Linear_Regression_Study_On_The_3D_Printing_Dataset.ipynb

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        "<a href=\"https://colab.research.google.com/github/akshayugalmogale/3d-Printing-Regression-Model/blob/main/Linear_Regression_Study_On_The_3D_Printing_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "## Linear Regression analysis of the 3D Prinitng Dataset"
      ],
      "id": "262fcf37"
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    {
      "cell_type": "markdown",
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      "source": [
        "### In this notebook, we will perform simple linear regression analysis of the 3D printing dataset and study the various relationships existing between the target variables labels and the predictor varibale features."
      ],
      "id": "07a267a0"
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    {
      "cell_type": "markdown",
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      "source": [
        "## 1. Importing the 3D Print Dataset & Important Python Libraries"
      ],
      "id": "80e95668"
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      "source": [
        "# Importing the libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "id": "833ca1d5"
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    {
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      "source": [
        "#To enable matplot visualization\n",
        "%matplotlib inline"
      ],
      "id": "c0ea2ddf"
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      "metadata": {
        "id": "e3342517"
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      "source": [
        "#Importing the Dataset using .read_csv() function of pandas\n",
        "#We will call the dataset as 'printer'\n",
        "\n",
        "printer = pd.read_csv('/content/3DPrinting_data.csv')"
      ],
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              "   layer_height  wall_thickness  infill_density infill_pattern  \\\n",
              "0          0.02               8              90           grid   \n",
              "1          0.02               7              90      honeycomb   \n",
              "2          0.02               1              80           grid   \n",
              "3          0.02               4              70      honeycomb   \n",
              "4          0.02               6              90           grid   \n",
              "\n",
              "   nozzle_temperature  bed_temperature  print_speed material  fan_speed  \\\n",
              "0                 220               60           40      abs          0   \n",
              "1                 225               65           40      abs         25   \n",
              "2                 230               70           40      abs         50   \n",
              "3                 240               75           40      abs         75   \n",
              "4                 250               80           40      abs        100   \n",
              "\n",
              "   roughness  tension_strength  elongation  \n",
              "0         25                18         1.2  \n",
              "1         32                16         1.4  \n",
              "2         40                 8         0.8  \n",
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          "metadata": {},
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        }
      ],
      "source": [
        "#Let's check few rows of the dataset using the .head() function of pandas\n",
        "printer.head()"
      ],
      "id": "bbaec3a5"
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## Few information regarding the dataset. I will put them here\n",
        "\n",
        "```\n",
        "`# This is formatted as code`\n",
        "```\n",
        "\n",
        "\n",
        "- The dataset contains **12 columns**.\n",
        "- The **first 9 columns** i.e from layer_height to fan_speed are **features**.\n",
        "- The **last 3 columns** i.i from roughness to elongation are **labels**. So, we will be **predicting** these **three** based on the **9 features**.\n",
        "- The various **units** of the **nine features** are as follows:\n",
        "                   1. Layer Height in mm\n",
        "                   2. Wall Thickness in mm\n",
        "                   3. Infill Density in %\n",
        "                   4. Infill Pattern in either Grid or Honeycomb\n",
        "                   5. Nozzle Temperature in Degree C\n",
        "                   6. Bed Temperature in degree C\n",
        "                   7. Print speed in mm/s\n",
        "                   8. Material in either abs or pla\n",
        "                   9. Fan Speed in %\n",
        "- The **units** of the **labels** are as follows\n",
        "                   1. Roughness in micro metre\n",
        "                   2. Tension Strength in MPa\n",
        "                   3. Elongation in %\n",
        "        "
      ],
      "id": "3c926cbe"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50 entries, 0 to 49\n",
            "Data columns (total 12 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   layer_height        50 non-null     float64\n",
            " 1   wall_thickness      50 non-null     int64  \n",
            " 2   infill_density      50 non-null     int64  \n",
            " 3   infill_pattern      50 non-null     object \n",
            " 4   nozzle_temperature  50 non-null     int64  \n",
            " 5   bed_temperature     50 non-null     int64  \n",
            " 6   print_speed         50 non-null     int64  \n",
            " 7   material            50 non-null     object \n",
            " 8   fan_speed           50 non-null     int64  \n",
            " 9   roughness           50 non-null     int64  \n",
            " 10  tension_strength    50 non-null     int64  \n",
            " 11  elongation          50 non-null     float64\n",
            "dtypes: float64(2), int64(8), object(2)\n",
            "memory usage: 4.8+ KB\n"
          ]
        }
      ],
      "source": [
        "# Basic information of the dataset using .info() function of pandas\n",
        "printer.info()"
      ],
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "### Basic information\n",
        "- The dataset contains **50 rows** of data.\n",
        "- The columns `infill_pattern` and `material` consists of **categorical entries** (infill_pattern = grid or honeycomb and material = abs or pla)  **instead** of **numerical** entries.\n",
        "- In the **Machine Learning World** this is relatively a **very small dataset** interms of observations. **Still** we can **fit** a good **regression model** out of it and study them."
      ],
      "id": "3ef1ffb8"
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    {
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      "source": [
        "## 2. Data Visualization"
      ],
      "id": "57fb6484"
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    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "In this section we will **visualize** the **data** in the form of **heatmaps** and **pairplots** "
      ],
      "id": "fa527b66"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef4b1a19"
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      "source": [
        "Here we will consider the roughness as the only label and we will predict that using the 9 features.\n",
        "\n",
        "First, let's convert the categorical features into numerical ones using the `.replace()` function of numpy"
      ],
      "id": "ef4b1a19"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cb346ef"
      },
      "source": [
        "We will replace infill_pattern and material as follows\n",
        "*            `Grid` as **0** and `honeycomb` as **1**\n",
        "*            `abs` as **0** and `pla` as **1**"
      ],
      "id": "9cb346ef"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b613e2b3"
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      "outputs": [],
      "source": [
        "printer['infill_pattern'].replace(['grid','honeycomb'], [0,1], inplace = True)\n",
        "printer['material'].replace(['abs','pla'], [0,1], inplace = True)"
      ],
      "id": "b613e2b3"
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              "   layer_height  wall_thickness  infill_density  infill_pattern  \\\n",
              "0          0.02               8              90               0   \n",
              "1          0.02               7              90               1   \n",
              "2          0.02               1              80               0   \n",
              "3          0.02               4              70               1   \n",
              "4          0.02               6              90               0   \n",
              "5          0.02              10              40               1   \n",
              "6          0.02               5              10               0   \n",
              "7          0.02              10              10               1   \n",
              "8          0.02               9              70               0   \n",
              "9          0.02               8              40               1   \n",
              "\n",
              "   nozzle_temperature  bed_temperature  print_speed  material  fan_speed  \\\n",
              "0                 220               60           40         0          0   \n",
              "1                 225               65           40         0         25   \n",
              "2                 230               70           40         0         50   \n",
              "3                 240               75           40         0         75   \n",
              "4                 250               80           40         0        100   \n",
              "5                 200               60           40         1          0   \n",
              "6                 205               65           40         1         25   \n",
              "7                 210               70           40         1         50   \n",
              "8                 215               75           40         1         75   \n",
              "9                 220               80           40         1        100   \n",
              "\n",
              "   roughness  tension_strength  elongation  \n",
              "0         25                18         1.2  \n",
              "1         32                16         1.4  \n",
              "2         40                 8         0.8  \n",
              "3         68                10         0.5  \n",
              "4         92                 5         0.7  \n",
              "5         60                24         1.1  \n",
              "6         55                12         1.3  \n",
              "7         21                14         1.5  \n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02</td>\n",
              "      <td>1</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>230</td>\n",
              "      <td>70</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.02</td>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>240</td>\n",
              "      <td>75</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>75</td>\n",
              "      <td>68</td>\n",
              "      <td>10</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.02</td>\n",
              "      <td>6</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>250</td>\n",
              "      <td>80</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>92</td>\n",
              "      <td>5</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.02</td>\n",
              "      <td>10</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>200</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>24</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.02</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>205</td>\n",
              "      <td>65</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>55</td>\n",
              "      <td>12</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.02</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>210</td>\n",
              "      <td>70</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>14</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.02</td>\n",
              "      <td>9</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>215</td>\n",
              "      <td>75</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>75</td>\n",
              "      <td>24</td>\n",
              "      <td>27</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.02</td>\n",
              "      <td>8</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>220</td>\n",
              "      <td>80</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>30</td>\n",
              "      <td>25</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8b4e2c5-80fa-4395-86b4-50afe85e1abb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8b4e2c5-80fa-4395-86b4-50afe85e1abb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8b4e2c5-80fa-4395-86b4-50afe85e1abb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ],
      "source": [
        "#let's view the first 10 observations\n",
        "printer.head(10)"
      ],
      "id": "78aa74fb"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "940b17ab"
      },
      "source": [
        "Perfect. Now we have **converted** all the **categorical** variables **into numerical** ones."
      ],
      "id": "940b17ab"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "bceb30e1",
        "outputId": "4d38f830-f307-4e81-d29e-ba1490f4929c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Creating the heatmap\n",
        "# First create a space for the heatmap and then draw the heatmap inside the space\n",
        "\n",
        "fig, ax = plt.subplots(figsize = (20,7))\n",
        "\n",
        "#Title for the heatmap\n",
        "title = 'Correlation Heatmap of the 3D printing Dataset'\n",
        "plt.title(title, fontsize = 20)\n",
        "ttl = ax.title\n",
        "\n",
        "# Correlation heatmap using .heatmap() function of sns library\n",
        "sns.heatmap(printer.corr(), cbar = True, cmap = 'cool', annot = True, linewidths = 1, ax = ax)\n",
        "\n",
        "#enable visualization using .show() function of matplot\n",
        "plt.show()"
      ],
      "id": "bceb30e1"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "340aadff"
      },
      "source": [
        "# Model 1 : Predicting roughness based on 9 features.\n",
        "- Here we will be **building** a **multiple linear regression** model to **predict** `roughness` based on all the 9 features from `layer_height` to `fan_speed`"
      ],
      "id": "340aadff"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "87cce4cb"
      },
      "outputs": [],
      "source": [
        "# Defining features and labels\n",
        "X = printer.drop(['roughness','tension_strength','elongation'], axis = 1)\n",
        "y = printer['roughness']"
      ],
      "id": "87cce4cb"
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "226d4bb4"
      },
      "outputs": [],
      "source": [
        "#importing statsmodels library\n",
        "import statsmodels.api as sm"
      ],
      "id": "226d4bb4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "47ca87f3"
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      "source": [
        "# let's define a function for the multiple regression\n",
        "\n",
        "def linear_Regression(x,y):\n",
        "    \n",
        "    x = sm.add_constant(x)\n",
        "    \n",
        "    #defining the model, fitting the model and printing the results\n",
        "    multiple_model = sm.OLS(y,x).fit()\n",
        "    print(multiple_model.summary())"
      ],
      "id": "47ca87f3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "outputId": "de274468-a965-4b92-e15d-44231e061e52"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              roughness   R-squared:                       0.875\n",
            "Model:                            OLS   Adj. R-squared:                  0.851\n",
            "Method:                 Least Squares   F-statistic:                     35.95\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           3.83e-16\n",
            "Time:                        16:52:02   Log-Likelihood:                -248.19\n",
            "No. Observations:                  50   AIC:                             514.4\n",
            "Df Residuals:                      41   BIC:                             531.6\n",
            "Df Model:                           8                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                 -0.9534      0.159     -6.006      0.000      -1.274      -0.633\n",
            "layer_height        1269.4449     87.648     14.483      0.000    1092.437    1446.453\n",
            "wall_thickness         2.3342      2.189      1.066      0.293      -2.087       6.756\n",
            "infill_density        -0.0423      0.234     -0.181      0.857      -0.515       0.430\n",
            "infill_pattern        -0.1255     11.280     -0.011      0.991     -22.907      22.656\n",
            "nozzle_temperature    15.0562      2.529      5.953      0.000       9.948      20.164\n",
            "bed_temperature      -55.6225      9.279     -5.995      0.000     -74.362     -36.883\n",
            "print_speed            0.6496      0.206      3.153      0.003       0.233       1.066\n",
            "material             298.4514     58.364      5.114      0.000     180.582     416.321\n",
            "fan_speed              7.8989      1.238      6.379      0.000       5.398      10.399\n",
            "==============================================================================\n",
            "Omnibus:                        1.107   Durbin-Watson:                   1.467\n",
            "Prob(Omnibus):                  0.575   Jarque-Bera (JB):                0.749\n",
            "Skew:                           0.300   Prob(JB):                        0.688\n",
            "Kurtosis:                       3.018   Cond. No.                     3.59e+18\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.48e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ],
      "source": [
        "#calling the linear regression function\n",
        "linear_Regression(X,y)"
      ],
      "id": "b44cac1e"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbab9b9f"
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      "source": [
        "### Inference from model\n",
        "1. The **R-squared** value is **0.875 ot 87.5%** which states that **about 87.5%** of proportion of **variability** in data can be **explained** by this **linear regression model**. The **r-squared** is the most **important parameter** for a **model evaluation**. higher the value, better is the fit.\n",
        "\n",
        "2. The **p-value** is an **important measure** for evaluating the various **variables**. **Closer** is the **p-value to 1**, the **lesser** is that **feature variable** has to do with the **label variable**. In our model, **wall thickness, infill_density and infill_pattern** are all close to 1 meaning they **don't** play **much role** in roughness.\n",
        "\n",
        "3. Finally, the **expression** for this model can be written from the **coefficients** as follows\n",
        "\n",
        "`Roughness = -0.9534 + 1269.4449*layer_height + 2.3342*wall_thickness - 0.0423*infill_density - 0.1255*infill_pattern + 15.0562*nozzle_temperature - 55.6225*bbed_temperature + 0.6496*print_speed + 298.4514*material + 7.8989*fan_speed`\n",
        "\n",
        "4. The coefficients suggest the following\n",
        "   - In the **absence** of all the **parameters**, the **roughness decreases** by **0.9534 micro metre** by the constant term. This may be **due** to **other factors** like **ambient temperature** which are **not considered** for the model.\n",
        "   - A **positive coefficient value** states that an **increase in wall thickness** (example) by 1 mm results in **increase in roughness** by 2.3342 micro metre.\n",
        "   - Similarly, **a negative coefficient value** states that an **increase in bed temperature** by a degree C causes **55.6225 micro metre decrease** in roughness."
      ],
      "id": "cbab9b9f"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "defd5e4f"
      },
      "source": [
        "### Now let's eliminate `wall thickness`, `infill density` and `infill pattern` and build the model to see if there's any improvement."
      ],
      "id": "defd5e4f"
    },
    {
      "cell_type": "code",
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              "   layer_height  wall_thickness  infill_density  infill_pattern  \\\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8478fa0d-238d-4730-b3dd-fe79366e5d90 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8478fa0d-238d-4730-b3dd-fe79366e5d90');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ],
      "source": [
        "#check X\n",
        "X.head()"
      ],
      "id": "026086bf"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "90ce523f"
      },
      "outputs": [],
      "source": [
        "X = X.drop(['wall_thickness','infill_density','infill_pattern'], axis = 1)"
      ],
      "id": "90ce523f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "cac01282",
        "outputId": "853937fb-483a-4442-d2ab-ed77f8cd6468"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   layer_height  nozzle_temperature  bed_temperature  print_speed  material  \\\n",
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              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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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              "  "
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          },
          "metadata": {},
          "execution_count": 232
        }
      ],
      "source": [
        "X.head()"
      ],
      "id": "cac01282"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6a0a79f2",
        "outputId": "f14aba15-f606-48d2-aeeb-b6b7fd5a3304"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              roughness   R-squared:                       0.872\n",
            "Model:                            OLS   Adj. R-squared:                  0.857\n",
            "Method:                 Least Squares   F-statistic:                     59.78\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           1.67e-18\n",
            "Time:                        16:52:02   Log-Likelihood:                -248.88\n",
            "No. Observations:                  50   AIC:                             509.8\n",
            "Df Residuals:                      44   BIC:                             521.2\n",
            "Df Model:                           5                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                 -0.9307      0.151     -6.172      0.000      -1.235      -0.627\n",
            "layer_height        1246.5353     83.178     14.986      0.000    1078.901    1414.169\n",
            "nozzle_temperature    14.7774      2.398      6.163      0.000       9.945      19.610\n",
            "bed_temperature      -54.3045      8.815     -6.160      0.000     -72.070     -36.539\n",
            "print_speed            0.5538      0.180      3.070      0.004       0.190       0.917\n",
            "material             294.1610     56.159      5.238      0.000     180.981     407.341\n",
            "fan_speed              7.6993      1.177      6.542      0.000       5.328      10.071\n",
            "==============================================================================\n",
            "Omnibus:                        0.850   Durbin-Watson:                   1.367\n",
            "Prob(Omnibus):                  0.654   Jarque-Bera (JB):                0.720\n",
            "Skew:                           0.285   Prob(JB):                        0.698\n",
            "Kurtosis:                       2.857   Cond. No.                     3.59e+18\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.37e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ],
      "source": [
        "#calling the linear regression function\n",
        "linear_Regression(X,y)"
      ],
      "id": "6a0a79f2"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "643289c5"
      },
      "source": [
        "## Inference\n",
        "1. The **R-squared** value decreases to **87.2%** which is **not much decrease**.\n",
        "2. The **p-values** of all the **considered features** do play an **important role** in predicting the label.\n",
        "3. We can **improve** the **model** further by considering **interaction terms** based on the **correlation heatmap**.\n",
        "4. The various interaction terms are as follows\n",
        "    - Material and nozzle temperature (-0.78)\n",
        "    - Bed temperature and nozzle temperature (0.6)\n",
        "    - Fan speed and nozzle temperatue (0.6)\n",
        "    - Fan speed and bed temperature (1)"
      ],
      "id": "643289c5"
    },
    {
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              "   layer_height  nozzle_temperature  bed_temperature  print_speed  material  \\\n",
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      "source": [
        "#get the interaction terms by multiplying values\n",
        "\n",
        "inter_mn = X['material']*X['nozzle_temperature']\n",
        "inter_bn = X['bed_temperature']*X['nozzle_temperature']\n",
        "inter_fn = X['fan_speed']*X['nozzle_temperature']\n",
        "inter_fb = X['fan_speed']*X['bed_temperature']"
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      "id": "47d3f023"
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              "   layer_height  nozzle_temperature  bed_temperature  print_speed  material  \\\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ],
      "source": [
        "#adding these interaction terms to dataset using .concat() function of pandas\n",
        "#we will call this dataset as interaction\n",
        "\n",
        "interaction = pd.concat([X,inter_mn,inter_bn,inter_fn,inter_fb], axis = 1)\n",
        "\n",
        "#chenge column names of this interaction terms\n",
        "interaction = interaction.rename(columns = {0:'interct_mn', 1:'interact_bn', 2:'interact_fn',\n",
        "                             3:'interact_fb'})\n",
        "\n",
        "interaction.head(10)"
      ],
      "id": "11709e40"
    },
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              roughness   R-squared:                       0.925\n",
            "Model:                            OLS   Adj. R-squared:                  0.910\n",
            "Method:                 Least Squares   F-statistic:                     63.22\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           1.31e-20\n",
            "Time:                        16:52:02   Log-Likelihood:                -235.45\n",
            "No. Observations:                  50   AIC:                             488.9\n",
            "Df Residuals:                      41   BIC:                             506.1\n",
            "Df Model:                           8                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                 -1.0735      0.923     -1.163      0.252      -2.938       0.791\n",
            "layer_height        1246.5353     65.870     18.924      0.000    1113.508    1379.562\n",
            "nozzle_temperature     0.0050      0.004      1.146      0.259      -0.004       0.014\n",
            "bed_temperature      -57.8032     50.004     -1.156      0.254    -158.788      43.182\n",
            "print_speed            0.5538      0.143      3.877      0.000       0.265       0.842\n",
            "material            5516.1791   2836.453      1.945      0.059    -212.153    1.12e+04\n",
            "fan_speed             33.0427     27.526      1.200      0.237     -22.546      88.632\n",
            "interct_mn           -25.7036     12.956     -1.984      0.054     -51.869       0.462\n",
            "interact_bn            0.2588      0.227      1.138      0.262      -0.201       0.718\n",
            "interact_fn           -0.2032      0.174     -1.169      0.249      -0.554       0.148\n",
            "interact_fb            0.1662      0.138      1.206      0.235      -0.112       0.444\n",
            "==============================================================================\n",
            "Omnibus:                        0.934   Durbin-Watson:                   1.345\n",
            "Prob(Omnibus):                  0.627   Jarque-Bera (JB):                0.333\n",
            "Skew:                           0.132   Prob(JB):                        0.847\n",
            "Kurtosis:                       3.300   Cond. No.                     4.80e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 9.58e-32. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
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      ],
      "source": [
        "# NOw let's fit this model to the linear regression function\n",
        "\n",
        "linear_Regression(interaction,y)"
      ],
      "id": "5987bc4c"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "851ff072"
      },
      "source": [
        "## Model Summary\n",
        "- Alright now, we have an increased R-squared of 92.5% or 0.925 which is very high.\n",
        "- We will stop here and declare this model as the most suitable for predicting roughness"
      ],
      "id": "851ff072"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ff027989"
      },
      "source": [
        "## Similarly we will fit models for predicting tension strength and elongation as well. I will reduce the explanations here. The procedure is absolutely same starting with all features and then tuning for better results."
      ],
      "id": "ff027989"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52acd60a"
      },
      "source": [
        "# Model 2: Predicting Tension strength based on features"
      ],
      "id": "52acd60a"
    },
    {
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      "source": [
        "X = printer.drop(['roughness','tension_strength','elongation'], axis = 1)\n",
        "X.head()"
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      "id": "a10e8c0e"
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              "Name: tension_strength, dtype: int64"
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          },
          "metadata": {},
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      ],
      "source": [
        "y = printer['tension_strength']\n",
        "y"
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      "id": "680d24e8"
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          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strength   R-squared:                       0.673\n",
            "Model:                            OLS   Adj. R-squared:                  0.609\n",
            "Method:                 Least Squares   F-statistic:                     10.55\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           6.91e-08\n",
            "Time:                        16:52:02   Log-Likelihood:                -151.94\n",
            "No. Observations:                  50   AIC:                             321.9\n",
            "Df Residuals:                      41   BIC:                             339.1\n",
            "Df Model:                           8                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                  0.0663      0.023      2.863      0.007       0.020       0.113\n",
            "layer_height          55.5972     12.788      4.348      0.000      29.772      81.423\n",
            "wall_thickness         1.0687      0.319      3.346      0.002       0.424       1.714\n",
            "infill_density         0.1629      0.034      4.769      0.000       0.094       0.232\n",
            "infill_pattern        -1.1427      1.646     -0.694      0.491      -4.466       2.181\n",
            "nozzle_temperature    -1.0468      0.369     -2.837      0.007      -1.792      -0.302\n",
            "bed_temperature        3.8647      1.354      2.855      0.007       1.131       6.599\n",
            "print_speed           -0.0156      0.030     -0.519      0.607      -0.076       0.045\n",
            "material             -17.3051      8.515     -2.032      0.049     -34.502      -0.108\n",
            "fan_speed             -0.5719      0.181     -3.166      0.003      -0.937      -0.207\n",
            "==============================================================================\n",
            "Omnibus:                        0.265   Durbin-Watson:                   1.461\n",
            "Prob(Omnibus):                  0.876   Jarque-Bera (JB):                0.456\n",
            "Skew:                           0.060   Prob(JB):                        0.796\n",
            "Kurtosis:                       2.548   Cond. No.                     3.59e+18\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.48e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        },
        {
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            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ],
      "source": [
        "X = X.drop(['infill_pattern','print_speed','material'], axis = 1)\n",
        "X.head()"
      ],
      "id": "d888aa83"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9897844",
        "outputId": "cef196ff-3081-4009-8073-0eb9631fca84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strength   R-squared:                       0.634\n",
            "Model:                            OLS   Adj. R-squared:                  0.593\n",
            "Method:                 Least Squares   F-statistic:                     15.27\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           1.07e-08\n",
            "Time:                        16:52:02   Log-Likelihood:                -154.73\n",
            "No. Observations:                  50   AIC:                             321.5\n",
            "Df Residuals:                      44   BIC:                             332.9\n",
            "Df Model:                           5                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                  0.0191      0.004      4.358      0.000       0.010       0.028\n",
            "layer_height          56.6997     12.886      4.400      0.000      30.730      82.669\n",
            "wall_thickness         1.1478      0.290      3.965      0.000       0.564       1.731\n",
            "infill_density         0.1543      0.034      4.537      0.000       0.086       0.223\n",
            "nozzle_temperature    -0.3028      0.073     -4.141      0.000      -0.450      -0.155\n",
            "bed_temperature        1.1021      0.255      4.318      0.000       0.588       1.617\n",
            "fan_speed             -0.2052      0.043     -4.812      0.000      -0.291      -0.119\n",
            "==============================================================================\n",
            "Omnibus:                        0.429   Durbin-Watson:                   1.310\n",
            "Prob(Omnibus):                  0.807   Jarque-Bera (JB):                0.404\n",
            "Skew:                           0.202   Prob(JB):                        0.817\n",
            "Kurtosis:                       2.824   Cond. No.                     3.43e+18\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.54e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ],
      "source": [
        "linear_Regression(X,y)"
      ],
      "id": "d9897844"
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    {
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      "outputs": [
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          "data": {
            "text/plain": [
              "   layer_height  wall_thickness  infill_density  nozzle_temperature  \\\n",
              "0          0.02               8              90                 220   \n",
              "1          0.02               7              90                 225   \n",
              "2          0.02               1              80                 230   \n",
              "3          0.02               4              70                 240   \n",
              "4          0.02               6              90                 250   \n",
              "\n",
              "   bed_temperature  fan_speed  interct_mn  interact_bn  interact_fn  \\\n",
              "0               60          0           0        13200            0   \n",
              "1               65         25           0        14625         5625   \n",
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          },
          "metadata": {},
          "execution_count": 244
        }
      ],
      "source": [
        "interaction = pd.concat([X,inter_mn,inter_bn,inter_fn,inter_fb], axis = 1)\n",
        "\n",
        "interaction = interaction.rename(columns = {0:'interct_mn', 1:'interact_bn', 2:'interact_fn',\n",
        "                             3:'interact_fb'})\n",
        "\n",
        "interaction.head()"
      ],
      "id": "09ddb8f6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bc9adda",
        "outputId": "de83358e-11d2-4edd-f90e-0f61e2ae6bfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strength   R-squared:                       0.718\n",
            "Model:                            OLS   Adj. R-squared:                  0.663\n",
            "Method:                 Least Squares   F-statistic:                     13.03\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           4.06e-09\n",
            "Time:                        16:52:02   Log-Likelihood:                -148.27\n",
            "No. Observations:                  50   AIC:                             314.5\n",
            "Df Residuals:                      41   BIC:                             331.7\n",
            "Df Model:                           8                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                 -0.0870      0.068     -1.280      0.208      -0.224       0.050\n",
            "layer_height          56.6017     11.739      4.822      0.000      32.895      80.309\n",
            "wall_thickness         1.1373      0.268      4.242      0.000       0.596       1.679\n",
            "infill_density         0.1587      0.032      4.928      0.000       0.094       0.224\n",
            "nozzle_temperature     0.0004      0.000      1.319      0.194      -0.000       0.001\n",
            "bed_temperature       -4.7917      3.832     -1.251      0.218     -12.530       2.946\n",
            "fan_speed              2.1355      1.273      1.678      0.101      -0.435       4.706\n",
            "interct_mn             0.1191      0.096      1.235      0.224      -0.076       0.314\n",
            "interact_bn            0.0221      0.017      1.263      0.214      -0.013       0.057\n",
            "interact_fn           -0.0138      0.007     -2.022      0.050      -0.028   -1.34e-05\n",
            "interact_fb            0.0077      0.004      1.830      0.075      -0.001       0.016\n",
            "==============================================================================\n",
            "Omnibus:                        0.134   Durbin-Watson:                   1.472\n",
            "Prob(Omnibus):                  0.935   Jarque-Bera (JB):                0.207\n",
            "Skew:                          -0.114   Prob(JB):                        0.902\n",
            "Kurtosis:                       2.782   Cond. No.                     5.90e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 6.34e-32. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ],
      "source": [
        "linear_Regression(interaction,y);"
      ],
      "id": "3bc9adda"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7955076f"
      },
      "source": [
        "## No further improvement for tension strength in linear regression"
      ],
      "id": "7955076f"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5085ea80"
      },
      "source": [
        "# Model 3 : Predicitng Elongation based on the features"
      ],
      "id": "5085ea80"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "2ecb234c",
        "outputId": "67918d2c-b65f-4360-8352-fdf42ca5a71c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   layer_height  wall_thickness  infill_density  infill_pattern  \\\n",
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      ],
      "source": [
        "X = printer.drop(['roughness','tension_strength','elongation'], axis = 1)\n",
        "X.head()\n",
        "y= printer[\"elongation\"]"
      ],
      "id": "9daafae2"
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        {
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          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strength   R-squared:                       0.673\n",
            "Model:                            OLS   Adj. R-squared:                  0.609\n",
            "Method:                 Least Squares   F-statistic:                     10.55\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           6.91e-08\n",
            "Time:                        16:52:02   Log-Likelihood:                -151.94\n",
            "No. Observations:                  50   AIC:                             321.9\n",
            "Df Residuals:                      41   BIC:                             339.1\n",
            "Df Model:                           8                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                  0.0663      0.023      2.863      0.007       0.020       0.113\n",
            "layer_height          55.5972     12.788      4.348      0.000      29.772      81.423\n",
            "wall_thickness         1.0687      0.319      3.346      0.002       0.424       1.714\n",
            "infill_density         0.1629      0.034      4.769      0.000       0.094       0.232\n",
            "infill_pattern        -1.1427      1.646     -0.694      0.491      -4.466       2.181\n",
            "nozzle_temperature    -1.0468      0.369     -2.837      0.007      -1.792      -0.302\n",
            "bed_temperature        3.8647      1.354      2.855      0.007       1.131       6.599\n",
            "print_speed           -0.0156      0.030     -0.519      0.607      -0.076       0.045\n",
            "material             -17.3051      8.515     -2.032      0.049     -34.502      -0.108\n",
            "fan_speed             -0.5719      0.181     -3.166      0.003      -0.937      -0.207\n",
            "==============================================================================\n",
            "Omnibus:                        0.265   Durbin-Watson:                   1.461\n",
            "Prob(Omnibus):                  0.876   Jarque-Bera (JB):                0.456\n",
            "Skew:                           0.060   Prob(JB):                        0.796\n",
            "Kurtosis:                       2.548   Cond. No.                     3.59e+18\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.48e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
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          "metadata": {},
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      ],
      "source": [
        "X = X.drop(['infill_pattern','print_speed'], axis = 1)\n",
        "X.head()"
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          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strength   R-squared:                       0.667\n",
            "Model:                            OLS   Adj. R-squared:                  0.620\n",
            "Method:                 Least Squares   F-statistic:                     14.33\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           6.78e-09\n",
            "Time:                        16:52:03   Log-Likelihood:                -152.42\n",
            "No. Observations:                  50   AIC:                             318.8\n",
            "Df Residuals:                      43   BIC:                             332.2\n",
            "Df Model:                           6                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                  0.0646      0.023      2.840      0.007       0.019       0.111\n",
            "layer_height          56.3670     12.448      4.528      0.000      31.262      81.472\n",
            "wall_thickness         1.1117      0.280      3.967      0.000       0.547       1.677\n",
            "infill_density         0.1664      0.033      4.985      0.000       0.099       0.234\n",
            "nozzle_temperature    -1.0289      0.363     -2.833      0.007      -1.761      -0.296\n",
            "bed_temperature        3.7661      1.330      2.831      0.007       1.084       6.449\n",
            "material             -17.1036      8.392     -2.038      0.048     -34.028      -0.180\n",
            "fan_speed             -0.5565      0.177     -3.140      0.003      -0.914      -0.199\n",
            "==============================================================================\n",
            "Omnibus:                        0.578   Durbin-Watson:                   1.501\n",
            "Prob(Omnibus):                  0.749   Jarque-Bera (JB):                0.710\n",
            "Skew:                           0.195   Prob(JB):                        0.701\n",
            "Kurtosis:                       2.566   Cond. No.                     5.42e+18\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 1.02e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        },
        {
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          "name": "stderr",
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              "   bed_temperature  material  fan_speed  interct_mn  interact_bn  interact_fn  \\\n",
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        "\n",
        "interaction = interaction.rename(columns = {0:'interct_mn', 1:'interact_bn', 2:'interact_fn',\n",
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          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strength   R-squared:                       0.718\n",
            "Model:                            OLS   Adj. R-squared:                  0.654\n",
            "Method:                 Least Squares   F-statistic:                     11.30\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           1.54e-08\n",
            "Time:                        16:52:03   Log-Likelihood:                -148.26\n",
            "No. Observations:                  50   AIC:                             316.5\n",
            "Df Residuals:                      40   BIC:                             335.6\n",
            "Df Model:                           9                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "======================================================================================\n",
            "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------\n",
            "const                 -0.0781      0.164     -0.477      0.636      -0.409       0.253\n",
            "layer_height          56.6034     11.884      4.763      0.000      32.585      80.622\n",
            "wall_thickness         1.1375      0.271      4.191      0.000       0.589       1.686\n",
            "infill_density         0.1587      0.033      4.868      0.000       0.093       0.225\n",
            "nozzle_temperature     0.0004      0.001      0.482      0.633      -0.001       0.002\n",
            "bed_temperature       -4.3166      8.863     -0.487      0.629     -22.229      13.595\n",
            "material             -29.9509    502.401     -0.060      0.953   -1045.341     985.440\n",
            "fan_speed              1.8551      4.876      0.380      0.706      -8.000      11.711\n",
            "interct_mn             0.2557      2.295      0.111      0.912      -4.382       4.893\n",
            "interact_bn            0.0199      0.040      0.494      0.624      -0.062       0.101\n",
            "interact_fn           -0.0120      0.031     -0.389      0.700      -0.074       0.050\n",
            "interact_fb            0.0062      0.024      0.256      0.800      -0.043       0.056\n",
            "==============================================================================\n",
            "Omnibus:                        0.143   Durbin-Watson:                   1.471\n",
            "Prob(Omnibus):                  0.931   Jarque-Bera (JB):                0.217\n",
            "Skew:                          -0.118   Prob(JB):                        0.897\n",
            "Kurtosis:                       2.779   Cond. No.                     5.64e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 6.94e-32. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ],
      "source": [
        "linear_Regression(interaction,y)"
      ],
      "id": "27d281b9"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79e21f4f"
      },
      "source": [
        "## Elonagtion and Tension_strength are strongly correlated to one another and both don't behave linearly."
      ],
      "id": "79e21f4f"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1dc83e4"
      },
      "source": [
        "# Summary\n",
        "\n",
        "1. In this notebook, we have studied about the relationship between the 9 features and the 3 features.\n",
        "2. The label roughness behaves perfectly with the 9 features whereas tension strength and elongation behave linearly only for about 72% each which is pretty good but not quite accurate. They can be resolved by polynomial regression which is not the case of interest in here.\n",
        "3. So, this is how you perform a Linear regression study on a given dataset."
      ],
      "id": "b1dc83e4"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bca58ac3"
      },
      "source": [
        "## If you like the work, consider leaving an upvote.\n",
        "\n",
        "# THANKS!!!"
      ],
      "id": "bca58ac3"
    }
  ],
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