Simple_Linear_Regression_Newspaper_Data.ipynb

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      "name": "Simple Linear Regression Newspaper Data.ipynb",
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      "source": [
        "# ***SIMPLE LINEAR REGRESSION***\n",
        "\n",
        "\n",
        "1. Load Data\n",
        "\n",
        "2. EDA\n",
        "\n",
        "3. Built Regression equation\n",
        "\n",
        "4. Model parameters\n",
        " - beta0, \n",
        " - beta1, \n",
        " - t-values, \n",
        " - p-values, \n",
        " - r squared values\n",
        "\n",
        "5. Prediction\n",
        "\n"
      ],
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        "id": "B-_aUesbLw_3"
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      "cell_type": "markdown",
      "source": [
        "***1. Load Data***"
      ],
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      }
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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      "execution_count": 2,
      "outputs": []
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      "execution_count": 3,
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        "id": "z1m5wvwJLns-"
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      "source": [
        "data = pd.read_csv('/content/NewspaperData.csv')"
      ]
    },
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      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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              "            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"
            ],
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              "      <th></th>\n",
              "      <th>Newspaper</th>\n",
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              "      <th>0</th>\n",
              "      <td>Baltimore Sun</td>\n",
              "      <td>391.952</td>\n",
              "      <td>488.506</td>\n",
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              "    <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",
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      "cell_type": "markdown",
      "source": [
        "***2. EDA***"
      ],
      "metadata": {
        "id": "VFnOW4IkI-4r"
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      "cell_type": "code",
      "source": [
        "data.info()"
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          "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"
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      "source": [
        "#Correlation Coef\n",
        "data.corr()"
      ],
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            "text/plain": [
              "           daily    sunday\n",
              "daily   1.000000  0.958154\n",
              "sunday  0.958154  1.000000"
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              "      <th>daily</th>\n",
              "      <th>sunday</th>\n",
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              "  </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",
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        "import seaborn as sns"
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      "outputs": []
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      "outputs": [
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          "text": [
            "/usr/local/lib/python3.7/dist-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"
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f582ce3f550>"
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data.sunday)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "hsO2iCiJIqC5",
        "outputId": "1a6747a8-f3d4-4baa-dad6-50296be960a2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f582d71e690>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***3. Fitting a Linear Regression Equation***"
      ],
      "metadata": {
        "id": "5AxTLZ76JJaY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "tEwgOAtBIu9R"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = smf.ols(\"sunday~daily\", data = data).fit()"
      ],
      "metadata": {
        "id": "fJzbwDmTJO92"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x =\"daily\", y =\"sunday\", data = data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "NRZQtfY2J4sO",
        "outputId": "87a4ce18-d4f5-4731-878c-03fcaeb3a92b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f581f7383d0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#coeffecients\n",
        "model.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbamLij9KQtV",
        "outputId": "d1afb91e-db82-4691-a2fb-173e48419d06"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept    13.835630\n",
              "daily         1.339715\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#t-value, p-value\n",
        "print(model.tvalues,'\\n', model.pvalues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPZUO4l0Kaz8",
        "outputId": "1380f097-0111-4b55-f304-d447d099ffd0"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#R squared values\n",
        "model.rsquared"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPWbC6MyKyIL",
        "outputId": "00a3eb66-b3c4-493e-dfb7-0279bee71c92"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9180596895873295"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Prediction***"
      ],
      "metadata": {
        "id": "BijEc5zsMWw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict fro 200 and 300 daily circulation\n",
        "newdata = pd.Series([200,300])"
      ],
      "metadata": {
        "id": "9yz7gLwGL0ub"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YH2b3dIKRJGz",
        "outputId": "bd80c46f-437b-4b4d-f517-dac909224669"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    200\n",
              "1    300\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred = pd.DataFrame(newdata, columns = ['daily'])"
      ],
      "metadata": {
        "id": "Nw4M72w-Raqh"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "DqbX6EM0Ryix",
        "outputId": "25541426-51fd-4168-ffcd-5d49460517ee"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   daily\n",
              "0    200\n",
              "1    300"
            ],
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-b688093a-1f1f-4a88-accf-40b3572553ce');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(data_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdqvLXJ8R6gr",
        "outputId": "e519cbdf-1379-4ff0-f244-5e1114e6679b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    281.778581\n",
              "1    415.750057\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "j13j-92wSBT4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}