Basit_Dogrusal_Regresyon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basit Doğrusal Regresyon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basit doğrusal regrosyon , değişken arasındaki ilişkiyi tanımlamak için kullanılan doğrusal bir modelin yaklaşımıdır.Formülünü aslında hepimiz biliyoruz :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align} \n",
    "y=a+bx   (Dogrusal Fonksiyon Denklemi)\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align} \n",
    "y = a + bx + e (Basit Dogrusal Regresyon Denklemi)\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align} \n",
    "y = B_0 + B_1X + e (Basit Dogrusal Regresyon Denklemi)\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Modüller Eklenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### İstenilen veri alınır ve ortama gösterilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=sns.load_dataset(\"anscombe\")\n",
    "df=data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>I</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>I</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>I</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dataset     x     y\n",
       "0       I  10.0  8.04\n",
       "1       I   8.0  6.95\n",
       "2       I  13.0  7.58\n",
       "3       I   9.0  8.81\n",
       "4       I  11.0  8.33"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Verinin özelliklerine bakılır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 44 entries, 0 to 43\n",
      "Data columns (total 3 columns):\n",
      "dataset    44 non-null object\n",
      "x          44 non-null float64\n",
      "y          44 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 1.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### İstenilmeyen sütun silinir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"dataset\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      x     y\n",
       "0  10.0  8.04\n",
       "1   8.0  6.95\n",
       "2  13.0  7.58\n",
       "3   9.0  8.81\n",
       "4  11.0  8.33"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Değişkenlerin yapıları istatistiksel olarak incelenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>x</td>\n",
       "      <td>44.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.198837</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>8.00</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>y</td>\n",
       "      <td>44.0</td>\n",
       "      <td>7.500682</td>\n",
       "      <td>1.958925</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.1175</td>\n",
       "      <td>7.52</td>\n",
       "      <td>8.7475</td>\n",
       "      <td>12.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   count      mean       std  min     25%   50%      75%    max\n",
       "x   44.0  9.000000  3.198837  4.0  7.0000  8.00  11.0000  19.00\n",
       "y   44.0  7.500682  1.958925  3.1  6.1175  7.52   8.7475  12.74"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  İki değişken arasındaki ilişkinin değeri incelenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>x</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>y</td>\n",
       "      <td>0.816366</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "          x         y\n",
       "x  1.000000  0.816366\n",
       "y  0.816366  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Veriler yardımıyla histogram ve regresyon dağılımı bir arada incelenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1721d1bcb88>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Veriler yardımıyla dağılım,yoğunluk ve regresyon grafiği incelenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1721d6e0248>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"x\",\"y\",df,kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Basit doğrusal yöntemlerden bir tanesi olan statsmodel modülü eklenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bağımsız değişken : x ve bağımlı değişken : y olarak seçilip ayrı veri kümesine atanır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[[\"x\"]]\n",
    "Y=df[[\"y\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x\n",
       "0  10.0\n",
       "1   8.0\n",
       "2  13.0\n",
       "3   9.0\n",
       "4  11.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      y\n",
       "0  8.04\n",
       "1  6.95\n",
       "2  7.58\n",
       "3  8.81\n",
       "4  8.33"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bağımsız değişkenin olduğu tabloya sabit eklenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X=sm.add_constant(X)\n",
    "X.head();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model tanıtılır ve ardından ilişki tablosu gösterilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=sm.OLS(Y,X).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   83.92</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 01 Apr 2020</td> <th>  Prob (F-statistic):</th> <td>1.44e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:56:11</td>     <th>  Log-Likelihood:    </th> <td> -67.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    44</td>      <th>  AIC:               </th> <td>   138.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    42</td>      <th>  BIC:               </th> <td>   142.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>const</th> <td>    3.0013</td> <td>    0.521</td> <td>    5.765</td> <td> 0.000</td> <td>    1.951</td> <td>    4.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x</th>     <td>    0.4999</td> <td>    0.055</td> <td>    9.161</td> <td> 0.000</td> <td>    0.390</td> <td>    0.610</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.513</td> <th>  Durbin-Watson:     </th> <td>   2.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.469</td> <th>  Jarque-Bera (JB):  </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.339</td> <th>  Prob(JB):          </th> <td>   0.639</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.167</td> <th>  Cond. No.          </th> <td>    29.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] 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:                      y   R-squared:                       0.666\n",
       "Model:                            OLS   Adj. R-squared:                  0.659\n",
       "Method:                 Least Squares   F-statistic:                     83.92\n",
       "Date:                Wed, 01 Apr 2020   Prob (F-statistic):           1.44e-11\n",
       "Time:                        23:56:11   Log-Likelihood:                -67.358\n",
       "No. Observations:                  44   AIC:                             138.7\n",
       "Df Residuals:                      42   BIC:                             142.3\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          3.0013      0.521      5.765      0.000       1.951       4.052\n",
       "x              0.4999      0.055      9.161      0.000       0.390       0.610\n",
       "==============================================================================\n",
       "Omnibus:                        1.513   Durbin-Watson:                   2.327\n",
       "Prob(Omnibus):                  0.469   Jarque-Bera (JB):                0.896\n",
       "Skew:                           0.339   Prob(JB):                        0.639\n",
       "Kurtosis:                       3.167   Cond. No.                         29.1\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Daha küçük bir tablo getirilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>const</th> <td>    3.0013</td> <td>    0.521</td> <td>    5.765</td> <td> 0.000</td> <td>    1.951</td> <td>    4.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x</th>     <td>    0.4999</td> <td>    0.055</td> <td>    9.161</td> <td> 0.000</td> <td>    0.390</td> <td>    0.610</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Not : P > |t| bize bağımsız değişkenden çıkan katsayının istatistiksel olarak anlamlı olup olmadığını söylemektedir.Genel olarak kabul edilen değer x<0.05'dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Değişkenlerin katsayıları gösterilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    3.001295\n",
       "x        0.499932\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bağımsız değişkenin yani x'in tahmin y değerleri gösterilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     8.000614\n",
       "1     7.000750\n",
       "2     9.500409\n",
       "3     7.500682\n",
       "4     8.500545\n",
       "5    10.000341\n",
       "6     6.000886\n",
       "7     5.001023\n",
       "8     9.000477\n",
       "9     6.500818\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fittedvalues[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      y\n",
       "0  8.04\n",
       "1  6.95\n",
       "2  7.58\n",
       "3  8.81\n",
       "4  8.33"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tablonun Doğrusal Formülü : Y = 3.001295 + X * 0.499932"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "veri=sns.regplot(x=\"x\",y=\"y\",data=df,scatter_kws={\"color\":\"b\",\"s\":15},line_kws={\"color\":\"r\"},ci=None)\n",
    "veri.set_title(\"Y = 3.001295 + X * 0.499932\",fontsize=20);\n",
    "veri.set_xlabel(\"X Değişkeni\",fontsize=12);\n",
    "veri.set_ylabel(\"Y Değişkeni\",fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tahmin değerler = Tahmin değerleri temsil eden doğrusal çizgi \n",
    "##### Gerçek değerler = Nokta değerler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### İkinci yöntem olan sklearn modülü getirilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bağımsız değişken ve bağımlı değişkenler yeniden atanır bu sefer sabit kullanılmayacak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[[\"x\"]]\n",
    "Y=df[\"y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model tanıtılır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression().fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### B0 ve B1 katsayısı çağırılır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0012954545454535 [0.49993182]\n"
     ]
    }
   ],
   "source": [
    "print(model.intercept_,model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.49993182])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bağımsız değişkenin yani  x'in tahmin y değerleri gösterilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.00061364, 7.00075   , 9.50040909, 7.50068182, 8.50054545])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tahmin değer yapılır . Örneğin X=20 ise Y tahmin olarak kaçtır ? (Amaç bağımsız değişkene değer verdiğimizde bağımlı değişkeni bulmaktır)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.99993182])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Birden fazla x değeri de girilebilir. Örneğin X=20,X=30 ve X=48 değerleri için Y'nin tahmin olarak değerleri kaçtır ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.99993182, 17.99925   , 26.99802273])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[20],[30],[48]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gerçek ve tahmin y değerlerini bir arada görmek için iki veri tabloya eklenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "hesap=pd.DataFrame({\"gercek_y\":Y,\"tahmin_y\":model.predict(X)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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>gercek_y</th>\n",
       "      <th>tahmin_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.000750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.58</td>\n",
       "      <td>9.500409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.81</td>\n",
       "      <td>7.500682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.500545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10.000341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.000886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.001023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.000477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.82</td>\n",
       "      <td>6.500818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gercek_y   tahmin_y\n",
       "0      8.04   8.000614\n",
       "1      6.95   7.000750\n",
       "2      7.58   9.500409\n",
       "3      8.81   7.500682\n",
       "4      8.33   8.500545\n",
       "5      9.96  10.000341\n",
       "6      7.24   6.000886\n",
       "7      4.26   5.001023\n",
       "8     10.84   9.000477\n",
       "9      4.82   6.500818"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hesap[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fark hesaplanır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "hesap[\"fark\"]=hesap[\"gercek_y\"]-hesap[\"tahmin_y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "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>gercek_y</th>\n",
       "      <th>tahmin_y</th>\n",
       "      <th>fark</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.000614</td>\n",
       "      <td>0.039386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.000750</td>\n",
       "      <td>-0.050750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.58</td>\n",
       "      <td>9.500409</td>\n",
       "      <td>-1.920409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.81</td>\n",
       "      <td>7.500682</td>\n",
       "      <td>1.309318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.500545</td>\n",
       "      <td>-0.170545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10.000341</td>\n",
       "      <td>-0.040341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.000886</td>\n",
       "      <td>1.239114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.001023</td>\n",
       "      <td>-0.741023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.000477</td>\n",
       "      <td>1.839523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.82</td>\n",
       "      <td>6.500818</td>\n",
       "      <td>-1.680818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gercek_y   tahmin_y      fark\n",
       "0      8.04   8.000614  0.039386\n",
       "1      6.95   7.000750 -0.050750\n",
       "2      7.58   9.500409 -1.920409\n",
       "3      8.81   7.500682  1.309318\n",
       "4      8.33   8.500545 -0.170545\n",
       "5      9.96  10.000341 -0.040341\n",
       "6      7.24   6.000886  1.239114\n",
       "7      4.26   5.001023 -0.741023\n",
       "8     10.84   9.000477  1.839523\n",
       "9      4.82   6.500818 -1.680818"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hesap[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Farklar çizgi grafikle gösterilir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1721f04c488>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=hesap.index,y=hesap[\"fark\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Şekildede görüldüğü gibi farklar '-' ve '+' yani yön bulundurduğundan  '- ' lerden kurtulmak için farkların karesi alınır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "hesap[\"fark_kare\"]=hesap[\"fark\"]**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "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>gercek_y</th>\n",
       "      <th>tahmin_y</th>\n",
       "      <th>fark</th>\n",
       "      <th>fark_kare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.000614</td>\n",
       "      <td>0.039386</td>\n",
       "      <td>0.001551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.000750</td>\n",
       "      <td>-0.050750</td>\n",
       "      <td>0.002576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.58</td>\n",
       "      <td>9.500409</td>\n",
       "      <td>-1.920409</td>\n",
       "      <td>3.687971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.81</td>\n",
       "      <td>7.500682</td>\n",
       "      <td>1.309318</td>\n",
       "      <td>1.714314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.500545</td>\n",
       "      <td>-0.170545</td>\n",
       "      <td>0.029086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10.000341</td>\n",
       "      <td>-0.040341</td>\n",
       "      <td>0.001627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.000886</td>\n",
       "      <td>1.239114</td>\n",
       "      <td>1.535403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.001023</td>\n",
       "      <td>-0.741023</td>\n",
       "      <td>0.549115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.000477</td>\n",
       "      <td>1.839523</td>\n",
       "      <td>3.383844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.82</td>\n",
       "      <td>6.500818</td>\n",
       "      <td>-1.680818</td>\n",
       "      <td>2.825150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gercek_y   tahmin_y      fark  fark_kare\n",
       "0      8.04   8.000614  0.039386   0.001551\n",
       "1      6.95   7.000750 -0.050750   0.002576\n",
       "2      7.58   9.500409 -1.920409   3.687971\n",
       "3      8.81   7.500682  1.309318   1.714314\n",
       "4      8.33   8.500545 -0.170545   0.029086\n",
       "5      9.96  10.000341 -0.040341   0.001627\n",
       "6      7.24   6.000886  1.239114   1.535403\n",
       "7      4.26   5.001023 -0.741023   0.549115\n",
       "8     10.84   9.000477  1.839523   3.383844\n",
       "9      4.82   6.500818 -1.680818   2.825150"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hesap[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MSE değeri hesaplanır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.250856306818182"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = np.mean(hesap[\"fark_kare\"])\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RMSE değeri hesaplanır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1184168752384693"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse=np.sqrt(mse)\n",
    "rmse"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
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