Logistic2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7f939ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c64a09a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "veri=pd.read_csv(\"desktop/alzheimer.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acf90805",
   "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>Group</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>SES</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nondemented</td>\n",
       "      <td>M</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nondemented</td>\n",
       "      <td>M</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Demented</td>\n",
       "      <td>M</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Demented</td>\n",
       "      <td>M</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Demented</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Group M/F  Age  EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF\n",
       "0  Nondemented   M   87    14  2.0  27.0  0.0  1987  0.696  0.883\n",
       "1  Nondemented   M   88    14  2.0  30.0  0.0  2004  0.681  0.876\n",
       "2     Demented   M   75    12  NaN  23.0  0.5  1678  0.736  1.046\n",
       "3     Demented   M   76    12  NaN  28.0  0.5  1738  0.713  1.010\n",
       "4     Demented   M   80    12  NaN  22.0  0.5  1698  0.701  1.034"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c65da84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NORMALİZASYON İŞLEMLERİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13aabd2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373, 10)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.shape  #veri setinde 373 satır örneklem, 10 tane öznitelik mevcuttur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb3a1d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group     object\n",
       "M/F       object\n",
       "Age        int64\n",
       "EDUC       int64\n",
       "SES      float64\n",
       "MMSE     float64\n",
       "CDR      float64\n",
       "eTIV       int64\n",
       "nWBV     float64\n",
       "ASF      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84b9be73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a3d6e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(x=\"Group\",data=veri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bcc4c594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(x=\"Group\", hue=\"M/F\",data=veri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5ceaa7ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "veri[\"Age\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51d78fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group     0\n",
       "M/F       0\n",
       "Age       0\n",
       "EDUC      0\n",
       "SES      19\n",
       "MMSE      2\n",
       "CDR       0\n",
       "eTIV      0\n",
       "nWBV      0\n",
       "ASF       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a2901242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(veri.isnull(),yticklabels=False,cmap=\"viridis\")  #eksik verilerin görselleştirilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "62a84c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.drop([\"SES\",\"MMSE\"],axis=1,inplace=True)  #eksik veriler veri setinden kaldırılır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9dce9c27",
   "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>Group</th>\n",
       "      <th>M/F</th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nondemented</td>\n",
       "      <td>M</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nondemented</td>\n",
       "      <td>M</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Demented</td>\n",
       "      <td>M</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Demented</td>\n",
       "      <td>M</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Demented</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Group M/F  Age  EDUC  CDR  eTIV   nWBV    ASF\n",
       "0  Nondemented   M   87    14  0.0  1987  0.696  0.883\n",
       "1  Nondemented   M   88    14  0.0  2004  0.681  0.876\n",
       "2     Demented   M   75    12  0.5  1678  0.736  1.046\n",
       "3     Demented   M   76    12  0.5  1738  0.713  1.010\n",
       "4     Demented   M   80    12  0.5  1698  0.701  1.034"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "706698a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(veri.isnull(),yticklabels=False,cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e519c972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group    0\n",
       "M/F      0\n",
       "Age      0\n",
       "EDUC     0\n",
       "CDR      0\n",
       "eTIV     0\n",
       "nWBV     0\n",
       "ASF      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "62672ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f899ce28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group    0\n",
       "M/F      0\n",
       "Age      0\n",
       "EDUC     0\n",
       "CDR      0\n",
       "eTIV     0\n",
       "nWBV     0\n",
       "ASF      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "154bef6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group     object\n",
       "M/F       object\n",
       "Age        int64\n",
       "EDUC       int64\n",
       "CDR      float64\n",
       "eTIV       int64\n",
       "nWBV     float64\n",
       "ASF      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6c7bd9ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "Group=pd.get_dummies(veri[\"Group\"],drop_first=True)  #uygun tipte olmayan veriler dummy değişkeni ile değiştirilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a25bbb62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   Demented  Nondemented\n",
       "0         0            1\n",
       "1         0            1\n",
       "2         1            0\n",
       "3         1            0\n",
       "4         1            0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Group.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7d8c899d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=pd.get_dummies(veri[\"M/F\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "44552855",
   "metadata": {},
   "outputs": [
    {
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       "   M\n",
       "0  1\n",
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       "3  1\n",
       "4  1"
      ]
     },
     "execution_count": 29,
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   ],
   "source": [
    "sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c43a97f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Group=pd.get_dummies(veri[\"Group\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1aaafc8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=pd.get_dummies(veri[\"M/F\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f1e87d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.drop([\"Group\",\"M/F\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "96c7a5e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "veri=pd.concat([veri,Group,sex],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ef29f8b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              int64\n",
       "EDUC             int64\n",
       "CDR            float64\n",
       "eTIV             int64\n",
       "nWBV           float64\n",
       "ASF            float64\n",
       "Demented         uint8\n",
       "Nondemented      uint8\n",
       "M                uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6c5bb36a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>87</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1.037</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1688</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1.040</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.801</td>\n",
       "      <td>1.331</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1327</td>\n",
       "      <td>0.796</td>\n",
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       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.801</td>\n",
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       "      <td>1</td>\n",
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       "<p>373 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  EDUC  CDR  eTIV   nWBV    ASF  Demented  Nondemented  M\n",
       "0     87    14  0.0  1987  0.696  0.883         0            1  1\n",
       "1     88    14  0.0  2004  0.681  0.876         0            1  1\n",
       "2     75    12  0.5  1678  0.736  1.046         1            0  1\n",
       "3     76    12  0.5  1738  0.713  1.010         1            0  1\n",
       "4     80    12  0.5  1698  0.701  1.034         1            0  1\n",
       "..   ...   ...  ...   ...    ...    ...       ...          ... ..\n",
       "368   82    16  0.5  1693  0.694  1.037         1            0  1\n",
       "369   86    16  0.5  1688  0.675  1.040         1            0  1\n",
       "370   61    13  0.0  1319  0.801  1.331         0            1  0\n",
       "371   63    13  0.0  1327  0.796  1.323         0            1  0\n",
       "372   65    13  0.0  1333  0.801  1.317         0            1  0\n",
       "\n",
       "[373 rows x 9 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri   #veri ön işlemeden geçti.Veriseti işlemler için uygun hale getirildi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "836e42e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LOJİSTIK REGRASYON ANALİZİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a3c2923d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=veri.drop(\"Demented\",axis=1)  #hedef değişken \"demented\",geri kalan bileşenler özniteliklerdir\n",
    "y=veri[\"Demented\"]     \n",
    "\n",
    "#girdi ve çıktı verileri ayarlandı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "164b936c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train ve test işlemleri için veri seti parçalara ayıralım\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e4d0718d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,\n",
    "                                               random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9d70f13a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#tahmin etmesi için kullanıdıgımız hedef için ikili lojistik regresyon analizi kullanılır\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "10173ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "lg_model=LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a5872896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#eğitim verisi kullnarak modeli eğitelim\n",
    "lg_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b786a5d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9680851063829787"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.score(x_test,y_test)  #buradan anlaşılıyor ki modelimiz yeni veri tahmini %96 başarı ile tahmin edecek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9914c5da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9390681003584229"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.score(x_train,y_train) #modelin doğruluk skoru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5e7bd1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, max_iter=1000)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model=LogisticRegression(C=0.1,max_iter=1000)\n",
    "lg_model.fit(x_train,y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "71372559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9680851063829787\n",
      "0.931899641577061\n"
     ]
    }
   ],
   "source": [
    "print(lg_model.score(x_test,y_test))\n",
    "print(lg_model.score(x_train,y_train))  #modelin eğitim verisi üzerindeki skoru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "191ad7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MODEL DEĞERLENDİRME\n",
    "#modelin sınıflarının ne kadar doğru tahmin ettiğini bulmak için confusion kullanalım \n",
    "#test verisindeki değerleri bulalım\n",
    "\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e12996ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin=lg_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6ad78087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55,  1],\n",
       "       [ 2, 36]], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,tahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c21a550e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 55 tane true pozitif, 36 tane true negatif tahmin yapılmış 1 tahmin pozitif iken yanlış tahminde bulunmuş \n",
    "#2 pozitif değer de negatif bulunmuş"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0359fce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
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       "      <th>0</th>\n",
       "      <td>87</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.736</td>\n",
       "      <td>1.046</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1738</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1698</td>\n",
       "      <td>0.701</td>\n",
       "      <td>1.034</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  EDUC  CDR  eTIV   nWBV    ASF  Demented  Nondemented  M\n",
       "0   87    14  0.0  1987  0.696  0.883         0            1  1\n",
       "1   88    14  0.0  2004  0.681  0.876         0            1  1\n",
       "2   75    12  0.5  1678  0.736  1.046         1            0  1\n",
       "3   76    12  0.5  1738  0.713  1.010         1            0  1\n",
       "4   80    12  0.5  1698  0.701  1.034         1            0  1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3ea74edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#eğitilmiş sistemin doğru tahmin yapıp yapmadıgının kontrolü\n",
    "import numpy as np\n",
    "yeni_veri=np.array([[80,12,0.5,1698,0.701,1.034,0,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "17196e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=uint8)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.predict(yeni_veri)  #sistem 1 değeri verdi bu demek ki bu kişi demented olabilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f1d79794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.18534845e-02, -1.42147641e-01,  1.51854240e+00,\n",
       "         1.47436898e-03,  6.44653900e-03,  1.62621339e-01,\n",
       "        -1.68342173e+00,  4.10250486e-01]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.coef_ #modeldeki özniteliklerin tahmin edilen katsayıların görüntülenmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7188f5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BAYES ALGORİTMASI (NAIVE BAYES CLASSIFICATION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0d737e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "651a8f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3ffe6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "x,y=make_blobs(100,2,centers=2,cluster_std=1.5)  #girdi ve çıktı verileri x ve y değişkenlerine atanır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "505850ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1364a57c730>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(x[:,0],x[:,1],c=y, s=50, cmap='RdBu')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "917159eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "10cb6c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bbdfd273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)  #model fit edildi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bb063987",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng= np.random.RandomState(0)\n",
    "x_yeni=[3,-3]+[-5,-5]*rng.rand(1000,2)  #rastgele veriler üretelim ve bu verilerin sınıflarını tahmin edelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4cb95c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_yeni=model.predict(x_yeni)  #yeni verileri kurdugumuz modele göre tahmin ettik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c3e8f146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.49730041706948,\n",
       " 6.1037978078562025,\n",
       " -12.039482991794772,\n",
       " -0.4969175816316376)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(x[:,0],x[:,1],c=y, s=50, cmap='RdBu')\n",
    "lim=plt.axis()\n",
    "plt.scatter(x_yeni[:,0],x_yeni[:,1],c=y_yeni,s=20,cmap='RdBu',alpha=0.2)\n",
    "plt.axis(lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "666bc505",
   "metadata": {},
   "outputs": [],
   "source": [
    "#temel bilgiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "49f55160",
   "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>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "      <th>Demented</th>\n",
       "      <th>Nondemented</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>77.013405</td>\n",
       "      <td>14.597855</td>\n",
       "      <td>0.290885</td>\n",
       "      <td>1488.128686</td>\n",
       "      <td>0.729568</td>\n",
       "      <td>1.195461</td>\n",
       "      <td>0.391421</td>\n",
       "      <td>0.509383</td>\n",
       "      <td>0.428954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.640957</td>\n",
       "      <td>2.876339</td>\n",
       "      <td>0.374557</td>\n",
       "      <td>176.139286</td>\n",
       "      <td>0.037135</td>\n",
       "      <td>0.138092</td>\n",
       "      <td>0.488724</td>\n",
       "      <td>0.500583</td>\n",
       "      <td>0.495592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1106.000000</td>\n",
       "      <td>0.644000</td>\n",
       "      <td>0.876000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>71.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1357.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.099000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>0.729000</td>\n",
       "      <td>1.194000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>0.756000</td>\n",
       "      <td>1.293000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>0.837000</td>\n",
       "      <td>1.587000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        EDUC         CDR         eTIV        nWBV  \\\n",
       "count  373.000000  373.000000  373.000000   373.000000  373.000000   \n",
       "mean    77.013405   14.597855    0.290885  1488.128686    0.729568   \n",
       "std      7.640957    2.876339    0.374557   176.139286    0.037135   \n",
       "min     60.000000    6.000000    0.000000  1106.000000    0.644000   \n",
       "25%     71.000000   12.000000    0.000000  1357.000000    0.700000   \n",
       "50%     77.000000   15.000000    0.000000  1470.000000    0.729000   \n",
       "75%     82.000000   16.000000    0.500000  1597.000000    0.756000   \n",
       "max     98.000000   23.000000    2.000000  2004.000000    0.837000   \n",
       "\n",
       "              ASF    Demented  Nondemented           M  \n",
       "count  373.000000  373.000000   373.000000  373.000000  \n",
       "mean     1.195461    0.391421     0.509383    0.428954  \n",
       "std      0.138092    0.488724     0.500583    0.495592  \n",
       "min      0.876000    0.000000     0.000000    0.000000  \n",
       "25%      1.099000    0.000000     0.000000    0.000000  \n",
       "50%      1.194000    0.000000     1.000000    0.000000  \n",
       "75%      1.293000    1.000000     1.000000    1.000000  \n",
       "max      1.587000    1.000000     1.000000    1.000000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ae695a53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "      <th>Demented</th>\n",
       "      <th>Nondemented</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1475</td>\n",
       "      <td>0.696</td>\n",
       "      <td>1.184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  EDUC  CDR  eTIV   nWBV    ASF  Demented  Nondemented    M\n",
       "0  73.0  12.0  0.0  1475  0.696  1.184       0.0          1.0  0.0\n",
       "1   NaN   NaN  NaN  1483    NaN  1.190       NaN          NaN  NaN"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "711e27a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              77.000\n",
       "EDUC             15.000\n",
       "CDR               0.000\n",
       "eTIV           1470.000\n",
       "nWBV              0.729\n",
       "ASF               1.194\n",
       "Demented          0.000\n",
       "Nondemented       1.000\n",
       "M                 0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7f634ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              77.013405\n",
       "EDUC             14.597855\n",
       "CDR               0.290885\n",
       "eTIV           1488.128686\n",
       "nWBV              0.729568\n",
       "ASF               1.195461\n",
       "Demented          0.391421\n",
       "Nondemented       0.509383\n",
       "M                 0.428954\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.mean(axis=0,skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "16b138e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              7.640957\n",
       "EDUC             2.876339\n",
       "CDR              0.374557\n",
       "eTIV           176.139286\n",
       "nWBV             0.037135\n",
       "ASF              0.138092\n",
       "Demented         0.488724\n",
       "Nondemented      0.500583\n",
       "M                0.495592\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "03d8158e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "      <th>Demented</th>\n",
       "      <th>Nondemented</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>58.384228</td>\n",
       "      <td>-0.612874</td>\n",
       "      <td>-0.075146</td>\n",
       "      <td>56.995582</td>\n",
       "      <td>-0.147083</td>\n",
       "      <td>-0.037001</td>\n",
       "      <td>-0.295584</td>\n",
       "      <td>0.022723</td>\n",
       "      <td>-0.142862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUC</th>\n",
       "      <td>-0.612874</td>\n",
       "      <td>8.273329</td>\n",
       "      <td>-0.164966</td>\n",
       "      <td>130.213180</td>\n",
       "      <td>-0.001303</td>\n",
       "      <td>-0.096024</td>\n",
       "      <td>-0.363674</td>\n",
       "      <td>0.277977</td>\n",
       "      <td>0.127267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDR</th>\n",
       "      <td>-0.075146</td>\n",
       "      <td>-0.164966</td>\n",
       "      <td>0.140293</td>\n",
       "      <td>1.505477</td>\n",
       "      <td>-0.004796</td>\n",
       "      <td>-0.001518</td>\n",
       "      <td>0.149276</td>\n",
       "      <td>-0.145882</td>\n",
       "      <td>0.037523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eTIV</th>\n",
       "      <td>56.995582</td>\n",
       "      <td>130.213180</td>\n",
       "      <td>1.505477</td>\n",
       "      <td>31025.047911</td>\n",
       "      <td>-1.374393</td>\n",
       "      <td>-24.052858</td>\n",
       "      <td>-0.894592</td>\n",
       "      <td>3.764918</td>\n",
       "      <td>49.979597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nWBV</th>\n",
       "      <td>-0.147083</td>\n",
       "      <td>-0.001303</td>\n",
       "      <td>-0.004796</td>\n",
       "      <td>-1.374393</td>\n",
       "      <td>0.001379</td>\n",
       "      <td>0.001095</td>\n",
       "      <td>-0.005207</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>-0.004594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASF</th>\n",
       "      <td>-0.037001</td>\n",
       "      <td>-0.096024</td>\n",
       "      <td>-0.001518</td>\n",
       "      <td>-24.052858</td>\n",
       "      <td>0.001095</td>\n",
       "      <td>0.019069</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>-0.002246</td>\n",
       "      <td>-0.038438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Demented</th>\n",
       "      <td>-0.295584</td>\n",
       "      <td>-0.363674</td>\n",
       "      <td>0.149276</td>\n",
       "      <td>-0.894592</td>\n",
       "      <td>-0.005207</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>0.238851</td>\n",
       "      <td>-0.199919</td>\n",
       "      <td>0.062830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nondemented</th>\n",
       "      <td>0.022723</td>\n",
       "      <td>0.277977</td>\n",
       "      <td>-0.145882</td>\n",
       "      <td>3.764918</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>-0.002246</td>\n",
       "      <td>-0.199919</td>\n",
       "      <td>0.250584</td>\n",
       "      <td>-0.055111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>-0.142862</td>\n",
       "      <td>0.127267</td>\n",
       "      <td>0.037523</td>\n",
       "      <td>49.979597</td>\n",
       "      <td>-0.004594</td>\n",
       "      <td>-0.038438</td>\n",
       "      <td>0.062830</td>\n",
       "      <td>-0.055111</td>\n",
       "      <td>0.245611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Age        EDUC       CDR          eTIV      nWBV  \\\n",
       "Age          58.384228   -0.612874 -0.075146     56.995582 -0.147083   \n",
       "EDUC         -0.612874    8.273329 -0.164966    130.213180 -0.001303   \n",
       "CDR          -0.075146   -0.164966  0.140293      1.505477 -0.004796   \n",
       "eTIV         56.995582  130.213180  1.505477  31025.047911 -1.374393   \n",
       "nWBV         -0.147083   -0.001303 -0.004796     -1.374393  0.001379   \n",
       "ASF          -0.037001   -0.096024 -0.001518    -24.052858  0.001095   \n",
       "Demented     -0.295584   -0.363674  0.149276     -0.894592 -0.005207   \n",
       "Nondemented   0.022723    0.277977 -0.145882      3.764918  0.005788   \n",
       "M            -0.142862    0.127267  0.037523     49.979597 -0.004594   \n",
       "\n",
       "                   ASF  Demented  Nondemented          M  \n",
       "Age          -0.037001 -0.295584     0.022723  -0.142862  \n",
       "EDUC         -0.096024 -0.363674     0.277977   0.127267  \n",
       "CDR          -0.001518  0.149276    -0.145882   0.037523  \n",
       "eTIV        -24.052858 -0.894592     3.764918  49.979597  \n",
       "nWBV          0.001095 -0.005207     0.005788  -0.004594  \n",
       "ASF           0.019069  0.000561    -0.002246  -0.038438  \n",
       "Demented      0.000561  0.238851    -0.199919   0.062830  \n",
       "Nondemented  -0.002246 -0.199919     0.250584  -0.055111  \n",
       "M            -0.038438  0.062830    -0.055111   0.245611  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Varyans Hesaplamaları\n",
    "veri.cov() #Kovaryans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "23a45fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>CDR</th>\n",
       "      <th>eTIV</th>\n",
       "      <th>nWBV</th>\n",
       "      <th>ASF</th>\n",
       "      <th>Demented</th>\n",
       "      <th>Nondemented</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027886</td>\n",
       "      <td>-0.026257</td>\n",
       "      <td>0.042348</td>\n",
       "      <td>-0.518359</td>\n",
       "      <td>-0.035067</td>\n",
       "      <td>-0.079153</td>\n",
       "      <td>0.005941</td>\n",
       "      <td>-0.037726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUC</th>\n",
       "      <td>-0.027886</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.153121</td>\n",
       "      <td>0.257015</td>\n",
       "      <td>-0.012200</td>\n",
       "      <td>-0.241752</td>\n",
       "      <td>-0.258708</td>\n",
       "      <td>0.193060</td>\n",
       "      <td>0.089279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDR</th>\n",
       "      <td>-0.026257</td>\n",
       "      <td>-0.153121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022819</td>\n",
       "      <td>-0.344819</td>\n",
       "      <td>-0.029340</td>\n",
       "      <td>0.815473</td>\n",
       "      <td>-0.778049</td>\n",
       "      <td>0.202140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eTIV</th>\n",
       "      <td>0.042348</td>\n",
       "      <td>0.257015</td>\n",
       "      <td>0.022819</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.210122</td>\n",
       "      <td>-0.988877</td>\n",
       "      <td>-0.010392</td>\n",
       "      <td>0.042700</td>\n",
       "      <td>0.572549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nWBV</th>\n",
       "      <td>-0.518359</td>\n",
       "      <td>-0.012200</td>\n",
       "      <td>-0.344819</td>\n",
       "      <td>-0.210122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213476</td>\n",
       "      <td>-0.286903</td>\n",
       "      <td>0.311346</td>\n",
       "      <td>-0.249618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASF</th>\n",
       "      <td>-0.035067</td>\n",
       "      <td>-0.241752</td>\n",
       "      <td>-0.029340</td>\n",
       "      <td>-0.988877</td>\n",
       "      <td>0.213476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008312</td>\n",
       "      <td>-0.032495</td>\n",
       "      <td>-0.561647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Demented</th>\n",
       "      <td>-0.079153</td>\n",
       "      <td>-0.258708</td>\n",
       "      <td>0.815473</td>\n",
       "      <td>-0.010392</td>\n",
       "      <td>-0.286903</td>\n",
       "      <td>0.008312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.817174</td>\n",
       "      <td>0.259405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nondemented</th>\n",
       "      <td>0.005941</td>\n",
       "      <td>0.193060</td>\n",
       "      <td>-0.778049</td>\n",
       "      <td>0.042700</td>\n",
       "      <td>0.311346</td>\n",
       "      <td>-0.032495</td>\n",
       "      <td>-0.817174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.222146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>-0.037726</td>\n",
       "      <td>0.089279</td>\n",
       "      <td>0.202140</td>\n",
       "      <td>0.572549</td>\n",
       "      <td>-0.249618</td>\n",
       "      <td>-0.561647</td>\n",
       "      <td>0.259405</td>\n",
       "      <td>-0.222146</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age      EDUC       CDR      eTIV      nWBV       ASF  \\\n",
       "Age          1.000000 -0.027886 -0.026257  0.042348 -0.518359 -0.035067   \n",
       "EDUC        -0.027886  1.000000 -0.153121  0.257015 -0.012200 -0.241752   \n",
       "CDR         -0.026257 -0.153121  1.000000  0.022819 -0.344819 -0.029340   \n",
       "eTIV         0.042348  0.257015  0.022819  1.000000 -0.210122 -0.988877   \n",
       "nWBV        -0.518359 -0.012200 -0.344819 -0.210122  1.000000  0.213476   \n",
       "ASF         -0.035067 -0.241752 -0.029340 -0.988877  0.213476  1.000000   \n",
       "Demented    -0.079153 -0.258708  0.815473 -0.010392 -0.286903  0.008312   \n",
       "Nondemented  0.005941  0.193060 -0.778049  0.042700  0.311346 -0.032495   \n",
       "M           -0.037726  0.089279  0.202140  0.572549 -0.249618 -0.561647   \n",
       "\n",
       "             Demented  Nondemented         M  \n",
       "Age         -0.079153     0.005941 -0.037726  \n",
       "EDUC        -0.258708     0.193060  0.089279  \n",
       "CDR          0.815473    -0.778049  0.202140  \n",
       "eTIV        -0.010392     0.042700  0.572549  \n",
       "nWBV        -0.286903     0.311346 -0.249618  \n",
       "ASF          0.008312    -0.032495 -0.561647  \n",
       "Demented     1.000000    -0.817174  0.259405  \n",
       "Nondemented -0.817174     1.000000 -0.222146  \n",
       "M            0.259405    -0.222146  1.000000  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.corr() #korelasyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "514b005e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Değerlendirme Metrikleri(Evaluation Metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "29e27e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy= Number of correct predictions\\Total number of predictions\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2, \\\n",
    "                                                 random_state=42)\n",
    "\n",
    "model=LogisticRegression().fit(x_train,y_train)\n",
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fd7cd740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DummyClassifier on the same dataset:\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy = DummyClassifier(strategy='most_frequent',random_state=42)\n",
    "dummy.fit(x_train,y_train)\n",
    "dummy.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "90698fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "x,y = make_classification(n_samples=10000, n_classes=2, \\\n",
    "                         weights=[0.95,0.05], random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d02be573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(random_state=42).fit(x_train,y_train)\n",
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "96515055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  0],\n",
       "       [ 0, 10]], dtype=int64)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test,model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "47e8fafb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mglearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.1.9)\n",
      "Requirement already satisfied: imageio in c:\\programdata\\anaconda3\\lib\\site-packages (from mglearn) (2.9.0)\n",
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (from mglearn) (1.3.4)\n",
      "Requirement already satisfied: cycler in c:\\programdata\\anaconda3\\lib\\site-packages (from mglearn) (0.10.0)\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (from mglearn) (1.20.3)\n",
      "Requirement already satisfied: matplotlib in c:\\programdata\\anaconda3\\lib\\site-packages (from mglearn) (3.4.3)\n",
      "Requirement already satisfied: pillow in c:\\programdata\\anaconda3\\lib\\site-packages (from mglearn) (8.4.0)\n",
      "Requirement already satisfied: joblib in c:\\programdata\\anaconda3\\lib\\site-packages (from mglearn) (1.1.0)\n",
      "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from mglearn) (0.24.2)\n",
      "Requirement already satisfied: six in c:\\programdata\\anaconda3\\lib\\site-packages (from cycler->mglearn) (1.16.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->mglearn) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->mglearn) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->mglearn) (3.0.4)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas->mglearn) (2021.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->mglearn) (2.2.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->mglearn) (1.7.1)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install mglearn\n",
    "import mglearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "729751f3",
   "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": [
    "mglearn.plots.plot_binary_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4bc4a0b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        10\n",
      "           1       1.00      1.00      1.00        10\n",
      "\n",
      "    accuracy                           1.00        20\n",
      "   macro avg       1.00      1.00      1.00        20\n",
      "weighted avg       1.00      1.00      1.00        20\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,model.predict(x_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "698b6a71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity:  0.8297872340425532\n"
     ]
    }
   ],
   "source": [
    "#Sensitivity = TP/TP+FN\n",
    "\n",
    "print('Sensitivity: ', 39/(39+8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f62dcb9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specificity:  0.9950372208436724\n"
     ]
    }
   ],
   "source": [
    "#Specificty = TN/(FP+TN)\n",
    "\n",
    "print('Specificity: ',401/(2+401))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "23e8bcae",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'recall_score' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_17492/2223096111.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m                scoring='recall',n_jobs=-1)\n\u001b[0;32m      8\u001b[0m \u001b[0mgs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mrecall_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\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[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'recall_score' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid={\n",
    "    'n_estimators':[10,200],\n",
    "    'max_features':['auto','sqrt','log2',0.5],\n",
    "}\n",
    "gs=GridSearchCV(estimator=model, param_grid=param_grid, \\\n",
    "               scoring='recall',n_jobs=-1)\n",
    "gs.fit(x_train,y_train)\n",
    "recall_score(y_test,gs.best_estimator_.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35c5c49f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Değerlendirme Metrikleri(Evaluation Metrics)- NAIVE BAYES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d15dba5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Accuracy = Number of correct predictions\\Total number of predictions\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2, \\\n",
    "                                                 random_state=42)\n",
    "\n",
    "model=GaussianNB().fit(x_train,y_train)\n",
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8397830",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DummyClassifier on the same dataset:\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy = DummyClassifier(strategy='most_frequent',random_state=42)\n",
    "dummy.fit(x_train,y_train)\n",
    "dummy.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad486ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "x,y = make_classification(n_samples=10000, n_classes=2, \\\n",
    "                         weights=[0.95,0.05], random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c33cd853",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(random_state=42).fit(x_train,y_train)\n",
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5c5bbcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test,model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f972283",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install mglearn\n",
    "import mglearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c70e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "mglearn.plots.plot_binary_confusion_matrix() \n",
    "#confusion_matrix değerleri bir önceki kod çıktısında mevcuttur.bu değerler bu şekildeki ifadelere karşılık gelir.Bunları diğer metrik türlerini hesaplamada kullanacağız"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3890fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,model.predict(x_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7407f7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sensitivity = TP/TP+FN\n",
    "\n",
    "print('Sensitivity: ', 52/(52+64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b1cf87b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specificty = TN/(FP+TN)\n",
    "\n",
    "print('Specificity: ',1876/(8+1876))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f237c558",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid={\n",
    "    'n_estimators':[10,200],\n",
    "    'max_features':['auto','sqrt','log2',0.5],\n",
    "}\n",
    "gs=GridSearchCV(estimator=model, param_grid=param_grid, \\\n",
    "               scoring='recall',n_jobs=-1)\n",
    "gs.fit(x_train,y_train)\n",
    "recall_score(y_test,gs.best_estimator_.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
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