Assignment_Simple_Linear_Regression_Delivery_Time.ipynb

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      "name": "Assignment Simple Linear Regression Delivery Time.ipynb",
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        "   1) Delivery_time -> Predict delivery time using sorting time  \n",
        "  \n",
        "   Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using Python.\n"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# import libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import statsmodels as smf"
      ],
      "execution_count": null,
      "outputs": []
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        "outputId": "c3e2c5dd-9cb8-461f-ad27-3871152bb94e"
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      "source": [
        "dataset = pd.read_csv(\"delivery_time.csv\")\n",
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Delivery Time</th>\n",
              "      <th>Sorting Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21.00</td>\n",
              "      <td>10</td>\n",
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              "      <th>1</th>\n",
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              "      <td>24.00</td>\n",
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              "      <td>29.00</td>\n",
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            "text/plain": [
              "   Delivery Time  Sorting Time\n",
              "0          21.00            10\n",
              "1          13.50             4\n",
              "2          19.75             6\n",
              "3          24.00             9\n",
              "4          29.00            10"
            ]
          },
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      "source": [
        "dataset.shape"
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            "text/plain": [
              "(21, 2)"
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      "source": [
        "dataset.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21 entries, 0 to 20\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Delivery Time  21 non-null     float64\n",
            " 1   Sorting Time   21 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 464.0 bytes\n"
          ]
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        "Feature Engineering"
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      "source": [
        "# Rename the columns\n",
        "\n",
        "dataset_1 = dataset.rename({'Delivery Time':'Delivery_time', 'Sorting Time':'Sorting_time'}, axis=1)\n",
        "dataset_1"
      ],
      "execution_count": null,
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            "text/plain": [
              "    Delivery_time  Sorting_time\n",
              "0           21.00            10\n",
              "1           13.50             4\n",
              "2           19.75             6\n",
              "3           24.00             9\n",
              "4           29.00            10\n",
              "5           15.35             6\n",
              "6           19.00             7\n",
              "7            9.50             3\n",
              "8           17.90            10\n",
              "9           18.75             9\n",
              "10          19.83             8\n",
              "11          10.75             4\n",
              "12          16.68             7\n",
              "13          11.50             3\n",
              "14          12.03             3\n",
              "15          14.88             4\n",
              "16          13.75             6\n",
              "17          18.11             7\n",
              "18           8.00             2\n",
              "19          17.83             7\n",
              "20          21.50             5"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKCMnk1qX6N1"
      },
      "source": [
        "EDA AND VISUALIZATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "McHJCUdxFGaX",
        "outputId": "1d358a3d-b542-4f22-d2f0-d3e751b0717c"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.distplot(dataset_1['Delivery_time'],color='orange',hist_kws =dict(edgecolor = 'black'))"
      ],
      "execution_count": null,
      "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 0x7fd0706dbad0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "CnYRi8TSZWG3",
        "outputId": "746a067f-68c5-48a1-8e5d-4555d5962a85"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.distplot(dataset_1['Sorting_time'],color='orange',hist_kws =dict(edgecolor = 'black'))"
      ],
      "execution_count": null,
      "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 0x7fd070634750>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kf3BIsktZgu2"
      },
      "source": [
        "Outlier Detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EVlJbmmaE62",
        "outputId": "0af261e8-eba6-4041-c7c8-32750b00d722"
      },
      "source": [
        "dataset_1['Delivery_time'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    21.000000\n",
              "mean     16.790952\n",
              "std       5.074901\n",
              "min       8.000000\n",
              "25%      13.500000\n",
              "50%      17.830000\n",
              "75%      19.750000\n",
              "max      29.000000\n",
              "Name: Delivery_time, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "Q4OxtV_wdNan",
        "outputId": "47b10536-5ef3-47ad-d3c3-116731b700aa"
      },
      "source": [
        "dataset_1.boxplot(column= ['Delivery_time'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd070587410>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "BwWOqoLLeLVT",
        "outputId": "6a8238de-b6c0-4dc1-c8a7-19eed8623913"
      },
      "source": [
        "dataset_1.boxplot(column= ['Sorting_time'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0704ce490>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VN2iLsoieUUc"
      },
      "source": [
        "# There is no Outlier in the dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5sZdolTpidl-"
      },
      "source": [
        "Corelation Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "SEGpdZpAijEF",
        "outputId": "1ab268bd-fa80-45e8-c1ad-cb0fea29eb09"
      },
      "source": [
        "dataset_1.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Delivery_time</th>\n",
              "      <th>Sorting_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Delivery_time</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.825997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sorting_time</th>\n",
              "      <td>0.825997</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Delivery_time  Sorting_time\n",
              "Delivery_time       1.000000      0.825997\n",
              "Sorting_time        0.825997      1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5i547c5eZ3P"
      },
      "source": [
        "Fitting the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6H6-ec3epKM",
        "outputId": "6251de3d-cc43-4762-8bc9-29c68ff839cb"
      },
      "source": [
        "import statsmodels.formula.api as smf\n",
        "\n",
        "model = smf.ols(\"Delivery_time~Sorting_time\",data=dataset_1).fit()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "KPSoMKqCfZi3",
        "outputId": "11c3ff30-dc1f-470d-9309-b02b36967046"
      },
      "source": [
        "sns.regplot(x=\"Sorting_time\", y=\"Delivery_time\", data=dataset_1,color= 'magenta');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WR1ngA_5gJZH",
        "outputId": "25ac3c9f-d93b-44c3-daf6-54dd22b1e18a"
      },
      "source": [
        "#Coefficients\n",
        "model.params"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept       6.582734\n",
              "Sorting_time    1.649020\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kc0_QXRxgRmh",
        "outputId": "6389455e-4de2-405d-a9d5-a7453a41d527"
      },
      "source": [
        "#t and p-Values\n",
        "print(model.tvalues, '\\n', model.pvalues)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept       3.823349\n",
            "Sorting_time    6.387447\n",
            "dtype: float64 \n",
            " Intercept       0.001147\n",
            "Sorting_time    0.000004\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6J6n58lVhnAJ"
      },
      "source": [
        "Checking Accuracy of Model using r2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEGjuG_TgUn6",
        "outputId": "5a1f6867-3dd1-4682-e086-c49bee3009ac"
      },
      "source": [
        "#R squared values\n",
        "(model.rsquared,model.rsquared_adj)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6822714748417231, 0.6655489208860244)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "adgyDGkwgbx1"
      },
      "source": [
        " Determination Coefficient = rsquared value = 0.6820 ; good fit = 68.2%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6h6oLvDfhvW5"
      },
      "source": [
        "Model Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RftAC_qgkyf"
      },
      "source": [
        "# Manual Prediction"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lV8YPr9jgnLO",
        "outputId": "4ce7f341-4c6b-47f8-c040-47e7dcf53258"
      },
      "source": [
        "Delivery_time = 6.582734 + 1.649020*(15)  # lets assume sorting_time as 15\n",
        "Delivery_time"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31.318033999999997"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOmY6A2Rg_mb"
      },
      "source": [
        "# Automatic Prediction"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TnGwbFGhE_7"
      },
      "source": [
        "#Predict for 10,15,20,30 sorting_time\n",
        "newdata=pd.Series([10,15,20,30])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "r9oMTtW2hRqI",
        "outputId": "4762abfc-85c8-4e1a-b965-dddd746085af"
      },
      "source": [
        "data_pred=pd.DataFrame(newdata,columns=['Sorting_time'])\n",
        "data_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sorting_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sorting_time\n",
              "0            10\n",
              "1            15\n",
              "2            20\n",
              "3            30"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CMWjQ0chcIQ",
        "outputId": "3e51061e-ec5f-472f-9cd9-41d241bb0043"
      },
      "source": [
        "model.predict(data_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    23.072933\n",
              "1    31.318032\n",
              "2    39.563132\n",
              "3    56.053331\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qN3DeuuhhgnO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}