Part11: tqdm_model_building_with_progress_bar.ipynb

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      "metadata": {
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      "source": [
        "# **Progress Bar in Jupyter Notebook**\n",
        "\n",
        "Help available on video \"https://www.youtube.com/watch?v=8zm4L3rVreI\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yc04janmetd"
      },
      "source": [
        "# ! pip install tqdm"
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      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxa8jup1DNjt"
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        "from tqdm.notebook import tqdm\n",
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      "outputs": []
    },
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        "number_list = list(range(100))\n",
        "for x in tqdm(number_list):\n",
        "    sleep(0.05)\n",
        "print('Completed!')"
      ],
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      "outputs": [
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            "\n",
            "Completed!\n"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4tFGw2QFMz6N"
      },
      "source": [
        "# **Model Building**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKKr9EoSVbOV"
      },
      "source": [
        "### Reading in the Delaney Solubility Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHR0FBHEMyyL"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "dataset = pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv')\n",
        "\n",
        "X = dataset.drop(['logS'], axis=1)\n",
        "Y = dataset.iloc[:,-1]\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqqRRTtUVi7v"
      },
      "source": [
        "### Model Building with Progress Bar"
      ]
    },
    {
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        "scrolled": true,
        "colab": {
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        "outputId": "01f6ae08-3f4e-414b-b4d4-15befcf4a4af"
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      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "parameter_n_estimators = [100,200,300,400,500,600,700,800,900,1000]\n",
        "\n",
        "for i in tqdm(parameter_n_estimators):\n",
        "  model = RandomForestRegressor(n_estimators=i)\n",
        "  model.fit(X,Y)\n",
        "  Y_pred = model.predict(X)\n",
        "  r2 = r2_score(Y, Y_pred)\n",
        "  mse = mean_squared_error(Y, Y_pred)\n",
        "  print('Tree: %s, R2: %s, MSE: %s' % (i, r2, mse))"
      ],
      "execution_count": 5,
      "outputs": [
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          "text": [
            "Tree: 100, R2: 0.9801103862147522, MSE: 0.08734480684287356\n",
            "Tree: 200, R2: 0.9802001328551525, MSE: 0.0869506864212711\n",
            "Tree: 300, R2: 0.9802407790154765, MSE: 0.08677218969123225\n",
            "Tree: 400, R2: 0.9801437906127148, MSE: 0.08719811215492014\n",
            "Tree: 500, R2: 0.9802564595026368, MSE: 0.08670332917252065\n",
            "Tree: 600, R2: 0.9801073793335154, MSE: 0.08735801149650163\n",
            "Tree: 700, R2: 0.9805156207728644, MSE: 0.08556522808450624\n",
            "Tree: 800, R2: 0.9805292360810997, MSE: 0.08550543675417892\n",
            "Tree: 900, R2: 0.9804101032711955, MSE: 0.08602860590075347\n",
            "Tree: 1000, R2: 0.9805506976950876, MSE: 0.08541118854259704\n",
            "\n"
          ],
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      ]
    },
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        "id": "3U2narT-KLIM"
      },
      "source": [
        "------------------------------"
      ]
    }
  ]
}