8 2 3 Ridge Regresyon.ipynb

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 "cells": [
  {
   "source": [
    "# RIDGE REGRESYON\n",
    "## MODEL KURMA"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Gerekli Kütüphaneler"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import RidgeCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Verisetindeki kategorik değişkenleri dummy değişkenlere çevir\n",
    "# Dummy değişkenleri verisetinde tut\n",
    "# Diğer değişkenleri ve bağımlı değişkeni bağımsız değişkenlerin dışında bırak\n",
    "# Test Train ayrımını yap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"verisetleri\\Hitters.csv\")\n",
    "df = df.dropna()\n",
    "# Kategorik değişkenleri dummy değişkene çevirdik. Kategorik değişkenlerin bize sağlıyacağı bilgiyi daha iyi almak için OneHut encoding yakalaşımı yapmış olduk. \n",
    "dms = pd.get_dummies(df[['League', 'Division', 'NewLeague']])\n",
    "y = df[\"Salary\"]\n",
    "# Dummy değişkenleri veri setine aktarmadan önce veri setindeki bağımsız ve bağımlı değişkenleri çıkararak X_ değişkenine atadık.\n",
    "X_ = df.drop(['Salary', 'League', 'Division', 'NewLeague'], axis=1).astype('float64')\n",
    "# Dummy değişkenleri ile X_ yi birleştirdik.\n",
    "X = pd.concat([X_, dms[['League_N', 'Division_W', 'NewLeague_N']]], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "y,\n",
    "test_size=0.25,\n",
    "random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "5    594   169      4    74   51     35     11    4408   1133      19    501   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  \n",
       "5   336     194      A        W      282      421      25   750.0         A  "
      ],
      "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>AtBat</th>\n      <th>Hits</th>\n      <th>HmRun</th>\n      <th>Runs</th>\n      <th>RBI</th>\n      <th>Walks</th>\n      <th>Years</th>\n      <th>CAtBat</th>\n      <th>CHits</th>\n      <th>CHmRun</th>\n      <th>CRuns</th>\n      <th>CRBI</th>\n      <th>CWalks</th>\n      <th>League</th>\n      <th>Division</th>\n      <th>PutOuts</th>\n      <th>Assists</th>\n      <th>Errors</th>\n      <th>Salary</th>\n      <th>NewLeague</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>315</td>\n      <td>81</td>\n      <td>7</td>\n      <td>24</td>\n      <td>38</td>\n      <td>39</td>\n      <td>14</td>\n      <td>3449</td>\n      <td>835</td>\n      <td>69</td>\n      <td>321</td>\n      <td>414</td>\n      <td>375</td>\n      <td>N</td>\n      <td>W</td>\n      <td>632</td>\n      <td>43</td>\n      <td>10</td>\n      <td>475.0</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>479</td>\n      <td>130</td>\n      <td>18</td>\n      <td>66</td>\n      <td>72</td>\n      <td>76</td>\n      <td>3</td>\n      <td>1624</td>\n      <td>457</td>\n      <td>63</td>\n      <td>224</td>\n      <td>266</td>\n      <td>263</td>\n      <td>A</td>\n      <td>W</td>\n      <td>880</td>\n      <td>82</td>\n      <td>14</td>\n      <td>480.0</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>496</td>\n      <td>141</td>\n      <td>20</td>\n      <td>65</td>\n      <td>78</td>\n      <td>37</td>\n      <td>11</td>\n      <td>5628</td>\n      <td>1575</td>\n      <td>225</td>\n      <td>828</td>\n      <td>838</td>\n      <td>354</td>\n      <td>N</td>\n      <td>E</td>\n      <td>200</td>\n      <td>11</td>\n      <td>3</td>\n      <td>500.0</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>321</td>\n      <td>87</td>\n      <td>10</td>\n      <td>39</td>\n      <td>42</td>\n      <td>30</td>\n      <td>2</td>\n      <td>396</td>\n      <td>101</td>\n      <td>12</td>\n      <td>48</td>\n      <td>46</td>\n      <td>33</td>\n      <td>N</td>\n      <td>E</td>\n      <td>805</td>\n      <td>40</td>\n      <td>4</td>\n      <td>91.5</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>594</td>\n      <td>169</td>\n      <td>4</td>\n      <td>74</td>\n      <td>51</td>\n      <td>35</td>\n      <td>11</td>\n      <td>4408</td>\n      <td>1133</td>\n      <td>19</td>\n      <td>501</td>\n      <td>336</td>\n      <td>194</td>\n      <td>A</td>\n      <td>W</td>\n      <td>282</td>\n      <td>421</td>\n      <td>25</td>\n      <td>750.0</td>\n      <td>A</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(263, 20)"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Amacımız, \"salary\" oyuncuların maaşını ifade eder. Bu değeri diğer değişkenleri kullanarak tahmin etmeye çalışmak. Veriset Beyzbol oyuncularına ait veri setidir.\n",
    "# Oyuncu transfer ederken oyuncuya ne kadar bedel belirtmemiz gerekir?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HATIRLATMA : Model kurulmadan önce yapılması gereken işlemler\n",
    "# Görselleştirme\n",
    "# Betimsel istatistiklerine bakılması\n",
    "# Birbirleriyle çaprazlanarak veri setinin daha yakından tanınması\n",
    "# gibi KEŞİFÇİ VERİ ANALİZİ işlemlerininde gerçekleştirilmesi gerekir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelin kurulması\n",
    "ridge_model = Ridge(alpha=5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Ridge(alpha=5)"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "ridge_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ -1.81040449,   8.87872786,   7.12487047,  -3.31713488,\n",
       "        -2.01162162,   5.40691525,   7.25828404,  -0.13033385,\n",
       "        -0.25425913,  -0.71109275,   1.81452   ,   0.77083457,\n",
       "        -0.6768975 ,   0.26211894,   0.2646614 ,  -0.36947934,\n",
       "        76.19519054, -90.15523668, -16.00453535])"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "ridge_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alpha parametresi değiştirerek farklı sonuçları gözlemleyin.\n",
    "# alpha lambda ya karşılık gelmektedir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "-3.4251968687162844"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "ridge_model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([10.        ,  9.87878788,  9.75757576,  9.63636364,  9.51515152,\n",
       "        9.39393939,  9.27272727,  9.15151515,  9.03030303,  8.90909091,\n",
       "        8.78787879,  8.66666667,  8.54545455,  8.42424242,  8.3030303 ,\n",
       "        8.18181818,  8.06060606,  7.93939394,  7.81818182,  7.6969697 ,\n",
       "        7.57575758,  7.45454545,  7.33333333,  7.21212121,  7.09090909,\n",
       "        6.96969697,  6.84848485,  6.72727273,  6.60606061,  6.48484848,\n",
       "        6.36363636,  6.24242424,  6.12121212,  6.        ,  5.87878788,\n",
       "        5.75757576,  5.63636364,  5.51515152,  5.39393939,  5.27272727,\n",
       "        5.15151515,  5.03030303,  4.90909091,  4.78787879,  4.66666667,\n",
       "        4.54545455,  4.42424242,  4.3030303 ,  4.18181818,  4.06060606,\n",
       "        3.93939394,  3.81818182,  3.6969697 ,  3.57575758,  3.45454545,\n",
       "        3.33333333,  3.21212121,  3.09090909,  2.96969697,  2.84848485,\n",
       "        2.72727273,  2.60606061,  2.48484848,  2.36363636,  2.24242424,\n",
       "        2.12121212,  2.        ,  1.87878788,  1.75757576,  1.63636364,\n",
       "        1.51515152,  1.39393939,  1.27272727,  1.15151515,  1.03030303,\n",
       "        0.90909091,  0.78787879,  0.66666667,  0.54545455,  0.42424242,\n",
       "        0.3030303 ,  0.18181818,  0.06060606, -0.06060606, -0.18181818,\n",
       "       -0.3030303 , -0.42424242, -0.54545455, -0.66666667, -0.78787879,\n",
       "       -0.90909091, -1.03030303, -1.15151515, -1.27272727, -1.39393939,\n",
       "       -1.51515152, -1.63636364, -1.75757576, -1.87878788, -2.        ])"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "np.linspace(10, -2, 100) # rastgele sayılar üretir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdalar = 10**np.linspace(10, -2, 100)*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([5.00000000e+09, 3.78231664e+09, 2.86118383e+09, 2.16438064e+09,\n",
       "       1.63727458e+09, 1.23853818e+09, 9.36908711e+08, 7.08737081e+08,\n",
       "       5.36133611e+08, 4.05565415e+08, 3.06795364e+08, 2.32079442e+08,\n",
       "       1.75559587e+08, 1.32804389e+08, 1.00461650e+08, 7.59955541e+07,\n",
       "       5.74878498e+07, 4.34874501e+07, 3.28966612e+07, 2.48851178e+07,\n",
       "       1.88246790e+07, 1.42401793e+07, 1.07721735e+07, 8.14875417e+06,\n",
       "       6.16423370e+06, 4.66301673e+06, 3.52740116e+06, 2.66834962e+06,\n",
       "       2.01850863e+06, 1.52692775e+06, 1.15506485e+06, 8.73764200e+05,\n",
       "       6.60970574e+05, 5.00000000e+05, 3.78231664e+05, 2.86118383e+05,\n",
       "       2.16438064e+05, 1.63727458e+05, 1.23853818e+05, 9.36908711e+04,\n",
       "       7.08737081e+04, 5.36133611e+04, 4.05565415e+04, 3.06795364e+04,\n",
       "       2.32079442e+04, 1.75559587e+04, 1.32804389e+04, 1.00461650e+04,\n",
       "       7.59955541e+03, 5.74878498e+03, 4.34874501e+03, 3.28966612e+03,\n",
       "       2.48851178e+03, 1.88246790e+03, 1.42401793e+03, 1.07721735e+03,\n",
       "       8.14875417e+02, 6.16423370e+02, 4.66301673e+02, 3.52740116e+02,\n",
       "       2.66834962e+02, 2.01850863e+02, 1.52692775e+02, 1.15506485e+02,\n",
       "       8.73764200e+01, 6.60970574e+01, 5.00000000e+01, 3.78231664e+01,\n",
       "       2.86118383e+01, 2.16438064e+01, 1.63727458e+01, 1.23853818e+01,\n",
       "       9.36908711e+00, 7.08737081e+00, 5.36133611e+00, 4.05565415e+00,\n",
       "       3.06795364e+00, 2.32079442e+00, 1.75559587e+00, 1.32804389e+00,\n",
       "       1.00461650e+00, 7.59955541e-01, 5.74878498e-01, 4.34874501e-01,\n",
       "       3.28966612e-01, 2.48851178e-01, 1.88246790e-01, 1.42401793e-01,\n",
       "       1.07721735e-01, 8.14875417e-02, 6.16423370e-02, 4.66301673e-02,\n",
       "       3.52740116e-02, 2.66834962e-02, 2.01850863e-02, 1.52692775e-02,\n",
       "       1.15506485e-02, 8.73764200e-03, 6.60970574e-03, 5.00000000e-03])"
      ]
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "lambdalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Yukarıdaki oluşturduğumuz birbirinden farklı lambda değerlerine karşı, bulunan katsayıları gözlemleyelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_model = Ridge()\n",
    "katsayilar = []\n",
    "\n",
    "for i in lambdalar:\n",
    "    ridge_model.set_params(alpha = i)\n",
    "    ridge_model.fit(X_train, y_train)\n",
    "    katsayilar.append(ridge_model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  0.00325215, -0.18347884,  0.49582229,  0.05207147,\n",
       "         0.49852755,  0.35780758,  0.13050722,  0.29156508,  0.09017435,\n",
       "        -0.03363913,  0.00485343, -0.01129272,  0.00286803]),\n",
       " array([ 0.48716485,  0.55008855,  0.05001106,  0.27373331,  0.16616941,\n",
       "         0.32362115,  0.00664439, -0.20624638,  0.54243844,  0.03847238,\n",
       "         0.54495307,  0.38421156,  0.1254093 ,  0.28786002,  0.10106927,\n",
       "        -0.04436196,  0.00665481, -0.01472784,  0.00393228]),\n",
       " array([ 0.4489693 ,  0.65259294,  0.0547705 ,  0.31425899,  0.18207896,\n",
       "         0.39644748,  0.01135508, -0.22590778,  0.58107838,  0.02069636,\n",
       "         0.58985146,  0.40982593,  0.11684682,  0.28464147,  0.11252912,\n",
       "        -0.05811777,  0.00906349, -0.01922258,  0.00535701]),\n",
       " array([ 0.3968063 ,  0.78060342,  0.06016408,  0.36183375,  0.20000384,\n",
       "         0.48764781,  0.01772912, -0.24182798,  0.60954426, -0.00155238,\n",
       "         0.63339787,  0.4345655 ,  0.10451029,  0.28195087,  0.12423297,\n",
       "        -0.07562832,  0.01227822, -0.02510361,  0.00726512]),\n",
       " array([ 0.32937467,  0.9401416 ,  0.06621354,  0.41658487,  0.21993467,\n",
       "         0.60068406,  0.02617569, -0.25361874,  0.62624781, -0.02844544,\n",
       "         0.67621095,  0.45845871,  0.0880216 ,  0.27978824,  0.13598169,\n",
       "        -0.09768359,  0.01656543, -0.03279487,  0.00982478]),\n",
       " array([ 0.24531948,  1.13761151,  0.07286178,  0.4774922 ,  0.2415094 ,\n",
       "         0.73881463,  0.03717582, -0.2611302 ,  0.63023671, -0.05991964,\n",
       "         0.71922379,  0.48160498,  0.06698821,  0.27812741,  0.1476926 ,\n",
       "        -0.12508529,  0.02228264, -0.04284536,  0.01326611]),\n",
       " array([ 0.1435482 ,  1.37929734,  0.07999484,  0.54179368,  0.26393219,\n",
       "         0.90467818,  0.05128863, -0.26442052,  0.62116703, -0.0955613 ,\n",
       "         0.76350787,  0.50410208,  0.04109169,  0.27692596,  0.15935413,\n",
       "        -0.15855204,  0.02990935, -0.05596537,  0.0179032 ]),\n",
       " array([ 0.02363255,  1.67061387,  0.08753565,  0.60440494,  0.28597299,\n",
       "         1.09981641,  0.06915608, -0.26372195,  0.59926001, -0.13453482,\n",
       "         0.81009977,  0.52597367,  0.01019011,  0.27613096,  0.17096354,\n",
       "        -0.19858115,  0.04008731, -0.07307487,  0.0241626 ]),\n",
       " array([-0.11378671,  2.01514908,  0.09564972,  0.65756367,  0.30609314,\n",
       "         1.32423652,  0.0915081 , -0.2594123 ,  0.56525502, -0.17562381,\n",
       "         0.85987142,  0.54713291, -0.02559235,  0.27568181,  0.18246959,\n",
       "        -0.2452718 ,  0.05367214, -0.09536904,  0.0326191 ]),\n",
       " array([-0.26670385,  2.41363844,  0.10508862,  0.69098126,  0.32269376,\n",
       "         1.57613996,  0.11917122, -0.25199043,  0.52034556, -0.21743938,\n",
       "         0.91346331,  0.56741057, -0.06581575,  0.27551143,  0.19373753,\n",
       "        -0.29813006,  0.07179912, -0.12440747,  0.04403999]),\n",
       " array([-0.43167285,  2.86310628,  0.11765572,  0.6927572 ,  0.33439715,\n",
       "         1.85191019,  0.15308516, -0.24204868,  0.46608227, -0.25878513,\n",
       "         0.97127119,  0.58665136, -0.10973206,  0.2755476 ,  0.20454612,\n",
       "        -0.35589354,  0.09596621, -0.16223522,  0.05943915]),\n",
       " array([-0.6040179 ,  3.35644666,  0.13671717,  0.65112408,  0.34019682,\n",
       "         2.14635766,  0.19433224, -0.23023687,  0.40424807, -0.29906764,\n",
       "         1.03344795,  0.60484538, -0.15634765,  0.27571572,  0.21461709,\n",
       "        -0.41642724,  0.12813948, -0.21154533,  0.0801436 ]),\n",
       " array([-0.77830722,  3.88264504,  0.16761906,  0.55675758,  0.33933019,\n",
       "         2.45312403,  0.24418475, -0.21721953,  0.33673633, -0.3385693 ,\n",
       "         1.09987502,  0.62223123, -0.20451768,  0.27594368,  0.2236679 ,\n",
       "        -0.47674786,  0.17088798, -0.27589485,  0.10787726]),\n",
       " array([-0.94897769,  4.42767568,  0.21783329,  0.40503507,  0.33087526,\n",
       "         2.76512455,  0.30417652, -0.20363305,  0.26546633, -0.37843093,\n",
       "         1.17008688,  0.63931124, -0.25304248,  0.27616801,  0.23147139,\n",
       "        -0.53322691,  0.22755856, -0.35998977,  0.14486916]),\n",
       " array([-1.11095288,  4.97592285,  0.29665745,  0.19746729,  0.31327399,\n",
       "         3.07497739,  0.37620355, -0.19005043,  0.19234109, -0.42032842,\n",
       "         1.24318757,  0.65676164, -0.30074808,  0.27633977,  0.23790421,\n",
       "        -0.58199675,  0.3025042 , -0.47005898,  0.19399524]),\n",
       " array([-1.26011918,  5.51184871,  0.4143498 , -0.05827957,  0.28409378,\n",
       "         3.37545591,  0.46264872, -0.17695715,  0.11921725, -0.46597955,\n",
       "         1.31783559,  0.67527456, -0.34655253,  0.27642815,  0.24297057,\n",
       "        -0.61952664,  0.40138114, -0.61434165,  0.25896264]),\n",
       " array([-1.39359306,  6.02159062,  0.58069505, -0.34983106,  0.24025666,\n",
       "         3.66000488,  0.56650459, -0.16473766,  0.04784951, -0.51667315,\n",
       "         1.39235321,  0.69539447, -0.389528  ,  0.27642056,  0.24679657,\n",
       "        -0.64325304,  0.5315299 , -0.80371445,  0.34454253]),\n",
       " array([-1.5097815 ,  6.49419307,  0.80316402, -0.66248462,  0.17873841,\n",
       "         3.9232733 ,  0.69144007, -0.15367066, -0.02019658, -0.57295067,\n",
       "         1.46494569,  0.71739679, -0.42895941,  0.27631954,  0.24959917,\n",
       "        -0.6520776 ,  0.70245206, -1.05248262,  0.45685054]),\n",
       " array([-1.60827968,  6.92225972,  1.0850129 , -0.98166033,  0.09750881,\n",
       "         4.16152392,  0.84173537, -0.14393249, -0.08363246, -0.63446354,\n",
       "         1.5339501 ,  0.74122336, -0.46438666,  0.2761376 ,  0.2516403 ,\n",
       "        -0.64655042,  0.92638753, -1.37935217,  0.60366354]),\n",
       " array([-1.68966187e+00,  7.30195969e+00,  1.42381149e+00, -1.29468666e+00,\n",
       "        -3.62900944e-03,  4.37278186e+00,  1.02201054e+00, -1.35608227e-01,\n",
       "        -1.41494723e-01, -6.99968600e-01,  1.59802280e+00,  7.66468354e-01,\n",
       "        -4.95614137e-01,  2.75891667e-01,  2.53178738e-01, -6.28664546e-01,\n",
       "         1.21898388e+00, -1.80858680e+00,  7.94743507e-01]),\n",
       " array([-1.75521934,  7.63250922,  1.810921  , -1.59163889, -0.12260881,\n",
       "         4.55668845,  1.23670227, -0.12870825, -0.19313812, -0.76742926,\n",
       "         1.65621877,  0.79240809, -0.52268312,  0.27559826,  0.25443116,\n",
       "        -0.60134741,  1.60002415, -2.37132894,  1.04210968]),\n",
       " array([-1.80669081,  7.91538656,  2.23228563, -1.86540884, -0.25524844,\n",
       "         4.71414786,  1.4893048 , -0.12318631, -0.23819186, -0.83422019,\n",
       "         1.70797608,  0.81807576, -0.54581766,  0.27526998,  0.25554995,\n",
       "        -0.56784719,  2.09413745, -3.10702231,  1.36015201]),\n",
       " array([-1.8460197 ,  8.15355087,  2.67052913, -2.11139641, -0.39594744,\n",
       "         4.84691116,  1.7814807 , -0.11895504, -0.2765114 , -0.89743421,\n",
       "         1.75305172,  0.84237965, -0.56536284,  0.27491346,  0.25661944,\n",
       "        -0.53121227,  2.73135033, -4.06480085,  1.76540696]),\n",
       " array([-1.87515919,  8.35083325,  3.107865  , -2.32714395, -0.53872158,\n",
       "         4.95721543,  2.11225087, -0.115897  , -0.30814612, -0.95425156,\n",
       "         1.79145062,  0.8642477 , -0.58173065,  0.27452851,  0.2576669 ,\n",
       "        -0.49397252,  3.5472491 , -5.30458807,  2.27572284]),\n",
       " array([-1.89593262,  8.51152795,  3.52898445, -2.51203537, -0.67825363,\n",
       "         5.04752698,  2.47754217, -0.11387258, -0.33332037, -1.00228798,\n",
       "         1.82336571,  0.88276469, -0.59535973,  0.27410836,  0.25868123,\n",
       "        -0.45802465,  4.5824306 , -6.89746573,  2.9084461 ]),\n",
       " array([-1.90994639,  8.64011983,  3.92310473, -2.66702658, -0.81066339,\n",
       "         5.12038018,  2.87033297, -0.11272745, -0.3524116 , -1.03983364,\n",
       "         1.84912635,  0.89726768, -0.60668768,  0.2736409 ,  0.25963217,\n",
       "        -0.42465708,  5.88086742, -8.92461954,  3.67721061]),\n",
       " array([ -1.9185465 ,   8.74107098,   4.28473642,  -2.79433964,\n",
       "         -0.9338395 ,   5.17828063,   3.28144629,  -0.11230164,\n",
       "         -0.36591278,  -1.06594   ,   1.86914816,   0.90738272,\n",
       "         -0.61613119,   0.27311095,   0.26048537,  -0.39463809,\n",
       "          7.48688573, -11.47390082,   4.58699972]),\n",
       " array([ -1.92280945,   8.81862902,   4.61322124,  -2.89710054,\n",
       "         -1.0473443 ,   5.22364529,   3.70075885,  -0.11244021,\n",
       "         -0.3743809 ,  -1.08037534,   1.88388412,   0.91301087,\n",
       "         -0.62407042,   0.27250335,   0.26121161,  -0.36831577,\n",
       "          9.44075517, -14.63288659,   5.62749321]),\n",
       " array([ -1.92356056,   8.87666466,   4.91143543,  -2.97895449,\n",
       "         -1.15201594,   5.25876301,   4.1183964 ,  -0.11300313,\n",
       "         -0.37838604,  -1.08351161,   1.89378531,   0.9142872 ,\n",
       "         -0.63083652,   0.27180653,   0.26179136,  -0.34571301,\n",
       "         11.77349146, -18.47751473,   6.76539754]),\n",
       " array([ -1.92141509,   8.91856616,   5.18414497,  -3.04370749,\n",
       "         -1.24942102,   5.28576369,   4.52552842,  -0.11387199,\n",
       "         -0.37847759,  -1.07620488,   1.89928279,   0.9115363 ,\n",
       "         -0.63670275,   0.2710162 ,   0.26221639,  -0.32662596,\n",
       "         14.50228148, -23.05623072,   7.93741531]),\n",
       " array([ -1.9168372 ,   8.94720973,   5.43641484,  -3.09502361,\n",
       "         -1.34128685,   5.3065845 ,   4.91465054,  -0.11495231,\n",
       "         -0.37517654,  -1.05970019,   1.90079754,   0.90523597,\n",
       "         -0.64188083,   0.2701382 ,   0.26249027,  -0.31073979,\n",
       "         17.62856776, -28.37133375,   9.04636776]),\n",
       " array([ -1.91020764,   8.96500212,   5.67234167,  -3.13618385,\n",
       "         -1.42900845,   5.322925  ,   5.27955004,  -0.11617225,\n",
       "         -0.3689901 ,  -1.03555364,   1.8987753 ,   0.89598626,\n",
       "         -0.64652367,   0.26918984,   0.2626287 ,  -0.29776325,\n",
       "         21.14061583, -34.36165434,   9.96309192]),\n",
       " array([ -1.9018865 ,   8.97396885,   5.89427279,  -3.1699073 ,\n",
       "         -1.51330506,   5.33619318,   5.61526783,  -0.11747948,\n",
       "         -0.36043415,  -1.00554354,   1.89373069,   0.88447345,\n",
       "         -0.65073445,   0.26819911,   0.2626597 ,  -0.28756179,\n",
       "         25.02082282, -40.89283183,  10.53556402]),\n",
       " array([ -1.8922552 ,   8.97585015,   6.10259019,  -3.19824669,\n",
       "         -1.59408394,   5.34745951,   5.91823878,  -0.11883796,\n",
       "         -0.35004602,  -0.97154793,   1.8862764 ,   0.87142105,\n",
       "         -0.65458032,   0.26720152,   0.26262266,  -0.28024984,\n",
       "         29.25541136, -47.76155677,  10.6044871 ]),\n",
       " array([ -1.88172685,   8.9721717 ,   6.29603819,  -3.22257863,\n",
       "         -1.67053515,   5.35744342,   6.18654873,  -0.12022504,\n",
       "         -0.33837543,  -0.93539098,   1.87711554,   0.85752944,\n",
       "         -0.65810715,   0.26623519,   0.26256519,  -0.27620138,\n",
       "         33.84186587, -54.71675408,  10.02251324]),\n",
       " array([ -1.87072564,   8.96427673,   6.47244819,  -3.24369538,\n",
       "         -1.74141952,   5.36654802,   6.42009476,  -0.12162856,\n",
       "         -0.3259544 ,  -0.89868802,   1.86699113,   0.84341625,\n",
       "         -0.66135151,   0.2653354 ,   0.26253754,  -0.27596038,\n",
       "         38.78918874, -61.4943233 ,   8.673658  ]),\n",
       " array([ -1.85964908,   8.95332991,   6.62960686,  -3.26197325,\n",
       "         -1.80544929,   5.37493759,   6.62047039,  -0.12304314,\n",
       "         -0.3132593 ,  -0.86273123,   1.85660844,   0.82957352,\n",
       "         -0.6643471 ,   0.26452985,   0.26258525,  -0.28006946,\n",
       "         44.10843684, -67.8558909 ,   6.49041872]),\n",
       " array([ -1.8488328 ,   8.94031682,   6.76599454,  -3.27756575,\n",
       "         -1.86163425,   5.38263336,   6.79055551,  -0.12446563,\n",
       "         -0.30068277,  -0.82844672,   1.84655905,   0.81635288,\n",
       "         -0.66712531,   0.26383594,   0.26274191,  -0.28886809,\n",
       "         49.79512404, -73.62002534,   3.46727923]),\n",
       " array([ -1.83853302,   8.92605782,   6.88120801,  -3.29056742,\n",
       "         -1.90949634,   5.38959863,   6.93393754,  -0.12589019,\n",
       "         -0.28852654,  -0.79642622,   1.83727272,   0.80397943,\n",
       "         -0.66971175,   0.26326004,   0.26302387,  -0.3023224 ,\n",
       "         55.80914591, -78.67794982,  -0.33080214]),\n",
       " array([ -1.82892927,   8.91123827,   6.97602686,  -3.30111624,\n",
       "         -1.94912136,   5.3957956 ,   7.05434244,  -0.12730447,\n",
       "         -0.27701409,  -0.76700798,   1.82900728,   0.7925838 ,\n",
       "         -0.67212183,   0.26279885,   0.26342829,  -0.31993817,\n",
       "         62.06019241, -82.99251808,  -4.76760501]),\n",
       " array([ -1.82013957,   8.89644106,   7.05220527,  -3.30943185,\n",
       "         -1.98107762,   5.40121196,   7.15521511,  -0.12868815,\n",
       "         -0.26631147,  -0.74036794,   1.82187009,   0.78223736,\n",
       "         -0.6743585 ,   0.26244179,   0.26393489,  -0.34078382,\n",
       "         68.40592996, -86.58497542,  -9.64589406]),\n",
       " array([ -1.81223659,   8.88216502,   7.11212329,  -3.31580617,\n",
       "         -2.00626147,   5.40586611,   7.23950667,  -0.13001458,\n",
       "         -0.25654371,  -0.71658759,   1.81585603,   0.77297757,\n",
       "         -0.67641368,   0.26217392,   0.26451098,  -0.36361883,\n",
       "         74.66612748, -89.51629481, -14.73005735]),\n",
       " array([ -1.80525773,   8.86882244,   7.15842076,  -3.32056982,\n",
       "         -2.02572792,   5.40980189,   7.30964794,  -0.13125507,\n",
       "         -0.24780017,  -0.6956852 ,   1.81088769,   0.76481873,\n",
       "         -0.67827327,   0.26197859,   0.26511835,  -0.38709127,\n",
       "         80.64955348, -91.86889443, -19.77787797]),\n",
       " array([ -1.79920825,   8.85672296,   7.19369451,  -3.32405395,\n",
       "         -2.04054656,   5.41308056,   7.36763964,  -0.13238414,\n",
       "         -0.240131  ,  -0.6776179 ,   1.80684908,   0.75775145,\n",
       "         -0.67992366,   0.26183963,   0.2657202 ,  -0.40994665,\n",
       "         86.18494577, -93.73207465, -24.57357986]),\n",
       " array([ -1.79406187,   8.84605857,   7.22029406,  -3.32655991,\n",
       "         -2.05170136,   5.41577356,   7.41517936,  -0.13338387,\n",
       "         -0.23354184,  -0.66227339,   1.80360973,   0.75173836,\n",
       "         -0.68135748,   0.26174274,   0.26628654,  -0.43119   ,\n",
       "         91.14591671, -95.19217684, -28.95307797]),\n",
       " array([ -1.78976306,   8.83690199,   7.24021679,  -3.32834172,\n",
       "         -2.06003641,   5.41795666,   7.45376709,  -0.13424612,\n",
       "         -0.22799326,  -0.64946793,   1.80104019,   0.74671281,\n",
       "         -0.6825766 ,   0.26167617,   0.26679692,  -0.45016675,\n",
       "         95.463099  , -96.32699505, -32.81562242]),\n",
       " array([ -1.78623299,   8.82922177,   7.25508225,  -3.32960072,\n",
       "         -2.06623906,   5.41970537,   7.48476496,  -0.1349722 ,\n",
       "         -0.22340746,  -0.63895756,   1.79902077,   0.74258307,\n",
       "         -0.68359234,   0.26163086,   0.26724066,  -0.46656117,\n",
       "         99.12270867, -97.2033658 , -36.12157378]),\n",
       " array([ -1.7833782 ,   8.82290913,   7.26615816,  -3.33048896,\n",
       "         -2.07084791,   5.42109133,   7.509416  ,  -0.13557094,\n",
       "         -0.21968008,  -0.63045984,   1.79744589,   0.739241  ,\n",
       "         -0.68442333,   0.26160015,   0.26761525,  -0.48033703,\n",
       "        102.15545096, -97.87681855, -38.88050737]),\n",
       " array([ -1.78109993,   8.81780859,   7.27441217,  -3.33111713,\n",
       "         -2.07427357,   5.42217975,   7.5288397 ,  -0.13605601,\n",
       "         -0.21669368,  -0.6236789 ,   1.7962255 ,   0.73657218,\n",
       "         -0.68509255,   0.26157929,   0.26792401,  -0.49165312,\n",
       "        104.62138682, -98.39238568, -41.13535508]),\n",
       " array([ -1.77930191,   8.81374517,   7.28057067,  -3.33156368,\n",
       "         -2.07682349,   5.42302793,   7.54402153,  -0.13644325,\n",
       "         -0.21432963,  -0.61832752,   1.79528467,   0.7344648 ,\n",
       "         -0.68562444,   0.26156506,   0.26817367,  -0.5007805 ,\n",
       "        106.59540858, -98.78594571, -42.94724371]),\n",
       " array([ -1.77789585,   8.81054479,   7.28517355,  -3.33188334,\n",
       "         -2.07872539,   5.42368469,   7.55580725,  -0.13674874,\n",
       "         -0.21247661,  -0.61414271,   1.79456241,   0.7328161 ,\n",
       "         -0.68604264,   0.26155527,   0.26837251,  -0.5080364 ,\n",
       "        108.15589045, -99.08571269, -44.38358387]),\n",
       " array([ -1.7768044 ,   8.80804703,   7.28862022,  -3.33211392,\n",
       "         -2.08014706,   5.42419062,   7.56490537,  -0.13698747,\n",
       "         -0.21103564,  -0.61089419,   1.79400976,   0.73153586,\n",
       "         -0.68636864,   0.26154846,   0.268529  ,  -0.51373905,\n",
       "        109.37719305, -99.31365911, -45.51008002]),\n",
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      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "katsayilar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Farklı lambda değerlerine karşılık olacak şekilde farklı katsayıların nasıl oluştuğunu gösteren grafik.\n",
    "# y ekseni katsayıları, x ekseni lambdaları göstermektedir.\n",
    "ax = plt.gca()\n",
    "ax.plot(lambdalar, katsayilar)\n",
    "ax.set_xscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Burada farklı lambda değerlerine karşılık gelen katsayıları gözlemlemiş olduk."
   ]
  },
  {
   "source": [
    "## TAHMİN"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_model = Ridge().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tahmin edilen değerler\n",
    "y_pred = ridge_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 376.25245449,  803.38498121,  496.17669652,  112.69554648,\n",
       "        427.60020221, 1003.6309402 ,  153.45713944,  361.33880956,\n",
       "        483.29143665,  916.91439669])"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "y_pred[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "183     125.0\n",
       "229    1940.0\n",
       "286     940.0\n",
       "102     375.0\n",
       "153     100.0\n",
       "320     960.0\n",
       "135      90.0\n",
       "213     100.0\n",
       "308     750.0\n",
       "161     657.0\n",
       "Name: Salary, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "y_train[0:10]"
   ]
  },
  {
   "source": [
    "### TRAIN HATASI"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train seti hataları - hata kareleri ortalaması karekökü\n",
    "RMSE = np.sqrt(mean_squared_error(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "289.3447069600656"
      ]
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bu değer bizim train hatamızdır fakat henüz validate edilmemiştir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "123477.15188321876"
      ]
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "np.mean(-cross_val_score(ridge_model, X_train, y_train, cv=10, scoring=\"neg_mean_squared_error\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "351.3931585606338"
      ]
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "np.sqrt(np.mean(-cross_val_score(ridge_model, X_train, y_train, cv=10, scoring=\"neg_mean_squared_error\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Burada ikinci kısımda elde ettiğimiz hata ilkinden elde ettiğimiz hatadan büyük olduğuna dikkat ediniz. Hatanın küçük olması doğru olduğu anlamına gelmez. İkinci bulduğumuz hata daha doğru hatadır."
   ]
  },
  {
   "source": [
    "### TEST HATASI"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = ridge_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "RMSE = np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "356.80829057302395"
      ]
     },
     "metadata": {},
     "execution_count": 57
    }
   ],
   "source": [
    "RMSE"
   ]
  },
  {
   "source": [
    "## MODEL TUNNING - İLK GERÇEK MODEL TUNNİNG İŞLEMİ OLACAK "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# İlk gerçek model tunning işlemi dememizin sebebi lambda parametresinin dışardan verilmesi ve en uygun olanı bulmamız gerekmesindendir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "356.80829057302395"
      ]
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "ridge_model = Ridge().fit(X_train, y_train)\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "357.8154668279836"
      ]
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "source": [
    "ridge_model = Ridge(100).fit(X_train, y_train)\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "358.5637914255956"
      ]
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "source": [
    "ridge_model = Ridge(1000).fit(X_train, y_train)\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NEDEN MODEL TUNNING\n",
    "\n",
    "# Modellemede kullanmamız gereken ve dışardan vermemiz gerken lambda değerinin bulunması gerekir. Bunun için birçok deneme yanılma işleminin yapılması gerekir. Bu işlem model tunning işlemidir. En uygun parametre değerini bulmaya çalışıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdalar1 = np.random.randint(0, 1000, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdalar2 = 100**np.linspace(10, -2, 100)*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Farklı lambda değerlerinin otomatik verilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridgecv=RidgeCV(alphas=lambdalar2, scoring=\"neg_mean_squared_error\", cv=10, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([5.00000000e+19, 2.86118383e+19, 1.63727458e+19, 9.36908711e+18,\n",
       "       5.36133611e+18, 3.06795364e+18, 1.75559587e+18, 1.00461650e+18,\n",
       "       5.74878498e+17, 3.28966612e+17, 1.88246790e+17, 1.07721735e+17,\n",
       "       6.16423370e+16, 3.52740116e+16, 2.01850863e+16, 1.15506485e+16,\n",
       "       6.60970574e+15, 3.78231664e+15, 2.16438064e+15, 1.23853818e+15,\n",
       "       7.08737081e+14, 4.05565415e+1...\n",
       "       2.01850863e+00, 1.15506485e+00, 6.60970574e-01, 3.78231664e-01,\n",
       "       2.16438064e-01, 1.23853818e-01, 7.08737081e-02, 4.05565415e-02,\n",
       "       2.32079442e-02, 1.32804389e-02, 7.59955541e-03, 4.34874501e-03,\n",
       "       2.48851178e-03, 1.42401793e-03, 8.14875417e-04, 4.66301673e-04,\n",
       "       2.66834962e-04, 1.52692775e-04, 8.73764200e-05, 5.00000000e-05]),\n",
       "        cv=10, normalize=True, scoring='neg_mean_squared_error')"
      ]
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "source": [
    "ridgecv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6609705742330143"
      ]
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "# optimum parametre yani en iyi lambda değeri yani alfa\n",
    "ridgecv.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "## FİNAL MODELİ BULUNAN OPTİMUM ALFA YA GÖRE;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_tuned = Ridge(alpha=ridgecv.alpha_).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "356.88036842428204"
      ]
     },
     "metadata": {},
     "execution_count": 73
    }
   ],
   "source": [
    "# Final modeli üzerinden test hatası hesaplama\n",
    "y_pred = ridge_tuned.predict(X_test)\n",
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lambdalar1 için işlemlerin yapılması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridgecv1 =RidgeCV(alphas=lambdalar1, scoring=\"neg_mean_squared_error\", cv=10, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([ 95, 815, 425, 632, 894, 745,  69, 108, 765, 114, 361, 305, 114,\n",
       "       347, 974, 671, 303, 719,  56, 984, 867, 883, 272, 885, 179, 897,\n",
       "       888, 715, 724, 583, 618, 789, 447,  86, 679,  47, 524, 642, 557,\n",
       "       452, 459, 580, 465, 273,  58, 728,   7, 244, 766, 261, 438, 811,\n",
       "       712, 366, 528, 918, 227, 536, 915, 962, 327, 905, 248, 124, 637,\n",
       "       111, 599, 429, 465, 230, 756, 539, 223, 290,  53, 573, 551, 882,\n",
       "       506, 751, 328, 335, 820, 876, 260, 276, 202, 103, 103, 727, 944,\n",
       "       954, 490, 210, 456, 860, 223, 589, 347, 509]),\n",
       "        cv=10, normalize=True, scoring='neg_mean_squared_error')"
      ]
     },
     "metadata": {},
     "execution_count": 76
    }
   ],
   "source": [
    "ridgecv1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "metadata": {},
     "execution_count": 77
    }
   ],
   "source": [
    "ridgecv1.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_tuned1 = Ridge(alpha=ridgecv1.alpha_).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "356.33030600251544"
      ]
     },
     "metadata": {},
     "execution_count": 79
    }
   ],
   "source": [
    "y_pred1 = ridge_tuned1.predict(X_test)\n",
    "np.sqrt(mean_squared_error(y_test, y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}