yogeshchappewar/news_paper_main.ipynb

## news_paper_main.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Simple linear regression"
    },
    {
      "metadata": {
        "id": "UgLPjlenOAZU"
      },
      "cell_type": "markdown",
      "source": "# Import Data Set"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:51.805484Z",
          "start_time": "2023-01-20T14:14:50.699728Z"
        },
        "id": "t87KFKu3OAZV",
        "outputId": "9fff4407-61bf-480a-a972-cc45fa6cef2c",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\ndata = pd.read_csv(\"NewspaperData.csv\")\ndata.head()   ",
      "execution_count": 1,
      "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\n0       Baltimore Sun  391.952  488.506\n1        Boston Globe  516.981  798.298\n2       Boston Herald  355.628  235.084\n3  Charlotte Observer  238.555  299.451\n4   Chicago Sun Times  537.780  559.093"
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:51.833911Z",
          "start_time": "2023-01-20T14:14:51.808649Z"
        },
        "id": "gPex3jqaon5p",
        "outputId": "24ec8293-e1fb-42da-e5b8-d703c17fade9",
        "trusted": true
      },
      "cell_type": "code",
      "source": "data.info()  ",
      "execution_count": 2,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 34 entries, 0 to 33\nData 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\ndtypes: float64(2), object(1)\nmemory usage: 944.0+ bytes\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-21T05:10:41.666314Z",
          "start_time": "2023-01-21T05:10:41.524765Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nplt.plot(data.daily,data.sunday, \"bo\") \nplt.xlabel(\"daily\")\nplt.ylabel(\"sunday\") \nplt.title(\"scattetr plot\")",
      "execution_count": 17,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0.5, 1.0, 'scattetr plot')"
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "id": "whOIvCAzOAZZ"
      },
      "cell_type": "markdown",
      "source": "# Correlation"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:53.124358Z",
          "start_time": "2023-01-20T14:14:53.107840Z"
        },
        "id": "4Uaqam_BOAZZ",
        "outputId": "2118fe0b-d1d6-4474-cf45-8e8b0b4680b2",
        "trusted": true
      },
      "cell_type": "code",
      "source": "data.corr()",
      "execution_count": 4,
      "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>daily</th>\n      <td>1.000000</td>\n      <td>0.958154</td>\n    </tr>\n    <tr>\n      <th>sunday</th>\n      <td>0.958154</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "           daily    sunday\ndaily   1.000000  0.958154\nsunday  0.958154  1.000000"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:53.149331Z",
          "start_time": "2023-01-20T14:14:53.124752Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "data.describe() ",
      "execution_count": 5,
      "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\ncount    34.000000    34.000000\nmean    430.962471   591.202412\nstd     269.211470   376.418051\nmin     133.239000   202.614000\n25%     233.021500   327.769500\n50%     355.235500   436.712500\n75%     516.616500   699.735250\nmax    1209.225000  1762.015000"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:54.799087Z",
          "start_time": "2023-01-20T14:14:53.149331Z"
        },
        "id": "3fyE2q6-on5s",
        "outputId": "2bfd566b-dd44-4ce1-9922-a49f0ee6c3ca",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import seaborn as sns\nsns.distplot(data['daily'])",
      "execution_count": 6,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "C:\\Users\\yogesh\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n  warnings.warn(msg, FutureWarning)\n"
        },
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='daily', ylabel='Density'>"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:54.962059Z",
          "start_time": "2023-01-20T14:14:54.800593Z"
        },
        "id": "4VeREtJuon5u",
        "outputId": "ecbe8015-72a8-47f4-8d63-a3e4540897f0",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings('ignore')\nimport seaborn as sns\nsns.distplot(data['sunday']) ",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='sunday', ylabel='Density'>"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "id": "Hh2B9xPQOAZc"
      },
      "cell_type": "markdown",
      "source": "Fitting a Linear Regression Model"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.238126Z",
          "start_time": "2023-01-20T14:14:54.964008Z"
        },
        "id": "0SO63uMnOAZc",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import statsmodels.formula.api as smf\nmodel = smf.ols(\"sunday~daily\",data = data).fit()  ",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.270725Z",
          "start_time": "2023-01-20T14:14:55.239554Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.summary()  ",
      "execution_count": 9,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>         <td>sunday</td>      <th>  R-squared:         </th> <td>   0.918</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.915</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   358.5</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Fri, 20 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>6.02e-19</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>19:44:55</td>     <th>  Log-Likelihood:    </th> <td> -206.85</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    34</td>      <th>  AIC:               </th> <td>   417.7</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    32</td>      <th>  BIC:               </th> <td>   420.8</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>Intercept</th> <td>   13.8356</td> <td>   35.804</td> <td>    0.386</td> <td> 0.702</td> <td>  -59.095</td> <td>   86.766</td>\n</tr>\n<tr>\n  <th>daily</th>     <td>    1.3397</td> <td>    0.071</td> <td>   18.935</td> <td> 0.000</td> <td>    1.196</td> <td>    1.484</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 3.297</td> <th>  Durbin-Watson:     </th> <td>   2.059</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.192</td> <th>  Jarque-Bera (JB):  </th> <td>   1.990</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.396</td> <th>  Prob(JB):          </th> <td>   0.370</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.882</td> <th>  Cond. No.          </th> <td>    965.</td>\n</tr>\n</table><br/><br/>Notes:<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==============================================================================\nDep. Variable:                 sunday   R-squared:                       0.918\nModel:                            OLS   Adj. R-squared:                  0.915\nMethod:                 Least Squares   F-statistic:                     358.5\nDate:                Fri, 20 Jan 2023   Prob (F-statistic):           6.02e-19\nTime:                        19:44:55   Log-Likelihood:                -206.85\nNo. Observations:                  34   AIC:                             417.7\nDf Residuals:                      32   BIC:                             420.8\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept     13.8356     35.804      0.386      0.702     -59.095      86.766\ndaily          1.3397      0.071     18.935      0.000       1.196       1.484\n==============================================================================\nOmnibus:                        3.297   Durbin-Watson:                   2.059\nProb(Omnibus):                  0.192   Jarque-Bera (JB):                1.990\nSkew:                           0.396   Prob(JB):                        0.370\nKurtosis:                       3.882   Cond. No.                         965.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.564077Z",
          "start_time": "2023-01-20T14:14:55.273387Z"
        },
        "id": "WZPZ9lDqon5z",
        "outputId": "10812e3a-fcff-4062-943b-60763af144cc",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.regplot(x=\"daily\", y=\"sunday\", data=data);",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.588677Z",
          "start_time": "2023-01-20T14:14:55.564077Z"
        },
        "id": "ri7sldAcon50",
        "outputId": "c97e84b2-a34e-4307-c318-e5274c9fc1ed",
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Coefficients\nmodel.params ",
      "execution_count": 11,
      "outputs": [
        {
          "data": {
            "text/plain": "Intercept    13.835630\ndaily         1.339715\ndtype: float64"
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.597822Z",
          "start_time": "2023-01-20T14:14:55.591167Z"
        },
        "id": "otPpC9kzon51",
        "outputId": "018fb059-8181-4f4a-de4a-31059ed52bc9",
        "trusted": true
      },
      "cell_type": "code",
      "source": "#t and p-Values\nprint(model.tvalues, '\\n', model.pvalues)     ",
      "execution_count": 12,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Intercept     0.386427\ndaily        18.934840\ndtype: float64 \n Intercept    7.017382e-01\ndaily        6.016802e-19\ndtype: float64\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.613182Z",
          "start_time": "2023-01-20T14:14:55.599060Z"
        },
        "id": "GFrR1rSvon52",
        "outputId": "cc59c1bb-dae5-4417-c57c-826d2cfd984b",
        "trusted": true
      },
      "cell_type": "code",
      "source": "#R squared values\n(model.rsquared,model.rsquared_adj) ",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/plain": "(0.9180596895873295, 0.9154990548869336)"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "F2S7v7rdOAZf"
      },
      "cell_type": "markdown",
      "source": "# Predict for new data point"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.629695Z",
          "start_time": "2023-01-20T14:14:55.615782Z"
        },
        "id": "rqe52vmCOAZg",
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Predict for 200 and 300 daily circulation\nnewdata=pd.Series([200,250,350])  ",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.646171Z",
          "start_time": "2023-01-20T14:14:55.629695Z"
        },
        "id": "ff1dWLUron53",
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_pred=pd.DataFrame(newdata,columns=['daily'])  ",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T14:14:55.662585Z",
          "start_time": "2023-01-20T14:14:55.646171Z"
        },
        "id": "M1OmCWxvon54",
        "outputId": "a28d53ea-a14c-4aca-97c5-338dc9085712",
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.predict(data_pred)  ",
      "execution_count": 16,
      "outputs": [
        {
          "data": {
            "text/plain": "0    281.778581\n1    348.764319\n2    482.735795\ndtype: float64"
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "news_paper.ipynb",
      "provenance": []
    },
    "gist": {
      "id": "",
      "data": {
        "description": "news_paper_main",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}