Öznitelik Seçimi/Geri Eleme Yöntemi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>outlook</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sunny</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sunny</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>overcast</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rainy</td>\n",
       "      <td>70</td>\n",
       "      <td>96</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rainy</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rainy</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>overcast</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sunny</td>\n",
       "      <td>72</td>\n",
       "      <td>95</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sunny</td>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rainy</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sunny</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>overcast</td>\n",
       "      <td>72</td>\n",
       "      <td>90</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>overcast</td>\n",
       "      <td>81</td>\n",
       "      <td>75</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rainy</td>\n",
       "      <td>71</td>\n",
       "      <td>91</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     outlook  temperature  humidity  windy play\n",
       "0      sunny           85        85  False   no\n",
       "1      sunny           80        90   True   no\n",
       "2   overcast           83        86  False  yes\n",
       "3      rainy           70        96  False  yes\n",
       "4      rainy           68        80  False  yes\n",
       "5      rainy           65        70   True   no\n",
       "6   overcast           64        65   True  yes\n",
       "7      sunny           72        95  False   no\n",
       "8      sunny           69        70  False  yes\n",
       "9      rainy           75        80  False  yes\n",
       "10     sunny           75        70   True  yes\n",
       "11  overcast           72        90   True  yes\n",
       "12  overcast           81        75  False  yes\n",
       "13     rainy           71        91   True   no"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://bilkav.com/odev_tenis.csv\"\n",
    "\n",
    "df = pd.read_csv(url)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sunny</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>sunny</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>overcast</td>\n",
       "      <td>83</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>rainy</td>\n",
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      "text/plain": [
       "    outlook  temperature  humidity  windy  play\n",
       "0     sunny           85        85      0     0\n",
       "1     sunny           80        90      1     0\n",
       "2  overcast           83        86      0     1\n",
       "3     rainy           70        96      0     1\n",
       "4     rainy           68        80      0     1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()\n",
    "\n",
    "columns = [\"windy\",\"play\"]\n",
    "\n",
    "for col in columns:\n",
    "    df[col] = label_encoder.fit_transform(df[col])\n",
    "    \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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      "text/plain": [
       "   overcast  rainy  sunny\n",
       "0         0      0      1\n",
       "1         0      0      1\n",
       "2         1      0      0\n",
       "3         0      1      0\n",
       "4         0      1      0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dms = pd.get_dummies(df[\"outlook\"])\n",
    "dms.head()"
   ]
  },
  {
   "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>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
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       "      <th>1</th>\n",
       "      <td>80</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   temperature  humidity  windy  play\n",
       "0           85        85      0     0\n",
       "1           80        90      1     0\n",
       "2           83        86      0     1\n",
       "3           70        96      0     1\n",
       "4           68        80      0     1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"outlook\"], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overcast</th>\n",
       "      <th>rainy</th>\n",
       "      <th>sunny</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>85</td>\n",
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       "      <th>1</th>\n",
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       "      <td>80</td>\n",
       "      <td>90</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>96</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   overcast  rainy  sunny  temperature  humidity  windy  play\n",
       "0         0      0      1           85        85      0     0\n",
       "1         0      0      1           80        90      1     0\n",
       "2         1      0      0           83        86      0     1\n",
       "3         0      1      0           70        96      0     1\n",
       "4         0      1      0           68        80      0     1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([dms, df], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"play\"], axis=1)\n",
    "y = df[[\"play\"]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE >>> 0.14946437379855324 \n",
      "R²  >>> -2.7634149688383376\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression().fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\n",
    "    \"MSE >>>\", mean_squared_error(y_pred, y_test), \"\\n\"\n",
    "    \"R²  >>>\", r2_score(y_pred, y_test),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   play   R-squared:                       0.483\n",
      "Model:                            OLS   Adj. R-squared:                  0.160\n",
      "Method:                 Least Squares   F-statistic:                     1.493\n",
      "Date:                Sun, 25 Apr 2021   Prob (F-statistic):              0.292\n",
      "Time:                        06:29:00   Log-Likelihood:                -4.9501\n",
      "No. Observations:                  14   AIC:                             21.90\n",
      "Df Residuals:                       8   BIC:                             25.73\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1             3.4007      1.859      1.829      0.105      -0.887       7.688\n",
      "x2             2.9474      1.754      1.680      0.131      -1.098       6.993\n",
      "x3             2.8194      1.878      1.501      0.172      -1.512       7.151\n",
      "x4            -0.0143      0.025     -0.569      0.585      -0.072       0.044\n",
      "x5            -0.0142      0.014     -1.031      0.333      -0.046       0.018\n",
      "x6            -0.4108      0.268     -1.533      0.164      -1.029       0.207\n",
      "==============================================================================\n",
      "Omnibus:                        0.118   Durbin-Watson:                   1.317\n",
      "Prob(Omnibus):                  0.943   Jarque-Bera (JB):                0.340\n",
      "Skew:                           0.035   Prob(JB):                        0.844\n",
      "Kurtosis:                       2.240   Cond. No.                     2.86e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.86e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "X = np.append(arr = np.ones(14).astype(int), values = X.iloc[:,:])\n",
    "\n",
    "X_l = df.iloc[:,[0,1,2,3,4,5]].values\n",
    "X_l = np.array(X_l, dtype=float)\n",
    "\n",
    "model = sm.OLS(y, X_l).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   play   R-squared:                       0.462\n",
      "Model:                            OLS   Adj. R-squared:                  0.223\n",
      "Method:                 Least Squares   F-statistic:                     1.931\n",
      "Date:                Sun, 25 Apr 2021   Prob (F-statistic):              0.189\n",
      "Time:                        06:29:00   Log-Likelihood:                -5.2280\n",
      "No. Observations:                  14   AIC:                             20.46\n",
      "Df Residuals:                       9   BIC:                             23.65\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1             2.5317      1.021      2.480      0.035       0.222       4.841\n",
      "x2             2.1712      1.062      2.045      0.071      -0.231       4.573\n",
      "x3             1.9472      1.045      1.863      0.095      -0.417       4.311\n",
      "x4            -0.0171      0.012     -1.399      0.195      -0.045       0.011\n",
      "x5            -0.3586      0.242     -1.481      0.173      -0.906       0.189\n",
      "==============================================================================\n",
      "Omnibus:                        0.557   Durbin-Watson:                   1.461\n",
      "Prob(Omnibus):                  0.757   Jarque-Bera (JB):                0.554\n",
      "Skew:                          -0.005   Prob(JB):                        0.758\n",
      "Kurtosis:                       2.025   Cond. No.                     1.25e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.25e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "X_l = df.iloc[:,[0,1,2,4,5]].values\n",
    "X_l = np.array(X_l, dtype=float)\n",
    "\n",
    "model = sm.OLS(y, X_l).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   play   R-squared:                       0.345\n",
      "Model:                            OLS   Adj. R-squared:                  0.148\n",
      "Method:                 Least Squares   F-statistic:                     1.754\n",
      "Date:                Sun, 25 Apr 2021   Prob (F-statistic):              0.219\n",
      "Time:                        06:29:00   Log-Likelihood:                -6.6049\n",
      "No. Observations:                  14   AIC:                             21.21\n",
      "Df Residuals:                      10   BIC:                             23.77\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1             1.1471      0.261      4.395      0.001       0.565       1.729\n",
      "x2             0.7176      0.228      3.146      0.010       0.209       1.226\n",
      "x3             0.5176      0.228      2.269      0.047       0.009       1.026\n",
      "x4            -0.2941      0.249     -1.182      0.265      -0.849       0.260\n",
      "==============================================================================\n",
      "Omnibus:                        0.739   Durbin-Watson:                   1.608\n",
      "Prob(Omnibus):                  0.691   Jarque-Bera (JB):                0.678\n",
      "Skew:                           0.254   Prob(JB):                        0.712\n",
      "Kurtosis:                       2.049   Cond. No.                         2.25\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_l = df.iloc[:,[0,1,2,5]].values\n",
    "X_l = np.array(X_l, dtype=float)\n",
    "\n",
    "model = sm.OLS(y, X_l).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"temperature\",\"humidity\",\"play\"], axis=1)\n",
    "y = df[[\"play\"]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE >>> 0.2037760416666667 \n",
      "R²  >>> -3.3271889400921673\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression().fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\n",
    "    \"MSE >>>\", mean_squared_error(y_pred, y_test), \"\\n\"\n",
    "    \"R²  >>>\", r2_score(y_pred, y_test),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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