Multiple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "311d23ab",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9c8a35",
   "metadata": {},
   "source": [
    "Bu projede TV, Radyo ve Gazete ilanlarına göre satış rakamları var.\n",
    "Sales Sütunu hedef   yani y=df['Sales'].\n",
    "TV, radyo ve gazeteye ayrı ayrı ne kadar yatırım yaparsam satış rakamlarım ne kadar artar sorusuna cevap arayacağız \n",
    "ve Hangi reklam yönteminin daha başarılı olduğunu bulacağız \n",
    "Test olarak ayırdığımız veri ile tahmin ettiğimiz veriyi aynı grafikte line chart ile görselleştireceğiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aeb140d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kütüphaneleri import etmekle başlıyoruz\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b81baba7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33ad85ed",
   "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": [
    "#EDA-Exploratory Data Analysis\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d4bac44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6fed43b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TV         200 non-null    float64\n",
      " 1   Radio      200 non-null    float64\n",
      " 2   Newspaper  200 non-null    float64\n",
      " 3   Sales      200 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 6.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8bd24ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "91b5c6d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(numeric_only=True),annot=True,cmap='RdYlGn' );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4030838b",
   "metadata": {},
   "source": [
    "Corelasyon tablosundan görüyoruz ki tv ile sales arasında pozitif yüksek bir ilişki var \n",
    "ve arkasından radio ve onun da arkasından  newspaper geliyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7aac05e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0\n",
       "Radio        0\n",
       "Newspaper    0\n",
       "Sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6f99c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#verimizde  düzeltme yapılması gereken bir durum olmadığı için feature engineeringe gerek kalmıyor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f2d4a0c",
   "metadata": {},
   "source": [
    "## Modelleme-modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "22ed06a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.loc[:,['TV','Radio','Newspaper']]\n",
    "y=df.loc[:,['Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a1a6ee89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd4bf314",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbab18cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a734d0ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d5d6352a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin=lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0a125a25",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.4080242 ],\n",
       "       [20.88988209],\n",
       "       [21.55384318],\n",
       "       [10.60850256],\n",
       "       [22.11237326],\n",
       "       [13.10559172],\n",
       "       [21.05719192],\n",
       "       [ 7.46101034],\n",
       "       [13.60634581],\n",
       "       [15.15506967],\n",
       "       [ 9.04831992],\n",
       "       [ 6.65328312],\n",
       "       [14.34554487],\n",
       "       [ 8.90349333],\n",
       "       [ 9.68959028],\n",
       "       [12.16494386],\n",
       "       [ 8.73628397],\n",
       "       [16.26507258],\n",
       "       [10.27759582],\n",
       "       [18.83109103],\n",
       "       [19.56036653],\n",
       "       [13.25103464],\n",
       "       [12.33620695],\n",
       "       [21.30695132],\n",
       "       [ 7.82740305],\n",
       "       [ 5.80957448],\n",
       "       [20.75753231],\n",
       "       [11.98138077],\n",
       "       [ 9.18349576],\n",
       "       [ 8.5066991 ],\n",
       "       [12.46646769],\n",
       "       [10.00337695],\n",
       "       [21.3876709 ],\n",
       "       [12.24966368],\n",
       "       [18.26661538],\n",
       "       [20.13766267],\n",
       "       [14.05514005],\n",
       "       [20.85411186],\n",
       "       [11.0174441 ],\n",
       "       [ 4.56899622]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "00093bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.899438024100912"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,tahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ff81287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7815996615334502"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,tahmin)**.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "11c7efd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4d2226d9",
   "metadata": {},
   "outputs": [],
   "source": [
    " GBR=GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9db4c22e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fb669c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GBR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c1578891",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin=GBR.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fccc1044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9836443833464613"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,tahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2cc5705",
   "metadata": {},
   "outputs": [],
   "source": [
    "#En başarılı yöntem budur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7133b655",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Test verileri\n",
    "x = ['TV','Radio','Newspaper']\n",
    "# Tahmin edilen veriler\n",
    "y= [1.2, 1.8, 2.5, 3.7, 4.2]\n",
    "\n",
    "# Grafik çizimi\n",
    "plt.plot(x, label='Test Verileri')\n",
    "plt.plot(y, label='Tahmin Edilen Veriler')\n",
    "\n",
    "# Eksen etiketleri ve başlık\n",
    "plt.xlabel('Örnek Numarası')\n",
    "plt.ylabel('Değer')\n",
    "plt.title('Test Verileri ve Tahmin Edilen Veriler')\n",
    "\n",
    "# Lejant (açıklama) gösterimi\n",
    "plt.legend()\n",
    "\n",
    "# Grafik gösterimi\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44f60979",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}