N_O_Predictions_Carbon.ipynb

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      "name": "N/O_Predictions_Carbon.ipynb",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/TectoArc/Carbon-Predictions/blob/main/N_O_Predictions_Carbon.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSTDfSZga09t"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "\n",
        "sns.set_style('dark')\n",
        "plt.style.use('ggplot')\n",
        "plt.rcParams['xtick.labelsize'] = 14\n",
        "plt.rcParams['ytick.labelsize'] = 14"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uV9SC9Nqb6xK"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "94u52HMBcLNM",
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        },
        "outputId": "258bd317-d741-4ed9-c996-f1cc351a8075"
      },
      "source": [
        "df_train = pd.read_excel(\"Dataset_M.xlsx\", skiprows=1)\n",
        "df_train = df_train.drop(columns=[\"Reference\",\"Sample Number\"], axis=1)\n",
        "\n",
        "X  = df_train.drop('C', axis = 1)\n",
        "y = df_train['C']\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=44)\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "model_extr = ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mae',\n",
        "                    max_depth=32, max_features='auto', max_leaf_nodes=None,\n",
        "                    max_samples=None, min_impurity_decrease=0.0, min_samples_leaf=1,\n",
        "                    min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
        "                    n_estimators=500, n_jobs=None, oob_score=False,\n",
        "                    verbose=0, warm_start=True, random_state=100)\n",
        "\n",
        "y_extr = model_extr.fit(X_train_scaled, y_train).predict(X_test_scaled)\n",
        "X_scale = scaler.transform(X)\n",
        "y_pred_extr = model_extr.predict(X_scale)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py:407: FutureWarning: Criterion 'mae' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='absolute_error'` which is equivalent.\n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFWJ0JQPieqS"
      },
      "source": [
        "ML Predictios for Phase Boundary Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "lRRaWoSJc6rl",
        "outputId": "1f64d931-1201-4ca1-97cf-aebe2f9b29e6"
      },
      "source": [
        "df_N = pd.read_excel('Dataset_N_Predicted.xlsx', skiprows=1)\n",
        "df_N = df_N.drop(columns=[\"C\"], axis=1)\n",
        "print(df_N.shape, df_N.columns)\n",
        "df_N.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(101, 5) Index(['Pressure (Gpa)', 'Temperature (°C)', 'Fe', 'Ni', 'S'], dtype='object')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pressure (Gpa)  Temperature (°C)         Fe         Ni         S\n",
              "0              10              1450  48.684211  51.315789  0.000000\n",
              "1              10              1450  48.409064  20.690694  0.565165\n",
              "2              10              1450  48.137011  20.574415  1.123978\n",
              "3              10              1450  47.867998  20.459435  1.676544\n",
              "4              10              1450  47.601975  20.345734  2.222969"
            ],
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              "      <th>Pressure (Gpa)</th>\n",
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      "source": [
        "df_N_scaled = scaler.transform(df_N)\n",
        "y_pred_n = model_extr.predict(df_N_scaled)\n",
        "N = pd.DataFrame(y_pred_n, columns=[\"C_Pred_N\"])\n",
        "df_N2 = df_N.copy()\n",
        "df_N2[\"C_Pred_N\"] = N[\"C_Pred_N\"] "
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
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      "metadata": {
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          "height": 286
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        "outputId": "f8c2469b-ab01-4ec8-91e4-61d96cd4ce72"
      },
      "source": [
        "plt.scatter(df_N[\"S\"], y_pred_n, color='b')\n",
        "plt.ylabel('Carbon_predicted--->')\n",
        "plt.xlabel('Sulfur')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BHfx+SovxFHK0uCv6yt3i4mIkJycLtg0YMADFxcWuj4y8qv0du3r2NHdq6dAxyTub9AGuGRD5ItGJ//rrr8d7771nvZiqubkZO3bsQP/+/d01NvIge4u93cUrg4l8j+iWDU8++STefPNNzJkzx9qy4YYbbsBTTz3lzvGRh9hb7HVGx6kgrdaI7Oz6br8vEUnL6ZYNOp0O1dXV6N27t6Dc0504x+9+ti7o6mqOPy7OiNTUFjQ0yAVXAlvWDACgvl7OCh87+P2UFuMp5PIFXB3V19ejsLAQ1dXVmDJlCqqqqmA2m6FWq7s9SPIuS/9+Wy0dLI+ffFKJtWtbrI9tJfNRo2p4o3ciHyc68RcWFmLVqlVISkrC2bNnMWXKFJSXl+PDDz9kPX6AaN+/32LUqGuPNRoNBg/uukKHVwUT+TbRi7tbtmzB008/jUWLFllvvjJgwACcO3fObYMj/8Srgol8m+jEX1lZiZtvvlmwTalUorWVP91JiL3/iXyb6MSfkJCA//73v4Jtp0+fFtVdk4KLrRu9s8KHyHeInuOfPXs2cnJyMGzYMDQ3N2P9+vX45ptvsHDhQneOj/yQrYViVvUQ+Q7R5Zwmkwk1NTU4evQoKisrodFoMHbsWI9U9LCc0zcwntJiPKXFeAp1u5zTZDJh9uzZ2LJlC6ZMmSLZwCh4dOzkyV8ARN4jKvHL5XLExcWhvr4eMTEx7h4TBRjW9RP5FtFz/GPGjEFOTg5+//vfQ61WC/r0p6amumVwFBhY10/kW0Qn/oMHDwIAdu/eLdguk8m6fVtGCmys6yfyLaIT/5o1a9w5DgpgrOsn8i2i6/iBtkXeoqIiHD9+HGfPnoXJZHLXuCiAsK6fyLeIPuO/ePEiVq5cCaPRiJiYGFRVVSEkJATPPPMMe/KTQ6zrJ/ItohP/unXr8Lvf/Q6TJ0+GTCaD2WzGgQMHsG7dOuTk5LhzjBQAOjaAKylRICsrmuWdRF4gOvFfvnwZkyZNslbzyGQypKend1rsJeoKyzuJvEv0HP+wYcNw8uRJwbaTJ09i2LBhkg+KApuj8k4icj/RZ/wmkwlvvPEGkpKSoFarceXKFZw/fx7Dhw8XlHNmZWW5ZaAUOFjeSeRdohN/v3790K9fP+vjhIQEDBkyRPCa9hd1EdnD8k4i7xKd+O+///4uX/P+++93azAUHLKz63HqVIhguoflnUSe41Qdf1fy8vKkfDsKUJbyzowMPUaPbkJGhp4Lu0Qe5NTN1rsissMzkc37+xKRZ0ia+DnHT65gy2Yiz5I08RM5izX9RJ4n6Rw/p3rIWazpJ/I8SRP/oEGDpHw7CgKs6SfyPKemesrKynDhwgUYDAbB9jvuuAMA8Nxzz0k3MgoKrOkn8jzRiX/v3r3Ys2cPtFotwsLCBM9ZEr89eXl5+Oqrr1BWVgalUonk5GTMnDkTiYmJro2aAgZr+ok8T3Ti/+ijj/DKK69Aq9U6/SGFhYWYOHEibrjhBgDAzp07sWzZMvztb39DRESE0+9HgYMtm4k8T3TiDw0NRXx8vEsfsmjRIsHjBQsWYM6cOSgqKsLw4cNdek8KHGzZTORZohP/9OnT8fbbb+P+++9HVFSU4Dm53Lk14sbGRpjNZp7tUye2yju//DIEqaktqK+X80BAJAGZWWQN5vTp0+0+t3PnTqc+9PXXX0d5eTleffVVUQeN5uZmp97fQqlUoqWlxaV9qTNPxHPOHAV27HBc0RMRYcbgwWYkJZmxZEkrrr/erUNyG34/pcV4CoWGhtp9TnTir6ystPtcnz59RA9m69atOHbsGF588UXExsaK2qesrEz0+7en0Wig0+lc2pc680Q8MzPVOH48rOsX/o9Wa/Tbi734/ZQW4ykUFxdn9znRczR9+vRBnz59oFaroVQqoVarrdvE2rJlC/Lz8/HCCy+ITvoUXOyVd9rDi72InCd6jl+v1+Ptt99Gfn4+TCYTFAoFRo8ejUceeQQqlarL/Tdv3oxjx45h8eLFLi8SU+CzVd7ZlaNHw5CZqeb8P5FIohP/5s2bYTAYsGrVKvTp0weVlZXYsWMH3n777S7vurVx40YcPXoUCxcuREREBGpq2io4wsPDER4e3r1/AQWUjuWdEREmFBSEoLTU/ldVp1NAp2tbF2CfH6KuiZ7jf+yxx7B69WrBxVsGgwELFizAhg0bHO47bdo0m9szMzPtPtce5/h9g7fiaeneefGiAkVFIdDrHc9QZmTo/aLlM7+f0mI8hRzN8TtVx19XVyeY06+rq4NS2fVb7Nq1S+zHEHXSvs7fchCoqFCguFhpPdNv7/DhcGRlRXPah8gO0Yn/jjvuwEsvvYRJkyZZp3oOHDiAu+66y53jIxJofxDIyopGXl7n9aW6Ojny8lSc9iGyQ3Tinzp1Knr37o38/HxUVVUhJiYGU6ZMwYQJE9w5PiK7uloItlT8+MO0D5EniZ7j9ybO8fsGX4ynZern8OFw1NV1nvvXaFqRnNzikxU/vhhPf8Z4Ckkyxw8A//73v5Gfn4/q6mr07t0bt99+OyZMmMBbLpLXWKZ+7E37sOKHqDPRF3Bt27YNH3zwAUaOHIlZs2Zh5MiR2LdvH7Zv3+7O8RGJkp1dD63W6PA1vNiLqI3oxH/kyBG88MILmDhxItLS0jBx4kQsWrQIn332mTvHRySKpf4/I0OP0aOboNHYPqu3VPyUlPAOXxS8RCf+Hj16oEePHp22iblql8gTLNM+u3dfwdixTTZfY6n4mTEjhsmfgpbDOf6Kigrr39PT0/Haa6/h3nvvRUxMDK5cuYIPP/wQkyZNcvsgiZwlpuLnnns0GDu2yecWfYnczWFVj6NWzO0525bZWazq8Q3+Fs+uKn4s4uKMXun372/x9HWMp5Cjqh6Wc5Jo/hpPexU/9niq1bO/xtNXMZ5C3W7LbDKZsGDBAhiNjqsmiHyRmIqf9lj9Q4FOVOKXy+WQy+Uu3wmLyJvaV/zYq/bpqKKCC78UuERX9aSnp+ONN95AYWEhysvLUVFRYf1D5OssFT/79ulEnf0XFytZ9kkByyv33HUW5/h9Q6DEs32Hz676/ffs2YqBA1utvxSkXAAOlHj6CsZTiIu7JIlAjaflQHD0aJjNNs+2SFEJFKjx9BbGU0iyXj1EgcgyDZSZqRad+MvKQlBWdu0aAfYBIn8iOvG3trbik08+QWFhIerr6wXPLV26VPKBEXmaszd6b48XhJE/Eb24u3XrVhw6dAiDBw/G+fPnMXLkSNTW1uKmm25y5/iIPMbZss+OdDoF20GQXxCd+L/88ks8//zzSE9Ph0KhQHp6OhYuXIiCggJ3jo/IY9qXfaalNUGlMrn0Ppazf1YFka8SPdXT3NwMtVoNoO3+u01NTYiPj8eFCxfcNTYij7N3f9+IiLaDQEODvMtKIODa2T/n/skXiU788fHxOHfuHAYMGICkpCTs3r0bPXr0QExMjDvHR+Q17Q8CHYmtBLKc/aeltV38WF8vR2Rk20GkqUmJsLDe1u3tq4Ms719ervDJu4eRfxNdzvnTTz9BLpcjKSkJly9fxsaNG2EwGDB79mzceOONbh0kyzl9A+PZWUmJAjNmxNjtAuqsnj1b0b9/K37+WQm9Xi7Y7q7rCQIFv59C3arjLyoqwsmTJzFr1qxOz23btg233XYbUlJSuj9KB5j4fQPjaZsr1wFIxXJA0Gpbgv4gwO+nULeatOXl5WHw4ME2n0tNTcXevXtdHxlRAHC2HYSUrl5V4NSpUOTlqXDXXRouKpMoXSb+CxcuYOjQoTafu/nmm/Hzzz9LPigif+RKMzgptT8IsKSUHOky8Tc2NqKlpcXmc62trWhsbJR8UET+yptn/+2xpJQc6bKqJz4+Ht999x1GjBjR6bnvvvsO8fHxbhkYkT+znP3bKwcFgKamMISFtd0bWKeTo6gopNOCrq2FXrFYUkr2dJn4J02ahPXr18NkMmHEiBGQy+UwmUz4+uuvsWnTJjz00EOeGCeR33FUDgpYFiOrrY/bXzcQG9u5tLP9AcTWgcIetpOgjkSVc+7fvx+7du2C0WhEr169UFdXh5CQEEybNg2TJ092+yBZ1eMbGE9pdTeelgPCxYsK0QeBQK4C4vdTSJK2zHq9HsXFxWhoaEBERARSUlKgUom/j2l3MPH7BsZTWlLG05WSUk/dW9hT+P0UkqQts0qlslvdQ0TeZZlWcuaCMsu9hR1NR1Fgcn7FyEWFhYXIycnB448/jmnTpuHIkSOe+miioOFsSenRo2Gs+glCHkv8BoMB/fr1w9y5cxEaGuqpjyUKOs6UlOp0Ctb8ByGPJf60tDTMnDkTo0aNgkwm89THEgUtsW2mWfMffHjrRaIA1rHN9D33aGwu/lpq/g8eDAvYqh+6xi8Sv0ajcWk/pVLp8r7UGeMpLU/HU6MB7roL2LHD/mva2j60tX749NMeGDzYjKQkM5YsacX113tsqC7h91M8v0j8rpZosbxLWoyntLwRzz/9SYHjx8VV/TQ0yPDVVzJ89RVw/LjJ50s/+f0U6lZ3TiIKHK42krt4MQT3389F4EDBxE8UZFxtJHfpUghbPwcIj5ZzXrhwARcuXIDZbIZOp8OFCxf404zIS1y5uTxbPwcG0S0buqugoABLly7ttH3cuHGYP3++w33ZssE3MJ7S8rV4utL7JyND7zNX/vpaPL1Nkl493sTE7xsYT2n5cjxLShS4//4YXLrkeBG4Vy8T7rzT4BOln74cT2/g4i4ROSUxsRW7d1d1uQZQVyfntI8fYuInIpucWQNg1Y9/8Ys6fiLyjo5X/q5YEYnDh8NRV9f5nNFS9cMrf30fz/iJSBTLQeDOOw12X9O+6mf8+D6YO7c3fwX4ICZ+InJKdna9qPr/piY5Dh7swdp/H8SpHiJyimXuX0zVDyDs//PRR+EYMaIZKpUZ9fVy9O3byikhL2DiJyKnWap+xN7ty6KpSY4vvggXbOPBwPOY+InIJZYzf2cv+urI3sFg3LgmLF1axwOAGzDxE5HLOlb9LF7cC59/Hoampu4tH1rWB/LzQ1kl5Aa8cpdEYzylFajxdKX1gxhhYSbrlFBlpRyVlXJERZlRWyvDb35jRlycAs3NzZwu+h+2bCBJMJ7SCoZ4SvkrwFkhISao1SbExZmsLaiD6aDAxE+SYDylFUzxtPwKqKhQICLCBL1ehq+/DvX4wcCi/UEhUKeRmPhJEoyntII9nr50MGg/jRQovwqY+EkSjKe0GM/O3LU+4ArLrwK12oTaWplgPcEffiUw8ZMkGE9pMZ6OeXN9QAxbawiWRWdfODgw8ZMkGE9pMZ7idJwSAgCdTo5ff+1c1VNTY/Tq2kFH3lxLYOInSTCe0mI8pWWJZ/vpol9/bTv71mhavb6gDHSePnLnLwMmfpIE4yktxlNaYuLZ/qBQWipHdbUSzc0yD43QPnesJzDxkyQYT2kxntJyJZ6+VFnkiFZrxI4dVU4lf0eJny0biChotW85YdFxqshy9h0VZYZOJ/PKr4SLF0OwYkWkZDe2Z+InImrH1sGgPXtrCEDborO7ppAqKqS7lwETPxGRE7o6MADuWUuIjZVu8ZeJn4hIYh0PDramj5yZNtJqjcjOrpdsfEz8RERuZu9XgqP1BHeWezLxExF5iZhpI3fwrZolIiJyOyZ+IqIgw8RPRBRkmPiJiIIMEz8RUZDxi149REQkHZ7xExEFGSZ+IqIgw8RPRBRkmPiJiIIMEz8RUZBh4iciCjJM/EREQYaJn4goyARsW+ZPPvkEH374IWpqapCQkICHH34YgwYN8vawfF5hYSH27duH8+fPo7q6Gk8++STGjx9vfd5sNmP37t04fPgwGhoakJycjHnz5qFfv37eG7SPysvLw1dffYWysjIolUokJydj5syZSExMtL6G8RTv448/xqFDh1BZWQkASEhIwH333Ye0tDQAjKUzAvKM/9ixY9iyZQsyMjKQk5ODgQMH4pVXXoFOp/P20HyewWBAv379MHfuXISGhnZ6/oMPPsD+/fsxd+5cLF++HL169cJLL72ExsZGL4zWtxUWFmLixIlYtmwZFi9eDIVCgWXLlqGhocH6GsZTPLVajQcffBA5OTlYvnw5UlNTsXLlSly8eBEAY+kUc7czaDcAAAVlSURBVAB67rnnzOvWrRNsW7BggXn79u1eGpF/mjVrlvmzzz6zPjaZTObHHnvMvGfPHuu2pqYm8+zZs80HDx70wgj9S2Njo3natGnmr7/+2mw2M55SePjhh80HDx5kLJ0UcGf8LS0tOH/+PIYMGSLYfsstt+Ds2bNeGlVg+PXXX1FTU4NbbrnFui00NBSDBg1ibEVobGyE2WxGREQEAMazO0wmE/Lz82EwGDBw4EDG0kkBN8dfV1cHk8mEqKgowfbo6GicPn3aS6MKDDU1bbeIi46OFmyPiopCdXW1N4bkVzZv3oz+/fsjJSUFAOPpipKSEixatAhGoxHh4eFYuHAhEhMTrcmdsRQn4BI/kS/aunUrzp49ixdffBFyecD90PaYuLg4rFy5Enq9HidOnMCaNWuwePFibw/L7wTcN7BXr16Qy+Wora0VbK+pqel0NkDOscTPcqZqUVtb2+kXFl2zZcsW5Ofn44UXXkBsbKx1O+PpPKVSib59+yIpKQkzZ85E//79ceDAAcbSSQGX+JVKJZKSkvD9998Ltp8+fRoDBw700qgCw29+8xtER0cLYtvc3IyioiLG1o7Nmzdbk358fLzgOcaz+0wmE4xGI2PppICc6pk8eTJyc3MxYMAADBw4EJ9++imqqqpw9913e3toPs9gMKC8vBxAW120TqfDhQsXEBERAY1Gg/T0dOTl5SE+Ph7XXXcd9u7di/DwcIwZM8bLI/c9GzduxNGjR7Fw4UJERERYz0bDw8MRHh4OmUzGeDph+/btSEtLg1qthsFgwBdffIHCwkI8++yzjKWTAvYOXJYLuKqrq9GvXz/MmTMHgwcP9vawfF5BQQGWLl3aafu4ceMwf/5860Uyhw4dwtWrVzFgwADMmzdPcFEStZk2bZrN7ZmZmdbnGE/x1qxZg4KCAtTU1EClUkGr1eKee+7B0KFDATCWzgjYxE9ERLYF3Bw/ERE5xsRPRBRkmPiJiIIMEz8RUZBh4iciCjJM/EREQYaJn8gFS5YsweHDh62Pd+zYgXnz5uGxxx7z4qiIxAnIK3eJxCoqKsK2bdvwyy+/QC6XIyEhAXPmzMGAAQNEv4dOp8O+ffuwdu1a9oUhv8DET0FLr9fj1VdfxaOPPorRo0ejpaUFZ86cQUhIiFPvo9PpEBkZ6XLSb21thUKhcGlfIldwqoeC1uXLlwEAY8aMgVwuR2hoKIYMGQKtVotdu3bhzTfftL72119/xbRp09Da2ip4j++//x7Lli1DdXU1Zs+ebW0r8Mc//lHwuvnz51sbiO3atQurVq3Cm2++iTlz5uDIkSPu/YcSdcDET0Hruuuug1wux+rVq/Htt98K7oUr1i233ILnn38evXv3xrvvvov58+eL2u/kyZMYNWoUNm/ejLFjxzr9uUTdwcRPQUulUuHFF1+ETCbDW2+9hUcffRQ5OTmderq7Q0pKCm677TbrLw0iT+IcPwW1hIQE61l6aWkpcnNzsWXLFsTFxbn1c9VqtVvfn8gRnvET/U98fDzGjx+PX375BeHh4WhubrY+58yvgLCwMDQ1NVkfm0wm1NXVSTpWou5g4qegVVpain379uHKlSsA2qpz8vPzkZycjP79++PMmTPQ6XTQ6/V4//33Rb9vXFwcjEYjTp06hZaWFuzZswdGo9Fd/wwip3Gqh4JWjx498OOPP2L//v3Q6/VQqVS49dZbMWvWLKhUKvz2t7/FM888g8jISEyZMgUnT54U9b4qlQqPPvoo/vGPf8BkMuEPf/gDp3bIp/BGLEREQYZTPUREQYaJn4goyDDxExEFGSZ+IqIgw8RPRBRkmPiJiIIMEz8RUZBh4iciCjL/D/0uTagvvDa+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qoVCk6TQglFt"
      },
      "source": [
        "O (New Data) predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQMFu78wfeAa",
        "outputId": "dd04b7fc-dc8a-4694-c1e2-43752ff653c8"
      },
      "source": [
        "df_O = pd.read_excel('Dataset_O.xlsx', skiprows=1)\n",
        "df_O = df_O.drop(columns = ['C'], axis=1)\n",
        "print(df_O.shape, df_O.columns)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(13, 5) Index(['Pressure (Gpa)', 'Temperature (°C)', 'Fe', 'Ni', 'S'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "_LpmrcbfgwLK",
        "outputId": "eb1cc87b-01e7-4dae-92be-f6d1f745bdb0"
      },
      "source": [
        "df_O_scaled = scaler.transform(df_O)\n",
        "y_pred_o = model_extr.predict(df_O_scaled)\n",
        "O = pd.DataFrame(y_pred_o, columns=[\"C_Pred_O\"])\n",
        "df_O2 = df_O.copy()\n",
        "df_O2[\"C_Pred_O\"] = O[\"C_Pred_O\"]\n",
        "\n",
        "plt.scatter(df_O[\"S\"], y_pred_o, color='b')\n",
        "plt.ylabel('Carbon_predicted--->')\n",
        "plt.xlabel('Sulfur')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3XFx9nJheiy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "52ba6b7c-7754-4141-91e3-3a0027a56195"
      },
      "source": [
        "df_MC = pd.read_excel(\"Melt Composition.xlsx\")\n",
        "df_MC.head()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Unnamed: 0   P     T     Fe     Ni      S     O\n",
              "0  LP10-1450-a  10  1450  62.79  16.99  16.82  0.69\n",
              "1  LP10-1450-d  10  1450  57.54  17.74  20.17  1.14\n",
              "2  LG10-1450-a  10  1450  71.22  17.57   6.62  0.49\n",
              "3  LG10-1450-c  10  1450  62.20  16.02  17.87  0.95\n",
              "4  LG10-1450-d  10  1450  61.01  17.57  18.18  0.72"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12e45eb0-02f4-4f19-8c4d-d9fb37563aea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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        "df_MC.rename(columns={'P' : \"Pressure (Gpa)\", 'T' : 'Temperature (°C)'}, inplace=True)\n",
        "df_MC.head()"
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      "execution_count": 30,
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              "    Unnamed: 0  Pressure (Gpa)  Temperature (°C)     Fe     Ni      S     O\n",
              "0  LP10-1450-a              10              1450  62.79  16.99  16.82  0.69\n",
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              "   Pressure (Gpa)  Temperature (°C)     Fe     Ni      S\n",
              "0              10              1450  62.79  16.99  16.82\n",
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              "      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-f91bbd62-08bb-4982-9b0c-7dc32c31b57d 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-f91bbd62-08bb-4982-9b0c-7dc32c31b57d');\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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test_norm = scaler.transform(df_test)\n",
        "df_test_pred = model_extr.predict(df_test_norm)\n",
        "df_test[\"C_Pred_MC\"] = df_test_pred\n",
        "df_test.to_excel(\"Melt Composition Predictions.xlsx\")"
      ],
      "metadata": {
        "id": "43iEiBEFjC4U"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df_test[\"S\"], df_test_pred, color='b')\n",
        "plt.ylabel('Carbon_predicted--->')\n",
        "plt.xlabel('Sulfur')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "BNg1ME8dkFGl",
        "outputId": "1f12b1fa-52e7-4493-d1b2-117ffbed6383"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "mmzI413qlm_E"
      },
      "execution_count": 20,
      "outputs": []
    }
  ]
}