Copy_of_Grid_Search_&_Random_Search.ipynb

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      "name": "Copy of Grid Search & Random Search.ipynb",
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      "name": "python3",
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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/elyselee/Launch-Bootcamp-/blob/master/Copy_of_Grid_Search_%26_Random_Search.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d2887d75-0a67-4ae1-9432-24672711f77d\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d2887d75-0a67-4ae1-9432-24672711f77d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving train.csv to train (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "keY40wQR3zZd",
        "colab_type": "code",
        "outputId": "a4fc6558-57cd-47a2-ee0a-72d318710cf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "import pandas as pd\n",
        "traint = pd.read_csv('train (1).csv')\n",
        "traindf=pd.read_csv('train (1).csv')\n",
        "traindf.head()\n",
        "traint.head()\n"
      ],
      "execution_count": 0,
      "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>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>...</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>196.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>706</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>856</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>8</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>548</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>978</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>284</td>\n",
              "      <td>1262</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>460</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>162.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>486</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>434</td>\n",
              "      <td>920</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>608</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Shng</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>216</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>540</td>\n",
              "      <td>756</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>3</td>\n",
              "      <td>642</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>350.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>655</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>490</td>\n",
              "      <td>1145</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>3</td>\n",
              "      <td>836</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0   1          60       RL  ...        WD         Normal    208500\n",
              "1   2          20       RL  ...        WD         Normal    181500\n",
              "2   3          60       RL  ...        WD         Normal    223500\n",
              "3   4          70       RL  ...        WD        Abnorml    140000\n",
              "4   5          60       RL  ...        WD         Normal    250000\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UvTWXVq4KcT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import skew\n",
        "from scipy.stats.stats import pearsonr\n",
        "\n",
        "#MODEL CLEANED WITH ATTRIBUTION TO \"https://www.kaggle.com/apapiu/regularized-linear-models\"\n",
        "TARGET = 'SalePrice'\n",
        "NROWS = None\n",
        "y_train = np.log(traindf[TARGET]+1)\n",
        "\n",
        "\n",
        "traindf.drop([TARGET], axis=1, inplace=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UOw-r4a536Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#MODEL CLEANING\n",
        "alltrain =traindf.loc[:,'MSSubClass':'SaleCondition']\n",
        "numeric_feats = alltrain.dtypes[alltrain.dtypes != \"object\"].index\n",
        "\n",
        "skewed_feats = traindf[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness\n",
        "skewed_feats = skewed_feats[skewed_feats > 0.75]\n",
        "skewed_feats = skewed_feats.index\n",
        "\n",
        "alltrain[skewed_feats] = np.log1p(alltrain[skewed_feats])\n",
        "\n",
        "alltrain = pd.get_dummies(alltrain)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zNO-46v7UPh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#MODEL CLEANINNG\n",
        "alltrain = pd.get_dummies(alltrain)\n",
        "#filling NA's with the mean of the column:\n",
        "alltrain = alltrain.fillna(alltrain.mean())\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-j0b21V7hee",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Trainn_test_split\n",
        "#creating matrices for sklearn:\n",
        "SEED = 0\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train = np.array(alltrain[:traindf.shape[0]])\n",
        "\n",
        "x_fit,x_test,y_fit,y_test = train_test_split(x_train, y_train, train_size =0.33, \n",
        "                            random_state=SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGSdSiIe9UlC",
        "colab_type": "code",
        "outputId": "1e5d63d1-8dbe-4fca-e5b8-9624bfaf085c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        }
      },
      "source": [
        "alltrain.head()"
      ],
      "execution_count": 0,
      "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>MSSubClass</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>MSZoning_C (all)</th>\n",
              "      <th>MSZoning_FV</th>\n",
              "      <th>MSZoning_RH</th>\n",
              "      <th>MSZoning_RL</th>\n",
              "      <th>...</th>\n",
              "      <th>GarageFinish_Unf</th>\n",
              "      <th>GarageQual_Ex</th>\n",
              "      <th>GarageQual_Fa</th>\n",
              "      <th>GarageQual_Gd</th>\n",
              "      <th>GarageQual_Po</th>\n",
              "      <th>GarageQual_TA</th>\n",
              "      <th>GarageCond_Ex</th>\n",
              "      <th>GarageCond_Fa</th>\n",
              "      <th>GarageCond_Gd</th>\n",
              "      <th>GarageCond_Po</th>\n",
              "      <th>GarageCond_TA</th>\n",
              "      <th>PavedDrive_N</th>\n",
              "      <th>PavedDrive_P</th>\n",
              "      <th>PavedDrive_Y</th>\n",
              "      <th>PoolQC_Ex</th>\n",
              "      <th>PoolQC_Fa</th>\n",
              "      <th>PoolQC_Gd</th>\n",
              "      <th>Fence_GdPrv</th>\n",
              "      <th>Fence_GdWo</th>\n",
              "      <th>Fence_MnPrv</th>\n",
              "      <th>Fence_MnWw</th>\n",
              "      <th>MiscFeature_Gar2</th>\n",
              "      <th>MiscFeature_Othr</th>\n",
              "      <th>MiscFeature_Shed</th>\n",
              "      <th>MiscFeature_TenC</th>\n",
              "      <th>SaleType_COD</th>\n",
              "      <th>SaleType_CWD</th>\n",
              "      <th>SaleType_Con</th>\n",
              "      <th>SaleType_ConLD</th>\n",
              "      <th>SaleType_ConLI</th>\n",
              "      <th>SaleType_ConLw</th>\n",
              "      <th>SaleType_New</th>\n",
              "      <th>SaleType_Oth</th>\n",
              "      <th>SaleType_WD</th>\n",
              "      <th>SaleCondition_Abnorml</th>\n",
              "      <th>SaleCondition_AdjLand</th>\n",
              "      <th>SaleCondition_Alloca</th>\n",
              "      <th>SaleCondition_Family</th>\n",
              "      <th>SaleCondition_Normal</th>\n",
              "      <th>SaleCondition_Partial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.110874</td>\n",
              "      <td>4.189655</td>\n",
              "      <td>9.042040</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>5.283204</td>\n",
              "      <td>6.561031</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.017280</td>\n",
              "      <td>6.753438</td>\n",
              "      <td>6.753438</td>\n",
              "      <td>6.751101</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.444833</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>548</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.127134</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.044522</td>\n",
              "      <td>4.394449</td>\n",
              "      <td>9.169623</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.886532</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.652489</td>\n",
              "      <td>7.141245</td>\n",
              "      <td>7.141245</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.141245</td>\n",
              "      <td>0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>460</td>\n",
              "      <td>5.700444</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.110874</td>\n",
              "      <td>4.234107</td>\n",
              "      <td>9.328212</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>5.093750</td>\n",
              "      <td>6.188264</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.075346</td>\n",
              "      <td>6.825460</td>\n",
              "      <td>6.825460</td>\n",
              "      <td>6.765039</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.488294</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>608</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.761200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.262680</td>\n",
              "      <td>4.110874</td>\n",
              "      <td>9.164401</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.379897</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.293419</td>\n",
              "      <td>6.629363</td>\n",
              "      <td>6.869014</td>\n",
              "      <td>6.629363</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.448916</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>642</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.583519</td>\n",
              "      <td>5.609472</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.110874</td>\n",
              "      <td>4.442651</td>\n",
              "      <td>9.565284</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>5.860786</td>\n",
              "      <td>6.486161</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.196444</td>\n",
              "      <td>7.044033</td>\n",
              "      <td>7.044033</td>\n",
              "      <td>6.960348</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.695758</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>836</td>\n",
              "      <td>5.262690</td>\n",
              "      <td>4.442651</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 288 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  LotFrontage  ...  SaleCondition_Normal  SaleCondition_Partial\n",
              "0    4.110874     4.189655  ...                     1                      0\n",
              "1    3.044522     4.394449  ...                     1                      0\n",
              "2    4.110874     4.234107  ...                     1                      0\n",
              "3    4.262680     4.110874  ...                     0                      0\n",
              "4    4.110874     4.442651  ...                     1                      0\n",
              "\n",
              "[5 rows x 288 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUYQie1-Xzp8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Random Forest Random Search\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\"n_estimators\": [200, 500],\n",
        "    \"max_depth\": [3, None],\n",
        "    \"max_features\": [1, 3],\n",
        "    \"min_samples_split\": [5, 10],\n",
        "    \"min_samples_leaf\": [5, 10],\n",
        "    \"bootstrap\": [True, False]}\n",
        "\n",
        "rf_random_search =RandomizedSearchCV(RandomForestRegressor(random_state=0),param_grid,  n_iter=100, cv=5, verbose=0, n_jobs=-1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oukf8psSX9SO",
        "colab_type": "code",
        "outputId": "dd89c6c8-b284-42a7-e35f-90cbd57e075c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "#Random Forest Random Search Fit\n",
        "rf_random_search.fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:266: UserWarning: The total space of parameters 64 is smaller than n_iter=100. Running 64 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  % (grid_size, self.n_iter, grid_size), UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
              "                   estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                   criterion='mse',\n",
              "                                                   max_depth=None,\n",
              "                                                   max_features='auto',\n",
              "                                                   max_leaf_nodes=None,\n",
              "                                                   min_impurity_decrease=0.0,\n",
              "                                                   min_impurity_split=None,\n",
              "                                                   min_samples_leaf=1,\n",
              "                                                   min_samples_split=2,\n",
              "                                                   min_weight_fraction_leaf=0.0,\n",
              "                                                   n_estimators='warn',\n",
              "                                                   n_jobs=None, oob_score=False,\n",
              "                                                   random_state=0, verbose=0,\n",
              "                                                   warm_start=False),\n",
              "                   iid='warn', n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={'bootstrap': [True, False],\n",
              "                                        'max_depth': [3, None],\n",
              "                                        'max_features': [1, 3],\n",
              "                                        'min_samples_leaf': [5, 10],\n",
              "                                        'min_samples_split': [5, 10],\n",
              "                                        'n_estimators': [200, 500]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eA50NzO3aKkT",
        "colab_type": "code",
        "outputId": "ae60fef2-f9e7-4d65-bd72-54fbb75bc43c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "#Random Forest Random Search Best Params\n",
        "rf_random_search.best_params_ "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': False,\n",
              " 'max_depth': None,\n",
              " 'max_features': 3,\n",
              " 'min_samples_leaf': 5,\n",
              " 'min_samples_split': 5,\n",
              " 'n_estimators': 500}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIf_ymARaPh3",
        "colab_type": "code",
        "outputId": "e588ee89-7a12-4a7b-e476-9ad9864704f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Random Forest Random Search Score\n",
        "rf_score=rf_random_search.score(x_train,y_train)\n",
        "print(rf_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7431364189170747\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWGo5fkypxrg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Ridge Random Search\n",
        "from sklearn.linear_model import Ridge\n",
        "ridge = Ridge()\n",
        "parameters = {'alpha': [1, 5, 10]}\n",
        "ridge_random_search=RandomizedSearchCV(ridge, parameters, cv = 5)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JW4QAHyYqXqh",
        "colab_type": "code",
        "outputId": "3ffaf144-4cf3-4c6b-9436-7391dc9d3ca5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "#Ridge Random Search Fit\n",
        "ridge_random_search.fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:266: UserWarning: The total space of parameters 3 is smaller than n_iter=10. Running 3 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  % (grid_size, self.n_iter, grid_size), UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
              "                   estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                                   max_iter=None, normalize=False,\n",
              "                                   random_state=None, solver='auto',\n",
              "                                   tol=0.001),\n",
              "                   iid='warn', n_iter=10, n_jobs=None,\n",
              "                   param_distributions={'alpha': [1, 5, 10]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3rQgKeDtntU",
        "colab_type": "code",
        "outputId": "81427c20-085c-4163-f383-24878e2f9a84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Ridge Random Search Best Params\n",
        "ridge_random_search.best_params_ "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'alpha': 10}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqGP4_oQttDM",
        "colab_type": "code",
        "outputId": "8b0df098-f6e1-4bbd-b2f6-2bbe74d4d0d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Ridge Random Search Score\n",
        "ridge_score=ridge_random_search.score(x_train,y_train)\n",
        "print(ridge_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9299579771565469\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ERjDdxnGGnM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Gradient Boost Random Search\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "num_estimators = [500,1000]\n",
        "learn_rates = [0.02, 0.05]\n",
        "max_depths = [1, 2]\n",
        "min_samples_leaf = [5,10]\n",
        "min_samples_split = [5,10]\n",
        "\n",
        "parameters = {'n_estimators': num_estimators,\n",
        "              'learning_rate': learn_rates,\n",
        "              'max_depth': max_depths,\n",
        "              'min_samples_leaf': min_samples_leaf,\n",
        "              'min_samples_split': min_samples_split}\n",
        "\n",
        "gb_random_search =RandomizedSearchCV(GradientBoostingRegressor(loss='huber'), parameters, random_state=1, n_iter=100, cv=5, verbose=0, n_jobs=-1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjFUJO40G8Po",
        "colab_type": "code",
        "outputId": "10cd8d6a-da2f-4638-aadf-bb97636c614d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        }
      },
      "source": [
        "#Gradient Boost Random Search Fit\n",
        "gb_random_search.fit(x_train, y_train)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:266: UserWarning: The total space of parameters 32 is smaller than n_iter=100. Running 32 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  % (grid_size, self.n_iter, grid_size), UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
              "                   estimator=GradientBoostingRegressor(alpha=0.9,\n",
              "                                                       criterion='friedman_mse',\n",
              "                                                       init=None,\n",
              "                                                       learning_rate=0.1,\n",
              "                                                       loss='huber',\n",
              "                                                       max_depth=3,\n",
              "                                                       max_features=None,\n",
              "                                                       max_leaf_nodes=None,\n",
              "                                                       min_impurity_decrease=0.0,\n",
              "                                                       min_impurity_split=None,\n",
              "                                                       min_samples_leaf=1,\n",
              "                                                       min_samples_split=2,\n",
              "                                                       min_weight_fraction_leaf=0.0,\n",
              "                                                       n_estimators=1...\n",
              "                                                       subsample=1.0,\n",
              "                                                       tol=0.0001,\n",
              "                                                       validation_fraction=0.1,\n",
              "                                                       verbose=0,\n",
              "                                                       warm_start=False),\n",
              "                   iid='warn', n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={'learning_rate': [0.02, 0.05],\n",
              "                                        'max_depth': [1, 2],\n",
              "                                        'min_samples_leaf': [5, 10],\n",
              "                                        'min_samples_split': [5, 10],\n",
              "                                        'n_estimators': [500, 1000]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=1, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfbnIFrnJHpk",
        "colab_type": "code",
        "outputId": "6032d686-3c9d-43a3-9dae-9ebf9f355d19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "#Gradient Boost Random Search Best Params\n",
        "gb_random_search.best_params_ "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.05,\n",
              " 'max_depth': 2,\n",
              " 'min_samples_leaf': 5,\n",
              " 'min_samples_split': 5,\n",
              " 'n_estimators': 1000}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIbJ0qqxHBNz",
        "colab_type": "code",
        "outputId": "cc8c9899-d172-4e63-dc56-3d4ff78dfdcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Gradient Boost Random Search Score\n",
        "gboost_score=gb_random_search.score(x_train,y_train)\n",
        "print(gboost_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9593994982854908\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gdGqWRrx8PW",
        "colab_type": "code",
        "outputId": "babbf3b7-9978-47f6-9d9d-1138c6a236d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "#Bar graph of Random Search Scores\n",
        "objects = ('RandomForest','Ridge','GradientBoost')\n",
        "y_pos = np.arange(len(objects))\n",
        "performance = [rf_score, ridge_score,gboost_score]\n",
        "\n",
        "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
        "plt.xticks(y_pos, objects)\n",
        "plt.ylabel('Score')\n",
        "plt.title('Random Search Scores')\n",
        "plt.ylim(0.5, 1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zwS4rbbfXPhrYC1gBLEmyuKqumBinqt49Mv7BwDOHqkeSNLsh9xR2B5ZV1TVVdSdwPLDv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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWnHIBCA31J8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Gradient Boost Grid Search\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "num_estimators = [500,1000]\n",
        "learn_rates = [0.02, 0.05]\n",
        "max_depths = [1, 2]\n",
        "min_samples_leaf = [5,10]\n",
        "min_samples_split = [5,10]\n",
        "\n",
        "parameters = {'n_estimators': num_estimators,\n",
        "              'learning_rate': learn_rates,\n",
        "              'max_depth': max_depths,\n",
        "              'min_samples_leaf': min_samples_leaf,\n",
        "              'min_samples_split': min_samples_split}\n",
        "\n",
        "gb_grid_search = GridSearchCV(GradientBoostingRegressor(loss='huber'),\n",
        "                           parameters, cv=3, return_train_score=True)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3Jo4-8PK4Kn",
        "colab_type": "code",
        "outputId": "d6eaee48-7141-496a-f8d2-58417378c3af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "#Gradient Boost Grid Search Fit\n",
        "gb_grid_search.fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
              "             estimator=GradientBoostingRegressor(alpha=0.9,\n",
              "                                                 criterion='friedman_mse',\n",
              "                                                 init=None, learning_rate=0.1,\n",
              "                                                 loss='huber', max_depth=3,\n",
              "                                                 max_features=None,\n",
              "                                                 max_leaf_nodes=None,\n",
              "                                                 min_impurity_decrease=0.0,\n",
              "                                                 min_impurity_split=None,\n",
              "                                                 min_samples_leaf=1,\n",
              "                                                 min_samples_split=2,\n",
              "                                                 min_weight_fraction_leaf=0.0,\n",
              "                                                 n_estimators=100,\n",
              "                                                 n_i...\n",
              "                                                 presort='auto',\n",
              "                                                 random_state=None,\n",
              "                                                 subsample=1.0, tol=0.0001,\n",
              "                                                 validation_fraction=0.1,\n",
              "                                                 verbose=0, warm_start=False),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid={'learning_rate': [0.02, 0.05], 'max_depth': [1, 2],\n",
              "                         'min_samples_leaf': [5, 10],\n",
              "                         'min_samples_split': [5, 10],\n",
              "                         'n_estimators': [500, 1000]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUQapTV-FQGh",
        "colab_type": "code",
        "outputId": "de8728c0-aa13-4e77-a398-3879a0b1095b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "#Gradient Boost Grid Search Best Params\n",
        "gb_grid_search.best_params_  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.05,\n",
              " 'max_depth': 2,\n",
              " 'min_samples_leaf': 5,\n",
              " 'min_samples_split': 10,\n",
              " 'n_estimators': 1000}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8-i41qAFVyX",
        "colab_type": "code",
        "outputId": "d383beb7-2efe-4e8c-dcdf-de7580ee87ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Gradient Boost Grid Search Score\n",
        "gboost_grid_score=gb_grid_search.score(x_train, y_train)\n",
        "print(gboost_grid_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9594109612711633\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHeY41ALETrv",
        "colab_type": "code",
        "outputId": "cfdfba15-c8c5-476a-ea34-01d765fe447e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "#Showing with different parameter value num_estimators (5,10)\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "num_estimators = [5,10]\n",
        "learn_rates = [0.02, 0.05]\n",
        "max_depths = [1, 2]\n",
        "min_samples_leaf = [5,10]\n",
        "min_samples_split = [5,10]\n",
        "\n",
        "parameters = {'n_estimators': num_estimators,\n",
        "              'learning_rate': learn_rates,\n",
        "              'max_depth': max_depths,\n",
        "              'min_samples_leaf': min_samples_leaf,\n",
        "              'min_samples_split': min_samples_split}\n",
        "\n",
        "num_grid_search = GridSearchCV(GradientBoostingRegressor(loss='huber'),\n",
        "                           parameters, cv=3, return_train_score=True)\n",
        "num_grid_search.fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
              "             estimator=GradientBoostingRegressor(alpha=0.9,\n",
              "                                                 criterion='friedman_mse',\n",
              "                                                 init=None, learning_rate=0.1,\n",
              "                                                 loss='huber', max_depth=3,\n",
              "                                                 max_features=None,\n",
              "                                                 max_leaf_nodes=None,\n",
              "                                                 min_impurity_decrease=0.0,\n",
              "                                                 min_impurity_split=None,\n",
              "                                                 min_samples_leaf=1,\n",
              "                                                 min_samples_split=2,\n",
              "                                                 min_weight_fraction_leaf=0.0,\n",
              "                                                 n_estimators=100,\n",
              "                                                 n_i...,\n",
              "                                                 presort='auto',\n",
              "                                                 random_state=None,\n",
              "                                                 subsample=1.0, tol=0.0001,\n",
              "                                                 validation_fraction=0.1,\n",
              "                                                 verbose=0, warm_start=False),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid={'learning_rate': [0.02, 0.05], 'max_depth': [1, 2],\n",
              "                         'min_samples_leaf': [5, 10],\n",
              "                         'min_samples_split': [5, 10],\n",
              "                         'n_estimators': [5, 10]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ERPwnU1EbbY",
        "colab_type": "code",
        "outputId": "80ec35f5-0ce8-45b2-af2e-095628563d3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "#Gradient Boost Grid Search num_estimators Best Params\n",
        "num_grid_search.best_params_ "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.05,\n",
              " 'max_depth': 2,\n",
              " 'min_samples_leaf': 5,\n",
              " 'min_samples_split': 5,\n",
              " 'n_estimators': 10}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJL7YGwCEdV9",
        "colab_type": "code",
        "outputId": "0085ee5f-6dda-4eff-86ec-79a9c441df30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Gradient Bosot Grid Search num_estimators 10 Score\n",
        "num_grid_search=num_grid_search.score(x_train, y_train)\n",
        "print(num_grid_search)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.40309241636365023\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXbQjeMSEpiX",
        "colab_type": "code",
        "outputId": "afea6724-528d-4f56-ca75-e402092fd9b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "source": [
        "#Bar Graph of 1000 num_estimator Grid Search and 10 num_estimator\n",
        "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "objects = ('Num_estimator:10','Num_estimator:1000')\n",
        "y_pos = np.arange(len(objects))\n",
        "performance = [num_grid_search, gboost_grid_score]\n",
        "\n",
        "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
        "plt.xticks(y_pos, objects)\n",
        "plt.ylabel('Score')\n",
        "plt.title('Different Num_estimators')\n",
        "plt.ylim(0, 1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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o0aMv6lSKJCUlJSk8PFxvvPGGxo8frxo1aigkJESPPPKIOnXqVO6+AgMDlZSUpISEBD32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\nAABjEXQAAICx/h9PN1NxrgHkmwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OnVvK6OScJN",
        "colab_type": "code",
        "outputId": "e45cf4ab-d248-40fe-eec7-83fd561641c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "source": [
        "#Bar Graph of Random Search VS GridSearch\n",
        "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "objects = ('RandomSearch','GridSearch')\n",
        "y_pos = np.arange(len(objects))\n",
        "performance = [gboost_score, gboost_grid_score]\n",
        "\n",
        "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
        "plt.xticks(y_pos, objects)\n",
        "plt.ylabel('Score')\n",
        "plt.title('RandomSearch VS GridSearch')\n",
        "plt.ylim(0.959, 0.9595)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//Xet5s2bp9tvv12RkZHy8vLSuHHjlJiYKA+P/25K//79NWzYMLVt21axsbHa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\nVf+9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}