Regression_w_new_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Regression w/new_dataset.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install pycaret[full] --quiet\n",
        "!pip install numpy --upgrade\n",
        "!pip install explainerdashboard\n",
        "!pip uninstall matplotlib\n",
        "!pip install matplotlib==3.1.3\n",
        "!pip install Jinja2\n",
        "!pip install markupsafe==2.0.1\n",
        "!pip install tornado"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LoJ7O0ozYaBV",
        "outputId": "74f380a6-e3f6-4488-a3af-55157f3cf1ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.0+zzzcolab20220506162203 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
            "xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\n",
            "tensorflow 2.8.0+zzzcolab20220506162203 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "google-colab 1.0.0 requires ipykernel~=4.10, but you have ipykernel 6.13.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.33.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 6.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.19.5)\n",
            "Collecting numpy\n",
            "  Using cached numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)\n",
            "Installing collected packages: numpy\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.19.5\n",
            "    Uninstalling numpy-1.19.5:\n",
            "      Successfully uninstalled numpy-1.19.5\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.0+zzzcolab20220506162203 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
            "yellowbrick 1.3.post1 requires numpy<1.20,>=1.16.0, but you have numpy 1.21.6 which is incompatible.\n",
            "google-colab 1.0.0 requires ipykernel~=4.10, but you have ipykernel 6.13.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.33.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 6.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed numpy-1.21.6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: explainerdashboard in /usr/local/lib/python3.7/dist-packages (0.3.8.2)\n",
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            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib->dtreeviz>=1.3->explainerdashboard) (7.1.2)\n",
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            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests->dash-auth->explainerdashboard) (2.0.12)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->explainerdashboard) (3.1.0)\n",
            "Found existing installation: matplotlib 3.5.2\n",
            "Uninstalling matplotlib-3.5.2:\n",
            "  Would remove:\n",
            "    /usr/local/lib/python3.7/dist-packages/matplotlib-3.5.2-py3.7-nspkg.pth\n",
            "    /usr/local/lib/python3.7/dist-packages/matplotlib-3.5.2.dist-info/*\n",
            "    /usr/local/lib/python3.7/dist-packages/matplotlib/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axes_grid/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axes_grid1/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axisartist/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/mplot3d/*\n",
            "    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/tests/*\n",
            "    /usr/local/lib/python3.7/dist-packages/pylab.py\n",
            "Proceed (y/n)? "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9TMHH4GEYCdD"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_theme(style=\"darkgrid\")\n",
        "\n",
        "import jinja2\n",
        "import explainerdashboard\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/NewLungdata.csv')"
      ],
      "metadata": {
        "id": "CnYqouR6YWWF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "id": "GjcS-AOQdNDv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna('0')"
      ],
      "metadata": {
        "id": "jMZW0cGBr5vj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "iuZebnqodQJY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "j9WNsFOXdcGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "5sbH3XvRdmwS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(0)"
      ],
      "metadata": {
        "id": "epGCkZEZR6xQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***EDA***"
      ],
      "metadata": {
        "id": "9qNzWSU7dwgR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.relplot(x='age_at_dendrite_date', y='score', hue='age_at_dendrite_date', size=4, aspect=2, data=df)"
      ],
      "metadata": {
        "id": "gzBZjI3Fd5tN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.relplot(x='age_at_dendrite_date', y='score', hue='age_at_dendrite_date', size=4, aspect=2, kind='line', data=df)"
      ],
      "metadata": {
        "id": "NAKBz1rseHxY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc = {'figure.figsize':(15,8)})\n",
        "sns.histplot(data=df, x='age_at_dendrite_date', binwidth=3)"
      ],
      "metadata": {
        "id": "Y3ySkUhKgjza"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Regression\n"
      ],
      "metadata": {
        "id": "H2jtF45dmeEd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from pycaret.regression import *"
      ],
      "metadata": {
        "id": "55ELOHbumg_z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exp = setup(data=df, target='age_at_dendrite_date')"
      ],
      "metadata": {
        "id": "rfcArkalmmE7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best = compare_models(fold=3)"
      ],
      "metadata": {
        "id": "kAk5z8eIsEqv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(best)"
      ],
      "metadata": {
        "id": "ct-k143Jxb0M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#click individual tabs to see the visuals\n",
        "evaluate_model(best)"
      ],
      "metadata": {
        "id": "EYUKY45XsN_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_model(best)"
      ],
      "metadata": {
        "id": "VA1Eik2Iw5v8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = predict_model(best, data=df)\n",
        "predictions.head()"
      ],
      "metadata": {
        "id": "alIGQlOZx_qE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "dashboard(best)"
      ],
      "metadata": {
        "id": "L0NeG_XbzxZ6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Stacker Model**"
      ],
      "metadata": {
        "id": "TAuH0WAHHLBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = create_model('rf', fold=5)\n",
        "cat = create_model('catboost', fold=5)\n",
        "gbr = create_model('gbr', fold=5)"
      ],
      "metadata": {
        "id": "rN9-sjICHOaR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stacker = stack_models([rf, cat, gbr], fold=3)\n"
      ],
      "metadata": {
        "id": "2Wl-b-cFOs_w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_stacked = predict_model(stacker, data=df)\n",
        "predictions_stacked.head()"
      ],
      "metadata": {
        "id": "_-6lIG6RO_L6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "dashboard(stacker)"
      ],
      "metadata": {
        "id": "0TtajLp6POIG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}