Bölüm 8-dogrusal_regresyon_modelleri.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresyon Modelleri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daha önceki derslerimizde sınıflandırma yöntemlerinden karar ağaçlarını ve destek vektör makinelerini görmüştük. Bu algoritmaların nasıl çalıştığına bakmıştık. Her iki algoritmada da verilerimiz hedef değerleri ile birlikte verilmişti. Biz var olan bilgilerden yola çıkarak bir makine öğrenmesi gerçekleştirdik ardından yine veriseti içerisindeki bir takım veriyi test seti olarak kaydederek oluşturduğumuz modeli test veri seti üzerinde deneyerek ne kadar iyi sonuçlar bulduğumuzu göstermeye çalıştık. Bugün de kimi yerlerde sınıflandırmanın altında kimi yerlerde sınıflandırmadan farklı bir yöntem olarak ele alınan regresyon modellerine bakacağız. Genel regresyon denkleminde hedef değişkenleri sürekli değerler alır ve birer sınıf ifade etmezler. Ancak sürekli değişkenlerin kesikli hale getirilmesi ile regresyon modelleri sınıflandırma için kullanılabilir. Biz öncelikle genel regresyon denkleminden bahsedelim.\n",
    "\n",
    "$Y\\ =\\ \\beta_0+\\ \\beta_1\\ x_1+\\ \\beta_2\\ x_2+\\ \\ldots\\ +\\ \\beta_n\\ x_n$\n",
    "\n",
    "Bu denklemde $Y$ değişkeni hedef değişkeni $X$ değişkenleri ise özellik değişkenlerini göstermektedir. $\\beta$ katsayıları katsayıları sklearn içerisinde `coef_`, $\\beta_0$ ise `intercept_` olarak kaydedilir. Lineer regresyon analizinde 2 boyutlu düzlemde çalışıyorsak yani bir çıktı bir de özellik değişkenimiz varsa 2 değişkeni temsil eden en iyi çizgiyi çizmeye çalışırız. \n",
    "\n",
    "**Lineer Regresyon**: Bağımlı bir değişken($Y$) ile bağımsız bir değişken ($X$) serisinin arasındaki ilişkinin fonksiyonel biçimi.  \n",
    "\n",
    "**Lineer regresyon analizi**: «veriyi lineer bir çizgiye uydurma çalışması» veya «lineer modelleme»  \n",
    "\n",
    "**$Y$**: Çıktı, bağımlı, sonuç, etkilenen değişken  \n",
    "**$X$**: Girdi, bağımsız, neden(faktör), etkileyen değişken  \n",
    "\n",
    "Regresyon sorusu: Y’nin X’e bağlı koşullarını nasıl tahmin edebiliriz sorusudur."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`from sklearn.linear_model import LinearRegression` satırıyla lineer regresyon fonksiyonu çağrılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basit Lineer Regresyon Analizi\n",
    "Önce basit bir lineer regresyon modelini ele alalım. Basit lineer regresyon analizinde bir adet bağımsız değişken(x) bir adet de bağımlı değişken vardır. Örnekte kullanılacak olan veri setini [şu adresten](https://drive.google.com/file/d/1ek0LwwIjWrU-SY4Wn-oi976sVOF8_X3e/view?usp=sharing) indirebilirsiniz. Verisetini pandas çerçevesine alıp regresyon modelini kuralım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "veriseti = pd.read_csv('data/linear_model.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82.583220</td>\n",
       "      <td>134.907414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73.922466</td>\n",
       "      <td>134.085180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.887445</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61.839983</td>\n",
       "      <td>114.530638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.775940</td>\n",
       "      <td>31.376437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x           y\n",
       "0  82.583220  134.907414\n",
       "1  73.922466  134.085180\n",
       "2  34.887445         NaN\n",
       "3  61.839983  114.530638\n",
       "4  16.775940   31.376437"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veriseti.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Görüldüğü gibi veri setinde bazı değerler eksik girilmiş. Şimdi bu değerlerin sayısına bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    0\n",
       "y    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veriseti.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y değişkeninde 8 adet boş değer var. X değişkeninde ise boş değer yok. Y değişkenindeki boş değerleri ortalama ile dolduralım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "veriseti.y.fillna(value=veriseti.y.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi veri seti hakkında tanımlayıcı istatistik bilgilerine bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.099330</td>\n",
       "      <td>87.915132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.416763</td>\n",
       "      <td>45.150390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.016737</td>\n",
       "      <td>-6.236295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.012087</td>\n",
       "      <td>52.047526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.255856</td>\n",
       "      <td>87.915132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.442040</td>\n",
       "      <td>126.556343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.432294</td>\n",
       "      <td>166.164544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                x           y\n",
       "count  100.000000  100.000000\n",
       "mean    52.099330   87.915132\n",
       "std     29.416763   45.150390\n",
       "min      0.016737   -6.236295\n",
       "25%     29.012087   52.047526\n",
       "50%     55.255856   87.915132\n",
       "75%     77.442040  126.556343\n",
       "max     99.432294  166.164544"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veriseti.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Göze çarpan bir anormallik ya da aykırı değer olmadığı görülüyor. Şimdi verileri normalize edelim. Örneğin `MinMaxScaler` kullanarak normalizasyon gerçekleştirelim. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "veriseti.x = (veriseti.x - veriseti.x.min()) / (veriseti.x.max() -\n",
    "                                                veriseti.x.min())\n",
    "veriseti.y = (veriseti.y - veriseti.y.min()) / (veriseti.y.max() -\n",
    "                                                veriseti.y.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veri setini x ve Y değişkenlerine ayıralım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = veriseti.x\n",
    "y = veriseti.y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X ve y arasındaki ilişkiye bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'X y arasındaki ilişki')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X.values, y.values)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Y')\n",
    "plt.title('X y arasındaki ilişki')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grafikte x ile y arasında pozitif yönlü doğrusal bir ilişkinin olduğu görülüyor. Şimdi en uygun çizgiyi çizdirmeye çalışalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lineer_regresyon = LinearRegression()\n",
    "lineer_regresyon.fit(X.values.reshape(-1, 1), y.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelimiz kuruldu ve verilerimize uyduruldu. Şimdi lineer modelimizi görüntüleyelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.10692427])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(lineer_regresyon.intercept_)\n",
    "print(lineer_regresyon.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.83833805]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineer_regresyon.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elde edilen regresyon modeli: Y=[0.10692427]+[0.83833805]X\n"
     ]
    }
   ],
   "source": [
    "print(\"Elde edilen regresyon modeli: Y={}+{}X\".format(\n",
    "    lineer_regresyon.intercept_, lineer_regresyon.coef_[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi regresyonun performansına bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = lineer_regresyon.predict(X.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8971708595884971"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y, y_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelimiz %89 oranında verilere uyum sağladı."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diğer kullanabileceğimiz ölçüler:\n",
    "1. Mean Absolute Error(MAE): Ortalama Mutlak Hata\n",
    "$MAE = 1/n\\times\\sum{|y-y_{tahmin}|}$\n",
    "2. Mean squared error(MSE) Ortalama Karesel Hata\n",
    "$MSE = 1/n\\times\\sum{(y-y_{tahmin})^2}$\n",
    "3. Root Mean Squared Error (RMSE): Ortalama Karesel Hataların Karekökü\n",
    "$RMSE = \\sqrt{1/n\\times\\sum{(y-y_{tahmin})^2}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ortalama Mutlak Hata: 0.05159991870239849 \n",
      "Ortalama Karesel Hata: 0.006982254161764924\n"
     ]
    }
   ],
   "source": [
    "print(\"Ortalama Mutlak Hata: {} \\nOrtalama Karesel Hata: {}\".format(\n",
    "    mean_absolute_error(y, y_predicted), mean_squared_error(y, y_predicted)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE yani Root Mean Squared Error için sklearn içerisinde bir fonksiyon yok ancak bu fonksiyonu [şu adreste](https://stackoverflow.com/questions/17197492/is-there-a-library-function-for-root-mean-square-error-rmse-in-python) önerildiği gibi basitçe yazabiliriz.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(targets, predictions):\n",
    "    return np.sqrt(((predictions - targets)**2).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE değeri: 0.08355988368688005\n"
     ]
    }
   ],
   "source": [
    "print(\"RMSE değeri: {}\".format(rmse(y.ravel(), y_predicted.ravel())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08355988368688005"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y, y_predicted, squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Varmış..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regresyon grafiğini çizmek istersek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'X y arasındaki ilişki')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_x = np.array([0, 0.5, 0.99])\n",
    "plt.scatter(X.values, y.values)\n",
    "plt.plot(random_x,\n",
    "         lineer_regresyon.intercept_[0] +\n",
    "         lineer_regresyon.coef_[0][0] * random_x,\n",
    "         color='red',\n",
    "         label='regresyon grafiği')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Y')\n",
    "plt.title('X y regresyon analiz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Çoklu Regresyon Analizi\n",
    "\n",
    "Eğer bağımsız değişkenler birden fazla olursa bu kez çoklu regresyon modeli kurmamız gerekir. Basit lineer regresyonda olduğu gibi katsayılar ve kesme terimi bulunduktan sonra regresyon modeli kurulabilir. Çoklu regresyonda modele alınacak değişkenlerin seçilmesi en önemli aşamayı oluşturur. \n",
    "\n",
    "Bu çalışmada Boston veri setini kullanarak bir çoklu regresyon analizi yapalım. Boston veri seti ile ilgili bilgiler [şu adresten](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html) alınabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "boston_X, boston_y = load_boston(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sütun isimleri\n",
    "boston.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X değerlerini pandas çerçevesine dönüştürelim\n",
    "boston_X = pd.DataFrame(boston_X, columns=boston.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# örnek veri seti\n",
    "boston_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 13 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null float64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null float64\n",
      "TAX        506 non-null float64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 51.5 KB\n"
     ]
    }
   ],
   "source": [
    "# veri setini inceleyelim\n",
    "boston_X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  506.000000  \n",
       "mean    12.653063  \n",
       "std      7.141062  \n",
       "min      1.730000  \n",
       "25%      6.950000  \n",
       "50%     11.360000  \n",
       "75%     16.955000  \n",
       "max     37.970000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tanımlayıcı istatistikleri\n",
    "boston_X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# boş değer var mı\n",
    "boston_X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.055892</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.455621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.200469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.412995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.603800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.055892</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.053929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.590879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.613808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.602339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.496996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.625505</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.488676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.582764</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.543993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.374044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.200469  0.406583 -0.055892  0.420972 -0.219247  0.352734   \n",
       "ZN      -0.200469  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "INDUS    0.406583 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "CHAS    -0.055892 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "NOX      0.420972 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "RM      -0.219247  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "AGE      0.352734 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "DIS     -0.379670  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "RAD      0.625505 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "TAX      0.582764 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "PTRATIO  0.289946 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "B       -0.385064  0.175520 -0.356977  0.048788 -0.380051  0.128069 -0.273534   \n",
       "LSTAT    0.455621 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT  \n",
       "CRIM    -0.379670  0.625505  0.582764  0.289946 -0.385064  0.455621  \n",
       "ZN       0.664408 -0.311948 -0.314563 -0.391679  0.175520 -0.412995  \n",
       "INDUS   -0.708027  0.595129  0.720760  0.383248 -0.356977  0.603800  \n",
       "CHAS    -0.099176 -0.007368 -0.035587 -0.121515  0.048788 -0.053929  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.590879  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.613808  \n",
       "AGE     -0.747881  0.456022  0.506456  0.261515 -0.273534  0.602339  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.496996  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.488676  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.543993  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000 -0.177383  0.374044  \n",
       "B        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.366087  \n",
       "LSTAT   -0.496996  0.488676  0.543993  0.374044 -0.366087  1.000000  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# korelasyonlara bakalım.\n",
    "boston_X.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRIM     1.000000\n",
      "RAD      0.625505\n",
      "TAX      0.582764\n",
      "LSTAT    0.455621\n",
      "NOX      0.420972\n",
      "Name: CRIM, dtype: float64\n",
      "ZN       1.000000\n",
      "DIS      0.664408\n",
      "AGE      0.569537\n",
      "INDUS    0.533828\n",
      "NOX      0.516604\n",
      "Name: ZN, dtype: float64\n",
      "INDUS    1.000000\n",
      "NOX      0.763651\n",
      "TAX      0.720760\n",
      "DIS      0.708027\n",
      "AGE      0.644779\n",
      "Name: INDUS, dtype: float64\n",
      "NOX INDUS değişkenleri yüksek korelasyona sahip:  0.7636514469209145\n",
      "CHAS       1.000000\n",
      "PTRATIO    0.121515\n",
      "DIS        0.099176\n",
      "RM         0.091251\n",
      "NOX        0.091203\n",
      "Name: CHAS, dtype: float64\n",
      "NOX      1.000000\n",
      "DIS      0.769230\n",
      "INDUS    0.763651\n",
      "AGE      0.731470\n",
      "TAX      0.668023\n",
      "Name: NOX, dtype: float64\n",
      "DIS NOX değişkenleri yüksek korelasyona sahip:  0.7692301132258261\n",
      "INDUS NOX değişkenleri yüksek korelasyona sahip:  0.7636514469209145\n",
      "RM         1.000000\n",
      "LSTAT      0.613808\n",
      "INDUS      0.391676\n",
      "PTRATIO    0.355501\n",
      "ZN         0.311991\n",
      "Name: RM, dtype: float64\n",
      "AGE      1.000000\n",
      "DIS      0.747881\n",
      "NOX      0.731470\n",
      "INDUS    0.644779\n",
      "LSTAT    0.602339\n",
      "Name: AGE, dtype: float64\n",
      "DIS      1.000000\n",
      "NOX      0.769230\n",
      "AGE      0.747881\n",
      "INDUS    0.708027\n",
      "ZN       0.664408\n",
      "Name: DIS, dtype: float64\n",
      "NOX DIS değişkenleri yüksek korelasyona sahip:  0.7692301132258261\n",
      "RAD      1.000000\n",
      "TAX      0.910228\n",
      "CRIM     0.625505\n",
      "NOX      0.611441\n",
      "INDUS    0.595129\n",
      "Name: RAD, dtype: float64\n",
      "TAX RAD değişkenleri yüksek korelasyona sahip:  0.9102281885331822\n",
      "TAX      1.000000\n",
      "RAD      0.910228\n",
      "INDUS    0.720760\n",
      "NOX      0.668023\n",
      "CRIM     0.582764\n",
      "Name: TAX, dtype: float64\n",
      "RAD TAX değişkenleri yüksek korelasyona sahip:  0.9102281885331822\n",
      "PTRATIO    1.000000\n",
      "RAD        0.464741\n",
      "TAX        0.460853\n",
      "ZN         0.391679\n",
      "INDUS      0.383248\n",
      "Name: PTRATIO, dtype: float64\n",
      "B       1.000000\n",
      "RAD     0.444413\n",
      "TAX     0.441808\n",
      "CRIM    0.385064\n",
      "NOX     0.380051\n",
      "Name: B, dtype: float64\n",
      "LSTAT    1.000000\n",
      "RM       0.613808\n",
      "INDUS    0.603800\n",
      "AGE      0.602339\n",
      "NOX      0.590879\n",
      "Name: LSTAT, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# En yüksek mutlak 5 korelasyonu sıralayalım\n",
    "for col in boston.feature_names:\n",
    "    en_yuksek_degerler = abs(boston_X.corr()[col]).nlargest(\n",
    "        n=5)  # en yüksek korelasyona sahip 5 değeri alalım\n",
    "    print(en_yuksek_degerler)\n",
    "    # eğer 0.75'ten büyük değer varsa yazdır.\n",
    "    for index, value in en_yuksek_degerler.items():\n",
    "        if 1 > value >= 0.75:\n",
    "            print(index, col, \"değişkenleri yüksek korelasyona sahip: \", value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "şimdi yüksek korelasyona sahip özellikleri analizden çıkartalım. Neden mi çıkartıyoruz? [Şuradan](https://towardsdatascience.com/why-exclude-highly-correlated-features-when-building-regression-model-34d77a90ea8e).  \n",
    "- NOX INDUS\n",
    "- DIS NOX\n",
    "- RAD TAX\n",
    "\n",
    "NOX ve RAD değişkenlerini analizden çıkaralım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_X = boston_X.drop(['NOX', 'RAD'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi çoklu regresyon analizini gerçekleştirelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(boston_X,\n",
    "                                                    boston_y,\n",
    "                                                    test_size=0.33,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "coklu_regresyon = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coklu_regresyon.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.08  0.03 -0.05  3.45  4.16 -0.03 -1.18 -0.   -0.69  0.01 -0.57]\n",
      "19.11\n"
     ]
    }
   ],
   "source": [
    "#katsayılar\n",
    "print(coklu_regresyon.coef_.round(2))\n",
    "print(coklu_regresyon.intercept_.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_y_predicted = coklu_regresyon.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.827980073358347"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, boston_y_predicted, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6919945689994251"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, boston_y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12e0e2f7108>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cllcIlJkbQu1/7g3TBiSUETDwcMLAgbwPKh9JbQtpo1DgaGjgDV0H8P2N99pan9f92Af44pW18jFRA/C9SEl/7sFP8pMf+NK+xrEfy2UVBz5qCaV0YvpiJJmYDlyvrVYbbhktMiQG7soMu+uG1gfj6/EBcBeF4o2CgYviQLrxwFEC8MQCSXH42qusRaGM2im2G2vo6h7tl3FcXe2TFWqw6H9eTxDa/fdwYaHDZy+t7msc+zEpZVnMauQSitT4I2TgTt64WmPgVVu8YTFw8x7Oiel8MKM+/TirJ/Ic9oaeu7o8dzwDdwA+AgbsqhHCaBn4x84t8oEvXr/t81q6T+7ZHX+fEkq6aVECePsE8E5acOmAur9sZbJ2Ohi1DpsrTeCcmCPQwB07HmDgarhx4Lc4Ma2E4unxyJmoSyiHPR9MHPiEgdcY+Gg0cL90Yo4OEB585AL/z8PP7/gcrTVNEpJgFtj/kdFpp1mtjZqrcgigd/k9FFKR5Irr60m50A/cag7WUbPAQipCT+B73kgZ+LW1CsCLUkIZLgN3jN4BuK/Hg4EPAPghj8k5McUEwB0DHw2Al1EoIzySJ7kkvQ0IFkrTIiENZ8wD+wSwStesFrlXS9Hf7UbazarxXl45pMiVMWLghc3EDLzRaOAlA9/CiTmsgmSVE9NcL9aVBr7ZET4Kq0eheIfsWC0Kk0o/kVBKBj4CCUUqfGEm4igllKRQt2WxuVRMiYQsNAx8vwy0YuDV+/k1Bi52C+BptWAuLXd3eObwTNXGNmoNPK85MceGgQ+5Fkpi50i2SUIJxHA7Qu3XigEAP9wNXcoMT+gJAyexTrARMHBV05FHGRqV5pKk2Jk1uYbGeeQklH0C+BYauIvvBRBydxp4L6sB+NIh6eC1JKPDZlybzYURGgY+ujDCq6v9kg0XSvF2/w+5S14byntsjgOPtVmj41I/XypNYGv4HLas42oGTQB8hAxc10BglAw8LdRtJZSsKGiRlgC+X6/7VhJKKPcenthJq/FeWj4cCaVeQtYftRNTKgLfG10cuJ0vaaFY6Zl7obKEnwjfxXfJR4fyHu5UqLQBSxfGGiDHov6LHAIDl0rzrT/1Yd73uSt7ep3zFXnhBMDNzxEwcOoAPkJASHbBwIu0hyc0RTQHgNgn40gLhUCVDA5MgkZPtAHwdvk9DEooh8TAa2M7bKfVZitsPfDA80bS4CDJJdOxKTp6ddUAq7YnlFAPZy3VZb1cKhqY6wbIsWHgLoxwvwy8kxS8sNjl/MLeZECVGzIhgokGbn6OgIHXJZRROsWS3CyInViN7G2Yn7GTUPYnIYSdq3wp/ivMrz9TPab69ALjHN0tgHcsgJ+ebQzGlB+giaIuoYzaiWnqgfsjlFBeddJsuk4H1/a7G5acUN/kM6nKPISIYixqghdDiEJZT8zr9uqInjBwZxbA9QgSefSABj5aCQXY0ZEpUwvgjXlg/wDW6F4mFgVTvcvlY5FKSPwpFGIQwPM+/M7fgO7SLddxDPyNp2e4tNw7nKiEAQllxE7MWhTKaJyYkledcABuJSyryw5LXkpyyVlxg+OskRfjyMCrRJ5gn4RmrW/u1V4dvyWAB419ve/t7MgBuFC5aXJ3mFb70kelqWqtS+BOd5BRZGIqwamGkVC8/aYzZ5Yt1xyCsU7IvSY50WB8+fUvwhP/AV74yC2XcWGEbzg9Qz+XLHYOHlDrETKjTuculCIcMQO/Z65J6AuuumQeWTHkYYwpySX/PvxpfjT4DXKpaWoXhVKMnQa+31OHY+B7PVG4ssv+hIFXNTkOOxtzMAplNACeS41bazsxcOWaLsQvLwpF2872osa0Y50ggya5CPFUnYFbsE9uTZd3DPwNp430chihhHVH86iTSVwceOgffiJPIRWF0jRDn7tnGyUDd6zQ9DZ9+QCb5Ip7xSKnxAppIWmKMYtCkQWeDQPe74a+3jev23Msv52LXniHM3A9AOCH68jUY8DAk1o8tgvb2sq0BXDRmCYn3DeAeTabsc5mGzqh8JvkIiKoM3Ana/W3BnAh4HV3T/H13lNcuz6c0LWdrL7pjBrAc2niwEfBwJ3DuxF6nJ5tlun07jsNxXB6vBZpjymRMCO69DJJqyah7DVd//MvrfLTf/CsIWxDyrrWtSYO+50PG46B7/F+Ocn3jmfgOllDadNb8NAdmaoCz1E5xdIaaO/EwB2Ae9EUUgR7c2Je+iQsnQdA2KQdB4Zaa5o6RQUtChENbmQ7MPBOWtCOAl7ReZLfiH6C+Wffs7ux9Jb3XcdF2NNBJqL9Ma7f+xEjCQ3BClXFgQ+zB+VuzIUQxoHPsVZU6riOAEUUQ4mMCdNlAGbp0s8lTcz6NAC+N8B7+tH38n0f/R74F2fh1/7Myx4bgKqdyAL2ycBtPfU936/CMfA7GcC1RiRrLDNt/j5sBl6LpR5VWFqSSwQKD1XGaG9lKnMMvI0Uwd4Yx3vfCY/8K6DKunTOykwqmiJFBU0KL8JXm5yYsC0Dn4oE0cM/ZsaVrt9+HEUKP/cW+Nyv7n7sNXN9MBOvtb8T01P/Dc5/cF/vvdmMhGLjwA85IqPOwF8pX+Q1mYkoErIuobz8MUWpcV7Pii79JCO23ZDMBrG3679i+RPcJVbozLwW1q++7LHBYC5EsF8NfJ9OTHev/QOSUI5GV/q8j1A5i/puToj1wy9opUYfRpgWkp8I3sVJsUaaf/22zxOWgfvxDFKEe2Og6XoJwr6te+K07rRQtEjRoQHwoO6HcAy8v3LLJbuZ5E/6n0RcecI+9/bJPGrtKl6yxqUXz3P2rbsfvrM6gAf7YeDJWuXEfZlmUunFSFLp6wz8exd/ES9ZAH6oZIURxVBOBQ3LwGfokdkwVtgfAw/zdZaYYTl+PW/OPv+yxwaDjcj3rYFbCWWvn8cllY0sE1MI0RBCPCaE+LwQ4ikhxD+xj79KCPEpIcTzQoj3CCEOJlIdSgfmorYFmg7ZialrEsqoohqSXPFq7xqvFlcH9PBbzPat9JtTSC/Y23jzPlgGXwK4ZRBpVtASKTpoIUVEWNfAHShv48T8C/l74dSbWWEGUdwewNduXgLgxsou2PoWVm46XmvvmmeRQdEfrH2+T5NKozVlPfBD18DzioEfy2/Q1PZUZe9PJIqyi87LsWZmADwQCtW5UT4eCLlnySHMN1jXbW702NVmvyurM/D9SijWibnXDa90qI+wmFUKfJvW+quAtwB/XAjxNuAngZ/RWt8PrAB/7UBGCCWAL2EiK0bJwH09mrjitJC06dMWyY5OTJF3ybRPGDVQYg8ArpQBLQvggS3K76SSLLHRI1Eb6UWDR9HbSCgn9SKcfRuJaOwKwHtLJvZc7FMqc6ekbD8M3Ek82cuPlnGL3TQ1PvxMTLfRx4HHfH6D2GrTomTgOfkQGHirqL53f6NyUhuGv7cNIpYbrNPialcPLeejLoGG+6yQuN8wwpJsjioTUxuzsWmE9p8Gvg1wLeLfDXzvgYwQagzcAvgBM/CPn18c6OGoB5yYo2PgbRLa7FxXWxR9EmJCX6DEHqJQ3GKxEkzZFssy7QrAW0g/ItRbSChbOTGTgpbqQGOO3IvxdrEo0xWrfe4XwGWGxCP3GnsHcBftNAQG7vTSURWzco7vttogUn0iOxdKBr4PjXormyoq6czvVLp1sI8wwobssK5brBXB8Lpv1fAiImc/X4PTwPf6eSoGPkInphDCF0J8DrgJPAScB1a1LlfHZeDebV77DiHE40KIxxcWFvY3ys0AfoDZmM9cW+cv/PtP8dFzi+VjogbgtzhBDqk7e5JL2sIAeJpvD0qiSEkJiQIP6YX4uz0yus+ROQC34U8WwIu+dY5GLZQXE7KFhNKvhXpaU2kHHwWNWXKvgb+LKoZyzRYM2mchrkCnZCJCeQEBe7yGA/AhaOAlgPse/gg0cMfAp1PTxckxcOcjCBlOos20rDbusGcklEzE+ypm1ZIdZDRDqkNT+fNlNOUurUa6wn3q/htJwdeI5whzqwZ0Uv757z5z21NVmbE8ymqEWmuptX4LcAb4WuANWz1tm9c+qLV+QGv9wMmTJ/c3SgfgWA38AMMIV7rm2iu96j0G4sDrjO7mM/DPXwFXPnNg43GWFoaBe0JTJJ1tnyeKPomOiHwPLYLdM1CbuOOkg8hVlLNsTaY2sSeaQnlRyebMay3YpWsDIZcAQWYXd3OOwmsMlKTdzryOARxvnyctX+UUhEgv3L+Ekr98CSUvJZQRM/C+kTUaZKa2vaqiUIaxqcyoVXIbD9Hom+8uDWb2lcjT1l0a08coXEvAXUhut7XaPApFsa9E7qTf4T3Rj/PN6/8dgEefX+QXH7nA8ze3X4swHhp4aVrrVeDDwNuAOSGEi2I5Awwn5mcrs0fzBW3Sww8yjNClfndrZVDFAIBXwNV98rdB5SxcPn9g43GWZDlTwoCf2gnAZUZKSOh7ewOwOgPXmtgV5XcM3L6nH7VQfkzIFho4DGbMAn5mAbExi/RjQnV7AHcsbr+le32VUogQJcK9O63s+NUwGbjnlS3VDrNDjQs3bVoA94Qmz9MyrC4Sw4kDn9Or3AzNAbyVmO8uDaYsA9/D51WKNl10PMP8rCNrQ1jr9vNqxL4ZeCO5SSgksTLzwtXJ72U7BBRQa2k4wiiUk0KIOft7E/gO4BngQ4CLtP8B4H0HMkIoF9WCPngnpmtAUG9EwDYMPHv2IQCuLh18x/UiqRihTraPzvBkQkJE6HvovUgozmmnjTMz1q7kqJmA0iUINdooPyYmr/wEAwBe3YtCKpqF3Wwacyi/uasSps3ESG377ecZ6Jzci9BetA8AN/fWk+ktp4m9mpMPXDEr2LuG+nLM+UoavYpbpUmvdMRHFENpqzan11mMzwLQTg2A5+EMgdhjMausg49GxjO02qYA18Dc6q/AL34zLDy3twHa9Vv4DaJNBbZ+/6nr3FzfmVQopZnKjKTq8kDc5lwvl7yVjQMDPw18SAjxJPBp4CGt9fuBHwH+thDiHHAc+OUDGSFAskYmIjo0gd031N2POeZdZ+D1hVxqqukGs4ufNf+dH3yFRFUH7R0iJLwisQxcoLyQkF163esLJeveAuAqNczDj6bQQUxMVkoERVobTy0WvJtJZoT9v8YsKmgS3Q7AtWY6f3kA7qscKSKkFxLuV0KBlx2J4uSJ0DdhhPXHDsNc0bOo5lgs0i5+LZHn5ToxlVQcZ5215hmkFsxk5rvLo5k9x5nnPTN3dDyLCJpuwNUTls7Dtc/BxY/taYzuxFH4TcvAbU/QQvHOX32CX3/s0o6v72QFd2HG5hLD3OcaIHpbmKcyMgIQYk9j3q3dNpFHa/0k8NVbPH4Bo4cfvCVr9MQUmQ4B06booDKQtmLgwoJALqJKknjh0bLSnzqEsEaXIg9AtrHt8zyVkhIhhECLgNDqkIF/mwlUB/B0g4Z1eIXadXGx4YXNNvgRsc3iiwPodzeIdGgy8GqhhN20YNYBeHMOHTaJdYrWGrHdhE43ynrS+y0FG+qMwovQdgPbi+n+KuXI8h407FH+4ifgt94B73wEmvO7upaTJwLPI/RHx8CDjaqLTJH0SxnwFoDtr8LqRTj9Vbt+j7S3RlPk5M0TrNNmXhqgk9GMSeTZwwaRbKwQAjRmEbklSnUN3M5B1i5vfumO5grQFX6LsHZyTAuJ1pXfaztb7+ecEoMAntnNcYDobWGe9cccVJLM0UilT9bYEG2zkwHyABmv07S6NQB3YYQDYWnnH6bAB0yLqoM2nVSg7e0A4L5MKTwzXZQfEu42G67mtCv6GzSxDNwCubYMPGhMof2GKdbvwhnzPjecfyLZBOBUDFyEDZqkOx/bN65Xn2WfWa+Bzii8sDyB7MVkPZKmzsCffh+sXYKVF3d9Laf/GgZultqhMnDrxPQ2rlD4htEWab90YoabGy588t/Cu/4E7EGnz9aMZKKbJ1jT7fJx5QB8Dww865iEIL85hxfZ1PP6Wnffxx4B3EkoMmgOFPBy+RSr/Z3n2Xq/qADcnkjdNbZk4FrD+34YLn4cT2UUItzbePdgRwPA8z4bukWGZeDZQToxLQMfcGKa3wsvriSU8x/kSf/NwMFKOs50VjFwb4cICSFTpGcmv2Ogu2J9NX93yPoAACAASURBVAae99fLinKRzowEYyNNwoaRUJxDDEzky3WOmRfXGXgmmRE9NMKUtw1bNMgH7u0ttmGO+z0d7ztpKtA5cp8MPO+t1v+ofr/0cfOzt3zbayilOXezU4JX4HmHqoGfu7lh6scXkoYnERvX6U7dZ94/65UbYyAURVG7P6svmY18D+Gb+cZN80v7JGsYAM+1D1F7z4k8acfce681jxe1ANDFoLQHwPre+lI6HVr6rYHIGOfkXbsNgG8kNQbuTuOWgXe2msvLF+CzvwrP/h6BysnveAB/+3/mr3o/TiYOgYGntzJwF4VSMvB0A5Yv8OHs9cDhALhXA3A/3z4KxZcJKhgE8EJpw2R2YlY1tln01mjYgkSxsKFmNiojak4h7PXz1JWc7XNDW1lhEwOfoYsMp8Dz8KIWscjpJtvfr3TFLM6X9EmCfTLwUGdIUQG43gMLVL06A3cJSmtw/Qvm9y3qvWy2h565wXf+zEd4ccmeWuoa+AFnY7642OU7/vUjfPTcIkmuOBusAprezKvN+6e9gVwGXV9LXQvGe0hikhvWXzF1smTgfWK8wEQA7UVCKawGHrbnCGzxpzypjaWUUF7a9TWhWr8yGNTAnY9gtXcbBp7UGfhgWdktGfi1z5mf3UU8PWHgAKyliumm2ZUPUrJwwD2gbenKix1iARxYUFOGbRwGgNdA21UK3MoClZVZX9oLTSRAnsHPvBE+/xvbv0GNgcvOzfL32Bb9F3ZRx812mRZcWAbuFQkretqckGoMvJMWzIguKrbdgSyr6ve3P0H0l2oAvtckHGuhLgwD90M8oVF7iCbRyRqFtsvCnXReesxE58CuAPzmeoLWcO6GmScmlf5wnJjLNn/h8kqftJCcDcyJIZk1AC7zhKDmSJb1nIrO3gFcdWxyXvsEHdvwukeMH8T4QiNlYco07CJ3Q3bN3Anb83ixkXyyAQB3EsqVvXXlcgAetgdOpE5iuh0DX+/npRPTbX4uwmhLDfyqBfDeIoHKJwCeS0WSK45NN8i0jzrATMwtGbh2EkrDhKVZh2JXN0kJD6W8bV02CYvtATDUKYTWg+9H5sjYX4HeUlnre0urAbjesLG8mHDBrFCIokdPx0SBj7DsSGY22Uf26ROZI/QWGri2jsAgNgCe9rY/QWSrV1jXTfr+zL5Lf4ZkKBtGCFDs5cSWrnMTq+c7Bn7x4+BZt/kuJBR3rL5omzgHXsXAhyKhpB1YPLf1f1lQWu5mhoF7ptRr7gA86w2cbAYiqLoWjPeQXaw7joGfYENMAVb+CkN7/Qwe/2X4ua++rbaurP8hnpojtJu9O+UB1feh8mqst1xEwW/+NXjh0fIhF/qnLAN334HLVF3t3c6Jmd0ioRRyhyiUkoEv4E8YuEljBTjejkmJyj5zB2E965jbUgN3DNw6ETs0yAj2nXCyF3OySYG/I4BHOkO44vGeiUJRfev03Cksrq6r24pyHX+6BHA/79KlQeB7pYQisz5oTagSEmJWVZuiWwsjTAtmRA/RNIDolwC+/Tj0+jVu6nmCqFFGwOzVIp0jfcPAAVS+++t46Ro3rRyk3f26+HE4/RaIZ6B/ewB3scGXHID7HqE/RCfmJ/8tPPjNW8apOwfxai8z/TAtgBfzBsB11h+oY1OuJaVqAL57Bi56i6zrJlHcomMBPCHGs6c0JXPj+F2/fNsSGDpZpaMbtBoNwoYhIXm6hYQC2zsyF74EX/xNuPChaozKAXiLSEgKaXvL1hi42uF7STprtG2buIqB2zjwzYk8WsNVWwa3u2hCWr2DK9R6RADc3LQT0zEZwYGG7fXs4qszcGoMvA6IXZoUIkQc4InAWVD0UAg2/DkiuQ0Aak1MhhdaB5AfESCrxJ/N0StaV7Um8j6E5ggs7ELu+bOEQpLlOV7epWfj8L2SgSflouzriFXaZBtVZ/puJpmli9cygBjG5vrpDpmkfuc6N/Q8UaMxmO25BwvJ0X7FwPfiM/GzjVLPL9KOuS9XPwOv/AYTPrgLCaXjANxq4PU48D33VNzK1i4bMKtJXc5ceNtyNzfd4rkO06cRNvRR5cnAfVVO2khWq4S1nRh42oHP/MeSTXu9BZb0DHHo0/VMw5UeMYElEarIa+WGb62VUzeRrrNOi3bsEzTMXJHpFhIKbK+DO2dzLW+iDPe168J9ZufEVNrEem9rtQqL4S0SyqbXLV8wJSUac9BdJNQ5csLAHQOPyAgPNpHH7qj1FNnSCWIZuLIaeHNqjlxEh8LAg6JLIhqkXrtM591s7jjsuxAs3zrx7HhJNwHnYw/C/2s7JmRdiKchaOD3DDAkocl8zdMEv+iRCAPgJQPPk3Jx9olZ020j11hzceC+ZeBh0yzKLNnhBJEscJM5Go3W/hk4OdKLwTHwYpfX0Zqw6HDdAXi/axidzODMHzUAvgsJxS3qJRtfXI9CGQoDd6eAjVurV2TFIAM/o67BsdcQxNaxnfUH76tbS3VJYicG/sR/gN/+62ZTAxobL3JFn6ARevS8moTiyqfKtHqPHTKIwZx+1nWbVhQQ2dOak+nMh+tCw2Zjb8fAL33S/KwlZLlEHm0JSgXg1Wa6toMj09XmyZyEyg6ZmE4+ec23QdFnSq+XYb0HYUcCwF0px5PTMZkODhTAe6UTs9aFxzJwGQwC+OzcPIWIEPtMONmLRbJLKppkfotYbc2QulaacFozXkgsiqohdLYJwJfOmeNtkRogjloQtQl6ZjFnkQHePOsRyB6p5xi4ZVdZUi32sMkabbway+qkBTNUEkpsWVW+gxOzkS2z7s3jBTHRZgau9W3T27XWRORoL0L7e2TgWRdPy0EG3rUnium7oXWsAs+bz8DlJ7a8zOb6GINRKMMAcOtncC3HHv5xePK/mI9g5YHlXkaSS+5VV+D4a8o5oQvDwJVNVypPs3U2v1MdmAsfNj+vfwFkwdTaOZ7Rr6QR+vR9w8D7xHihk1CKKhnnNgw8yEwt8HbkEze3AvAOzNxrTorbhRKWAF7Lm9iGgSe5ZJYOArVjJErYN5Lieny6zAPJt6uFcvVzJm3+Vd8EwEm1hLrTGfj6JgZOkRnN7vFfGXoESC+TvFpcJS2qbiLOialsAouTJE4cO04hQnzHwJ96L/zGn9/fG7/vr8N/ejtc/eyW/x2qPqnfIgvaNLdh4J2uAegorpyYQOVY3KyBu4XaXzVAHLYgmiJMTN2HIjZAJtOEsOiROQCPzE+V90sGPjMzw7puE2TVIk36fVoiNcdJIG4Zhlak2wBE1iVSCWk0Dy6Koc6eH3sQ/s3XbP1aa1Jp8x0FdQa+yw3WAswK02TaN/VnHGA3j5l/joH/wT+E//63t7xMZxMrCzxRZsIOh4HbU876VbOpfeoX4VlTJc8x8JVuRpCuMavW4PhrCR2A5wmhzsl8m3Tj7k23BuBuU775pcG+lEWGvmgkCn3tC7B8Hl+lPKPODgB4jxjP3ntkXiXjpLcB8HydDm0C36MZx+TaH9x8sy5EUzB7ZmsJZfWl6vG6hOKctpsAPMsyHon/Jj/sv4/V/vZzpJmYe7PROG3IgdZlGGF3s/Ry7XNw6k1mowFm6KC8OxzAnQZ+fMpo4MgUrjwB7/+b8PxDQ32v0+kLfDD+O7zNe6Z0aLodXNpaCnnPTI7jx45TiKiq2XHpU/Ds7+69jrXW8MX/Cl96Pzz4LfCl373lKbHskXot8qBNU2/DwDu2lnfDMnC7iIQDnc0SinNc9lcqAI+nCazDVDUsE837hKpPbrP5AivRqDytEnziNkU0Syy7JUt2UQU4Bt40AC63A/Cu2Tjy5vGyeluW1D7r9SfNiWEHHbkoCkIhUV6EsBvYrgHcHrvXdZs+MSrtoCwD/6UnVnh2PUA78Fy9tK3UsPlYHfpemYk5FA3cfZ/rV0x0UbZREpkSwHs5JzIrMxx/DaHb1Is+ERmpBXDt7k2nLqHYe/5ffgA++E+rx688UXZ86r/0ObjxRQC+pM/SCLyKgeu6EzPdNQOPiw0SO65G6JMSojdLKFHbAvgWEopj3zP3DmwWni7MicNKf+V86K8wK3r8heBh1nrbn9La2QI90SINZssolrzYRgO/8RTc/ZXQPlE+pO50CcVp4CemagzcMaPtwon2YUppTkqjd50RC2UkSsnAgwa+0OTdFQrtMTU1hfSiqmZHXkv82Iv1ls1rv+XvgxfCS5+65SkN1Sf3W8igTYsacCTr8Lv/F3SX6DkGbrVmERgA99y92uzEdIw8WbVOzKZZIM6aFQOPVZ8iMBuDc2LqotLAvahVArX7/Noxf6tbBtaJKbeLhrEATus4wgJAntYWljvm71AjugwZDOIqCmW3pzTL2jZo0qOBynos3LyG1IJ/9qHrfOBCZj6bLAzb2+a6nbQoJRMYrEY4HAmlxsCXXzAP9c2ccLruWj/nRGbZ6PHXEgWBaZJQJKYMgmPgLgR2gIHb+9tdHJzLFz6MRvB+9fVES0/DtSeRIuCcvpdm5JOEJlzUxIE7DbzYtQYeyw59q6M3Qo+ECJ1vB+BbSCiXPgHRNJz9+kEJRRVIgpIUOAlW9MzmfI9Ypnnp0VuvZ206W2Q9PIn2ozIVvyxmVY8D7y6aDfXk66FVAfgkCqUWRmjC9tJKB+wt7fDKvVlSSI5hJtkcnfIo7MIIla2QJrvLdGnQik29jcCFZblolF1EKgyYO/adeiO0T1ZAVrOm7pEHbWQ4RZsEqTRPXFwh++SDRlq4+FH6VgNvlAzcTBwvsePZzMBLCWXF/G4lFGeiddx83rxPQ/Uogra9rAXwvNLA/bhF2D5WXQ+qkEIroeCAfzuNtWc+t2yeLLt4Z3UG5hrm7qBpSxs3rP24ZOB6jxLKhm7R0zE67SI7i6zR5p3fcj8regqBNpEGRX/bk1Y3K7jveKv8+2Wl0r/06UEQzfvlBqbXrpixAFcWzXqo15mZT15C4cH8fUSBR0IIeUJMRhbY71nWNPDYFu6y36lKO6T1DfSFj3Ct9Xo+od5IUPTgS+9nuXkfGSGNwCcLahJK4O79LqNQtKYpO6T2Gs3QJyEaDD3MOlZCeYXZcDbPg8ufhjMPGCJR2yx8XSCFf8t88JPKIX320m9tMyzNnFyk3ziJ9sOyPEAVRlir9rnwrPl54nWDDPyASsnCkQHwnFbk04g8wyJkWum6QwTwbio5htm558VG6dD0kEj8MixN95bo0KQd+Ugvroou5TVA3Is5AJ89A1MntzxVNHWfImijwjZt+ix1U97+i4/S/+gvmCeknTLDseEYuJ045UTdxHz7VgoqNXDrxHQWTFkAz/o0SVDWix+UGnjFwINGm9aUAQCd17R1qCIHXHhj1kMqzdNXNzEy+7lV6wSeA/A9M3DXwioqNzC5RwklD6fpE5t70l9hRU/zyuMtVrUFvetPmp/bJHB1U8lXnJou/w79XWjg178In/yFwceSdXjXH4Mn3l09Vptbcu0K+aJJznKtu7JaZMVZfY3V6G4IYkLfMlrHwEsAdxr4ggFGMN+pLPBkwqWbtc3/8qd5pvnVPK1eaR5bOseN5muJfA/PExTBFL8n/yifVG/Es6c/obIKhHcC8KyDhyILLYBHPqkONwG4Y+C2e+PmKJz+KkyfNhtRul6GOgobyufmlCoB3Hy2x/QbeNXih7eMMOplkmN6jaJ5EjwL4FKVTkyla9EsixbAT77OjNOuFz1h4AXTjYDYN7qYJ7MDYeD9THJMmEU8T6dMkxVaomo7uNdfpqObtOIA5UcVA8/3y8CNntdp3mMZ+K0A3sIAuA7bREJy6eYKf4KPM1tYtp5ukFoAd8XwhZUQSgAv+gM9BjsbFrC6S5D3+ORLfZ6ztzXXPlHbALJIN0w8uQVgF5JGkZWda8K4zcz09MB1RVq1UzNPcjpswvufvMr3/JtHeWm5xsbtyUO1jpeRLqUkolQt1Xt7AC/LLPhxKcPsugWfJQXx1Dw9YkTew+svscI0Z+ZbrLIJwLe5bictOHusRRSY5RUMaODbAPgnfh5+/+8PZisuPm9yEOrNou3cuqjuwtu4Ru/68+bjuubTNQB/lbjGSsOAcugLA4h5QkxO4WL+6wA+dRcETeMbsXKbdv+/fB5UwRd5Lef0vUhbifNq4zXEof2cQcD/lv8tPq7ejO/kCllj4OkOEooF98LKMI3AN2WRtwLw5q2F08yLE7NxN2ZMTLt9X19LlAiq+WBPTmFq7uV/Cf+0SdB57vdvGdZiJ2Va9BDNWbRvIqNyqQeksFIHX3jWgPbsGfN32xCgO56Bryc50w3TqDcjMLv6QTDwrOC4MBN3TnQqBq4lWnilpuqnq3Rp0I58lBdVcbVlc9+9AfjG9Qv0ifnnH7qxJYBLpWmToMIpdGxA8sqNBX4w+F3Oq9PmSdlG6fBr2WgPN2GDtDaeWihhYDvPd1YW0XmP51cUFztmSvSJadpQLqehayuvBDbNmSIh6zvdfYqZWcO0V9bW6GeSpnTdeCwDD1yJ0D7nbnbQGi4sVqcC2VmgryOixlQJ4KUG3l8uE6p2AvDCSi46aOwjCsUATGP6GH1tADxIV1nRU7xivsWKY+DXHAO/9bq5VGSFoh0H3DNrPq/rSg87MPArj5t6K/VrLtrOM/XPa1niM/qVeCojuGpCGR2JqGKbNfeJ66y1TKccIQSpiPGLrumrahl4CeCdBa4U02yoCPI+ed/ci/J0aXXjhdQA69XQXPdy9GoaoQFzl23qCUoGPqiB78DASwA389vzBKmIzGkbjGO86BsJxdVo33w9mZo55qQgq4P7OkfWANxJKGFq7uVz7bfSFy3zHWyyxY2UGXqErTm0X1X3rEtVZT2UhWfh5FdUzRvapgewvtMBXGs41o4sgIcm6sPtvlvoxdvajafh179/WwDoZUWpgc/TKZN6PC1R+OWRPEpXDAOPArRf69Be7B3As0Lxxae/yBV1nBsbaQXgNSaWZilNkaHCNsKCaO/KM7zJu8jnT/3PZNpneWWZzHbGcRKHm7BhsjWARxbA++uLkPfpqJCl3Cy8HnGZTOE2SWHlldAxcJmSWdYfNaeYmTFMe2NjjaVuykxZC9wycAsiXtEv08zrDFxuLLDEDM04LB2lhSsd7PRvtuhX+YmfL+u8OIelF0T70sBzAuZmpo2OW/SIsxVWmebu2catDFymt9T3cGysHQecnjXfQ/TBf8SZR/+u+TxbVSNM1iqwrjPOrQDczq0vacOs292LwK2NBk6wzozo02m/snxpRkScm/ldhA7Ac/MZujf5/ErEmgwp0i7ra66BwaB/Zzkx4PQs9wHwYvAqGpaBR4H5v8D3qggoldWiUG7PwLXb7MFEeLn74WS5qFURgs2MvkiNo7IEcLsJ6cKcoB2A200hzlYNUWlPcy78CqOhgzlZWSfpytoqgVDE7TnwTWhrnue0i2V+NPgNfGQVSrjwrNG/rWnryNT+wfTDhCMC4P/uL72V//xDX4/vCXJCM6lKBn77zLjSPvdr8NzvmQSWLaybVhLKnNgo0+o9jITiADwu1gwDj33jmXZNHvbBwH/+Q+doJde57p008e7tk2ax1IA27drjbNRGNKxGeMNMtj/6jd9OlyZLS0vkLuTOgp9nx1t2hocBHdx1ni82FhAyo6djFlOz8BIaBC70zAK41zCLPgxjlBaGgdusyrjVZm7GLKzOxgYr3ZwZ0TMeeDsegEzEeDLh4lKPJgmXVypwUt2bLOkZ2pGPH9ayPWEAwAdKjHaXjPTwlHFCuWxUHcS3MK7bWrrOhm5ycrph0sGLPnG+RtefIQo8UpuZOnDq2+TIdI7vqdjnnrkmc6KD99iDNG+Y+P4tGfiVz1S/57cDcMvA1dmBS7j6JlmhmG4E3CdM+rerAw6QiYjYnoqkZbrI1DDVIuELqzGJjsj6XTrrZs6UTbztPVxIzPjfk38TPPC/sqDnaASDDDzwRAngA3Hgu2DgdQDPvbhsPlHO26hdAXT9elobAPfjGkO3/U11gRRhLTLGfJZGscaamGG2GfKUuN+EAOZ9rrz3H9L/ua8DpVhfNd91c3q+nE8yT/iq5AneGfwOrxMvmZN6sg4bV3l09Tj/7HefYa2XI5tGQrnjNfC6SS/EUznS9c/r7YGBv/CI+VkDg7r1soLjpRNzEwMXfhmW52tZMnARxESOgW8F4FrDR/6l0TO3sI+dW+Q+fwk5fcZE29hjV11GSV2N6ngaYSWUe9ZMyu49X/FWOjRJe+tI9/5WqnATztPShFdBFYmiZCn9+B2z2PvE3MwsgIu4BHCvbwHcvncY+EbKKrKyH2ar2ebYnGHave4GS92UWbqouFqQYOrJ+DLhzNLH+Wz8Q6ws1BxR3SWW9TTNyCeIrAbuNO1apmBWjyNfs/0MLfOW9vle0Kgxrt0BuOqvsqZbzLciEho08lUinZIE5jN4jVkT1VG3Tdd2x+l2HPA1r5zjHbOfBpki7L3eUgOvH93rDlo3Z2qPqZ5j4BWA39RzZZGlTCpmmyGvDsy6SKer5+UioinN/FaWgXsyK+faDTlNn4g86dLbMABe+ncsa+3JgGPtiIeS15P8sZ/i0nKPu61UNADgnmPgeTX+nTRw9/06fRsoRFz276wAfKpi4HUAlzmgNzFw8/8hBcqrSyjmXjXyVdbFDHPNiCfkq41ufvWzTD33WzTlBmvrK3TXzf1uzxwrNyWZp+XGMiesr8xutv/xfMyDj1zgW37qQ1wt7IktuMMTeeomhYm7TjcMExF5b3flL3vLVVH+LYoAgfU415yYPZtAZDTwoDySAyYKJfbRQVx6ptVWUSjJKnzoJ0yizha2vLrGrF6j0zhtEpYcgNcSK1zikIin8ZoGRF+XPc0NcRd+a47Mb1H018sQOgfgLpQL4FJhJ3V2a2XCZmI2tISIHua1qWiUyR+B1Qp9y8B9TxgHk0yRSZeejplqhLTaZmxJr8NKL2NGdNFOPrFWeCa64970PA2Rky9XDWW93iJLzNKKgpKBO0adrFZA73R3wMRjQ3nE165LUJ2B7xLAZbJBlyZTjYDcbxDZjNckMvHwM624LNhUvWjw2p2ahPL2rz3L/z5jGvB6rjP6JgC/vpbw7Gc+Uj3g2KrMyxDB+vxO1xdIdchqfE9Zt/w5dW9JIrJCEQceZyML1FN3V5f2YprKMnC7GXs6L9fDIrP0iZFpl37HgF9Z0tdukCkhb7rHzKXLKz2eu7HBm+4xgFoCeE1CCWVtbe7AwNXSeVIdUEzdWz4m/YigZOBm3O/5/LKV+cSgJOOkliA2NX2g1MA9XaBEUK0H+521izU6/gxzrZBPpK8y//eJn2fWNtW+fv0afYszQWuujCOXRUZox3WMDSOb2RDC59S9/Lu/+FaEEDzqQtXvdAmlbtILCXSOl64itXUW7MaR+eKjgF08tb6LdUv6XdoiRcWzxCIv5QEXhUINwHs0aAQ+IogJhSTNcrL+VgBuJ9kWk1cpTdAx33K/dY9h4FO3MvDCVj8U8RRB0yyeWdHlasOUCFXhFKQbJrUdymgPr7bzv5RbIHUMvJZFOGMnbE/HdLR5bepVAB5bCSay7w3YaKAUmfXoE9GOA6NPAll/g6VOxjR9vEb1GrAFwVRa1lfur9vvTmuCZIklPU078stIF9c+r7NY76xeZ+AOwM3zXK14ETZKCWnbKJQrT8C/eWv53ai0R4+Y6Tggt2UDAArbkGKmGZY1r8sORJuSebqlhBLA5cfh5tMQtqqCaJsyMR9+5jrHVp4sC4eVbHXloql7DQMAnm0sscIUbzl7nBuYMTyvz5R1Y9JCEQU+9wTrdHVcbrpgGHhbm3unwxYKYZyYNoknnDlFoiN01iPpWva6CcAzQt5oAfyR5xbJpS4BPbKhknUJxVXOlFpUNXm2MLnwHBf1KVqNao1JL65OAJZwvO+ZNT7w9E0jkwwwcPu8oHGLhBJoiRIhviuz7ABcrtH1Z5lthVzOZ9CzrzDZ0NYWb14n67hQ2JkqszdLy41lzp7U9cKzZAScOvs6/vib7+bbX38Xn12yNeTvdCdm3aQXEeicIFvjsnZgtwhf+E349T+3/QtfeMSE+ITtbRm4K06vT9xvftqNwUeia04QgNRr43miTDhJ0z6BMuCh6rp8Wou13mRL3YxT2haOmrqHjSQvHR91AJc2IsCLpwhrgLg8ZRwmIp7Cy7tV1prd8V3cK8B1u9hLbd0uiI5uEGCLdflNetj4a9EsnaGt3C7mVgUGmQjxZQ+d9egTG8AKGigEedJlpZcxJRL85iBjlUGTJil3WQD3klWjIWYdPJmypGeMhFLG7Frmt3qNnraySl0DXx0E8DLLLoirjNHtShtce9L4Q2y9D5X1SHTEVCNA+hWAy9gc62ebIauYz/OCtsz2FgnFMvAogOc+YBpBvO67SzDezMB7Cxc5KdZ4TFrnl2PgTv9uzA52S+ots6qneOCV81zXx1jUM+TxPD4KZEEmFVHgccpbZ1HPlhEiYE8/1kTQoCDAVxnry2Y9vO3N95sTWN4rSYOrvucklEwHvPG0mYMPf8mc3N587yYGXpNQYgvgS8wiss62xcjE0nku6HtoRUH5mPLjkum6+drTDZ6/0TH3Jd2OgW9yYloJxdsURjil1ukFs8w2zVjTU18NUFajXFm+Se7ky8Zs1YmqqAB8HpMvsnb5GV5Up/if3mqcxt/6+ru4qaarMR2QHUEANzcjkAkvukXUW4JnfscsmO3StF94BP3Kb0BNndpWA3eA7Z34CgBEfxmlND7KAHhtJ80Dl65uJYd+tzxuOp0SuIWBP3t9g7/8rsfoZQXX1xLuFTb2eeYMSkM3tEBbB3Dbkd5vThO0KkDsHzM9OYPmLC3dJ0v65CICG3PsKsIB3NBWW9wE4Nf08fI5p08eLxl47jcQfoTSgillO6W0qs3jKqeYT15C5wb02nEAQpCLGJV2We5mzHhpGTVT3uOgSUNUTWJnRdc4Mu3nXdKztOOgZOClU3LjBpf0XQAUW9WFw1plJQAAIABJREFUdgzctXkL49JnsVW4n/mQvYGfOu+T2NOEDKpMStoVgC/bUMIXlJl7clNzEec3mYoDMy+b89CcLxn45lT65qKJaPlI+hX2c1iwdgB+9x8Z7JbUW2aNNm+9b54PybfwB/IBZm38PUVCVkhi3+MEqywwRxxUS7wO4AQRhQjxdMHl62Y9fMMbX0PuxYiiT2FJQ7TJiZkRloD9qQvLTMUBrzxm7lUY3CqhNJT5rhb0NpEjALLAX32BF/TdtONqw1FBo2o+YedtlwbP3dgwTbLrDNydhPxbJZTAAriLTRcyA5nT1l16wRxzTbNO1o+/BYDfKL4NgI3VhaqeTzyD51oVFpnpfIX1laWSjcXL3OQY3/1HTFjv/3D/CW5iyJiMBk+hw7QjB+B1j+4FbWOge8vVhF+/duuL1q7A4nOca38Njy/HZGuDEorOeqg8xbcOUXHSLCY/WUVqvSUDd3VBXPebotbNXNQlFFeTwUbNPP7cS0TnPsDTV9a4ttbnHrGIFj6+rV62kQuz29cAXFnZI2jOELVqTsFTbwIgbs/QFgmRTgfqLvh+JaGUR/5NEsqCV6X8nrnrWFkytvCaJuyPkGm9Yd+neu8L3is51X8BkXXpExnAwnQtIjMAPiWSgdR8MPHZTVJOe+Z+zNLl8kqvLNu6xDTN0Cdy2Z52YQb9BV6yJ66BEqOrVkN3zi7LxLyoWZ1AHIBrDe99Z1X0yIUjOoDM++VpwpVNAPBsSYHZZsiSNN+7Iw9p2jdJRr/2fXD+g7UwQt8ATGMW/LCMt74lCsW2r8vmXmPH4Bj48zB1ypSxrTkxvWSVFT3Na05O8Wvx9/H3ix/k2KwtEpanZIVh4HN69RYGXp8bXmCakXgqo+gaOfLUyePooGkaY1vSEInCNv6oNPBXzLeYigMKpXnD6Wk8G+M+GIVi3qth/Qg39WCdnAFbu4RQORf06QEGrv2G0fa1rjHwmOccAx/QwF0Gbgyeb+Zd4sIIjYRSEhqVl9FrSTjHXMusk8tn/xQvvvn/4L8qUwo2WV9GuKJYjRlEWEWhRBbAj9mM7ShZgqm7mGmYa800Qubu+0q+L/0xFk9/062feUh29AC8xoJLBt65UYUGblUn+FO/AMLjk9HXcVPNkq0Ogvy5f/UtfOaXfpggsXqsZeBBtoJUmgCF9gYB3Hnxna4mu2ZCrOo2XrpWVczbJKGceOkD/FL006RfeC831hPOiEXU1GmmWoZxmkiUuzYBuGXgjWkacUyiQxId0r7bjLM9PYepn5cjaw4TlwwDhgEpRMlkcusI7DcrJ9fx+TmaFqRd5cFMROZ4DjTbFfu/GNxHpBPmOudJiMtYYBm08JUJD2zTh3gQwAlbRkLBMfAOLy0PMvBW5Jex5s4p2c6WWPJOUGhvsJbKJgbuANALGvheYMIdnYSSrpvGzudtuy13GrEALoqEREdMN4KydjRQ1niZaYTclFNoBBf1KXPJxNaDef734fwHB5yYJYB7QdVYd5MGru28eP39RrYr/RiLz5l5GDYHGHiYrbKq28y3Il51os2pmZjI1r7J0l4pocwUyyxsBnC/CucUoWHgvsrQyRobtJhtRRC1CORgGGte67zUaDSNRDNj5pZzYEJNA/dF2UO0YuA7ALjt73lBnaYdVeMlaOChzQZsAbxLg8VOShZObWLgSfkawKbTr6G1JqBAe3UNPC/9ZllUAfjVvM0j9/4gi/a0kG4sEhYdE3kUTZU+FZ1nRPZkcMLrst7LmJEreNOnBj7Wt77uLh7TbyDwJ1EopdXTUi/pu5B4xhnlWNZmAN+4AY/9Enzl9/Gl7C4W9CxBfzDTcSZf4PTCxwhdyrkF8ChbQ1kGbrzYFSAqyyx9yxSlZeDX9TFT8Mjt3MngT8/KN2946meQVz/P93ifwjv7dUw3zITfSHJU6wQvXHyRxY4FJcuao5ZZkB2aPKtfwak5I+O0p+eYIqEhsoGkAb+24WzQok+jvFbm+mTO3FM+5/j8HC0byy0tA80wk6+rY9pxNRF7c0aznc2um2O3zT7TYYsWKc/f7NDgVgYuwiZ3i5XS6XbM6xkGbk8/y3qadhyUNc217egyrTfQ03eTEKEsA5fJRukwds0sdOG6EjXxfY+coJwbToo6d9ls4DeWzPfd7dhoBduceSoOyjoW67pFu2XGMtsM+DX57Xz4Df+Ero3WydJ+BR4b1+mmBYEnjHRRMvBoWw3cS1fJREzQNiekxDnCN66ZsqhBs5J6tKaRr9MLTFz6D3zDfbzzm19TynhZ0icrFA1P0SzWWNSzAxKKrGvgYdMCeI7I1unQMmVvoxaxThA1mSpJ+qWEMjtlNgsXOugcmFAxcN+rJBRXu34BF/q3hYRiydcL+jStuGLgZf5A3i83FHdCXFOtwfriJQO3c74xA8k6UmnTlcoLSgAXsqpmmkVzvPauKVqRz2MvLHNxqVfeG/prTGOKyCFE2cxby5qE4nV48doNmiKjfez0wMf6rjfdTSP0uHe+yUHZbQFcCPEKIcSHhBDPCCGeEkL8Dfv4MSHEQ0KI5+3P+QMbZd1qAL6qp1hnypSRdLYZwD/2s2YBf/Pf5dpaYhIPZGeA1QRacq+6ynz/BbPg54wjopGvUjgN3PMHwvK0jasuHWUWSEqpwskojoFbCcWzm8Sx9DLf/9QP0RUtxJ/4Sabt0Ws9KVgRsxTrN/nPj1t2mW0gtSButokDj+fUGT6m3lwuIr85gyc082yg60f/GgPv6CYdGtVCsADeOF7FCZ86foypmeMs6FmWYvN4Lsxn7tEYON5On/1Kw24xsd3ORNiiSWZ1wvxWAI9apsmDtbujZICBL4sZ4sAjKhl4Rn/VSF7N+dP0icrv7h+8+wPldVbWbaSOy8SMm/iesPXjDfi40LjllZWBn2sb5nFfmubMU40AYSNqVvQUM03zuWdbIS/q0/xB+K1k2jq+0uQWAG/HgdnQEtsb0Q8RWiFQyJoGLpUmytdJgxmaDdcv1AKnq/sRNitZJe8T6Iw8NGz2T7/lXv7qN76qDLnM0j5pocpQ2BVvjpPTNdKx6XTmAgL8bL1sh+bHLQIkjaICxyztm6xbIk5Mmfe6e8bMszoD30pCcU5M1yiaZA2e/cBg45Klc2ThLMtMDzBwUdbOSSHrIfF40xkj+S0VjU1RKOZ7/60n7Sk6noF0A6k1IdIAuF2/QlUMPI/miQOft736OI88t8DFpR5nj7XJollm6TAteiirYbv1X2QJDUtA5ulw/aqR8eZPnqFurzrR5gv/+I/xNWcPDhp3w8AL4P/UWr8BeBvww0KINwI/Cjystb4feNj+feBWZ5jrtFjW01XT0bA12EEkT+Dxd8FXfT8cf40BcMsEdC2U0Lee9jd1PsGGNwNBROK1aBVrJtQPCWIQwIUNz3LNW91R+JpzFloAV33XULgDsiBKl7mij/NZ/800VJ9/O/d3YOokMyUDL9jw5zgu1vjDpw1bD9I1VpmiEfoEvsdflj/Gz/LnmbdHPweSx8TGgMd7kIE36ahGqacXVkLx519RPufU8WOcnG3zdenP8+T8d5lbKFxqfaMszgRw/5lTXHROxRqA+402TZEa+QRukVDciQUAL+RE0OPyqtHAM6+JF7YQQpSnHSFTblw16eLzJ8+Q1mpE92+aWtgbullW46NIKLRHEIQ2c9cvu5Jn9pSU283LxZPn/S4oRaAyEkITQWLLBqwwXW6uLlrhwkLXbAw4ecG+98Y1Oqks/QEDEgoQIgcYuCs3UMQzNGwBMleaoKwOGTYNOClZssZiU2x9WXYg7ZEViuPazL2/92e/mRNTdQCvvicviE12os4J8w6JZ8sk2Kih6aLy45hNKiMj5PiUmVOvvWuKuVbI/aeq77dyYgrwfBSi7N9aOjH7K/Bb74CP/kz1AZaeZ7V5FhADG07ZgCHrobMOXd3gLWfnmYoDrmeRYfNOkrLfwadestJPPA3pupVAJdoL8XyfQnsmvd8CuLSJQ990/wleXOrx+MVlzh5voeM5ZkWXGfpl/SHfauBFntKwcfczbDBj79Vd91Zrqbwn/sGKHLe9utb6mv7/23vzKMmu+s7zc98ea661l0pVKrSU1kJVYhVCICSLRZIBA8Y2yN0+YDfghW6PYWxsw0FtsHvsGfsM+4EZ3IhlbOwBt+22jQHrGI1lJBBIQhuCApVUqspacov1LXf+uPe+eBEZkUtVruT7nlOnMiMjI2+8eO/7vvf726T8lv56BngY2AXcBpg+l58GfnqlFtmFDClNyTInpS4eCbbwpLUTmVXgs88odXT+CwB4ZqrBpKXuhieOKXUbxkmaRjfOFLO2riZ0hijGU8SpAne6PHBTEZkSko5WH6ebwJu1zoVAc4ognOS0rPArjbfz6977OLb1WkDlGIOa/3lGDDMqZvnuk6eYmGnhNk9xWk/+BjWtZHu1kNoWJuo+JqY7qgXSakaAOgVqBGlKYqgnw4tqp3AiKJbZWg1IsPC1EgqFeo2G6Fz8AJfuqPKorgaMnc7PXL9EgRYltGrsUeBD1UwQdvxCRkQtVeCzznBnC21ZhNKGuM2pZ1S3xq0799CUHkIH9UZCdYP7ETtURSHKx27h4liWJvCOAg9NSwJ9E4vNzaxVSwOFkRVgWwLLELgsp4GplMBPZgi81Wmp21HgWkVmgpgAvpV0VWKemG4xJGpIfzhtHBa26rpxU5NjDbvTwTFspOeUmTBv0FHgKog5ItWNqjDSvaVPMp+T7RWIhaqp8KMZWrq5lZlbOkonKB/qXUYLVYUJ8EvX7uMr//nFXQTVlQcOJMJJ885TD/zH/6asj2yg/9QTHHN2U/Ts9BhDZ3Rfu9UgrM9QI2D7UMCztpY5WncB2fHqNYH/aEqnPWoLJUokrlAeuBDqfLDiNlIHzc3UqRddqALkk/WQ80eL2KURhrUCtwrqnDVWVdRWdiVAWdbYIdSNNRjqPt6rgSXdHoQQe4FnA/cA26SUx0CRPLB1wO+8VQhxrxDi3omJc5+eIzIKc4qSUuDAj63zeLReITydJXCV3zrrjtJox5yph+zZoyqunnryCKAGm7p0clNrjjrRWs4Q5WRG3cGFykKxM2l5tu5Jkk771idkR4GrCyCqZfK/m5MUoykmqXI8GeJL0xeyo6p+v5JR4Cel2rKNyBm++shx/NZpTlEl0AoncC22VzOEqklynKlUjUG3Ah8dHacmC6kPHOutujXcIXCcAtv06/q6v0WkLZSmyKTVobaH3xeKwLMZG26hTFG0KAlN4D0KvFDKfL/lYkrJLFONkGT2BFPWCMXMFrotXLV1n1ZEvWX7Lpr4WJEaaLE1OUEsHJ4RW9OxdjJq0sRTuxVN4GYqeWx6Y0Q1WlGcBsbiVj21KYwFZQUdBZ5aKJpcJmZayiOlR4G3Z4mb0yqAGTaVcg6GOjnRVkyYCWJOzKh2A1ZxhFKxSCKFKlLS6/r0vRM8eloTUtRMMyesYqfcHMD2DbEoBT6caHIsd1+S2d2r5fgk2kIJ4hqh7o0SlDrnUvo5tBokUYumdBjTit5zrC51D1kLRQe0hUNZqJtb6oE/rlu2mrqIdg2mn+IIO9g5nBEldG5MrWaNZmOGuvTZVg24aFuZH85mdjmQfgbH60JVNOue4HHcsVAAdT4kIXH9FDXpp8Oe928ppd0jzx8r4pbHGBI1KtRxiorknT4KHGC/pTmn3B3EXA0smsCFEGXgi8BvSCnnn42UgZTy41LKw1LKw1u2bDmbNXZDE3gDH9v1OKMJ/Am5i2fkKDIzK6+uyfyvH4t4ZlpdoJdfrAKUJ59RCrwZJp1iBaCh87Bb3jBVOU2oFTjC7rp5OHqr6erpNJ0gZrcHHjcyPl1zkmoyhSx2cq+Nj11wbWxLnXzHY/Xal1ab/N0Dz2A3lAJ39AVSLbjsHs1YEVqBByKkkCm2yVo+u7eNM0vHQklaszSlq7JObE8Fy6zOjcEEvyKdeta2ugMxjm1RG1LHMuu7C7dISbQpGwvFm5uFot7wKJS3E5jeHJNHOWWPd/nsISr9LtG7mMrIOG3LQ8RNZlsRu8RJasF2QsvvlFyHSoH7jhoy0Ja6/TCditYiTR59ZiZtp5u0651AoVa8rm8UeMdCqWbU4VBZp+61W7QylaFe44SyULLj5ExOtNXtgZ+YaVIVNdzyKNWCSxNPtUPQa5lNPP6ve7TVF9ZTkeBWOucPgJPp3NiKE4YS/bdLPZoqe3P3g7SquShn01zlYrFzLjW0rRK1m7RbTVrSZbw8uKqwU0qvpw8Jh4oe/9cUBZqi0MmuMsdHj4R7LNzCjqHuXZ6tybXdrHcp8Iu2VXi6qdbx748eATrZOy3pcuRkXSlw7YE7RGn2WoiDlYQks6rPuznPhRBcd5Hipz1jJeziCKNWjapoYOv6B8vrHGc/Q+AXiqdUpkrPjXU1sCgCF0K4KPK+U0ppZg8dF0Ls0D/fAfQvb1xu6G3MNCX2jBY5hTq4D7S2cUyO4YeT6ZZ29pQi8G+d9jk2pR7btmMXCYLaaeWVN9shtpA8gVKiLU99CJE/zAizzDRD7aF1giCzMkgzMtJhsfqEPN7jgcvmNC2pSKk9e5phZigMdy4s03JUCEElcJhpRjwVKnK44Tz4l8cm8NunGdnayRb5yM8f4l03X9I5JhmVKzIXqfFeZ2SBi7YPqcwJveWU7Rp1fGVZFEZS4jLpYebEjo3StOdG0u0dl6svMil3eIrAUwXeS+BmG1/ZAYVhvGgWmxhr5mlOiPEuBR4KNbxDNiaJpIVfHKYtAuyoQ+CN4k49l1SpbBG3aEl3XgVepsldj02kNo9sd/rpCL1ttwsVWtLlGTmSxieMlQIwNqzeV9hu0Kh1Uu4KrQnloRtlmPHAA6vbAz8xrRS4Xxml7Ls0cYnbdWLdIOyaC3eryUAAYZPmrFLghV4C1+dg3FZZKEPRaXXcve5dk8x8hrYTkFguHm1KsoHU1YulcidVtKWDpWGrSdiq08ZNLZR+6ApioiwUT2h7crhKTWTmrRpho7OPHq+X2DXcfY4Ze7LdrBM3Z6gTsK0S8LID29ixTand//1vvomUUgVaUVlTPzxVUwo8rBOHbWWR6s8gEkqBy7pqnOZn0ixfccUOAtfiwPYKFEYYEnWGRB2hG7KZ6z/RCrypueIicZS2P6ryz1cZi8lCEcAngYellH+S+dGXgdv117cDX1r+5c2FCW5NSkXgRoF/u7mtY1/oQGb7jBpI+80TgmOT6mLdMVKhZg+T6AIKM/Pvx2VVhdUK9BixYIQRMct0I8ImBsvG1pV9tUxGhkl3s3VK0wwF1Rze+JWtGZ6SKnJeP32MimjgVbemSmb7UEfVKwIP+UFLXUw37RH81CWjjIhZnn9Fh7Av3l5JrQ61iEy5esbnRCgFOkuBC7dVqMsAS0+iVwQeqO1+MJwG7bbq1y3o92eq90K7mwwAtu09wJfj5/PU8KHOg24BnxZF+lsoKdlXtqd9wneLCayoznFGuwg80gpctKaYFSqVK7J87KRJrRWxjTOExW3dPTO0heI7HQ88tVe0fVQUTf7lsQkKOhtGZuZMWpr0/KDAq9vv4874ZakCD1w7vbFtGVbHPAnb6UBhgEJzopMDDjoLRRe19HjgE9OzlEUTpzhKOXD0EN8mNZ0VU64MpTnehHUaU4rAS9VupedqomvpdsLl6PQc+wQ6N/dEChzXJbE8huQMlpCqshGoVDppgZH2h6OwqYqEcLpuYr1wtfJOpw+JznO3jY0wo224uLgV2dIBSG0LHan7qZhJX09fW1FT2Uo1GbC16rN3vMTv/czzACgkNZphQqgJvIXLDydqaTl90pzGI0ptLDVPIETUT3FGVrrSLK+7aAsPve9mdQ0EwxRlnTL1tLeKsVDjqE0gQpoFdRPZK55B9Dneq4HFKPAXAm8CXiqEuF//ewXwQeBGIcTjwI36+xVHN4GXuD/Zz0l/Nw8le3FHVBrP1HGVtRBPP8Mphjg6HfLwMaW+dgwFNP0xqtFppJQ0W0qdeeMX8Mvt3+Cxna8BQAYjVEWd6VoDGwmWg21btKTDrCykgSpD4J5ulB/bAdOinBK4Hc6kPVuaJ1S+q1Me44JxRWzbMydtxXeZaUY8VtM/E6f42GtVwyqK3aqrC1mSdLq3oRE2s7LAxdsrzFLACTtZDg3pK8LMKPChgstHf+EQrzusjqWp3oucuQR+YOcIvxb+KqfGDncedEv4skVVmAb8pe5fMkE5rcABLhEqDevppIfAdaWg056iptPcIivATZrMNCOGxCwURklsP21jYMVNQuFhWQJbB61MForUBVFlmnzrx5MUMbZLI1XghsCLns335F4St9iVfWN88O0j6qKOwyatTG+WoHmCsqnCBPUeM0HMrAKf1b2mKQxTdG1aUmXY1GbVueQXK3imGVXYoFWbJJGC6nB3FoobZHLSgVJ4eq59Aum50cLFsW0S22NMe9120RB4Jshshlq3m8iwRRtX3ZwGwChwQ+SJVr2JFAyVSkxLdWz/Z3gQgVRl6jqz5owss3O4+9x1A2Ph1BFhjcgudAqT9M2/Sp3ZVkSkhVgbhyOnainpJo0ZrcDVZ6D6v7Swp37McTnSReCgOm0C6poALJL0ZmBnqoN92jQLKmjpiAR3aDtrgcVkofyrlFJIKa+UUh7U//5OSnlKSnmDlPJC/f8SJiucw4J12t60LLF7pMC35EX8h/LHmKbEoSvVlv5HR1QfZVE7kaYvffXRE4wU1da64Y+zRUzSDBPautvd1pEK9f2v4NILVUmz0PPs6lMTupTeSRXdLIVUgacd+yJFDqNDQ0zRIXA3mk0VePuEWpdb2cL+rSWEgK2ZtKlqwWG6GfJ0TVJ3htVOwniGpXniB95gAg+FWu/esRI1GaiGW0mMFarOe0XPgdELVNGIxs2Xb08DVCZ3OHF6iBi4ZEdVBVSz3qUmwOdv08G67O4AOj5sdUd6kRywFIE/GY90eeCR5WElIV44Q9NWrxPbyu+uNZpURQNRHCGxXdUzQ0qsuE1bB15tW+WBGwtFaPuoSJM4SShpBS6iDoEb37WgiaLSozgNgW8b1QQRdaYSAWwTZ3oUeMdC8UXcVYnZNJ0Yg2Hl1wsfGTWpZwjcBBWJGoS1SWYpMFbuITpNLGbQRSkcoMCNJYGDYwuk5REIdWwcXYFrSBPAKavzNmq3kZGyprK9SnrRKeTRRV1CW4fCpVr0mEwKyMIodzVUIsE/3PcI6L5Bk1TmWChmIlTUamBHdWRWDGhSrYoa9XaU9o1v4fLDk7VMPxQ1Ucf05I6Ew876o9jN09yTHOiqVO1CIXOT1DcDJ52x2iIgpFXqkHZvFeZqYfDtdJ3CEPgUJUZKLkMFN1XX1x26Av4/OPWU6qPsNk4wgfogfjBR44DuotYOtrBFPE69HaUWiud5/Pdfem76d1xTGTdzRt/BbRxLVfbVZJCeyKa6q5gYAq9yplZSW8MkwY9rnGCYtrRxzqiATWF4G794YB9X7h7uSsOqBOq9tKKEerCN4qIJvAQIQHYFqkAFAtt2Cc+xiAwJt2exojpNoVLmeOUfd+ZN9sAocDORPouy7/DP/+V6tmSzEbRF8pqLfPg3FmWhXCJUQPnJaISDGQUe61JvP56h7RsCD3BbbVqz6sJ3SqNIe1IppSTCiltp5owtBKG0sUxbVk3gjkgo08DVwWsrQ+CuIXDP+N7dl4gh8LGqtlCidqp8Z2XANnGG1oAgptejwEOTYqrJIrQ83KhJs66tnnKVYsHsmBrEjSmmKaaZIAZeoAlck1ihfaovgVt699PCw7esrrYUnj7fyaahagKPQ5VG2MZR/v4AmJFq5pxOjG0hfKoFlw+1X8Vlrzyf03/9PQA+e9d3ueHgBMIpEeKwo4fAXW0fRe0GXtJAZAL0hlQrNJhtRfhhg5Z0OW+0qBS46Uio871lqsBdxkK1Q/9Gchm3OAM0bDZVU7+WScuVYZuANrE/oppnxa35r88VxIYrpTdpclOyRNlXQZUokZQ8m21jY8yKMs3TihAKrVPMOmOpyjVpQnFhlFFmqLfjVIE7bndwxi+ok6VWm1FZKJadKvBaRoGbrJiyrBFKm20jFZ6Oh5AzxxRRImmIElOUGGoopVka2cbF2yu88TndY7EqgcNTk4oMwtJ2VVVqep2XxhkIITqKo0eBx9jEum9LqmBas9hRnbbJ7faKnd/vgUk9k/5cAgfYNVzoshhSgq6dAGHPWU+aajX2rJS4DlhPkmDxo3a120KxPGwZUoxnCXW/7MQJ8GSLUAf03PJIh4h0m89Q33RSD9zYK5ny8HHRyQ6yo0ZaHGQU6EIKfLxaJEYgw1bqvx63d7BVTOoslE4XO7N9L9gJrVApcCllp2ulvpHFlq/y2DMEXip3LJS4McWsLHTt2qBj40VtdVPyw6m+ForxwFMFniFwv2wIvGOVWWaqetiCWBXylBdhodiZICaoz7EaONyTHOD+8nVMSXWMk/oZfvjkk9Rt9dn2ZqEY/z9pNwhkAyfIxnp8YjvQCjwmbqs89Ut3VJmsh8ygm82ZBnVWR4ED1Kv7eYaxNF12DrLFUnoCkOuo7pxmMDRu0LE21yCFEDYggduphVKkEjhpNeJ5o6qCb8bfRqFxHJKEcnSahj+eNqA3W33LDXCJqLdjWimBd18UJl+5Wa/hCBXFti3BjCyofh2GwDXB2ULSwGPXcIEn5biyP7S/V6yOMC1LlKQikMpo/w+7GrjpjFxZ2bV4CwU6NkoPYd5rX8WPKqrPcaqG27M4cZN2n8ySXqTl171e9iAYBTd7Qq0pk9cLwNh++NVvwb4XpyrnPHGcaWeM2bCjfAHVhD9pU5azxDrIJu2AgBaxLsTwyqOd9xypUVeRvjHZltCzLZW1YEedbJEtmTxnO2mmo+E8feM2N5Js6mD2+9GSR4iLjFrpgImwuodtZCwUJ1AXua3VvAfTesrTZD2kpKfjmBtZbKsZkKbRWLlcpaxwGASDAAAgAElEQVRbxbaaNURrmqZdnlPd56dKtZV62n0VuAl2SlWlmiXwYkUHRjMK3NYKPImaiFgFMZfigZvc69jy0xvfQ09PM4U6l64ch5kzJ5ixKoyVvDl2hl9Uz/NPP4JDonZtGSRehQp1aq1IxSJwuXSHOk+OtLVvbZqdOd0EfnqbKu4zTdjmoI8Ct3RvHU/PFRVOoZM6uI6DmOsKIqjy59GNfCW5mrLvMFJUJ+Ee3ZO4VdrJjuQZZiaP4xDTLmxJG9CbO7zj+jgiod5qE7XVBeW63SdmoPNhW41ZrcAdHEvw9vDX+ZPoZygaL1DbKqC2ptuHAo7KLQgZw4lHABgaGWNan7QJArfUPyBZyWzXneFdSn1PHVVKtqd8eg78/gR+5Nr/xshL3qGWalR2exY3bhAtgsClGc/Wmw44CIboaxNz7RODsf2K2PV7spBMCHVMsr0wYtvDStpUqKUkZ3LOhS7SCipjnZFVcQsnaacNz2whOCMrBKGe7xj2V+BO3KRZVxdlUNAK3DMKvL+FkhJ43CbR1kV5+wVsFZNUfFsVqpjZjVr9KQJXts2JmRZVodejySKxA5y4mVbJVqrDVHVQsTYzg92eIXTmHlPf84ikRRI2GNN9UPrt2GwzMQlXka3VuTmVhuYSuLFQkrCFFYeEwuvebfVgjgLXr5/YQXrj+97T06kCf842C6d5huNhkZ3Dc8/Fgr4x7Tr2T+r7y1/Z9fPEH6Iq6tRaMTJs0sJLxdpH7p0hFB6JnisqzDxL3ZztxLjKYhmowAtzFTioPHJfCwHhFTpEnxP44uA5Nr8X/QcekvuoBi4jOi/1/DFdirz1Ki4UTzHxw4cAkKVtqfdt0pRsVw/ubTYJdbtSt1eBa78tbNZSArctwSNyD8cZ7fICswQ+VHDTrBOpZ3AGpZG0WdAM5VSR9SJLFsGYbozzzANqm2Yt8FEZcu7xwN/+kmdx8+UqWm6qR2nN4skmUZ/UwF4YC8UaRMa96FXg88Hx0i370UQRSNZCkZZHMZnFEzG2vqBMqwC3rgpcnNJY2qeZqIkrW2nXPcsSnKZKIZqCJMaJVCoawC8fUiRSF0XcpEVbBwCN6jMWSm/a3Esv2crrD+8mcG0i4SKiNnHYoClddp53AUXR4vq9QaeMHtI0wiFXMtNQguFMvc0QmsADc3MKcJJWWmwVFCsMDalzt96YxYtnSfwqvbAsQRsXGbao6MrHLOmkz/M6FoptCWSmMK0ypEWF1bG9jAKXUQsraZEsMF3d66nENL6zdIL0xve9Y9M4uj3v5aMJw8xytFWYk4ECUPBcFThNZnkguYCDl1/e/QS/SpU6tXaEjFq0pcMl2ytUfIe/e/A4P47HaB3XcwIyFkqCxbERlfrqD1LgwdwgJqhjF6QKPGOh9Mv6WQVsOALPJt6Xg05vBqPA3b3PxRKS+JG/B8Cubuf5+8e4es8w1+xVJ46xS5rNZuqBu143gQtNLIrAY4TlYGfsgGImGm+yHtpWQDXoEHh0TBG4Xx6i7aqTYMaee2EZGLIQAkrj2h8/9p3FBUhSC2WwqjaeJrMn8JNGVwn8IJiLvHc02kCYYGf91OJsF61gjrTVcemyUGyPLbonh62DbFLbAF5DT1UqDKc9KohaOLLd1XVvUlRVe9/GGdy4zkndNPPgsMoNn3VG1M1MtxYo6PeZWig9Cvy6i7bwRz9zFaDznJM2MmzQFh6Wbs3rN070ELj6XMs9FsqQqKnPwAxf1hNokpbutWI7jOreMa36LIWkhgjmEjgoVS2jZlr5SB+id12fSFq00USsbyxN6RIUMjdzcxPW9oCMWqpv+ALDeV2nuxeKyb7BLaTn9o9P1xkfGdaNzJqMWTOclpU5OeCgislaWjF/b/hFXRlKAFZBKfB6K4KoRQuXasHlq795Pd/5/Zt4xtrGSENZKEaB/9DZz7eK11LTs0170whT2E7nGGaOZSgcCrrHufByC2XJ8DL+X9ZCOU8T+PjFytsaefIfAfBHdjBe9vmrt72QPVqlG7JutluEYaQf61EXeruZtGoqC8W21QxMfW4Ws7MGhbm7K6/vmBxDIuCZBwEoVEbTUuW6M5jATcBstOhhD2sF3pyEAZZLF9Ig5uCLLBzeT0u6yKPfxCXqGlowCIYc3UJ/4piDtPpPDrZQstBK5ymtwEs9CrygmwZ5WrVZes2F5on099Pq06iFJ9tdXffOmB4ctZN4cY1TQl9wui973RnGl8rHbkmHckFnFXkOBdfuLpjqQWSpQiMZNtVN3Hi00093WslCSmQVRzLdiJBSMt0IGaJGklF6wi3gyxaENZq63/hYJaAlXWqzM5RlXU1H74O28BBRq9PCoE9Q2ncsmnhph0kzIrCrQhI6gUytLmXUxpHhwgRuLJTUA1evb7kBQ8XOTmbXaBGCIUT9JBXqTMrynBRCUDuLpr7ZJBffMvfnxSHlgbdj3WzLpejZbKmo67BV3s0Qus2wvkl+vvwm/s8tv6t64cDgNEJQn5+wu4RIhJvGsiyvCCN71fMKq19GDxuQwM0d03csPMdKfW1TGFMeHucIOxlv6oyPsZ1zXsPYJa1mk0hbKHbv5GjXRMCVhSL0RWia9TuZG4lJWwttn2rBIcShEWzDmVTpjKWhkVRpttwRBsFYKONlX+VJGyxFgbuDVXW1XOBhuYfkR3cDIPsU5/TCZLCYhj4LIvv3e3PA+0FbI2Y2ZyFL4JlJJgW9xTfFFMXmCTWgwvEy7VRn1Y0p2/BMmHSyk/hJnUlbX2g6ONxwR/BpEbdqNPHSz8BzLP72167l557bnSmURaILjYhaRMJXmTUAJx7uq8BLrqQdJ7SihMlGmyFRQxS6CdwjhHadtm7RO1ryaOBRn5rAFXHXWLssQt34yzSP6qfAPa1o26ZCUl8HNWtAsZXxd2NF4NlunP2QBjFTC0UdS9srdO1kdo8U1Od+5ggAZyin4qoXbeHxRLKDK5/9nDk/s4Jh7YFHELcJjbevYY/uTb8281EtSxAnklaksoEGKnBQa/QrXYH4EKdD4G4Az/0VePs9C1ucK4QNR+AmiGIutFdcsYMv/qcXdJ0APwguBVRe7ujI3Duj55tJ8k2iUJdg9/rSxmtNWmk/cFABmt5IvCHw2A7SreJ0sENt3YHq0HjakjIMBt+pUwKveOrE0ZkXiyLwRSjw4YLHA8k+rOMP6t9Z2OJ4evsN3N5+F9bo+QuvAToWCizOQgkMgWsFnj22GcVXrCoCN5WS1ehkWp1pmTafNR2YzCjwKUsfw6mj2CRMOsZG0q0U/BGV4dCaopmZ7QlwwZbyvAot1oVGVtxU+fKV7VDdDU/d203g2n8tO+p8mG6ETDVChkUNK0PglldQO452LW0eFrg2LeEh9HoLlf7nT6hvJqmF0sdqcW2lwCOtaoU+vi2rN1e/oI697dLGxY4aWCRdzdz6wesJYpobl+0XKXkO5uHdI0X1uetGVm9+6dW87ED/zKwv2q/k487PcWD73PcjgqqyUNqxyv/v8eirO5/Vea5ei2MJojhL4PMo8MLwnOMYCVeV1+v3hePPyY5ZTWxgAnfT7w+d360OTw5dAagm8lvKc086Y6G02y1iPSoqG5EHUi+5IFpKgWuCt4XoCrQBxJlouyHhM676UCNpMTI0pNLdADnPVsu8p7RNpxl3VpwnB9zAX9gDrxZcvisvSG8sok9xTi9sv8i/JFfNmz7WhawCX4yFolVeqsDdrALvfHalYXUMTPvP0eRUWp1p6+BcaAZLZ4hmWmgS1Wpv2tHHUmexRL4m9MYZGlJN41kspK1IU8StzizSXVfD0R4C1+dO0dUE3gyZrIeMWrWu3t6O8ffDKaLM5xiKgGJb5TNXhvqfP7HwcGVIRTSUddGHbD3H4nvJ+fzQUrsKQ8it3swWt5h+diEujs66sNwFCNyx2FrxUztztKp3xaUyliXSTJRUgU+rzqHP2runQ/o9uHvrG/CufE06OLkLwRA+Ic1GDStuEYvu9e3ee3H6tbGLbEsQS0kzjLFEJ+WxL3ZcBduv7HooEk5awWt5C8eQVhobrhLT3OXnKyhobz8Ex+EEI1xWmXvSmSBmu0uB9xC4ZREKj4A2rog7BG6JOdVoplpROgUc26LsO5x0lKKYochI2ed41USrB5NxNWuhgCLwiYfnL+IxSIOY8yjwosuDyb7OW1yEAjd5svMd7y50dSZcBIEXR0mEk/aL7ro5Zrbsvg5i2nrNVeqccEyPCl2gZXpMZ24i09YQxKQE3nBHQVgdAi9oa6Zxmhoe2xf7PlEer5O0kUgSQ267D8PDX1Zf9yjwoq0IfKoRMdVQQcxstoMp4x9hhsTpEHVs+2zVLWKHRvqfC5Hw8Akp0yDxK/TTlZ5t8ZbwN9lZDHgLHV84cnvbHRTTzy4ULl6kg3bz2HOgro273/3SlIyLukeLuTFVA5fJesju4UJ3lsc8ouazb3keUsr+P9THVzansJJ2KqQMxs+7KP3aKHDbEoShsrF8x+7qPz4HN90x56GIzt9wvIUtyJXGBlbggy+00nlXUJc+pxnunnKtYT7MdrtNHOoya2vu64VW0JksYzxw2+rKQAHS9CqTo1wNHI4JFZWepUjJsylrD9fk1vbDUFFVuu0b18RqFPhiCDxNIxx8ke0aLvC43NUpNQ8WJvCbLtvOH772ijRNc0FYVmYy+CI88Of+Mj+68eNEWktklX7Xlt1UwwWd92cyewxBJLpFqcgqRdtRKZyawGO3pMgpCQGR7oi8cFKVmM/nifZA2qpS1ElanZS8XZnGXj1phEVbBc6mm8pCqWby26FTxj8iZrriE7FdYAyV3+2V+gcxY8vFFyEVUU9HgPXClLqb+I05vibAnqK8VTUbQ2XaBLrgyF5AgZvXTknR2JKZRmmAGvKbzbOep4+2bYmueFMXUgKfwU66s48ARHGMpinqcowCt4gSSSuMB6cQzoNsh0V7HRD4xlPgzsKKcM+WKr8VvpV2ZQ8v73eHNc3d263ONPs+BB7bQRoUMqRvibkK3BSOmGyIaiYXvG6VEELwrEuuJPqKz0VXPpdB8B2br/6XF6e57WmDqcV44OVtgGC+gp/zRotsH6nwRLiPi6NHcRZhcZR9hzdcMziQ1xduUU2QWYwHPrQb55JR+PLXgO4gplGIs6JEWfdadoPOmkNPXcCOVuBm8IOVsR8cy6LmDFPUBJ54msBb0+CVUkVfDKcIreH5FVkvbA+XEClFZ6jFjquUwpfJnCBmQStw6+lvkdQSCrLR9XmZ0vERZqhnyEG6BZyWboI1II0wtnx86pRpIAYEjz1bHUMzcMFYIrI34HnzB8H0ULdcAj2Y2Ox0Fg2TGOCY68JhrOSpdMBFKvB5YSokW1NqJ9TTDgMhmAl2ETSeSIebOJYgThKaYbKkm7VBmCFwJ8gtlCXDWCi9PSqy2DNa4n8kz+fQ8IDMCb3VCsM21iALBYidQpqWlc1C6fXAjQI3k8yrBZcfRkpxt21FEM7o+fB7JxY84FuzaWsjOnC4mCDJgVvgl+/qzl7pgxfuH+ee+/dwsfNoOlVo2eEWVRuBRVZvjmSGBGTTM00Qs26VMa/kZXYNiQ7ymo6QZiqSyBCNZcGsGGbLjA7cuuXOjcUtpr1PSnKW0FoaQUnbV72myewW/DJsvRSOP5ixUNSnXrATXCKuvevN7EfflDMeuK+rQH0RITLHrsu66JNdAsrG81EeOEH/HZsRPyZLJJ3YVOjJbMko4ki4+LEK2jlLJXBjaWhhc/j80U5aplHgtrf4Ng290MfXas/Myf838Mb3wpNPMFxR16adBjHj+VMIByDJCL3cQjkL+IuwUMbLnsoH7RPABFKyjttN4liPU+sNYqIsEVPZZplAlG+nuefp82xTbq4utAu3lvmfTzokUvQtfV40Ln8tvOmvVa7pQrBs2HHlgk97wbPG+PfkEiJpYVdWqPjAnNiLrN4seTauLfB60jNN5WDT6dxo/EzBidQqzoy1S3QDKdvrVuBdxVN+pUMYXhG30CGPeIE8514Ix8MlIqDdPQlplx5wkZJUpx94QBtbRuxW42S7rITse7Mz8QkrSxQDFHhi+3iEVGhgBf1vzJ2BC0aBa3uhl8Czr2t5BLpwJZ0+tVgYC0Ur8HfeeBF/8vqD+n3o910cm9svZ7Ewtlo4jSvD9DrMYmiHykQpF9Ta944VeWJillO19lkp8KyFYrolriU2HIEvxkIRQvCOlz6L1x7a3f8JthlO2kbqrWLf8na3mFHg6m79oZ+7mnfeeFHX00xTIHPR/e6rLuX1z72AH8mttIJzaDPp+LD/pWf/+33w/P1j/I/keVzX+j9wqyvUQc0EMhepwIUQDBe9OTsbM7yj7XYIJih0XlNq9eqYC8kQeEaxWgJmrEyudVDpePNeuTMwAUgW0Rumd32eiPBF2E2ye56v/jcd6rQ4cImpOEow/L/xCzhZ3A/br+j73pxsX27tjUvEwNz6xPbwRUhZNAZWa6YKXBP5iM4SGRsdfI7GlkdJ6k6N52ihdMHsTs6lAMb06Q5n8Gj3z1M3u1h9E33ZgW2EseSeH5yeP4VwAGJT8yHt1FZdS2w4C6XoOQwXXfaOz7/tetv1zxr8Q33go7CNPSiNEMArUkGlOpksFNNXpQvGX/M6ubv/9dVXcN++z7NnOQY5LyO2VtRQ2MeOCzUPcyWwRAIHVX0624q6HrPSIFuHtIKgQCIFlpDYuirR08Rit/XUeb9bgaephKCsiYyFEhQzlkw/opkHwvHxUTNT4yy5XfkG2HYpDGkBYWYlxiFjgYQIvpFczqkXfJhf2trJCgoyuwE3Y28ZpRc6JbwBBSPGzvGIBtoshsDNLmds535kaSt7Lr1m4HtMLC8VMd5SFXhqofT5PbPzOJdBwPomUIymcay4/41i74tg2xVpPOnQ+SMMF1U2zMBOhPMgtjp9j9aevjcggXuOxTfe9dKufOElw1goUSujwOd+HJZXTIOY1oAGVNCxUNyerI5DB68++zWuIF6wf5zHjs/2zdBZFizRQgGV4hj3pIuZoFnidxR0wXdo4FGiha2rQwPfJZQ2jiZwJ2OhWJboInCrUIG6XpdXSvu+QyeLaLGwXGWh+IS0sznBlqWCmQZCKDJLQkb9BCLV0nW4p1WtlbFhvGKHwId1PxRrgLIGReABbTXnc1AWSs/QYcpbEf/L4/O+R2l7qvc1ak7oktBjoXTBWCiFRVb49oNXJsGimkyC1TPQ22DHlfCf/jX91rEtXnrJVv7qW0+dlQI38a6W8DgHc3TZsOEsFFCpZn0T+xcLU3gTZSyUPgrc9oqUdRqh6KfQNczWzSucZTBmlfHqZ+/iqt1DfVt4LguM4lqCAn/JJVu5/qLu3YpJW8uWmxdcO+2P4eriKN+xaeHi6bF2WW/SsQRTQhFaTfoErpvxwEsEmSkvYomFGbarVK9PmE6GH/xkD+KQUU+RYQs3TatLkVGqQTHr++tA+IA+KKCqKtWUoXgggQshcG2RZqEsBl1DH5bq+c5noSyHAheCtlNOWwMvVGhkcKOu+jwbD9y0yG3Rx65ZA2w4Bb4s0CeWDNsQR2DT2eZm4AQlfD0zUMyjwM3WslRaoayOZcZV5w3zpXdcu3J/wFR4LoHAf+XF++c8VjQN/TPl47YlaOIDM6oXOKolaBuHId3m0+1R4FN6rF6NgkpT9DoKvFAckO2xCFhuQFFX5XkLkZvtQBwy7Kl0wBYuw8UeAs8QnZfZGaTEPsAaAdU10ijlQYFOUCrcWUrfjszONCicrYUyjwKfb1j3IhC5Zba0JvWfW5wFdt1FW/Ac65zywHMCX0toMo6itppYbtPXQslaIpYz+FDt2z4Gj8LI0CI79v2kIyWcc9tk7hpT1sHeXbu6HjftewNd3Rq4NjU8bDSB9yjwSak+l1kZKOvNrMstdqUlWktMC8tOcfIWKorSFkqHwL15FbjIptYZa2ceYu4qepqH6D3Hmr98vAfZdgbBWVsofX7Pr8ALfg0O3Lq01+xB5FUZ10Ms7EXuoEq+w+/fcmnagnopMA3W2mJpGUsrhU1K4DpvO1EBKGCghWJgzRNxTivUluih/sTCDFleRLvaeeHMtVAAQuGDhKKubvUdi9PSVXOd6Qz5BaXYJ2NFaDUCFbjKWCjYLiE2LvHSCTwTuFwwwGe7EIdUXU3g0u1qsapeMKMgs8duEQq8y/+d53mubQ3sO9IXGQulUDhLC6WfMhYCbnr/0l6vDxKvyrh4Gpg7FnE+/PxzF9mcrffvaQ/ciIi1xqYmcIdYzbuEvgo8exGJPhZLCqN+lrgF/4nFZa/RvZTPIU4BMH4RXHgT7Hle18Nt4TOTFCjpXhuerSwUA9/P5FMLwaQwBF5QxRsZCwWgiY9Lfcl5vdm0ur4BtCwsF5KIqqPOtzbOvAq8y34y5+F8CjxLXvO0MOjNtV8IWWVfLC4xxmNE0RKze5YC6VcZ1T2/HX/l87INgYe5Al9D6FQgjwiHmETYWP3IJptPPF/OZ07g3dh9SP07VwRV+Pm/mPNwaPlMx2UqWkkKIQi1ImpJh8DLlDvbglrkETtFZloFKlkC18TYEgEVWV9Ua4EsnOwUp4VISnvgFU3gwgnmZkHYrmpbLOPMYAw6CnYeZW1l//48BH7rVTu5YMsS3meGwD1/iUS89RIYPr/TEmIFIIKh1Ptfcp762cBUcS8wXm61sDkJPKvAiZFiwGHoslDmOVSlLerCW6OpHJsNM84oxG2ytBBlgkvZ/F5LqAb+jeIuJppDbHXtbguFjiXjLdEi6CotX4jAtQde1oU8A4OebgHasz0WysIKvKtPyTzP+62bL5l/nT3oyuxYYqUqO58Nv/Hdpf3OEmFlqkjdVehNYvoerRcFviHTCM8ZtqmMi3CJ08khc+AuksAvfiW845tQWaHKxhxduHPsHby/8O6ux1IFjtvV48KxBFEi+ffnf4Q/in5W/czvtlBMDxQ/WJoC7wocLqjAPYgjSpYe5TXoZmFeMxvEXIQHbi3SA18qrGx14wIDHdYCdrFD4EsuNDqrP2gU+Po4FgsSuBDiU0KIE0KIBzOPjQoh/kkI8bj+/xyy8dcAmrBdEeMQpdOz5yCbFTAfgVsWjM1Ng8uxMrjgvN3sPX9v12NmGksLt2tu6nDR49Rsm9PeDiapqCyUHgsl1MrSX6rHm7XVFvLAbQfiNiVbKXB/IIEXutbW9VifSfOdpSzOQlkqzI2hjXPuMY0VQHbU35ILjc4CZs5nvE4slMUo8P8buLnnsXcD/yylvBD4Z/39xoEQJJabKvB+OeBA90U0SKXnWHW86+ZL+JM3HOx6LE4VuNdV5LVntMgz000m66plQuDpKskX/Sbsf4n6Xd0DJVvUsyjYS1Dg2kIpWorAi4MI3BBxVoGPXgDjF3dXd/bA0f50W/j9A/JnCdPOIBLL95rLCbc0tx3vSsIUNkVL7Fy5UliQwKWUdwGnex6+Dfi0/vrTwE8v87pWHFITuEO8KAWeE/j6RjQgvWvPmPoMn5hQOeKBayuCu+F3U0Ub61YIxSUT+BLsBduFOCLQhWGl4jwK3Am6RUVpDN7x77Dl4v6/QycHutk7oPgcYbz1aJ2kzfUiW50arAKBm6rreKNYKAOwTUrVD1P/P7AvqRDirUKIe4UQ905MTJzln1sB2G6aRjiwq1hWgYsV6huSY1kgB6R37RlVhPboMyrVrF8PnURbFMWlVtJ2+cMLbN8tB5KQQESE0qZaGvB8Nzir/HlXK/CWvcwEroOYvQOD1w0yttKqWChGgS81oLtCWPEgppTy41LKw1LKw1vWU2c+203TCAduObsUeE7g6xmml3evUjTVdo8fn8W2BG6fHOiRIUUCi63kS9FloSxGgYf4hIQ4ajJ7PzjBWQ04MO0D2s7yEng67Wi9EngmYLvkXi1nATMcOenTe3wtcLYEflwIsQNA/39i+Za0OhC2l6YRDlbguYWyUWCmsfQqxfGyR8G1mWlFAztY7hzX6Z9LVb5dFsriPHA7aREUSrzxOQPG1LmFs1Tg6lw9pwEifWCqG5N5mrmtKTIKfMFiquWA/syXOvxjpXC2BP5l4Hb99e3Al5ZnOasHYbu4QgUxF2Wh5AS+rtEJLvUMthUiVeEDR2i5BUAsPU0ua6EsmIWiPHCiJpYbpL255+Dq2+H5b1/aOlg5AjevK9eJ5zsH2cyc1SBVZ30p8AVZSQjxOeB6YFwIcRT4feCDwP8jhPgl4MfA61ZykSsBYXt4IsaRUU7gPwEwTZf6pXedN1rk0eMzgxv4P+tl0K4tPU1uKVkotlLgRK35bxQHXrW0NWiYAF4yYGLP2cIMy1iNIpmzgrZQIiyc+VJ9lwkm93/DELiU8o0DfnTDMq9ldWG7BFaMm8RYg+7cuQe+YSA1gfYbbHv+mCK3gUNA9r8kTSlcEsyNX1gL3+AtF+I2RM0V6Q1iytz37lrEAOwlwFRijlbXaatkx6MtfBLEqpSVt4vbmZYFzhTPrhnWcmNzVmKCInCR4Itk8MVnu52GPLkCX9fopHfNJccFLZSzhVHSTmFh9W472kJpr0xFo74p+KXBQx/O7nX1Wu11GsQE3NLIqmSgAMSFLVzZ+iQT5UtX5e8thM1L4JaLb0V4Iu4/0NjA2Ci5Al/f0AQm+5CjIfBzGsPXD+nEmUUQsmUslJVR4LgF5Z9feNPyvq7Z0azDMnoDEVQRK9jxMAvTitd11kdV6uaVlbaHJ9q4Iu4/0NjALUBrKlfg6xzGm5R9LJTzNIGfzQSWeTHfyLA5z1VphMoDXwE1KwTc+mfL/7rGJlrHBE4wpI7rKsCMo3OXMtVoBbE+VrEWsF18EeGKZP7SY+OD5wS+rmF6dsg+ZLp7pIAQK6DA0zbCiyBw3Q98xRT4SiG1UNYxgfvVVTumpo6gXz3BWmDzspLt4lsxVhLPT865hbIhkG6h+1zIgWuze6RAtXeAwrliKQMLdD/wBbNQ1hvSXcb69Wnt+N4AAA+cSURBVMA5+HMwe3xV/pSxUJYyGHolsYkJ3GNXxYUoyhX4TwDSftgD1PDH33R47hDhc4VlKRJfDCHb3sp64CuFNFC7jtd8xc+s2p8y80S9XIGvMSwHT0SwGA9cPz/H+oVppzoomHVgxwoNnHb8xc1CtVyQCYSNDabA138WymrC0d73elHg6+M2shawPbWljaP5ydn0pcgJfF3DKHBrtcfa2YtV4Pr8ac+ubzXbC2cJmTabAE5qoawP6lwfq1gLmKyAJFwgjVATQt6NcF3DNF2yVmMuYha2vzhCNru8jUbg9gawUFYRhri9daLAN6+sNKXNcbiAhVIEhPI7c6xbhOOX8dHoFrZtfd7CT15OON7islCycZaNpGbdAF7xv8GzNnbh9XLBWCfOOuGDzUvgprQ5WUQQM7dP1j28IOCD0Rv50+IyVyIuhMP/EUYXMU4vew6t55S8fnjOW9Z6BesGTlrIkxP42kIPmSVZwAMvjHaG4OZYtyj76jOsBqvc9vTady7ueRtVgefoQtFT51lxuWsKzhKbmMAXqcCf/za49LbVW1eOs8Il2yt89BcO8aILx9d6Kf2xlN7hOdYt9m8p8bE3HeLFF6+P4TQ5gcsF0ggLI+pfjnUNIQQ3X768nfiWFVauwH8SIITgpy5bP+fZ+jBy1gK2p8gblnWKd44cfZHNdMoVeI5lwuYl8KzvnZfJ51hp5Ao8xwpg8xJ41pNcr/P+cvzkoCuImSvwHMuDTUzgbv+vc+RYCWR3fLkCz7FMyAkc8jzvHCuPPI0wxwpg8xK4lSvwHKuIPI0wxwpg8xJ47oHnWE3kQcwcK4BNTOC5As+xisjTCHOsAHICh9wDz7HyyBV4jhXA5mWuLgtl8x6GHKuEPI0wRRiGHD16lGazudZLWXcIgoDdu3fjuotzBTYvc+VBzByriTyNMMXRo0epVCrs3bsXIdZHX+31ACklp06d4ujRo+zbt29Rv5NbKJAHMXOsPLpiLpubwJvNJmNjYzl590AIwdjY2JJ2JudE4EKIm4UQjwohvi+EePe5vNaqo+uC2rwbkRyrhK40ws1N4EBO3gOw1ONy1gQuhLCBDwEvBy4F3iiEuPRsX2/VkacR5lhNGAvF9iEnrxzLhHNR4M8Bvi+l/IGUsg18Htg4jbPzNMIcqwlzjm3yAOZmRbm8MkNhzoXAdwFPZr4/qh/rghDirUKIe4UQ905MTJzDn1tmWHkaYY5VhDnfcvtkwyOKorVeQopzYa5++0A55wEpPw58HODw4cNzfr5myFoouQLPsdLIFXhfvO9vHuJ7T08v62teurPK799y2YLPe//738+dd97Jeeedx/j4OIcOHeLVr341b3/725mYmKBYLPKJT3yCSy65hF/8xV9kdHSUb3/721x99dW8733v41d/9Ve59957EULwB3/wB7zqVa/iM5/5DH/2Z39Gu93muc99Lh/+8Iex7U676pMnT3LLLbfwnve8h1e+8pXn/F7PhcCPAudlvt8NPH1uy1lFZAOXuQLPsdKwbEDkCnyd4N577+WLX/wi3/72t4miiKuvvppDhw7x1re+lY9+9KNceOGF3HPPPbztbW/jq1/9KgCPPfYYX/nKV7Btm3e9612Mjo7ywAMPIKVkcnKShx9+mC984Qt84xvfwHVd3va2t3HnnXfy5je/GYDjx49z6623cscdd3DjjTcuy/s4F+b6JnChEGIf8BTws8DPLcuqVgN5EDPHasN2cwXeg8Uo5ZXAv/7rv3LbbbdRKBQAuOWWW2g2m9x999287nWvS5/XarXSr1/3utelavorX/kKX/jCFwCVOTIyMsKdd97JfffdxzXXXANAo9Fg69atgCpeuuGGG/jQhz7Ei1/84mV7H2dN4FLKSAjxDuAfABv4lJTyoWVb2UrDytMIc6wybA8cb+Hn5VhxSDnXzU2ShOHhYe6///6+v1Mqleb9fSklt99+Ox/4wAfm/MxxHA4dOsQ//MM/LCuBn1MeuJTy76SUF0kp90sp/+tyLWpVkBfy5FhtWE6uwNcJrr32Wv7mb/6GZrPJ7Owsf/u3f0uxWGTfvn38xV/8BaAI+Tvf+U7f37/pppv4yEc+kj7vzJkz3HDDDfzlX/4lJ06cAOD06dP86Ec/ApRK/9SnPsUjjzzCBz/4wWV7H3klZu/XOXKsFGw398DXCa655hpuvfVWrrrqKl7zmtdw+PBhhoaGuPPOO/nkJz/JVVddxWWXXcaXvvSlvr//nve8h5MnT7Jnzx4OHDjA3XffzaWXXsodd9zBTTfdxJVXXsmNN97IsWPH0t+xbZvPf/7zfO1rX+PDH/7wsrwP0W8rsFI4fPiwvPfee1ft782LOIT3j6uvf+uHUBxd2/Xk+MnHHx+AnQfhjZ9b65WsKR5++GEOHDiw1stgdnaWcrlMvV7nuuuu4+Mf/zhXX331kl7j6aef5mMf+xjve9/7lm1d/Y6PEOI+KeXh3uduXgWezTzJFXiO1YDt5Ap8HeGtb30rBw8e5Oqrr+a1r33tksn761//Oi996UtxnLWLoW3e6J0QyvtOwtwDz7E6ePabYGz/Wq8ih8ZnP/vZc/r966+/nkceeWSZVnN22LwEDiorIAnzPPAcq4MX/9ZaryDHTxg2r4UCnfRBy57/eTly5MixDrHJCdxT9kneHS5HjhwbEJubwC03D2DmyJFjw2JzE7jt5gHMHDk2ISYnJxfMxT5y5AiXX375Of2dF7zgBef0+wthkxO4l5fR58ixCbEYAl8O3H333Sv6+pubvXIFniPH2uLv3w3PPLC8r7n9Cnj5/OXq7373u3niiSc4ePAgL3nJS/jud7/LmTNnCMOQO+64g9tuU7Np4jjmLW95C3fffTe7du3iS1/6EoVCgeuvv55nP/vZ3HfffUxMTPDnf/7nfOADH+CBBx7gDW94A3fccQegBjnMzs7y9a9/nfe+972Mj4/z4IMPcujQIT7zmc+c82i5nMDzFMIcOTYdPvjBD/Lggw9y//33E0UR9XqdarXKyZMned7znsett94KwOOPP87nPvc5PvGJT/D617+eL37xi/zCL/wCAJ7ncdddd/Gnf/qn3Hbbbdx3332Mjo6yf/9+3vnOdzI2Ntb1N7/97W/z0EMPsXPnTl74whfyjW98g2uvvfac3sfmZi/LzS2UHDnWEgso5dWAlJLf/u3f5q677sKyLJ566imOHz8OwL59+zh48CAAhw4d4siRI+nvGZK/4ooruOyyy9ixYwcAF1xwAU8++eQcAn/Oc57D7t27ATh48CBHjhzJCfycYNIIc+TIsWlx5513MjExwX333Yfruuzdu5dmswmA73daH9i2TaPRSL83P7Msq+t5lmX1HbvW+1rLMZptkwcxnTyNMEeOTYhKpcLMzAwAU1NTbN26Fdd1+drXvpa2gN0IyBV47oHnyLHpMDY2xgtf+EIuv/xyrrnmGh555BEOHz7MwYMHueSSS9Z6eYvG5m0nC/D4V6A9A5e9eq1XkiPHpsF6aSe7XrGUdrKbW35e+LK1XkGOHDlynDU2tweeI0eOHBsYOYHnyJFj1bGa1u1GwlKPS07gOXLkWFUEQcCpU6dyEu+BlJJTp04RBIsffL25PfAcOXKsOnbv3s3Ro0eZmJhY66WsOwRBkBb7LAY5gefIkWNV4bou+/btW+tl/EQgt1By5MiRY4MiJ/AcOXLk2KDICTxHjhw5NihWtRJTCDEBnG2jgXHg5DIuZ7WQr3t1sRHXvRHXDPm6VxPnSym39D64qgR+LhBC3NuvlHS9I1/36mIjrnsjrhnyda8H5BZKjhw5cmxQ5ASeI0eOHBsUG4nAP77WCzhL5OteXWzEdW/ENUO+7jXHhvHAc+TIkSNHNzaSAs+RI0eOHBnkBJ4jR44cGxQbgsCFEDcLIR4VQnxfCPHutV5PPwghzhNCfE0I8bAQ4iEhxK/rx98rhHhKCHG//veKtV5rL4QQR4QQD+j13asfGxVC/JMQ4nH9/8harzMLIcTFmWN6vxBiWgjxG+vxeAshPiWEOCGEeDDzWN/jKxT+TJ/r3xVCXL3O1v3fhBCP6LX9tRBiWD++VwjRyBz3j66zdQ88L4QQ/6s+3o8KIX5qbVZ9lpBSrut/gA08AVwAeMB3gEvXel191rkDuFp/XQEeAy4F3gv85lqvb4G1HwHGex77I+Dd+ut3A3+41utc4Bx5Bjh/PR5v4DrgauDBhY4v8Arg7wEBPA+4Z52t+ybA0V//YWbde7PPW4fHu+95oa/R7wA+sE9zjb3W72Gx/zaCAn8O8H0p5Q+klG3g88Bta7ymOZBSHpNSfkt/PQM8DOxa21WdE24DPq2//jTw02u4loVwA/CElHJdjhOXUt4FnO55eNDxvQ34c6nwb8CwEGLH6qy0G/3WLaX8RyllpL/9N2DxvU9XCQOO9yDcBnxeStmSUv4Q+D6KczYENgKB7wKezHx/lHVOjEKIvcCzgXv0Q+/QW85PrTcrQkMC/yiEuE8I8Vb92DYp5TFQNydg65qtbmH8LPC5zPfr/XjD4OO7kc73/4jaLRjsE0J8WwjxL0KIF63VouZBv/NiIx3vOdgIBC76PLZucx+FEGXgi8BvSCmngY8A+4GDwDHgj9dweYPwQinl1cDLgbcLIa5b6wUtFkIID7gV+Av90EY43vNhQ5zvQojfASLgTv3QMWCPlPLZwH8GPiuEqK7V+vpg0HmxIY73IGwEAj8KnJf5fjfw9BqtZV4IIVwUed8ppfwrACnlcSllLKVMgE+wDrdnUsqn9f8ngL9GrfG42brr/0+s3QrnxcuBb0kpj8PGON4ag47vuj/fhRC3A68Cfl5qI1lbEKf01/ehvOSL1m6V3ZjnvFj3x3s+bAQC/yZwoRBin1ZbPwt8eY3XNAdCCAF8EnhYSvknmcez/uWrgQd7f3ctIYQoCSEq5mtUkOpB1DG+XT/tduBLa7PCBfFGMvbJej/eGQw6vl8G3qyzUZ4HTBmrZT1ACHEz8C7gVillPfP4FiGErb++ALgQ+MHarHIu5jkvvgz8rBDCF0LsQ63731d7fWeNtY6iLuYfKjL/GOqu/jtrvZ4Ba7wWtfX6LnC//vcK4L8DD+jHvwzsWOu19qz7AlQU/jvAQ+b4AmPAPwOP6/9H13qtfdZeBE4BQ5nH1t3xRt1gjgEhSvH90qDji9rSf0if6w8Ah9fZur+P8ozNOf5R/dzX6vPnO8C3gFvW2boHnhfA7+jj/Sjw8rU+X5byLy+lz5EjR44Nio1goeTIkSNHjj7ICTxHjhw5NihyAs+RI0eODYqcwHPkyJFjgyIn8Bw5cuTYoMgJPEeOHDk2KHICz5EjR44Niv8f2uwUeSP8yaAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.plot(y_test, label='gerçek')\n",
    "plt.plot(boston_y_predicted, label='tahmin')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.4 64-bit ('base': conda)",
   "language": "python",
   "name": "python37464bitbaseconda7ed53fe53b85464eb341fdc51fc0e993"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}