Predict_Elongation_of_3D_Print_Material.ipynb

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        "<a href=\"https://colab.research.google.com/github/akshayugalmogale/3d-Printing-Regression-Model/blob/main/Predict_Elongation_of_3D_Print_Material.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": "code",
      "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\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "ZgUAngS_4ndS"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "printer = pd.read_csv('/content/3DPrinting_data.csv')"
      ],
      "metadata": {
        "id": "wChMZ4OB4rth"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "printer['infill_pattern'].replace(['grid','honeycomb'], [0,1], inplace = True)\n",
        "printer['material'].replace(['abs','pla'], [0,1], inplace = True)"
      ],
      "metadata": {
        "id": "nEzNuQ9H5XNu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#importing statsmodels library\n",
        "import statsmodels.api as sm\n",
        "# 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())"
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      "source": [
        "# Model 3 : Predicitng Elongation based on the features"
      ]
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              "   layer_height  wall_thickness  infill_density  infill_pattern  \\\n",
              "0          0.02               8              90               0   \n",
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              "4          0.02               6              90               0   \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",
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              "4                 250               80           40         0        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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        "printer.head()"
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              "   layer_height  wall_thickness  infill_density  infill_pattern  \\\n",
              "0          0.02               8              90               0   \n",
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              "2          0.02               1              80               0   \n",
              "3          0.02               4              70               1   \n",
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              "   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",
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        "X = printer.drop(['roughness','tension_strength','elongation'], axis = 1)\n",
        "y = printer['tension_strenght']\n",
        "X.head()"
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          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strenght   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:                        16:17:11   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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          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strenght   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:                        16:17:11   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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      "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",
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        "#adding these interaction terms to dataset using .concat() function of pandas\n",
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        "\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)"
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              "   layer_height  wall_thickness  infill_density  nozzle_temperature  \\\n",
              "0          0.02               8              90                 220   \n",
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              "      <td>17600</td>\n",
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              "      <td>8000</td>\n",
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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  material  fan_speed  interct_mn  interact_bn  interact_fn  \\\n",
              "0               60         0          0           0        13200            0   \n",
              "1               65         0         25           0        14625         5625   \n",
              "2               70         0         50           0        16100        11500   \n",
              "3               75         0         75           0        18000        18000   \n",
              "4               80         0        100           0        20000        25000   \n",
              "\n",
              "   interact_fb  \n",
              "0            0  \n",
              "1         1625  \n",
              "2         3500  \n",
              "3         5625  \n",
              "4         8000  "
            ],
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              "                                                     [key], {});\n",
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              "\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:       tension_strenght   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:                        16:17:11   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"
          ]
        },
        {
          "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.022461,
          "end_time": "2022-07-07T12:50:39.554347",
          "exception": false,
          "start_time": "2022-07-07T12:50:39.531886",
          "status": "completed"
        },
        "tags": [],
        "id": "79e21f4f"
      },
      "source": [
        "## Elonagtion and Tension_strength are strongly correlated to one another and both don't behave linearly."
      ]
    }
  ]
}