Regresyon_Modelleri_ile_Abalone_Yas_Tahmini.ipynb

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   "id": "bc3ee721",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Deniz Kulağı (Abalone) Yaş Tahmini  \n",
    "\n",
    "   &ensp;<sup>Edanur Yılmaz (121521014)</sup>  \n",
    "   &ensp;<sup>24.11.2024</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb467689",
   "metadata": {},
   "source": [
    "## İçerik\n",
    "• [Veri İncelemesi ve Ön İşlemesi](#Veri-İncelemesi-ve-Ön-İşlemesi)  \n",
    "• [Basit Doğrusal Regresyon](#1.Basit-Doğrusal-Regresyon)    \n",
    "• [Çoklu Doğrusal Regresyon](#2.Çoklu-Doğrusal-Regresyon)    \n",
    "    &emsp;- [Özellik Eliminasyonu (Feature Selection)](#2.1.Özellik-Eliminasyonu-(Feature-Selection))   \n",
    "    &emsp;- [K-Fold](#2.2.K-Fold)  \n",
    "    &emsp;- [Grid Search Cross-Validation ile Hiperparametre Ayarlama](#2.3.Grid-Search-Cross-Validation-ile-Hiperparametre-Ayarlama)    \n",
    "• [Çoklu Doğrusal Olmayan Regresyon (MLPRegressor)](#3.Çoklu-Doğrusal-Olmayan-Regresyon)    \n",
    "    &emsp;- [Grid Search Cross-Validation ile Hiperparametre Ayarlama](#3.1.Grid-Search-Cross-Validation-ile-Hiperparametre-Ayarlama)    \n",
    "• [K-En Yakın Komşular Algoritması](#4.K-En-Yakın-Komşular-Algoritması)  \n",
    "    &emsp;- [Grid Search Cross-Validation ile Hiperparametre Ayarlama](#4.1.Grid-Search-Cross-Validation-ile-Hiperparametre-Ayarlama)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8fee6a",
   "metadata": {},
   "source": [
    "#### Gerekli Kütüphaneler\n",
    "Kullanacağımız kütüphaneleri ve belirli paketleri(kütüphane ve modülden) ekliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd8ed8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import missingno as msno\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, KFold, GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e6525f",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Veri İncelemesi ve Ön İşlemesi  \n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97fdc7ea",
   "metadata": {},
   "source": [
    "Kullanılan veri seti: [Abalone Dataset by Rodolfo Mendes](https://www.kaggle.com/datasets/rodolfomendes/abalone-dataset) \n",
    "\n",
    "Kaynak: [Abalone - UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/1/abalone)\n",
    "\n",
    "<sup>Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994) \n",
    "\"The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone (H. rubra) from the North Coast and Islands of Bass Strait\", Sea Fisheries Division,   \n",
    "Technical Report No. 48 (ISSN 1034-3288)</sup>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0319da43",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_data = pd.read_csv(\"abalone.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9f979f",
   "metadata": {},
   "source": [
    "Veri setinin; 1 'object', 6 'float64', 1 de 'int64' veri tipinde 8 farklı değişkenden oluştuğunu ve kayıp gözlem bulundurmadığını görebiliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3786b4a2",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4177 entries, 0 to 4176\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Sex             4177 non-null   object \n",
      " 1   Length          4177 non-null   float64\n",
      " 2   Diameter        4177 non-null   float64\n",
      " 3   Height          4177 non-null   float64\n",
      " 4   Whole weight    4177 non-null   float64\n",
      " 5   Shucked weight  4177 non-null   float64\n",
      " 6   Viscera weight  4177 non-null   float64\n",
      " 7   Shell weight    4177 non-null   float64\n",
      " 8   Rings           4177 non-null   int64  \n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 293.8+ KB\n"
     ]
    }
   ],
   "source": [
    "abalone_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1620b4e5",
   "metadata": {},
   "source": [
    "Veriyi setinin, cinsiyet('M', 'F', 'I')('I' burada infant yani bebek anlamındadır.), uzunluk, çap, yükseklik, tam ağırlık, soyulmuş ağırlık, iç organ ağırlığı, kabuk ağırlığı, halka sayısı(1.5 eklendiğinde yıl olarak yaşı vermektedir.) değişkenlerinden oluştuğunu görebiliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f6b75f8",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Diameter</th>\n",
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       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>15</td>\n",
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       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
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       "      <td>0.2255</td>\n",
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       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>9</td>\n",
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       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>10</td>\n",
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       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
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       "      <th>4172</th>\n",
       "      <td>F</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.8870</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.2490</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>M</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.9660</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2605</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>M</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.1760</td>\n",
       "      <td>0.5255</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>F</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1.0945</td>\n",
       "      <td>0.5310</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>M</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.9485</td>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>12</td>\n",
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       "     Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "0      M   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1      M   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2      F   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3      M   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4      I   0.330     0.255   0.080        0.2050          0.0895   \n",
       "...   ..     ...       ...     ...           ...             ...   \n",
       "4172   F   0.565     0.450   0.165        0.8870          0.3700   \n",
       "4173   M   0.590     0.440   0.135        0.9660          0.4390   \n",
       "4174   M   0.600     0.475   0.205        1.1760          0.5255   \n",
       "4175   F   0.625     0.485   0.150        1.0945          0.5310   \n",
       "4176   M   0.710     0.555   0.195        1.9485          0.9455   \n",
       "\n",
       "      Viscera weight  Shell weight  Rings  \n",
       "0             0.1010        0.1500     15  \n",
       "1             0.0485        0.0700      7  \n",
       "2             0.1415        0.2100      9  \n",
       "3             0.1140        0.1550     10  \n",
       "4             0.0395        0.0550      7  \n",
       "...              ...           ...    ...  \n",
       "4172          0.2390        0.2490     11  \n",
       "4173          0.2145        0.2605     10  \n",
       "4174          0.2875        0.3080      9  \n",
       "4175          0.2610        0.2960     10  \n",
       "4176          0.3765        0.4950     12  \n",
       "\n",
       "[4177 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c9e29f",
   "metadata": {},
   "source": [
    "Halka sayısı tahmini değil yaş tahmini olarak tanımlamak istediğim için halka sayısına 1.5 ekleyerek bir yaş sütunu oluşturuyorum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e3f3ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_data[\"value\"] = 1.5\n",
    "abalone_data[\"Rings\"] = abalone_data[\"Rings\"] + abalone_data[\"value\"]\n",
    "abalone_data.drop(\"value\", axis='columns', inplace=True)\n",
    "abalone_data = abalone_data.rename({\"Rings\":\"Age\"}, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dded34be",
   "metadata": {},
   "outputs": [
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       "      <td>M</td>\n",
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       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
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       "      <td>F</td>\n",
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       "      <th>3</th>\n",
       "      <td>M</td>\n",
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       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>11.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
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       "      <td>F</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.8870</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.2490</td>\n",
       "      <td>12.5</td>\n",
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       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>M</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.9660</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2605</td>\n",
       "      <td>11.5</td>\n",
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       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>M</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.1760</td>\n",
       "      <td>0.5255</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>10.5</td>\n",
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       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>F</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1.0945</td>\n",
       "      <td>0.5310</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>11.5</td>\n",
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       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>M</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.9485</td>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4177 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "0      M   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1      M   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2      F   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3      M   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4      I   0.330     0.255   0.080        0.2050          0.0895   \n",
       "...   ..     ...       ...     ...           ...             ...   \n",
       "4172   F   0.565     0.450   0.165        0.8870          0.3700   \n",
       "4173   M   0.590     0.440   0.135        0.9660          0.4390   \n",
       "4174   M   0.600     0.475   0.205        1.1760          0.5255   \n",
       "4175   F   0.625     0.485   0.150        1.0945          0.5310   \n",
       "4176   M   0.710     0.555   0.195        1.9485          0.9455   \n",
       "\n",
       "      Viscera weight  Shell weight   Age  \n",
       "0             0.1010        0.1500  16.5  \n",
       "1             0.0485        0.0700   8.5  \n",
       "2             0.1415        0.2100  10.5  \n",
       "3             0.1140        0.1550  11.5  \n",
       "4             0.0395        0.0550   8.5  \n",
       "...              ...           ...   ...  \n",
       "4172          0.2390        0.2490  12.5  \n",
       "4173          0.2145        0.2605  11.5  \n",
       "4174          0.2875        0.3080  10.5  \n",
       "4175          0.2610        0.2960  11.5  \n",
       "4176          0.3765        0.4950  13.5  \n",
       "\n",
       "[4177 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26876b5a",
   "metadata": {},
   "source": [
    "Cinsiyet değişkeninin 'object' olduğunu biliyoruz. Aldığı değerlere bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ff9b039",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'F', 'I'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data[\"Sex\"].unique() # I: Infant"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640dd753",
   "metadata": {},
   "source": [
    "Cinsiyet değişkeninin aldığı değerlere karşılık gelen sayısal değerler atıyoruz.   \n",
    "(Sayısal değer ataması daha zor olan bir durum olsaydı panda kütüphanesindeki 'get.dummies' fonksiyonunu kullanabilirdik.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7bb4a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_map(x):\n",
    "    return x.map({\"M\" : 1,\n",
    "                  \"F\" : 2,\n",
    "                  \"I\" : 3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f2b0326",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_data[\"Sex\"] = binary_map(abalone_data[\"Sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07d98955",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>2</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.8870</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.2490</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>1</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.9660</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2605</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>1</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.1760</td>\n",
       "      <td>0.5255</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>2</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1.0945</td>\n",
       "      <td>0.5310</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>1</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.9485</td>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4177 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "0       1   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1       1   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2       2   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3       1   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4       3   0.330     0.255   0.080        0.2050          0.0895   \n",
       "...   ...     ...       ...     ...           ...             ...   \n",
       "4172    2   0.565     0.450   0.165        0.8870          0.3700   \n",
       "4173    1   0.590     0.440   0.135        0.9660          0.4390   \n",
       "4174    1   0.600     0.475   0.205        1.1760          0.5255   \n",
       "4175    2   0.625     0.485   0.150        1.0945          0.5310   \n",
       "4176    1   0.710     0.555   0.195        1.9485          0.9455   \n",
       "\n",
       "      Viscera weight  Shell weight   Age  \n",
       "0             0.1010        0.1500  16.5  \n",
       "1             0.0485        0.0700   8.5  \n",
       "2             0.1415        0.2100  10.5  \n",
       "3             0.1140        0.1550  11.5  \n",
       "4             0.0395        0.0550   8.5  \n",
       "...              ...           ...   ...  \n",
       "4172          0.2390        0.2490  12.5  \n",
       "4173          0.2145        0.2605  11.5  \n",
       "4174          0.2875        0.3080  10.5  \n",
       "4175          0.2610        0.2960  11.5  \n",
       "4176          0.3765        0.4950  13.5  \n",
       "\n",
       "[4177 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b049025",
   "metadata": {},
   "source": [
    "Veri setinde kayıp gözlemimizin olmadığını biliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0ca17f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex               0\n",
       "Length            0\n",
       "Diameter          0\n",
       "Height            0\n",
       "Whole weight      0\n",
       "Shucked weight    0\n",
       "Viscera weight    0\n",
       "Shell weight      0\n",
       "Age               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f9487d8",
   "metadata": {},
   "source": [
    "Bütün veri setinin %1'i kadar değere NaN atıyorum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c78e8b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xy = [(row, col) for row in range(abalone_data.shape[0]) for col in range(abalone_data.shape[1])]\n",
    "random.seed(121521014)\n",
    "for row, col in random.sample(xy, int(round(.01*len(xy)))):\n",
    "    abalone_data.iat[row, col] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc891a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Sex               37\n",
       " Length            52\n",
       " Diameter          44\n",
       " Height            37\n",
       " Whole weight      39\n",
       " Shucked weight    41\n",
       " Viscera weight    44\n",
       " Shell weight      39\n",
       " Age               43\n",
       " dtype: int64,\n",
       " 376)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data.isnull().sum(), abalone_data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3914fe1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xdepU/Pz8OHfu3FNl+2NiYjDGkDNnTgA+//xz4PEo6NhhAOdxUqtWLby9vcmbNy9z585lxowZ3Lx58zm2SOTvc567Dx06xK1bt+zP8+fPT/v27WnRogUAo0aNYuPGjcDvhwGGDx9O4cKFadas2XNsgYiIiEjCoidjEREREREREfnXTZkyhSlTpgCPO3uio6PtTp+FCxfy7rvvUrFiRWrWrMkHH3xAYGAg4eHhuLm52cs6wwCPHj2iQYMGLF68mEePHtG1a1cmTJhAaGioRj1LgufsyG/RogVLly5l9uzZABw5coTbt28Dj0fvW5ZF06ZN8fX1Zc2aNQQGBgLYZf5jcwYEPvroI3LlysW0adPYt29fnP+eSGKwdOlS3njjDQYPHhwnDFCgQAE6depE06ZN+f777xk0aBCbNm0Cnh0GqFOnDvv27SNLlixPHS8iCdHvVUgSERH5OxQEEBEREREREZF/1YEDB2jXrh29e/dmzpw5wOOOS3jc6dO4cWOuX79OqVKlyJUrFz/88APDhg2jadOmhIWFxQkDDBs2jH79+hEZGUnDhg3Jnj0748aNw9/fn7Nnz5IpUyZ1+kiC5uLiYnfOlypVCoA5c+ZQvHhxxo0bx507dwDsigCffvopSZIkITg4mOHDhwO/lfl3/v+MGTN46aWXCAgIICAggNDQUHr27MnNmzdVIUMStCc7Px89eoS/vz9LliwhKCgoThggX758dOvWjSZNmnD48GECAwN/Nwzg7u6Ol5cXoGkxJGFx7qeRkZGEhoYCj6sfOSskHTp0SKEAERH5x+hJQERERERERET+VTlz5qRfv348fPiQTz75xB4B/eDBA8aPH0/q1KlZtGgRmzZtYtu2bcyYMYOsWbOybNkyGjRoECcMYFkWgwcPZsqUKeTMmRM/Pz9q1qzJl19+aY/8VKePJHRPds6Hh4fj4+PD8OHDmThxInfu3LE7hSpXrkyPHj2Ijo6mb9++NG/enJ9++onr169z//59OnTowJo1ayhUqBDu7u506NCBokWLcu3aNS5fvhwfzRP5Q4wx9n6+Z88epk2bxrp168iYMSO3bt1i2bJlDB8+nF9//dX+Tb58+ejevfsfCgOIJESWZREWFsbMmTMZNWoU58+ft8ORn332GaVKlWLYsGHxvJUiIvKicIvvDRARERERERGRF1vq1Knp0qULbm5uBAYG0rNnT3se59OnT9OjRw/q1asHQKZMmciUKRPZsmWjXr16rFmzhgYNGrBw4UK8vb2JjIzEw8OD1q1bU6VKFZIlS4abmxve3t4KAUiiFRAQgJeXFz169CAwMBBjDB06dCBVqlRkyJCBli1b4uvry8CBA5k9ezabN28GHo90/vnnn8maNSsTJkwAHo8s9fT05MGDB1y4cIGiRYvGZ9NEfpczBDB37lzat29PaGgoRYsWJW3atKRMmZKrV68yY8YMAHr37k2aNGmAx2GAHj16YFkWc+bMYciQIURERPDhhx/a6xRJyK5fv86WLVtYu3YtV69eZdKkSSxdupSAgAD8/Px03hYRkX+MZRSTFBEREREREZHn4M6dO0ycOJHAwEDSpElDo0aNWL16NevWrSNfvnxPdeQfPXqUOnXq8MMPP1CtWrWnwgCxR5PG/lskMYmOjrZHg86ePZuePXty69YtBg4cSPv27UmdOjUAMTExHDlyhP79+3PlyhVOnDhBnjx5yJMnD+PHjydjxoz2OvPnz8+jR4/YuXMn/v7+8dEskT9kw4YNVK1alVSpUhESEmKHwi5cuMCsWbOYM2cOt27dok2bNnHCAAAnT55k7NixzJw5k/fff5/ly5eTJEmS+GqKyB8WFRXFmjVrGDJkCKdOneL111/nyy+/JHPmzIwbN47q1avH9yaKiMgLQkEAEREREREREXluYocBUqZMSVhYGOvWraN8+fJxOkSdfi8MoNH/ktj80bDK/woDwOMpNRwOB2fPniVr1qx4eXnh4+Njf9+jRw+Cg4OpWrUqc+fOxdfX999ojsgf4gxuPcnhcBAdHU3Lli2ZP38+EydOpF27dsBv4Zh79+6xevVqAgMDuXnzJq1ataJPnz5xwgDHjh1j7ty5dOrUiSxZsjy3don8XREREXz99dfUr1+fX375BW9vb8aOHUuLFi2Ax8fIk9PIiIiI/FmaGkBERERERERE/jXOzk/nC+2UKVPSoUMHAAIDAwFYv3495cuXx83N7akX34UKFWLZsmXUqVOHNWvWULlyZTZu3IiXl1d8NEfkL4m9Xx85coTvv/+e77//niRJklChQgVy5sxpj+hv1qwZAD179mTQoEEA9jQBAEmTJsWyLIoVK/ZUsKBLly6MHz8ePz8/goODFQKQeDVt2jTCw8Np0qTJU/ui83g4ffo07u7ulCtXDnhc+cIZCEuRIgU1atTgzp07BAYGsmjRIowx9OnTh7Rp0wJQsGBBRo4ciZubmwJikqh4enpy/Phxfv75Zzw8PAgLC+O7777j559/JlOmTKpyJCIi/wgFAURERERERETkXxF7BHRYWBg+Pj4YY0iZMiXt2rXDzc2Nfv36MX78eHLmzEmbNm1wcXF5Zhjg888/p1y5cnz55ZeEhoYqCCCJhjHG3p8XLFhA586duX37tv39mDFjKFKkCJMmTaJIkSLAs8MAHTt2JGXKlPYx5fz/e/fusWjRIpYuXcqePXvw9/dnw4YNvPLKK8+tjSJPOnDgAG3atCFVqlT4+PjQsGHDp87b0dHRREVFERUVxQ8//ECePHmeGgGdLFkyateuzeLFizl8+DBLlizB1dWVPn362OEYZ3BAIQBJbMLDw8maNSt169Zl8+bNTJs2jYiICAYMGEDmzJk17ZGIiPxtqi0jIiIiIiIiIv8K58vrqVOn8uGHH3Lx4kUsy8IYQ+rUqQkICGDIkCEADBgwgFmzZgHYYYDYChYsyJ49e7h06RJp0qR56nuRhMp5HCxevJiPPvqIe/fuMXjwYDZs2MCMGTOoVKkSBw8epFSpUnzxxRf275o1a8bIkSNJkyYNgwYNYtKkSfz6669PrT9FihT89NNPnDhxgtq1a7Njxw7y5MnzvJon8kwFChSgX79+WJbF8ePHnwoBOBwOvL29eeuttwD45ptvAOxrhJMxhowZM/LRRx/h6emJm5sbU6ZMYcqUKTx8+PD5NUjkH+S8h+nUqRO7d+8mKCiIQYMGkTdvXubMmcOQIUO4fPlynONB9z0iIvJXKAggIiIiIiIiIv+o2J04t2/fZvLkyezYsYMuXbrECQOkSpWKtm3bEhgYyK+//krPnj3/Zxggb968+Pn5ERMTo3lzJVE5evQoPXr0AGDRokX069ePSpUq8fHHH9OlSxfSpElDWFgYO3fuBH7r8HGGAfz8/BgwYADz5s17qpMU4NNPP2X79u189tlnZM2a9Tm3TiQuh8NB0qRJ6dWrFzNmzGDcuHEAnDp1ijt37gC/TQ1QqlQpAAYPHsyqVauAuGGAmJgYALuaTL9+/UiVKhUzZ87k8OHD9n9PJCGLfd4G4tzDOKeFqVChAoMHDyZv3rzMnj07ThggKirK/s3mzZufWp+IiMjv0VOziIiIiIiIiPxjHA6HPQL6+vXrXL58mXbt2pEhQwa2bdtGhw4duHTpkt3RkzJlSjp06GCHAXr16vU/wwCg8s+S+Jw5c4YrV67Qv39/ateubX++b98+OnXqxK1bt+jXrx+DBw8G4nYSNWvWjL59+1KgQAFq1qwZp0y0ZVn2MVKoUCFSpEjxnFok8vuc525vb2+qVasGwKxZsyhevDiLFy/m3r179rK1atXik08+AeDjjz9m3bp1wG+VNJxl/zds2ECGDBmoXbs2zZo148KFC0yePNn+74kkFLH3b4g7TdK+ffuYNGkS/fv3Z8OGDTx48MD+zsvLi4oVKz4VBrhw4QLu7u44HA769u1L7dq17WpKIiIi/xe3+N4AEREREREREXkxxJ4LffHixQwZMoTTp0+TLl06bty4AcD69euxLIuQkBB7/ltnGAAgMDCQXr164eLiQtOmTdXBIy+EPXv2APDmm2/anx08eJB27dpx7NgxevXqZYcA4HGIxrIs0qVLB0Dbtm1p0qQJPj4+xMTExAnD6BiRhCj2fhkdHc0PP/xAdHQ0I0aMwNXVlbp16+Lr6wtA165duXHjBnPmzKFatWpMmTKFSpUq4efnR1RUFH369GHLli00adKEVKlS0bx5cyZNmsSGDRs4e/YsOXPmjKdWisQ1btw4jh49Sp8+fciRIwfwW6hl3rx5tG7dmoiICHv52rVr89FHH1G5cmUAPD09qVixIpZl0b9/f2bPns3Nmzf55JNPmDdvHtOmTSNDhgw0a9bs+TdOREQSJQUBREREREREROQfEXsu9IYNG5IuXTpCQkIoW7YsoaGhzJs3j23btrFu3Tosy2LixInPDAMMGTKEjz/+mCRJklCvXr34bJLInxJ75Cc8rpARu0PU2QG0b9++OCGAoKAgACIjIzHGMG3aNO7du0dgYCA+Pj64uLjg4+MDqCKGJD5ubm7069cPb29vgoODCQwMBLDDAOnSpWPQoEEkTZqUkJAQ2rRpwyuvvELq1Km5f/8+p06dIlu2bAwdOhR4XEo9Y8aMHD9+nAcPHsRjy0R+c/bsWcaNG8elS5dInjw5HTp04JVXXgFg7dq1NG3aFIB27dphWRbbtm1jxYoVnD59mvv371O/fn3gcRjgvffew9XVlWHDhrFmzRrWrVuHw+EgV65cbNmyhcyZMz8VChMREXkWBQFERERERERE5B9z/vx5Bg4cCMDEiRPjlEHPlSsXVatWpW/fvqxduxaACRMmkCVLljhhgNDQUBYsWGDPHS2SWDhDAOfPnydr1qx2CKBEiRJMnjyZY8eOkTNnTtq3b/9UCCAiIgJPT09u3brFxIkTKV26NF5eXhrxL4mWMxgTHR2Nj48PXbt2xeFwMHbsWDsMUKdOHVKmTEnmzJmZMGECefLkYeHChXz//fecO3cOPz8/3nvvPaZPn27PpR4TE8ODBw/ImTMn2bJli8cWivzmlVdeYcSIEYwePZpp06YRExNDhw4dyJUrF0uWLCFZsmTMmjWLmjVrAnDo0CFmz57NzJkzGTx4MMYYGjRoAPxWGSB37tyMGDGCq1ev4u/vT79+/ciQIYNCACIi8odZxhgT3xshIiIiIiIiIonDnj17yJIlC/7+/s/8/tChQ5QsWZIKFSqwefNmIO6o6JiYGPbt20eHDh34/vvvqVq1KhMmTODll1+2O43u3buHZVkkT55cL7sl0Zk9ezYTJkxgyZIl5MqVC3h8XJQqVYqYmBj8/f25cOECvXv3ZtiwYcBvIQB4PGf6ypUrmTp1Ki1btoxTYUAkIXuyIsazhIaGMnr0aMaOHYuXlxeBgYF2GMDpxo0b3L59m8uXL5MlSxYyZsxI0qRJ7e+7dOnC+PHjqVOnDjNnzrSrZYjEF+e+73A4WL58OUFBQZw+fZrmzZvTtm1b6tSpQ+XKlRk1alSc3/30009MnjyZ8ePH88orr9C/f387DPB7FWZ0XyQiIn+GIsUiIiIiIiIi8ofMmTOHsmXLMmHCBC5fvvzMZa5du4bD4SA6Ohrn2IPYI5pdXV0pUaIELVq0wNPTkx07dtChQwcuXbpkv/BOkSIFyZMnxxijl92SqNy/f58NGzZw9OhR1q1bZ3/+2muvMXr0aAAuXLhA1apV7RAAPB79aYyhR48erFy5kooVK1K7dm2FACTRcDgc9v564sQJVq5cyeDBg5k1axYHDhywl/Px8aF79+507tyZsLAwAgMDWbZsGXfv3rWXSZs2Lblz5+add94hV65ccUIAn3zyCePHjydjxowEBQUpBCAJgmVZGGNwcXGhVq1a9OnTh9y5czNz5kxmzpxJaGioPU1AdHS0/bts2bLRrl07OnbsyLlz5xgyZAiLFi2y1xkVFWUv67yX0n2RiIj8GQoCiIiIiIiIiMj/yeFwEBkZSdasWZ/q2IktR44ceHp6cvHiRU6dOoUxhieLESZJkoR69eqRLFkyHj16xPr16+nevTtXr14FsJdXJ6gkNsmTJ6dFixa4urryySefsH37dvu75s2b07lzZwDWrVvHuHHjOHXqFFeuXOHcuXPUq1eP4OBgsmfPzowZM0iZMiUOhyOeWiLyxzk7QAEWLVrEe++9R61atQgMDKRFixa8++67NGrUiPDwcOBxGKBHjx506dLFDgMsXbrUDgM8ee6/ffs2a9asoXz58owePZrMmTOzZcsWTQsgCcqzwgA5cuRgwoQJXLp0iTt37gDg5hZ3tuasWbPSvn37OGGAJUuWAODu7v7c2yEiIi8Wt/97ERERERERERH5r3NxcaFp06Z4e3tz8uRJ6tSpAzwu8+wckWmMwc/Pj5IlS7Jz507WrFlD3rx57e+cnTsRERGkTZuWvHnzkjVrVk6fPs3y5cvJli0bffv2JVmyZPHTSJE/4cmyzVFRUbi7u1OxYkUGDx5M3759mTx5Mrlz5yZTpkwkTZqU3r17kzx5cgYPHkzXrl1JnTo1SZIk4eHDh9y7d4/ixYuzfPlyXnrpJZV/lkTDeRwsXLiQxo0bA9C7d2/8/f25f/8+EydOZNGiRVy4cIEFCxbg7+9vVwYAGDt2LIGBgXYHauxpApwWLlzIsWPHqFq1KmPGjCF79uzPr4Ei/wfn9cB5LDj3ZYfDQUhICPv372f27Nm8++67FClS5KnfO8MAlmUxefJkOnbsSJIkSfjwww+fc0tERORFY5knY/kiIiIiIiIiIk9wvuSO3Tk5Y8YMjh49Svfu3Xn55ZftZRcsWMBHH30EPJ4vvUmTJvZ3sedCT5UqFc2bN6dSpUr2XOjLli2jaNGif2iuaZH4Env//OqrryhUqBCenp728XH16lU++ugjDh48yKxZs6hdu7Y9vzPAqlWrWLt2Lfv37yc6Opq8efNSrlw5GjduTJo0aRQCkETn0KFDVK1alfv37zN37lzq1q1rf7dmzRoaNmzIo0ePmDp1Kq1atbKPh9DQUEaPHk1ISAh3795l9uzZNGzY8Knz/82bNzlz5gz58uV7ZlBAJCH46aefePnll+3zt8PhYNmyZYwbN47Dhw/TtGlTevXqRY4cOZ75+wsXLjB8+HC2bNnCgQMH8PPze56bLyIiLyBNDSAiIiIiIiIi/ydnp4zz5fbZs2cJDAxk8uTJTJkyhUuXLtnLNmrUiMGDBwPQrFkzJk2axP379wHsEEDnzp25e/cub775Jq+99hply5blp59+Yvr06XH+eyIJkXP/nDFjBq+//jpvv/0233//PWFhYQCkT5+eWrVqERoaSu/evfn1119xcXGx54auXr06s2fP5siRIxw5coR169bRpUsX0qRJg8PhUAhAEp0jR45w69YtBg0aFCcEsHfvXoYMGcKjR4/o27cvrVq1An6b79w5TUDTpk3JkSMHb7/99lPnf2MMadOmpVSpUgoBSII1e/Zsypcvz5IlS4iJiQEe7+d16tShS5cuFCxYkIULFzJhwgTOnTv3zHX4+/vTr18/vvvuO/z8/Oz1iIiI/FWqCCAiIiIiIiIif1pkZCQLFixg7NixnD9/nrZt29K+fXuyZMliLxMYGGgHAqpUqUKBAgXw9/dn3bp1rF+/noIFC7J161bSpUvHpk2bqFatGu+++y5r1661O4lEEiJjDFeuXOGVV14hIiICAD8/Pxo3bky9evUoXLgwkZGR1K5dm3Xr1tGqVSvGjRtHkiRJ7GoCT1a9UBUMSawcDgfVqlVj48aNHD58mKJFiwJw8OBBAgICOHbsGL169SIoKMj+zc2bN0mbNq3977CwMKKiokiePLkqYkiiEPuc7XA4aNOmDbNmzaJIkSJ06tSJevXqxakMsHz5coKCgjh9+jQtWrSgc+fOvPLKK39o/SIiIn+VW3xvgIiIiIiIiIgkLg6HAw8PDxo3boybmxvDhw9n8uTJAHHCAIGBgWTIkIFhw4axfv161q9fb68jX758rFu3jnTp0gEQHh5OdHQ0bm5uCgFIgmdZFhkzZiQ4OJj27duTL18+MmXKxKhRo1i8eDGDBw+mSZMmTJw4kbNnz7J69WqqV6/Oe++9F2cdT65TJKH7vQCLM9zy6NEj4PdDANHR0URGRjJ27Fh8fHzo27cvDocDLy8vvLy8MMYoBCAJXuypXo4ePcqdO3ewLItXX32Vr776iilTpmBZFnXr1sXV1RUXFxdq1aoFQFBQEDNmzAD4n2EAXRNEROSfoCCAiIiIiIiIiPyfYnf+uLi4YIzB3d2d+vXrY4xhxIgRzwwDBAQE8Oabb3Lu3Dl27dpF0qRJyZEjB1WrVo0zGnTp0qUAlC1b9vk2TOQPeLLz0xlaadCgATt27GDLli3Mnz+funXr0rt3b5o1a8aWLVuoU6cOrVq1YuDAgUyfPp333nsPy7LidCKJJBaxj4Pr16+TPn16+9+FCxdm/fr1/PLLL3zzzTfPDAFERETg6enJlStXmDlzJu+//z5AnGNBnZ+S0Blj7H127ty5fPLJJ9y8eZOMGTPi5eUFwP79+wkLC8PFxYXatWv/bhjA1dWVtm3bkitXrnhrj4iIvNgUBBARERERERGRZ4rd6XPv3j0iIiK4ceMGmTNnxtfXF8AOAwC/WxmgYMGCFCxYkBo1ajzzvzNgwACWLVtG3rx57ZfkIgmJ8zj4+eefSZs2LZ6enjgcDnx9falVqxZr1qyhU6dObN++nYIFCzJr1iymT5/Ozp07yZcvHy+//DIrV65kzJgxdO3aVSEASZScx8H06dNZsmQJM2fOxN/fH4AiRYoA0KZNG1566SVOnjzJJ5988lQIAKBDhw7cvHmTd9555/k3QuRvch4HK1eupFmzZqRPn57Zs2fTpEkTIiIi2LFjB2PHjmX37t0MHz4c4JlhgFGjRjFx4kSSJUvGoEGDVAlDRET+FXrqEBEREZGnGGPiexNERCSexQ4BrF27loYNG1K8eHFKlChB2bJladWqFXfu3AHAw8OD+vXr07t3bzJlysTkyZMJCQnh0qVLcdYX+/oSGhrKhQsXaNCgAUOHDiV9+vR8/vnnZMqU6fk2VOQPmjlzJqVLl2bIkCHcvXvX7syvX78+9evXZ+/evcycOZOiRYsyZswYtm7dStq0aTlw4AAnTpzA1dWVKVOmcOjQoXhuichfd+fOHebPn8+uXbv49ttv7c+rV69O3bp1uXv3LqdOnaJVq1aMGDECeFxBwxme6datG+vWreODDz6gcuXK8dUMkb/MGMOtW7eYOHEiACNGjKBJkyYAeHp68v777zN+/HhatGjB6dOnGTlyJMuWLSMmJgbADgN07NiRChUq0Lp1a4UARETkX2MZveUVERERkVhid/z8+uuvpE6dOp63SERE4tOcOXP4+OOPAXjttdcA+PHHH7l16xbFixdn7NixvPrqq3h6ehIVFcXixYsZPnw4ly9fpm3btnTo0IHMmTPHWacxhmXLljF69GiOHDnCq6++yoIFC1QaVxKsBw8e0LVrV1asWMHdu3cpUKAA48ePJ3/+/KRJk4a7d+9SvHhxvL292b59uz3txeXLl5k7dy5r167l66+/5uWXX+brr7/W/ZUkajNmzKBVq1ZkzZqVAwcOkC5dOgDCwsKoVKkSe/bsIUOGDMydO5dXXnmFl156iXv37tGjRw/mz5/PK6+8wu7du/Hz89M0GZIgLVu2jKxZs1KsWLFnTldx48YNSpQogYuLCydPniRJkiTExMTg4uJiL3/27Fm74tHrr79Ou3btqFevnt3pb4whPDwcLy8vYmJiFAYQEZF/hYIAIiIiIvJMq1aton///owePZqKFSvG9+aIiEg82LRpE5UrV8bX15dx48bx0UcfER0dTUREBO+++y4HDhzAz8+PvXv3kjVrVizLihMGuHr1KvXr16dfv35kzJgxzrp//fVX5syZg7u7O7Vq1eKll16Kp1aK/DF3797lwoULdOnShd27d5MiRQrq169P3bp1KVOmDJMnT6Z9+/a0aNGCzz77zP5dWFgY165dY/To0XTr1o1s2bKp81MSBed+6gwKR0dH4+bmhjGGmjVrsnr1aj7++GMmTJiAt7c3ALdu3aJZs2Zs2LABLy8v0qdPT4oUKbh27RrXr1+ncOHCrFmzhsyZM6vzUxKklStXUqtWLd544w0mTZpE4cKFn1rm3LlzFC1aFDc3Nw4dOkSOHDmeua4vv/ySihUr8ujRI0qUKEGXLl2oXbu2zv8iIvLc6IojIiIiIk/ZvXs39erV48cff+Tnn3+O780REZHnzOFwEBYWxvTp0wEYO3YsH330EQBubm5cunSJ+/fvA1C3bl2yZcuGZVk4HA7c3d2pX78+ffr0wd3dnR07duDj4xNn/cYYUqdOTadOnWjbtq1CAJIo+Pr6UrhwYbZv387QoUPJli0bU6dOpUqVKsycOZMyZcpQsmRJFi9ezNKlS+3feXl5kTVrViZNmkS2bNmIjo5WJ5AkeMYYez89e/Ys8Pj8D4/nSB8/fjzZs2dn8+bN7N27F3g8BUCaNGlYt24dgwcPply5cly5coUffviB3LlzM2DAALZs2aIQgCRo+fLlo1y5chw4cIDNmzc/c5l06dJRsGBBHjx4wLlz54BnT69XqlQp6tati5ubG8eOHWP48OFs27btX91+ERGR2PTUISIiIiJPvbSYN28elmUxdepUWrRoEU9bJSIi8cXFxYXQ0FD27t3LG2+8Yc99C3Dw4EHq16/PiRMn6N27N2PGjInzOwB3d3fq1q3LlClT2L17N76+vnGuNc6yuW5ubnbHkkh8uH379p9a3tl52bt3b2bPns0nn3xCaGgoLVu2ZPz48eTLl4/Q0FDWrl3LtWvXnrkO7fOSGDjP0zNmzCBPnjx07dqVH374wZ7nPE2aNLRs2ZIrV64wY8YM4PG+HRERAUC/fv1Yt24dP/30E+fOnWPXrl0MHDiQtGnT4nA4FAKQBMkYQ65cuZg2bRqjR4+mV69ewOMqRrHvY5InT06pUqWIiYmhXbt2nDlzBsuy4iwTFRUFPD4ucubMSZs2bTh+/DghISH2tUfFmkVE5N+mIICIiIiI2C/6Dh48yMmTJzl16hQNGza0O34cDkd8bp6IiMSDhw8fcvv2bVKmTGl/duDAAQICAjh27Bi9evVi2LBh9nc///wz48aNs19qe3h4UKtWLfz8/IiJiXnmHLsi8WnEiBFUrVqV48eP/+HfuLq62mXSCxYsyIgRI1ixYgVlypRhzpw5rFq1Cng8v/SmTZv+rU0XeS5OnjxJly5dABg3bhyNGzcmODiYsLAwvLy8qF69OoUKFWLFihUEBwcD4OnpGefZ4aWXXiJDhgxx1quKGJJQOTvzs2fPTteuXQGYOHEi7777LkeOHInTcd+3b18qVKjAhQsX6NSpE2fPnrWrIzkrJAF899135M6dm/bt2/P222+zYcMGOzyjeyMREfm36a5LRERERADYtWsXb775JoGBgfz4449ky5YNeDzyTS/rREReXM8KezkcDrvz/vz584SFhbF//37atGljhwCCgoIACA8PB+DYsWMMHDiQ+fPnP7U+jfyUhObXX39l6dKlHDhwgF69enHixIk//Ftnx42zQ6h69ep89tlnhISE2Pt6TEwMw4cPJzIyUiM+JdHKmzcv1atXB6BIkSL8/PPP9OrVi3LlynHkyBFy5MjBhAkTAJg0aRL79+8Hfr+jX52ekhjEPsffv3+fzz//nG+//ZZPPvmEb7/91j6nJ02alJ49e1K0aFG2bt1Ko0aNOHLkCC4uLri4uOBwOOjWrRuHDx8md+7cZM2alU6dOgGPw2IPHjzQ9UFERP51eqMrIiIiIgBERESQL18+1qxZw82bN+1ShgoBiIi82Jzn+cWLF7Nu3Tr7s+zZs1OzZk1+/PFHgoKCaN++/VMhgIiICJIkSQLAoEGD8PHx4dVXX42fhoj8CalTp2bu3Lm8/fbbbNy4ke7du/+pMAAQpwx0jhw5aNWqFQcOHKBSpUqUKFGCrVu34uHhoc5PSRSe7JCMjIwEHlcCKFSoEADbt28nICCAo0ePUqFCBbp164avry8jR47k4sWLrF27FmOMOjflhWBZFsmTJ2fmzJm8//77fPHFF3Tu3Jlvv/0Wh8OBZVmULFmSwYMHU7JkSb7++mtKly5N9erV+fjjj3nrrbcYO3YsOXLkoG3btsDjQE2KFCm4cOECYWFhuj6IiMi/zjK6MxMRERERIDo6mi+++IKgoCC++OILUqZMydatW9WhIyLyH3Do0CHeeOMNqlatymeffUb69OkB+OyzzwgICMDNzY3o6Gj69u3LkCFDAHj06BHe3t4AtG3blqlTp9KqVSvGjh2Ll5dXvLVF5I9wlvf//vvvad++PXv37uW9995j9OjR5MuX7y+tMyYmBldXV3uUZ/LkyYmOjsbNze0f3nqRf5bzeAC4fv26fQ2IiorCxcWF6dOn07ZtW7p06UJwcDBbtmwhJCSEDRs2kClTJsqWLcuXX37JL7/8wvbt2yldunScdYokVg6HAxcXF3788Ufat2/Pli1bKFWqFOPGjaNw4cK4uLgQHR3NpUuXGDp0KMuWLePRo0fA42kyihYtypIlS8icOTMAly5dokCBAuTLl8+uoCEiIvJv0vAuERERkf+o2KN1HA4Hbm5ulC1blp49e/L2229z584dhgwZwpkzZ+J5S0VE5N+WIUMGSpYsyd69ezl48KD9eatWrWjcuDHR0dEkSZKEd955h9DQUAA7BNC5c2emTp3Kq6++yuDBg/Hy8tJoUEnwnPM4FyhQgPHjx1OqVCm2bNlCly5d/nRlACdXV1eMMSRLlozkyZNjjFEIQBIFZ4e9c+TztGnTAHB3d8fV1ZUqVapQunRpxo4dy+rVq3nvvfdYt24do0aNwsXFhQULFnDhwgWioqJo0aIFly9fVghAEpXfu29x7sfZs2cnJCSE9957jy+//JLOnTvz3Xff2c/R2bJlY9asWWzfvp2VK1cSEhLC+vXrWb9+vR0CAAgKCuLBgwe8+uqrREVF6X5JRET+daoIICIiIvIf8kdG5kRFRbFr1y4GDRrEgQMHqF+/PgMHDiRnzpzPaStFROR5iX1dmDx5Mu3bt8ff358DBw7YI0IB6tWrx7Jly/D29uatt97ijTfe4OHDh+zcuZMjR46QPXt2du7cSebMme1R0SIJ2ZP3RMeOHaNr167s3LmT999/nxEjRpA/f/543EKR5+vGjRsEBASwevVqAD766CM6duxI7ty58fb2Zv/+/bz99tuUL1+eOXPmkC5dOgD27t3L+vXr+eyzz7h37x558+Zlz549pEqVKh5bI/LHOUf9A/z8889cu3aNX375BT8/P7Jnz07q1KntZZ9VGaBIkSJ/KPjSt29fhg8fjr+/P7t27eLll1/+19okIiLipCCAiIiIyH9E7BfeX3/9NTt37mTHjh289NJLvPTSS3Tq1ImUKVPi7u5uhwECAwM5ePAg9erVIzAwUGEAEZFELvbL7mepVq0a69ato23btowaNcoe9Q/Qv39/1qxZw/Hjx+3PXnrpJd566y2Cg4Px8/NTCEAShdj3RBs2bGDp0qVcv36db775hl9//RWADz74gGHDhv3laQJEEqPbt2+zd+9eAgICuH79Orly5eLDDz+kY8eO+Pn5MWDAAIYOHWpPBeMUFhbGkSNH6N+/P7Nnz8bf3///vN6IJASxrweff/45Q4YMiXOf8/rrr1O/fn06dOhgf/a/wgBPhsxu3brF6dOnCQ4OZs2aNfj5+bF9+3by5Mnz/BopIiL/aQoCiIiIiPzHrFq1iubNm3P37l3g8dyFERER5M6dm27dulG1alXSpUtHVFQUX3zxBQMHDlQYQETkBTNp0iTOnTtHt27dyJAhg12+/Ny5c1SsWJHo6Gjmzp1LmTJliIiIwNPTE4CffvqJY8eOcfv2bWJiYnjrrbfIlCkTPj4+CgFIojN37lyaNWuGh4cH9evXx9vbmzt37rB06VKMMVSuXJkRI0YoDCAvvCc7L0+ePMnMmTP5/PPP+fnnn8mdOzdTp04lMjKS3r1789NPP7F582aKFy8O/BYyc/5/dHS0psWQRMV5PQBo1qwZyZIl4+rVqyxfvhxjDB07dmTcuHH28rHDAKVLl2bcuHEULlz4qcoAZ86coVq1apw9e5ayZcsybdo0cuTI8TybJiIi/3EKAoiIiIj8h2zevJn3338fd3d3AgMDqVChAgBz5sxh0aJFJE2alIYNG9K7d29SpEhBdHQ0u3btssMAjRo1olevXuTNmzeeWyIiIn/Vpk2bqFy5MgDlypWjatWqNG/enKRJkxIaGkpwcDCBgYHUqVOHJUuWAI+njXF3d//ddf6RqWdEEpJdu3bx7rvv4unpybx586hRo4b93Z49e/j444/56aefqFSpEqNGjVIYQP4znOfz+/fvc+bMGfr06cOOHTvw9fWladOmXLhwgc2bN9O4cWOGDRtGmjRpnvqtSGKya9cuqlSpgru7O9OmTaNu3br2dyNGjKBPnz4ArFmzhipVqgBgWVacMEC+fPlYvHjxM6eUOXLkCD/88APly5cnbdq0z6dRIiIi/5/qM4mIiIj8R1y8eJH+/fsDMGHCBHr37k3x4sUpXrw49erVI2XKlPzyyy+4ubmRIkUKANzc3Hj77bcZNGgQpUqVYsGCBYwdO5aoqKj4bIqIiPwNV65cwdvbG29vb/bt20fXrl2pWLEiJ06cwMfHh6ZNm5IzZ06WLVtmj377XyEAQB0/kuh89dVXxMTE0LNnTzsEYIyxK10sX76cwoULs2nTJvr27RunVLTIi8x5Pk+ePDnFixdn27ZtDB8+nJdffpnx48dz5MgRwsPD2b59OydPnnzmb0USA+f4yC1bthAWFkZQUFCcEMC+fftYsWIFAH369KFq1apYlmXv59mzZyckJIQSJUrwyy+//G4n/6uvvkrdunUVAhARkXihIICIiIjIf8Qvv/zCyZMnadasGa1bt7Y//+677+jTpw8XLlygbdu2DB06NM7vnGGAnj17UqFCBTp37vx/dgiJiEjC43zh3bx5c2rWrMmjR4/o378/tWrV4vDhw7z11lv069cPT09PFi1aBMDMmTP56quv4nOzRf5RzuNg7969AKRPnx6AmJgYLMvC1dUVYwyFCxdm8uTJeHh4sHbtWvr06aMwgPznxMTEANCzZ0/mzp1Lt27duHz5Mm5ubpw/f55PP/0UFZuVxMqyLMLCwti4cSN+fn7Url3b/u7gwYO0a9eOI0eO0KtXrzjPyA8ePLD/zp49O0uXLuXMmTOkT58eh8Pxu/8tERGR+KAggIiIiMgLzvkyYvfu3YSFhZEnTx77u2PHjtG2bVv27dtHmzZtCAkJsb+7fv060dHRwOMwQMWKFVm1apVK44qIJBJPvoy2LIuIiAgARo0aRfbs2dm2bRsTJ05k9erVvPrqqwQFBfHmm2/y3XffERAQwKlTp9i8eTOAOnvkheDsjHHez9y9excAV1fXOMs4HA5ef/11u/Nn/fr1fPLJJxw9evT5brBIPHIGYwAKFizIqFGjWLFiBW+88QZ58uRh/Pjx6uCURC0yMpKIiIg490gHDx4kICCAY8eO0atXL4KCgoDH91W3b99m4sSJbNy4EXh8b/Tyyy+TNm1aHA4HLi7qbhERkYRFVyYRERGRROxZnTJPdvw4X0b4+fkBv43sOXbsGAEBARw8eJA2bdowadIkAPsFyOjRoylXrpy9PldXV3x8fP6dhoiIyD/Oef5fuXIl06dPB8DT0xMAX19f2rZty+7duxk6dCiVKlVi69atDB8+nAcPHtCiRQvWrVuHw+FgyJAh7N69W5098kLJnj07AJMmTXpm577z+MmYMSMAxYoVY/PmzYwePdoOSor8FzjP/c7njurVqzNr1iz27dtHtmzZdDxIohD7ufnRo0f23ylSpCBHjhw8evQIFxcXjh8//swQQHh4OC4uLvz00098+umnfPvtt0Dckf4KAYiISEKkq5OIiIhIImZZFpGRkZw/f57w8PA43+3atYvly5fb/3a+yN64cSMrV66kbdu29miH2CEAZyfR7t27OX/+PLdv335OrRERkX/agQMHqFWrFq1bt6ZFixacPn2asLAwPD09qVWrFsWLF2fatGmsWrUKeFz++fPPP6ddu3Z26duYmBh1fkqi83sVLJyByEaNGlGxYkUuX77M+PHjuXjxYpzloqKiAMiZMyeZMmWiWbNmVK9enSFDhuDm5vbvbrzIP+R/VXL5s1VeLMuyf5M9e3Z8fX0xxuh4kETB2WE/d+5chg8fTnh4uB14L1KkCHfv3qVq1ao0bNiQY8eO8cknn9ghgIiICJIkSQJA3759efjwISVKlIifhoiIiPxJllFtPxEREZFEKyYmhvnz57Ny5Urq1KlD9erV8fHxYcmSJTRo0IAqVaowadIkMmfOTFRUFHXr1mXDhg2kTZuWK1eu0K5dOyZOnAg8HuXgfMHRunVrpk+fzsCBA+nbt69e8ImIJFJXr15lx44d9OzZk6tXr5IvXz5q165Nq1atyJAhA/v27ePtt9+mSpUqzJw5k5QpUwJw7949jh07RsOGDXF1dWX37t1kyZIlnlsj8scYY+xOn/v373Pv3j3u3r1LlixZ8Pb2xt3dnejoaBYuXMjAgQO5ffs2TZs2pU2bNuTJkydOeee6deuyZcsWrl69ipeXFwDR0dG6N5IEL/Z+fPjwYY4fP87+/ft55ZVXeOutt3jjjTfieQtFnq9z585RvHhxkiVLxoEDB+yg/J07d3j99df54YcfAOjevTujRo0CfntGdjgcdO3alQkTJlCvXj2mTZtGsmTJ4q0tIiIif5QqAoiIiIgkYtHR0dy8eZP169czaNAgDhw4wPz582nQoAEpUqSgdu3aZM6cGQB3d3dq1KiBn58fV65coVixYva8t4AdAggMDGT69OmUKFGCFi1a6EW3iEgi5ufnR6NGjdi2bRutW7fmzp07BAYG8s4777B7926KFClC7969Wb16NStWrLB/lyxZMkqXLs3hw4c5fPgwWbJksUdSiyRksUMAa9asoU6dOhQsWJDChQtTrFgxOnbsyLfffoubmxu1atWiZcuWJE2alGnTptGqVSvWrVvHTz/9xI0bN2jTpg2ff/45pUqVijP1ku6NJKEzxtghgIULF1KlShWaN2/OzJkz6d27N6VKlWLUqFFPVcIQeZFlyJCBWrVq8fPPP9OxY0f785QpUzJ16lQ7GHD+/Hnu3btHVFQUSZIkISIigjZt2jBhwgTy5s3LmDFjSJYs2Z+uqiEiIhIfVBFAREREJJG7evUqI0aMYPr06fj6+nLt2jVSpUrFlClTqF27NvC4coCrqysAgwYNIiQkhLCwMOrUqcMHH3xAsWLFuHnzJmPGjGHhwoWkTZuWPXv2kCtXrvhsmoiI/AOcHaN3797l+PHjBAYGsnPnTpImTUqrVq3w8/Nj6dKlXLp0ifXr11OsWDEg7mjS2H+LJAazZ8+mefPmALz99ttER0dz48YNzpw5g5+fH3PnzqVChQo8evSI2bNnM3/+fL766ivgcRDGzc2NO3fukC1bNr744gsyZcoUJ2QgkhgsWLCAjz76CMuy6NevH1WrVuXIkSN0796dR48e0aFDB9q2bUvOnDnje1NF/lFPnq+dz8PXrl3jtdde4/Lly0ybNo2WLVsCj6eD2bJlCx06dODixYtkzZqVvHnz4uHhwYkTJzh79iy5c+dmy5YtZM6cOc7ztYiISEKmIICIiIhIIhb7BUelSpXYsWMHxhgCAgLskv/OlxSxO3HGjRvHnDlzOHbsGADe3t48evQIgKJFi7JgwQJy584dDy0SEZHnYfDgwSxbtoyTJ0+SNWtWXFxc+OWXX2jdujWBgYGkSJEivjdR5C/btGkTVapUIWXKlIwbN45GjRoRGhqKq6sr9evXZ82aNQCcPn2anDlzEhUVxYULF5gzZw6bN2/m3Llz5MmTh1y5cjFixAj8/PzU6SOJzq5du6hZsyZubm6MGzeOBg0aABASEkLXrl2Jjo4GHk8J1rlzZwWA5YUR+7n35s2bpE2bFvjtuXjZsmU0a9aMkiVLMmvWLDJlymT/9sKFC7Rv354TJ07YFTPy589P6dKlGTBgAOnTp9f1QEREEhUFAUREJNH6vRE5GrEm/zXGGM6cOUPevHlxd3fHsiz8/f0ZMmQIFStWjDN3Yezj4/vvv2fbtm2sX7+eiIgI/P39KV++PO+//z4ZMmSIr+aI/E9PnvudpZqfPO9r1KbIs8V+ef3VV1+xdOlSxo4da3+fPHlytm3bRvHixeNrE0X+MmMM0dHRNGrUiM8//5xZs2bRtGlT+/vDhw8TEBDAt99+S9euXRk9evRT6wgNDeXhw4ekS5eOqKgoPDw81Okjic79+/dp27YtixYt4rPPPqNFixYADBkyhIEDB5I0aVIGDhzIzJkzOX36NO3bt6dt27YKAssLZfbs2QwZMoThw4dTt25d+/OLFy/SrVs3Vq5cyahRo+jevTvweNo9Nzc3IiIiuHfvHufPn8fFxYUCBQrg4uKi64GIiCRKCgKIiEii8KyOH2enz5kzZ7hz5w5hYWGUKFECHx+f+NpMkXgTERFBYGAg6dOn5+rVq4wfP54sWbIwZMgQqlatire3t73sk8dTVFQU7u7u8bHZIn9JaGgo33//Pa+99hqWZdkv5GbNmsWDBw/o1KlTfG+iSIL25HVgxYoVjB8/ni+//BKAX375BT8/v/jaPJG/5ebNm+TJk4fcuXPb+zTAwYMHCQgI4NixY/Tq1YugoCD7u8jISDw8PIDHzxmWZWFZlkJlkmg8ua+ePHmS/Pnz07ZtW0JCQgCYMGECPXr0wMPDgwMHDpA/f34mTJhA586dARQGkBfKrl27KF++vP3vli1bUqtWLd555x0A1q5dy4cffoiLiwtbt26lXLlygAaWiIjIi0dXNRERSfAWLVrE+vXr7dKFMTEx9oPZwoULee+99yhdujTly5enWLFiLFq0iOvXr8fnJos8F7HznJ6engwdOpTOnTvTs2dPWrVqxaVLl+jfvz/r1q2zy/7DbyOondzd3YmJiXlqnSIJUUxMDEuWLOHjjz+ma9euOBwOXF1dmTZtGi1atGDixIlcuHAhvjdT5Ll68rz+f3myY7NmzZpMmzaNzz77jMuXL9tl0EUSumfdt9y9e5c7d+7g6elJZGQk8L9DANeuXWPmzJl2CWgXFxf7GFEIQBKD2CGA8+fPA5A3b15CQkLs6QAOHjzI5MmTcXNzY+PGjeTPnx+Ajh072iOlQ0JCmDhxIidPnoyHVoj8s1599VVy5swJQO7cuZk7dy7169ena9euAHzwwQcEBQXhcDiYPn06V69eBZ6uMiYiIpLY6comIiIJ2qZNm2jUqBEDBw5k586dREVF2WXYlixZQuPGjbl06RJvvPEGefLk4cyZM3Tq1Inx48fbL/NEXiSxX3jHxMQQFRXFnTt3AOxjI1WqVPTr14+AgIBnhgGcyy1cuJBVq1bF+UwvvCWhsyyLDBkycPHiRcaPH8/w4cOZNm0abdq0IWPGjAQHB+Pv7x/fmynyr3qy8/PJl9Z/JhjgXFeePHlo3rw5GTNmVNlbSRRid34eO3aMe/fuAZAiRQpSp07NgwcP8PDw4NChQ88MAYSHhwOPp8gICgriwIED8dMQkb/JeRzMmjWLihUrMnnyZADatm3Lm2++CcDevXs5e/YsgwcPpnTp0jgcDjtonzNnTlxcXChRogRTpkxh/vz5CoNJohYVFUXy5MkJCQkhZcqU5MqVi927d5MuXTrGjRtHsWLFWLVqFRUqVKBatWqsW7eOr7/+GkD7voiIvHAUBBARkQQtW7Zs1K1blxMnTtC/f3927txJREQEDx48YOTIkaRLl44lS5awZ88eDhw4wIABA0iePDnjx49nwoQJCgPICyX2C+8vv/ySbt26UaZMGUqXLk2LFi2YMWOGvWy6dOno06dPnDDA2rVriYqKAmDYsGE0btyYmTNn8uDBg3hpj8hf4eLiQtmyZZkxYwbp06dnwIABtGnThpdeeomZM2dSrVo1QNUt5MXlLFsOj+c7/+yzz2jQoAFDhw5lzpw5wJ8bzRY7AOb8WyEASYiePK8799fRo0dTp04dOwCTLFkyihcvztdff03Hjh1p164dx44do2fPnnYIICIigiRJkmCMYeTIkTx8+JA8efI83waJ/IO2bNlCy5YtuXHjBmnTprU/dzgchIaGsnHjRgCyZ88OPL5OOI+ZTJkykTZtWmrVqsXbb79NmzZtdB2QRCP2tSEsLAzAnvauYMGCVKlShTVr1nDz5k0OHDhA//79uXnzJo0bN2b48OGkTp0aFxcXunTpwq1bt3B1ddVzhIiIvFAsoyubiIgkcD/++COBgYEsXLiQ4sWLM3ToUPz9/SlQoACjR4+mffv29rLh4eEsW7aMESNGcPHiRdq0aUOHDh14+eWX47EFIv+sVatW0bBhQ8LDw0mbNi0RERHcv38fgGbNmhESEoKXlxcAN27csEdMp0mThooVK/Lw4UOWLFlCsmTJ+PLLLylQoEB8NkfkL2vRogWzZs3Csiw+/PBDVqxYATweBeR8ASjyIokdCJs/fz5dunTh9u3bcZapVKkS/fv3p2jRovac5yKJ2c8//8xLL730zIDLnTt3yJo1K/nz5+fLL7+0P3fO/ew0aNAg+vfvD8CjR4/w9vbGGEO7du2YOnUqLVu2ZNy4cfb9k0hC9+Q85u3bt2fmzJnMnTuXOnXqPLV88+bNmT17NrNnz6ZJkyZxKr+ULVuWO3fucPToUSIjI/Hw8FBlGEl05s6dy+HDh2nYsCFvvPGG/fnevXupVKkSWbNmZd26dWTMmJEffviBvn37snbtWlxdXYmJicEYQ4sWLZgwYQJJkiSJx5aIiIj8s1QRQEREErzs2bMzcOBAGjZsyOHDhxkwYACbN2/G19eX9957D3j8IsQYQ5IkSahfvz69e/fm5ZdfZsqUKUycOFGVAeSFsXPnTmrXro0xhk8//ZSzZ8/yzTffsG/fPlKkSMHs2bN5//33CQ0NxRhjVwbo3r077u7uzJgxgyVLlpAjRw4OHjyoEIAkWocPH2bevHn4+vqSNGlSVq1aRe/evYmOjsbd3V1lPeWF5AwBLFmyhCZNmvDo0SOCgoJYs2YNK1asoFSpUmzatIkOHTqwatUqHQeS6AUHB1O1alW++OKLZ055ERMTg2VZ+Pr6Ar+NDP3ggw8IDAy0l4s9ZYy3tzcAnTt3ZurUqRQpUoQhQ4bg5eWlUaCSaDhDAFu3bmXnzp18+eWXNGjQwA4BOI8X5z5dtGhRAIKCgjh48KAdIu7UqRN79uzh1VdfjROkVAhAEpPTp0/Tv39/Jk+ezIcffsjUqVPt6gClS5dm0KBBnDhxgoULF+Lu7k7evHlZtWoV48ePp2zZsvb91fnz53XvJCIiLxy3+N4AERGR/8U50uGVV16xR/EsXLiQixcvcvfuXfshzbIsLMvCGIO7uzv16tUDYPjw4UyZMgVAlQEkUTPGcPfuXYYPH47D4WDcuHG0bt0aeDwXrpeXF/7+/hw9epSXX34ZHx8f4PExlDZtWnr06EHNmjXZsGEDL730EuXLlydz5szx2SSRv6VYsWIMGDCA/Pnz4+LiQvPmzRk5ciTGGIYPH26P7tGLbHnRHD16lJ49e2JZFrNmzbLveeDxOf/EiRMcOXKE27dva/+XRO3XX39l69atHD16lKCgIFxcXHjrrbdwcXGxq2MYY+z/PTlCul27doSHhzNixAiaNGnC2rVryZEjB5ZlsXXrVo4cOUL27NlZvXo16dKl0zVDEp0tW7ZQqVIlatSowYMHD/Dz8wPiVkZydnA2a9aMXbt2sXLlSj788EPSpEmDi4sLx48fJ3v27AwdOlTVlCTRyp07N0uWLGHt2rWMGjWKtm3bsmvXLmrXrk3NmjXp0KEDGzZsYMSIEVSrVo38+fMDjytplCtXjvXr1zNu3Dg+++wzfHx84lRgEhERSew0NYCIiCQIsV/mOR+4Yr+Mi4iIwNPTk9OnTxMUFMSGDRu4c+cOnTp1YujQoXanZ+x1RUdHs2TJEoYPH87PP/9M3bp1GTBgAJkyZYqXNor8XZcvX6ZgwYKULFmS9evX258fPXqUNm3acPDgQdq2bUtISEg8bqXIP895Xnd28jzZ2RMZGcny5cvp3Lkzt27dokePHgwfPhwXFxc7MPZk545e8ElC5dw3f69TctGiRTRq1IgBAwbEGfG8f/9+OnTowLfffkvv3r0ZNmxYnPU9edyIJAbHjx9n6NChLFu2jNKlSxMYGEiZMmXsffnSpUvkzp2bSpUq2dPDxBYdHc2CBQvo1asXv/76q31NyJgxI6VLlyY4OBg/Pz+FACRR2rlzJ0OGDGH//v1ERUXRrFkzZs6cCfDM5+rQ0FA6d+7M3r17OXv2LGnTpiVHjhwsWbKETJky6TiQROnJ+5sVK1YwZswYvv76a7y9valevTpjxoxh/fr1tGzZkjfffJPFixeTLl26OOsJDw8nSZIkREdH4+amsZMiIvLi0FsAERFJECzL4uHDh6xcuZLjx48Dv3XaTJ06lfLly3Pv3j1y585N//79qVy5Ml5eXmzevJmtW7cSFRUVZ13GGNzc3KhXrx59+/bFy8uLnTt3kjRp0nhpn8g/4eLFi9y7dy9OmOXYsWN2CKBNmzZxQgDXr1+PExgQSSxil392OBz2i+zQ0FAePXr0VHloDw8PatasyYQJE0iTJg2ffvopvXv3xuFw4Orqal9PAgMDmThxIoBCAJIgBQcH06ZNG6Kjo+2qFk7ODP+mTZsAKF68uP2dMwj27bff0qtXLzsEAHDt2jUAO0Qjkpjkz5+f/v37U7NmTfbu3UtgYCC7d++29+WIiAgcDsfv3uO7ubnRtGlT9u7dy65du5gzZw6LFi1i3759TJ8+XSEASdTKlStHYGAglStXBmD58uWsWbMG+O2ZGLCvJz4+PkyaNImtW7eyYcMGvvjiCzZs2KAQgCRqzhCAc3+vWbMmM2fOZPTo0Xh5eTFnzhzeeecdjDEULlyYI0eOsHbtWmJiYuJMB+Pp6QmgEICIiLxwdGUTEZEEY+/evXTt2hVfX19mzpxJsWLF+Oyzz2jbti1p06blxIkTvPnmm+TIkYMBAwYAj6cJGD58OF5eXpQvXz5OCURnGKB27dokSZKEN954A19fX40ClUTLud/evHkTgG+++Yb27dvbIYBJkyYBv41m2LVrl10x4+2334637Rb5o/bu3Uv+/PlJmTKl/WLO+XJv1apVTJkyhStXruDl5UWTJk0oX748efLkAR6/vKtevToAHTt25NNPP8XhcPDpp58C0LdvX4YPH46/vz/NmjVTMEwSnB9++IE+ffoQFRVFsmTJGD58OG5ubnbnjPMakDJlSuC3wMyBAwdo06YNx44do1evXgQFBQGPK2VYlkX//v1xdXVl2rRpqgggiVK+fPns6hcrVqwgMDCQgQMHUq5cOdzc3IiMjCQ6Ohp4XBLdxcXF7viMjo7Gw8ODHDlykCNHDkqVKhVn3cYYdX5KouEMR1qWZZf/L1OmDA6HA4fDwdq1a5kyZQrp0qXjjTfeiFNxz9XVFYfDgYeHB1myZCFLlixx1qvjQBKaKVOmkCNHDipUqPCHlo+9v+fOnZvcuXNTtWpVOnfuzObNm+nevTuZMmXi/v37LF68mLp165IsWbI4vxcREXkRKQggIiIJRp48eciXLx+bN2+mW7dulCpViuHDh5MxY0YmTpzIm2++aS/7yiuv2GGABQsW2H8/Kwzg7u5OjRo1ADTSQRK1AgUKULBgQQ4fPsyKFSuYMGHCUyGAiIgIkiRJAsC4ceN48OABWbNmjc/NFvlDpk+fTuvWrenatSv9+vXD19fXfuE9d+5cmjVrBoCPjw+hoaF8//33dpnokiVLAnHDAJ07dyY4OJjvvvsOgB07dpApUyZ27dpF0qRJFQqTBCdLlizMmzeP7t27ExwcjMPhYOTIkXHCAPD4Hgjgiy++wN3dnd69ez8VAnBOqXT79m127txJ5syZiYyMxMPDI97aJ/J3PCsM4Obmhre3N8mTJ4+zrPNYiV0RBuDu3bv4+vrGWVbXAUnoYt+vOKc8cnV1jRPsevvtt7Esi8jISDZv3oyXlxeffPLJU2GA3wuDKSQmCc3WrVtp164dKVKkYM2aNbz11lt/6Hexz+kOhwN/f3/mz5/P6tWrmTNnDrt27cLFxYVdu3YxfPhw+75JRETkRWaZ2DVwRERE4pHD4eDcuXMMGjSIxYsXA5A2bVoWL15MuXLlgKfndP7xxx8ZNGgQCxYsoFixYgwePNgOA6iTRxKb/2ufjYmJoUOHDkydOpVkyZLx4MEDunTpQnBwMACPHj3C29sbeNwJOmHCBNq1a8eoUaPw8vJ6Lm0Q+SuioqKYPXs2gYGBPHr0iICAAHr16oWvry9Hjhzh/fffJyYmhmHDhlGhQgW2bt3KihUr2LlzJwULFmTChAlxXhBGRkbyxRdf0KpVKy5duoS7uzvFihVjyZIlZM6cWaEwSbAiIyNZu3YtHTp04Pr163Tp0sUOAzhHf54+fZrq1atz/fp1fH19uXDhAr1797anA3CGAIwx1KpVy66m0bp163huncgf93v3RCdOnCAwMJAVK1bw3nvvUa5cOXr16oWXlxevvvoq169fx9PTk2TJkhEVFYUxhpQpU3LlyhUKFSrErFmzFIiRRCP2cbBr1y7Wr1/P3r178fX1JXPmzHTo0IHChQvby3/xxReMGjWKzZs388EHH9CzZ0/eeOONp9Ylkhg0bdqUefPmkTZtWpYtW0aZMmX+9DocDgcuLi4YYwgLCyMoKIgJEyaQMmVKdu/ejb+//z+/4SIiIgmNERERSWCGDBliLMsylmWZnDlzmtOnTxtjjImOjn7m8ufOnTONGzc2lmWZ4sWLm82bN5vIyMjnuckif5vD4bD/PnfunDl16pTZt2+fiYmJibPcgwcPTKFChYxlWSZt2rQmLCzsqXUNGDDAWJZlChcubC5evPivb7vIP+H+/ftm3rx55uWXXzY+Pj7mk08+MTExMWbTpk3Gsiwzb968OMt///33pmHDhsayLFOwYEGze/fup9Z59epVM3/+fLNhwwZz69YtY8zvX0tEEoqIiAjz+eefmwwZMhjLskzXrl1NVFSU/X10dLTp2bOncXV1NZZlmRo1atjfxV6uW7duxrIsU7FiRXP79u3n2gaRvyP2PdHPP/9sjh07Fuf748ePm5o1a9r3Qm5ubiZ9+vTmpZdeMp6ensbb29ukTJnSeHp6GsuyjKenp0mfPr25cOHC826KyF8W+ziYPXu2cXd3N5ZlGR8fH5MiRQpjWZbx8vIyY8eOjXO/v3v3blOpUiVjWZapVq2a2b9//zPXKZJQxX6+bd68ubEsy6RLl8588cUX/8j6t2zZYm7cuGGMiXvfJCIi8qJSRQAREUkwYmJiuHbtGmXLliU0NJQsWbLw1Vdf8dprrzF+/HhKlCjxu791VgZYunQpmTNnZsaMGZQtW/b5bbzIP2TdunV07tyZBw8ecOvWLapXr07VqlVp3LixPYL53Llz1KhRg+PHj5MjRw4aNWpEgQIFCA8PZ9GiRaxfv5506dLxxRdfkDt37nhukcgf9+DBA1atWsWAAQPs0dD379/nm2++Yf/+/QD2qGiACxcu0L9/fxYuXEiBAgWYOHGiXRkg9nJOzlFBIgnd/6oMABAeHk6jRo1YuXIlmTJlomPHjpQrV45s2bLx66+/0q9fP5YuXUq2bNnYs2cPL730kvZ/SRRMrFHL69evZ8SIEezfv/+pqhbHjx9n8ODBbNy4kYwZMzJs2DDKly9PVFQUkZGR+Pj48PDhQ8LDw/H29iZJkiSkTp1aFWEk0Vm5ciW1atUiefLkjBo1ivfffx83NzfmzZvHiBEjePjwIW3btqV///6kTp0agD179jBixAi2bNlC9erV6dix4x8urS4SX7755huKFi0K/FbdCKBFixbMmjXrb1UGgKefA3RfJCIi/xnxHEQQEZH/uCdHOxtjzM6dO83OnTvN1atXTfXq1Y1lWea1114zhw8ffmrZ2CM7z549az744AOTKVMmc/Xq1X91u0X+Dc6Rz859PlWqVMbNzc2kSpXK9OrVK86IhWvXrpl3333XuLm52b+xLMskSZLElC1b1pw5cyYeWyLy192/f9/MnTvXZMmSxWTIkMEUKlTIFCtWzISHhz9zNP/58+dNo0aNnlkZQCPfJDH7vyoDPHr0yLRs2dIeGerh4WH8/PyMl5eXsSzLlChRwly6dMkYo0oYkjjEPmfPmjXLvsdp06aNWbly5VPLHD161NSqVctYlmXKlCljdu/ebcLDw+OsM/azxrOeO0QSsvPnz5uiRYsay7LMggUL4nz31VdfGX9/f2NZlhk4cOBTv929e7epUqWKsSzLfPzxxyYiIuI5bbXInzdy5EhjWZYZPXq0/Vns8/m/URlARETkv0IVAUREJEFYsmQJV69epUuXLnE+P336NH369GH16tWUKFGCkJAQihUrBkB0dDRubm4YYwgPD8fLy4tz586RMmVKUqdOrYS3JBrGGB49ekSDBg3YtWsXwcHBtGzZku+//56NGzcSHBzMrVu3nhoRGhkZyZYtWzh8+DC3bt3Cy8uLihUrUrhwYdKmTRvPrRL565yVAQYOHMjFixdJlSoVX3/9Nf7+/s88t8euDFCkSBFGjhxJhQoV4mnrRf4688Qczv9XZYDIyEjWr1/P9u3b2bx5Mw6Hg6JFi1KmTBkaNWqkEdCSKK1Zs4bq1auTLl06Pv30Uxo3bvy7y544cYLAwEBWrFjBW2+9xcCBAylTpoyeAeSFcPjwYcqVK0edOnWYOXOm/fm+ffvo0KED3333HX369GHo0KHP/P327duZO3cuw4YNI0uWLM9rs0X+tHHjxtG1a1cAxowZQ+fOnYF/pzKAiIjIf068xhBERESMMd98842xLMukSZPGTJ48+anvz5w587uVARwOh+nZs6d5++23zd27d+3PNeJHEps7d+6YtGnTmh49esT5/P79+2bZsmW/OyJUJLH7vfO1szJAnjx5jGVZpkGDBubXX381xjx7pP/58+dNkyZNjGVZpkKFChr5JonCH6laER4e/j8rAzg9ePDAPHjwIM5nuh+SxObKlSumTJkyT42A/l/78vHjx+3KAOXKlTObN2/Wvi8vhEmTJhnLssyQIUPszw4cOGAKFSpkLMsyvXv3jrP8jz/+aM6fPx/nM+f9kCrDSEI3bdo0u8rd2LFj7c9VGUBEROTvUURaRESeOxOrGI0xBjc3N3r16kVoaCiTJ09m0qRJcZbPmTMnI0aM4MMPP+Srr76iXbt2fPXVVwD07NmTUaNGcfz4cSIiIuzfaBSQJGTmGQWZXF1d8fLyolq1asDj+c0BkiVLRrVq1ZgwYQLp06dn7Nix9OzZk+joaAD7/0USK+f5eu3ataxbt87+PFmyZHz44Yf06tWLrFmzsnz5ckaOHMndu3exLOup48jf35/+/fvTvn17ZsyYgYeHx3Nth8if5XA47NH/Fy5c4ODBg0ybNo09e/Zw6tQpezlPT0+qVKnCxIkTn3kdcF4vvL298fHxsdcNuh+SxOf69et89913vP/++zRs2BB4fN/0v/blfPnyERgYSJ06ddi1axfTp0+3jwuRxCxZsmTA4+MC4NChQwQEBHDs2DF69epFUFAQgP0cvGjRIkqVKsWVK1fsdTjvh1QZRhK6Vq1aMXnyZAC6du3KuHHjgMf3Qc59fMaMGXz88cfcvHmTOnXqsHv37vjaXBERkUTDLb43QERE/ltMrJK3K1euZNasWRw+fJgkSZIQHh7OiRMnmDhxIi4uLrRp08b+Xc6cORk5ciSurq6sWLGCkiVLkjNnTk6dOkWWLFnYvXs36dKl03QAkuDFPgb27dvHnj17OHHiBGXLlsXV1ZVTp05RsmTJOPuxh4cH1apVw7IsOnTowNixYwHs8tDa7yWx2717Nx9++CEFChTAzc2NSpUqAZA8eXKqV68OwIABA5g0aRKWZdGrVy98fX2f2vezZ8/O2LFjcXV1taePEUmIYndsLl++nGHDhnH69GkiIiLw8PDAx8eH/v3706hRI9KkSUOSJEmoUqUKwFPXAXd396fK/+uaIImN8/7o0KFD3L9/H29vb4wx/2cIwClfvnz07t2bFClS0LdvX7uUtEhCZ56YEia2YsWK4evry9dff838+fMJDg7m2LFj9OzZM04IwNPTk8jISD7//HNSpEhBkiRJnmcTRP4xAQEBALRt29aeKqBz5852GMDT05MZM2YAMGvWLOrUqaNpAkRERP4PejMmIiLPlfMlx7x582jatCnJkyenTZs2vPLKK1y/fp1FixZx9uxZRo4ciWVZ9oMgQI4cOZgyZQoZM2YkJCSE0NBQ3nnnHWbNmkXGjBk1B64kCs5jYPXq1TRo0IDw8HAAlixZgsPhYN++fbRo0QJXV9c4nZweHh588MEHwONOoIkTJ/Lw4UMmTZqkzk5JdJ586Z0kSRI++ugjFi9ezMiRI3E4HFSuXBl4PBoudhggJCQEwA4DPLku53VAx4UkZM59du7cuTRr1gyAmjVrEhkZye3bt9m3bx9du3bl+PHjtG/fnsKFC5MkSRKqVq0K/BYGcHV1ZdiwYbi7u8dbW0T+Cc5jIlOmTMBvFTOcFWCe1VF68+ZNjh07Rvny5QEoVKiQfV+k5wJJDGLv28ePH+fUqVOkTp2acuXKAY+Ph8qVK7Nw4UJ+/PFHbt269VQlAE9PTxwOBy1btuT7779n4MCBJE+ePN7aJPJ3KQwgIiLyD3v+sxGIiMh/3VdffWWSJUtmXF1dzfLly+N8980335hu3boZLy8vkyVLFjNlyhT7u9hzfX7zzTfmp59+Mvfu3TPGaM5DSVy+/vprkyJFCuPu7m569eplBg0aZOrVq2fPiThy5Eh72SfnuI2IiDDLly837u7uJkWKFObatWvPe/NF/pbYc6KvXbvWNG/e3OTIkcPky5fPWJZl3NzcTKlSpczGjRvj/O7+/ftm7ty55uWXXzY+Pj6mZ8+e5s6dO0+tUySx2LVrl/Hy8jKpU6c2y5YtM8Y8PueHhYWZqVOnmqRJkxrLskyjRo3Mjz/+aP8uIiLCfP755yZTpkzGsiwzaNCg+GqCyF/y5L1NbN988419P7Ry5Ur789jneed9/8mTJ41lWWbx4sX/3saK/Eti79PLli0z2bNnN5ZlmYCAAPPDDz/Y323atMlkzJjRWJZlihUrZh48ePDUurp162YsyzJlypQxN2/efC7bL/JvmzJlin09GDt2rP15eHi4/Xfz5s2NZVkmXbp0Zvfu3fGwlSIiIgmfhsmI/EvM/yjvJvJfd/z4cR4+fEjHjh2pWbMmgF3CuUiRIqRNmxZPT0+Cg4MZPXo0DoeDtm3b4uLiQlRUFO7u7hQpUsRenzFGI34kQXvymnDgwAHu37/PzJkz7ZGgN27c4LXXXqNr16706tULDw8POnfujIuLy1OVAapUqcLnn39O7ty5SZ8+fby0SeSvij0S+uOPP8bT05P69euTKVMmChUqxI4dO9i3bx+DBw/GsiwqVqwIPF0ZYNq0aTx8+JCgoCCNfJNExXlN2L59O+Hh4YwcOZLatWsDj0v6J0mShNatW5MuXTrat2/PwoULyZYtG4MGDQJ+qxATHh5OcHAwTZs2jcfWiPx5znuaBQsWkCZNGvs8D1CkSBG6detGcHAwM2fOJFOmTBQvXhzLsnA4HDgcDtzc3DDG0KdPH0Bzn0vi9GRlGBcXFwYOHEijRo3Ili2bvVzFihUZNmwYnTp14siRIzRu3JiSJUvy9ttvc/XqVaZMmcKmTZvw9/e3jylNGyaJSexnZRNrSpg/WxmgbNmy7N+/n9dffz1+GiLyF/xe/4H6FUTkn6QggMi/IPbF+urVq6RPn94uayjyX+Z8IXHixAkAu+PGGBOnhHOmTJlo0qQJx48fZ926dUydOhVjDO3atcPd3f2pFxs6tiShc+6jy5YtI23atERFRVGqVCk7BACQLl06OnfujJeXF23atInzsuPJMICnpyfVqlV7/g0R+ZN+7wXG3r17admyJR4eHsyfP98OhQFs2LCB2bNns3LlSgIDAzHGUKlSJeC3MIBlWbRp04atW7cybNiw59YekX+CZVlER0ezceNGADvc6DzPO/+/evXq3L9/n2bNmjFkyBDeeustuwS6h4cHderUoWbNmnh5eakMuiQ6+/bt46OPPsLLy4t169ZRrlw5+5pRtWpV9u3bx+bNm/H29qZ169aUL18eFxcX+16oe/furFmzhooVK/Luu+/Gc2tE/prt27fTunVrUqdOTUhICHXr1o3zvfN60KRJE5IlS8ann37Kpk2bWLNmjd0ZClCuXDnmzJlDpkyZdD2QRCH2M4LD4SA0NBR3d3csyyJJkiT2cn80DHD//n3Wrl1L5syZn39jRP6i2O947t27x507d3j48CGpU6fGz8/PXk6hABH5uxQEEPkXOC/O06dPJyQkhD179pAiRYp43iqR+Oe8wc2ZMycAly5dwhiDw+F46mVFzpw5adKkCevWreP48eMEBwfj5eXFxx9/rNENkigdO3aMevXqAVC8ePE4D3axtW7dGmPMUy87XFxc9GJPEo2bN2+SNm3ap15YOF9i7N+/n+joaHr37m2HACIjI/Hw8KBy5cpkypQJT09PFi9ezNChQzHG8P777wOPwwDVqlXDy8uLN998kxQpUujliCQ6bm5upEmTBnd3d6Kjo4HfniFcXFzsfbpJkybs37+f6dOnc/jwYcqXL2+/NPTw8LDXp2uDJDYlS5akWbNmzJ49m9q1a7N06VIqVKgAwFtvvUWnTp0YMWIEK1asYP/+/TRr1ozChQsTGRnJvHnz2LJlC/7+/kyfPp0UKVJoBLQkKs5Rz4sWLSIyMpKgoCA7BBB7X44dDqtRowb58uXjxIkTLFy4EBcXFzJkyMDbb79NuXLl8PX11bOCJAqx79s3bdrEvHnz2LNnD25ubqRMmZIePXrw5ptvkjVrVuCPhQGWLVvG7du3SZUqlY4DSRSclS8AVq1axfjx4/nqq68IDw8nWbJk1KtXj5o1a/Luu+9iWZaed0Xkb1EQQORf8ujRI/r3709kZCSRkZHxvTkiCUquXLnw9vZm7dq1nDhxgvz588d5WHPe4JYsWZKMGTPy5ptvsnHjRsaOHUumTJk06kcSpYIFC9KmTRumTJnC4cOHeeONN7h27ZpdNSa2J192uLi40LFjR73QkEQhODiYXbt2MWTIkDjTuMR2/PhxAPz9/QGIiYnBw8PDPv8XKlSINm3acPbsWQ4dOsSnn36KMYbKlSsDjyvK1KpVy/6tjg1JjPz8/IiKiiIkJITChQvj6+trf+csg+7i4kKOHDkA+O677+zvRBIz53l75syZeHh4MG3aNOrWrRsnDFC3bl2SJk3K4sWLWbRo0VPVX0qXLs3ChQvJmDGjrgOS6FiWxb1799i0aRN+fn7UqFEDiNsx5BQ7DJArVy5y5cplLx/bs8L1IglN7M7M2bNn07x5cwBy5MiBu7s7x44do1mzZtSsWZOAgADKlCkD/O8wgDNMnCpVKh0Hkmg86zioWLEiLi4uXLx4kenTp7Nx40YCAwNp3ry57v9F5G9RXFrkX2CMwcvLi1deeYW7d+9y8uTJ+N4kkefKGPPMzx0OBwBlypShcuXK3L17l3r16nHx4kVcXV2JiYkBsEfGGWO4cuUKpUqVIiAggFOnTrFo0SJ7OZHEwrnPTpo0ifbt2wNw4MABdu7caae7nzxuAgICmDx5MvD4JceUKVOe70aL/AWXL19m3rx5bNy4keDgYC5fvhzne+cLDGdFjB9//BH47brhPB4ASpUqxQcffIDD4eDLL78kKCiI7du32+tyLqeXfZLYOO9zWrVqRebMmTlw4ADLly8nPDwc+G3fdl47nEGADBkyAAoCSOIS+/4m9nnbuX9PmTKF1q1bc+fOHerWrRvnPF+5cmWmTZvGunXraN++PbVr16ZTp04sXbqU1atXqwy6JGqhoaGEhoaSIkUKkiZNCvz++d3FxYXQ0FD7387rCPx2XKkihiQGzn183bp1NG/enJQpUzJ37lyOHTvGd999x4YNGyhWrBjLli2je/fu9rMCxH0+7tq1K0FBQQBxKiTpOJDEZMuWLbRs2ZKUKVOycOFCNm7cyPr169mzZw9Nmzbll19+oWXLlnz11Vfxvakiksjp6ijyL7AsC8uyeOONNwC4c+dOPG+RyPPjcDjsh7sff/yR06dPc+DAASDuQ1lISAhvvvkmJ0+epGbNmvz444/2Szx3d3cABgwYgLu7O5UrV+ajjz4iU6ZMdgeTSEIXu3Pf1dXVfmE3YcIEOnfuDECjRo3Ytm2bfcw8KwwwZswYvL29KV269PPbeJG/KHPmzIwZM4ayZcvi5ub21Dydzo6fokWLArB582ZCQ0Nxc3Ozv3OOhAb48MMPSZUqFeXKlePAgQMEBwdz9OhRezmRhMB57n5Wh6eTs5MfHk8LAJAnTx7ef/99rl69yqRJk1i9ejUPHjzAsiwiIyPt+6H58+cD2BU2fi9wKZLQPFnG1rIs+37o98IA9erVY8eOHfZvfHx8qFy5MhMmTGDp0qWMHTuW2rVrkzJlSo38lETN09MTT09PLl68yMGDB3E4HM88vzscDqKjo1m7di0bNmwAfruOgO6HJPG5desWISEhAIwZM4bGjRvj4eGBm5sbKVKk4Pbt2xhjqFSpEtmzZ4/z24CAAKZOnQrA+PHjefjw4XPffpG/yxhDdHQ0ixcvxuFwMGrUKOrXr29/f+rUKb799lsAevfuTYkSJeJrU0XkBaEggMjf9KwHtYiICOC3UTtffPEF8NtoaJEXVexShosXL6ZKlSqUKlWKkiVLUr16dVasWMGjR48ASJ06NcHBwbz22mt88803lCxZksmTJ7Np0ya+/fZbmjRpwowZMyhevDhp0qShQIEC9vzq586di7c2ivwvsa8JzlCYk5ubm319GDNmDF26dAHgvffeY8eOHb8bBujcuTOXL18mf/78//bmi/wtzn23fPnyTJ48mTlz5gCwfft2vvnmG+C30fvvvPMOefPm5ZtvvqFp06YYY+IEZpz3TDExMdy+fZu6detSo0YNtm3bxtq1a+MsIxLfLl68aP/tDIE5z+lr166lWbNmlChRgjZt2jBz5kx72VSpUtGtWzcqVqzI0aNH6du3L8OGDePnn3/G4XAQFRVFx44dWblyJcWKFaNKlSqAOn0k8XDuq6NGjaJSpUrA4/uh3wsDtGjRgtu3b8cJAzivLbE7SZ3nf438lIQu9n197FH8MTExpEyZknfffZewsDC2b9+Oi4tLnDCkczkXFxcePnxI79692bhxo/08IZJY3b17lyNHjlC5cmWaNGkCPL5e7N+/n3bt2nH27Fl69+7N4MGDn/n7Vq1aMXfuXL766iuSJk2qgKQkOs7pYbZv307hwoVp2rSp/d2BAwdo27YtR48epVevXnGmRtL5X0T+MiMi/4gzZ86YmzdvmpiYGBMdHW2MMeabb74xnp6eplatWvZyMTEx8bWJIs/NwoULjWVZxrIsU6xYMePr62ssyzJZsmQxQ4YMMffv3zfGGBMVFWXOnDljKlWqZC9vWZZxc3MzlmWZHDlymEuXLtnr7dGjh7Esy/Tu3Tu+mibyuxwOh/338ePHzdy5c01AQICZOHGi2b17t/1dWFiY/XfXrl3t/X779u1PrUcksXly/129erWxLMvUqFHDfPfdd3G+O3z4sMmQIYOxLMs0atTomfdIjRs3NqlSpTI3b940mzZtMl5eXiZp0qTm1KlT/2o7RP6ovn37GsuyzIYNG4wxj+/1ncfBnDlz4tzfuLq6GsuyTOfOnU1kZKS9jjNnzpj69eublClTGsuyTPLkyU2uXLnMyy+/bCzLMq+88op9P6RnCUlMHA6HuXv3rvH09DSWZZk6derY30VFRdl/O5+fjTGmSpUqxrIskzp1arNt27bnur0i/6TY90RHjhwxo0ePNjt27IizzIoVK+xrxNSpU+N8Fx4ebox5fN5v3LixsSzLjBw5Ms7xIpIYrVu3zliWZZo2bWp/duDAAVOoUKFnvu+5cOGC2bdv3zPXFftaIpKYnDlzxvj4+Jg333zTPHz40Bjzv4+Da9eumRkzZpjTp0/Hx+aKSCKnIIDIP8D5ks/X19cUKlTI1KhRw3z66afmk08+Md7e3iZHjhzmhx9++N3f60FOEjvnSw6Hw2F+/vlnkzt3bpMhQwazYMECY4wxx44dMz169DDp0qUzyZMnN/369bPDAE6TJk0yrVu3Nvnz5zdVq1Y1PXr0MFevXo2zTPny5Y2Pj4/ZvHnz82mYyB8U+0Xf2rVr7c4b5/+SJ09uOnToYC+jMID8V2zdutWUKFHCuLq6mnr16plvv/3W/i4qKsosW7bMpE+f3liWZcqUKWPWr19vjh49as6fP29atmxpLMsy77zzjomIiDDGGFOjRg1jWZb5/PPP46lFInF17NjRPodv3LjR/nzfvn0madKkJmXKlGbixIlm5cqVZsyYMfayH3/8sXnw4IG9/C+//GLmzZtnypcvb5ImTWosyzKFChUyTZo0MVeuXDHG6JlBEq+TJ08aPz8/OxjmFLsDx/n3vn37TNq0aY1lWSZdunR2yEYkMYl9L//5558bf39/Y1mW+eCDD8zly5fjLDt69Gj72vDpp5+aX375Jc73zuvM66+/bm7evPlctl/k33TkyBGTJEkSU6VKFWOMMV9//fUzOz+dz8whISEmefLkcZ4jRBIr5/Xhzp07JkeOHCZv3rzGGGO+++67/3kcrF+/3qRMmdIsXLjw+W+0iCR6CgKI/E2PHj0yffv2NaVKlTIFChQwPj4+cTp/LMsyHh4epmrVqqZly5Zm+PDhZt++febEiRPxveki/4jYLzlu375tzp07Z9zd3c2UKVPiLHfjxg3z2WefmZdeeul3wwDGPL4ZNubpZLdzxN2bb75prl279s83ROQf4Bz9bFmW6d69u9m+fbtZvXq1SZYsmbEsy3z44Yf2ss8KA7i6usbpSBJ5EezatcuULl3aWJb1VBggLCzMbN261WTJksW+Z/L09LQryWTPnj3OC/N69eoZy7LMpEmT4qElIr+Jff/Tp08f+9zv7LQMCQkxlmWZpUuXxvndrl277A7RZs2a2SOAYjt37pz59ttvTWhoqH2tUAhAEivnPf3p06ftDv7fCwMYY8ypU6dMypQpTYkSJYxlWSZ//vz2yGiRxCD29WHWrFn29WHYsGHm6tWrcUL0TkFBQfZyRYsWNQ0aNDAfffSR3SmULVs2+35IlWEksbt27ZrJlCmTSZIkienfv78pXLiwsSzL9OrVy14mdkWM119/3WTIkMH8+OOP8bXJIn9J7PO8812nMY/36/DwcFO5cmW7YpLzfN+zZ097Oedx4HA4TJkyZUzSpEnNV1999dy2X0ReHAoCiPyDoqKizKVLl8zatWvNlClTTM2aNU3evHmNZVnG3d3dLnceu2R6iRIlTHBwsMpZSaI3Y8YMU6pUKdO/f3+TLl06c+PGDWNM3BfXd+/eNdOnT7fDAP3797dHwzmXi/1iIyoqyoSFhdmjQtOnT68yWJJgHTx40GTMmNEkT57cTJ8+3f587ty5JkmSJPY1oGbNmvZ3scMAzqkvkidPbkJDQ1UVQBKl2Ptt7HubnTt3/m4YwJjHo6G7d+9u3n33XZMuXTrz5ptvmhYtWjw1Ku711183adOmNUeOHPlX2yHye5z3K0924Pfq1cu+x9+6dasJDg425cqVi/M75/Gxd+9e89JLLz0VBvi95wFdDyQx+L391OFw2FNh/F4YICYmxt7/f/nlF/PKK6+YL774wgwYMMD89NNP//7Gi/wFz+rQj80ZEE6XLp1ZsmTJU7978u/Zs2ebggULmiRJktjXk0yZMpnq1avb90MKhUli8L/uW5z78KRJk0ySJEnsqWOeNQI6JibGNG/e3FiWZbp27apQmCRa06dPN/Xq1TPHjx+P8/mhQ4fsSmBPHgePHj0yxjw+Dtq2bWssyzLNmzc3oaGhz3XbReTFoCCAyF8Q+6bW2WnpLFkb26NHj+yb1m7dupnly5ebzp07myZNmpicOXOalClTGjc3N3VsSqJ38+ZNU7FiRWNZlsmTJ0+cIMCTIxb+Vxgg9rEVHh5uQkJC7HR43rx5zcmTJ59fo0SecOHChWee640x5v79+6ZZs2bGsiwzevRo+/OxY8caDw8P4+bmZubOnWu//K5evbq9TOwwQN++fc0333zz7zVC5F/wRzsp/68wgPPF4Pnz501YWNhTL/t69+5tLMsy5cuXV2lciVdnzpwxGTNmNOvXr4/zeewwQKFChUzlypWf+u3vhQH0Uk8Ss9j3++fOnTMHDx4033zzjbl48aIx5vF+7zzHxw4DVK9e/akKYR9//LFxd3e3f2uM5oCWhOncuXP230/eC924ccOULFnSWJYVp4yz8ziIjo42165dM6dOnYrzu59++sl89dVXZsGCBWb+/Pnmhx9+eCo4L5KQ/V/XA6cTJ06YmjVrGg8PD5MpUyaza9eup9bVpUsXe1oM5/slhSMlMYgdFLt586Y9SLBly5bm+++/t5eLjo42Y8aMsasLDx8+/Kl1tW/f3liWZYoUKWJXR9VxICJ/loIAIn9Q7Ivs3bt3zbVr18zZs2fN+fPn4yz35KjmyZMnG8uyzIQJE+Isd+vWLXP16tWn5ocTSawOHjxo6tevb9zd3Y1lWWbkyJH2d0/epN67d8/MmDHDZMyY0aRKlcp07tz5maVxd+zYYapWrWq6dOny1IOjyPM0ffp04+PjY5YuXfrMMMDly5eNv79/nNH+M2bMMMmSJTNubm5mx44dxpjH87o5S55XrVrVXtaZ9hZJbGK/7Pvyyy/NsGHDTI0aNUzdunXNhAkTnurs37lzp3nrrbeMZVmmbt26cb533kM9ec1wOBymXbt2dmWYs2fP/mvtEfkjnPM1FytW7KkytbHDACVLloxTBtrJ+e8vv/zSZMyY0R7h4+zsEUnIpk+fbg4ePGj/O/b+vXDhQpMpUyZ7qpeMGTOaJUuW2Ms4rxlnzpwx6dKlM5ZlmTJlyphJkyaZnTt3mkaNGhnLsky5cuWeOYWYSEIxdOhQY1mWWbly5TO///HHH42Pj48pXbp0nM/v379vvv32W/Puu++abNmyGcuyTOvWrc327dv/539PnT6SEP2V60HsZ4cdO3aYihUrGldXV5MlSxbTqlUrs2LFCjN58mTz9ttvG8uyTNasWc2lS5eMMZoWQxKH2PvppUuXzIULF0ynTp1Mzpw5jWVZpk2bNnHCAJcuXTJDhgyxwwDlypUzn3zyienataspVqyYsSzL5MiRwz4OFAoTkb9CQQCRPyD2zey2bdvMO++8Y49i8PLyMu3atTO7d+9+6gWHMcasWLEiTulDZ1lEXbjlRRF7fz9w4IBp2LChcXNzM4UKFYoz1/mzwgCzZs0yHh4eJleuXObu3bvPXP+vv/4aZ8S0yPPmcDhMQECAXZ4z9ssOp19//dXMmjXLLld+6NAhU6RIEePm5maPGI2OjjZ37twxtWvXNq6urk+VxRVJbGKf1+fOnWu8vb3jTIHknOriyZfk/ysMENuDBw/MlClTzGuvvWa/AFFlGEkITp06ZUqUKGE8PT3N8OHDTXh4eJwRy927d7ePgTlz5hhjHh8vzyoH/eWXXxp/f39jWZbp0qXL822IyJ+0du1a+37oySlaFi9ebO/3JUqUMEWKFLH/PWTIEPPrr78aY357djh79qxd+cuyLPveKEeOHHZYXp2fkhDFxMTYAcVUqVI9s5rXDz/8YLy9vc3rr79u7t27Z4wx5vjx46Z79+52CMbf39+4uLgYy7LMhx9+aK5evfq8myLyl/2d60Hsyl6HDh0yXbp0eeo5IkWKFOaDDz7QtBiSqMS+b1mwYIHJmTOncXd3N76+viZZsmT2/v1kZYDbt2+bNWvW2AEx5/+yZMliGjVqZK5cuWKM0XEgIn+dggAif8Ly5cvti3GpUqXM+++/bzJkyGAsyzKFCxc2ISEhT12UT5w4YZIlS2YKFCig+awk0fsjL+MOHDhgateubVxcXEyFChXMzp07f/f3d+7cMYsWLTI///zzH16/SHyIiIgw3bp1M7Vq1bLP5U/ur6GhofbL7QkTJhjLskz//v2NMXEDM84RRKlSpTKWZZlGjRo9p1aI/DuWLVtmhyPHjh1rvv/+e7Nv3z7Tp08f+75p7NixcX4TOwzQoEEDc/jw4Weue+rUqaZEiRKmRYsWT1VhEokvYWFhZtCgQcayLFOwYEG7KkDs4GLsygDOQNjvhQF27dplXn31Vc2FLgleWFiY+fDDD41lWSZbtmzm66+/NsY8DkQWK1bMpEuXzixbtswY8/hl9ejRo+0Rbv37938qDPDzzz+bkSNHmsqVK5t3333XtG/fXi+7JcF6cjRyjx49TK1atZ5aLjo62ty8edO8++67xrIsU7t2bdOmTRv73r906dImJCTE/D/27jxKrrJeG/ZvN52kIMcQgUhABhEhHgblVcJ8mBRoEceXl4MEFBA+QA4yeZRBUHFAhAMCDoAoQQEJg4KAtgwJ8wwJU5QQAonQZCQDGSpJdz3fH5wuu5MOIKn03lV9XWv16s7e1Q93sap6V9W+9/MsWbIk3X777dWrRJddbgaKbGWPB51T/af05gVTzzzzTDrnnHPSt7/97fTjH/84Pfzww9WZYRwPqDedZZihQ4emiy66KE2ZMiWNGzcufec730mDBg2qLg3WtQyQ0pvLrj744INp1KhR6Q9/+EN69dVXq7NHeh4AK0MRAN6h+++/Pw0cODCtvvrq6Ve/+lV1+2uvvZYOP/zwlGVZGjx4cHX6507z5s1Lw4YNS+utt161yQr1qOsHH88//3y6995704UXXpjuvPPObusjpvTmMgH7779/tQzQ9XmxopP9XtRSVF3X8uy84vOPf/xjGjVqVI8Fr/b29rT77runLMvSjTfemFJ68/nTuaTAJZdckrbffvt0++23p80226z6oQkUXU9/v59//vk0bNiwlGVZ+v3vf99t3/jx49OGG26YsixLp5xyynJjjB49ujrt5zHHHFOdNWlZL7/8co/Lx0Cepk6dWr3CreuyMF0fx6eeemq1DHDbbbellFZcBug8RlgLnaLqfGyWy+W0//77V0/+jB07Nk2ZMiVlWZZ++tOfLvd7I0eOrJbnzzjjjDRz5syUUs/HlM73G94XUDSdj9cXXnghfe9731tu/+WXX56uv/76btvGjBmTdtppp+px4D3veU867bTT0vTp07v9rT/qqKO6zSADRVer40HXmQFWxMUi1JuJEydW3x9fe+21y+2//vrr08c+9rHq0mDLlgF64nkArCxFAHgbHR0dacmSJemYY45JWZal8847r9v+5557Lg0fPjxlWZa+9rWvLfe7ixcvTnvttVfKsqzbNOlQT7q+6Lz22mvThz/84bT66qunLMtSc3NzWmuttdLll1/ebTrDRx55pFoG2Guvvd5RGQCKqmsR5oknnug25XnnyZuU/vnYPuigg1KWZemXv/zlcr+/3377pfXWWy+l5INuiq3zcTt//vzqz8v+/R49enTq169fOv7447ttv++++6pTPn/7299e4X+jtbU1ffGLX0yTJ09ebp9jBXnq/Pvc9WRN5/Og8/tjjz1WvcKz8+/9sr/zTsoAndug6Dpf83Q9+bPpppuma665Jm2zzTbV2THa29u7vfa58sor07rrrrtcGaDztlAPZs+enTbeeOM0ePDg9Pzzz1e3P/zww9XlLW6++eZuvzNp0qR0zz33pFtuuaXbUkhdjyc77LBDGjp0aHrhhRd65X5ALazq44HXRdSrp556Kr33ve9Ne+yxR3VbR0dHt8f3zTffXF0a7Oijj+5WBlh29hmAWlAEgC46P7Rb9sOIBQsWpK222ip96EMf6nbCZ+zYsWnHHXdMWZal//qv/+r2O10P3Icffnhaa621TGlL3fvtb39b/TD7qKOOSqeeemo69NBDU3NzcxowYEA6+eSTu72AffTRR7uVAcaMGZNfeHiH3u5Dh8WLF6djjjkmNTc3p/XWWy/dcMMN3Y4NKb15tUOWZWmdddZJf/zjH6vbTz/99JRlWRoxYkQql8s+4KDwXnzxxfSpT30qXXHFFT2WAc4555yUZVm6/PLLq9seeuih9NGPfjRlWZZOPfXUbuNNmTKl24fnKaXqzBpOBlEUnY/1iRMnpqOPPjpdf/311Sv9Ox+nHR0dadGiRenUU09Nzc3Nae+9965+6F2pVLo9nt9pGQCKpPN5sHTp0m4nano6+fNv//ZvKcuy5WbHe6uTP53LBEC9eOGFF9JWW22VsixLl156aXX70qVL04knnlh9Ltx0003VfT39ne983dPR0ZG+/vWvpyzL0gEHHJAWLFiw6u8EvAuOB/DO3XLLLSnLsrTddtulRYsWrbDgcuGFF1bfHxx55JFp/PjxecQF+ghFAEhvnui/8cYb09FHH91jC3vy5MlpyJAh6f/8n/9TffE6bty4agng2GOP7Xb7qVOnpt/+9rdpzpw5KaU3p4n7+9//vurvCKxC9913Xxo0aFBaY401lpv++YwzzlhuHdxOnWWAAQMGpG233Tbdf//9vRkb3rFlP6hrbW1Nf/jDH9Kvf/3rdMcdd6Q33nijum/x4sXp+OOPT1mWrbAMcNJJJ1WfF5/85CfTtttum7IsS+uvv36aMGFCr9wnWBnlcjntueeeKcuytOOOO6Zrrrmm20nQlFI699xzU5Zl6Sc/+UlK6c2llHoqAXR+6P3jH/84tbS0pFdffdVJUApt5syZafDgwSnLsrT55punQw45pPravquHH364ur7zueee223fisoAXQtiUESdf+NnzZqVLr744rTTTjtV/86n9M/HdrlcTl/4whdSlmWpf//+6fvf//5yr4d6Ovmz2mqrpRNPPDHNnj171d8ZqKHzzjsvZVmWNt544/S3v/2tur29vT1985vf7LEM0NPVne3t7emII45IWZalD3/4w9WZ9bw2omgcD6BnK/p7/fTTT6e11147bbDBBtW/7V0f+11/3m677arvD4444oj0j3/8Y9WGBvosRQD6vNdffz0dcsgh6T3veU/Ksix97nOfW27N55kzZ6YPf/jDaa211kpvvPFGeuGFF9IOO+ywXAlg0aJFKaWU7r777uVa4lCvOl+kdp7sv/jii7vtf+yxx6pr5H7zm9+sbu/64fdjjz2WPvnJT6YNN9wwTZs2rXeCw7+g6xTol112WfrMZz5TXfqi8/s222yTrrrqqjRx4sSU0ptlgM6reHoqA0ydOjV95zvfqb6xK5VKaZtttun2oSEUXWtra/r4xz+esixLw4cP71YGSCmlBx98MA0cODB96UtfSvfff3/1eNBTCWD+/Plp4403TrvuumuaP39+r98X+Fc888wzaYcddkjNzc1p7bXXTqVSKX3gAx9IV199dXrllVe63fbSSy+t/q2//fbbu+3r+nz59re/nbIsS0OGDEmLFi1ywodC6nxN9I9//CN96lOfqs5wtPfee6fp06dXb9fTyZ8NN9ww3XvvvSscM6WUfve736Wmpqa03nrr9ViugSLqfAzPnj077brrrmngwIHpyiuvTCl1n1lyRWWAzr/3L7zwQvrZz35WLU1+7GMfS1OmTKn+PhSJ4wH800svvZTuuuuubu9je3otv2TJkrTrrrumLMvSIYccUr1918d+e3t7am9vTzvssEPacccd0x577JGyLEs/+tGPVv0dAfokRQD6tOnTp1fbdx/72MfS7bffvsJ12Q455JCUZVk66KCD0vDhw1OWZelrX/tadX/X8sB+++2XBg4cmO67775Vfh9gVeuc3vZjH/tYGjx4cHr55ZerL3bfavrnhQsXdvv32LFj04wZM1JK1ryiWDo/uJg1a1Y64IADUpZl6b3vfW/acsst01e+8pW08847p4033rj6wceBBx6YHnvssZRSz2WAZctkTzzxRPrrX/+aHnjggepzAIqu64cao0ePrv6tX7YMMHHixOo0uRtssEHKsix9+9vfrv5u5/OhUqmkgw46qDp7gA+7qQfnnXdeWmONNdJuu+2W/vu//ztts802qX///unzn//8ch9uf/WrX01ZlqUvfOELafLkyd32dX28//jHP64WyqBoOl+jv/TSS2mzzTZLWZalT33qU2nChAnp9ddfX+72XU/+dE4L/cEPfjA9/vjjKxw7pZSuv/76aqFGIYZ6smTJknTyySenLMvStttu2+11TkpvXQZI6c3XVOutt17aYIMN0v/3//1/aerUqdXfgyJxPIB/evnll9Oaa66ZsixL//f//t900UUXpZT++ZjtfEx3Pg/uueeetOGGG6b3vOc96ayzzqqWAToLAJ0/f+hDH0qnn356+vOf/1xdVuPuu+/u7bsH9AGKAPRZs2bNSltvvXV1+p1lT9x06jyYP/roo2nTTTetXu3z1a9+tXqbzpkAUkrpv//7v1OWZelLX/qSRisNZbvttkvrr79+9U3fgw8+2GMJoL29Pc2aNSsdeuihPZZhlAAoks43YdOnT69e9bzrrrump556qnqVw/z589Mrr7ySDj744PS+970vNTU1pV133TU9+OCDKaWelwlY0TEF6smyZYCPfOQj3coAnVfA3XzzzdXXR//xH//R41jf+MY3qstkdF1bFIqo62N/r732SoMGDUqXX355Gjt2bDrssMNSlmWpqakp/eQnP0kvvvhiSunNJZQ22WSTNHjw4OoSSl1P7Cx7kqfz+QNF0fm4b2trS8OGDUtZlqWTTz75bX+v87H8r578ScnJT+rT9OnT04c+9KGUZVm65JJLUkrdjxtvVwZ46KGH0sMPP5wWLFhQvT0UieMB/FOlUkkjRoyovv5vampKWZalvffeO51//vnVQlfX27/++uvpBz/4QRo8eHBae+2104knnpjmzZvX7XYnnnhiyrKsOrvMaaedlrIsS0cffXRKyWenQG0pAtAnLVy4MB188MEpy7I0YsSItGTJkpRS9xeeyx5w58yZk84888y07rrrpgEDBqT/+Z//SbNmzep2m843ex/+8IfTSy+9tMrvB/SGzjeBn/jEJ1KWZemWW25J9913X9pmm22WKwF0lmKef/75VCqV0mGHHZZLZngnupYAOj/MO+KII5Zby7DzdvPnz08XXHBB2mKLLapXRIwbNy6l9GYZ4IQTTljhMgFQr7p+sH3HHXekbbfdNmVZlrbffvv0u9/9rvqB3y9+8YtqGeDoo49ON9xwQ3r22WfTAw88kD73uc9VPwjsvOLHBxsURdfH4rInclJ68zXNBhtskHbeeefqbEeXXXZZ9bmw2267pauvvjqllNLpp5+esixLm266qVmQqEuLFi2qzm7x5S9/ubr9rd4nd93/Tk7+QD1Y0ZXJnY/1s846q3oBSE+/83ZlgLf770DeHA/gn5544om01lprpf79+6ejjjoqvf/976++9/3gBz+YLr300uUe4y+//HI644wz0vve977qTMQ//OEP04UXXpj222+/lGVZ2nrrrVNbW1tKKaU777yzemEKQK0pAtAnTZgwIW2yySZps802W66FvewbsTfeeKN6wn/OnDnpG9/4Rho8eHAaMGBA+shHPpLOPvvsdNJJJ1XX/1l//fWt/0zhdb5h6yzBvJXO58Q111yT+vXrl3bZZZe05ZZbpizL0mmnnVa9XdcroPfaa680YMCAdNttt9U4OdRG1xJA51SHJ5xwQvVxvOwVCZ3PmXK5nC677LK06aabpgEDBqSTTjqpeoxYsmRJtQyw0UYbpauvvloZgMJ7J8eDjo6O6nPil7/8ZfVDj+222y5dffXV1X3XXHNNdUrDLMvSoEGDUnNzc/UDjX/84x8pJVf8UBydj/9p06Z1m+Gr6/uBhQsXpu9973vLLQs2bty49IMf/CANGDAgZVmWjjrqqPTCCy9UZ0v68pe/vNwySVB0c+bMSVtuuWXacMMN0+zZs1NKK36f/Nxzz6XHHnssvf76693+ri978ueJJ57otfzwbnUeDzo/H3o7Dz74YPU1zrXXXtvjbbqWAd773veu8HZQRI4H8KZKpZLmzp1bXebukksuSXPnzk0XX3xx2mmnnVKWZal///5pnXXWSWeddVYaN25c9b31a6+9lq6++urqxSRdvz760Y+mKVOmVP87d911V8qyLH3+85/P664CDUwRgD7pyiuvTFmWpX333Tel9M+rmDtfsM6ePTu1tramgw8+OH3kIx9JQ4cOTUcccUS68847U0opXXHFFWmHHXbodgBfb7310v/7f/+vOj0oFN2LL76Yjj/++Opa529nwoQJ1Re5WZalY445prqv6wcmnSdC999//zR37tya54ZamTFjRnUmgBEjRlSPASs6Idr5gceiRYvSd77znZRlWfrABz6Qnn766eptuq4bOmzYsOWmf4MiervjQedz47HHHksDBw5Mzc3NaejQodVlAq6++urqzAAPP/xwOvfcc9Oee+6Zdt9993TIIYekkSNHVgszSgAUzcsvv1y9Sueaa65J06ZNSym9eVKo8+/+iy++mLbccss0YMCAdMMNN3T7/ccffzztsssuKcuy9PGPfzzts88+qbm5OQ0bNiz98Y9/7O27Ayvlvvvuq344vXTp0mqhsfMk6auvvpouu+yytOeee6bVV1+9Wgr7+te/3q0U3PXkz6BBg7q9VoKimjRpUtptt93Sf/3Xf6Vp06ZV3+Ou6Kr9M888szorwLx583q8XXt7ezr11FNTlmVp880371Y6gyJzPIDubrjhhpRlWVp99dWrj+NKpZLOO++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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(abalone_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893a6d22",
   "metadata": {},
   "source": [
    "Atadığımız NaN'lar rastegele olduğu için kayıp gözlemler arasında bir korelasyon olmasını beklemiyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49c8e3f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(abalone_data) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5269e85",
   "metadata": {},
   "source": [
    "Kayıp gözlemleri, her sütunun(değişkenin) kendi ortalaması ile dolduruyorum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d40e05a6",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.8870</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.2490</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.9660</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2605</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.1760</td>\n",
       "      <td>0.5255</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1.0945</td>\n",
       "      <td>0.5310</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.9485</td>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4177 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "0     1.0   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1     1.0   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2     2.0   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3     1.0   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4     3.0   0.330     0.255   0.080        0.2050          0.0895   \n",
       "...   ...     ...       ...     ...           ...             ...   \n",
       "4172  2.0   0.565     0.450   0.165        0.8870          0.3700   \n",
       "4173  1.0   0.590     0.440   0.135        0.9660          0.4390   \n",
       "4174  1.0   0.600     0.475   0.205        1.1760          0.5255   \n",
       "4175  2.0   0.625     0.485   0.150        1.0945          0.5310   \n",
       "4176  1.0   0.710     0.555   0.195        1.9485          0.9455   \n",
       "\n",
       "      Viscera weight  Shell weight   Age  \n",
       "0             0.1010        0.1500  16.5  \n",
       "1             0.0485        0.0700   8.5  \n",
       "2             0.1415        0.2100  10.5  \n",
       "3             0.1140        0.1550  11.5  \n",
       "4             0.0395        0.0550   8.5  \n",
       "...              ...           ...   ...  \n",
       "4172          0.2390        0.2490  12.5  \n",
       "4173          0.2145        0.2605  11.5  \n",
       "4174          0.2875        0.3080  10.5  \n",
       "4175          0.2610        0.2960  11.5  \n",
       "4176          0.3765        0.4950  13.5  \n",
       "\n",
       "[4177 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data = abalone_data.apply(lambda x: x.fillna(x.mean()))\n",
    "abalone_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5eed82a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d65ffa",
   "metadata": {},
   "source": [
    "Aşağıda değişkenlerle ilgili önemli istatistiki bilgileri görebiliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b1f73e3",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.955314</td>\n",
       "      <td>0.523918</td>\n",
       "      <td>0.407786</td>\n",
       "      <td>0.139496</td>\n",
       "      <td>0.829684</td>\n",
       "      <td>0.359699</td>\n",
       "      <td>0.180672</td>\n",
       "      <td>0.239142</td>\n",
       "      <td>11.428399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.824774</td>\n",
       "      <td>0.119481</td>\n",
       "      <td>0.098625</td>\n",
       "      <td>0.041664</td>\n",
       "      <td>0.488494</td>\n",
       "      <td>0.221132</td>\n",
       "      <td>0.109029</td>\n",
       "      <td>0.138725</td>\n",
       "      <td>3.201742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.445000</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.094500</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.806000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.172000</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>11.428399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.615000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>0.500500</td>\n",
       "      <td>0.251500</td>\n",
       "      <td>0.327000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>2.825500</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>30.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sex       Length     Diameter       Height  Whole weight  \\\n",
       "count  4177.000000  4177.000000  4177.000000  4177.000000   4177.000000   \n",
       "mean      1.955314     0.523918     0.407786     0.139496      0.829684   \n",
       "std       0.824774     0.119481     0.098625     0.041664      0.488494   \n",
       "min       1.000000     0.075000     0.055000     0.000000      0.002000   \n",
       "25%       1.000000     0.450000     0.350000     0.115000      0.445000   \n",
       "50%       2.000000     0.540000     0.420000     0.140000      0.806000   \n",
       "75%       3.000000     0.615000     0.480000     0.165000      1.150000   \n",
       "max       3.000000     0.815000     0.650000     1.130000      2.825500   \n",
       "\n",
       "       Shucked weight  Viscera weight  Shell weight          Age  \n",
       "count     4177.000000     4177.000000   4177.000000  4177.000000  \n",
       "mean         0.359699        0.180672      0.239142    11.428399  \n",
       "std          0.221132        0.109029      0.138725     3.201742  \n",
       "min          0.001000        0.000500      0.001500     2.500000  \n",
       "25%          0.188000        0.094500      0.130000     9.500000  \n",
       "50%          0.340000        0.172000      0.235000    11.428399  \n",
       "75%          0.500500        0.251500      0.327000    12.500000  \n",
       "max          1.488000        0.760000      1.005000    30.500000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "53dd8a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(abalone_data[list(abalone_data.columns)], orient=\"h\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914aad97",
   "metadata": {},
   "source": [
    "Aykırı değerleri aşağıdaki gibi bulabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "53f5116a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outliers(variable_name):\n",
    "    Q1 = np.percentile(abalone_data[variable_name], 25)\n",
    "    Q3 = np.percentile(abalone_data[variable_name], 75)\n",
    "    IQR = Q3 - Q1\n",
    "    Lower_bound = (Q1 - 1.5*IQR) \n",
    "    Upper_bound = (Q3 + 1.5*IQR)\n",
    "    \n",
    "    Lower_outliers = abalone_data[variable_name][(abalone_data[variable_name] < Lower_bound)]\n",
    "    Higher_outliers = abalone_data[variable_name][(abalone_data[variable_name] > Upper_bound)]\n",
    "    \n",
    "    return Lower_outliers, Higher_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ad9f3c",
   "metadata": {},
   "source": [
    "'Age' değişkeni için aykırı değerlere bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fcb92fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(236     2.5\n",
      "237     4.5\n",
      "238     4.5\n",
      "306     4.5\n",
      "520     4.5\n",
      "526     4.5\n",
      "719     3.5\n",
      "720     4.5\n",
      "894     4.5\n",
      "1054    4.5\n",
      "1429    4.5\n",
      "2115    4.5\n",
      "2371    4.5\n",
      "2711    4.5\n",
      "3472    4.5\n",
      "3521    4.5\n",
      "3801    4.5\n",
      "Name: Age, dtype: float64, 6       21.5\n",
      "7       17.5\n",
      "9       20.5\n",
      "32      19.5\n",
      "33      20.5\n",
      "        ... \n",
      "3929    17.5\n",
      "3930    22.5\n",
      "3931    18.5\n",
      "3944    21.5\n",
      "3947    17.5\n",
      "Name: Age, Length: 260, dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "print(outliers(\"Age\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbc79cd",
   "metadata": {},
   "source": [
    "Bütün değişkenler arasındaki ilişkiye aşağıdaki gibi bakılabilir fakat fazla değişkenimiz olduğu için ekrana yazdırmıyorum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "32352eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sbn.pairplot(abalone_data[list(abalone_data)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d2f302",
   "metadata": {},
   "source": [
    "Eğer istediğimiz değişkenler arasındaki ilişkiye bakmak istersek aşağıdaki fonksiyonu kullanabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4f9bdc2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation_plot(variable_name1, variable_name2):\n",
    "    return sbn.jointplot(x = variable_name1,\n",
    "             y = variable_name2,\n",
    "             data = abalone_data,\n",
    "             kind = \"reg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f116c44a",
   "metadata": {},
   "source": [
    "## 1.Basit Doğrusal Regresyon\n",
    "&ensp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08982e6e",
   "metadata": {},
   "source": [
    "Basit doğrusal regresyon modelimizde kullanacağım bağımsız değişkeni 'Shell weight' olarak seçiyorum. Bağımlı değişkenimiz ise 'Age'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf974673",
   "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>Shell weight</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.150</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.070</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.210</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.155</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.055</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Shell weight   Age\n",
       "0         0.150  16.5\n",
       "1         0.070   8.5\n",
       "2         0.210  10.5\n",
       "3         0.155  11.5\n",
       "4         0.055   8.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_variables = abalone_data.loc[:, ['Shell weight', 'Age']]\n",
    "slr_variables.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f51c304d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x24b55cfd370>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"Shell weight\", y=\"Age\", data=slr_variables, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337f3cfc",
   "metadata": {},
   "source": [
    "Modelimizi oluşturmadan önce verimizi eğitim ve test seti olarak bölüyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d38e7167",
   "metadata": {},
   "outputs": [],
   "source": [
    "slr_train, slr_test = train_test_split(slr_variables,\n",
    "                                       train_size = 0.8,\n",
    "                                       test_size = 0.2,\n",
    "                                       random_state = 121521014)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04aed430",
   "metadata": {},
   "source": [
    "%80'e %20 böldüğümüz veriden, eğitim setine 3341 ve test setine 836 gözlem düştüğünü görebiliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "31b60a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3341, 836)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(slr_train), len(slr_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc60731",
   "metadata": {},
   "source": [
    "Bağımsız değişken için ayrı bağımlı değişken için ayrı eğitim ve test serileri oluşturalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d67a8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = slr_train['Shell weight']\n",
    "y_train = slr_train['Age']\n",
    "X_test = slr_test['Shell weight']\n",
    "y_test = slr_test['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "124efa64",
   "metadata": {},
   "source": [
    "Kullanacağımız yöntemi adlandıralım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "577877c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sLR = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259ed991",
   "metadata": {},
   "source": [
    "Modeli oluşturmadan önce boyut düzenlemeleri yapıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae15a519",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.values.reshape(-1, 1)\n",
    "X_test = X_test.values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56bcef59",
   "metadata": {},
   "source": [
    "Modeli oluşturalım ve fit edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b80b5b65",
   "metadata": {},
   "outputs": [],
   "source": [
    "sLR_Model = sLR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c908fa",
   "metadata": {},
   "source": [
    " Sabit katsayı:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a5f06db9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.014116077330247"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sLR_Model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295021f9",
   "metadata": {},
   "source": [
    "Eğim katsayısı:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1757c4ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.30008561])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sLR_Model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c7d472",
   "metadata": {},
   "source": [
    " Belirtme katsayısına($R^2$) baktığımızda modelin tahminlerinin iyi olmayacağını söyleyebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a667f4db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38462834474038676"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sLR_Model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7bea35",
   "metadata": {},
   "source": [
    "Modeli aşağıdaki gibi görselleştirebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "91eaa895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 29.7)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = sns.regplot(x=\"Shell weight\", y=\"Age\", data=slr_train, ci= None,\n",
    "        scatter_kws={\"color\":\"r\", \"s\":9})\n",
    "graph.set_title(\"Model Denklemi: AGE = 8 + SHELL WEIGHT*14.3\")\n",
    "graph.set_ylabel(\"Age\")\n",
    "graph.set_xlabel(\"Shell weight\")\n",
    "plt.xlim(-0.5, 2)\n",
    "plt.ylim(bottom = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac36757",
   "metadata": {},
   "source": [
    "Bağımsız değişken eğitim seti üzerinden tahmin değerleri elde edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "71cde6ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_train = pd.DataFrame(sLR_Model.predict(X_train), index = y_train.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f19ab29",
   "metadata": {},
   "source": [
    "Eğitim setinin gerçek değerleri ve tahmin edilen değerleri ile ilgili bir tablo oluşturalım.  \n",
    "Bu tablo hata, hatalar karesi ve orantılı hataları göstermekte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "37f2a042",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.concat([y_train, y_hat_train], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9322a573",
   "metadata": {},
   "outputs": [],
   "source": [
    "def residuals(df):\n",
    "    df.columns = [\"Real Age\", \"Predicted Age\"]\n",
    "    df[\"Residual\"] = df[\"Real Age\"] - df[\"Predicted Age\"]\n",
    "    df[\"SQR Residual\"] = df[\"Residual\"]**2\n",
    "    df[\"Proportional Residual\"] = abs(df[\"Residual\"]/df[\"Real Age\"])\n",
    "    np.mean(df[\"SQR Residual\"])\n",
    "    np.mean(df[\"Proportional Residual\"])\n",
    "    np.median(df[\"Proportional Residual\"])\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3d28808b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\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>Real Age</th>\n",
       "      <th>Predicted Age</th>\n",
       "      <th>Residual</th>\n",
       "      <th>SQR Residual</th>\n",
       "      <th>Proportional Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>11.428399</td>\n",
       "      <td>8.586120</td>\n",
       "      <td>2.842279</td>\n",
       "      <td>8.078551</td>\n",
       "      <td>0.248703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3589</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>13.898601</td>\n",
       "      <td>-1.398601</td>\n",
       "      <td>1.956086</td>\n",
       "      <td>0.111888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>9.529925</td>\n",
       "      <td>-1.029925</td>\n",
       "      <td>1.060746</td>\n",
       "      <td>0.121168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2733</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>9.515625</td>\n",
       "      <td>-1.015625</td>\n",
       "      <td>1.031494</td>\n",
       "      <td>0.119485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>14.663656</td>\n",
       "      <td>7.836344</td>\n",
       "      <td>61.408289</td>\n",
       "      <td>0.348282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Real Age  Predicted Age  Residual  SQR Residual  Proportional Residual\n",
       "2417  11.428399       8.586120  2.842279      8.078551               0.248703\n",
       "3589  12.500000      13.898601 -1.398601      1.956086               0.111888\n",
       "1255   8.500000       9.529925 -1.029925      1.060746               0.121168\n",
       "2733   8.500000       9.515625 -1.015625      1.031494               0.119485\n",
       "469   22.500000      14.663656  7.836344     61.408289               0.348282"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c279ba",
   "metadata": {},
   "source": [
    "Bağımsız değişken test seti üzerinden tahmin değerleri elde edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6360da63",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test = pd.DataFrame(sLR_Model.predict(X_test), index = y_test.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fdcfea4",
   "metadata": {},
   "source": [
    "Test setinin gerçek değerleri ve tahmin edilen değerleri ile ilgili bir tablo oluşturalım.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "daba0045",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.concat([y_test, y_hat_test], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4b344400",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Real Age</th>\n",
       "      <th>Predicted Age</th>\n",
       "      <th>Residual</th>\n",
       "      <th>SQR Residual</th>\n",
       "      <th>Proportional Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>8.5</td>\n",
       "      <td>9.801627</td>\n",
       "      <td>-1.301627</td>\n",
       "      <td>1.694232</td>\n",
       "      <td>0.153133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2826</th>\n",
       "      <td>10.5</td>\n",
       "      <td>10.209179</td>\n",
       "      <td>0.290821</td>\n",
       "      <td>0.084577</td>\n",
       "      <td>0.027697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2238</th>\n",
       "      <td>9.5</td>\n",
       "      <td>9.587125</td>\n",
       "      <td>-0.087125</td>\n",
       "      <td>0.007591</td>\n",
       "      <td>0.009171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>22.5</td>\n",
       "      <td>13.376648</td>\n",
       "      <td>9.123352</td>\n",
       "      <td>83.235548</td>\n",
       "      <td>0.405482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3689</th>\n",
       "      <td>10.5</td>\n",
       "      <td>13.376648</td>\n",
       "      <td>-2.876648</td>\n",
       "      <td>8.275105</td>\n",
       "      <td>0.273966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Real Age  Predicted Age  Residual  SQR Residual  Proportional Residual\n",
       "2283       8.5       9.801627 -1.301627      1.694232               0.153133\n",
       "2826      10.5      10.209179  0.290821      0.084577               0.027697\n",
       "2238       9.5       9.587125 -0.087125      0.007591               0.009171\n",
       "428       22.5      13.376648  9.123352     83.235548               0.405482\n",
       "3689      10.5      13.376648 -2.876648      8.275105               0.273966"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e5857d",
   "metadata": {},
   "source": [
    "Test setinden tahmin edilen değişkenleri ve hata değerlerini görselleştirelim. \n",
    "Büyük bir hata oranına sahip olduğunu söyleyebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1cde831e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(x=y_hat_test, y=df_test[\"Residual\"], lowess = True, line_kws=dict(color=\"darkred\"))\n",
    "plt.title('Residual Plot')\n",
    "plt.xlabel('Predicted Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c293d42",
   "metadata": {},
   "source": [
    "## 2.Çoklu Doğrusal Regresyon\n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5571e386",
   "metadata": {},
   "source": [
    "Modelimizi oluşturmadan önce verimizi eğitim ve test seti olarak bölüyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c4d73cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_DF_train, abalone_DF_test = train_test_split(abalone_data,\n",
    "                                         test_size=0.2,\n",
    "                                         random_state=121521014)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914fee52",
   "metadata": {},
   "source": [
    "Bütün değişkenler sayısal olduğu için hepsini 0 ve 1 arasında ölçeklendirelim. \n",
    "Bu işlem için Minmax normalizasyonu yapalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "258e23ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "scalerMM = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b484a78a",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.955314</td>\n",
       "      <td>0.523918</td>\n",
       "      <td>0.407786</td>\n",
       "      <td>0.139496</td>\n",
       "      <td>0.829684</td>\n",
       "      <td>0.359699</td>\n",
       "      <td>0.180672</td>\n",
       "      <td>0.239142</td>\n",
       "      <td>11.428399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.824774</td>\n",
       "      <td>0.119481</td>\n",
       "      <td>0.098625</td>\n",
       "      <td>0.041664</td>\n",
       "      <td>0.488494</td>\n",
       "      <td>0.221132</td>\n",
       "      <td>0.109029</td>\n",
       "      <td>0.138725</td>\n",
       "      <td>3.201742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.445000</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.094500</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.806000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.172000</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>11.428399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.615000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>0.500500</td>\n",
       "      <td>0.251500</td>\n",
       "      <td>0.327000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>2.825500</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>30.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sex       Length     Diameter       Height  Whole weight  \\\n",
       "count  4177.000000  4177.000000  4177.000000  4177.000000   4177.000000   \n",
       "mean      1.955314     0.523918     0.407786     0.139496      0.829684   \n",
       "std       0.824774     0.119481     0.098625     0.041664      0.488494   \n",
       "min       1.000000     0.075000     0.055000     0.000000      0.002000   \n",
       "25%       1.000000     0.450000     0.350000     0.115000      0.445000   \n",
       "50%       2.000000     0.540000     0.420000     0.140000      0.806000   \n",
       "75%       3.000000     0.615000     0.480000     0.165000      1.150000   \n",
       "max       3.000000     0.815000     0.650000     1.130000      2.825500   \n",
       "\n",
       "       Shucked weight  Viscera weight  Shell weight          Age  \n",
       "count     4177.000000     4177.000000   4177.000000  4177.000000  \n",
       "mean         0.359699        0.180672      0.239142    11.428399  \n",
       "std          0.221132        0.109029      0.138725     3.201742  \n",
       "min          0.001000        0.000500      0.001500     2.500000  \n",
       "25%          0.188000        0.094500      0.130000     9.500000  \n",
       "50%          0.340000        0.172000      0.235000    11.428399  \n",
       "75%          0.500500        0.251500      0.327000    12.500000  \n",
       "max          1.488000        0.760000      1.005000    30.500000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "49884455",
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = list(abalone_DF_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0bde51cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_DF_train[variables] = scalerMM.fit_transform(abalone_DF_train[variables])\n",
    "abalone_DF_test[variables] = scalerMM.transform(abalone_DF_test[variables])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ad6d481b",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3341.000000</td>\n",
       "      <td>3341.000000</td>\n",
       "      <td>3341.000000</td>\n",
       "      <td>3341.000000</td>\n",
       "      <td>3341.000000</td>\n",
       "      <td>3341.000000</td>\n",
       "      <td>3341.000000</td>\n",
       "      <td>3341.000000</td>\n",
       "      <td>3341.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.472126</td>\n",
       "      <td>0.588425</td>\n",
       "      <td>0.568765</td>\n",
       "      <td>0.123772</td>\n",
       "      <td>0.292526</td>\n",
       "      <td>0.265585</td>\n",
       "      <td>0.238021</td>\n",
       "      <td>0.236407</td>\n",
       "      <td>0.289350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.412081</td>\n",
       "      <td>0.168890</td>\n",
       "      <td>0.176228</td>\n",
       "      <td>0.037639</td>\n",
       "      <td>0.172711</td>\n",
       "      <td>0.162131</td>\n",
       "      <td>0.143040</td>\n",
       "      <td>0.138847</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.101770</td>\n",
       "      <td>0.155634</td>\n",
       "      <td>0.138673</td>\n",
       "      <td>0.123766</td>\n",
       "      <td>0.128743</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.617021</td>\n",
       "      <td>0.598214</td>\n",
       "      <td>0.123894</td>\n",
       "      <td>0.284827</td>\n",
       "      <td>0.251761</td>\n",
       "      <td>0.227123</td>\n",
       "      <td>0.233034</td>\n",
       "      <td>0.288683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716312</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>0.146018</td>\n",
       "      <td>0.405501</td>\n",
       "      <td>0.370412</td>\n",
       "      <td>0.331139</td>\n",
       "      <td>0.321357</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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": [
       "               Sex       Length     Diameter       Height  Whole weight  \\\n",
       "count  3341.000000  3341.000000  3341.000000  3341.000000   3341.000000   \n",
       "mean      0.472126     0.588425     0.568765     0.123772      0.292526   \n",
       "std       0.412081     0.168890     0.176228     0.037639      0.172711   \n",
       "min       0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "25%       0.000000     0.489362     0.464286     0.101770      0.155634   \n",
       "50%       0.500000     0.617021     0.598214     0.123894      0.284827   \n",
       "75%       1.000000     0.716312     0.696429     0.146018      0.405501   \n",
       "max       1.000000     1.000000     1.000000     1.000000      1.000000   \n",
       "\n",
       "       Shucked weight  Viscera weight  Shell weight          Age  \n",
       "count     3341.000000     3341.000000   3341.000000  3341.000000  \n",
       "mean         0.265585        0.238021      0.236407     0.289350  \n",
       "std          0.162131        0.143040      0.138847     0.133663  \n",
       "min          0.000000        0.000000      0.000000     0.000000  \n",
       "25%          0.138673        0.123766      0.128743     0.208333  \n",
       "50%          0.251761        0.227123      0.233034     0.288683  \n",
       "75%          0.370412        0.331139      0.321357     0.333333  \n",
       "max          1.000000        1.000000      1.000000     1.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_DF_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c37cbb",
   "metadata": {},
   "source": [
    "Bağımsız değişkenlerimiz için ayrı bağımlı değişkenimiz için ayrı eğitim ve test serileri oluşturalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fd302e57",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_y_train = abalone_DF_train.pop(\"Age\")\n",
    "a_X_train = abalone_DF_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bb8c9f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_y_test = abalone_DF_test.pop(\"Age\")\n",
    "a_X_test = abalone_DF_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc54a1d",
   "metadata": {},
   "source": [
    "#### 2.1.Özellik Eliminasyonu (Feature Selection)\n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8e8c85",
   "metadata": {},
   "source": [
    "Özyinelemeli Özelliğin Eliminasyonu(RFE) yöntemini de kullanarak bir doğrusal regresyon modeli oluşturalım ve fit edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8a57125a",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm_RFE = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e8b760fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_RFE.fit(a_X_train, a_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d8878913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a_X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee78aeea",
   "metadata": {},
   "source": [
    "6 bağımsız değişken seçelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cc0d4a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe = RFE(lm_RFE, n_features_to_select = 6)\n",
    "rfe = rfe.fit(a_X_train, a_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdebbbd",
   "metadata": {},
   "source": [
    "RFE'nin seçtiği değişkenler aşağıdaki gibi görülebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "632462e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Sex', False, 3),\n",
       " ('Length', False, 2),\n",
       " ('Diameter', True, 1),\n",
       " ('Height', True, 1),\n",
       " ('Whole weight', True, 1),\n",
       " ('Shucked weight', True, 1),\n",
       " ('Viscera weight', True, 1),\n",
       " ('Shell weight', True, 1)]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(a_X_train.columns, rfe.support_, rfe.ranking_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf3af084",
   "metadata": {},
   "source": [
    "Seçtiğimiz değişkenlerle tahminlerde bulunalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6d37357a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = rfe.predict(a_X_train)\n",
    "y_pred_test = rfe.predict(a_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df94f43",
   "metadata": {},
   "source": [
    "RMSE ve MAPE değerini aşağıdaki fonksiyon ile hesaplayabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e6c91258",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE(actual, predicted):\n",
    "    MSE = mean_squared_error(actual, predicted)\n",
    "    RMSE = np.sqrt(MSE)\n",
    "    return RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "31c48093",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAE(actual, predicted):\n",
    "    MAE = np.mean(np.abs((actual - predicted)))\n",
    "    return MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1513f23a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAPE(actual, predicted):\n",
    "    MAPE = np.mean(np.abs((actual - predicted) / actual))\n",
    "    return MAPE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2176669",
   "metadata": {},
   "source": [
    "Hem gerçek hem de tahmin değerleri normalleştrilmiş olduğundan normalizayon işleminin tersini almamıza gerek yok. Eğer almak isteseydik Minmax normalizayonunun tersi aşağıdaki gibi alınabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "97dbdd52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred_train_inv = y_pred_train * (max(y_pred_train) - min(y_pred_train)) + min(y_pred_train)\n",
    "#y_pred_test_inv = y_pred_test * (max(y_pred_test) - min(y_pred_test)) + min(y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474bbb55",
   "metadata": {},
   "source": [
    "Eğitim seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım. \n",
    "RMSE, değeri büyük değil. 0'a yakınlık modelin uyumunun fazla olduğu anlamına geliyor.\n",
    "$R^2$ değeri kötüdür. Model iyi tahmin yapamamaktadır. İlişkinin yaklaşık olarak %50'sini açıklayabilmektedir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8c15c0d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09506914022944844\n"
     ]
    }
   ],
   "source": [
    "RMSE_lm_train = RMSE(a_y_train, y_pred_train)\n",
    "print(RMSE_lm_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "91db5947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4939568443999556"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2_lm_train = r2_score(a_y_train, y_pred_train)\n",
    "R2_lm_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026bdd57",
   "metadata": {},
   "source": [
    "Test seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım. \n",
    "Değerler için eğitim setiyle aynı yorumlarda bulunabiliriz.   \n",
    "Ayrıca yaptığımız MAE değeri de büyük değil. 0'a yakınlık modelin uyumunun fazla olduğu anlamına geliyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "99fe140f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09340857882340643\n"
     ]
    }
   ],
   "source": [
    "RMSE_lm_test = RMSE(a_y_test, y_pred_test)\n",
    "print(RMSE_lm_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c0e02ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5018197912233097"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2_lm_test = r2_score(a_y_test, y_pred_test)\n",
    "R2_lm_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0a452a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.066177457242716"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE_lm_test = MAE(a_y_test, y_pred_test)\n",
    "MAE_lm_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b8ada3",
   "metadata": {},
   "source": [
    "Test setinden tahmin edilen değişkenleri ve hata değerlerini görselleştirelim. \n",
    "Büyük bir hata oranına sahip olduğunu söyleyebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9bbf39e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_residuals(actual, predicted):\n",
    "    residuals = actual - predicted\n",
    "    sns.residplot(x=actual, y=residuals, lowess = True, line_kws=dict(color=\"darkred\"))\n",
    "    plt.title('Residual Plot')\n",
    "    plt.xlabel('Predicted Age')\n",
    "    plt.ylabel('Residuals')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "43bf6102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_residuals(a_y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43bec3a1",
   "metadata": {},
   "source": [
    "### 2.2.K-Fold\n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2420e8e9",
   "metadata": {},
   "source": [
    "K-fold ile subsetler oluşturup cross_val_score ile performansına bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e400ffc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "folds = KFold(n_splits = 5, shuffle = True, random_state = 121521014)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc6781e",
   "metadata": {},
   "source": [
    "$R^2$ değerleri her subset için aşağıdaki gibidir. Performans oldukça düşüktür."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5289004d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.48456065, 0.51201015, 0.43860337, 0.52463094, 0.44717822])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_CV0 = LinearRegression()\n",
    "scores_CV0 = cross_val_score(lm_CV0, a_X_train, a_y_train,\n",
    "    scoring='r2', cv=folds)\n",
    "scores_CV0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44906703",
   "metadata": {},
   "source": [
    "MSE, değerleri büyük değil. 0'a yakınlık modelin uyumunun fazla olduğu anlamına geliyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "700115c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00903091, -0.01014118, -0.00976259, -0.00804762, -0.00913481])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_CV1 = LinearRegression()\n",
    "scores_CV2 = cross_val_score(lm_CV1, a_X_train, a_y_train,\n",
    "    scoring='neg_mean_squared_error', cv=folds)\n",
    "scores_CV2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f33f1021",
   "metadata": {},
   "source": [
    "#### 2.3.Grid Search Cross-Validation ile Hiperparametre Ayarlama\n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e39043e",
   "metadata": {},
   "source": [
    "Çapraz doğrulamada kullanacağımız subsetleri oluşturalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "586c4c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "fold_CVGS = KFold(n_splits=5, shuffle=True, random_state=121521014)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec6105a2",
   "metadata": {},
   "source": [
    "Seçilecek bağımsız değişken sayısını belirlemek için ilgili hiperparametrenin potansiyel değerlerini belirleyelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "426b3112",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyper_params_CVGS = [{\"n_features_to_select\": list(range(1, 9))}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3f8138",
   "metadata": {},
   "source": [
    "Doğrusal regresyon modelini oluşturup fit ettikten sonra RFE yöntemini de kullanarak bir GridSearch modeli oluşturalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2d0e821c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm_CVGS = LinearRegression()\n",
    "lm_CVGS.fit(a_X_train, a_y_train)\n",
    "rfe_CVGS = RFE(lm_CVGS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "17ab0d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_CVGS = GridSearchCV(estimator = rfe_CVGS,\n",
    "                         param_grid= hyper_params_CVGS,\n",
    "                         scoring=\"r2\",\n",
    "                         cv=fold_CVGS,\n",
    "                         verbose=1,\n",
    "                         return_train_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c5a5e2",
   "metadata": {},
   "source": [
    "Modeli fit edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e3780089",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=121521014, shuffle=True),\n",
       "             estimator=RFE(estimator=LinearRegression()),\n",
       "             param_grid=[{&#x27;n_features_to_select&#x27;: [1, 2, 3, 4, 5, 6, 7, 8]}],\n",
       "             return_train_score=True, scoring=&#x27;r2&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=121521014, shuffle=True),\n",
       "             estimator=RFE(estimator=LinearRegression()),\n",
       "             param_grid=[{&#x27;n_features_to_select&#x27;: [1, 2, 3, 4, 5, 6, 7, 8]}],\n",
       "             return_train_score=True, scoring=&#x27;r2&#x27;, verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: RFE</label><div class=\"sk-toggleable__content fitted\"><pre>RFE(estimator=LinearRegression(), n_features_to_select=8)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LinearRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=121521014, shuffle=True),\n",
       "             estimator=RFE(estimator=LinearRegression()),\n",
       "             param_grid=[{'n_features_to_select': [1, 2, 3, 4, 5, 6, 7, 8]}],\n",
       "             return_train_score=True, scoring='r2', verbose=1)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_CVGS.fit(a_X_train, a_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8988f8ca",
   "metadata": {},
   "source": [
    "Model sonuçlarına bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4ccf4afe",
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_features_to_select</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.012909</td>\n",
       "      <td>0.004157</td>\n",
       "      <td>0.004059</td>\n",
       "      <td>0.002054</td>\n",
       "      <td>1</td>\n",
       "      <td>{'n_features_to_select': 1}</td>\n",
       "      <td>0.380117</td>\n",
       "      <td>0.378662</td>\n",
       "      <td>-0.093868</td>\n",
       "      <td>0.404471</td>\n",
       "      <td>...</td>\n",
       "      <td>0.284933</td>\n",
       "      <td>0.190039</td>\n",
       "      <td>8</td>\n",
       "      <td>0.385331</td>\n",
       "      <td>0.385602</td>\n",
       "      <td>0.371367</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.391067</td>\n",
       "      <td>0.382536</td>\n",
       "      <td>0.006710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.013165</td>\n",
       "      <td>0.005053</td>\n",
       "      <td>0.002962</td>\n",
       "      <td>0.002423</td>\n",
       "      <td>2</td>\n",
       "      <td>{'n_features_to_select': 2}</td>\n",
       "      <td>0.390252</td>\n",
       "      <td>0.390341</td>\n",
       "      <td>0.301003</td>\n",
       "      <td>0.416546</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372770</td>\n",
       "      <td>0.039322</td>\n",
       "      <td>7</td>\n",
       "      <td>0.392095</td>\n",
       "      <td>0.391488</td>\n",
       "      <td>0.399846</td>\n",
       "      <td>0.385561</td>\n",
       "      <td>0.397788</td>\n",
       "      <td>0.393356</td>\n",
       "      <td>0.005052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.012388</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>0.003679</td>\n",
       "      <td>3</td>\n",
       "      <td>{'n_features_to_select': 3}</td>\n",
       "      <td>0.464083</td>\n",
       "      <td>0.486986</td>\n",
       "      <td>0.298217</td>\n",
       "      <td>0.498960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.430413</td>\n",
       "      <td>0.073778</td>\n",
       "      <td>6</td>\n",
       "      <td>0.467898</td>\n",
       "      <td>0.460067</td>\n",
       "      <td>0.480689</td>\n",
       "      <td>0.459458</td>\n",
       "      <td>0.481395</td>\n",
       "      <td>0.469901</td>\n",
       "      <td>0.009573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012113</td>\n",
       "      <td>0.005074</td>\n",
       "      <td>0.003944</td>\n",
       "      <td>0.001984</td>\n",
       "      <td>4</td>\n",
       "      <td>{'n_features_to_select': 4}</td>\n",
       "      <td>0.472683</td>\n",
       "      <td>0.495760</td>\n",
       "      <td>0.326329</td>\n",
       "      <td>0.499132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.442017</td>\n",
       "      <td>0.065026</td>\n",
       "      <td>5</td>\n",
       "      <td>0.477206</td>\n",
       "      <td>0.469325</td>\n",
       "      <td>0.487503</td>\n",
       "      <td>0.470545</td>\n",
       "      <td>0.489711</td>\n",
       "      <td>0.478858</td>\n",
       "      <td>0.008429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.009906</td>\n",
       "      <td>0.003261</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.002073</td>\n",
       "      <td>5</td>\n",
       "      <td>{'n_features_to_select': 5}</td>\n",
       "      <td>0.473559</td>\n",
       "      <td>0.511734</td>\n",
       "      <td>0.416181</td>\n",
       "      <td>0.517533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.470646</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>4</td>\n",
       "      <td>0.496470</td>\n",
       "      <td>0.485248</td>\n",
       "      <td>0.495679</td>\n",
       "      <td>0.485779</td>\n",
       "      <td>0.504923</td>\n",
       "      <td>0.493620</td>\n",
       "      <td>0.007371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.007948</td>\n",
       "      <td>0.002242</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>0.002514</td>\n",
       "      <td>6</td>\n",
       "      <td>{'n_features_to_select': 6}</td>\n",
       "      <td>0.475280</td>\n",
       "      <td>0.513587</td>\n",
       "      <td>0.414135</td>\n",
       "      <td>0.515245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.471013</td>\n",
       "      <td>0.040476</td>\n",
       "      <td>3</td>\n",
       "      <td>0.497939</td>\n",
       "      <td>0.486671</td>\n",
       "      <td>0.497280</td>\n",
       "      <td>0.488072</td>\n",
       "      <td>0.506229</td>\n",
       "      <td>0.495238</td>\n",
       "      <td>0.007170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.006132</td>\n",
       "      <td>0.002162</td>\n",
       "      <td>0.002936</td>\n",
       "      <td>0.003854</td>\n",
       "      <td>7</td>\n",
       "      <td>{'n_features_to_select': 7}</td>\n",
       "      <td>0.484169</td>\n",
       "      <td>0.508463</td>\n",
       "      <td>0.438016</td>\n",
       "      <td>0.515528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.478572</td>\n",
       "      <td>0.031471</td>\n",
       "      <td>2</td>\n",
       "      <td>0.507671</td>\n",
       "      <td>0.487755</td>\n",
       "      <td>0.504599</td>\n",
       "      <td>0.488137</td>\n",
       "      <td>0.515719</td>\n",
       "      <td>0.500776</td>\n",
       "      <td>0.011088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.007210</td>\n",
       "      <td>0.002362</td>\n",
       "      <td>0.003819</td>\n",
       "      <td>0.001922</td>\n",
       "      <td>8</td>\n",
       "      <td>{'n_features_to_select': 8}</td>\n",
       "      <td>0.484561</td>\n",
       "      <td>0.512010</td>\n",
       "      <td>0.438603</td>\n",
       "      <td>0.524631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.481397</td>\n",
       "      <td>0.034113</td>\n",
       "      <td>1</td>\n",
       "      <td>0.507683</td>\n",
       "      <td>0.499169</td>\n",
       "      <td>0.504609</td>\n",
       "      <td>0.497872</td>\n",
       "      <td>0.515806</td>\n",
       "      <td>0.505028</td>\n",
       "      <td>0.006464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.012909      0.004157         0.004059        0.002054   \n",
       "1       0.013165      0.005053         0.002962        0.002423   \n",
       "2       0.012388      0.002296         0.006597        0.003679   \n",
       "3       0.012113      0.005074         0.003944        0.001984   \n",
       "4       0.009906      0.003261         0.001036        0.002073   \n",
       "5       0.007948      0.002242         0.002052        0.002514   \n",
       "6       0.006132      0.002162         0.002936        0.003854   \n",
       "7       0.007210      0.002362         0.003819        0.001922   \n",
       "\n",
       "   param_n_features_to_select                       params  split0_test_score  \\\n",
       "0                           1  {'n_features_to_select': 1}           0.380117   \n",
       "1                           2  {'n_features_to_select': 2}           0.390252   \n",
       "2                           3  {'n_features_to_select': 3}           0.464083   \n",
       "3                           4  {'n_features_to_select': 4}           0.472683   \n",
       "4                           5  {'n_features_to_select': 5}           0.473559   \n",
       "5                           6  {'n_features_to_select': 6}           0.475280   \n",
       "6                           7  {'n_features_to_select': 7}           0.484169   \n",
       "7                           8  {'n_features_to_select': 8}           0.484561   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  ...  \\\n",
       "0           0.378662          -0.093868           0.404471  ...   \n",
       "1           0.390341           0.301003           0.416546  ...   \n",
       "2           0.486986           0.298217           0.498960  ...   \n",
       "3           0.495760           0.326329           0.499132  ...   \n",
       "4           0.511734           0.416181           0.517533  ...   \n",
       "5           0.513587           0.414135           0.515245  ...   \n",
       "6           0.508463           0.438016           0.515528  ...   \n",
       "7           0.512010           0.438603           0.524631  ...   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  split0_train_score  \\\n",
       "0         0.284933        0.190039                8            0.385331   \n",
       "1         0.372770        0.039322                7            0.392095   \n",
       "2         0.430413        0.073778                6            0.467898   \n",
       "3         0.442017        0.065026                5            0.477206   \n",
       "4         0.470646        0.040468                4            0.496470   \n",
       "5         0.471013        0.040476                3            0.497939   \n",
       "6         0.478572        0.031471                2            0.507671   \n",
       "7         0.481397        0.034113                1            0.507683   \n",
       "\n",
       "   split1_train_score  split2_train_score  split3_train_score  \\\n",
       "0            0.385602            0.371367            0.379310   \n",
       "1            0.391488            0.399846            0.385561   \n",
       "2            0.460067            0.480689            0.459458   \n",
       "3            0.469325            0.487503            0.470545   \n",
       "4            0.485248            0.495679            0.485779   \n",
       "5            0.486671            0.497280            0.488072   \n",
       "6            0.487755            0.504599            0.488137   \n",
       "7            0.499169            0.504609            0.497872   \n",
       "\n",
       "   split4_train_score  mean_train_score  std_train_score  \n",
       "0            0.391067          0.382536         0.006710  \n",
       "1            0.397788          0.393356         0.005052  \n",
       "2            0.481395          0.469901         0.009573  \n",
       "3            0.489711          0.478858         0.008429  \n",
       "4            0.504923          0.493620         0.007371  \n",
       "5            0.506229          0.495238         0.007170  \n",
       "6            0.515719          0.500776         0.011088  \n",
       "7            0.515806          0.505028         0.006464  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_CVGS = pd.DataFrame(model_CVGS.cv_results_)\n",
    "results_CVGS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c0b829",
   "metadata": {},
   "source": [
    "Farkı daha iyi görebilmek için sonuçları görselleştirelim. 8 değişkenin performansının en iyi olduğu görülebiliyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e7fecf7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def opt_num_of_features(results_CVGS):\n",
    "    plt.figure(figsize=(16,6));\n",
    "    plt.plot(results_CVGS[\"param_n_features_to_select\"], \n",
    "             results_CVGS[\"mean_train_score\"]);\n",
    "    plt.plot(results_CVGS[\"param_n_features_to_select\"], \n",
    "             results_CVGS[\"mean_test_score\"]);\n",
    "    plt.xlabel('number of features');\n",
    "    plt.ylabel('r-squared');\n",
    "    plt.title(\"Optimal Number of Features\");\n",
    "    plt.legend(['test score', 'train score'], loc='upper left');\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1422bf25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt_num_of_features(results_CVGS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0484214",
   "metadata": {},
   "source": [
    "Değişken sayımızı 8 olarak seçtiğimzde $R^2$ değerinin çok az arttığı görülebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d810b91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_features_optimal = 8\n",
    "lm_final = LinearRegression()\n",
    "lm_final.fit(a_X_train, a_y_train)\n",
    "rfe_final = RFE(lm_final, n_features_to_select=n_features_optimal)\n",
    "rfe_final = rfe_final.fit(a_X_train, a_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cc015440",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test_final = lm_final.predict(a_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a52244e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09307253136263738\n"
     ]
    }
   ],
   "source": [
    "RMSE_lm_test_final = RMSE(a_y_test, y_pred_test_final)\n",
    "print(RMSE_lm_test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4a3a0e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5053978577036267"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2_lm_test_final = r2_score(a_y_test, y_pred_test_final)\n",
    "R2_lm_test_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534e9a73",
   "metadata": {},
   "source": [
    "## 3.Çoklu Doğrusal Olmayan Regresyon\n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a30f13",
   "metadata": {},
   "source": [
    "Bağımlı değişkenlerimizi y ve bağımsız değişkenlerimizi X olarak tanımlayalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1aa9572e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = abalone_data[\"Age\"]\n",
    "X = abalone_data.drop(\"Age\", axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64256bea",
   "metadata": {},
   "source": [
    "Eğitim ve test olarak bölelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "780ef7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_X_train, abalone_X_test, abalone_y_train, abalone_y_test = train_test_split(X, y,\n",
    "                                                                                    test_size=0.2,\n",
    "                                                                                    random_state=121521014)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5150e36",
   "metadata": {},
   "source": [
    "Standartlaştırma ile veriyi ölçeklendirelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7b04b5ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "scalerANN = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "33eef6e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_X_train_scaled = scalerANN.fit_transform(abalone_X_train)\n",
    "abalone_X_test_scaled = scalerANN.transform(abalone_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9606b41d",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_y_train = np.array(abalone_y_train)\n",
    "abalone_y_test = np.array(abalone_y_test)\n",
    "\n",
    "abalone_y_train = abalone_y_train.reshape(-1, 1)\n",
    "abalone_y_test = abalone_y_test.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6f8304fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_y_train_scaled = scalerANN.fit_transform(abalone_y_train)\n",
    "abalone_y_test_scaled = scalerANN.transform(abalone_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "beccd897",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_y_train_scaled_1d = abalone_y_train_scaled.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67458c0",
   "metadata": {},
   "source": [
    "Çok Katmanlı Algılaycı(MLP) regresyon modelimizi oluşturalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "479ddf07",
   "metadata": {},
   "outputs": [],
   "source": [
    "aNN = MLPRegressor(max_iter=250, random_state=121521014).fit(abalone_X_train_scaled, abalone_y_train_scaled_1d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b33595",
   "metadata": {},
   "source": [
    "Hata değerini hatalar karesi ile heapladığını, gizli katmanda 100 nöron olduğunu, 8 değişkenimizin olduğunu ve 200 iterasyon ayptığımızı görebiliriz.   \n",
    "(Model hatası 200 iterasyonda 0'a tam olarak yaklaşılamadığını belirtiyor.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "88a3a3c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('squared_error', 0.20162756489251066, (100,), 8, 234)"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#?aNN\n",
    "aNN.loss, aNN.loss_, aNN.hidden_layer_sizes, aNN.n_features_in_, aNN.n_iter_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f896c88",
   "metadata": {},
   "source": [
    "Hatanın zamanla 0'a yaklaştığını aşağıdaki grafikten görebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "cb644ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24b5c71e390>]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(aNN.loss_curve_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "b93aec3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_aNN_pred_train = aNN.predict(abalone_X_train_scaled)\n",
    "y_aNN_pred_test = aNN.predict(abalone_X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "544e8c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_aNN_pred_train = y_aNN_pred_train.reshape(1, -1)\n",
    "y_aNN_pred_test = y_aNN_pred_test.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ddd366",
   "metadata": {},
   "source": [
    "Hataları hesaplamadan önce tahmin değerlerinin standartlaştırılmış halini tersine çeviriyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "bd51e4b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_aNN_pred_train_inv = scalerANN.inverse_transform(y_aNN_pred_train)\n",
    "y_aNN_pred_test_inv = scalerANN.inverse_transform(y_aNN_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "b9709a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_aNN_pred_train_inv = y_aNN_pred_train_inv.reshape(-1, 1)\n",
    "y_aNN_pred_test_inv = y_aNN_pred_test_inv.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fac765",
   "metadata": {},
   "source": [
    "Eğitim seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım. \n",
    "RMSE, değeri 0'dan büyük yani model uyumlu değil. \n",
    "$R^2$ değeri kötüdür. Model iyi tahmin yapamamaktadır. İlişkinin yaklaşık olarak %60'ını açıklayabilmektedir. (Doğrusal regresyona kıyasla RMSE ve $R^2$ değerleri artmıştır.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "fe2d5f29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.038591509244177\n"
     ]
    }
   ],
   "source": [
    "aNN_RMSE_train = RMSE(abalone_y_train, y_aNN_pred_train_inv)\n",
    "print(aNN_RMSE_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "e6232015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5960325608251144"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aNN_R2_train = r2_score(abalone_y_train, y_aNN_pred_train_inv)\n",
    "aNN_R2_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16eb96d9",
   "metadata": {},
   "source": [
    "Test seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım.\n",
    "Eğitim seti ile aynı yorumlar yapılabilir. $R^2$ değeri daha düşüktür."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "8961219a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1019999913276957\n"
     ]
    }
   ],
   "source": [
    "aNN_RMSE_test = RMSE(abalone_y_test, y_aNN_pred_test_inv)\n",
    "print(aNN_RMSE_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "99ecb88f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.562018388000804"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aNN_R2_test = r2_score(abalone_y_test, y_aNN_pred_test_inv)\n",
    "aNN_R2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20842f96",
   "metadata": {},
   "source": [
    "Test setinden tahmin edilen değişkenleri ve hata değerlerini görselleştirelim. Hata oranının küçük olduğu söylenemez."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "df60c5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_residuals(abalone_y_test, y_aNN_pred_test_inv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bea3bd3",
   "metadata": {},
   "source": [
    "#### 3.1.Grid Search Cross-Validation ile Hiperparametre Ayarlama\n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad3647d",
   "metadata": {},
   "source": [
    "Hiperparametre ayarlamak adına bazı değişkenler için potansiyel değerler belirliyoruz. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "e98e802f",
   "metadata": {},
   "outputs": [],
   "source": [
    "aNN_params = {'random_state': [418],\n",
    "              'hidden_layer_sizes': [(50, 50), (100,100), (100,)],\n",
    "              'alpha': [0.1, 0.001, 0.0001]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "002ed8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "aNNfCV = MLPRegressor(random_state=121521014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "be2918a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "abalone_y_train_scaled_1d = abalone_y_train_scaled.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8067be",
   "metadata": {},
   "source": [
    "Izgara Arama Çapraz Doğrulama yapyoruz. (10 fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "b6defbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n"
     ]
    }
   ],
   "source": [
    "aNN_CVGS = GridSearchCV(aNNfCV, aNN_params,\n",
    "                        cv=10,\n",
    "                        n_jobs=-1,\n",
    "                        verbose=2).fit(abalone_X_train_scaled, abalone_y_train_scaled_1d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b440eb8",
   "metadata": {},
   "source": [
    "En iyi parametrelere bakıyoruz. alpha değeri varsayılan 0.0001 değerinden 0.1'e ve hidden_layer_sizes varsayılan (100,)'den (100, 100)'e değişti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "2567c799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.1, 'hidden_layer_sizes': (100, 100), 'random_state': 418}"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aNN_CVGS.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344e22af",
   "metadata": {},
   "source": [
    "Modelimizi en iyi parametre değerleriyle oluşturuyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "9dac62f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "aNN_CVGS_model = MLPRegressor(random_state=aNN_CVGS.best_params_[\"random_state\"],\n",
    "                              hidden_layer_sizes=aNN_CVGS.best_params_[\"hidden_layer_sizes\"],\n",
    "                              alpha=aNN_CVGS.best_params_[\"alpha\"]).fit(abalone_X_train_scaled, \n",
    "                                                                        abalone_y_train_scaled_1d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc84840a",
   "metadata": {},
   "source": [
    "Hatanın zamanla 0'a yaklaştığını aşağıdaki grafikten görebiliriz. Parametreleri tune etmeden önceki halinde 200'üncü iterasyonda bile 0'a yaklaşamazken, bu haliyle yaklaşık 100 iterasyonda 0'a yaklaştığını görebiliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "06c62d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24b5c70a960>]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eKSkp4TxK2Oy2M13FWiwAAERWlybdfrkcYhjGWUskfr9f3/rWt/Tkk09q7Nix3XLPNsuWLVNtbW3wKC0tDeMJwtf2lpDEjs0AAETa2Yc8OhEfHy+bzdZh5KOysrLDCIkk1dXV6f3331dRUZEefvhhSVIgEJBhGLLb7dqyZYuuu+46JSYmhnzPNk6nU06nM5zmX5Ao6xdGWFiLBQCAiAprhMXhcCgjI0P5+fntzufn52v27Nkdro+Li9Pu3btVXFwcPBYtWqRx48apuLhYM2fOlCRlZmZ2uOeWLVvOek+zWK0W2axsgAgAgBnCGmGRpKVLlyonJ0fTp09XZmam1q5dq5KSEi1atEhSa6mmrKxMzz//vKxWq9LT09t9f+jQoXK5XO3OL168WPPmzdOPf/xj3X777XrllVf0xhtvaPv27Rf4eN0rymaRP2DIy1osAABEVNiBJTs7W9XV1VqxYoXKy8uVnp6uzZs3KzU1VZJUXl5+3jVZvmz27NnasGGDHnvsMX3ve9/TqFGjlJeXFxyB6S2irFY1KcCkWwAAIizsdVh6q55eh0WSpv0gXydOebXlO/M0NiG2R/4NAAAuJT2yDsulzs4cFgAATEFgCUNweX7eEgIAIKIILGFw2Fu7y8cICwAAEUVgCUNbSYiF4wAAiCwCSxgoCQEAYA4CSxjaluenJAQAQGQRWMJwZoSFwAIAQCQRWMJASQgAAHMQWMJgt7EOCwAAZiCwhMFBSQgAAFMQWMJwZoSFkhAAAJFEYAkDk24BADAHgSUMbYHFxwgLAAARRWAJQ9s6LKx0CwBAZBFYwsAICwAA5iCwhIE5LAAAmIPAEoYo1mEBAMAUBJYw2FnpFgAAUxBYwkBJCAAAcxBYwuBo2605QGABACCSCCxhaCsJeX2UhAAAiCQCSxgoCQEAYA4CSxiiKAkBAGAKAksYoigJAQBgCgJLGIIr3TLCAgBARBFYwsDCcQAAmIPAEobgpFtKQgAARBSBJQx26+kRFkpCAABEFIElDFF2XmsGAMAMBJYwRFlPT7plLyEAACKKwBKGtkm3XkZYAACIKAJLGNpKQoywAAAQWQSWMLSVhJjDAgBAZBFYwhBlZx0WAADMQGAJgz04wkJJCACASCKwhMHBbs0AAJiCwBKGtpIQk24BAIgsAksY2kpCXn9AhkFoAQAgUggsYWgrCUmSL0BgAQAgUggsYbCfXjhOoiwEAEAkEVjCEPWFERZWuwUAIHIILGGIajfCQmABACBSCCxhsFgsslvbFo+jJAQAQKQQWMIUxVosAABEHIElTG0TbwksAABETpcCy+rVq5WWliaXy6WMjAxt27at02u3b9+uOXPmaPDgwYqOjtb48eP1s5/9rN0169atk8Vi6XA0NTV1pXk96sxqt5SEAACIFHu4X8jLy9OSJUu0evVqzZkzR7/85S81f/587dmzRyNGjOhwfUxMjB5++GFdfvnliomJ0fbt2/Xggw8qJiZGDzzwQPC6uLg47du3r913XS5XFx6pZzHCAgBA5IUdWJ555hndd999uv/++yVJK1eu1Ouvv641a9YoNze3w/VTp07V1KlTg3+PHDlSmzZt0rZt29oFFovFosTExK48Q0QxhwUAgMgLqyTk9XpVWFiorKysduezsrJUUFAQ0j2KiopUUFCgq6++ut35+vp6paamavjw4br11ltVVFR0zvs0NzfL4/G0OyKhrSTESrcAAEROWIGlqqpKfr9fCQkJ7c4nJCSooqLinN8dPny4nE6npk+froceeig4QiNJ48eP17p16/Tqq69q/fr1crlcmjNnjvbv39/p/XJzc+V2u4NHSkpKOI/SZcGSkI8RFgAAIiXskpDUWr75IsMwOpz7sm3btqm+vl5///vf9eijj2r06NG68847JUmzZs3SrFmzgtfOmTNH06ZN089//nM9++yzZ73fsmXLtHTp0uDfHo8nIqGlrSTESrcAAEROWIElPj5eNputw2hKZWVlh1GXL0tLS5MkTZ48WceOHdMTTzwRDCxfZrVadeWVV55zhMXpdMrpdIbT/G5hbysJ8ZYQAAARE1ZJyOFwKCMjQ/n5+e3O5+fna/bs2SHfxzAMNTc3n/Pz4uJiJSUlhdO8iHDwlhAAABEXdklo6dKlysnJ0fTp05WZmam1a9eqpKREixYtktRaqikrK9Pzzz8vSVq1apVGjBih8ePHS2pdl+Xpp5/WI488Erznk08+qVmzZmnMmDHyeDx69tlnVVxcrFWrVnXHM3Yru/X0W0JMugUAIGLCDizZ2dmqrq7WihUrVF5ervT0dG3evFmpqamSpPLycpWUlASvDwQCWrZsmQ4dOiS73a5Ro0bpqaee0oMPPhi8pqamRg888IAqKirkdrs1depUbd26VTNmzOiGR+xeUfbTgYVJtwAARIzFMIyLYqjA4/HI7XartrZWcXFxPfbv3P+b9/TG3ko9dcdkfXNGx4XyAABA6EL9/WYvoTBREgIAIPIILGGiJAQAQOQRWMIUZW19S8gXILAAABApBJYwRbFbMwAAEUdgCVOUvXWExUtJCACAiCGwhIml+QEAiDwCS5gGxzgkScfrOl+pFwAAdC8CS5iS3NGSpPLaRpNbAgDApYPAEqakAS5JUnlNk8ktAQDg0kFgCdOZEZYmXSSLBAMA0OsRWMKU5G4dYWls8au2scXk1gAAcGkgsITJFWXToNMTb49SFgIAICIILF2QGNc6ylLhYeItAACRQGDpguTTE28ZYQEAIDIILF3Aq80AAEQWgaULEk9PvC2vZYQFAIBIILB0QTJrsQAAEFEEli5oKwlVeAgsAABEAoGlC9rWYjla08jicQAARACBpQva5rA0+wI62cDicQAA9DQCSxc47TbF929dPI43hQAA6HkEli4KvinExFsAAHocgaWLWIsFAIDIIbB0URJrsQAAEDEEli46M8JCYAEAoKcRWLrozH5ClIQAAOhpBJYuOrNjMyMsAAD0NAJLFyUPOFMSYvE4AAB6FoGlixJOj7B4fQFVn/Ka3BoAAC5uBJYuctitiu/vlCRVMPEWAIAeRWC5AEy8BQAgMggsF6BtLRYm3gIA0LMILBegbS2WoyzPDwBAjyKwXIAzq91SEgIAoCcRWC5A0gBWuwUAIBIILBeAERYAACKDwHIBgpNua5sUCLB4HAAAPYXAcgES4lyyWKQWv8HicQAA9CACywWIslk15PTicZSFAADoOQSWC9Q28ZZXmwEA6DkElguUHJzHwggLAAA9hcBygRKDbwoxwgIAQE8hsFyg5LbVbgksAAD0GALLBRo2sDWwHKk+ZXJLAAC4eBFYLtDEpDhJ0icVdWrxB0xuDQAAF6cuBZbVq1crLS1NLpdLGRkZ2rZtW6fXbt++XXPmzNHgwYMVHR2t8ePH62c/+1mH6zZu3KiJEyfK6XRq4sSJeumll7rStIhLHdxPsS67vL6A9h+rN7s5AABclMIOLHl5eVqyZImWL1+uoqIizZ07V/Pnz1dJSclZr4+JidHDDz+srVu3au/evXrsscf02GOPae3atcFrduzYoezsbOXk5GjXrl3KycnRwoULtXPnzq4/WYRYLBalJ7slSR+V1ZrcGgAALk4WwzDCWlN+5syZmjZtmtasWRM8N2HCBC1YsEC5ubkh3eOOO+5QTEyM/vd//1eSlJ2dLY/Ho9deey14zc0336yBAwdq/fr1Id3T4/HI7XartrZWcXFxYTzRhfvhn/foV9sOKWdWqn6wID2i/zYAAH1ZqL/fYY2weL1eFRYWKisrq935rKwsFRQUhHSPoqIiFRQU6Oqrrw6e27FjR4d73nTTTee8Z3NzszweT7vDLOnDTo+wHGWEBQCAnhBWYKmqqpLf71dCQkK78wkJCaqoqDjnd4cPHy6n06np06froYce0v333x/8rKKiIux75ubmyu12B4+UlJRwHqVbtQWWveUe+Zh4CwBAt+vSpFuLxdLub8MwOpz7sm3btun999/XL37xC61cubJDqSfcey5btky1tbXBo7S0NMyn6D5pg2MU47CpqSWgA8d5vRkAgO5mD+fi+Ph42Wy2DiMflZWVHUZIviwtLU2SNHnyZB07dkxPPPGE7rzzTklSYmJi2Pd0Op1yOp3hNL/HWK0WTUp2693DJ7S7rFbjEmPNbhIAABeVsEZYHA6HMjIylJ+f3+58fn6+Zs+eHfJ9DMNQc3Nz8O/MzMwO99yyZUtY9zRbcB4LbwoBANDtwhphkaSlS5cqJydH06dPV2ZmptauXauSkhItWrRIUmuppqysTM8//7wkadWqVRoxYoTGjx8vqXVdlqefflqPPPJI8J6LFy/WvHnz9OMf/1i33367XnnlFb3xxhvavn17dzxjRKQPa53ZTGABAKD7hR1YsrOzVV1drRUrVqi8vFzp6enavHmzUlNTJUnl5eXt1mQJBAJatmyZDh06JLvdrlGjRumpp57Sgw8+GLxm9uzZ2rBhgx577DF973vf06hRo5SXl6eZM2d2wyNGxuTTIyx7yj3yBwzZrOee0wMAAEIX9josvZWZ67BIkj9gKP3x19XY4tcbS+dp9FDmsQAAcD49sg4LOmezWjQxubWjd1MWAgCgWxFYutHk4MRb8xaxAwDgYkRg6UaTGGEBAKBHEFi60eThpyfeHvUoELgopgYBANArEFi60egh/eW0W1Xf7NPhala8BQCguxBYupHdZtWEpNPrsRxlHgsAAN2FwNLNJrPiLQAA3Y7A0s1Y8RYAgO5HYOlmbXsK7S6rlZ+JtwAAdAsCSzcblxCrWJdddU0+RlkAAOgmBJZuZrdZNeuywZKkdw5UmdwaAAAuDgSWHnDV6HhJ0jufEVgAAOgOBJYeMOd0YHnv8Ek1tfhNbg0AAH0fgaUHjBoSo4Q4p7y+gAqPnDS7OQAA9HkElh5gsViCoyzbKQsBAHDBCCw9pG0eSwGBBQCAC0Zg6SFtIywfltWqtqHF5NYAANC3EVh6SEKcS6OH9pdhSDsOMsoCAMCFILD0oDOvN1eb3BIAAPo2AksPmj3q9AJyzGMBAOCCEFh60KxRg2W1SAerTuloTaPZzQEAoM8isPSgOFeUpqQMkMQoCwAAF4LA0sPmjGKZfgAALhSBpYe1vd78zoFqGYZhcmsAAOibCCw9bFrqALmirDpe16yi0hqzmwMAQJ9EYOlhTrtNX5mcJEn6TcFhcxsDAEAfRWCJgHvnpEmS/vxhuSpqm0xuDQAAfQ+BJQLSh7k1Y+Qg+QKGXvj7EbObAwBAn0NgiZB7rxopSXpx5xE1tfjNbQwAAH0MgSVCbpyYqGEDonWyoUUvF5WZ3RwAAPoUAkuE2KwWfXv2SEnSc+8c5hVnAADCQGCJoIVXpqifw6Z9x+pUcIANEQEACBWBJYLc0VH6esZwSdL/bD9kcmsAAOg7CCwR1lYW+tu+Sh04Xm9uYwAA6CMILBF22ZD+un78UBmG9N0/fCifP2B2kwAA6PUILCZ44quT1N9p1/tHTur/vPmZ2c0BAKDXI7CYIGVQP/3ngnRJ0rN/3a/CIydMbhEAAL0bgcUkC6YO09emDlPAkBZvKJanqcXsJgEA0GsRWEy04vZJShkUrc9PNup7L39kdnMAAOi1CCwminVFaWX2VNmsFr1SfFQb3i0xu0kAAPRKBBaTZaQO1JLrx0iS/uOl3dq8u9zkFgEA0PsQWHqBh64drezpKQoY0r+sL9LfPjlmdpMAAOhVCCy9gNVq0Y/umKyvTkmWL2Bo0QsfqOCzKrObBQBAr0Fg6SVsVot+unCKbpyYIK8voPuff18FBwgtAABIBJZeJcpm1f/51lTNHROvBq9f3/rVTj2yvkilJxrMbhoAAKbqUmBZvXq10tLS5HK5lJGRoW3btnV67aZNm3TjjTdqyJAhiouLU2Zmpl5//fV216xbt04Wi6XD0dTU1JXm9WlOu01rc6brGxnDZbFIf9x1VNf/9G3lbt6r2kbWagEAXJrCDix5eXlasmSJli9frqKiIs2dO1fz589XScnZX8ndunWrbrzxRm3evFmFhYW69tprddttt6moqKjddXFxcSovL293uFyurj1VHxftsOm/vjFFf3rkKs0ZPVhef0C/3HpQ1z79lja8W6JAwDC7iQAARJTFMIywfv1mzpypadOmac2aNcFzEyZM0IIFC5SbmxvSPSZNmqTs7Gx9//vfl9Q6wrJkyRLV1NSE05R2PB6P3G63amtrFRcX1+X79DaGYeitT4/rR3/eq/2Vrbs7Txnu1pO3p+uKlAHmNg4AgAsU6u93WCMsXq9XhYWFysrKanc+KytLBQUFId0jEAiorq5OgwYNane+vr5eqampGj58uG699dYOIzBf1tzcLI/H0+64GFksFl07bqg2L56r7906UbFOu3Z9XqsFq97Roxs/VH2zz+wmAgDQ48IKLFVVVfL7/UpISGh3PiEhQRUVFSHd46c//alOnTqlhQsXBs+NHz9e69at06uvvqr169fL5XJpzpw52r9/f6f3yc3NldvtDh4pKSnhPEqfE2Wz6r6r0vTXf71a/zBtuCRpw3ul+urPt2tv+cUZ1gAAaNOlSbcWi6Xd34ZhdDh3NuvXr9cTTzyhvLw8DR06NHh+1qxZuuuuuzRlyhTNnTtXv/vd7zR27Fj9/Oc/7/Rey5YtU21tbfAoLS3tyqP0OUNjXfrpwin63YOZSnK7dLDqlBasekd575UozOoeAAB9RliBJT4+XjabrcNoSmVlZYdRly/Ly8vTfffdp9/97ne64YYbzt0oq1VXXnnlOUdYnE6n4uLi2h2Xkhlpg/Tnf5mra8YNUbMvoO9u3K3v5BXrzU8qdaT6lPxMzAUAXETs4VzscDiUkZGh/Px8fe1rXwuez8/P1+23397p99avX697771X69ev1y233HLef8cwDBUXF2vy5MnhNO+SMyjGof+550r9YusB/XTLp3q5+KheLj4qSXLYrEqLj9E/zhqhu2amymo9/wgYAAC9VViBRZKWLl2qnJwcTZ8+XZmZmVq7dq1KSkq0aNEiSa2lmrKyMj3//POSWsPK3Xffrf/+7//WrFmzgqMz0dHRcrvdkqQnn3xSs2bN0pgxY+TxePTss8+quLhYq1at6q7nvGhZrRb98zWjNWPkID33zmF9VlmvQ9Wn5PUFtO9Ynb7/ysd6bXeFfvL1y5UyqJ/ZzQUAoEvCDizZ2dmqrq7WihUrVF5ervT0dG3evFmpqamSpPLy8nZrsvzyl7+Uz+fTQw89pIceeih4/p577tG6deskSTU1NXrggQdUUVEht9utqVOnauvWrZoxY8YFPt6lY/rIQZo+svXNK3/A0NGaRuXvOab/en2fdhys1s0rt2r5LRN154yUkOYbAQDQm4S9DktvdbGuw3KhDled0r/9YZfeO3xSkjR5mFv/MG2Ybp2SrPj+TklSIGDow7Ja/e2TSjV6fbr3qjQluaPNbDYA4BIR6u83geUS4A8Yeu6dQ/qv1/ep2ReQ1LrZ4rwx8RoU49Tbn1aqqt4bvH5Avyj919dbN2IEAKAnEVjQQVV9s/6066heKj6qXaU17T7r77Rr3th4lZxo0Edlreu6fHv2SD06f7xcUTYTWgsAuBQQWHBOB4/X64+7ytXQ4tPVY4Zo+shBctitavb59V9/2af/u/2QJGlCUpz++ZpRumbcEMW6okxuNQDgYkNgwQV585NK/X+/36UTp1pLRVE2izJHxevGiQkaObifnHabXFFWuaJsSj39NwAA4SKw4IJVepr0P+8c1paPK3Sw6lSn1yXGufT0N6boqjHxEWwdAOBiQGBBt/qssl75e47p7U8rVdPQomZfQE0tfnkaW3TK65ck3X9Vmv7t5nGMtgAAQkZgQUQ0ev36zz/v0Ys7W9feGZ8Yqx/dMVmj4vsr1mVnhV0AwDkRWBBRb+w5pu9u/FDVp868Hm2zWjQgOkpDYp26cuQgZY4arFmXDdagGIeJLQUA9CYEFkRcZV2THn/lY2399HiwTHQ24xNjNTE5TmMTYjU2ob/GDI1Vktslu639Xpw+f0CHqxu0/1idbFaLrhs/tMM1AIC+jcACUzX7/KppaNGJU14dqT6lvx88oYIDVfr0WH2n33FHR2lwf4cGxzhU1+TTweOn5PUHgp+PGdpf//GVCbpm3JCzbi9gGIYaW/w62dCi2oYWDRsQLXc/XsUGgN6MwIJe6XhdswqPnNCnx+q1v7Je+4/VdQgmX9TPYdOYof115ESDahpaJElXjY7XI9eNVk1jiz4uq9VHRz3aW+5Rdb233X1sVouuHDlQN0xI0A0TEjQyPiYizwgACB2BBX2GP2CotrFF1fXNqj7l1YlTXrmirBozNFbDBkTLarWotrFFq9/8TM+9c7jTcNMmymZRf6ddJ08HnDYD+kUpymaV3WqRzWpRdJRNiW6XktwuJbmjNWxAtFIH99NlQ/orvr+DTSIBIAIILLgolZ5o0E9e36c3P6lUyqB+mjwsTunD3JqUHKdEd7QGREepn8Mmi8Wi0hMNemPvMb2x95h2HjwhXyD0/9RjXXalxcco1mWX3Xom5IxPjFX2jBEaNoDNIQGgOxBYgC+oa2pRRW2TfAFD/oChFn9Ap5r9Kq9tVHltk8prG/X5yUYdqjqlsppGnev/K6wW6YYJCcrJTNWcUfG8ug0AFyDU3297BNsEmCbWFRXyXkhNLX4dqW7Q4epTamrxy+c35AsE1Oj16/WPj2nHwWpt2XNMW/YcU7LbpakjBmrycLcuH+5W+jC34thzCQC6HSMsQJj2H6vTC38/oo0flKm+2dfh8/j+To0c3E+pg2M0cnA/jU2M1cSkOA0fGM28GAD4EkpCQA9r8PpUVFKjDz+v1Yeft/7fsprGTq+Pddo1PilWg2Occtitctqtcthb58dYLBZZLRZZLdKg/g7dNClRo4b0j+DTAIA5CCyACWobW1Ryupx0pPqUDh4/pb0Vdfqssk4t/vD+Xy19WJxunzJMN01KVLTDJl8gIJ/fUMAwFO2wKc4VJVdU9+zb5PUFZMiQw2YNexSoqcWvNW8d0B8KP9dXJidq8Q1j1d9JtRlAaAgsQC/i9QV04Hi99lXUqa6pdfNIrz8gry8gf6A1hAQMKWAY2ldRp237q+QP4a0mh82quGi7hsS6NGxAtIYPbD0uHz5AV44ceN7w0eD16ed/+0y/3n5IXl9ANqtF/Rw2xTjsGjGon65MG6jpIwcpI3Vgh7k5hmHo9Y8r9IM/7W03spQQ59Rjt0zUrZcnUQIDcF4EFqAPq65v1uaPKvTH4qN678gJGUbr+jI2a2vpqLHFf843mSRpYlKcHph3mW65PElRX9rSwDAMvfZRhX7wpz0qr206b3ssFiltcIyGxjk1NNalobFO7a3w6J3PqiVJyW6X7p49UuvfLdGR6gZJ0uxRg7Vg6jD1c9jUz2FTdJRdJxu8+vRYnfYfq9enx+rU32XXD25PV/owd9c6CkCfR2ABLhKGYXQYqQgEDNV7fapr8snT2KIKT5M+P9moz082qKS6QW/tO67Gltb9nJLcLt0xbZhcdptaAoZ8/oCKS2tUcKA1bAwfGK3Hb5ukWZcNUoPXr1PNPtU3+7S33KP3Dp/Ue4dPBEPIlznsVi2ad5n+6ZrRinbY1NTi19qtB7Xqzc/U7Dv3An9toqNs+ln2Fbo5PfECeglAX0VgAS5hJ0959eLOI1pXcERV9c1nvcZht2rR1aP0z9eMOu9cmEpPkz47Xq/jdc2q9DSrsq51VCZn1kiNGNyvw/WlJxq05u0DOlrTqAavX41evxq8PvV32jXm9KaXo4f213PvHNa2/VWSpH+/eZz+6epRlJGASwyBBYCaWvx6tfio3jt8QjarRXabRXarVbEuu76eMVypg83dX8nnD+gHf9qj3+w4Ikm6/Ypk3TwpUQNjHBrYz6EB/aJks1ralb8GxThkY7E+4KJBYAHQZ/zvjsN64o97QppoHN/fqVsmJ+q2KcmaNmKgrFaLDMNQeW2T9pZ7dPD4qeDqxeW1TapratH1ExJ018zUs44GATAXgQVAn1JwoEq/KTis43XNqmlo0ckGr2oaW845uTjZ7VLKoH7ad6wuuJt3ZywW6eqxQ5QzK1VzRsd32yvhAC4MgQVAn/flCcct/oC2f1alPxYf1ZY9x9qtNGyzWjRqSIzGJrTu8p3kdinRHa2AYSjvvVK9/enxdvdOcruUOrifRg6O0eD+DjntNrmirHJF2dTgbd2e4Uj1KR2pblBdU4smD3crI3WQpqcO1OXD3TrZ0KIj1adUcqJBZTWNShsco6xJiRoU4wj7OStqm/SnD4/KGWXTsAEuDRvQT0kDXIp12ts9vz9g6Hhds8prG1VR2yRXlE1Xjx3Cflbo0wgsAC5qTS1+bf30uGobWzQhKU6jh/Y/56jJ4apTenHnEW36oEzVp7w90iarRZqZNljzJyfqhgkJSj7Prt7HPE1a89YB/fbdEnk7eavKZrXIfvpoOr1uzxfNHROvZxZeoSGxzvO2r+1/7pnYjN6EwAIAZ2EYhk42tARXIz5c1aDaxhY1+/xqagmoqcUvh92q1EGn94OK7ydXlE3FpTV6//Rr3p+fbJQryqrUQTFKGdRPSW6XPig5qY+Petr9WymDojVj5GDNvGyQxibEBt+Wqm9u3dbhi0Fl2ogBGtzfqaM1jTpa06iTnZS4bFaLEmKdSnC7tLfco6aWgOL7O/Wz7CmaO2bIWb+z/1idNrxXqpeLylTX5NOQWKfiY50a0t+py4bEKGtiQnA+EGAYht47fFID+kVpbEJsj/97BBYA6CH1zT7FOGwdRipKqhv0l4/L9ZePKrTr89qQJhFPTx2o79w4VrNHDW53vwavTw1ev/wBQy3+1pEVV5RN8f2dwbek9h+r08O/LdK+Y3WyWKQH5l6maakDgzuM1za26JXioyo8cvK87RgS69RNkxI0Pz1Jsy4bzJtYl6Bmn1+vFB3V/91+UJ8eq5cryqrXFs9TWnzPvk1IYAEAE9U3+1R45KTePVStnQdP6GhNo/o57Ypx2BTjtGtgjEN3XjlCc0YPvqASTVOLXyv+tEe/3VnS6TU2q0XXjx+qb85I0Zihsaqqb25dU6euWYVHTuqNvcdU13RmPtCQWKe+OiVZC64YpvRhcTIMqaymUXvLPdpfWS+71aKBMQ4NjnFoYIxDwwZEa2iss1tKTYZh6KMyj3yBgK5IGRDSPQMBQ8Wf1yjOZddl8f0ZKQqT1xfQr7Yd1HPvHO6wbtOMtEHa8P/O6tE+JbAAwCVk8+5yrSs4LJ8/ILutdRdwh92qK0cO0jcyhmtonKvT73p9ARUcqNJfPqrQ6x9XtCtHDRsQrdrGlnYTnM+mrXwwLiFWg/s7dMxzZnJwVb1XVosUZbPKbrPIYbNqYnKcZo8arNmj4pUyqJ8qPU3a+EGZ/lBYqgPHT0mS0uJjtHB6iv4hY5iGxp69/QeP1+vRTbv17qETwXZMGzFQGakDlTUxQWMiUNLoy06c8mrRC4XB/ktyu/T/zBmpq0YP0dd/UaAGr18/uH2ScjJH9lgbCCwAgLB5fQFt/fS4Xi4uU/6eY8EtFhw2q0YP7a9xibGySKo+5dXJBq+q670qr21UCNWvTiXGuXS8vjlYQnNFWWWzWHTK27q9hM1q0bXjhujqsUM0e3S8LouPkS9g6FfbDmrlG/vl9QXkimrdL6up5czkZatFWjg9RUuzxnYaeL6oLexdKj49Vqf7f/O+Sk40qL/Trie/OklfvSI5uPfYbwoO6/FXP1aMw6bXvzNPwwf2zDpGBBYAwAWpa2pRUUmNktwujYyP6bCJZpumFr8+q2zd0HLfsTrVNrQoIc6l5AGtr5bH92991bt1bo3RWi47fEIFB6pVXFoj3+mgkpE6UAunD9dXJifJarHozx+Wa8N7JfqgpKbdv5fkdinaYdPB0yMxc8fE60dfm6xEt0sfH/Wo8MhJbd9/XG/ua32VPcZh0z9fO1r3XZXW7k2yBq9POw+e0Lb9Vdq2/7j2V9ZrSsoAfT1juG67PEkD+p15Rd3T1KJPyutkt1k0MSmuz6/j8+YnlXpkfZHqm30aMaiffn3P9A6jUYGAoey1O/Te4ZOaOyZez987o0feMCOwAAB6vVPNPu36vEaJcS5dNqT/Wa/59Fidtnxcoe2fVemDIzXy+ltHUdzRUfr+rRN1x7RhZ/0hff/wCf3gz3u1q7RG0plXxG1Wi2yndz33dTI05LBZdd34obJapY+PetptABp1OrRMHTFQyQNcrSsr1zTpaG2jahtbNGJQP40a0rpf1pih/XXFiAFy2iMfcLy+gN7aV6lNH5Tp/SMn5fW1Pq/PbwT7cGbaIK25K6PT9YMOHq/X/P/epmZfQD/5+uVaOD2l29tJYAEAXHQavX69f+SESk806saJCeddfyYQMPTHD4/qJ3/Zp7Kaxg6fDxsQrblj4jV3zBBNSIrV3z6p1B8KP9cnFXVnvbbZ51dVfXjr+AzoF6XbpyTr6xkpSh8WF/YoxclTXu36vEa7Smu16/Ma7S33yGG3Kr6/U/H9HRrc3yl3dJRiHDZFO+zq57Bpb7lHf9x1tNPX4yXpzhkj9ORXJ8lhP3cZ7JdvH1Dua58o1mXXG0uvVsI55kN1BYEFAIDT/AFDlXVN8gcMBQKS3zDktFuV5HadNUB8fLRWr398TDEOmyYluzUpOU4DYxwyDEOfn2zUByUnVVRSo+pTXiW7XUoeEK3kAdGKc9l1pLpBnx2v12eV9frw89p2b96MT4zVmIRYeX1+eX0Bef0BWWRRtMOmfqcPw5DKa5tUUds6avPFN7jCNTTWqQVTh+nm9ES5o6NaFyG0WRXjsLUreZ2Lzx/QP6wp0N6KOj37zam6OT2xy+05GwILAAAm8wcMbf+sSr9/v1Rb9hzrdEXj80mLj9GU4W5NSRmgycPcMiRV1TW3vqJe71V9ky+4dk+D168B/aL01SnJmjM6vlvW1Pmssl4WizSqk7LdhQj199ve7f8yAACQ1Dpv5uqxrW841Ta0aMueCnmafHLYrXKePgxDp4NGa+AwDJ3eC+vMxOX+TnN/rkcP7f6gEi4CCwAAEeDuF6Vv9MCk1UvFpfPCOQAA6LMILAAAoNcjsAAAgF6PwAIAAHo9AgsAAOj1CCwAAKDX61JgWb16tdLS0uRyuZSRkaFt27Z1eu2mTZt04403asiQIYqLi1NmZqZef/31Dtdt3LhREydOlNPp1MSJE/XSSy91pWkAAOAiFHZgycvL05IlS7R8+XIVFRVp7ty5mj9/vkpKSs56/datW3XjjTdq8+bNKiws1LXXXqvbbrtNRUVFwWt27Nih7Oxs5eTkaNeuXcrJydHChQu1c+fOrj8ZAAC4aIS9NP/MmTM1bdo0rVmzJnhuwoQJWrBggXJzc0O6x6RJk5Sdna3vf//7kqTs7Gx5PB699tprwWtuvvlmDRw4UOvXrw/pnizNDwBA3xPq73dYIyxer1eFhYXKyspqdz4rK0sFBQUh3SMQCKiurk6DBg0KntuxY0eHe950003nvGdzc7M8Hk+7AwAAXJzCCixVVVXy+/1KSEhodz4hIUEVFRUh3eOnP/2pTp06pYULFwbPVVRUhH3P3Nxcud3u4JGSwnLHAABcrLo06fbLW3EbhnHW7bm/bP369XriiSeUl5enoUOHXtA9ly1bptra2uBRWloaxhMAAIC+JKzND+Pj42Wz2TqMfFRWVnYYIfmyvLw83Xffffr973+vG264od1niYmJYd/T6XTK6XSG03wAANBHhRVYHA6HMjIylJ+fr6997WvB8/n5+br99ts7/d769et17733av369brllls6fJ6Zman8/Hx95zvfCZ7bsmWLZs+eHXLb2uYOM5cFAIC+o+13+7zvABlh2rBhgxEVFWX8+te/Nvbs2WMsWbLEiImJMQ4fPmwYhmE8+uijRk5OTvD63/72t4bdbjdWrVpllJeXB4+amprgNe+8845hs9mMp556yti7d6/x1FNPGXa73fj73/8ecrtKS0sNSRwcHBwcHBx98CgtLT3n73zYrzVLrQvH/eQnP1F5ebnS09P1s5/9TPPmzZMkffvb39bhw4f11ltvSZKuueYavf322x3ucc8992jdunXBv//whz/oscce08GDBzVq1Cj98Ic/1B133BFymwKBgI4eParY2NiQ5tOEyuPxKCUlRaWlpbwu3U3o0+5Ff3Yv+rN70Z/d72LrU8MwVFdXp+TkZFmtnU+t7VJguZSwvkv3o0+7F/3ZvejP7kV/dr9LtU/ZSwgAAPR6BBYAANDrEVjOw+l06vHHH+cV6m5En3Yv+rN70Z/di/7sfpdqnzKHBQAA9HqMsAAAgF6PwAIAAHo9AgsAAOj1CCwAAKDXI7Ccx+rVq5WWliaXy6WMjAxt27bN7Cb1Cbm5ubryyisVGxuroUOHasGCBdq3b1+7awzD0BNPPKHk5GRFR0frmmuu0ccff2xSi/uW3NxcWSwWLVmyJHiO/gxPWVmZ7rrrLg0ePFj9+vXTFVdcocLCwuDn9Gd4fD6fHnvsMaWlpSk6OlqXXXaZVqxYoUAgELyGPu3c1q1bddtttyk5OVkWi0Uvv/xyu89D6bvm5mY98sgjio+PV0xMjL761a/q888/j+BT9LCQN+u5BLXtm/SrX/3K2LNnj7F48WIjJibGOHLkiNlN6/Vuuukm47nnnjM++ugjo7i42LjllluMESNGGPX19cFrnnrqKSM2NtbYuHGjsXv3biM7O9tISkoyPB6PiS3v/d59911j5MiRxuWXX24sXrw4eJ7+DN2JEyeM1NRU49vf/raxc+dO49ChQ8Ybb7xhfPbZZ8Fr6M/w/Od//qcxePBg409/+pNx6NAh4/e//73Rv39/Y+XKlcFr6NPObd682Vi+fLmxceNGQ5Lx0ksvtfs8lL5btGiRMWzYMCM/P9/44IMPjGuvvdaYMmWK4fP5Ivw0PYPAcg4zZswwFi1a1O7c+PHjjUcffdSkFvVdlZWVhiTj7bffNgzDMAKBgJGYmGg89dRTwWuampoMt9tt/OIXvzCrmb1eXV2dMWbMGCM/P9+4+uqrg4GF/gzPd7/7XeOqq67q9HP6M3y33HKLce+997Y7d8cddxh33XWXYRj0aTi+HFhC6buamhojKirK2LBhQ/CasrIyw2q1Gn/5y18i1vaeREmoE16vV4WFhcrKymp3PisrSwUFBSa1qu+qra2VJA0aNEiSdOjQIVVUVLTrX6fTqauvvpr+PYeHHnpIt9xyi2644YZ25+nP8Lz66quaPn26vvGNb2jo0KGaOnWqfvWrXwU/pz/Dd9VVV+mvf/2rPv30U0nSrl27tH37dn3lK1+RRJ9eiFD6rrCwUC0tLe2uSU5OVnp6+kXTv3azG9BbVVVVye/3KyEhod35hIQEVVRUmNSqvskwDC1dulRXXXWV0tPTJSnYh2fr3yNHjkS8jX3Bhg0b9MEHH+i9997r8Bn9GZ6DBw9qzZo1Wrp0qf7jP/5D7777rv7lX/5FTqdTd999N/3ZBd/97ndVW1ur8ePHy2azye/364c//KHuvPNOSfw3eiFC6buKigo5HA4NHDiwwzUXy28WgeU8LBZLu78Nw+hwDuf28MMP68MPP9T27ds7fEb/hqa0tFSLFy/Wli1b5HK5Or2O/gxNIBDQ9OnT9aMf/UiSNHXqVH388cdas2aN7r777uB19Gfo8vLy9MILL+i3v/2tJk2apOLiYi1ZskTJycm65557gtfRp13Xlb67mPqXklAn4uPjZbPZOiTTysrKDikXnXvkkUf06quv6s0339Tw4cOD5xMTEyWJ/g1RYWGhKisrlZGRIbvdLrvdrrffflvPPvus7HZ7sM/oz9AkJSVp4sSJ7c5NmDBBJSUlkvjvsyv+7d/+TY8++qi++c1vavLkycrJydF3vvMd5ebmSqJPL0QofZeYmCiv16uTJ092ek1fR2DphMPhUEZGhvLz89udz8/P1+zZs01qVd9hGIYefvhhbdq0SX/729+UlpbW7vO0tDQlJia261+v16u3336b/j2L66+/Xrt371ZxcXHwmD59uv7xH/9RxcXFuuyyy+jPMMyZM6fDa/affvqpUlNTJfHfZ1c0NDTIam3/k2Kz2YKvNdOnXRdK32VkZCgqKqrdNeXl5froo48unv41bbpvH9D2WvOvf/1rY8+ePcaSJUuMmJgY4/Dhw2Y3rdf7p3/6J8PtdhtvvfWWUV5eHjwaGhqC1zz11FOG2+02Nm3aZOzevdu48847ecUxDF98S8gw6M9wvPvuu4bdbjd++MMfGvv37zdefPFFo1+/fsYLL7wQvIb+DM8999xjDBs2LPha86ZNm4z4+Hjj3//934PX0Kedq6urM4qKioyioiJDkvHMM88YRUVFwWU0Qum7RYsWGcOHDzfeeOMN44MPPjCuu+46Xmu+lKxatcpITU01HA6HMW3atOBruTg3SWc9nnvuueA1gUDAePzxx43ExETD6XQa8+bNM3bv3m1eo/uYLwcW+jM8f/zjH4309HTD6XQa48ePN9auXdvuc/ozPB6Px1i8eLExYsQIw+VyGZdddpmxfPlyo7m5OXgNfdq5N99886z/m3nPPfcYhhFa3zU2NhoPP/ywMWjQICM6Otq49dZbjZKSEhOepmdYDMMwzBnbAQAACA1zWAAAQK9HYAEAAL0egQUAAPR6BBYAANDrEVgAAECvR2ABAAC9HoEFAAD0egQWAADQ6xFYAABAr0dgAQAAvR6BBQAA9HoEFgAA0Ov9/1HHCnrUvn5EAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(aNN_CVGS_model.loss_curve_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "722b6580",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_aNN_CVGS_train = aNN_CVGS_model.predict(abalone_X_train_scaled)\n",
    "y_pred_aNN_CVGS_test = aNN_CVGS_model.predict(abalone_X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "ac1f09fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_aNN_CVGS_train = y_pred_aNN_CVGS_train.reshape(1, -1)\n",
    "y_pred_aNN_CVGS_test = y_pred_aNN_CVGS_test.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c554e7ab",
   "metadata": {},
   "source": [
    "Hataları hesaplamadan önce tahmin değerlerinin standartlaştırılmış halini tersine çeviriyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "445e7b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_aNN_CVGS_train_inv = scalerANN.inverse_transform(y_pred_aNN_CVGS_train)\n",
    "y_pred_aNN_CVGS_test_inv = scalerANN.inverse_transform(y_pred_aNN_CVGS_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "207ae2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_aNN_CVGS_train_inv = y_pred_aNN_CVGS_train_inv.reshape(-1, 1)\n",
    "y_pred_aNN_CVGS_test_inv = y_pred_aNN_CVGS_test_inv.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb726279",
   "metadata": {},
   "source": [
    "Eğitim seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım. \n",
    "RMSE, değeri 0'dan büyük yani model uyumlu değil. \n",
    "$R^2$ değeri kötüdür. Model iyi tahmin yapamamaktadır. Model ilişkinin yaklaşık olarak %61'ini açıklayabilmektedir. Tune edilmemiş haline göre performans artmıştır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "363c0498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9965041407526043"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aNN_CVGS_RMSE_train = RMSE(abalone_y_train, y_pred_aNN_CVGS_train_inv)\n",
    "aNN_CVGS_RMSE_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "2b72c819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6125404500336715"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aNN_CVGS_R2_train = r2_score(abalone_y_train, y_pred_aNN_CVGS_train_inv)\n",
    "aNN_CVGS_R2_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354a6a00",
   "metadata": {},
   "source": [
    "Test seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım.\n",
    "RMSE değeri tune edilmemiş haline kıyasla daha büyüktür. Performans kötüleşmiştir. $R^2$ değeri tune edilmemiş haline göre daha küçüktür. Model yaklaşık olarak %56'sını açıklayabilmektedir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "732fcaf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1046394512216926"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aNN_CVGS_RMSE_test = RMSE(abalone_y_test, y_pred_aNN_CVGS_test_inv)\n",
    "aNN_CVGS_RMSE_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "b7d4594f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5609177593475658"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aNN_CVGS_R2_test = r2_score(abalone_y_test, y_pred_aNN_CVGS_test_inv)\n",
    "aNN_CVGS_R2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c115dda",
   "metadata": {},
   "source": [
    "Performans, eğitim seti için artmış fakat test seti için azalmıştır.  \n",
    "(Aynı parametrelerle random_state eklemeden denediğimde hidden_layer_sizes değerinin (50, 50) seçildiği ve hem eğitim hem de test seti performansının arttığı bir durum oldu. RMSE her ikisi için de azalırken $R^2$ değerleri arttı. $R^2$ sırasıyla 0.63 ve 0.568'di.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a5d82d",
   "metadata": {},
   "source": [
    "Test setinden tahmin edilen değişkenleri ve hata değerlerini görselleştirelim. Hata oranının küçük olduğu söylenemez."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "975c93a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_residuals(abalone_y_test, y_pred_aNN_CVGS_test_inv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb7fe7d",
   "metadata": {},
   "source": [
    "## 4.K-En Yakın Komşular Algoritması\n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b243519c",
   "metadata": {},
   "source": [
    "Modelimizi oluşturup fit ediyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "81710d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "kNN = KNeighborsRegressor().fit(abalone_X_train_scaled, abalone_y_train_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e0b790",
   "metadata": {},
   "source": [
    "'minowski'(p=2) metriğini kullanıyoruz. 5 komşu kullanıldığını ve ağırlıklandırma yöteminin 'uniform' olduğunu görebiliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "c2260675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('minkowski', 5, 2, 'uniform')"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#?kNN\n",
    "kNN.metric, kNN.n_neighbors, kNN.p, kNN.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "ddbf4e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_kNN_train = kNN.predict(abalone_X_train_scaled)\n",
    "y_pred_kNN_test = kNN.predict(abalone_X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2250166e",
   "metadata": {},
   "source": [
    "Hataları hesaplamadan önce tahmin değerlerinin standartlaştırılmış halini tersine çeviriyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "df02ff3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_kNN_train_inv = scalerANN.inverse_transform(y_pred_kNN_train)\n",
    "y_pred_kNN_test_inv = scalerANN.inverse_transform(y_pred_kNN_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37eb54b2",
   "metadata": {},
   "source": [
    "Gerçek ve tahmin değerlerinin ideal uyuma sahip olup olmadığını aşağıdaki foknsiyonu kullanarak bulabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "f0c1c057",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_kNN(actual, predicted):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.scatter(actual, predicted, label='Predicted vs Actual')\n",
    "    plt.plot([min(actual), max(actual)], \n",
    "             [min(actual), max(actual)], color='red', linestyle=\"dashed\", linewidth=2, label='Ideal fit')\n",
    "    plt.title('KNN Regression: Predicted vs Actual')\n",
    "    plt.xlabel('Actual Age Progression')\n",
    "    plt.ylabel('Predicted Age Progression')\n",
    "    plt.legend()\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e8ebf6",
   "metadata": {},
   "source": [
    "İdeal uyuma yakınlık fazla değil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "4769ef98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_kNN(abalone_y_test, y_pred_kNN_test_inv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0006794",
   "metadata": {},
   "source": [
    "Eğitim seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım. \n",
    "RMSE, değeri 0'dan büyük yani model uyumlu değil. \n",
    "$R^2$ değeri kötüdür. Model iyi tahmin yapamamaktadır. İlişkinin yaklaşık olarak %65'ini açıklayabilmektedir. (Diğer modellere göre $R^2$ değeri daha iyidir.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "da855f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9025994333434617"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_RMSE_train = RMSE(abalone_y_train, y_pred_kNN_train_inv)\n",
    "kNN_RMSE_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "81f6e310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6481312766436943"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_R2_train = r2_score(abalone_y_train, y_pred_kNN_train_inv)\n",
    "kNN_R2_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a06eb8",
   "metadata": {},
   "source": [
    "Test seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım. \n",
    "$R^2$ değeri daha eğitim setine göre çok daha düşüktür. Overfit durumu olabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "426311c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.320130129886025"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_RMSE_test = RMSE(abalone_y_test, y_pred_kNN_test_inv)\n",
    "kNN_RMSE_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "e0e25d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46640082940297967"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_R2_test = r2_score(abalone_y_test, y_pred_kNN_test_inv)\n",
    "kNN_R2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "616ba2bf",
   "metadata": {},
   "source": [
    "#### 4.1.Grid Search Cross-Validation ile Hiperparametre Ayarlama\n",
    "&nbsp;<sup>[İçeriğe git.](#İçerik)</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9243de44",
   "metadata": {},
   "source": [
    "Hiperparametre ayarlamak adına bazı değişkenler için potansiyel değerler belirliyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "48191a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "kNN_params = {\"n_neighbors\":np.arange(1,30,1),\n",
    "              \"weights\": [\"uniform\", \"distance\"],\n",
    "              \"p\":[1, 2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "31492e3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "kNNfCV = KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b77553c",
   "metadata": {},
   "source": [
    "Izgara Arama Çapraz Doğrulama yapyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "e217007d",
   "metadata": {},
   "outputs": [],
   "source": [
    "kNN_CVGS = GridSearchCV(kNN, kNN_params).fit(abalone_X_train_scaled, abalone_y_train_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b6e5146",
   "metadata": {},
   "source": [
    "En iyi parametrelere bakıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "1a895177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 27, 'p': 2, 'weights': 'distance'}"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_CVGS.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c83fb4f",
   "metadata": {},
   "source": [
    "Modelimizi en iyi parametre değerleriyle oluşturuyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "cdb687f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "kNN_CVGS_model = KNeighborsRegressor(n_neighbors=kNN_CVGS.best_params_[\"n_neighbors\"], \n",
    "                                     weights=kNN_CVGS.best_params_[\"weights\"],\n",
    "                                     p=kNN_CVGS.best_params_[\"p\"]).fit(abalone_X_train_scaled, \n",
    "                                                                     abalone_y_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "7ac6cdd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_kNN_CVGS_train = kNN_CVGS_model.predict(abalone_X_train_scaled)\n",
    "y_pred_kNN_CVGS_test = kNN_CVGS_model.predict(abalone_X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "4f6a20af",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_kNN_CVGS_train_inv = scalerANN.inverse_transform(y_pred_kNN_CVGS_train)\n",
    "y_pred_kNN_CVGS_test_inv = scalerANN.inverse_transform(y_pred_kNN_CVGS_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c80d89",
   "metadata": {},
   "source": [
    "İdeal uyuma yakınlık parametre ayarı yapılmadan önceki halinden daha iyidir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "5b26a156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_kNN(abalone_y_test, y_pred_kNN_CVGS_test_inv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d854ceb",
   "metadata": {},
   "source": [
    "Eğitim seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım.   \n",
    "RMSE, değeri oldukça düşüktür. Model uyumludur.\n",
    "$R^2$ değeri mükemmeldir. İlişkinin yaklaşık olarak %100'ünü açıklayabilmektedir.  \n",
    "Eğitim seti olduğu için bu duruma iyi diyemeyiz overfit durumu olması söz konusudur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "d7a79d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.5403047620910447e-17"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_CVGS_RMSE_train = RMSE(abalone_y_train, y_pred_kNN_CVGS_train_inv)\n",
    "kNN_CVGS_RMSE_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "a125a5c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_CVGS_R2_train = r2_score(abalone_y_train, y_pred_kNN_CVGS_train_inv)\n",
    "kNN_CVGS_R2_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de4f4234",
   "metadata": {},
   "source": [
    "Test seti tahminleri için RMSE ve $R^2$ değerlerinde bakalım. \n",
    "RMSE değeri parametre ayarı yapılmadan önceki halinden daha düşüktür.  \n",
    "$R^2$ değeri parametre ayarı yapılmadan önceki halinden daha yüksektir fakat diğer modellere kıyasla performansı düşüktür."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "b1bf02f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.212777534956045"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_CVGS_RMSE_test = RMSE(abalone_y_test, y_pred_kNN_CVGS_test_inv)\n",
    "kNN_CVGS_RMSE_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "518d29a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5146377835555165"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNN_CVGS_R2_test = r2_score(abalone_y_test, y_pred_kNN_CVGS_test_inv)\n",
    "kNN_CVGS_R2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e1b8113",
   "metadata": {},
   "source": [
    "Test seti sonuçlarına bakacak olursak en başarılı model, tune edilmemiş Çok Katmanlı Algılaycı(MLP) regresyon modelidir. Tune edildiğinde çok daha az iterasyonla yakın bir sonuca ulaşılabilir."
   ]
  }
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