MachineLearning Projects/Google_Brain.ipynb

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    "colab": {
      "name": "Google Brain.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOJsiSCgJ0up42rbFuU/XWM",
      "include_colab_link": true
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      "name": "python"
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DeependraParichha1004/Codes/blob/main/Google_Brain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCVw9QpAZ9Mk"
      },
      "source": [
        "<img title=\"\" alt=\"ventilator diagram\" src=\"https://raw.githubusercontent.com/google/deluca-lung/main/assets/2020-10-02%20Ventilator%20diagram.svg\" style=\"user-select: auto;\">\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UgDBmmxJWNcT"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aYwMJepWT9i"
      },
      "source": [
        "train_set=pd.read_csv('train.csv')\n",
        "test_set=pd.read_csv('test.csv')\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
        },
        "id": "f5ZGUf9EaZGp",
        "outputId": "02756c14-8087-49ac-d056-54923b606dce"
      },
      "source": [
        "train_set.isna().sum()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id           0\n",
              "breath_id    0\n",
              "R            0\n",
              "C            0\n",
              "time_step    0\n",
              "u_in         0\n",
              "u_out        0\n",
              "pressure     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "zOSa-DZO9SJ9",
        "outputId": "09444492-7e20-42fa-c1fa-6b9b15244d46"
      },
      "source": [
        "df_sel=train_set[(train_set['pressure']>2.638) & (train_set['pressure']<24.78)]\n",
        "df_sel"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "            id  breath_id   R   C  time_step       u_in  u_out   pressure\n",
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      "metadata": {
        "id": "4h2XqNZeW8Fh"
      },
      "source": [
        "train=train_set.fillna(0)\n",
        "test=test_set.fillna(0)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "train"
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      "execution_count": 93,
      "outputs": [
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              "            id  breath_id   R   C  time_step       u_in  u_out   pressure\n",
              "0            1          1  20  50   0.000000   0.083334      0   5.837492\n",
              "1            2          1  20  50   0.033652  18.383041      0   5.907794\n",
              "2            3          1  20  50   0.067514  22.509278      0   7.876254\n",
              "3            4          1  20  50   0.101542  22.808822      0  11.742872\n",
              "4            5          1  20  50   0.135756  25.355850      0  12.234987\n",
              "...        ...        ...  ..  ..        ...        ...    ...        ...\n",
              "460034  460035       9531  50  10   1.075398   0.000000      1   7.735650\n",
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              "\n",
              "[460039 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jy9C9FCpacGY"
      },
      "source": [
        "Q3=train['pressure'].quantile(0.25)\n",
        "Q1=train['pressure'].quantile(0.75)\n",
        "IQR=Q3-Q1\n",
        "lower_limit=Q1-(1.5*(IQR))\n",
        "upper_limit=Q1+(1.5*(IQR))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szAKNZ2Idz6Q",
        "outputId": "91b044aa-1149-493a-f0f7-98b2f793e4cf"
      },
      "source": [
        "lower_limit,upper_limit"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24.78391990417188, 2.6387440870387895)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3jNC1NpwiCU9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "mUZpfEnoiRGe",
        "outputId": "026541ce-34f0-470b-c111-9d0b83ac6771"
      },
      "source": [
        "ax=sb.boxplot(x=train['pressure'])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQNUeVjhlMxA",
        "outputId": "3189b696-c3e0-4302-b0d3-5c902ecf4bfc"
      },
      "source": [
        "train['C'].value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10    171879\n",
              "50    152240\n",
              "20    135920\n",
              "Name: C, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ydUSqpFhYvdl",
        "outputId": "2d9bdb78-4842-468e-f81c-6cda3b38553c"
      },
      "source": [
        "test['u_out'].value_counts()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    283726\n",
              "0.0    173450\n",
              "Name: u_out, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_M6UkCFRQJLF"
      },
      "source": [
        "from palettable.scientific.sequential import Acton_14,Bamako_12"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcv7touRi81t",
        "outputId": "776b0969-fff7-49f8-a929-f4fb171c1fcb"
      },
      "source": [
        "train['R'].unique()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([20, 50,  5])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhbCuvHBlPxt"
      },
      "source": [
        "# def count_plot(col):\n",
        "#   cat_tr=train[col].value_counts().index.astype(str)\n",
        "#   val_tr=train[col].value_counts().values\n",
        "#   cat_te=test[col].value_counts().index.astype(str)\n",
        "#   val_te=test[col].value_counts().values\n",
        "#   #fig,(ax1,ax2)=plt.subplots(figsize=(8,6),ncols=2)\n",
        "#   # sb.axes_style(\"whitegrid\")\n",
        "#   # ax1=sb.barplot(x=cat_tr, y=val_tr,capsize=0.1,palette=pal)\n",
        "#   # sb.axes_style(\"whitegrid\")\n",
        "#   # ax2=sb.barplot(x=cat_te, y=val_te,capsize=0.1,palette=pal)\n",
        "#   # #ax.bar(cat,val,width=0.3)\n",
        "#   plt.subplot(1,2,1)\n",
        "#   plt.bar(cat_tr,val_tr,color=['yellowgreen', 'Purple', 'wheat'],width=0.6)\n",
        "#   plt.subplot(1,2,2)\n",
        "#   plt.bar(cat_te,val_te,color=['turquoise','skyblue','crimson'],width=0.8)\n",
        "#   plt.tight_layout()\n",
        "#   plt.show()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_DSz3yCL6gCP"
      },
      "source": [
        "# def count_plot(col):\n",
        "#   cat_tr=train[col].value_counts().index.astype(str)\n",
        "#   val_tr=train[col].value_counts().values\n",
        "#   cat_te=test[col].value_counts().index.astype(str)\n",
        "#   val_te=test[col].value_counts().values\n",
        "#   fig_1,(ax_1,ax_2)=plt.subplots(figsize=(10,6),ncols=2,nrows=1)\n",
        "\n",
        "#   ax_1.bar(cat_tr,val_tr,color=['yellowgreen', 'Purple', 'wheat'],align='center',width=0.6)\n",
        "\n",
        "#   ax_2.bar(cat_te,val_te,color=['turquoise','skyblue','crimson'],width=0.8)\n",
        "\n",
        "import seaborn as sb\n",
        "def count_plot(col1,col2):\n",
        "  \n",
        "  fig=plt.figure(figsize=(8,6))\n",
        "  ax=sb.countplot(x=col1,hue=col2,linewidth=2,ec='k',data=train_set)\n"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "cHYtrVicJLmz",
        "outputId": "e2cf7590-6a79-4017-8f1e-659e26d8a3f2"
      },
      "source": [
        "count_plot('R','C')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgoAAAF5CAYAAAD+nwKnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbhUlEQVR4nO3de3SU9b3v8c9MwgQCxmQScgGUiyimomRJltS1QDYpGnRFdLvhhJ1aW4FWbGmh3RAoksQCykrCVtgQLioLvB3pWW2lJKVEKW63iLpKJbYxiB4ugiYhkAu3QtLMM+cPak6p+cEMzMwzM7xff8n8Mjxfsp4V3/nN88w4vF6vVwAAAN1w2j0AAAAIX4QCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYxdo9QLhqbT0jy+ItJgAA0c3pdCgpqbdxnVAwsCwvoQAAuOrx0gMAADAiFAAAgBGhAAAAjLhGAQCAbng8nWptPabOzg67RwkIpzNGvXr1UZ8+18rhcPj8PEIBAIButLYeU8+e8erdO92v/7GGI6/XK4+nU6dOtam19Zjc7lSfn8tLDwAAdKOzs0O9eydEfCRIksPhUGxsDyUmJquj45xfzyUUAAAwiIZI+EcOh1OSf7f+EwoAAMCIaxQAALDJG29s0y9/+aoOHz6k+Ph4DR06TI88MlUjRmTZPVoXQgEAABts2vSKXnnlRc2d+3Pdcced6tGjh95/f5d27nybUAAA4Gp2+vRprV+/Tj//eYnGjs3penz06Ls0evRdNk72dVyjAABAiNXW/lkdHR26665/sXuUS2JHAQCuEllZN9s9gk9qaj6xe4SgO3nyhK69NlGxseH/v2F2FAAACLGEhGt14kSbOjs77R7lksI/ZQAAATX222V2j9Ctt18ttHuEkBk+/Db16NFD77zz3xo3brzd41wUOwoAAIRYnz59NG3aDD3zTJn+53/+W+fOnVNnZ6fee+9drV69wu7xLsCOAgAANvj3f39YycnJevHF9Vq0aKHi43tr2LCb9cgjU+0e7QKEAgAANrnnnnt1zz332j3GRfHSAwAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiPdRAADgEoL9gVrh/EFY7CgAABABVq1arsmTJ2r06GwdOPB/ux4/fPhzPfbYo5oy5SE99tijOnLkcECPy44CAAA+CvQHavnzQVhjxvyLJk+eoh/96PsXPL5s2VI99NBk5ebep+rqrSovf1r/9V9rAzYjOwoAAESAESOylJaWfsFjra0t+vTTTzR+fK4kafz4XH366SdqbW0N2HEJBQAAItTRo0eVkpKqmJgYSVJMTIxSUvqqqelowI5BKAAAAKOQhcJbb72lBx98UA888IAmTpyoN954Q5J08OBB5efnKzc3V/n5+Tp06FDXc4KxBgBAtEhLS9Px403yeDySJI/Ho+PHjyk1NS1gxwhJKHi9XhUWFqqsrEy//e1vVVZWpnnz5smyLJWUlKigoEDV1dUqKChQcXFx1/OCsQYAQLRISnJr6NCbtH17tSRp+/Zq3XjjMCUlJQXsGCG768HpdOrUqVOSpFOnTik1NVWtra2qq6vThg0bJEl5eXlavHixWlpa5PV6A77mdrtD9c8FAEQhf+5SCLTly8v19ttvqaWlWbNn/0gJCdfqlVf+j+bOXaAlS0q0YcMLuuaaa1RU9IuAHjckoeBwOLR8+XL98Ic/VHx8vM6cOaPnnntODQ0NSktLu+AijNTUVDU0NMjr9QZ8jVAAAESq2bPnavbsuV97fODAQXr++ReDdtyQhEJnZ6fWrVun1atXa+TIkfrTn/6k2bNnq6wssPejBlJych+7RwCAq1LfvtfYPYIkqanJqdjY86/Q19Z+avM0geN0Ov36HockFPbu3aumpiaNHDlSkjRy5Ej16tVLcXFxOnr0qDwej2JiYuTxeNTU1KSMjAx5vd6Ar/mjufm0LMsbjG8HAOAijh07ZfcIkiTLstTZadk9RsBZlnXB99jpdFz0l+OQXMyYnp6uxsZGHThwQJK0f/9+NTc3a+DAgcrMzFRVVZUkqaqqSpmZmXK73UpOTg74GgAA8I/D6/WG5NfmLVu26Pnnn5fD4ZAk/eQnP9H48eO1f/9+zZ8/XydPnlRCQoJKS0s1ZMgQSQrKmq/YUQAQbb76YKNAvw1xoHx1oWC4fEBSY+PnSk8faPcYAffP/65L7SiELBQiDaEAINoQCv4hFM7jQ6EAALiEq/ljpgkFAAAiwIkTbVq8uFhffvmFevTooQEDrtfcuQuUlJSk2tq/qLz8abW3tysjI0PFxYuVlBSYa/MIBQAAfLRl1r0B/fsmrvi9z1/rcDhUUPCIbr89W5JUUbFCa9eu1Lx5C7V4cZEWLHhSI0ZkaePGF7RmzUotWFASkBn5UCgAACJAQsK1XZEgSbfcMlyNjY3at2+vXC6XRozIkiQ9+OAkvfXW9oAdl1AAACDCWJal11//tUaPvktHjzYqPf3/v1dQYmKiLMvSyZMnAnIsQgEAgAjz7LPlio/vpX/7t/8V9GNxjQIAABFk1arl+uKLwyotfVZOp1NpaelqbGzoWm9ra5PT6VRCwrUBOR47CgAARIh16yq0b99eLV36n3K5XJKkYcMy1d7ero8+qpEkbd78K40bNz5gx2RHAQAAH/lzl0KgHTiwXy+/vEHXXXe9ZsyYKknKyOinpUuXqahokcrLn1ZHR4fS08/fHhkohAIAABFgyJAbtHPn7m7Xbr11hF566ZdBOS6hAADAJYTzOycGG9coAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMOKuBwAALiEr6+ag/v3hfFcFoQAAQISYNOl+uVwuuVxxkqTHH/+xRo26U7W1f1F5+dNqb29XRsb5N1xKSnIH5JiEAgAAPrp7yaSA/n1vLvyV389ZsqRUQ4YM7fqzZVlavLhICxY8qREjsrRx4wtas2alFiwoCciMXKMAAEAE27dvr1wul0aMyJIkPfjgJL311vaA/f3sKAAAEEF+8YsiSV7demuWHnvsRzp6tFHp6Rld64mJibIsSydPngjIJ0iyowAAQISoqHheL774mp5//iVJXj37bFnQj0koAAAQIdLS0iVJLpdL//qvk/WXv3yktLR0NTY2dH1NW1ubnE5nQHYTJEIBAICIcPbsWZ0+fVqS5PV6tX17tYYOvUnDhmWqvb1dH31UI0navPlXGjdufMCOyzUKAAD46HLuUgiUlpZmLVxYKMuy5PFYGjRosP7jP+bL6XSqqGiRysufVkdHh9LTz98eGSiEAgAAEaB//wHasOF/d7t2660j9NJLvwzKcQkFAAAuIZzfOTHYuEYBAAAYEQoAAMCIUAAAwMDr9do9QkB5vZYkh1/PIRQAAOhGbKxLZ86cjIpY8Hq96uz8m9rajsvl6unXc7mYEQCAbiQl9VVr6zGdPt1m9ygB4XTGqFevPurTx783YiIUAADoRkxMrFJSMi79hVGOlx4AAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMAoZKHQ3t6ukpIS3XPPPbr//vtVVFQkSTp48KDy8/OVm5ur/Px8HTp0qOs5wVgDAAC+C1kolJeXKy4uTtXV1aqsrNSsWbMkSSUlJSooKFB1dbUKCgpUXFzc9ZxgrAEAAN+FJBTOnDmjzZs3a9asWXI4HJKklJQUNTc3q66uTnl5eZKkvLw81dXVqaWlJShrAADAP7GhOMiRI0eUmJioVatW6YMPPlDv3r01a9Ys9ezZU2lpaYqJiZEkxcTEKDU1VQ0NDfJ6vQFfc7vdofjnAgAQNUISCh6PR0eOHNE3vvENzZs3Tx999JFmzJihFStWhOLwlyU5uY/dIwDAValv32vsHgH/ICShkJGRodjY2K6XA0aMGKGkpCT17NlTR48elcfjUUxMjDwej5qampSRkSGv1xvwNX80N5+WZXmD8e0AAFzEsWOn7B7hquJ0Oi76y3FIrlFwu90aNWqU3n33XUnn70pobm7WoEGDlJmZqaqqKklSVVWVMjMz5Xa7lZycHPA1AADgH4fX6w3Jr81HjhzRggUL1NbWptjYWM2ePVtjx47V/v37NX/+fJ08eVIJCQkqLS3VkCFDJCkoa75iRwFAtMnKulmSNPbbZTZP0r23Xy2UJNXUfGLzJFeXS+0ohCwUIg2hACDaEAroTli89AAAACIToQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABjF2j0AIElZWTfbPYJPamo+sXsEAAgpdhQAAIAROwoIK3cvmWT3CN16c+Gv7B4BAGzBjgIAADAiFAAAgBGhAAAAjAgFAABgxMWMAAD4IBJu4w7GLdzsKAAAACN2FAAA8EM43sYdzFu42VEAAABG7CgE0NX6+hUAIHr5HArr16/XtGnTvvb4hg0b9OijjwZ0KARPJMQMEAiRcK4T7ogEPodCRUVFt6GwZs0aQuGfjP12md0jfM3brxbaPQIAIAJdMhTee+89SZJlWXr//ffl9Xq71r744gv17t07eNMh4LbMutfuEbo1ccXv7R4BUepqu/AMCLRLhsITTzwhSWpvb9eCBQu6Hnc4HOrbt68WLlwYvOkAAFedSHjZ6GpyyVDYsWOHJKmwsFBlZeG3pQ4AAILH52sU/jESLMu6YM3p5C5LAEBg8BJpePE5FD7++GMtWrRI+/btU3t7uyTJ6/XK4XBo7969QRsQAADYx+dQmD9/vsaNG6enn35aPXv2DOZMAAAgTPgcCl9++aV++tOfyuFwBHMeAAAQRny+uODuu+/Wzp07gzkLAAAIMz7vKLS3t2vmzJkaOXKkUlJSLljjbggAAKKTz6EwdOhQDR06NJizAACAMONzKMycOTOYcwAAgDDkcyh89VbO3bnzzjsDMgwAAAgvPofCV2/l/JXW1lb97W9/U1pamv7whz8EfDAAAGA/n0Phq7dy/orH49GaNWv4UCgAAKLYZb/3ckxMjGbMmKEXXnghkPMAAIAwckUf0vDuu+/yBkwAAEQxn196GDt27AVRcPbsWXV0dKikpCQogwEAAPv5HArl5eUX/LlXr14aPHiw+vTpE/ChAABAePA5FO644w5J5z9i+vjx40pJSeHjpQEAiHI+/5/+9OnTKiws1G233aa77rpLt912m+bNm6dTp04Fcz4AAGAjn0NhyZIlOnv2rCorK/XnP/9ZlZWVOnv2rJYsWRLM+QAAgI18funhnXfe0fbt29WrVy9J0uDBg7V06VLdfffdQRsOAADYy+cdhbi4OLW0tFzwWGtrq1wuV8CHAgAA4cHnHYVJkyZp6tSp+t73vqd+/fqpvr5eGzdu1OTJk4M5HwAAsJHPofD4448rLS1NlZWVampqUmpqqqZPn04oAAAQxXx+6eGpp57S4MGDtXHjRm3dulUbN27UDTfcoKeeeiqY8wEAABv5HApVVVUaPnz4BY8NHz5cVVVVAR8KAACEB59DweFwyLKsCx7zeDxfewwAAEQPn0MhOztbK1as6AoDy7K0cuVKZWdn+3XAVatWadiwYfr0008lSTU1NZo4caJyc3M1depUNTc3d31tMNYAAIDvfA6FJ554Qrt27dLo0aM1adIkjRkzRrt27VJRUZHPB/v4449VU1Oj/v37SzofG3PnzlVxcbGqq6uVnZ2tZcuWBW0NAAD4x+dQSE9P1+uvv67Vq1dr2rRpqqio0G9+8xulp6f79PyOjg4tWrRITz75ZNdjtbW1iouL69qVmDJlirZt2xa0NQAA4B+fb4+UJKfTqaysLGVlZfl9oBUrVmjixIkaMGBA12MNDQ3q169f15/dbrcsy1JbW1tQ1hITE/2eGwCAq5lfoXC59uzZo9raWs2ZMycUhwuI5GQ+Phtf17fvNXaPgCjC+YRAC8Y5FZJQ+OMf/6j9+/frW9/6liSpsbFR06ZN03e+8x3V19d3fV1LS4ucTqcSExOVkZER8DV/NDeflmV5L/efjCh17BiflorA4XxCoF3OOeV0Oi76y7HP1yhciR/84AfauXOnduzYoR07dig9PV3r16/X9OnTde7cOe3evVuStGnTJk2YMEHS+fdoCPQaAADwT0h2FEycTqfKyspUUlKi9vZ29e/fX+Xl5UFbAwAA/rElFHbs2NH137fffrsqKyu7/bpgrAEAAN/ZuqMAIHJlZd1s9wgAQiAk1ygAAIDIxI4CgCuyZda9do/QrYkrfm/3CEBUYEcBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEXc9AGGK9ykAEA7YUQAAAEbsKABhbuy3y+weoVtvv1po9wgAQoAdBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMAoJKHQ2tqq73//+8rNzdX999+vmTNnqqWlRZJUU1OjiRMnKjc3V1OnTlVzc3PX84KxBgAAfBeSUHA4HJo+fbqqq6tVWVmp6667TsuWLZNlWZo7d66Ki4tVXV2t7OxsLVu2TJKCsgYAAPwTklBITEzUqFGjuv6clZWl+vp61dbWKi4uTtnZ2ZKkKVOmaNu2bZIUlDUAAOCfkF+jYFmWXnvtNeXk5KihoUH9+vXrWnO73bIsS21tbUFZAwAA/okN9QEXL16s+Ph4Pfzww3rzzTdDfXifJSf3sXsEhKG+fa+xewREEc4nBFowzqmQhkJpaak+//xzrV27Vk6nUxkZGaqvr+9ab2lpkdPpVGJiYlDW/NHcfFqW5b2Cfy2i0bFjp+weAVGE8wmBdjnnlNPpuOgvxyF76eGZZ55RbW2tKioq5HK5JEnDhw/XuXPntHv3bknSpk2bNGHChKCtAQAA/4RkR+Gzzz7TunXrNGjQIE2ZMkWSNGDAAFVUVKisrEwlJSVqb29X//79VV5eLklyOp0BXwMAAP4JSSjceOON2rdvX7drt99+uyorK0O2BgAAfMc7MwIAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIyiNhQOHjyo/Px85ebmKj8/X4cOHbJ7JAAAIk7UhkJJSYkKCgpUXV2tgoICFRcX2z0SAAARJypDobm5WXV1dcrLy5Mk5eXlqa6uTi0tLTZPBgBAZHF4vV6v3UMEWm1trebNm6ff/e53XY/dd999Ki8v1y233OLT39HaekaW5d+35r77vuXX1yPybN36h5Adi/Mp+oXyfJI4p64Gl3NOOZ0OJSX1Nq7HXslA0exi3zSTDz74IAiT4GrF+YRA45zC5YjKlx4yMjJ09OhReTweSZLH41FTU5MyMjJsngwAgMgSlaGQnJyszMxMVVVVSZKqqqqUmZkpt9tt82QAAESWqLxGQZL279+v+fPn6+TJk0pISFBpaamGDBli91gAAESUqA0FAABw5aLypQcAABAYhAIAADAiFAAAgBGhAAAAjAgFAABgxDszwm85OTlyuVyKi4uTJM2ZM0djxoyxeSpEktbWVhUWFurw4cNyuVwaOHCgFi1aJLfbrZqaGhUXF6u9vV39+/dXeXm5kpOT7R4ZEcD0s4lz6spweyT8lpOTo7Vr1+qmm26yexREqLa2Nu3bt0+jRo2SJJWWlurEiRNasmSJcnNztXTpUmVnZ2v16tU6cuSIli5davPEiATd/WyyLItz6grx0gOAkEtMTOyKBEnKyspSfX29amtrFRcXp+zsbEnSlClTtG3bNrvGRBTgnLpyvPSAyzJnzhx5vV6NHDlSP/vZz5SQkGD3SIhQlmXptddeU05OjhoaGtSvX7+uNbfbLcuy1NbWpsTERBunRKT4559NnFNXjh0F+O3VV1/Vli1b9Otf/1per1eLFi2yeyREsMWLFys+Pl4PP/yw3aMgwvGzKTgIBfjtq0/hdLlcKigo0IcffmjzRIhUpaWl+vzzz7V8+XI5nU5lZGSovr6+a72lpUVOp5Pf/OCT7n42cU5dOUIBfvnrX/+qU6dOSZK8Xq+2bt2qzMxMm6dCJHrmmWdUW1uriooKuVwuSdLw4cN17tw57d69W5K0adMmTZgwwc4xESFMP5s4p64cdz3AL0eOHNGPf/xjeTweWZalG264QQsXLlRqaqrdoyGCfPbZZ8rLy9OgQYPUs2dPSdKAAQNUUVGhDz/8UCUlJRfcypaSkmLzxAh3F/vZxDl1ZQgFAABgxEsPAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgxGc9ALBVTk6Ojh8/rpiYGMXHx2vMmDEqKipS79697R4NgNhRABAG1q5dqz179mjz5s2qq6vTc889Z/dIAP6OUAAQNvr27avRo0dr7969do8C4O8IBQBho7GxUe+8846uv/56u0cB8He8hTMAW+Xk5Ki1tVXS+Q/2+eY3v6mVK1cqISHB5skASOwoAAgDFRUV2rNnj15++WUdOHCgKxwA2I9QABA27rjjDj300EMqLS21exQAf0coAAgr3/3ud7Vr1y598skndo8CQIQCgDDjdrv1wAMPqKKiwu5RAIiLGQEAwEWwowAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACM/h8zefKSDecqjwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1q-UuBI8JRKh",
        "outputId": "46a5107e-36e0-4c39-b14f-6c1510a42247"
      },
      "source": [
        "train.isna().sum()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id           0\n",
              "breath_id    0\n",
              "R            0\n",
              "C            0\n",
              "time_step    0\n",
              "u_in         0\n",
              "u_out        0\n",
              "pressure     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "I89nq0PmKgiJ",
        "outputId": "10691820-b3d8-440e-e2e6-b9bf4576c83e"
      },
      "source": [
        "cat_c=train['C'].value_counts().index.astype(int)\n",
        "val_c=train['C'].value_counts().values\n",
        "cat_c1=test['C'].value_counts().index\n",
        "val_c1=test['C'].value_counts().values\n",
        "fig_1,(ax_1,ax_2)=plt.subplots(figsize=(15,8),ncols=2,nrows=1)\n",
        "\n",
        "explode = (0.1, 0.1, 0) \n",
        "ax_1.pie(x=val_c,explode=explode,colors=['yellowgreen','lightskyblue','gold'],startangle=90,labels=cat_c,autopct='%1.2f%%',textprops={'fontweight':'bold','fontsize':12},wedgeprops={'linewidth':1,'edgecolor':'k'},shadow=True,pctdistance=0.6)\n",
        "ax_1.add_artist(plt.Circle((0,0),0.4,fc=\"white\"))\n",
        "\n",
        "ax_1.legend(loc=3)\n",
        "\n",
        "explode = (0, 0.1, 0) \n",
        "ax_2.pie(x=val_c1,explode=explode,colors=['magenta','wheat','salmon'],labels=cat_c1,autopct='%1.2f%%',textprops={'fontweight':'bold','fontsize':12},wedgeprops={'linewidth':1,'edgecolor':'k'},shadow=True,pctdistance=0.6)\n",
        "ax_2.add_artist(plt.Circle((0,0),0.4,fc=\"white\"))\n",
        "\n",
        "ax_2.legend(loc=3)\n",
        "\n",
        "ax_1.plot()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 129
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKRZAgfwz7GY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7226e41b-43d0-4eef-9232-43ea64229bf5"
      },
      "source": [
        "train_set.max()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id           460039.000000\n",
              "breath_id      9531.000000\n",
              "R                50.000000\n",
              "C                50.000000\n",
              "time_step         2.745190\n",
              "u_in            100.000000\n",
              "u_out             1.000000\n",
              "pressure         64.820992\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "whbN1T5GyttN",
        "outputId": "8ff5ddf2-ecd6-4337-f4a9-656d2f988034"
      },
      "source": [
        "train_set"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>breath_id</th>\n",
              "      <th>R</th>\n",
              "      <th>C</th>\n",
              "      <th>time_step</th>\n",
              "      <th>u_in</th>\n",
              "      <th>u_out</th>\n",
              "      <th>pressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0</td>\n",
              "      <td>5.837492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.033652</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0</td>\n",
              "      <td>5.907794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.067514</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0</td>\n",
              "      <td>7.876254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.101542</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0</td>\n",
              "      <td>11.742872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.135756</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>0</td>\n",
              "      <td>12.234987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460034</th>\n",
              "      <td>460035</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.075398</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>7.735650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460035</th>\n",
              "      <td>460036</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.107040</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>6.399909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460036</th>\n",
              "      <td>460037</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.138719</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>6.540513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460037</th>\n",
              "      <td>460038</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.170342</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>6.470211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460038</th>\n",
              "      <td>460039</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.202019</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>6.329600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>460039 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            id  breath_id   R   C  time_step       u_in  u_out   pressure\n",
              "0            1          1  20  50   0.000000   0.083334      0   5.837492\n",
              "1            2          1  20  50   0.033652  18.383041      0   5.907794\n",
              "2            3          1  20  50   0.067514  22.509278      0   7.876254\n",
              "3            4          1  20  50   0.101542  22.808822      0  11.742872\n",
              "4            5          1  20  50   0.135756  25.355850      0  12.234987\n",
              "...        ...        ...  ..  ..        ...        ...    ...        ...\n",
              "460034  460035       9531  50  10   1.075398   0.000000      1   7.735650\n",
              "460035  460036       9531  50  10   1.107040   0.000000      1   6.399909\n",
              "460036  460037       9531  50  10   1.138719   0.000000      1   6.540513\n",
              "460037  460038       9531  50  10   1.170342   0.000000      1   6.470211\n",
              "460038  460039       9531  50  10   1.202019   0.000000      1   6.329600\n",
              "\n",
              "[460039 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jDwdrTFeNl2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "fe395c01-9a5f-4ad8-c5b1-de51e4f00ea2"
      },
      "source": [
        ""
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nHAAAIAoFHApfXcq5NzfccENIhgEAANEl4FD46lLOX+no6NCXX36ptLQ0vf766yEfDAAAOC/gUPjqUs5f8fv9Wrt2LTeFAgCgH+vztZfj4uI0b948Pffcc6GcBwAARJFLuknD22+/zQWYAADoxwJ+6+Hmm28+Lwq6u7t15swZlZWVhWUwAADgvIBDoaqq6ryvBw4cqNGjR2vw4MEhHwoAAESHgEPhO9/5jqRzt5j+/PPPNWzYMG4vDQBAPxfw/+lPnjypRYsW6brrrtNNN92k6667TosXL1ZXV1c45wMAAA4KOBTKy8vV3d2turo6/fnPf1ZdXZ26u7tVXl4ezvkAAICDAn7rYceOHdq2bZsGDhwoSRo9erRWrVqlqVOnhm04AADgrICPKCQkJKi9vf28xzo6OuTxeEI+FAAAiA4BH1EoLCzU7Nmz9aMf/UgjRoxQc3Ozamtrdccdd4RzPgAA4KCAQ+Gee+5RWlqa6urq1NraqtTUVM2dO5dQAACgHwv4rYdHH31Uo0ePVm1trV599VXV1tbqqquu0qOPPhrO+QAAgIMCDoX6+nqNHz/+vMfGjx+v+vr6kA8FAACiQ8Ch4HK5ZFnWeY/5/f4LHgMAAP1HwKGQm5urNWvW9ISBZVl68sknlZubG9QGn3rqKY0bN0779++XJDU2NqqgoEB5eXmaPXu22traer43HGsAACBwAYfCQw89pHfeeUc33nijCgsL9d3vflfvvPOOli1bFvDGPvroIzU2NiozM1PSudh48MEHVVpaqoaGBuXm5mr16tVhWwMAAMEJOBTS09P18ssv6+mnn9acOXNUXV2t3/3ud0pPTw/o+WfOnNHy5cv18MMP9zzW1NSkhISEnqMSM2fO1JYtW8K2BgAAghPwxyMlye12Kzs7W9nZ2UFvaM2aNSooKNDIkSN7HmtpadGIESN6vk5OTpZlWers7AzLmtfrDXpuAAAuZ0GFQl998MEHampq0sKFCyOxuZBISemft88ePnyI0yPENF6/C/Ga9B2vHUItHPtUREJh586dOnDggL73ve9Jko4ePao5c+Zo1qxZam5u7vm+9vZ2ud1ueb1eZWRkhHwtGG1tJ2VZdl9/5ah1/Dh3+7wUvH4X4jXpO147hFpf9im32/W1fxwHfI7CpfjJT36it956S9u3b9f27duVnp6u559/XnPnztXp06e1a9cuSdKGDRs0bdo0Seeu0RDqNQAAEJyIHFEwcbvdqqysVFlZmXw+nzIzM1VVVRW2NQAAEBxHQmH79u09/z1x4kTV1dX1+n3hWAMAAIFz9IgCgNiXnX210yMACKOInKMAAABiE0cUAFySzQtudXqEXhWs+YPTIwD9AkcUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGPGph8sMn3kHAASDIwoAAMCIIwqXGT7zDgAIBkcUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAAKOIhEJHR4fuuusu5eXlafr06Zo/f77a29slSY2NjSooKFBeXp5mz56ttra2nueFYw0AAAQuIqHgcrk0d+5cNTQ0qK6uTldeeaVWr14ty7L04IMPqrS0VA0NDcrNzdXq1aslKSxrAAAgOBEJBa/Xq0mTJvV8nZ2drebmZjU1NSkhIUG5ubmSpJkzZ2rLli2SFJY1AAAQnIifo2BZll566SVNmTJFLS0tGjFiRM9acnKyLMtSZ2dnWNYAAEBw4iO9wRUrVmjQoEG68847tXXr1khvPmApKYOdHgFRaPjwIU6PgH6E/QmhFo59KqKhUFFRocOHD+uZZ56R2+1WRkaGmpube9bb29vldrvl9XrDshaMtraTsiz7En5b9EfHj3c5PQL6EfYnhFpf9im32/W1fxxH7K2Hxx57TE1NTaqurpbH45EkjR8/XqdPn9auXbskSRs2bNC0adPCtgYAAIITkSMKH3/8sdatW6dRo0Zp5syZkqSRI0equrpalZWVKisrk8/nU2ZmpqqqqiRJbrc75GsAACA4EQmFb33rW9q3b1+vaxMnTlRdXV3E1gAAQOC4MiMAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjAgFAABgRCgAAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCo34bCwYMHVVRUpLy8PBUVFenQoUNOjwQAQMzpt6FQVlam4uJiNTQ0qLi4WKWlpU6PBABAzOmXodDW1qY9e/YoPz9fkpSfn689e/aovb3d4ckAAIgtLtu2baeHCLWmpiYtXrxYr7zySs9jt912m6qqqnTNNdcE9DM6Ok7JsoJ7aW677XtBfT9iz6uvvh6xbbE/9X+R3J8k9qnLQV/2KbfbpaSkRON6/KUM1J993Ytm8t5774VhElyu2J8QauxT6It++dZDRkaGjh07Jr/fL0ny+/1qbW1VRkaGw5MBABBb+mUopKSkKCsrS/X19ZKk+vp6ZWVlKTk52eHJAACILf3yHAVJOnDggEpKSnTixAkNHTpUFRUVGjNmjNNjAQAQU/ptKAAAgEvXL996AAAAoUEoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAi6qoqJCU6ZM0bhx47R///6ex7lDJ/qqo6NDd911l/Ly8jR9+nTNnz+/514sjY2NKigoUF5enmbPnq22tjaHp0WsmDJliqZNm6YZM2ZoxowZ2rFjhyT2qUtmAxexc+dOu7m52Z48ebK9b9++nsdnzZplb9q0ybZt2960aZM9a9Ysp0ZEjOno6LDffffdnq9/+ctf2r/4xS9sv99v33LLLfbOnTtt27bt6upqu6SkxKkxEWP+/t8o27bZp0KAIwq4qNzc3Asuf80dOnEpvF6vJk2a1PN1dna2mpub1dTUpISEBOXm5kqSZs6cqS1btjg1JvoB9qlLx02h0CctLS1KS0tTXFycJCkuLk6pqalqaWnhUtkIimVZeumllzRlyhS1tLRoxIgRPWvJycmyLEudnZ3yer0OTolYsXDhQtm2rZycHP3sZz9jnwoBjigAcNSKFSs0aNAg3XnnnU6Pghj34osvavPmzfrtb38r27a1fPlyp0fqFwgF9Al36EQoVFRU6PDhw3riiSfkdruVkZGh5ubmnvX29na53W7+8kNAvvr3x+PxqLi4WO+//z77VAgQCugT7tCJS/XYY4+pqalJ1dXV8ng8kqTx48fr9OnT2rVrlyRpw4YNmjZtmpNjIkb89a9/VVdXlyTJtm29+uqrysrKYp8KAW4KhYsqLy/Xa6+9ps8//1xJSUnyer165ZVXuEMn+uzjjz9Wfn6+Ro0apQEDBkiSRo4cqerqar3//vsqKyuTz+dTZmamqqqqNGzYMIcnRrQ7cuSI7r33Xvn9flmWpauuukpLly5Vamoq+9QlIhQAAIARbz0AAAAjQgEAABgRCgAAwIhQAAAARoQCAAAwIhQAAIAR93oA4Li6ujrV1NTo4MGDSkxM1NVXX6158+b13MgHgHMIBQCOqqmp0bPPPqtHHnlEN954o6644grt2LFDr7/+OqEARAEuuATAMV1dXbrpppu0cuVK3XrrrU6PA6AXnKMAwDEffPCBfD6fpk6d6vQoAAwIBQCO6ezsVFJSkuLjeRcUiFaEAgDHeL1edXR06OzZs06PAsCAUADgmOuvv14ej0fbtm1zehQABoQCAMcMGTJE9913n5YvX65t27apu7tbX375pd58801VVlY6PR4A8akHAFFg8+bNqq2t1SeffKLExERdc801mjdvniZOnOj0aMBlj1AAAABGvPUAAACMCAUAAGBEKAAAACNCAQAAGBEKAADAiFAAAABGhAIAADAiFAAAgBGhAAAAjP4PtMzJb1D+D3MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "WQc2pTPcxDfw",
        "outputId": "04e48be8-f35f-485c-c239-8b4fbbb712cd"
      },
      "source": [
        "corr=train_set.corr('pearson')\n",
        "fig=plt.figure(figsize=(10,6))\n",
        "ax=sb.heatmap(corr,cmap='RdBu_r',annot=True)\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "AOzoenQCBY-i",
        "outputId": "da5ee8e3-950d-484b-9e60-7b5442c51aa2"
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.7.0'"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "4QQg9FsmMrZV",
        "outputId": "74d50531-c699-4869-8404-40f13cecb553"
      },
      "source": [
        "df_sel"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>breath_id</th>\n",
              "      <th>R</th>\n",
              "      <th>C</th>\n",
              "      <th>time_step</th>\n",
              "      <th>u_in</th>\n",
              "      <th>u_out</th>\n",
              "      <th>pressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0</td>\n",
              "      <td>5.837492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.033652</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0</td>\n",
              "      <td>5.907794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.067514</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0</td>\n",
              "      <td>7.876254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.101542</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0</td>\n",
              "      <td>11.742872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.135756</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>0</td>\n",
              "      <td>12.234987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460034</th>\n",
              "      <td>460035</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.075398</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>7.735650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460035</th>\n",
              "      <td>460036</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.107040</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>6.399909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460036</th>\n",
              "      <td>460037</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.138719</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>6.540513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460037</th>\n",
              "      <td>460038</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.170342</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>6.470211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460038</th>\n",
              "      <td>460039</td>\n",
              "      <td>9531</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>1.202019</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>6.329600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>414554 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            id  breath_id   R   C  time_step       u_in  u_out   pressure\n",
              "0            1          1  20  50   0.000000   0.083334      0   5.837492\n",
              "1            2          1  20  50   0.033652  18.383041      0   5.907794\n",
              "2            3          1  20  50   0.067514  22.509278      0   7.876254\n",
              "3            4          1  20  50   0.101542  22.808822      0  11.742872\n",
              "4            5          1  20  50   0.135756  25.355850      0  12.234987\n",
              "...        ...        ...  ..  ..        ...        ...    ...        ...\n",
              "460034  460035       9531  50  10   1.075398   0.000000      1   7.735650\n",
              "460035  460036       9531  50  10   1.107040   0.000000      1   6.399909\n",
              "460036  460037       9531  50  10   1.138719   0.000000      1   6.540513\n",
              "460037  460038       9531  50  10   1.170342   0.000000      1   6.470211\n",
              "460038  460039       9531  50  10   1.202019   0.000000      1   6.329600\n",
              "\n",
              "[414554 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5cbrQKcMtda"
      },
      "source": [
        "X=train.iloc[:,:-1].values\n",
        "Y=train.iloc[:,-1].values"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtUEDCpZM5eK"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=0)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "8mZU1gN6ZWIa",
        "outputId": "a3853e19-a250-423c-bd63-fe14b462aaab"
      },
      "source": [
        "test"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "            id  breath_id   R   C  time_step       u_in  u_out\n",
              "0            1          0   5  20   0.000000   0.000000    0.0\n",
              "1            2          0   5  20   0.031904   7.515046    0.0\n",
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              "4            5          0   5  20   0.127644  26.320956    0.0\n",
              "...        ...        ...  ..  ..        ...        ...    ...\n",
              "457171  457172      14394  50  50   1.631394   2.526626    1.0\n",
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              "457174  457175      14394  50  50   1.727811   3.472706    1.0\n",
              "457175  457176      14394  50  50   1.759665   3.697572    0.0\n",
              "\n",
              "[457176 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YVaZqI1VQa8",
        "outputId": "a8cdab87-82b3-4502-c33c-c19a3badaae3"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rfr=RandomForestRegressor(n_estimators=64,criterion='squared_error')\n",
        "rfr.fit(x_train,y_train)\n"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(n_estimators=64)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCwIhKQNXaul"
      },
      "source": [
        "y_pred=rfr.predict(x_test)"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v65X6g4sXNjm",
        "outputId": "fd1b5ca1-0b2f-43cd-9cb4-4f2be6be43c1"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "score=r2_score(y_pred,y_test)\n",
        "score"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7981788251303706"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWNAQ9j4bVdT"
      },
      "source": [
        "sample=pd.read_csv('sample_submission.csv')\n",
        "ids=test_set['id']"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BO6TVPmqZCBw",
        "outputId": "21946385-c304-44ff-cf12-1c7207b47dac"
      },
      "source": [
        "pred=rfr.predict(test_set)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:439: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
            "  f\"X has feature names, but {self.__class__.__name__} was fitted without\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_dByr30bqb9",
        "outputId": "a12d2ed3-5632-49c2-d91b-fd50a8009579"
      },
      "source": [
        "ids"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              1\n",
              "1              2\n",
              "2              3\n",
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              "           ...  \n",
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              "711393    711394\n",
              "711394       711\n",
              "Name: id, Length: 711395, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvttqnZfZShG",
        "outputId": "aab09abc-961d-4938-8146-1bd4921f3690"
      },
      "source": [
        "pred.shape"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4024000,)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "fgMUjDb6Yufo",
        "outputId": "50c11299-eb53-47dc-fcdc-1e365b6d7658"
      },
      "source": [
        "output=pd.DataFrame({'id':ids,'pressure':pred})\n",
        "output.to_csv('submission_1.csv')\n",
        "output"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "              id  pressure\n",
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              "...          ...       ...\n",
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              "4023999  4024000  6.070368\n",
              "\n",
              "[4024000 rows x 2 columns]"
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          "metadata": {},
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          "height": 423
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        "outputId": "c8030747-76c5-4f34-adaf-7f1ddc8d9f2c"
      },
      "source": [
        "test_set=pd.read_csv('test.csv')\n",
        "test_set"
      ],
      "execution_count": 133,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "              id  breath_id   R   C  time_step       u_in  u_out\n",
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              "...          ...        ...  ..  ..        ...        ...    ...\n",
              "4023995  4023996     125748  20  10   2.530117   4.971245      1\n",
              "4023996  4023997     125748  20  10   2.563853   4.975709      1\n",
              "4023997  4023998     125748  20  10   2.597475   4.979468      1\n",
              "4023998  4023999     125748  20  10   2.631134   4.982648      1\n",
              "4023999  4024000     125748  20  10   2.665301   4.985373      1\n",
              "\n",
              "[4024000 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIjdcFYtcAGm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}