZMH3SgbQMHLBVWcvAAUyfdGDd7lFLsngaeLWnF04jlq4AnoiIfynaNQ84LW2fBtxSVD5V0m6SRpB1ZD8YEWuBDZKOSNc8tegcM7N+r31MGxdOGU3b4BZEVqO4cMpo2se01e0etaxnMRP4T0m/Bl4vFNYw6+yRwGeAZZIeTmX/CMwG5kg6A3gWOCldb7mkOcDjZCOpzoqIQo/NmcBVQAtZP8YdNcRtZtZvtI9pq2ty6K6WZPF9YAGwDHij1gtHxH2U7m8AOKbMObOAWSXKF5N1jpuZWS+oJVlsjogvNzwSMzPLrVr6LBZKmiZpSPr19b6S9m14ZGZmlhu11Cw+lZ5nFpXVPHTWzMz6vlrW4B7RjEDMzCy/avlR3qmlyiPimvqHY2ZmeVRLM9ThRdu7k41keohsjiYzM+sHammG+mLxa0l7Az9uWERmZpY72zPr7Ktkv642M7N+opY+i1t5a+K+XYBRwJxGBmVmZvlSS5/Ft4q2NwO/jYjVDYrHzMxyqJY+i7th6/UsJO3bk/UszMysb6ulGWoa8E2y9SzeIJvvyT/KMzPrR2pphppOtp7FC40OxszM8qlh61mYmdnOo5HrWZiZ2U6iYetZmJnZzsPrWZiZWVVez8LMzKryehZmZlaV17MwM7OqyiYLSUdHxAJJU0rtj4ibGheWmZnlSaWaxQfIRkF9pMS+AJwszMz6ibLJIiLOS8+nNy8cMzPLo6qjoSSdLWkvZX4o6SFJxzUjODMzy4dahs5+LiJeBo4D/gQ4HZjd0KjMzCxXakkWSs/HAz+KiEeKysqfJF0p6XlJjxWVnS+pU9LD6XF80b6ZklZKWiFpUlH5WEnL0r5LJFW9t5mZ1VctyWKJpF+QJYv5kvaktmk/rgImlyj/14g4ND1uB5A0CpgKHJzO+a6kAen4y4BpZEu5jixzTTMza6BaksUZwAzg8Ih4FXgbWVNURRFxD1DrAkknAh0R8XpErAJWAuMlDQH2ioj7IyKAa4D2Gq9pZmZ1UssvuM+PiK8XvV4PXAKcsp33/IKkU4HFwFciYh3QBjxQdMzqVLYpbXcvLykt1DQNoLW1lUWLFpU8rqurq+y+vHCM9eEY68Mx1k9fiXMbEVHxQdacNDNt7wbMI0sgtZw7HHis6HUrMICsRjMLuDKVfwf4dNFxVwAfAw4HfllUfhRway33Hjt2bJSzcOHCsvvywjHWh2OsD8dYP3mPE1gcJb5Ta2mGOh0YLWkmcCuwMCLO387E9FxEbImIN4AfAOPTrtXAsKJDhwJrUvnQEuVmZtZEZZOFpMMkHQaMAf4d+CTwFHB3Ku+x1AdR8FGgMFJqHjBV0m6SRpB1ZD8YEWuBDZKOSKOgTgVu2Z57m5nZ9qvUZ3Fxt9frgFGpPICjK11Y0k+ACcD+klYD5wETJB2azn8G+DxARCyXNAd4HNgMnBURW9KlziRrCmsB7kgPMzNrokrTfUzckQtHxMkliq+ocPwssn6M7uWLgUN2JBYzM9sxtYyGQtKHyH4DsXuhLCK+0aigzMwsX2qZG+p7ZP0VXyT75fZJwLsbHJeZmeVILaOh3h8RpwLrIuIC4K/YeuSSmZnt5GpJFhvT86uSDiD7oZxXzzMz60dq6bO4TdJg4CLgIbKRTD9sZFBmZpYvtazB/c20eaOk24DdI+KlxoZlZmZ5UmkN7pJrb6d9XoPbzKwfqVSz+CnwcHrA1mtYeA1uM7N+pFKy+BjZkNn3kk2x8ZOIWNmUqMzMLFfKjoaKiJsjYirwAeBp4GJJ90n6QNOiMzOzXKhl6OxrwEvAy8AeFP2K28zM+odKHdwTgZPJphH/JfDvaZ4mMzPrZyr1WdwFPArcR7bo0alphTsAIuJLDY7NzMxyolKyqLrOtpmZ9Q+Vpii/upmBmJlZftXSwW1mZv2ck4WZmVXlZGFmZlVVnUhQ0u7AGWy7Ut7nGhiXmZnlSC01ix8D7wQmAXcDQ4ENjQzKzMzypZZk8ecR8TXglTRC6kPA6MaGZWZmeVJLstiUntdLOgTYGxjesIjMzCx3alkp73JJ+wBfA+YBg9K2mZn1E7WslFdYQvVu4E8bG46ZmeVR1WYoSftJ+rakhyQtkfRvkvZrRnBmZpYPtfRZdADPky2G9HHgBeCGaidJulLS85IeKyrbV9Kdkp5Kz/sU7ZspaaWkFZImFZWPlbQs7btEkrrfy8ysv5u7tJMjZy9gxIyfceTsBcxd2lnX69eSLPaNiG9GxKr0+CdgcA3nXQVM7lY2A7grIkaSzWo7A0DSKGAq2W85JgPflTQgnXMZMA0YmR7dr2lm1q/NXdrJzJuW0bl+IwF0rt/IzJuW1TVh1JIsFkqaKmmX9PgE8LNqJ0XEPcCL3YpPBAoTFF4NtBeVd0TE6xGxClgJjJc0BNgrIu6PiACuKTrHzMyAi+avYOOmLVuVbdy0hYvmr6jbPZR9B5fYIW0AAhDZCnlvpF27AF0RsVfVi0vDgdsi4pD0en1EDC7avy4i9pF0KfBARFybyq8A7gCeAWZHxLGp/CjgnIj4cJn7TSOrhdDa2jq2o6OjZFxdXV0MGjSoWvi9yjHWh2OsD8dYP42Ic1nnS2X3jW7bu0fXmjhx4pKIGNe9vNIU5Xv26A47plQ/RFQoLykiLgcuBxg3blxMmDCh5HGLFi2i3L68cIz14RjrwzHWTyPiPHf2AjrXb9ymvG1wC188pT73qmkiQUn7SBov6X8UHtt5v+dS0xLp+flUvhoYVnTcUGBNKh9aotzMzJLpkw6kZeCArcpaBg5g+qQD63aPWobO/g1wDzAfuCA9n7+d95sHnJa2TwNuKSqfKmk3SSPIOrIfjIi1wAZJR6RRUKcWnWNmZkD7mDYunDKatsEtiKxGceGU0bSPaavbPWr5BffZwOFkfQoTJR1EljQqkvQTYAKwv6TVwHnAbGCOpDOAZ4GTACJiuaQ5wOPAZuCsiCj01pxJNrKqhawf446a/3VmZv1E+5i2uiaH7mpJFq9FxGuSkLRbRDwpqWrdJiJOLrPrmDLHzwJmlShfDBxSQ5xmZtYgtSSL1ZIGA3OBOyWtw/0GZmb9Si1zQ300bZ4vaSHZrLM/b2hUZmaWKxWThaRdgEcLv5OIiLubEpWZmeVKxdFQEfEG8IikdzUpHjMzy6Fa+iyGAMslPQi8UiiMiBMaFpWZmeVKLcmi6jBZMzPbudXSwb1VP4WkI4FPkS2GZGZm/UAtNQskHUqWID4BrAJubGBMZmaWM2WThaT3kK0xcTLwR7IFjxQRE5sUm5mZJXOXdnLR/BWsWb+RAwa3MH3SgQ39xXZ3lWoWTwL3Ah+JiJUAkv6hKVGZmdmbCosbFdasKCxuBDQtYVQaOvsx4Pdkix/9QNIxlJ4y3MzMGqgZixtVUzZZRMTNEfFJ4CBgEfAPQKukyyQd16T4zMz6vTUl1qqoVN4IVacoj4hXIuK6tDrdUOBh0trZZmbWeAcMbulReSPUtPhRQUS8GBHfj4ijGxWQmZltrRmLG1VT09BZMzPrPYVO7LyOhjIzs5xo9OJG1fSoGcrMzPonJwszM6vKycLMzKpysjAzs6qcLMzMrConCzMzq8rJwszMqnKyMDOzqnolWUh6RtIySQ9LWpzK9pV0p6Sn0vM+RcfPlLRS0gpJk3ojZjOzHTF3aSdHzl7Ass6XOHL2AuYu7eztkHqkN2sWEyPi0IgYl17PAO6KiJHAXek1kkaRLcJ0MDAZ+K6kAaUuaGaWR4X1KDrTLLGF9Sj6UsLIUzPUicDVaftqoL2ovCMiXo+IVcBKYHzzwzMz2z55WI9iRykimn9TaRWwDgjg+xFxuaT1ETG46Jh1EbGPpEuBByLi2lR+BXBHRPy0xHWnAdMAWltbx3Z0dJS8f1dXF4MGDar3P6uuHGN9OMb6cIw7ZlnnS29ut7bAc0XLUIxu27sXIipv4sSJS4pafN7UWxMJHhkRayT9CXCnpCcrHFtqdb6SGS4iLgcuBxg3blxMmDCh5AUXLVpEuX154RjrwzHWh2PcMefOXvBmE9RXRm/m4mXZV2/b4Ba+eMqEXoysdr3SDBURa9Lz88DNZM1Kz0kaApCen0+HrwaGFZ0+FFjTvGjNzHZMHtaj2FFNTxaS9pC0Z2EbOA54DJgHnJYOOw24JW3PA6ZK2k3SCGAk8GBzozYz237tY9q4cMpo2tLKdm2DW7hwyuhenXK8p3qjGaoVuFlS4f7XR8TPJf0XMEfSGcCzwEkAEbFc0hzgcWAzcFZEbCl9aTMza4SmJ4uI+A3wvhLlfwSOKXPOLGBWg0MzM2uIwtDZjZu2wLC3hs4CfaZ2kaehs2ZmO6WdYeisk4WZWYOtWb+xR+V55GRhZtZgB6SO7VrL88jJwsyswXaGobO99aM8M7N+o9CJnfVRbKBtcAvTJx3YZzq3wTULM6ugr8+UmiftY9r41YyjGd22N7+acXSfShTgmoWZlbEzDPe0+nHNwsxK2hmGe1r9uGZhuTR3aScXzV/BmvUbOaAPtu/uDHaG4Z5WP65ZWO4ULxQT9M2FYnYGO8NwT6sfJwvLHTd/5MPOMNzT6sfNUJY7bv7Ih51huKfVj5OF5c4Bg1veXCime7k1V/uYNtrHtLFo0aI+s0iPNYaboSx3Jh70jh6Vm1njOVlY7ix88g89KjezxnMzlG2jt4etus/CLH9cs7Ct5GHYqodsmuWPk0XO9PZcPHkYtuohm2b542aoHJm7tJPpP32ETVvizbl4pv/0EaB5c/HkoQmoeMimf8Ftlg9OFjlywa3Ls0RRZNOW4IJblzftizIvw1YLQzbNLB/cDFWk0AQ0YsbPeqUJaN2rm3pU3ghuAjKzUlyzSLZqAqJ3moDywE1AZlaKk0WShyYgAVGmvJncBGRm3bkZKslDE1CpRFGp3MysWZwscqStTCdyuXIzs2bpM8lC0mRJKyStlDSj3tcf3DKwR+WN4M5lM8urPpEsJA0AvgN8EBgFnCxpVD3vcf4JBzNwl617BwbuIs4/4eB63qai9jFtXDhl9Js1ibbBLVw4ZbT7D8ys1/WVDu7xwMqI+A2ApA7gRODxet0gL6OAPCW0meWRIvLffSrp48DkiPib9PozwF9GxBe6HTcNmAbQ2to6tqOjo+T1urq6GDRoUGOD3kGOsT4cY304xvrJe5wTJ05cEhHjttkREbl/ACcBPyx6/Rng25XOGTt2bJSzcOHCsvvywjHWh2OsD8dYP3mPE1gcJb5T+0SfBbAaGFb0eiiwppdiMTPrd/pKsvgvYKSkEZLeBkwF5vVyTGZm/Uaf6OCOiM2SvgDMBwYAV0bE8l4Oy8ys3+gTyQIgIm4Hbu/tOMzM+qM+MRpqe0j6A/DbMrv3B15oYjjbwzHWh2OsD8dYP3mO8wWAiJjcfcdOmywqkbQ4Sg0NyxHHWB+OsT4cY/30lTi76ysd3GZm1oucLMzMrKr+miwu7+0AauAY68Mx1odjrJ++EudW+mWfhZmZ9Ux/rVmYmVkPOFmYmVlVO12ykHSlpOclPVZUdoOkh9PjGUkPp/LhkjYW7ftek2IcJmmhpCckLZd0dirfV9Kdkp5Kz/sUnTMzLfy0QtKkXozxIklPSnpU0s2SBqfypr+XFWI8X1JnUSzHF52Tl/cxb5/J3SU9KOmRFOcFqTxPn8lyMebpM1kuxtx8JrdbqdkF+/ID+B/AYcBjZfZfDHw9bQ8vd1yDYxwCHJa29wT+m2xRp/8LzEjlM4B/TtujgEeA3YARwNPAgF6K8Thg11T+z0UxNv29rBDj+cBXSxyfm/cxh59JAYPS9kDg18AROftMlosxT5/JcjHm5jO5vY+drmYREfcAL5baJ0nAJ4CfNDWobiJibUQ8lLY3AE8AbWQLOl2dDrsaaE/bJwIdEfF6RKwCVpItCNX0GCPiFxGxOR32ANkMwL2iwvtYTm7ex8L+HH0mIyK60suB6RHk6zNZMsacfSbLvY/lNP193F47XbKo4ijguYh4qqhshKSlku6WdFSzA5I0HBhD9hdIa0SshexLBviTdFgb8Lui01ZT+UuxkTEW+xxwR9HrXnsvS8T4hdQscWVR00ke38fcfCYlDUjNYc8Dd0ZE7j6TZWIs1uufyQox5u4z2RP9LVmczNZ/wa0F3hURY4AvA9dL2qtZwUgaBNwI/H1EvFzp0BJlTRnzXC5GSecCm4HrUlGvvZclYrwM+DPg0BTXxYVDS5zeq+8jOfpMRsSWiDiU7C/z8ZIOqXB4r7yXlWLMy2eyTIy5+0z2VL9JFpJ2BaYANxTKUtXvj2l7CVl74XuaFM9Asi+P6yLiplT8nKQhaf8Qsr9MoJcWfyoTI5JOAz4MnBKp4bW33stSMUbEc+k/7BvAD3irWp+39zFXn8miGNYDi4DJ5OwzWSbGXH0mS8WYt8/k9ug3yQI4FngyIlYXCiS9Q9KAtP2nwEjgN40OJLVTXwE8ERH/UrRrHnBa2j4NuKWofKqk3SSNSHE+2BsxSpoMnAOcEBGvFpU3/b2sEOOQosM+ChRGxuXmfUzy9Jl8R9EoopZCbOTrM1kyxpx9JsvFmJvP5HZrZm96Mx5kVfq1wCayrH1GKr8K+Ntux34MWE42GuEh4CNNivGvyaqajwIPp8fxwH7AXcBT6XnfonPOJfvLaAXwwV6McSVZG2uh7Hu99V5WiPHHwLJUPg8Ykrf3MYefyfcCS1Ocj/HW6Kw8fSbLxZinz2S5GHPzmdzeh6f7MDOzqvpTM5SZmW0nJwszM6vKycLMzKpysjAzs6qcLMzMrConC7NE0kclhaSDtvP8rupHlT33s5L+UDQr6TWSTpA0Yzuv1y5p1PbGY9bdrr0dgFmOnAzcB0wlmyV0K5IGRMSWBt7/hoj4Qreyedt5rXbgNuDxHYrILHHNwow35246EjiDLFkUyicoW4/ierIfVSFprqQlytYrmNbtOhdLekjSXZLekcr+l6T/UrbGwY2S3l5jTJ+VdKmkPSWtStOGIGkvZWtgDCx1bUnvB04ALkq1lD+rx3tk/ZuThVmmHfh5RPw38KKkw4r2jQfOjYhCs87nImIsMA74kqT9UvkewEMRcRhwN3BeKr8pIg6PiPeRTVF+RpkYPlnUDHV6oTCyqc0XAR9KRVOBGyNiU6lrR8R/ktVIpkfEoRHx9Pa9JWZvcbIwy5wMdKTtjvS64MHI1hoo+JKkR8jWThhGNp8PwBu8NSngtWRTfQAcIuleScuAU4CDy8RwQ/pyPzQiftRt3w+BQgI5HSjsr/XaZjvEfRbW76WawdFkX7wBDABC0v9Oh7xSdOwEssnh/ioiXpW0CNi9zKULc+lcBbRHxCOSPgtM6GmMEfErZcuEfoBsJbXCRHQ7fG2zWrhmYQYfB66JiHdHxPCIGAas4q2aQbG9gXUpURxEtmRmwS7pWgCfIussh2w51bWpz+GUHYjzGrKJMotrHeWuvSHtM6sLJwuzrMnp5m5lN5J94Xf3c2BXSY8C3yRriip4BThY0hKymso3UvnXyFbHu5Ns2u/tdR2wD1svllTu2h3AdGWrxLmD23aYZ5016yMkfRw4MSI+09uxWP/jPguzPkDSt4EPkq3XYdZ0rlmYmVlV7rMwM7OqnCzMzKwqJwszM6vKycLMzKpysjAzs6r+PzHmtrIfrioqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y,x)\n",
    "plt.ylabel(\"Araba Maksimum Hız\")\n",
    "plt.xlabel(\"Araba Fiyat\")\n",
    "plt.title(\"Araba Fiyat Hız Grafiği\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bfef53f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c5c60a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 Milyon TL'lik Bir aracın Hızı :  [[871.66401826]]\n"
     ]
    }
   ],
   "source": [
    "y_tahmin = lr.predict(x)\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,y_tahmin,color=\"r\")\n",
    "plt.ylabel(\"Araba Max Hız\")\n",
    "plt.xlabel(\"Araba Fiyat\")\n",
    "plt.title(\"Araba Hız ve Fiyat Grafiği\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "araba_fiyat = 10000\n",
    "print(\"10 Milyon TL'lik Bir aracın Hızı : \",lr.predict((np.array([araba_fiyat]).reshape(-1,1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "86c902cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000000e+00, 6.0000000e+01, 3.6000000e+03, 2.1600000e+05,\n",
       "        1.2960000e+07],\n",
       "       [1.0000000e+00, 7.0000000e+01, 4.9000000e+03, 3.4300000e+05,\n",
       "        2.4010000e+07],\n",
       "       [1.0000000e+00, 8.0000000e+01, 6.4000000e+03, 5.1200000e+05,\n",
       "        4.0960000e+07],\n",
       "       [1.0000000e+00, 1.0000000e+02, 1.0000000e+04, 1.0000000e+06,\n",
       "        1.0000000e+08],\n",
       "       [1.0000000e+00, 1.2000000e+02, 1.4400000e+04, 1.7280000e+06,\n",
       "        2.0736000e+08],\n",
       "       [1.0000000e+00, 1.5000000e+02, 2.2500000e+04, 3.3750000e+06,\n",
       "        5.0625000e+08],\n",
       "       [1.0000000e+00, 2.0000000e+02, 4.0000000e+04, 8.0000000e+06,\n",
       "        1.6000000e+09],\n",
       "       [1.0000000e+00, 2.5000000e+02, 6.2500000e+04, 1.5625000e+07,\n",
       "        3.9062500e+09],\n",
       "       [1.0000000e+00, 3.0000000e+02, 9.0000000e+04, 2.7000000e+07,\n",
       "        8.1000000e+09],\n",
       "       [1.0000000e+00, 4.0000000e+02, 1.6000000e+05, 6.4000000e+07,\n",
       "        2.5600000e+10],\n",
       "       [1.0000000e+00, 5.0000000e+02, 2.5000000e+05, 1.2500000e+08,\n",
       "        6.2500000e+10],\n",
       "       [1.0000000e+00, 7.5000000e+02, 5.6250000e+05, 4.2187500e+08,\n",
       "        3.1640625e+11],\n",
       "       [1.0000000e+00, 1.0000000e+03, 1.0000000e+06, 1.0000000e+09,\n",
       "        1.0000000e+12],\n",
       "       [1.0000000e+00, 2.0000000e+03, 4.0000000e+06, 8.0000000e+09,\n",
       "        1.6000000e+13],\n",
       "       [1.0000000e+00, 3.0000000e+03, 9.0000000e+06, 2.7000000e+10,\n",
       "        8.1000000e+13]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polinomsal_regresyon = PolynomialFeatures(degree= 4)\n",
    "\n",
    "x_polinom = polinomsal_regresyon.fit_transform(x)\n",
    "x_polinom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "453eb5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2 = LinearRegression()\n",
    "lr2.fit(x_polinom,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f16dea21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0qlSpwksvvcSYMWOIioqicePGTJo0iXnz5jFw4EBSU1OJi4vjzTffpGEpqBs15oRkZbnB6UaMgF9+gcRE17Lob3+DChWCHV3AlcSVwr3ACp/HQ4FvVPV04BvvMSLSGOgFNAEuBl71EkrYysjI4PPPP6dZs2Y8+uijtGzZkiVLljBixAi/rggyMjKYN28eo0aNypmg5/XXXwfc0Nvvvfce/fr1yxng7tdff2XixInMmzePhx9+mPLly7Nw4ULatWuXUy311FNPsXDhQpYsWZJzBdOoUSNmz57NwoUL+de//sVDDz0UiLfDmNCWnu5uHDdp4loRHTjgOqCtXw/33lsmEgIE+EpBRGoD3YEngEHe6h5AJ295ApAEPOitn6SqacAGEVkLnAPkPUaDH0ZdPIrkIAydnT32Ebgrhf79+3PuuefmDF53wQUXsHv3bvbv31/gca666ioAWrduzcaNGwH48ccfue+++4Cjh94G6Ny5MwkJCSQkJFCxYkUuv/xyAJo1a8aSJUsAaN68OTfccAM9e/akZ8+eAOzfv59+/fqxZs0aRIT09PRiey+MCXmpqfDmm/DMM65qqHlzmDTJJYYQ730cCIGuPhoFPAD4fipXV9XtAKq6XUSyK+YSgbk+223x1h1FRAYAAwCqV69+zPSVFStWPGoimszMzAInpgmEuLg4vvvuu5zHaWlpZGZmkpKSkhOLqpKSksLhw4c5cuQIycnJpKWlER0dTXJyMpmZmWRkZJCcnExqairp6ekkJyeTlZXFoUOHco6TmZnJwYMHOXz4MCKSsz77wz05OZkjR47k7DNp0iS+//57pk+fzmOPPca8efMYOnRoztXEpk2b6N69O8nJyRw6dCgnhtwOHz5c6NSh/khJSSmW44SK0lSe0lQWOLY8kQcPUmvqVOp8+CHl9u5lf5MmbBoxgj1t27rexz7/w6EoUOcnYElBRC4DdqjqAhHp5M8ueaw7ZpIBVR0LjAVo06aNdup09KFXrFhx1JVBMK4UgGNes1OnTnz66ac88sgjJCUlccopp5CYmEhsbCzlypUjISGBmJgYYmJiSEhIIDIykgoVKpCQkEBaWhoiQkJCAh06dGDKlClcdtllrF69mq1bt9KqVStWrVqVcxxwSSE+Pp6EhISc16hQoQKbN2+me/fudO3aldq1ayMiHDp0iAYNGpCQkMDkyZNzXqt8+fJERUXl+f7FxsbSshg66CQlJZH7HIaz0lSe0lQW8CnPrl2u1dDLL7thKbp2hYceouJ559E8TIaigMCdn0BeKbQHrhCRS4FY4CQReQf4Q0RqelcJNYEd3vZbgDo++9cGtgUwvhI1fPhwbrnlFpo3b0758uWZMGHCCR3nb3/7G0OGDDlm6G1/ZGZmcuONN7J//35Ulfvuu49KlSrxwAMP0K9fP55//nkuuOCCE4rLmFBXbudOGDQI/vMfN2DdVVe53sdt2gQ7tNCiqgH/wd1DmOYtjwSGestDgWe85SbAYiAGqA+sByILOm7r1q01t+XLlx/1+MCBA8dsE85CpTy53+cTNXPmzGI5TqgoTeUpNWVZs0b173/XzOho1chI1b59VZctC3ZURVaU8wPM13w+V4PRo/kp4AMR6Q9sBq4FUNVlIvIBsBzIAO5S1fwnJjDGmIIsXermMZg0CaKj2X7JJSS++CLUqxfsyEJaiSQFVU3CtTJCVXcDXfLZ7glcSyVjjDkxc+e6oSimToX4eLj/frjvPtasWkWiJYRClcoeze7qyASKvb8m5KjCN99Aly7Qrh3MmQOPPeaamD7zDNSsGewIw0apSwqxsbHs3r3bPrgCRFXZvXs3sbGxwQ7FGNf7+NNPoW1buPBCWLECnnvOJYN//hOqVAl2hGGn1I2SWrt2bbZs2cLOnTsB156+NH2AhUJ5YmNjqV27dlBjMGVcRga8/76rJlq2DE47zbUq6tfPjV5qTlipSwrR0dHUr18/53FSUlKxtKcPFaWtPMYcl7Q0N/H9M8+44SeaNIF334XrroOoUvdxFhT2LhpjQl9KCowd62Y5274dzjkHXngBLrsMIkpdLXhQWVIwxoSuPXvglVfgxRfdcpcu8M470LmzG4rCFDtLCsaY0LN9u7sSeO01d5VwxRWu93HbtsGOrNSzpGCMCR0bNsDIkfDf/7qhrHv1crOcNWsW7MjKDEsKxpjgW77c9T6eONENV33zzfDAA9CgQbAjK3MsKRhjgmf+fDfD2ZQpUL68m8xm0CA325kJCksKxpiSpQqzZ7tk8OWXUKmS62j2j39A1arBjq7Ms6RgjCkZqjB9uksGP/wA1au7/ga33QYnnRTs6IzHkoIxJrAyM2HyZJcMliyBunXh1VfdfYO4uGBHZ3KxXh/GmMA4cgTGjYNGjVwrovR0eOstWLMG7rjDEkKIsisFY0zxOngQ3njD9T7esgVat4aPPoKePa33cRiwpGCMKR779sHo0TBqlJsH+fzz3ZXCRRdZ7+MwYknBGFM0O3a4RDB6NBw4AN27u97H7dsHOzJzAiwpGGNOzObNroro9dfd6KXXXed6H7doEezITBFYUjDHWPz7Yhb9vohykeWIjoymXGQ5txzhs1zI+uiIaKIiopB8qg0+WbiVkTNW0atOMg8/9S1DujWkZ0vrsBQKCj03q1bB00/D22+7aqG+fV3v4zPOCF7QZUig/3csKZgcmVmZjPhuBMNnDSdLs4rlmHkljfSMSPYezEI1ks2/RbAjNZo+n0bR4PuTqFkxnuhIl1CiI6KPXs79ONdydiIqbLmgYxR2vMiIyGJ5X0LVJwu3MuzjpaSmZ0Id2LovlWEfLwWgJzvcpDaTJ0NsLNx1l5v/uE6dIEdddhR4foopMVhSMABsObCFGz++kVmbZtGnWR8ePf9RVJUjmUdIz0rnSOYRt5zps1zI+vy2nbbkN6Iy01BJp2JkBrs0E9VMNu9JIT5OycjKID0znfSsdNIz093jPJbTs9zjkiRIgUkn/XA6Jy0/ya8ElLNfCSS7wo6RfVU3csYq94Hjo8mGJVTv9Sisnuc6mQ0bBgMHwimnFPp+qSqKkqVZqHq/0aOWs5/zd7sTPcbq5NXEb4vPd7vjjSNQxyiozKNnruGAHoEoWJxSC+hAanomI2esKrmkICJ1VPW3XOtqqOrvxRKBCbpPV37KrVNvJS0jjQk9J3BT85vyrfYpDvV//Izy3vJttTJ4bqn7M5Qj8GP/7sd1LFUlUzP9Sh6FLRfpGN52W3/fSpWqVY7ZLjUjleQjycf1WsV1teavqIgoMjMjITYCIYpH1mQRwWGSEjP5tragcbFoOVB5haw3XvLrAy/k/BLsAIpBtPu1MKUD0AGAbftSi+3w/lwpbBCRD4H+qnrIWzcdaFVsUZigSE1PZchXQxj982ha1WzFe1e/x/LNFejw9Ey27UulVqW4gNT116oUx9Y8/ohrVTr+zkwiQpS4b7qhICkpiU6dOhXLsbI0q8ST3Vs/rKPK7xto8scqKqYdJDUqluXVGrK91mlcdU59BCFCIhDxfiNHLfvzXFG2K8oxlv26jObNmgcs3pIoc5fnZrN9/2FAuKlBFqPcVPQn9L+TH3/+k5YC3wHfich1qroOsEbHYW75zuX0mtyLpTuWMqjtIEZ0GcHnS3f9WV9JYOorAYZ0a3jU6wDERUcypFvDYnuN0iBCIoiJiiGGEpiIPj0dJk5k2H8/JGHjWtZVSWTD1X25o2IXomJjeeWKZmHfEKDS75Xo1LBTsMMokqEXN8/534kUV3Va3P87/iQFVdVXRWQx8D8ReRBC8brQ+ENVef2X1xn4xUDiy8Uzvc90Ljn9EoA865OLu74S/kwwI2esApJJDNAVifFDaqqb0OaZZ2DzZhJatGDe02O4P+M0rqt7iGq/Jdi5CSEl8b/jT1IQAFX9XkS6AO8DjYotAlNi9qbuZcC0AUxePpmLTruIt658ixrxNXKez69esjjrK7P1bJlIz5aJJCUl8Y8bOhX78U0hDhxwU10+/7zrfNa+PYwZAxdfzDkifAd2bkJUoP93/EkKl2YvqOp2EbkA+GuxR2IC6offfqD3R73ZlryNZy58hvv/ej8RcvQ4NMVZ129C1K5d8OKL8PLLsH8/dOsGDz8MHTsGOzITIvxJCr3zaYkyu5hjMQGQmZXJk3OeZHjScOpWqsv3t37POYnn5Lmt1fWXYlu2wHPPwdixrsroqqtc09LWrYMdmQkx/iSFhIBHYQIid9+D17q/xkkx+U9m4ltfGcjWR6YErV3reh9PmABZWXDjjfDgg3DmmcGOzISoQpOCqj5WEoGY4uXb92B8j/H0PauvX30PsusrTZhbsgSeegrefx+io2HAABg8GOrVC3ZkJsTlmxRE5GUKaGWkqvcUdGARicVVMcV4rzNZVR8VkeHA3wGvhS0Pqep0b59hQH8gE7hHVWf4X5TwlD2OSWHfzP0d7+RwxmGGfDmEV35+hZY1WjLpmkmccbKNSVNmzJ3rZjj73/8gIQGGDHG9j2vUKHRXY6DgK4X5PsuPAY8e57HTgAtUNUVEooE5IvK599wLqvqs78Yi0hjoBTQBagFfi8gZqnp0G8lS5KhxTMi/X4C/452s2LmC6ydff1Tfg5ioEmjjboJLFb75xiWDmTPh5JPh8cfd2ESVKwc7OhNm8k0Kqjohe1lEBvo+9oeqKpDiPYz2fgrq39ADmKSqabhe1GuBc4Afj+d1w4m//QIK205VGbdwHPd8fg/x5eL5rM9nXHr6pZhSLisLpk51yeDnn6FWLdfEdMAAqFAh2NGZMCXus7uQjUR+UdXjHtZCRCKBBcBfgNGq+qBXfXQzcAB3NXK/qu4VkVeAuar6jrfvOOBzVZ2c65gDgAEA1atXbz1p0qQCY0hJSSE+Pv54Qy8RS7fuz/e5ZokV89yuehz84dNqtH71SJ5d/Syzds6idaXWDGs0jJNjTg5IvIEQyufnRJREeSQzk2rffsup775LhU2bSK1Vi829e/N7165ouXLF9jp2bkJbUcrTuXPnBaraJq/nAjpgjFf100JEKgFTRKQp8BrwOO6q4XHgOeBW8h4645iMpapjgbEAbdq00cLGmSnOsWiK28NPfZtnv4DESnFHdUrx3e7+Zn8OIJdw0nqSdz7H1uStPH3h0wz+6+Bj+h6EulA+PycioOU5fBjGj3e9jzdsgKZNYeJE4q69loZRURR3w2E7N6EtUOXJ9xNERJJF5ICIHACaZy9nrz+eF1HVfUAScLGq/qGqmaqaBbyOqyIC2AL4DsxeG9h2PK8TboZ0a0hc9NHj8+fVLyD3dkomB8t9wPKM+4iMiOT7W7/ngfYPhF1CMH5KSXEznNWvD3fcAdWquWqjxYuhd2+ICo3BAE3pUNA9hSL1TxCRU4B0Vd0nInHAhcDTIlJTVbd7m10J/OotTwUmisjzuBvNpwPzihJDqPO3X4DvdvsyNrKv/Esc0MX0btKbMZeNKbDvgQlje/a4nscvvgh798KFF8LEidCpk5vxzJgACORXjJrABO++QgTwgapOE5G3RaQFrmpoI3AbgKouE5EPgOVABnBXaW55lM3ffgE9WyZyUqVVXDnxPjKjMhl/qf99D0yY2b7d3TB+7TU4eBB69nS9j8/Juye6McUpYElBVZcALfNYf1MB+zwBPBGomMLZntQ99Jrci8rlKvPlrV9a34PSaP16GDnSjVqakeGqhoYOdfcOjCkhVhkZJh746gH2pO7hyVZPWkIobZYtc72P33sPIiPhllvggQfgtNOCHZkpg/xOCiJyku/2qronIBGZY8zeNJtxC8fxwF8foEF0g2CHY4rLzz+7PgaffOL6FQwcCIMGuf4GxgRJoc1VROQ2EfkDWILrc7CAo3s7mwBKy0jjtmm3Ua9SPf55/j+DHY4pKlXX6/iii9w9glmz4NFHYdMm18LIEoIJMn+uFAYDTVR1V6CDMcd65vtnWLlrJdP7TKdCOeulGrZUYdo0d2Uwd64bi2jkSLjtNjdGkTEhwp+ksA44FOhAzLFW717NE989wfVNrs+ZMtOEmcxM+OADePJJWLrUjVL62mtw880QGxvs6Iw5hj9JYRjwg4j8hBvkDih8lFRTNKrK7dNuJzYqllEXjwp2OOZ4paXB22+7uQzWroXGjd3j6693Q1kbE6L8SQr/Ab4FlgJZgQ3HZHt7ydvM3DiTMd3HHDWPsgltEampMGqUuz+wdSu0aQMffww9ekCE9Tg3oc+fpJChqoMCHonJsevQLgbNGES72u34e+u/Bzsc44+9e2H0aNqNHAkHDrhex2++6XohWwdDE0b8SQozvZFJ/8fR1UfWJDVAhnw1hP1p+xl7+VgbzyjU/fEHvPACvPoqJCezv107qj77LPz1r8GOzJgT4k9S6OP9HuazTgHrWRMASRuTGL9oPMM6DKNpNevJGrI2bXKth8aNgyNH4LrrYOhQft27l06WEEwY82eO5volEYhxU2neNu02Tqt8Gv933v8FOxyTl5UrXe/jd9911UL9+rnex6ef7p5PSgpqeMYUVaFJQUT65rVeVd8q/nDKtqfmPMXq3auZceMMykeXD3Y4xteCBa5Z6ccfu6akd98N998PtWsHOzJjipU/1Udn+yzHAl2AXwBLCsVo5a6VPDnnSfo060PXBl2DHY7J9t138MQTMGMGVKwIDz0E994Lp5wS7MiMCQh/qo/+4ftYRCoCbwcsojIou09C+ejyPN/1+WCHY1Thiy9c7+M5c1wCePJJN8FNxYqF729MGDuRUVIP4SbAMcVk/KLxzNo0i7GXjaV6fPVgh1N2ZWa66qERI2DRIqhTx01yc+utUN6q80zZ4M89hf/x51zJEUBj4INABlWWpBxJYchXQ+hwagf6t+of7HDKpiNH3I3jp56C1avhjDNcH4M+faBcuWBHZ0yJ8udK4Vmf5Qxgk6puCVA8Zc74RePZnbqbkReNtD4JJe3QIdekdORI+O03aNkSPvwQrrzSzWtgTBnkzz2FWXD0fAoiUsU6rxVdlmbx4k8v0rZ2W9rWbhvscMqO/ftdZ7MXXoCdO6FDBxg7Frp1s97Hpszzp/poAPA4kIob+0iwzmvFYtrqaazds5YnLrAZSEvEzp1uXKJXXnFDUVx8sWtN1LFjsCMzJmT4U300BJtPISBemPsCp1Y8lavOvCrYoZRuv/0Gzz3nrgYOH4arr4Zhw6BVq2BHZkzIsfkUgmTR74tI2pjEyItGEhVhU2UHxJo1bujqt95yzUxvvBEefBAaNQp2ZMaELJtPIUhemPsCFaIr8LdWfwt2KKXP4sWuX8GHH7rWQ7fdBoMHQ926wY7MmJBn8ykEwfbk7by39D1ub3M7lWIrBTuc0uOHH1wfg88+c1NcPvAADBwI1a3vhzH+svkUguDVn18lIyuDe8+9N9ihhD9V+OorlwxmzYKTT4Z//xvuugsqVQp2dMaEHZtPoYSlpqcyZsEYrmh4BQ2qNAh2OOErKws++cQlgwULIDHRNTH9+9+hQoVgR2dM2LL5FErYO0veYdehXdzX9r5ghxKe0tNh0iR3z2DFCmjQAF5/HW66CWJigh2dMWHP5lMoQarKqJ9G0bJGS86re16wwwkvhw+7oSeeeQY2boRmzeC99+CaayDKWm8ZU1zy/W8SkQtU9VsRybMRvap+HLiwSqcv133J8p3LeavnW4j1nPVPcjKMGeP6GfzxB7Rt6wap697deh8bEwAFfcU6H9fq6PI8nlPAksJxemHuC9SMr8n1Ta8Pdiihb/dueOkl97NvH1x0ket9fP75lgyMCaB8k4KqPur9vqXkwim9lu9czox1M/h3539TLtJG3szXtm3uquA//4GDB93gdMOGwdlnF76vMabI/Bn76F7gTSAZeB1oBQxV1S8L2S8WmA3EeK8zWVUfFZEqwPtAPWAjcJ2q7vX2GQb0BzKBe1R1xokVK7g+WbiVkTNWsW1fKrUqxTGkW0OmbxlFbFQst7W5LdjhhaZ169z9gvHj3bwGvXvD0KHQpEmwIzOmTPFnrOZbVfUA0BWoBtwCPOXHfmnABap6FtACuFhE2gJDgW9U9XTgG+8xItIY6AU0AS4GXhWRsBu/+JOFWxn28VK27ktFga37Uhny8RwmLHqLvs37UrV81WCHGFIqbNgAN9zg5jAYP95NaLN6Nbz9tiUEY4LAn2Yb2RW4lwJvqupi8eMuqaoqkOI9jPZ+FOgBdPLWTwCSgAe99ZNUNQ3YICJrgXOAH/0qSYgYOWMVqemZR63bqdM4kpXGvW2ts1qOn36CJ5/k7E8/df0KBg2C++6DWrWCHZkxZZq4z+4CNhB5E0gE6gNnAZFAkqq2LvTg7pv+AuAvwGhVfVBE9qlqJZ9t9qpqZRF5BZirqu9468cBn6vq5FzHHAAMAKhevXrrSZMmFRhDSkoK8fHxhYVabJZu3X/U43RN57GNA6gVU4/Xzi76/MslXZ5ipUqlhQup++67VP7lF9ITEthw2WXsuP56MkrJ3MdhfX5yKU1lASuPr86dOy9Q1TZ5PefPlUJ/XPXPelU9JCIn46qQCqWqmUALEakETBGRpgVsntfVxzEZS1XHAmMB2rRpo506dSowhqSkJArbpjg9/NS3bN2XmvM4JXIWB8rtJTH9wWKJo6TLUyyysmDaNNf7+KefoEYNePZZogcMYNuCBeFXngKE5fnJR2kqC1h5/OXPPYXhqvqLqu7zHu/DVff4zds3CXev4A8RqQng/d7hbbYFqOOzW21g2/G8TigY0q0hcdHuVoiiHIj6lHJ6Kk9cfEOQIwuCjAyYOBHOOgt69IAdO1yfgw0b4P773aB1xpiQ4k9SONVrFYSIxABTgDWF7SQip3hXCIhIHHAhsBKYCvTzNusHfOotTwV6iUiMiNQHTgfm+V+U0NCzZSJPXtWMxEpxpEUsJT1iPf1b3MWVrWoHO7SSk5bmJrRp2NDdRM7KcjeOV692w1jHxgY7QmNMPvypProFeNdLDJ1x9fwv+LFfTWCCd18hAvhAVaeJyI/AByLSH9gMXAugqstE5ANgOZAB3OVVP4Wdni0T6dkykR6TXuSH36ryXPd/BDukkpGS4pLBc8+5/gZnn+2Wr7gCIvz5/mGMCbaChrnwnavwRdy8Ct8Ds0Sklar+UtCBVXUJ0DKP9buBLvns8wRQKiYsXrtnLf9b9T8e7vgwcdFxwQ4nsPbscfMev/iiW+7cGSZMgC5drPexMWGmoCuF53I93gs09tYrcEGggioN3lz4JhESwZ1n3xnsUAJn+3Y3XPVrr7mrhMsvd72P27ULdmTGmBNU0DAXnUsykNJEVXnv1/e48LQLqZlQM9jhFL8NG2DkSPjvf91Q1tdf73ofN28e7MiMMUXk15jDItId19M45w6hqv4rUEGFu5+2/sSGfRsY3ml4sEMpXsuXw1NPuRZFERFw881uysu//CXYkRljiok/Yx+NAcrjbjK/AVxDGLYKKkkTl04kNiqWno16BjuU4jF/vutjMGUKlC8P99zjeiDXLkMtqowpI/xpEvJXVe0L7FXVx4B2HN2fwPjIyMrg/WXvc9kZl3FSzEnBDufEqbo5j7t1c62IZs6ERx6BTZvg+ectIRhTSvlTfZTdPfeQiNQCduOGvDB5+HbDt+w4uIM+TfsUvnEoUoXp092VwQ8/QPXq8PTTcPvtcFIYJzljjF/8SQrTvE5oI4FfcC2P3ghkUOFs4tKJVIypyCWnXxLsUI5PZiZMnuzmPl68GE491TUzvfVWiCvlTWqNMTn8maP5cW/xIxGZBsSq6v6C9imrUtNT+XjFx1zb+Fpio8Kk1+6RI6638dNPw5o1rhfy+PHQpw9ERwc7OmNMCSuo81qeczN7z9kczXn4bM1nJB9Jpk+zMKg6OngQ3ngDnn0WtmyBVq3clULPnhAZdtNYGGOKSUFXCpOBRd4PHD2Kqc3RnIeJSydSI74Gnep1CnYo+du3D1591XU627ULzjvPJYeuXa33sTGmwKRwNXA90Bw3aN17qrq2RKIKQ/sO7+OzNZ9xZ5s7iYwIwW/aO3bAqFEwejQcOACXXup6H3foEOzIjDEhpKAezVNwcyBUwM2K9pw3l8LDqjqrpAIMFx+v+JgjmUdCr+po82ZXRfT662700muvdb2PWx4zLJUxxvjV+ugwsB84AJyKT69m86eJSyfSoHID2tTKczKjkrdqlbt5/Pbb7vFNN8GDD7obycYYk4+CbjR3Bnrj5kn+GnhRVeeXVGDhZHvydmZunMnDHR/Gj+mrA2vRItfHYPJkiImBO+6AwYNdE1NjjClEQVcK3wBLgDlADNBXRPpmP6mq9wQ4trDxwbIPyNIsejftHbwgvv/eJYPp010ns6FDYeBAqFYteDEZY8JOQUnBr3mYDUxZOYWm1Zpy5ilnluwLq8KXX7pkMHs2VK0KTzwBd94JlSqVbCzGmFKhoBvNE0oykHC1+9Bu5myew9AOQ0vuRbOy3OB0I0bAL7+4cYhefBH+9jc3YJ0xxpwgv4bONvn7fO3nZGomVzS8IvAvlp5O9S++cPcJVq50Q1a/8Ya7iVyuXOBf3xhT6llSKKKpq6ZSI75GYFsdpabCm2/CM89w5qZNbjKb995zzUut97ExphjZbOpFkJaRxhdrv+DyMy4nQgLwVh44AM88A/Xrw113Qa1aLBkxwrUw6tXLEoIxptj5M8lOLNCfY2deuzWAcYWFWZtmkXwkufirjnbtgpdegpdfdsNSdO0KDz0E553HnlmzbDgKY0zA+PP19m2gBtANmAXUBpIDGVS4mLpqKnFRcXSp36V4Drh1q5vRrG5dePxxuOAC+PlnmDEDzj/fkoExJuD8uafwF1W9VkR6qOoEEZkIzAh0YKFOVZm6aipdG3QlLrqI8w2sXeuqiSZMcPMa3HCD633cuHHxBGuMMX7y50oh3fu9T0SaAhWBegGLKEws/mMxvx34rWhVR0uXugTQsCG89ZZrUrp2rUsOlhCMMUHgz5XCWBGpDDwCTAXiveUybeqqqQjCZWdcdvw7z53rZjibOhXi490wFPfdBzVqFH+gxhhzHPyZeS176s1ZwGmBDSd8TF01lXZ12lGtgp/DSKjCN9+4ZPDtt1ClCvzrX3D33VC5cmCDNcYYPxVafSQiJ4vIyyLyi4gsEJFR3hDaZdaWA1tYsH0BV5zhR9VRVhZ88gm0bQsXXeQ6nT3/PGzaBI88YgnBGBNS/LmnMAnYgZt05xpgF/B+IIMKddNWTwMo+H5CRga8+67raHblla6Z6X/+A+vXu6qi+PgSitYYY/znzz2FKqr6uM/jf4tIzwDFExamrprKX6r8hUZVGx375OHD7kbx00/Dhg3QtKlLDtddB1HWgdwYE9r8uVKYKSK9RCTC+7kO+CzQgYWqlCMpfLPhG64444qj505ISYHnnoPTToPbb4dTToFPP4XFi6FPH0sIxpiwkG9SEJFkETkA3AZMBI54P5OA+wo7sIjUEZGZIrJCRJaJyL3e+uEislVEFnk/l/rsM0xE1orIKhHpVtTCBcKX677kSOaRP6uO9uyBxx5zHc4GD4Yzz3Q3lOfOhSuugAgbScQYEz4KGjo7oYjHzgDuV9VfRCQBWCAiX3nPvaCqz/puLCKNgV644TRqAV+LyBmqmlnEOIrV1FVTqRxbmfbRp8GQITBmjLtK6NEDhg2Dc88NdojGGHPC/KrT8PopnM7RYx/NLmgfVd0ObPeWk0VkBZBYwC49gEmqmgZsEJG1uKlAf/QnxpKgqny5+nO67q1CVIPTIT0devd2s5w1bRrs8IwxpshEVQveQORvwL24MY8WAW2BH1X1Ar9fRKQeMBtoCgwCbgYOAPNxVxN7ReQVYK6qvuPtMw74XFUn5zrWAGAAQPXq1VtPmjSpwNdOSUkhvhha+pTfuJHUKWPp1OpHxk6LpHuNS9jcqxeHEwvKc8WvuMoTKqw8oas0lQWsPL46d+68QFXzHu9fVQv8AZbirhAWeY8bAe8Xtp/P/vHAAuAq73F1IBJ3P+MJ4L/e+tHAjT77jQOuLujYrVu31sLMnDmz0G0KNG+e6pVXqoKO6hitDEc3rfypaMcsgiKXJ8RYeUJXaSqLqpXHFzBf8/lc9af66LCqHhYRRCRGVVeKSEN/spGIRAMfAe+q6sdeEvrD5/nXgWnewy1AHZ/dawPb/HmdYqcKs2a56S6/+srNd/zPf/JV/R85PXkjpzY8JyhhGWNMoPnTNGaLiFQCPgG+EpFP8ePDWlx7zXHAClV93md9TZ/NrgR+9ZanAr1EJEZE6uPuYczzpxDFRhWmTYP27aFzZ1iyxI1eumkT6f/8P5K2/cCFp11YoiEZY0xJ8mfsoyu9xeEiMhM3SuoXfhy7PXATsFREFnnrHgJ6i0gLQIGNuCavqOoyEfkAWI5ruXSXllTLo8xM+PBDNy7RkiWueeno0XDLLRDnhsX+afMcDqYf5NtFNaj/3WfUqhTHkG7ugmnkjFVs25eas65ny5K9z2CMMcWlwKQgIhHAElVtCqCqs/w9sKrOAfKaFWZ6Afs8gbvPUDLS0uDtt13v47VroVEj1xu5d2+Ijj5q05e//wg0goPJDYkEtu5LZciHi0EgPdPdrN+6L5VhHy8FsMRgjAlLBVYfqWoWsFhETi2heErGwYMwahQ0aAB//ztUrAgffQTLlkHfvsckBIDP13xFOf0Lkfx5tz89S3MSQrbU9ExGzlgV6BIYY0xA+HOjuSawTETmAQezV6pqMU9MXIIWLnSD0p13Howb5+ZALmCqywNpB0jOWsFJmdf6dfht+1KLK1JjjClR/iSFxwIeRUnr0MElhhYt/No8aWMSSBZxWWf5tX2tSkWcntMYY4LEnxvNR91HEJH2QB/cpDvhy8+EAPD1+q+JiYyjYmQT0rL+XB8dIUfdUwCIi47MuQFtjDHhxq/R2kSkhYg8IyIbgX8DKwIaVYj5ev3XdK5/Pk9f1ZrESnEIkFgpjpHXnsXIa846at2TVzWzm8zGmLCV75WCiJyBG6CuN7AbN7GOqGrnEootJGw5sIUVu1bQv2V/erZMzPMD35KAMaa0KKj6aCXwHXC5qq4FEJFCh8wubb5Z/w2AdVozxpQJBVUfXQ38jptk53UR6ULe/Q5Kta83fE21CtVoVr1ZsEMxxpiAyzcpqOoUVb0eNwBeEm5ineoi8pqIdC2h+IJKVfl6/dd0qd+FCLHJcowxpV+hn3SqelBV31XVy/hz+OyhgQ4sFCzbuYzfU363qiNjTJlxXF9/VXWPqv5Hj2MuhXD29fqvAbufYIwpO6xOpABfr/+aM04+g1Mrlq5RPowxJj+WFPKRkZXBrE2z6FK/S7BDMcaYEmNJIR+Lfl9EypEUzq97frBDMcaYEmNJIR+jf/gfAIPfSaP9U9/yycKtQY7IGGMCz58B8cqcTxZuZfLSr4iiJpGcbPMkGGPKDLtSyMMzX6zgkCwjJqtJzjqbJ8EYUxZYUsjDpgOryZIDxGY1PWq9zZNgjCntLCnkITZ+NQAxuZKCzZNgjCntLCnkoVb1jUTpyURp9Zx1Nk+CMaYssKSQi6qy4cB82tbuQO1K5W2eBGNMmWKtj3LZsG8DW5O38nDHh7nj7DIxmocxxuSwK4VcZm+aDcB5dc8LciTGGFPyLCnkMnvTbKrEVeHMU84MdijGGFPiLCnk8t3m7+h4akebP8EYUybZJ5+P31N+Z+2etXQ4tUOwQzHGmKCwpODjx99+BKB9nfZBjsQYY4LDkoKPH377gXKR5WhVs1WwQzHGmKCwpODjhy0/0KZWG2KiYoIdijHGBEXAkoKI1BGRmSKyQkSWici93voqIvKViKzxflf22WeYiKwVkVUi0i1QseUlLSON+dvm89fafy3JlzXGmJASyM5rGcD9qvqLiCQAC0TkK+Bm4BtVfUpEhgJDgQdFpDHQC2gC1AK+FpEzVDUzUAF+snArI2esYuu+VNIjVnIk5ghvz4rhw28/I7FSHEO6NbRezMaYMiVgVwqqul1Vf/GWk4EVQCLQA5jgbTYB6Okt9wAmqWqaqm4A1gLnBCq+TxZuZdjHS9nqjXyaGrEcgOjMRgA5cyjY5DrGmLKkRO4piEg9oCXwE1BdVbeDSxxANW+zROA3n922eOsCYuSMVaSm/3kRkhaxkqisGkSSU5tlcygYY8ocUdXAvoBIPDALeEJVPxaRfapayef5vapaWURGAz+q6jve+nHAdFX9KNfxBgADAKpXr9560qRJBb5+SkoK8fHxx6xfunV/zrKq8sjGWzmjfHP6Vr/vmG2bJVb0t7gBl195wpWVJ3SVprKAlcdX586dF6hqm7yeC+iAeCISDXwEvKuqH3ur/xCRmqq6XURqAju89VuAOj671wa25T6mqo4FxgK0adNGO3XqVGAMSUlJ5LXNw099m1N1lCF/cCB2L+t3N+a5HUe/JYmV4vjHDQW/RknKrzzhysoTukpTWcDK469Atj4SYBywQlWf93lqKtDPW+4HfOqzvpeIxIhIfeB0YF6g4hvSrSFx0ZGAqzoCKJd19HwJNoeCMaasCeSVQnvgJmCpiCzy1j0EPAV8ICL9gc3AtQCqukxEPgCW41ou3RXIlkfZrYpGzljF0oOrEY2hnNYjUoRMVWt9ZIwpkwKWFFR1DiD5PN0ln32eAJ4IVEy59WyZSM+WibQb9zBREWfz3S09SuqljTEmJJX5Hs1pGWks3L6Qtoltgx2KMcYEXZlPCov/WExaZhrn1j432KEYY0zQlfmk8NOWnwA4N9GSgjHGlPmkMHfrXGol1KL2SbWDHYoxxgRdmU8KP235iXMTz8W1oDXGmLKtTCeFXYd2sW7vOtrWtpvMxhgDZTwp/Lz1ZwDOSQzYuHvGGBNWymRS+GThVlo89iW9J7wDKtwzYZ+NhmqMMQR47KNQ9MnCrQz5cDHpWcqRcmuI1jocOBzNkMmLAawHszGmTCtzVwojZ6wiPUtRlLSINZTLOh2A9Ey1YbKNMWVemUsK27yRUTNlJ1myjxgvKfg+Z4wxZVWZSwq1KsUBkCZrACiXdcYxzxljTFlV5pLCkG4NiY4QjkSsAY2inNYHIDpSbJhsY0yZV+ZuNGffSL5p6jrKZdVFiKZy+WgevbyJ3WQ2xpR5ZS4pAPRoUYuoLzfQp/F1/Ofy7sEOxxhjQkaZqz4CWL93PfsO7+PsxLODHYoxxoSUMpkU0rPSufrMq2lXu12wQzHGmJBSJquPGlVtxOTrJgc7DGOMCTll8krBGGNM3iwpGGOMyWFJwRhjTA5LCsYYY3JYUjDGGJPDkoIxxpgclhSMMcbksKRgjDEmh6hqsGM4YSKyE9hUyGZVgV0lEE5JsfKEttJUntJUFrDy+Kqrqqfk9URYJwV/iMh8VW0T7DiKi5UntJWm8pSmsoCVx19WfWSMMSaHJQVjjDE5ykJSGBvsAIqZlSe0labylKaygJXHL6X+noIxxhj/lYUrBWOMMX6ypGCMMSZHqU4KInKxiKwSkbUiMjTY8fhDRDaKyFIRWSQi8711VUTkKxFZ4/2u7LP9MK98q0SkW/Aiz4nnvyKyQ0R+9Vl33PGLSGvvfVgrIi+JiJR0Wbw48irPcBHZ6p2jRSJyqc9zIVseEakjIjNFZIWILBORe731YXl+CihPuJ6fWBGZJyKLvfI85q0v2fOjqqXyB4gE1gGnAeWAxUDjYMflR9wbgaq51j0DDPWWhwJPe8uNvXLFAPW98kYGOf7zgFbAr0WJH5gHtAME+By4JITKMxwYnMe2IV0eoCbQyltOAFZ7MYfl+SmgPOF6fgSI95ajgZ+AtiV9fkrzlcI5wFpVXa+qR4BJQI8gx3SiegATvOUJQE+f9ZNUNU1VNwBrceUOGlWdDezJtfq44heRmsBJqvqjur/wt3z2KVH5lCc/IV0eVd2uqr94y8nACiCRMD0/BZQnP6FeHlXVFO9htPejlPD5Kc1JIRH4zefxFgr+gwkVCnwpIgtEZIC3rrqqbgf3jwBU89aHSxmPN/5Ebzn3+lByt4gs8aqXsi/nw6Y8IlIPaIn7Nhr25ydXeSBMz4+IRIrIImAH8JWqlvj5Kc1JIa86tHBof9teVVsBlwB3ich5BWwbrmXMll/8oV6u14AGQAtgO/Cctz4syiMi8cBHwEBVPVDQpnmsC4fyhO35UdVMVW0B1MZ9629awOYBKU9pTgpbgDo+j2sD24IUi99UdZv3ewcwBVcd9Id3SYj3e4e3ebiU8Xjj3+It514fElT1D++fNwt4nT+r7EK+PCISjfsAfVdVP/ZWh+35yas84Xx+sqnqPiAJuJgSPj+lOSn8DJwuIvVFpBzQC5ga5JgKJCIVRCQhexnoCvyKi7uft1k/4FNveSrQS0RiRKQ+cDruBlOoOa74vUvkZBFp67Wa6OuzT9Bl/4N6rsSdIwjx8nivPQ5YoarP+zwVlucnv/KE8fk5RUQqectxwIXASkr6/JT0HfaS/AEuxbVIWAc8HOx4/Ij3NFxrgsXAsuyYgZOBb4A13u8qPvs87JVvFUFqoZOrDO/hLtnTcd9Y+p9I/EAb3D/zOuAVvN73IVKet4GlwBLvH7NmOJQH6ICrRlgCLPJ+Lg3X81NAecL1/DQHFnpx/wr801tfoufHhrkwxhiTozRXHxljjDlOlhSMMcbksKRgjDEmhyUFY4wxOSwpGGOMyWFJwZQZInKliKiINDrB/VMK3yrffW8WkZ0+I3e+JSJXyAmO3isiPUWk8YnGY0x+ooIdgDElqDcwB9eRcXjuJ0UkUlUzA/j676vq3bnWnWiHyp7ANGB5kSIyJhe7UjBlgjc+Tntc57NePus7iRuTfyKuwxMi8ok3IOEyn0EJs7d/TkR+EZFvROQUb93fReRnbxz8j0SkvJ8x3Swir4hIgohs8IZsQEROEjevRnRexxaRvwJXACO9q44GIjJeRK4pjvfKlG2WFExZ0RP4QlVXA3tEpJXPc+fgeo9nV8fcqqqtcb1C7xGRk731FYBf1A1YOAt41Fv/saqerapn4YZv7p9PDNf7VB/dkr1S3bDPSUB3b1Uv4CNVTc/r2Kr6A+4KY4iqtlDVdSf2lhhzLEsKpqzojZtTA+93b5/n5qkbjz7bPSKyGJiLG3DsdG99FvC+t/wObpgFgKYi8p2ILAVuAJrkE8P73od4C1V9M9dzbwDZieIWIPt5f49tTLGwewqm1PO+6V+A+4BV3Kx8KiIPeJsc9Nm2E24gsnaqekhEkoDYfA6dPUbMeKCnqi4WkZuBTscbo6p+LyL1ROR83OxZ2YO4FfnYxhwPu1IwZcE1wFuqWldV66lqHWADf37T91UR2OslhEa46RCzRXjHAuiDu2kNbirI7d49gRuKEOdbuAH4fK8i8jt2svecMcXKkoIpC3rj5qbw9RHugz23L4AoEVkCPI6rQsp2EGgiIgtwVx7/8tY/gpvx6yvcUMcn6l2gMi4xZMvv2JOAISKyUEQaFOE1jTmKjZJqTIjwWg/1UNWbgh2LKbvsnoIxIUBEXsZNwXppsGMxZZtdKRhjjMlh9xSMMcbksKRgjDEmhyUFY4wxOSwpGGOMyWFJwRhjTI7/B5yHTMD5yyaXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_tahmin2 = lr2.predict(x_polinom)\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,y_tahmin,color=\"r\",label=\"Doğrusal\")\n",
    "plt.plot(x,y_tahmin2,color=\"g\",label=\"Polinomsal\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Araba Maksimum Hız\")\n",
    "plt.xlabel(\"Araba Fiyatı\")\n",
    "plt.title(\"Araba Hız ve Fiyt Grafiği\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8724fa5e",
   "metadata": {},
   "source": [
    "### R Kare ile Regresyon Modellerinin Değerlendirilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0e0e80ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures \n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1bb80255",
   "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>deneyim</th>\n",
       "      <th>maas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deneyim  maas\n",
       "0      0.5  2500\n",
       "1      0.0  2250\n",
       "2      1.0  2750\n",
       "3      5.0  8000\n",
       "4      8.0  9000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dogrusal_regresyon_veriseti_20220209.csv\", sep=\";\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a15f555b",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(df.deneyim,df.maas)\n",
    "plt.xlabel(\"Deneyim (Yıl)\")\n",
    "plt.ylabel(\"Maaş(TL)\")\n",
    "plt.title(\"Deneyim Maaş İlişkisi\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "46d758ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.deneyim.values.reshape(-1,1)\n",
    "y = df.maas.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "581e7323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineer_reg = LinearRegression()\n",
    "lineer_reg.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd1bf655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polinomsal_regresyon = PolynomialFeatures(degree= 2)\n",
    "\n",
    "x_polinom = polinomsal_regresyon.fit_transform(x)\n",
    "\n",
    "\n",
    "poly_reg = LinearRegression()\n",
    "poly_reg.fit(x_polinom,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8621ce40",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "y_tahmin_lineer = lineer_reg.predict(x)\n",
    "y_tahmin_poly = poly_reg.predict(x_polinom)\n",
    "\n",
    "plt.scatter(df.deneyim,df.maas)\n",
    "plt.plot(x,y_tahmin_lineer,color=\"red\",label=\"Doğrusal\")\n",
    "plt.scatter(x,y_tahmin_poly, color=\"g\",label=\"Polinomsal\")\n",
    "plt.xlabel(\"Deneyim (YIL)\")\n",
    "plt.ylabel(\"Maas (TL)\")\n",
    "plt.title(\"Deneyim Maas İliskisi\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1b8ab1",
   "metadata": {},
   "source": [
    "# Sınıflandırma\n",
    "- 1. Lojistik Regresyon \n",
    "- 2. K-En Yakın Komşu \n",
    "- 3. Destek Vektör Makinesi \n",
    "- 4. Karar Ağacı \n",
    "- 5. Rastgele Orman\n",
    "- 6. Sınıflandırma Modellerinin Değerlendirilmesi\n",
    "#ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4f4f1b64",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures \n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "47a6c2de",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis     class  \n",
       "0      98.672917                 -0.254400  Abnormal  \n",
       "1     114.405425                  4.564259  Abnormal  \n",
       "2     105.985135                 -3.530317  Abnormal  \n",
       "3     101.868495                 11.211523  Abnormal  \n",
       "4     108.168725                  7.918501  Abnormal  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ca36c552",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.countplot(data[\"class\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2832a21e",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  class  \n",
       "0      98.672917                 -0.254400      1  \n",
       "1     114.405425                  4.564259      1  \n",
       "2     105.985135                 -3.530317      1  \n",
       "3     101.868495                 11.211523      1  \n",
       "4     108.168725                  7.918501      1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"class\"] = [1 if each == \"Abnormal\" else 0 for each in data[\"class\"]]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "027b9345",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 310 entries, 0 to 309\n",
      "Data columns (total 7 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   pelvic_incidence          310 non-null    float64\n",
      " 1   pelvic_tilt numeric       310 non-null    float64\n",
      " 2   lumbar_lordosis_angle     310 non-null    float64\n",
      " 3   sacral_slope              310 non-null    float64\n",
      " 4   pelvic_radius             310 non-null    float64\n",
      " 5   degree_spondylolisthesis  310 non-null    float64\n",
      " 6   class                     310 non-null    int64  \n",
      "dtypes: float64(6), int64(1)\n",
      "memory usage: 17.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c2862cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b803ce46",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "y_data =data.drop([\"class\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "78f7f772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(x_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8dc4ad0",
   "metadata": {},
   "source": [
    "### Veriyi Normalize Edelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ddd67ef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc3ffce",
   "metadata": {},
   "source": [
    "### Eğitim Test Bölünmesi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f846d667",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e606c936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train  (6, 263)\n",
      "x_test  (6, 47)\n",
      "y_train  (263,)\n",
      "y_test  (47,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.15,random_state=42)\n",
    "\n",
    "x_train = x_train.T\n",
    "x_test = x_test.T\n",
    "y_train = y_train.T\n",
    "y_test = y_test.T\n",
    "\n",
    "print(\"x_train \",x_train.shape)\n",
    "print(\"x_test \",x_test.shape)\n",
    "print(\"y_train \",y_train.shape)\n",
    "print(\"y_test \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5b6e05",
   "metadata": {},
   "source": [
    "### Sklearn Logistik Regresyon Modeli Eğitimi ve Testi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7222262d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "934fa604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(x_train.T,y_train.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "eaab2f11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Doğruluğu : 0.7872340425531915\n"
     ]
    }
   ],
   "source": [
    "test_dogrulugu = lr.score(x_test.T,y_test.T)\n",
    "print(\"Test Doğruluğu : {}\".format(test_dogrulugu))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb6fadb",
   "metadata": {},
   "source": [
    "### K-En Yakın Komşu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "44f0ae75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "19051052",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis     class  \n",
       "0      98.672917                 -0.254400  Abnormal  \n",
       "1     114.405425                  4.564259  Abnormal  \n",
       "2     105.985135                 -3.530317  Abnormal  \n",
       "3     101.868495                 11.211523  Abnormal  \n",
       "4     108.168725                  7.918501  Abnormal  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "12cb3608",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.scatterplot(data=data,x = \"lumbar_lordosis_angle\", y = \"pelvic_tilt numeric\",hue=\"class\")\n",
    "plt.xlabel(\"Lomber Lordoz Açısı\")\n",
    "plt.ylabel(\"Pelvik Eğim\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "853c6587",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  class  \n",
       "0      98.672917                 -0.254400      1  \n",
       "1     114.405425                  4.564259      1  \n",
       "2     105.985135                 -3.530317      1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"class\"] = [1 if each == \"Abnormal\" else 0 for each in data[\"class\"]]\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ecdb47f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "32e007ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "533d64e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.15,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d2679d89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 En Yakın Komşu Modeli Test Doğruluğu 0.6382978723404256 \n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "komsu_sayisi = 4\n",
    "knn = KNeighborsClassifier(n_neighbors= komsu_sayisi)\n",
    "knn.fit(x_train,y_train)\n",
    "\n",
    "prediction = knn.predict(x_test)\n",
    "print(\"{} En Yakın Komşu Modeli Test Doğruluğu {} \".format(komsu_sayisi,knn.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb92e87f",
   "metadata": {},
   "source": [
    "### En İyi K Değerinin Bulunması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "44a5e585",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "score_list = []\n",
    "for each in range(1,50):\n",
    "    knn2 = KNeighborsClassifier(n_neighbors=each)\n",
    "    knn2.fit(x_train,y_train)\n",
    "    score_list.append(knn2.score(x_test,y_test))\n",
    "    \n",
    "plt.plot(range(1,50), score_list)\n",
    "plt.xlabel(\"k Değerleri\")\n",
    "plt.ylabel(\"Doğruluk\")\n",
    "plt.title(\"En İyi K Değerinin Bulunması\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f016ba5",
   "metadata": {},
   "source": [
    "## Destek Vektör Makinesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "52df200e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "820ad672",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis     class  \n",
       "0      98.672917                 -0.254400  Abnormal  \n",
       "1     114.405425                  4.564259  Abnormal  \n",
       "2     105.985135                 -3.530317  Abnormal  \n",
       "3     101.868495                 11.211523  Abnormal  \n",
       "4     108.168725                  7.918501  Abnormal  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "270bf913",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  class  \n",
       "0      98.672917                 -0.254400      1  \n",
       "1     114.405425                  4.564259      1  \n",
       "2     105.985135                 -3.530317      1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"class\"] = [1 if each == \"Abnormal\" else 0 for each in data[\"class\"]]\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5d3b023e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "135d46cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "456f02ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.15,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9deea3e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Destek Vektör Makinesi Modeli Test Doğruluğu: 0.7872340425531915\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(random_state=1)\n",
    "svm.fit(x_train,y_train)\n",
    "\n",
    "print(\"Destek Vektör Makinesi Modeli Test Doğruluğu: {}\".format(svm.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13afea17",
   "metadata": {},
   "source": [
    "## Karar Ağacı "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bfd5ee7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cac18c86",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  class  \n",
       "0      98.672917                 -0.254400      1  \n",
       "1     114.405425                  4.564259      1  \n",
       "2     105.985135                 -3.530317      1  \n",
       "3     101.868495                 11.211523      1  \n",
       "4     108.168725                  7.918501      1  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv\")\n",
    "data[\"class\"] = [1 if each == \"Abnormal\" else 0 for each in data[\"class\"]]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c343b234",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "087245d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "25a989e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.15,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4a31aabf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Karar Ağacı Modeli Test Doğruluğu: 0.851063829787234\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)\n",
    "\n",
    "print(\"Karar Ağacı Modeli Test Doğruluğu: {}\".format(dt.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3a5a40",
   "metadata": {},
   "source": [
    "## Rastgele Orman "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "dc3df0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bd4ac9e5",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  class  \n",
       "0      98.672917                 -0.254400      1  \n",
       "1     114.405425                  4.564259      1  \n",
       "2     105.985135                 -3.530317      1  \n",
       "3     101.868495                 11.211523      1  \n",
       "4     108.168725                  7.918501      1  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv\")\n",
    "data[\"class\"] = [1 if each == \"Abnormal\" else 0 for each in data[\"class\"]]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2bb8f364",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cce96244",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ddca1012",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.15,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "481840bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rastgele Orman Modeli Test Doğruluğu: 0.8723404255319149\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100,random_state=1)\n",
    "rf.fit(x_train,y_train)\n",
    "\n",
    "print(\"Rastgele Orman Modeli Test Doğruluğu: {}\".format(rf.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7eae0f",
   "metadata": {},
   "source": [
    "## Konfucion Matris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a6890f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "dd7201aa",
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  class  \n",
       "0      98.672917                 -0.254400      1  \n",
       "1     114.405425                  4.564259      1  \n",
       "2     105.985135                 -3.530317      1  \n",
       "3     101.868495                 11.211523      1  \n",
       "4     108.168725                  7.918501      1  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv\")\n",
    "data[\"class\"] = [1 if each == \"Abnormal\" else 0 for each in data[\"class\"]]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6e877475",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "53101433",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6e528b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.15,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d703ae9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rastgele Orman Modeli Test Doğruluğu: 0.8723404255319149\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100,random_state=1)\n",
    "rf.fit(x_train,y_train)\n",
    "\n",
    "print(\"Rastgele Orman Modeli Test Doğruluğu: {}\".format(rf.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4d0bd75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  3],\n",
       "       [ 3, 27]], dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = rf.predict(x_test)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "30a8172f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidths=0.5,linecolor=\"white\",fmt=\".0f\")\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce590e6",
   "metadata": {},
   "source": [
    "## Kümeleme "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb96d2e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}