machinelearning2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "818efadf",
   "metadata": {},
   "source": [
    "### K-En Yakın Komşu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e843479",
   "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": 13,
   "id": "faf47dc6",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0fe60b34",
   "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": 15,
   "id": "05833348",
   "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": 15,
     "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": 16,
   "id": "987bf958",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c1af223",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d0174d22",
   "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": 19,
   "id": "5ef61768",
   "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": "3596c5da",
   "metadata": {},
   "source": [
    "### En İyi K Değerinin Bulunması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d3f29e9",
   "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": "7842694a",
   "metadata": {},
   "source": [
    "## Destek Vektör Makinesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97e68c4f",
   "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": 22,
   "id": "2ee16eba",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ortopedik_hastalarin_biyomekanik_ozellikleri_20220209.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "171a0581",
   "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": 23,
     "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": 24,
   "id": "e6eeccc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3a575801",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f9ab124",
   "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": 27,
   "id": "e42f1225",
   "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": "1c7e4ec1",
   "metadata": {},
   "source": [
    "## Karar Ağacı "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4267947b",
   "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": 29,
   "id": "11dd8f48",
   "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": 29,
     "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": 30,
   "id": "f49a2ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3ac83e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8e890082",
   "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": 33,
   "id": "177b326f",
   "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": 34,
   "id": "b1ea9cf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Karar Ağacı Modeli Test Doğruluğu: 0.8085106382978723\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": "e706d73c",
   "metadata": {},
   "source": [
    "## Rastgele Orman "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eb9e4ec2",
   "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": 36,
   "id": "76a8ac6e",
   "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": 36,
     "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": 37,
   "id": "6b015c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "65c7bfdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b7fb8110",
   "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": 40,
   "id": "380bd069",
   "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": "b524aeda",
   "metadata": {},
   "source": [
    "## Konfucion Matris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2c5c46ce",
   "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": 42,
   "id": "bfd325b7",
   "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": 42,
     "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": 43,
   "id": "dc1d246b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"class\"].values\n",
    "x_data = data.drop([\"class\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dece3f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d1701290",
   "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": 46,
   "id": "8fbd9a79",
   "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": 47,
   "id": "4ec9ba34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  3],\n",
       "       [ 3, 27]], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "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": 48,
   "id": "e5b41ea5",
   "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": "10486b23",
   "metadata": {},
   "source": [
    "# Kümeleme "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31d8624",
   "metadata": {},
   "source": [
    "### K-Ortalama Küme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c1587b7c",
   "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": 50,
   "id": "7e91c751",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.668249</td>\n",
       "      <td>24.027857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.896214</td>\n",
       "      <td>32.383457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.852587</td>\n",
       "      <td>26.578297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.705927</td>\n",
       "      <td>26.579424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.767424</td>\n",
       "      <td>22.519448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y\n",
       "0  31.668249  24.027857\n",
       "1  14.896214  32.383457\n",
       "2  31.852587  26.578297\n",
       "3  25.705927  26.579424\n",
       "4  26.767424  22.519448"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.random.normal(25,5,1000)\n",
    "y1 = np.random.normal(25,5,1000)\n",
    "\n",
    "x2 = np.random.normal(55,5,1000)\n",
    "y2 = np.random.normal(60,5,1000)\n",
    "\n",
    "x3 = np.random.normal(55,5,1000)\n",
    "y3 = np.random.normal(15,5,1000)\n",
    "\n",
    "x = np.concatenate((x1,x2,x3),axis= 0)\n",
    "y = np.concatenate((y1,y2,y3),axis= 0)\n",
    "\n",
    "dictionary = {\"x\":x,\"y\":y}\n",
    "\n",
    "data = pd.DataFrame(dictionary)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3bec7150",
   "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.figure()\n",
    "plt.scatter(x1,y1)\n",
    "plt.scatter(x2,y2)\n",
    "plt.scatter(x3,y3)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"K-Ortlama Kümeleme Yöntemi için Oluşturulan Veri Seti\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "58782f8c",
   "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.figure()\n",
    "plt.scatter(x1,y1,color=\"black\")\n",
    "plt.scatter(x2,y2,color=\"black\")\n",
    "plt.scatter(x3,y3,color=\"black\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"K-Ortlama Kümeleme Yöntemi için Oluşturulan Veri Seti\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6eea9b",
   "metadata": {},
   "source": [
    "## K Değerinin Seçimi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f82d0d71",
   "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": [
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\n",
    "for k in range(1,15):\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(data)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(range(1,15),wcss)\n",
    "plt.xticks(range(1,15))\n",
    "plt.xlabel(\"Küme Sayısı (K)\")\n",
    "plt.ylabel(\"wcss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f5d22865",
   "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": [
    "k_ortalama = KMeans(n_clusters=3)\n",
    "kumeler = k_ortalama.fit_predict(data)\n",
    "\n",
    "data[\"label\"] = kumeler\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(data.x[data.label == 0],data.y[data.label == 0],color=\"red\",label=\"Kume 1\")\n",
    "plt.scatter(data.x[data.label == 1],data.y[data.label == 1],color=\"green\",label=\"Kume 2\")\n",
    "plt.scatter(data.x[data.label == 2],data.y[data.label == 2],color=\"blue\",label=\"Kume 3\")\n",
    "plt.scatter(k_ortalama.cluster_centers_[:,0],k_ortalama.cluster_centers_[:,1],color=\"yellow\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"3-Ortalama Kümeleme Sonucu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8cda1ed",
   "metadata": {},
   "source": [
    "## Hiyerarşik Kümeleme "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a1800487",
   "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": 56,
   "id": "36f8c351",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.844395</td>\n",
       "      <td>17.442267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.056521</td>\n",
       "      <td>29.030702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.160240</td>\n",
       "      <td>34.598305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.363514</td>\n",
       "      <td>21.423114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.307373</td>\n",
       "      <td>21.031307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y\n",
       "0  27.844395  17.442267\n",
       "1  28.056521  29.030702\n",
       "2  24.160240  34.598305\n",
       "3  22.363514  21.423114\n",
       "4  22.307373  21.031307"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.random.normal(25,5,20)\n",
    "y1 = np.random.normal(25,5,20)\n",
    "\n",
    "x2 = np.random.normal(55,5,20)\n",
    "y2 = np.random.normal(60,5,20)\n",
    "\n",
    "x3 = np.random.normal(55,5,20)\n",
    "y3 = np.random.normal(15,5,20)\n",
    "\n",
    "x = np.concatenate((x1,x2,x3),axis=0)\n",
    "y = np.concatenate((y1,y2,y3),axis=0)\n",
    "\n",
    "dictionary = {\"x\":x,\"y\":y}\n",
    "\n",
    "data = pd.DataFrame(dictionary)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9e5f83a4",
   "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.figure()\n",
    "plt.scatter(x1,y1)\n",
    "plt.scatter(x2,y2)\n",
    "plt.scatter(x3,y3)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"Hiyerarşik Kümeleme Yöntemi için Veri Seti\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9073a7e3",
   "metadata": {},
   "source": [
    "## Dendiogram Gösterimi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3399d0a8",
   "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": [
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "\n",
    "merg = linkage(data,method=\"ward\")\n",
    "dendrogram(merg,leaf_rotation=90)\n",
    "plt.xlabel(\"Veri Noktaları\")\n",
    "plt.ylabel(\"Oklid Mesafesi\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "809592c5",
   "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": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "hiyerarsi_kume = AgglomerativeClustering(n_clusters=3,affinity=\"euclidean\",linkage=\"ward\")\n",
    "kume = hiyerarsi_kume.fit_predict(data)\n",
    "\n",
    "data[\"label\"] = kume\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(data.x[data.label == 0],data.y[data.label == 0],color=\"red\",label=\"Kume 1\")\n",
    "plt.scatter(data.x[data.label == 1],data.y[data.label == 1],color=\"green\",label=\"Kume 2\")\n",
    "plt.scatter(data.x[data.label == 2],data.y[data.label == 2],color=\"blue\",label=\"Kume 3\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"Hierarşik Kümeleme Sonucu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0012f2ef",
   "metadata": {},
   "source": [
    "##  Temel Bileşen Analizi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e4b3d610",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ac8ba0e7",
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>sinif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   sinif  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "\n",
    "data = iris.data\n",
    "feature_names = iris.feature_names\n",
    "y= iris.target\n",
    "\n",
    "df = pd.DataFrame(data,columns=feature_names)\n",
    "df[\"sinif\"] = y\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d5c24716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "varience ratio:  [0.92461872 0.05306648]\n",
      "sum:  0.977685206318795\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2, whiten=True)# Whiten = Normalizasyon\n",
    "pca.fit(data)\n",
    "\n",
    "x_pca = pca.transform(data)\n",
    "\n",
    "print(\"varience ratio: \",pca.explained_variance_ratio_)\n",
    "\n",
    "print(\"sum: \", sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "79773647",
   "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": [
    "df[\"p1\"] = x_pca[:,0]\n",
    "df[\"p2\"] = x_pca[:,1]\n",
    "\n",
    "color = [\"red\",\"green\",\"blue\"]\n",
    "\n",
    "for each in range(3):\n",
    "    plt.scatter(df.p1[df.sinif == each],df.p2[df.sinif == each], color=color[each],label=iris.target_names[each])\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel(\"p1\")\n",
    "plt.ylabel(\"p2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c4758a",
   "metadata": {},
   "source": [
    "# PCA ve Kernel PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ab618da6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = make_circles(n_samples=1_000,factor=0.3,noise=0.05,random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,stratify=y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dece7f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_, (train_ax, test_ax) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(8, 4))\n",
    "\n",
    "train_ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train)\n",
    "train_ax.set_ylabel(\"Feature #1\")\n",
    "train_ax.set_xlabel(\"Feature #0\")\n",
    "train_ax.set_title(\"Training data\")\n",
    "\n",
    "test_ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test)\n",
    "test_ax.set_xlabel(\"Feature #0\")\n",
    "_ = test_ax.set_title(\"Testing data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "91e81a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA, KernelPCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "kernel_pca = KernelPCA(\n",
    "    n_components=None, kernel=\"rbf\", gamma=10, fit_inverse_transform=True, alpha=0.1\n",
    ")\n",
    "\n",
    "X_test_pca = pca.fit(X_train).transform(X_test)\n",
    "X_test_kernel_pca = kernel_pca.fit(X_train).transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "191ef80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (orig_data_ax, pca_proj_ax, kernel_pca_proj_ax) = plt.subplots(\n",
    "    ncols=3, figsize=(14, 4)\n",
    ")\n",
    "\n",
    "orig_data_ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test)\n",
    "orig_data_ax.set_ylabel(\"Feature #1\")\n",
    "orig_data_ax.set_xlabel(\"Feature #0\")\n",
    "orig_data_ax.set_title(\"Testing data\")\n",
    "\n",
    "pca_proj_ax.scatter(X_test_pca[:, 0], X_test_pca[:, 1], c=y_test)\n",
    "pca_proj_ax.set_ylabel(\"Principal component #1\")\n",
    "pca_proj_ax.set_xlabel(\"Principal component #0\")\n",
    "pca_proj_ax.set_title(\"Projection of testing data\\n using PCA\")\n",
    "\n",
    "kernel_pca_proj_ax.scatter(X_test_kernel_pca[:, 0], X_test_kernel_pca[:, 1], c=y_test)\n",
    "kernel_pca_proj_ax.set_ylabel(\"Principal component #1\")\n",
    "kernel_pca_proj_ax.set_xlabel(\"Principal component #0\")\n",
    "_ = kernel_pca_proj_ax.set_title(\"Projection of testing data\\n using KernelPCA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b557be24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "x = [[0,0,1],[0,1,0],[1,0,0],[0,1,1],[0,1,0],[0,1,1]]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "af73722c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [1, 0],\n",
       "       [0, 0],\n",
       "       [1, 1],\n",
       "       [1, 0],\n",
       "       [1, 1]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel = VarianceThreshold(threshold=0.8*(1-0.8))\n",
    "sel.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2a630845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f2e33975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new = SelectKBest(chi2, k=2).fit_transform(X, y)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c10aa92b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e0be109b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 3)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsvc = LinearSVC(C=0.01, penalty=\"l1\", dual=False).fit(X, y)\n",
    "model = SelectFromModel(lsvc, prefit=True)\n",
    "X_new = model.transform(X)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "77ae87b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsvc = LinearSVC(C=0.01, penalty=\"l2\", dual=False).fit(X, y)\n",
    "model = SelectFromModel(lsvc, prefit=True)\n",
    "X_new = model.transform(X)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b267ed",
   "metadata": {},
   "source": [
    "### Ağaç Temelli Öznitelik Seçimi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cd66eebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "daac552b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12791752, 0.06239127, 0.37942084, 0.43027037])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = ExtraTreesClassifier(n_estimators=50)\n",
    "clf = clf.fit(X, y)\n",
    "clf.feature_importances_  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "06289718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SelectFromModel(clf, prefit=True)\n",
    "X_new = model.transform(X)\n",
    "X_new.shape   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b593600",
   "metadata": {},
   "source": [
    "## Model Seçimi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "93faad83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "32a21ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "\n",
    "x = iris.data\n",
    "y= iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "66afc732",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x - np.min(x)) / (np.max(x) - np.min(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "90b7f366",
   "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=0.30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb0cba0",
   "metadata": {},
   "source": [
    "## Çapraz Doğrulama ile Model Eğitimi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f5f91114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d22036b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ortalama Doğruluk:  0.9536363636363637\n",
      "Doğrulukların Standart Sapması  0.07390523304255509\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "fold_sayisi = 10\n",
    "dogruluklar = cross_val_score(estimator=knn, X = x_train,y=y_train,cv=fold_sayisi)\n",
    "print(\"Ortalama Doğruluk: \",np.mean(dogruluklar))\n",
    "print(\"Doğrulukların Standart Sapması \",np.std(dogruluklar))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a30527b",
   "metadata": {},
   "source": [
    "## Izgara Araması Çapraz Doğrulama "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f2c5aba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "df14dbdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "\n",
    "x = iris.data\n",
    "y= iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f2ca0a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =(x - np.min(x)) / (np.max(x) - np.min(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "38505e9d",
   "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=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7db26be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f040f98b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "grid = {\"n_neighbors\":np.arange(1,50)}\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "knn_cv = GridSearchCV(knn,grid,cv=10)\n",
    "knn_cv.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0e854bab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi K Değeri:  {'n_neighbors': 13}\n",
      "En iyi K Değerine Göre Doğruluk Değeri 0.9800000000000001\n"
     ]
    }
   ],
   "source": [
    "print(\"En iyi K Değeri: \",knn_cv.best_params_)\n",
    "print(\"En iyi K Değerine Göre Doğruluk Değeri\",knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "615993de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi Hiper Parametre {'C': 1000.0, 'penalty': 'l2'}\n",
      "En iyi Hiper Parametreye Göre Doğruluk 0.9800000000000001\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "grid = {\"C\":np.logspace(-3,3,7),\"penalty\":[\"l1\",\"l2\"]}\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg_cv = GridSearchCV(logreg,grid,cv=10)\n",
    "logreg_cv.fit(x,y)\n",
    "\n",
    "\n",
    "print(\"En iyi Hiper Parametre\",logreg_cv.best_params_)\n",
    "print(\"En iyi Hiper Parametreye Göre Doğruluk\",logreg_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46fbcd85",
   "metadata": {},
   "source": [
    "# Deep Learning\n",
    "\n",
    "#### egitim.csv dosyası elimizde olmadığı için hata almanız normal. Eğitim dosyası yüklendikten sonra çalıştırırsanız hata almayacaksınız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a968a336",
   "metadata": {},
   "outputs": [],
   "source": [
    "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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1eb5d483",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3ee652ba",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'egitim.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_9348/1168617692.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mveri\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"egitim.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Verinin Boyutu: \"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mveri\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    481\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 482\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 811\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    813\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1038\u001b[0m             )\n\u001b[0;32m   1039\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m    220\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \"\"\"\n\u001b[1;32m--> 222\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    700\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'egitim.csv'"
     ]
    }
   ],
   "source": [
    "veri = pd.read_csv(\"egitim.csv\")\n",
    "print(\"Verinin Boyutu: \",veri.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9799f380",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_filte0 = 0\n",
    "label_filte1 = 1\n",
    "\n",
    "veri.pd.concat([veri[veri[\"label\"] == label_filte0],\n",
    "               veri[veri[\"label\"] == label_filte1]], axis = 0)\n",
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d13124cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_veri = veri[\"label\"]\n",
    "\n",
    "X_veri = veri.drop([\"label\"],axis=1)\n",
    "print(\"X veri : \", X_veri.shape)\n",
    "print(\"Y veri : \", V_veri.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6a885bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "resim_boyutu = int(np.sqrt(X_veri.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e9ef9c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "resim1 = X_veri.iloc[900].values\n",
    "resim1 = resim1.reshape(resim_boyutu,resim_boyutu)\n",
    "plt.imgshow(resim1,cmap=\"gray\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "246f8a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "resim2 = X_veri.iloc[8000].values\n",
    "resim2 = resim2.reshape(resim_boyutu,resim_boyutu)\n",
    "plt.imgshow(resim2,cmap=\"gray\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "797de4d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_egitim, X_test, Y_egitim, Y_test = train_test_split(X_veri,Y_veri,test_size=0.15,random_state=42)\n",
    "\n",
    "print(\"X egitim: \",X_egitim.shape)\n",
    "print(\"X test: \",X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b61f9209",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_egitim = X_egitim.T\n",
    "x_test = X_test.T\n",
    "y_egitim = Y_egitim.values.reshape(-1,1).T\n",
    "y_test = Y_test.values.reshape(-1,1).T\n",
    "print(\"x egitim: \",x_egitim.shape)\n",
    "print(\"x test: \",x_test.shape)\n",
    "print(\"y egitim: \",y_egitim.shape)\n",
    "print(\"y egitim: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c1cde0",
   "metadata": {},
   "source": [
    "## Katmanlı Sinir Ağları "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe692a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "def layerSayisiniParametrelendir(x_train,y_train):\n",
    "    parameters = {\"weight\":np.random.rand(3,x_train.shape[0]) * 0.1,\n",
    "                 \"bias1\":np.zeros(3,1),\n",
    "                 \"weight2\":np.random.randn(y_train.shape[0],3) * 0.1},\n",
    "                 \"bias2\":np.zeros((y_train.shape[0],1))}\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d0fc277",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    \n",
    "    y_head = 1/(1+np.exp(-z))\n",
    "    return y_head\n",
    "sigmoid(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da8baab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ileriYayilmaNN(x_train,parameters):\n",
    "    \n",
    "    Z1 = np.dot(parameters[\"weight1\"],x_train +parameters[\"bias1\"])\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = Z1 = np.dot(parameters[\"weight2\"],A1 +parameters[\"bias2\"])\n",
    "    A2 = sigmoid(Z2)\n",
    "    \n",
    "    cache = {\"Z1\":Z1,\n",
    "            \"A1\":A1,\n",
    "            \"Z2\":Z2,\n",
    "            \"A2\":A2}\n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60bf2892",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maliyetHesaplaNN(A2,Y,parameters):\n",
    "    logprobs = np.multiply(np.log(A2),Y)\n",
    "    cost = -np.sum(logprobs)/Y.shape[1]\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "822a7122",
   "metadata": {},
   "outputs": [],
   "source": [
    "def geriyeYayilmaNN(parameters, cache, X, Y):\n",
    "\n",
    "    dZ2 =cache[\"A2\"]-Y\n",
    "\n",
    "    dW2 = np.dot(dZ2, cache[\"A1\"].T)/X.shape[1]\n",
    "\n",
    "    db2 = np.sum(dZ2, axis = 1,keepdims=True)/X.shape[1]\n",
    "\n",
    "    dZ1 = np. dot (parameters [\"weight2\" ]. T, dz2)*(1 - np.power (cache[\"A1\"], 2))\n",
    "\n",
    "    dw1 = np.dot (dZ1, X. T)/X. shape[1]\n",
    "\n",
    "    db1 = np.sum (dz1,axis = 1,keepdims=True)/X.shape[1]\n",
    "\n",
    "    grads = {\"dweight1\": dw1,\"dbias1\": db1,\"dweight2\": dw2,\"dbias2\": db2}\n",
    "\n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e2a6e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parametreGuncelleNN(parameters, grads, learning_rate= 0.01):\n",
    "\n",
    "    parameters = {\"weight\": parameters[\"weight1\"]-learning_rate*grads[\"dweight1\"],\n",
    "                  \"bias1\":parameters[\"bias1\"]-learning_rate*grads[\"dbias1\"],\n",
    "                  \"weight2\":parameters[\"weight2\"]-learning_rate*grads[\"weight2\"],\n",
    "                  \"bias2\":parameters[\"bias2\"]-learning_rate*grads[\"bias2\"]}\n",
    "    return parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c088f0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tahminNN(parameters, x_test):\n",
    "\n",
    "# x test ileri yayilma girdisidir.\n",
    "\n",
    "    A2, cache = ileriYayilmaNN(x_test, parameters)\n",
    "\n",
    "    Y_prediction = np.zeros((1,x_test.shape[1]))\n",
    "\n",
    "    # eğer z 0.5'den büyükse, 1 sınıfına ait resimler\n",
    "\n",
    "    # eğer z 0.5'den küçükse, e sfina ait resimler\n",
    "\n",
    "    for i in range(A2. shape[1]):\n",
    "\n",
    "        if A2[0,i]<= 0.5: \n",
    "            Y_prediction[0,i] = 0\n",
    "\n",
    "        else:\n",
    "            Y_prediction[0,i] = 1\n",
    "\n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddac289f",
   "metadata": {},
   "source": [
    "## 2 Katlamlı Sinir Ağı Oluşturma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b006958",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ikikatmanNN(x_train, y_train,x_test,y_test, num_iterations): \n",
    "    cost_list = []\n",
    "    index_list = []\n",
    "    # ilklendirme\n",
    "    parameters = layerSayisiniParametreleriIlklendir(x_train, y_train)\n",
    "    \n",
    "    for i in range(0, num_iterations):\n",
    "        # ileri yayılım\n",
    "        A2, cache = ileriYayilmaNN(x_train, parameters)\n",
    "        # maliyet hesapla\n",
    "        cost = maliyetHesaplaNN(A2, y_train, parameters)\n",
    "        # geriye yayılım\n",
    "        grads = geriyeYayilmaNN(parameters, cache, x_train, y_train)\n",
    "        # parametreleri güncelle\n",
    "        parameters = parametreGuncelleNN (parameters, grads)\n",
    "    \n",
    "        if i % 100 == 0:\n",
    "            cost_list.append(cost)\n",
    "            index_list.append(i)\n",
    "    print (\"İterasyon: %i, Maliyet: %f\" % (i, cost))\n",
    "    plt.plot (index_list, cost_list) \n",
    "    plt.xticks (index_list, rotation='vertical')\n",
    "    plt.xlabel(\"Iterasyon Sayisi\")\n",
    "    plt.ylabel(\"Maliyet\")\n",
    "    plt.show()\n",
    "    \n",
    "    #predict\n",
    "    y_prediction_test = tahminNN(parameters,x_test)\n",
    "    y_prediction_train = tahminNN(parameters,x_train)\n",
    "    \n",
    "    # eğitim ve test doğruluğu \n",
    "    print(\"Eğitim Doğruluğu: {} %\".format(\n",
    "    100-np.mean(np.abs(y_prediction_train-y_train) * 100)))\n",
    "    print(\"Test Doğruluğu {} %\".format(\n",
    "    100 - np.mean(np.abs(y_prediction_test-y_test))))\n",
    "    \n",
    "\n",
    "parameters = ikikatmanNN(x_egitim,y_egitim,x_test,y_test,num_iterations=900)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a8e8428",
   "metadata": {},
   "source": [
    "## N-Katmanlı Sinir Ağı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d008f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_egitim, x_test, y_egitim, y_test = \n",
    "                    x_egitim.T, x_test.T, y_egitim.T, y_test.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2634e7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# yapay sinir ağları kütüphaneleri\n",
    "\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from keras.models import Sequential # initialize neural network Library from keras.layers import Dense # build our Layers Library\n",
    "\n",
    "# modelimiz için bir metot yazalım \n",
    "def siniflandiriciMethot():\n",
    "    classifier = Sequential() # initialize neural network =\n",
    "    classifier.add(Dense(units = 8, kernel_initializer = 'uniform', activation = \"relu\", input_dim = x_egitim.shape[1]))\n",
    "    classifier.add(Dense(units = 8, kernel_initializer = 'uniform', activation = \"relu\"))\n",
    "    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = \"sigmoid\"))\n",
    "    classifier.compile(optimizer = \"adam\", loss = 'binary crossentropy', metrics = [\"accuracy\"])\n",
    "    return classifier\n",
    "\n",
    "siniflandirici = KerasClassifier(build_fn = siniflandiriciMethot, epochs = 5) \n",
    "dogruluk = cross_val_score(estimator = siniflandirici, X = x_egitim, y = y_egitim, cv =3 )\n",
    "\n",
    "ortalama = dogruluk.mean()\n",
    "\n",
    "varyans = dogruluk.std()\n",
    "\n",
    "print(\"Doğruluğun ortalaması: \"+ str(ortalama))\n",
    "print(\"Doğruluğun Varyansı: \"+ str(varyans))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "758f9ca0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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