4_Makine_Öğrenmesi_Modelinin_Oluşturulması.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Makine Öğrenmesi Uygulanması"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kütüphanelerin Eklenmesi\n",
    "## Algoritmanın Uygulanması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kütüphanemizi ekliyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV,cross_val_score\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import numpy as np\n",
    "from sklearn import model_selection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### son halini almış temiz verisetini okuyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = pd.read_excel(r\"kopya.xlsx\")\n",
    "df_2.drop(\"Unnamed: 0\", axis = 1, inplace = True)\n",
    "df = df_2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ilçe', 'türü', 'kategori', 'yapı_durumu', 'net_metrekare', 'alan',\n",
       "       'oda_sayısı', 'bina_yaşı', 'dairenin_katı', 'kat_sayısı', 'ısıtma',\n",
       "       'aidat', 'banyo_sayısı', 'wc_sayısı', 'site_içerisinde', 'eşya_durumu',\n",
       "       'kullanım_durumu', 'krediye_uygunluk', 'yatırıma_uygunluk', 'yapı_tipi',\n",
       "       'takas', 'fiyat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Çalışmak istediğimiz sütunları aldık."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"ilçe\",\"türü\",\"net_metrekare\",\"alan\",\"oda_sayısı\",\"bina_yaşı\",\"ısıtma\",\"site_içerisinde\",\n",
    "         \"eşya_durumu\",\"banyo_sayısı\",\"wc_sayısı\",\"dairenin_katı\",\"fiyat\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>ilçe</th>\n",
       "      <th>türü</th>\n",
       "      <th>net_metrekare</th>\n",
       "      <th>alan</th>\n",
       "      <th>oda_sayısı</th>\n",
       "      <th>bina_yaşı</th>\n",
       "      <th>ısıtma</th>\n",
       "      <th>site_içerisinde</th>\n",
       "      <th>eşya_durumu</th>\n",
       "      <th>banyo_sayısı</th>\n",
       "      <th>wc_sayısı</th>\n",
       "      <th>dairenin_katı</th>\n",
       "      <th>fiyat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>190</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>239000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>143</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>159000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>140</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>385000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>814</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>815</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>816</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>817</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>700000</td>\n",
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       "    <tr>\n",
       "      <td>818</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>819 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ilçe  türü  net_metrekare  alan  oda_sayısı  bina_yaşı  ısıtma  \\\n",
       "0       6     1            165   190           6          4       5   \n",
       "1       6     1             85    95           2          0       5   \n",
       "2       6     1            130   143           4          2       5   \n",
       "3       6     1             92   105           2          0       5   \n",
       "4       6     1            120   140           5          3       5   \n",
       "..    ...   ...            ...   ...         ...        ...     ...   \n",
       "814     5     1             85   103           2          7       6   \n",
       "815     5     4             25    41           0          5       6   \n",
       "816     5     1             65    74           1          7       6   \n",
       "817     5     1             85   103           2          7       6   \n",
       "818     5     1             74    96           1          7       6   \n",
       "\n",
       "     site_içerisinde  eşya_durumu  banyo_sayısı  wc_sayısı  dairenin_katı  \\\n",
       "0                  0            0             1          2             16   \n",
       "1                  1            0             1          1              5   \n",
       "2                  1            0             2          0              5   \n",
       "3                  1            0             1          1             19   \n",
       "4                  1            0             1          1             10   \n",
       "..               ...          ...           ...        ...            ...   \n",
       "814                1            0             2          0             13   \n",
       "815                1            1             1          0             10   \n",
       "816                1            0             1          0             11   \n",
       "817                1            0             2          0             19   \n",
       "818                1            0             1          0             15   \n",
       "\n",
       "      fiyat  \n",
       "0    430000  \n",
       "1    239000  \n",
       "2    400000  \n",
       "3    159000  \n",
       "4    385000  \n",
       "..      ...  \n",
       "814  750000  \n",
       "815  360000  \n",
       "816  550000  \n",
       "817  700000  \n",
       "818  600000  \n",
       "\n",
       "[819 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bağımlı ve Bağımsız değişkenleri ayırdık"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"fiyat\"], axis = 1)\n",
    "y = df[\"fiyat\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eğitim ve Test verisinin ayrılması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 144)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GridSearchCV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"colsample_bytree\":[0.4,0.5,0.6],\n",
    "         \"learning_rate\":[0.01,0.02,0.09],\n",
    "         \"max_depth\":[2,3,4,5,6],\n",
    "         \"n_estimators\":[100,200,500,2000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(xgb, params, cv = 10, n_jobs = -1, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### En uygun parametreleri giriyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb1 = XGBRegressor(colsample_bytree = 0.5, learning_rate = 0.09, max_depth = 4, n_estimators = 2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modelimizi eğitiyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb = xgb1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tahmin yapıyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 246285.84, 1144784.  ,  658234.56,  609021.2 ,  381962.12],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb.predict(X_test)[15:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### tahmin edilen ve gerçek verileri karşılaştırabiliriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "489     250000\n",
       "431    1750000\n",
       "279     635000\n",
       "677    1350000\n",
       "200     400000\n",
       "Name: fiyat, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[15:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### modelin skorunu hesapladık. 0-1 arası değer döner bize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8500945754454254"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999980594753378"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### valide edilmiş (doğrulanmış hata oranımızı buluyoruz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5663891.709316989"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(-1*(cross_val_score(model_xgb, X_test, y_test, cv=10, scoring='neg_mean_squared_error'))).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### model de parametrelerin önemini görüyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance = pd.DataFrame({\"Importance\": model_xgb.feature_importances_},\n",
    "                         index=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ilçe</td>\n",
       "      <td>0.032922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>türü</td>\n",
       "      <td>0.334605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>net_metrekare</td>\n",
       "      <td>0.059210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>alan</td>\n",
       "      <td>0.062562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>oda_sayısı</td>\n",
       "      <td>0.022739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bina_yaşı</td>\n",
       "      <td>0.044012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ısıtma</td>\n",
       "      <td>0.008799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>site_içerisinde</td>\n",
       "      <td>0.000181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>eşya_durumu</td>\n",
       "      <td>0.000624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>banyo_sayısı</td>\n",
       "      <td>0.236852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wc_sayısı</td>\n",
       "      <td>0.190893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>dairenin_katı</td>\n",
       "      <td>0.006601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Importance\n",
       "ilçe               0.032922\n",
       "türü               0.334605\n",
       "net_metrekare      0.059210\n",
       "alan               0.062562\n",
       "oda_sayısı         0.022739\n",
       "bina_yaşı          0.044012\n",
       "ısıtma             0.008799\n",
       "site_içerisinde    0.000181\n",
       "eşya_durumu        0.000624\n",
       "banyo_sayısı       0.236852\n",
       "wc_sayısı          0.190893\n",
       "dairenin_katı      0.006601"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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