Newspaper_Feasability_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4bf489b0",
   "metadata": {},
   "source": [
    "## 1. Import Necessary Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad05edf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d247ae2",
   "metadata": {},
   "source": [
    "## 2. Data Collection & Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af70a16f",
   "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>Newspaper</th>\n",
       "      <th>daily</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baltimore Sun</td>\n",
       "      <td>391.952</td>\n",
       "      <td>488.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Globe</td>\n",
       "      <td>516.981</td>\n",
       "      <td>798.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boston Herald</td>\n",
       "      <td>355.628</td>\n",
       "      <td>235.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Observer</td>\n",
       "      <td>238.555</td>\n",
       "      <td>299.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Sun Times</td>\n",
       "      <td>537.780</td>\n",
       "      <td>559.093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Newspaper    daily   sunday\n",
       "0       Baltimore Sun  391.952  488.506\n",
       "1        Boston Globe  516.981  798.298\n",
       "2       Boston Herald  355.628  235.084\n",
       "3  Charlotte Observer  238.555  299.451\n",
       "4   Chicago Sun Times  537.780  559.093"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data = pd.read_csv(\"NewspaperData.csv\")\n",
    "newspaper_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1f92ba",
   "metadata": {},
   "source": [
    "## 3. Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b92df30",
   "metadata": {},
   "source": [
    "### 3.1 Perform Initial Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8582a1b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "333ccfcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Newspaper    0\n",
       "daily        0\n",
       "sunday       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "966cbf4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34 entries, 0 to 33\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Newspaper  34 non-null     object \n",
      " 1   daily      34 non-null     float64\n",
      " 2   sunday     34 non-null     float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 944.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "newspaper_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29024152",
   "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>daily</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>430.962471</td>\n",
       "      <td>591.202412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>269.211470</td>\n",
       "      <td>376.418051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>133.239000</td>\n",
       "      <td>202.614000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>233.021500</td>\n",
       "      <td>327.769500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>355.235500</td>\n",
       "      <td>436.712500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>516.616500</td>\n",
       "      <td>699.735250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1209.225000</td>\n",
       "      <td>1762.015000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             daily       sunday\n",
       "count    34.000000    34.000000\n",
       "mean    430.962471   591.202412\n",
       "std     269.211470   376.418051\n",
       "min     133.239000   202.614000\n",
       "25%     233.021500   327.769500\n",
       "50%     355.235500   436.712500\n",
       "75%     516.616500   699.735250\n",
       "max    1209.225000  1762.015000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83df3e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Newspaper     object\n",
       "daily        float64\n",
       "sunday       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc426dab",
   "metadata": {},
   "source": [
    "### 3.2 Perform Assumptions Check"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aebe21e",
   "metadata": {},
   "source": [
    "# ==========================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44755bfd",
   "metadata": {},
   "source": [
    "## 1. Linearity Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6fac0420",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='daily',y='sunday',data=newspaper_data)\n",
    "plt.title('Daily Vs Sunday')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c34abf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='daily', y='sunday', data=newspaper_data)\n",
    "plt.title(\"Daily Vs Sunday\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de776e6",
   "metadata": {},
   "source": [
    "### Linearity Test is Failed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d26ba0",
   "metadata": {},
   "source": [
    "## 2. Normality Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80d0a793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(a=newspaper_data['daily'],hist = False)\n",
    "plt.title(\"Daily Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d83c7a3c",
   "metadata": {},
   "source": [
    "### Normality Test is Failed "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada6b946",
   "metadata": {},
   "source": [
    "## 3. Multicollinerity Test\n",
    "\n",
    "Here cannot be check because only one input.\n",
    "So, Multicollinearity test is Pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cdfe6f",
   "metadata": {},
   "source": [
    "##  4. AutoRegession Test\n",
    "\n",
    "Here we don't have input features with datetime datatype.\n",
    "So, No AutoRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a342861",
   "metadata": {},
   "source": [
    "## 5. Homoscedasticity Test II 6. Zero Residual mean\n",
    "\n",
    "This can be performed only after **Model Training**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c799f02",
   "metadata": {},
   "source": [
    "# ============================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781d3597",
   "metadata": {},
   "source": [
    "## 4. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d683888",
   "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>Newspaper</th>\n",
       "      <th>daily</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baltimore Sun</td>\n",
       "      <td>391.952</td>\n",
       "      <td>488.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Globe</td>\n",
       "      <td>516.981</td>\n",
       "      <td>798.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boston Herald</td>\n",
       "      <td>355.628</td>\n",
       "      <td>235.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charlotte Observer</td>\n",
       "      <td>238.555</td>\n",
       "      <td>299.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Sun Times</td>\n",
       "      <td>537.780</td>\n",
       "      <td>559.093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Newspaper    daily   sunday\n",
       "0       Baltimore Sun  391.952  488.506\n",
       "1        Boston Globe  516.981  798.298\n",
       "2       Boston Herald  355.628  235.084\n",
       "3  Charlotte Observer  238.555  299.451\n",
       "4   Chicago Sun Times  537.780  559.093"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f2cc721",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newspaper_data = newspaper_data.drop(\"Newspaper\",axis =1).head()\n",
    "del newspaper_data[\"Newspaper\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8303887c",
   "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>daily</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>391.952</td>\n",
       "      <td>488.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>516.981</td>\n",
       "      <td>798.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>355.628</td>\n",
       "      <td>235.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>238.555</td>\n",
       "      <td>299.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>537.780</td>\n",
       "      <td>559.093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     daily   sunday\n",
       "0  391.952  488.506\n",
       "1  516.981  798.298\n",
       "2  355.628  235.084\n",
       "3  238.555  299.451\n",
       "4  537.780  559.093"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7cf17f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "daily     0\n",
       "sunday    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c49f9537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "daily     float64\n",
       "sunday    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c9c783",
   "metadata": {},
   "source": [
    "## 5. Model Building\n",
    "\n",
    "Linear Regression model can be built by using any of the 2 libraries:\n",
    "\n",
    "1. Statmodels\n",
    "2. sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6c0f7f",
   "metadata": {},
   "source": [
    "### 1. Using statmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "282c31a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1a8beaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.OLS at 0x252734d78e0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model = smf.ols(formula = \"sunday~daily\", data = newspaper_data)\n",
    "linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "167786ea",
   "metadata": {},
   "source": [
    "## 6. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "82c64323",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = linear_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99e966ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    13.835630\n",
       "daily         1.339715\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c924766b",
   "metadata": {},
   "source": [
    "## 7. Model Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d5c6c8",
   "metadata": {},
   "source": [
    "### Manual Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa6639dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147.80713"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = mx+c\n",
    "# If x=100 y= ??\n",
    "\n",
    "(1.339715*100)+13.835630"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "56b82c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "281.77862999999996"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = mx+c\n",
    "# If x=200 y= ??\n",
    "\n",
    "(1.339715*200)+13.835630"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "35d578bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "415.75012999999996"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = mx+c\n",
    "# If x=300 y= ??\n",
    "\n",
    "(1.339715*300)+13.835630"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3727bc5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "549.72163"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = mx+c\n",
    "# If x=400 y= ??\n",
    "\n",
    "(1.339715*400)+13.835630"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d5d2c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "683.69313"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = mx+c\n",
    "# If x=500 y= ??\n",
    "\n",
    "(1.339715*500)+13.835630"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa6c3e1",
   "metadata": {},
   "source": [
    "### Automatic Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d72d553f",
   "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>daily</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>391.952</td>\n",
       "      <td>488.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>516.981</td>\n",
       "      <td>798.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>355.628</td>\n",
       "      <td>235.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>238.555</td>\n",
       "      <td>299.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>537.780</td>\n",
       "      <td>559.093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     daily   sunday\n",
       "0  391.952  488.506\n",
       "1  516.981  798.298\n",
       "2  355.628  235.084\n",
       "3  238.555  299.451\n",
       "4  537.780  559.093"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newspaper_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "308b43fa",
   "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>daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   daily\n",
       "0    100\n",
       "1    200\n",
       "2    300\n",
       "3    400\n",
       "4    500"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.DataFrame(data = {\"daily\":[100,200,300,400,500]})\n",
    "data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0ac57d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    147.807106\n",
       "1    281.778581\n",
       "2    415.750057\n",
       "3    549.721533\n",
       "4    683.693009\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.predict(data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21acc17",
   "metadata": {},
   "source": [
    "## 8. Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881fde0d",
   "metadata": {},
   "source": [
    "### ---------------PARK-----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1646bbb3",
   "metadata": {},
   "source": [
    "## 9. Model Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2a3d28b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "535c3d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "dump(linear_model,open('Newspaper_intell.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "31284977",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c30718b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_itelligence_file = load(open('Newspaper_intell.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8a019f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    147.807106\n",
       "1    281.778581\n",
       "2    415.750057\n",
       "3    549.721533\n",
       "4    683.693009\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_itelligence_file.predict(data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885441a1",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "* Assumptions Tests are failed for this model.\n",
    "* This Model is not good for the future prediction regarding the Sunday publications.\n",
    "* So, we need some transformations or feature Engineering is needed here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2fb85e",
   "metadata": {},
   "source": [
    "# THE END!!!!"
   ]
  }
 ],
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