Predict_Tension_Strength_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_Tension_Strength_of_3D_Print_Material.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "printer = pd.read_csv('/content/3DPrinting_data.csv')"
      ],
      "metadata": {
        "id": "wChMZ4OB4rth"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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": 142,
      "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())"
      ],
      "metadata": {
        "id": "sOlKHBDq6jr0"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
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      "metadata": {
        "id": "Z3qvVv8k6kma"
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      "execution_count": 143,
      "outputs": []
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        "tags": [],
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      "source": [
        "# Model 2: Predicting Tension strength based on features"
      ]
    },
    {
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      "outputs": [
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              "   layer_height  wall_thickness  infill_density  infill_pattern  \\\n",
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              "   nozzle_temperature  bed_temperature  print_speed  material  fan_speed  \n",
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      ],
      "source": [
        "X = printer.drop(['roughness','tension_strength','elongation'], axis = 1)\n",
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
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              "Name: tension_strength, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 145
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      ],
      "source": [
        "y = printer['tension_strength']\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
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      "outputs": [
        {
          "output_type": "stream",
          "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:                        16:54:32   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"
          ]
        }
      ],
      "source": [
        "linear_Regression(X,y)"
      ]
    },
    {
      "cell_type": "code",
      "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']"
      ],
      "metadata": {
        "id": "bV7qC_Yk9UST"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
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        "id": "8ZAexPLX9Vrh",
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      "execution_count": 148,
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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",
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              "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",
              "   interct_mn  interact_bn  interact_fn  interact_fb  \n",
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              "   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  \n",
              "0               60          0  \n",
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              "2               70         50  \n",
              "3               75         75  \n",
              "4               80        100  "
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      "source": [
        "X = X.drop(['infill_pattern','print_speed','material'], axis = 1)\n",
        "X.head()"
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      "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:                        16:54:32   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"
          ]
        },
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          "name": "stderr",
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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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              "   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",
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              "   bed_temperature  fan_speed  interct_mn  interact_bn  interact_fn  \\\n",
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          "metadata": {},
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      ],
      "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()"
      ]
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    {
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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:                  0.663\n",
            "Method:                 Least Squares   F-statistic:                     13.03\n",
            "Date:                Mon, 14 Nov 2022   Prob (F-statistic):           4.06e-09\n",
            "Time:                        16:54:32   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"
          ]
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      ],
      "source": [
        "linear_Regression(interaction,y);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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          "end_time": "2022-07-07T12:50:38.978384",
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        "tags": [],
        "id": "7955076f"
      },
      "source": [
        "## No further improvement for tension strength in linear regression"
      ]
    }
  ]
}