Simple_Linear_Regression_news_paper.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UgLPjlenOAZU"
   },
   "source": [
    "# Import Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:50:59.361735Z",
     "start_time": "2021-10-09T07:50:58.490183Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "t87KFKu3OAZV",
    "outputId": "9fff4407-61bf-480a-a972-cc45fa6cef2c"
   },
   "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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"NewspaperData.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:50:59.473088Z",
     "start_time": "2021-10-09T07:50:59.365724Z"
    }
   },
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "whOIvCAzOAZZ"
   },
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:50:59.611720Z",
     "start_time": "2021-10-09T07:50:59.478081Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "4Uaqam_BOAZZ",
    "outputId": "2118fe0b-d1d6-4474-cf45-8e8b0b4680b2"
   },
   "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\n",
       "daily   1.000000  0.958154\n",
       "sunday  0.958154  1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:04.278657Z",
     "start_time": "2021-10-09T07:50:59.617702Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: 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": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(data['daily'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:04.591823Z",
     "start_time": "2021-10-09T07:51:04.281648Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: 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='sunday', ylabel='Density'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import seaborn as sns\n",
    "sns.distplot(data['sunday'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hh2B9xPQOAZc"
   },
   "source": [
    "Fitting a Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:04.873363Z",
     "start_time": "2021-10-09T07:51:04.594812Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "0SO63uMnOAZc",
    "outputId": "d755b8ff-516d-45b1-aab6-6dbf24284151"
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "model = smf.ols(\"sunday~daily\",data = data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:05.441721Z",
     "start_time": "2021-10-09T07:51:04.881343Z"
    }
   },
   "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.regplot(x=\"daily\", y=\"sunday\", data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:05.472443Z",
     "start_time": "2021-10-09T07:51:05.448505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    13.835630\n",
       "daily         1.339715\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Coefficients\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:05.743059Z",
     "start_time": "2021-10-09T07:51:05.478425Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept     0.386427\n",
      "daily        18.934840\n",
      "dtype: float64 \n",
      " Intercept    7.017382e-01\n",
      "daily        6.016802e-19\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# t-values and p-values\n",
    "print(model.tvalues, '\\n', model.pvalues)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:05.882194Z",
     "start_time": "2021-10-09T07:51:05.748045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9180596895873295, 0.9154990548869336)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R squared values\n",
    "(model.rsquared,model.rsquared_adj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F2S7v7rdOAZf"
   },
   "source": [
    "# Predict for new data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:06.068669Z",
     "start_time": "2021-10-09T07:51:05.888181Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "rqe52vmCOAZg",
    "outputId": "1578e5b2-5135-41d4-875a-e580cf87dc6b"
   },
   "outputs": [],
   "source": [
    "#Predict for 200 and 300 daily circulation\n",
    "newdata=pd.Series([392,517])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:06.268806Z",
     "start_time": "2021-10-09T07:51:06.079641Z"
    }
   },
   "outputs": [],
   "source": [
    "data_pred=pd.DataFrame(newdata,columns=['daily'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-09T07:51:06.424397Z",
     "start_time": "2021-10-09T07:51:06.275797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    539.003815\n",
       "1    706.468160\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "news_paper.ipynb",
   "provenance": []
  },
  "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": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}