8 5 3 TEMEL BİLEŞEN ANALİZİ.ipynb

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 "cells": [
  {
   "source": [
    "# TEMEL BİLEŞEN ANALİZİ"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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  PutOuts  Assists  Errors  Salary  \n",
       "1   414     375      632       43      10   475.0  \n",
       "2   266     263      880       82      14   480.0  \n",
       "3   838     354      200       11       3   500.0  \n",
       "4    46      33      805       40       4    91.5  \n",
       "5   336     194      282      421      25   750.0  "
      ],
      "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>PutOuts</th>\n      <th>Assists</th>\n      <th>Errors</th>\n      <th>Salary</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>632</td>\n      <td>43</td>\n      <td>10</td>\n      <td>475.0</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>880</td>\n      <td>82</td>\n      <td>14</td>\n      <td>480.0</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>200</td>\n      <td>11</td>\n      <td>3</td>\n      <td>500.0</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>805</td>\n      <td>40</td>\n      <td>4</td>\n      <td>91.5</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>282</td>\n      <td>421</td>\n      <td>25</td>\n      <td>750.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df = pd.read_csv(\"./verisetleri/Hitters.csv\")\n",
    "df.dropna(inplace = True)\n",
    "df = df._get_numeric_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Verileri 2 veya 3 bileşen ile temsil edebilirmiyiz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STANDARTLAŞTIRMA İŞLEMİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = StandardScaler().fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[-0.6029005 , -0.59567545, -0.5285512 , -1.2061115 , -0.52206292],\n",
       "       [ 0.51254171,  0.49225957,  0.72996619,  0.44151497,  0.79405962],\n",
       "       [ 0.62816682,  0.73648988,  0.95878753,  0.40228577,  1.02631654],\n",
       "       [-0.56209164, -0.46245892, -0.18531919, -0.61767348, -0.36722498],\n",
       "       [ 1.29471156,  1.35816704, -0.87178322,  0.75534858, -0.0188396 ]])"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df[0:5, 0:5] # Beş satrı ve 5 değişkenin gözlemlenmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# İki bileşenli dönüştürme\n",
    "pca = PCA(n_components = 2)\n",
    "# Veriye uygulanması\n",
    "pca_fit = pca.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bileşenlerin gözlemlenmesi\n",
    "# Önce dataframe dönüştürülmelidir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "bilesen_df = pd.DataFrame(data = pca_fit, columns = [\"Birinci_bileşen\", \"İkinci_bileşen\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     Birinci_bileşen  İkinci_bileşen\n",
       "0           0.076848        1.653525\n",
       "1           0.337127       -2.320560\n",
       "2           3.408362        0.755757\n",
       "3          -2.642221       -0.361486\n",
       "4           1.071681       -1.511674\n",
       "..               ...             ...\n",
       "258        -0.152111       -0.331665\n",
       "259         3.241373       -0.391942\n",
       "260        -1.206322       -0.625714\n",
       "261         2.084856       -2.059851\n",
       "262         1.977963       -0.208100\n",
       "\n",
       "[263 rows x 2 columns]"
      ],
      "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>Birinci_bileşen</th>\n      <th>İkinci_bileşen</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.076848</td>\n      <td>1.653525</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.337127</td>\n      <td>-2.320560</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.408362</td>\n      <td>0.755757</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-2.642221</td>\n      <td>-0.361486</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.071681</td>\n      <td>-1.511674</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>-0.152111</td>\n      <td>-0.331665</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>3.241373</td>\n      <td>-0.391942</td>\n    </tr>\n    <tr>\n      <th>260</th>\n      <td>-1.206322</td>\n      <td>-0.625714</td>\n    </tr>\n    <tr>\n      <th>261</th>\n      <td>2.084856</td>\n      <td>-2.059851</td>\n    </tr>\n    <tr>\n      <th>262</th>\n      <td>1.977963</td>\n      <td>-0.208100</td>\n    </tr>\n  </tbody>\n</table>\n<p>263 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "bilesen_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0.45245466, 0.24246801])"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# Açıklanan varyans\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Yukarıdaki sonuç : Birinci_bileşen ce veri setinin içinde bulunan değişkenliğin % 45 i açılanabilmiş. Benzer şekilde İkinci_bileşen ce veri setinin içerisinde bulunan değişkeliğin % 24 ü açılanabilmiş.\n",
    "# Bu iki değeri toplarsak yaklaşık olarak % 68 gibi bir değerle veri setinin içerisindeki değişkenlik açıklanabilmiş olur. İyi bir oran olarak kabul edilebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bileşenlere erişmek için"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 0.19506377,  0.19410007,  0.19690453,  0.19491272,  0.22956638,\n",
       "         0.20673717,  0.271085  ,  0.31970454,  0.32077315,  0.30810145,\n",
       "         0.32761493,  0.32977446,  0.30573146,  0.08303783,  0.00159189,\n",
       "        -0.00529271,  0.24914187],\n",
       "       [-0.38407792, -0.37764493, -0.22866298, -0.37459128, -0.31026531,\n",
       "        -0.23115759,  0.26820405,  0.19641268,  0.18589726,  0.13386448,\n",
       "         0.17692929,  0.17283361,  0.19698297, -0.16295204, -0.17624626,\n",
       "        -0.20978299, -0.05452611]])"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([-0.38407792, -0.37764493, -0.22866298, -0.37459128, -0.31026531,\n",
       "       -0.23115759,  0.26820405,  0.19641268,  0.18589726,  0.13386448,\n",
       "        0.17692929,  0.17283361,  0.19698297, -0.16295204, -0.17624626,\n",
       "       -0.20978299, -0.05452611])"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "pca.components_[1]"
   ]
  },
  {
   "source": [
    "### OPTIMUM BİLEŞEN SAYISINI BULMA"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FARKLI BİLEŞEN SAYILARINA GÖRE AÇILANAN VARYANSIN NASIL DEĞİŞTİĞİNİ İNCELEYİP BUNA GÖRE BİLEŞEN SAYISINA KARAR VERMEK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA().fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cumsum() Kümülatif toplamın amacı :\n",
    "# İndirilecek bileşen sayısı belirtilmezde değişken sayısı kadar yeni bileşen oluşturulur. Bu durmda değişken sayısı kadar oluşan bileşenlerin kümülatif (cumsum) toplamlarını alırsak, işte ikinci bileşene geldiğimizde birinci ile beraber düşünüldüğünde verisetindeki değişkenliğin ne kadarının açıklandığını öğrenebiliriz. Örneğin benzer şekilde veri setinin içerisindeki değişkenliğin, kümülatif olarak baktığımızda üç bileşence ne kadarının açıklandığını görebiliriz. Bu sebeple bir kümülatif toplam işlemi gerçekleştirilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel(\"Bileşen Sayısı\")\n",
    "plt.ylabel(\"Kümülatif Varyans Oranı\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([4.52454661e-01, 2.42468012e-01, 1.01696801e-01, 5.39343967e-02,\n",
       "       4.16143455e-02, 3.08119274e-02, 2.86933912e-02, 1.47589096e-02,\n",
       "       1.06379741e-02, 7.78968542e-03, 5.72654636e-03, 3.49183444e-03,\n",
       "       3.16734133e-03, 1.57268674e-03, 8.27965421e-04, 2.82821576e-04,\n",
       "       7.06999352e-05])"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 bileşenle 15 tane değişkenin verisini taşıyabiliyorsak bunu uygulayabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FİNAL VERİ SETİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components = 3)\n",
    "pca_fit = pca.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0.45245466, 0.24246801, 0.1016968 ])"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Yaklaşık olarak 3 bileşen ile % 80 verisetindeki değişkenlik açıklanabilr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# son"
   ]
  }
 ]
}