Coklu_Dogrusal_Regresyon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e7892fa",
   "metadata": {},
   "source": [
    "# Çoklu Doğrusal Regresyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57757dd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "ad = pd.read_csv(\"Advertising.csv\", usecols = [1,2,3,4])\n",
    "df = ad.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "144e8895",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "482c784d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sales değişkenini dışarıda bırakıp tüm bağımsız değişkenleri seçme işlemi\n",
    "X = df.drop(\"sales\", axis = 1)\n",
    "y = df[\"sales\"]\n",
    "\n",
    "# Test ve train ayırma işlemleri\n",
    "# Her seferinde farklı ifadeler gelmesin diye random_state kullandık\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eac97773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 3)\n",
      "(40, 3)\n",
      "(160,)\n",
      "(40,)\n"
     ]
    }
   ],
   "source": [
    "# Test ve train gözlemlerinin boyutlarına bakalım\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1228f01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veri setinin tüm hâlini oluşturan traning isimli değişken oluşturuyoruz\n",
    "training = df.copy()\n",
    "training.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c7b646",
   "metadata": {},
   "source": [
    "## Statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d3a469ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Model oluşturma\n",
    "lm = sm.OLS(y_train, X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "561ea2cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>sales</td>      <th>  R-squared (uncentered):</th>      <td>   0.982</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.982</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   2935.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 13 Apr 2023</td> <th>  Prob (F-statistic):</th>          <td>1.28e-137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:54:28</td>     <th>  Log-Likelihood:    </th>          <td> -336.65</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   160</td>      <th>  AIC:               </th>          <td>   679.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   157</td>      <th>  BIC:               </th>          <td>   688.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0531</td> <td>    0.001</td> <td>   36.467</td> <td> 0.000</td> <td>    0.050</td> <td>    0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>radio</th>     <td>    0.2188</td> <td>    0.011</td> <td>   20.138</td> <td> 0.000</td> <td>    0.197</td> <td>    0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>newspaper</th> <td>    0.0239</td> <td>    0.008</td> <td>    3.011</td> <td> 0.003</td> <td>    0.008</td> <td>    0.040</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.405</td> <th>  Durbin-Watson:     </th> <td>   1.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>  15.574</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.432</td> <th>  Prob(JB):          </th> <td>0.000415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.261</td> <th>  Cond. No.          </th> <td>    13.5</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                  sales   R-squared (uncentered):                   0.982\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.982\n",
       "Method:                 Least Squares   F-statistic:                              2935.\n",
       "Date:                Thu, 13 Apr 2023   Prob (F-statistic):                   1.28e-137\n",
       "Time:                        22:54:28   Log-Likelihood:                         -336.65\n",
       "No. Observations:                 160   AIC:                                      679.3\n",
       "Df Residuals:                     157   BIC:                                      688.5\n",
       "Df Model:                           3                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "TV             0.0531      0.001     36.467      0.000       0.050       0.056\n",
       "radio          0.2188      0.011     20.138      0.000       0.197       0.240\n",
       "newspaper      0.0239      0.008      3.011      0.003       0.008       0.040\n",
       "==============================================================================\n",
       "Omnibus:                       11.405   Durbin-Watson:                   1.895\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               15.574\n",
       "Skew:                          -0.432   Prob(JB):                     0.000415\n",
       "Kurtosis:                       4.261   Cond. No.                         13.5\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model uygulama\n",
    "model = lm.fit()\n",
    "\n",
    "# Modelin çıktılarına ulaşma\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9341c716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0531</td> <td>    0.001</td> <td>   36.467</td> <td> 0.000</td> <td>    0.050</td> <td>    0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>radio</th>     <td>    0.2188</td> <td>    0.011</td> <td>   20.138</td> <td> 0.000</td> <td>    0.197</td> <td>    0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>newspaper</th> <td>    0.0239</td> <td>    0.008</td> <td>    3.011</td> <td> 0.003</td> <td>    0.008</td> <td>    0.040</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coefli tabloya erişmek istersek\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eec8ae1",
   "metadata": {},
   "source": [
    "## skicit-learn ile model oluşturma "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "70d88ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9790673381226274"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "model = lm.fit(X_train, y_train)\n",
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0c8cfe59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04472952, 0.18919505, 0.00276111])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a205d2db",
   "metadata": {},
   "source": [
    "## Tahmin\n",
    "\n",
    "Model denklemi:\n",
    "\n",
    "Sales = 2.97 + TV*0.04 + radio*0.18 % newspaper^0.002\n",
    "\n",
    "Örneğin 30 birim Tv harcaması, 10 birim radio harcaması, 40 birim de gazete harcaması olduğunda satışların tahmini değeri ne olur?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4145a322",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = [[30], [10], [40]]\n",
    "new_data = pd.DataFrame(new_data).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "166a2243",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([6.32334798])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2a07c0cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6447277656443373"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eğitim setinin hatasına ulaşma\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_train, model.predict(X_train)))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8fcb17f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7815996615334506"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test setinin hatasına ulaşma\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, model.predict(X_test)))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6aa51a",
   "metadata": {},
   "source": [
    "## Model Tuning / Model Doğrulama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0909622c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"sales\", axis = 1)\n",
    "y = df[\"sales\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size = 0.20, \n",
    "                                                    random_state = 42)\n",
    "lm = LinearRegression()\n",
    "model = lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ed8ea111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6447277656443373"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_train, model.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a1b0a89d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7815996615334506"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cba536a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8957008271017817"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "76a20605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8853562237979616"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Modelimizin 10 tane rastgele r2 değerinin ortalaması ile doğruluğu\n",
    "cross_val_score(model, X, y, cv = 10, scoring = \"r2\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "84d0154e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6513523730313335"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eğitim hatası için model doğruluğu yukarıdaki train sqrt değeri ile karşılaştırabiliriz\n",
    "# Başında eksi olması +'ya çevirme\n",
    "np.sqrt(-cross_val_score(model, \n",
    "                X_train, \n",
    "                y_train, \n",
    "                cv = 10, \n",
    "                scoring = \"neg_mean_squared_error\")).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d7715a0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8462778823997088"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test hatası için model doğruluğu yukarıdaki train sqrt değeri ile karşılaştırabiliriz \n",
    "np.sqrt(-cross_val_score(model,\n",
    "                X_test, \n",
    "                y_test, \n",
    "                cv = 10, \n",
    "                scoring = \"neg_mean_squared_error\")).mean()"
   ]
  }
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