{"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom google.colab import drive\nimport os\ndrive.mount('/content/drive')\n# Establecer ruta de acceso en drive\nimport os\nprint(os.getcwd())\nos.chdir(\"/content/drive/My Drive\")\nprint(os.getcwd())","metadata":{"id":"yhPMn72ngWAW","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"459c1403c84642ddafe0465fa92bb2ae","outputId":"0f82fc9b-ae2b-4415-b6f4-78fdff70c0d0","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":404,"user_tz":300,"timestamp":1637196003371},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n/content/drive/My Drive\n/content/drive/My Drive\n"}],"execution_count":102},{"cell_type":"markdown","source":"# Lectura de archivos","metadata":{"id":"TPUjYGtohL7n","cell_id":"9a507c907d6c4095b994d66a5e8ead89","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"train=pd.read_csv('train_datathon.csv',sep=',')\ntrain.head()","metadata":{"id":"8TFYk9q5hNHW","colab":{"height":256,"base_uri":"https://localhost:8080/"},"cell_id":"5f0d1600f7dc49bb92c3bddd375093db","outputId":"a365301d-89d9-401a-905c-e8988afa8edf","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":524,"user_tz":300,"timestamp":1637196006350},"deepnote_cell_type":"code"},"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\n0   1          60       RL  ...        WD         Normal    208500\n1   2          20       RL  ...        WD         Normal    181500\n2   3          60       RL  ...        WD         Normal    223500\n3   4          70       RL  ...        WD        Abnorml    140000\n4   5          60       RL  ...        WD         Normal    250000\n\n[5 rows x 81 columns]"},"metadata":{},"execution_count":103}],"execution_count":103},{"cell_type":"code","source":"test=pd.read_csv('test_datathon.csv',sep=',')\ntest.head()","metadata":{"id":"hDU12M1fhYv9","colab":{"height":226,"base_uri":"https://localhost:8080/"},"cell_id":"80122ecf5ebe46d49546ed36dfce25c2","outputId":"b0590286-391d-4be8-9b24-155f27338678","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":512,"user_tz":300,"timestamp":1637196010023},"deepnote_cell_type":"code"},"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>HeatingQC</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1461</td>\n      <td>20</td>\n      <td>RH</td>\n      <td>80.0</td>\n      <td>11622</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>NAmes</td>\n      <td>Feedr</td>\n      <td>Norm</td>\n      <td>1Fam</td>\n      <td>1Story</td>\n      <td>5</td>\n      <td>6</td>\n      <td>1961</td>\n      <td>1961</td>\n      <td>Gable</td>\n      <td>CompShg</td>\n      <td>VinylSd</td>\n      <td>VinylSd</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>CBlock</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>No</td>\n      <td>Rec</td>\n      <td>468.0</td>\n      <td>LwQ</td>\n      <td>144.0</td>\n      <td>270.0</td>\n      <td>882.0</td>\n      <td>GasA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>896</td>\n      <td>0</td>\n      <td>0</td>\n      <td>896</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>TA</td>\n      <td>5</td>\n      <td>Typ</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>Attchd</td>\n      <td>1961.0</td>\n      <td>Unf</td>\n      <td>1.0</td>\n      <td>730.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>140</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>120</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>MnPrv</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1462</td>\n      <td>20</td>\n      <td>RL</td>\n      <td>81.0</td>\n      <td>14267</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>NAmes</td>\n      <td>Norm</td>\n      <td>Norm</td>\n      <td>1Fam</td>\n      <td>1Story</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1958</td>\n      <td>1958</td>\n      <td>Hip</td>\n      <td>CompShg</td>\n      <td>Wd Sdng</td>\n      <td>Wd Sdng</td>\n      <td>BrkFace</td>\n      <td>108.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>CBlock</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>No</td>\n      <td>ALQ</td>\n      <td>923.0</td>\n      <td>Unf</td>\n      <td>0.0</td>\n      <td>406.0</td>\n      <td>1329.0</td>\n      <td>GasA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>1329</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1329</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</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>0</td>\n      <td>NaN</td>\n      <td>Attchd</td>\n      <td>1958.0</td>\n      <td>Unf</td>\n      <td>1.0</td>\n      <td>312.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>393</td>\n      <td>36</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>Gar2</td>\n      <td>12500</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1463</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>74.0</td>\n      <td>13830</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>Gilbert</td>\n      <td>Norm</td>\n      <td>Norm</td>\n      <td>1Fam</td>\n      <td>2Story</td>\n      <td>5</td>\n      <td>5</td>\n      <td>1997</td>\n      <td>1998</td>\n      <td>Gable</td>\n      <td>CompShg</td>\n      <td>VinylSd</td>\n      <td>VinylSd</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>TA</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>791.0</td>\n      <td>Unf</td>\n      <td>0.0</td>\n      <td>137.0</td>\n      <td>928.0</td>\n      <td>GasA</td>\n      <td>Gd</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>928</td>\n      <td>701</td>\n      <td>0</td>\n      <td>1629</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>1</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>1997.0</td>\n      <td>Fin</td>\n      <td>2.0</td>\n      <td>482.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>212</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>MnPrv</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1464</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>78.0</td>\n      <td>9978</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>Gilbert</td>\n      <td>Norm</td>\n      <td>Norm</td>\n      <td>1Fam</td>\n      <td>2Story</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1998</td>\n      <td>1998</td>\n      <td>Gable</td>\n      <td>CompShg</td>\n      <td>VinylSd</td>\n      <td>VinylSd</td>\n      <td>BrkFace</td>\n      <td>20.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>PConc</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>No</td>\n      <td>GLQ</td>\n      <td>602.0</td>\n      <td>Unf</td>\n      <td>0.0</td>\n      <td>324.0</td>\n      <td>926.0</td>\n      <td>GasA</td>\n      <td>Ex</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>926</td>\n      <td>678</td>\n      <td>0</td>\n      <td>1604</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>1</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>Attchd</td>\n      <td>1998.0</td>\n      <td>Fin</td>\n      <td>2.0</td>\n      <td>470.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>360</td>\n      <td>36</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>6</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1465</td>\n      <td>120</td>\n      <td>RL</td>\n      <td>43.0</td>\n      <td>5005</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>HLS</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>Gtl</td>\n      <td>StoneBr</td>\n      <td>Norm</td>\n      <td>Norm</td>\n      <td>TwnhsE</td>\n      <td>1Story</td>\n      <td>8</td>\n      <td>5</td>\n      <td>1992</td>\n      <td>1992</td>\n      <td>Gable</td>\n      <td>CompShg</td>\n      <td>HdBoard</td>\n      <td>HdBoard</td>\n      <td>None</td>\n      <td>0.0</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>ALQ</td>\n      <td>263.0</td>\n      <td>Unf</td>\n      <td>0.0</td>\n      <td>1017.0</td>\n      <td>1280.0</td>\n      <td>GasA</td>\n      <td>Ex</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>1280</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1280</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>Gd</td>\n      <td>5</td>\n      <td>Typ</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>Attchd</td>\n      <td>1992.0</td>\n      <td>RFn</td>\n      <td>2.0</td>\n      <td>506.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>0</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>144</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"     Id  MSSubClass MSZoning  ...  YrSold  SaleType SaleCondition\n0  1461          20       RH  ...    2010        WD        Normal\n1  1462          20       RL  ...    2010        WD        Normal\n2  1463          60       RL  ...    2010        WD        Normal\n3  1464          60       RL  ...    2010        WD        Normal\n4  1465         120       RL  ...    2010        WD        Normal\n\n[5 rows x 80 columns]"},"metadata":{},"execution_count":104}],"execution_count":104},{"cell_type":"code","source":"print(train.shape)\nprint(test.shape)","metadata":{"id":"KLbOGjpUhchd","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"8a7b0e60f05648d2a4d70a040426f6cb","outputId":"3c3f40ce-dbde-44c5-bc71-24a5fdc21be6","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":11,"user_tz":300,"timestamp":1637194347360},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"(1460, 81)\n(1459, 80)\n"}],"execution_count":4},{"cell_type":"markdown","source":"# Analisis exploratorio\n\n","metadata":{"id":"itI2DXvwhoeR","cell_id":"bea47282fbc34acf8cfe0223be254788","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"train.info()","metadata":{"id":"oeqY8kgZh0Lp","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"f3d382a2d0c4450d84d730c3fdc9ac85","outputId":"fe490fca-9118-4359-e424-9d785b09d3cf","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":488,"user_tz":300,"timestamp":1637194348779},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1460 entries, 0 to 1459\nData columns (total 81 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   Id             1460 non-null   int64  \n 1   MSSubClass     1460 non-null   int64  \n 2   MSZoning       1460 non-null   object \n 3   LotFrontage    1201 non-null   float64\n 4   LotArea        1460 non-null   int64  \n 5   Street         1460 non-null   object \n 6   Alley          91 non-null     object \n 7   LotShape       1460 non-null   object \n 8   LandContour    1460 non-null   object \n 9   Utilities      1460 non-null   object \n 10  LotConfig      1460 non-null   object \n 11  LandSlope      1460 non-null   object \n 12  Neighborhood   1460 non-null   object \n 13  Condition1     1460 non-null   object \n 14  Condition2     1460 non-null   object \n 15  BldgType       1460 non-null   object \n 16  HouseStyle     1460 non-null   object \n 17  OverallQual    1460 non-null   int64  \n 18  OverallCond    1460 non-null   int64  \n 19  YearBuilt      1460 non-null   int64  \n 20  YearRemodAdd   1460 non-null   int64  \n 21  RoofStyle      1460 non-null   object \n 22  RoofMatl       1460 non-null   object \n 23  Exterior1st    1460 non-null   object \n 24  Exterior2nd    1460 non-null   object \n 25  MasVnrType     1452 non-null   object \n 26  MasVnrArea     1452 non-null   float64\n 27  ExterQual      1460 non-null   object \n 28  ExterCond      1460 non-null   object \n 29  Foundation     1460 non-null   object \n 30  BsmtQual       1423 non-null   object \n 31  BsmtCond       1423 non-null   object \n 32  BsmtExposure   1422 non-null   object \n 33  BsmtFinType1   1423 non-null   object \n 34  BsmtFinSF1     1460 non-null   int64  \n 35  BsmtFinType2   1422 non-null   object \n 36  BsmtFinSF2     1460 non-null   int64  \n 37  BsmtUnfSF      1460 non-null   int64  \n 38  TotalBsmtSF    1460 non-null   int64  \n 39  Heating        1460 non-null   object \n 40  HeatingQC      1460 non-null   object \n 41  CentralAir     1460 non-null   object \n 42  Electrical     1459 non-null   object \n 43  1stFlrSF       1460 non-null   int64  \n 44  2ndFlrSF       1460 non-null   int64  \n 45  LowQualFinSF   1460 non-null   int64  \n 46  GrLivArea      1460 non-null   int64  \n 47  BsmtFullBath   1460 non-null   int64  \n 48  BsmtHalfBath   1460 non-null   int64  \n 49  FullBath       1460 non-null   int64  \n 50  HalfBath       1460 non-null   int64  \n 51  BedroomAbvGr   1460 non-null   int64  \n 52  KitchenAbvGr   1460 non-null   int64  \n 53  KitchenQual    1460 non-null   object \n 54  TotRmsAbvGrd   1460 non-null   int64  \n 55  Functional     1460 non-null   object \n 56  Fireplaces     1460 non-null   int64  \n 57  FireplaceQu    770 non-null    object \n 58  GarageType     1379 non-null   object \n 59  GarageYrBlt    1379 non-null   float64\n 60  GarageFinish   1379 non-null   object \n 61  GarageCars     1460 non-null   int64  \n 62  GarageArea     1460 non-null   int64  \n 63  GarageQual     1379 non-null   object \n 64  GarageCond     1379 non-null   object \n 65  PavedDrive     1460 non-null   object \n 66  WoodDeckSF     1460 non-null   int64  \n 67  OpenPorchSF    1460 non-null   int64  \n 68  EnclosedPorch  1460 non-null   int64  \n 69  3SsnPorch      1460 non-null   int64  \n 70  ScreenPorch    1460 non-null   int64  \n 71  PoolArea       1460 non-null   int64  \n 72  PoolQC         7 non-null      object \n 73  Fence          281 non-null    object \n 74  MiscFeature    54 non-null     object \n 75  MiscVal        1460 non-null   int64  \n 76  MoSold         1460 non-null   int64  \n 77  YrSold         1460 non-null   int64  \n 78  SaleType       1460 non-null   object \n 79  SaleCondition  1460 non-null   object \n 80  SalePrice      1460 non-null   int64  \ndtypes: float64(3), int64(35), object(43)\nmemory usage: 924.0+ KB\n"}],"execution_count":5},{"cell_type":"markdown","source":"Analisis de nulos (OJO)","metadata":{"id":"TSVgcRegipFX","cell_id":"29c79e5bf68a47b1886ab74f8bd65ad1","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"pd.set_option('display.max_rows', None)  # or 1000\nserie=(train.isnull().sum()/train.shape[0])*100\nserie=serie.sort_values(ascending=False)\nserie= serie[serie>0]\nserie","metadata":{"id":"1Gylq61XiH1h","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"5d3015866aaf47ea89708ac76c14e91b","outputId":"6e248c76-f9ea-49ee-b46e-fac41b86e341","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":415,"user_tz":300,"timestamp":1637194355430},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"PoolQC          99.520548\nMiscFeature     96.301370\nAlley           93.767123\nFence           80.753425\nFireplaceQu     47.260274\nLotFrontage     17.739726\nGarageCond       5.547945\nGarageType       5.547945\nGarageYrBlt      5.547945\nGarageFinish     5.547945\nGarageQual       5.547945\nBsmtExposure     2.602740\nBsmtFinType2     2.602740\nBsmtFinType1     2.534247\nBsmtCond         2.534247\nBsmtQual         2.534247\nMasVnrArea       0.547945\nMasVnrType       0.547945\nElectrical       0.068493\ndtype: float64"},"metadata":{},"execution_count":6}],"execution_count":6},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.figure(figsize=(10,6))\nserie.plot(kind='bar')","metadata":{"id":"neDk_3kfir0K","colab":{"height":453,"base_uri":"https://localhost:8080/"},"cell_id":"961f0bcaa03d45d9ad5e1a8d235a2860","outputId":"8532f162-57b4-433c-87bd-e9ab0ae0fb9d","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":523,"user_tz":300,"timestamp":1637194357896},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":"<Figure size 720x432 with 1 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":7},{"cell_type":"code","source":"pip install missingno","metadata":{"id":"QH4RJyA4Y6DJ","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"cc483c00c68740278e0d766467f6a193","outputId":"334523eb-924f-4599-f7de-4fbc9546f09d","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":4961,"user_tz":300,"timestamp":1637194381669},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Requirement already satisfied: missingno in /usr/local/lib/python3.7/dist-packages (0.5.0)\nRequirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from missingno) (0.11.2)\nRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from missingno) (3.2.2)\nRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from missingno) (1.4.1)\nRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from missingno) (1.19.5)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->missingno) (0.11.0)\nRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->missingno) (2.4.7)\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->missingno) (1.3.2)\nRequirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->missingno) (2.8.2)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->missingno) (1.15.0)\nRequirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn->missingno) (1.1.5)\nRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn->missingno) (2018.9)\n"}],"execution_count":8},{"cell_type":"code","source":"import missingno as msno\nmsno.matrix(train)","metadata":{"id":"UEVWlQEsY6in","colab":{"height":400,"base_uri":"https://localhost:8080/"},"cell_id":"afce66a5d29f46648198954711ce4a60","outputId":"1aa962df-432e-45fc-f772-3381a956615e","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":1505,"user_tz":300,"timestamp":1637194415708},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 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size 1800x720 with 2 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":9},{"cell_type":"code","source":"msno.heatmap(train)","metadata":{"id":"b7ZW8sp5ZPtm","colab":{"height":662,"base_uri":"https://localhost:8080/"},"cell_id":"4f869a79407447efb716ed5e8ad89dcc","outputId":"88b62d17-b8e2-421c-fe59-b3df634f7995","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":1992,"user_tz":300,"timestamp":1637194472374},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":"<Figure size 1440x864 with 2 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":10},{"cell_type":"code","source":"msno.dendrogram(train)","metadata":{"id":"x5X8vLwDZa4e","colab":{"height":992,"base_uri":"https://localhost:8080/"},"cell_id":"745b7f7814f24c9bb5b6df8a62aefa66","outputId":"b9a3d87e-c86f-4643-f733-5dd4e969b4b2","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":2620,"user_tz":300,"timestamp":1637194518303},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 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size 1800x2016 with 1 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":11},{"cell_type":"markdown","source":"Comenzar a mirar las variables con mayor cantidad de nulos y explorar si tienen algo que aportar","metadata":{"id":"4km5v_6ZUs4C","cell_id":"11422ed74e0f43308b24927a81a1a45f","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"train.PoolQC.value_counts() # Decision: borrar","metadata":{"id":"OuR4Qz8qjIYc","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"4a7e30ad156d4485aa386b731c28b89f","outputId":"bc9f333b-00cf-4d88-a470-44f8dd42dee9","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":417,"user_tz":300,"timestamp":1637194536582},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Gd    3\nFa    2\nEx    2\nName: PoolQC, dtype: int64"},"metadata":{},"execution_count":12}],"execution_count":12},{"cell_type":"code","source":"train.MiscFeature.value_counts() # Decision: borrar","metadata":{"id":"8k2BE1zJjngW","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"e5d2d03a99094522adb554af1da92716","outputId":"ee98f3ae-d443-4b9c-ecad-66670a7b3b5e","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":45,"user_tz":300,"timestamp":1637194537901},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Shed    49\nOthr     2\nGar2     2\nTenC     1\nName: MiscFeature, dtype: int64"},"metadata":{},"execution_count":13}],"execution_count":13},{"cell_type":"code","source":"train.Alley.value_counts() # Decision: borrar","metadata":{"id":"zC0AmBEhjwFw","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"5ec6b740a1c04c00976b944efbc1af18","outputId":"8321d29c-2eb4-468f-eb33-1c2ca0d46fa6","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":5,"user_tz":300,"timestamp":1637194538933},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Grvl    50\nPave    41\nName: Alley, dtype: int64"},"metadata":{},"execution_count":14}],"execution_count":14},{"cell_type":"code","source":"train.Fence.value_counts() # Decision: borrar","metadata":{"id":"vY11ZYwGj1Nm","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"f96b13499c314878a37acdd26133341c","outputId":"2b7ba17e-839a-4780-edc7-c83aaa0cf3fe","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":44,"user_tz":300,"timestamp":1637194540225},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"MnPrv    157\nGdPrv     59\nGdWo      54\nMnWw      11\nName: Fence, dtype: int64"},"metadata":{},"execution_count":15}],"execution_count":15},{"cell_type":"code","source":"train.FireplaceQu.value_counts() # Decision: Con este se podria hacer otra tecnica en vez de borrar la columna","metadata":{"id":"Y7RJ-SHZj8-O","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"3ca561714c354fa7b608fdd3bf6b4c4d","outputId":"ebef4ab2-9ec4-43e3-d163-b441dc48b4b0","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637194541289},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Gd    380\nTA    313\nFa     33\nEx     24\nPo     20\nName: FireplaceQu, dtype: int64"},"metadata":{},"execution_count":16}],"execution_count":16},{"cell_type":"code","source":"train.LotFrontage.isnull().sum() # Decision: Reemplazar con alguna tecnica de interpolacion","metadata":{"id":"yXqGuzNIkJE-","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"6affda4055374db1b5c4a8568af5f6c0","outputId":"895a75c8-eee8-4b90-bbe5-2fdf8443b8ce","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":7,"user_tz":300,"timestamp":1637194544762},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"259"},"metadata":{},"execution_count":17}],"execution_count":17},{"cell_type":"code","source":"train.GarageFinish.value_counts() # Decision: Alguna tecnica de reemplazo","metadata":{"id":"hMhofhJ5kz4G","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"664cfec1bbb44dd58d78aa70ad6762b7","outputId":"4e79ce31-036e-4986-9dbc-e1fce710b98b","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":5,"user_tz":300,"timestamp":1637194546464},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Unf    605\nRFn    422\nFin    352\nName: GarageFinish, dtype: int64"},"metadata":{},"execution_count":18}],"execution_count":18},{"cell_type":"code","source":"train.GarageQual.value_counts() # Decision: Alguna tecnica de reemplazo","metadata":{"id":"EZ9rrqlgk32j","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"7284daa860904f8aa1e46413ecde1a2c","outputId":"c0700df0-e7c6-4812-abc0-8e04a7108445","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637194547861},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"TA    1311\nFa      48\nGd      14\nPo       3\nEx       3\nName: GarageQual, dtype: int64"},"metadata":{},"execution_count":19}],"execution_count":19},{"cell_type":"code","source":"train.GarageCond.value_counts() # Decision: Reemplazar con alguna tecnica ","metadata":{"id":"MBYinXXPkWqM","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"a9382212e77346c09912ae4de9cbe2dc","outputId":"7b99cce1-18cc-4ab3-a4a5-b319b482a759","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":5,"user_tz":300,"timestamp":1637194549135},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"TA    1326\nFa      35\nGd       9\nPo       7\nEx       2\nName: GarageCond, dtype: int64"},"metadata":{},"execution_count":20}],"execution_count":20},{"cell_type":"code","source":"train.GarageType.value_counts() # Decision: Reemplazar con alguna tecnica","metadata":{"id":"bIUCOCeEkjr1","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"6f9e0543a51f404199bcf620273fd1f9","outputId":"feb249cb-3036-4bc3-f2e6-0f8c104079f7","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637194549944},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Attchd     870\nDetchd     387\nBuiltIn     88\nBasment     19\nCarPort      9\n2Types       6\nName: GarageType, dtype: int64"},"metadata":{},"execution_count":21}],"execution_count":21},{"cell_type":"code","source":"train.BsmtExposure.value_counts() # Decision: Tecnica reemplazro","metadata":{"id":"L_TOw2GClBd2","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"0dedcd28489d441e8caf5fe9749da36d","outputId":"db24ae24-62ae-40c0-a1b5-430545d4cd56","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":8,"user_tz":300,"timestamp":1637194551746},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"No    953\nAv    221\nGd    134\nMn    114\nName: BsmtExposure, dtype: int64"},"metadata":{},"execution_count":22}],"execution_count":22},{"cell_type":"code","source":"train.GarageYrBlt.isnull().sum() # Decision: Reemplazar con alguna tecnica de interpolacion","metadata":{"id":"AYW1-2SnkoAs","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"3a79c847c820461fbc7529f3b3ef0329","outputId":"2e43e7b6-14b5-4e18-a8aa-3f09e4ca0ecf","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":388,"user_tz":300,"timestamp":1637194554465},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"81"},"metadata":{},"execution_count":23}],"execution_count":23},{"cell_type":"code","source":"train.BsmtFinType2.value_counts() # Decision: Tecnica reemplazro","metadata":{"id":"vWyQNbyxlVP5","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"6aedb4447f444783ad68c10b1ed29f2f","outputId":"96a1fd09-72cf-4b3a-f846-46b26559bc8d","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":7,"user_tz":300,"timestamp":1637194555524},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Unf    1256\nRec      54\nLwQ      46\nBLQ      33\nALQ      19\nGLQ      14\nName: BsmtFinType2, dtype: int64"},"metadata":{},"execution_count":24}],"execution_count":24},{"cell_type":"code","source":"train.BsmtFinType1.value_counts() # Decision: Tecnica reemplazro","metadata":{"id":"_5pFtefjlesw","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"da8f550766684ad5a56253d8cf3e0a16","outputId":"a5fe5bd3-dc7f-4aac-c170-a9aef947dfe0","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":5,"user_tz":300,"timestamp":1637194556449},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Unf    430\nGLQ    418\nALQ    220\nBLQ    148\nRec    133\nLwQ     74\nName: BsmtFinType1, dtype: int64"},"metadata":{},"execution_count":25}],"execution_count":25},{"cell_type":"code","source":"train.BsmtCond.value_counts()# Decision: reemplzar moda","metadata":{"id":"x9VRvFkamMnK","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"090cf53d11d14701ac95a470aa640ad7","outputId":"7b487b79-8de0-4068-f671-21285271767e","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":5,"user_tz":300,"timestamp":1637194557451},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"TA    1311\nGd      65\nFa      45\nPo       2\nName: BsmtCond, dtype: int64"},"metadata":{},"execution_count":26}],"execution_count":26},{"cell_type":"code","source":"train.BsmtQual.value_counts()# Decision: reemplzar moda","metadata":{"id":"rL107j01mZtB","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"8be41cb04bb94b8a81dbec151f34cd35","outputId":"893d5beb-755d-493e-f3e4-6603d39dbd51","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637194558698},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"TA    649\nGd    618\nEx    121\nFa     35\nName: BsmtQual, dtype: int64"},"metadata":{},"execution_count":27}],"execution_count":27},{"cell_type":"code","source":"train.MasVnrArea.isnull().sum()","metadata":{"id":"qo4Ggud0mxpI","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"e0a3c12c6f1046d499e7e47fc4a0711c","outputId":"337bc775-94c4-4aeb-b3c6-8f699075c650","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":7,"user_tz":300,"timestamp":1637194560044},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"8"},"metadata":{},"execution_count":28}],"execution_count":28},{"cell_type":"code","source":"train.MasVnrType.value_counts()","metadata":{"id":"VIGrx0WDm9oX","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"9cf887eaee6340739df06dd21e24295e","outputId":"55faf27b-4e7f-47da-adc7-b3bfd5ca81f4","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":5,"user_tz":300,"timestamp":1637194562282},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"None       864\nBrkFace    445\nStone      128\nBrkCmn      15\nName: MasVnrType, dtype: int64"},"metadata":{},"execution_count":29}],"execution_count":29},{"cell_type":"code","source":"train.Electrical.value_counts()","metadata":{"id":"HNcncBVhnGcu","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"5df570e334914ce294d80a0b75618b95","outputId":"7a1a90b7-aa91-4b6d-a6f0-03e98a99c596","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":5,"user_tz":300,"timestamp":1637194563381},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"SBrkr    1334\nFuseA      94\nFuseF      27\nFuseP       3\nMix         1\nName: Electrical, dtype: int64"},"metadata":{},"execution_count":30}],"execution_count":30},{"cell_type":"code","source":"train.shape","metadata":{"id":"IviYSCrkjqyP","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"1e784806ef0044e5be94cb122d2186b3","outputId":"fcb20a97-0be7-4a6b-870e-f438fd2e245a","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637194565074},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"(1460, 81)"},"metadata":{},"execution_count":31}],"execution_count":31},{"cell_type":"markdown","source":"# Borrado de columnas","metadata":{"id":"f4UKftB-jjtb","cell_id":"0876112cbeb84500a8b79c62ef40413e","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"col_del=['PoolQC','MiscFeature','Alley','Fence','Id'] # Columnas a borrar que no aportan\ncol_inter= ['LotFrontage','GarageYrBlt','MasVnrArea'] # Columnas a interpolar\ncol_cat= ['FireplaceQu','GarageFinish','GarageCond','GarageType','BsmtExposure','BsmtFinType2','BsmtFinType1','BsmtCond',\n          'BsmtQual','MasVnrType','Electrical'] # Columnas cateogoricas","metadata":{"id":"VLmHyjDKjk1y","cell_id":"64f33f40260a49cd9e8a083ac03525b5","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":374,"user_tz":300,"timestamp":1637194601565},"deepnote_cell_type":"code"},"outputs":[],"execution_count":32},{"cell_type":"code","source":"# Borrar variables\ntrain_x=train.drop(labels=col_del, axis=1) # Borrando las columnas en col_del\ntrain_x.columns # Verificando las columnas que quedaron","metadata":{"id":"NPaKejTjnNc5","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"0f7cfab2839f42cc985166d2895c4227","outputId":"6b3df9e3-3c12-42ac-bc0d-e1ea169afbd1","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":359,"user_tz":300,"timestamp":1637194604826},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Index(['MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n       'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope',\n       'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle',\n       'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'RoofStyle',\n       'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea',\n       'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond',\n       'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2',\n       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC',\n       'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n       'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd',\n       'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType', 'GarageYrBlt',\n       'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond',\n       'PavedDrive', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\n       'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n       'SaleCondition', 'SalePrice'],\n      dtype='object')"},"metadata":{},"execution_count":33}],"execution_count":33},{"cell_type":"code","source":"for i in col_inter: # reemplazar por la mediana en las columnas numericas en col_inter\n  train_x[i] = train_x[i].fillna(train_x[i].median())","metadata":{"id":"2rukUqzZpM4f","cell_id":"4c1d68b7399d445e898786ac60fc0954","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":2,"user_tz":300,"timestamp":1637194608857},"deepnote_cell_type":"code"},"outputs":[],"execution_count":34},{"cell_type":"code","source":"train_x.MasVnrArea.isnull().sum() # Verificar que funciono","metadata":{"id":"wi0GRNbAqzFh","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"2c8c34fa1984462188d70bea59f59c25","outputId":"70989898-1c1b-4027-ff9a-4da8d7ad8c66","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":5,"user_tz":300,"timestamp":1637194610731},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{},"execution_count":35}],"execution_count":35},{"cell_type":"code","source":"for i in col_cat: # Reemplazar los vacios por Desconocido\n  train_x[i] = train_x[i].fillna('Desconocido') ","metadata":{"id":"tv40bacirLN-","cell_id":"40db9eb6f8b24e26ac04a6d04ed06745","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":8,"user_tz":300,"timestamp":1637194612069},"deepnote_cell_type":"code"},"outputs":[],"execution_count":36},{"cell_type":"code","source":"train_x.Electrical.isnull().sum() # Verificando que funciona","metadata":{"id":"2lJoEtbIrULP","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"03f5624817d940afa6833eafd7504443","outputId":"57b2b3de-8710-407b-fc84-dda40c5c716c","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637194613690},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{},"execution_count":37}],"execution_count":37},{"cell_type":"code","source":"train_x.SaleCondition.dtype","metadata":{"id":"cDC6ggflskna","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"4a0171421c4d40bc916ed44d14095cd8","outputId":"879943b0-1911-4c39-b31e-49c1514f6cc5","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637194615017},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"dtype('O')"},"metadata":{},"execution_count":38}],"execution_count":38},{"cell_type":"code","source":"train_x.SaleCondition.value_counts()","metadata":{"id":"NBfqcMdDs0eP","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"febb390ed99c47669c37873ac63e9978","outputId":"00ca8a0c-0d26-44b3-f87b-a2d4006f83e3","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":7,"user_tz":300,"timestamp":1637194616761},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Normal     1198\nPartial     125\nAbnorml     101\nFamily       20\nAlloca       12\nAdjLand       4\nName: SaleCondition, dtype: int64"},"metadata":{},"execution_count":39}],"execution_count":39},{"cell_type":"markdown","source":"# Recodificar variables categoricas","metadata":{"id":"FvHKuhLvru4I","cell_id":"dfdfa7e560034850a7830ce0c529fc8a","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"alldata=[] # lista para guardar resultados de One hot Encoding\nalldata1=[] # lista para guardar resultados de \nvariables=[] # ir guardando las variables\nfor i in train_x.columns: # iterar sobre todas las columnas\n  if train_x[i].dtype == 'O': # Quedarme con las que sean tipo object\n    if len(train_x[i].unique()) ==2: # Si solo tienen dos categorias aplicar One Hot Encoding\n      print('Variable 2 categorias:',i)\n      y= pd.get_dummies(train_x[i]) # Aplicar One Hot Encoding\n      alldata.append(y) # ir agregando las columnas resultantes en alldata\n      variables.append(i)\n    else:\n      pass\n    print('--------------------')\n    if len(train_x[i].unique()) >2: # Si las categorias tienen mas de 2 categorias aplicar LabelEncoder\n      print('Variable >2 categorias:',i)\n      z= pd.DataFrame()\n      z[i]=train_x[i].astype('category').cat.codes # Aplicar Label Encoder\n      alldata1.append(z) # Agregar a la lista alldata1\n      variables.append(i) #\n  if train_x[i].dtype == 'float64': # Si la columna es numerica entonces normalizar z score\n    train_x[i]=(train_x[i]-train_x[i].mean())/(train_x[i].std()) # yapo\ndata_y=pd.concat(alldata, axis=1) # concatenar las columnas obtenidas One hot Encoding\ndata_z=pd.concat(alldata1, axis=1) # concatenar las columnas de Label Encoder\ntrain_y= pd.concat([data_y,data_z,train_x],axis=1) # Unir todos los datasets One Hot Encoding, LabelEncoder, Z score\ntrain_y=train_y.drop(labels=variables, axis=1) # Borrar las columnas ya recodificadas\ntrain_y.head() # mostrar que quedo ","metadata":{"id":"ZEMWMo6rrwwT","colab":{"height":1000,"base_uri":"https://localhost:8080/"},"cell_id":"37cc776686ad4fefafcd486b1475abba","outputId":"6cd7db54-e17e-4d8e-9a88-1d1780a904ea","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":414,"user_tz":300,"timestamp":1637194622701},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"--------------------\nVariable >2 categorias: MSZoning\nVariable 2 categorias: Street\n--------------------\n--------------------\nVariable >2 categorias: LotShape\n--------------------\nVariable >2 categorias: LandContour\nVariable 2 categorias: Utilities\n--------------------\n--------------------\nVariable >2 categorias: LotConfig\n--------------------\nVariable >2 categorias: LandSlope\n--------------------\nVariable >2 categorias: Neighborhood\n--------------------\nVariable >2 categorias: Condition1\n--------------------\nVariable >2 categorias: Condition2\n--------------------\nVariable >2 categorias: BldgType\n--------------------\nVariable >2 categorias: HouseStyle\n--------------------\nVariable >2 categorias: RoofStyle\n--------------------\nVariable >2 categorias: RoofMatl\n--------------------\nVariable >2 categorias: Exterior1st\n--------------------\nVariable >2 categorias: Exterior2nd\n--------------------\nVariable >2 categorias: MasVnrType\n--------------------\nVariable >2 categorias: ExterQual\n--------------------\nVariable >2 categorias: ExterCond\n--------------------\nVariable >2 categorias: Foundation\n--------------------\nVariable >2 categorias: BsmtQual\n--------------------\nVariable >2 categorias: BsmtCond\n--------------------\nVariable >2 categorias: BsmtExposure\n--------------------\nVariable >2 categorias: BsmtFinType1\n--------------------\nVariable >2 categorias: BsmtFinType2\n--------------------\nVariable >2 categorias: Heating\n--------------------\nVariable >2 categorias: HeatingQC\nVariable 2 categorias: CentralAir\n--------------------\n--------------------\nVariable >2 categorias: Electrical\n--------------------\nVariable >2 categorias: KitchenQual\n--------------------\nVariable >2 categorias: Functional\n--------------------\nVariable >2 categorias: FireplaceQu\n--------------------\nVariable >2 categorias: GarageType\n--------------------\nVariable >2 categorias: GarageFinish\n--------------------\nVariable >2 categorias: GarageQual\n--------------------\nVariable >2 categorias: GarageCond\n--------------------\nVariable >2 categorias: PavedDrive\n--------------------\nVariable >2 categorias: SaleType\n--------------------\nVariable >2 categorias: SaleCondition\n"},{"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>Grvl</th>\n      <th>Pave</th>\n      <th>AllPub</th>\n      <th>NoSeWa</th>\n      <th>N</th>\n      <th>Y</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>SalePrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>-0.220799</td>\n      <td>8450</td>\n      <td>7</td>\n      <td>5</td>\n      <td>2003</td>\n      <td>2003</td>\n      <td>0.513928</td>\n      <td>706</td>\n      <td>0</td>\n      <td>150</td>\n      <td>856</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>8</td>\n      <td>0</td>\n      <td>1.017250</td>\n      <td>2</td>\n      <td>548</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>0</td>\n      <td>2</td>\n      <td>2008</td>\n      <td>208500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0.460162</td>\n      <td>9600</td>\n      <td>6</td>\n      <td>8</td>\n      <td>1976</td>\n      <td>1976</td>\n      <td>-0.570555</td>\n      <td>978</td>\n      <td>0</td>\n      <td>284</td>\n      <td>1262</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>6</td>\n      <td>1</td>\n      <td>-0.107890</td>\n      <td>2</td>\n      <td>460</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>0</td>\n      <td>5</td>\n      <td>2007</td>\n      <td>181500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>-0.084607</td>\n      <td>11250</td>\n      <td>7</td>\n      <td>5</td>\n      <td>2001</td>\n      <td>2002</td>\n      <td>0.325803</td>\n      <td>486</td>\n      <td>0</td>\n      <td>434</td>\n      <td>920</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>6</td>\n      <td>1</td>\n      <td>0.933906</td>\n      <td>2</td>\n      <td>608</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>0</td>\n      <td>9</td>\n      <td>2008</td>\n      <td>223500</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>70</td>\n      <td>-0.447787</td>\n      <td>9550</td>\n      <td>7</td>\n      <td>5</td>\n      <td>1915</td>\n      <td>1970</td>\n      <td>-0.570555</td>\n      <td>216</td>\n      <td>0</td>\n      <td>540</td>\n      <td>756</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>7</td>\n      <td>1</td>\n      <td>0.808890</td>\n      <td>3</td>\n      <td>642</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>0</td>\n      <td>2</td>\n      <td>2006</td>\n      <td>140000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>0.641752</td>\n      <td>14260</td>\n      <td>8</td>\n      <td>5</td>\n      <td>2000</td>\n      <td>2000</td>\n      <td>1.366021</td>\n      <td>655</td>\n      <td>0</td>\n      <td>490</td>\n      <td>1145</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>9</td>\n      <td>1</td>\n      <td>0.892234</td>\n      <td>3</td>\n      <td>836</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>0</td>\n      <td>12</td>\n      <td>2008</td>\n      <td>250000</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   Grvl  Pave  AllPub  NoSeWa  N  ...  PoolArea  MiscVal  MoSold  YrSold  SalePrice\n0     0     1       1       0  0  ...         0        0       2    2008     208500\n1     0     1       1       0  0  ...         0        0       5    2007     181500\n2     0     1       1       0  0  ...         0        0       9    2008     223500\n3     0     1       1       0  0  ...         0        0       2    2006     140000\n4     0     1       1       0  0  ...         0        0      12    2008     250000\n\n[5 rows x 43 columns]"},"metadata":{},"execution_count":40}],"execution_count":40},{"cell_type":"code","source":"train_y.info()","metadata":{"id":"ImuspMKM24Ar","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"e473026831bb46b8abce82bcad29976d","outputId":"44cb5590-4cd4-43f1-f805-55ed941b604f","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":489,"user_tz":300,"timestamp":1637194632910},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1460 entries, 0 to 1459\nData columns (total 43 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   Grvl           1460 non-null   uint8  \n 1   Pave           1460 non-null   uint8  \n 2   AllPub         1460 non-null   uint8  \n 3   NoSeWa         1460 non-null   uint8  \n 4   N              1460 non-null   uint8  \n 5   Y              1460 non-null   uint8  \n 6   MSSubClass     1460 non-null   int64  \n 7   LotFrontage    1460 non-null   float64\n 8   LotArea        1460 non-null   int64  \n 9   OverallQual    1460 non-null   int64  \n 10  OverallCond    1460 non-null   int64  \n 11  YearBuilt      1460 non-null   int64  \n 12  YearRemodAdd   1460 non-null   int64  \n 13  MasVnrArea     1460 non-null   float64\n 14  BsmtFinSF1     1460 non-null   int64  \n 15  BsmtFinSF2     1460 non-null   int64  \n 16  BsmtUnfSF      1460 non-null   int64  \n 17  TotalBsmtSF    1460 non-null   int64  \n 18  1stFlrSF       1460 non-null   int64  \n 19  2ndFlrSF       1460 non-null   int64  \n 20  LowQualFinSF   1460 non-null   int64  \n 21  GrLivArea      1460 non-null   int64  \n 22  BsmtFullBath   1460 non-null   int64  \n 23  BsmtHalfBath   1460 non-null   int64  \n 24  FullBath       1460 non-null   int64  \n 25  HalfBath       1460 non-null   int64  \n 26  BedroomAbvGr   1460 non-null   int64  \n 27  KitchenAbvGr   1460 non-null   int64  \n 28  TotRmsAbvGrd   1460 non-null   int64  \n 29  Fireplaces     1460 non-null   int64  \n 30  GarageYrBlt    1460 non-null   float64\n 31  GarageCars     1460 non-null   int64  \n 32  GarageArea     1460 non-null   int64  \n 33  WoodDeckSF     1460 non-null   int64  \n 34  OpenPorchSF    1460 non-null   int64  \n 35  EnclosedPorch  1460 non-null   int64  \n 36  3SsnPorch      1460 non-null   int64  \n 37  ScreenPorch    1460 non-null   int64  \n 38  PoolArea       1460 non-null   int64  \n 39  MiscVal        1460 non-null   int64  \n 40  MoSold         1460 non-null   int64  \n 41  YrSold         1460 non-null   int64  \n 42  SalePrice      1460 non-null   int64  \ndtypes: float64(3), int64(34), uint8(6)\nmemory usage: 430.7 KB\n"}],"execution_count":41},{"cell_type":"markdown","source":"# Definir entrenamiento y test ","metadata":{"id":"PU43Avcb4Tq4","cell_id":"acf223aa85dd4e8baee03c9224f67416","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"# traigo estos modelos \nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nX = train_y.drop(columns=['SalePrice']) # matriz de diseño\ny = train_y.SalePrice # vector respuesta","metadata":{"id":"Q5O8GHkg4Vph","cell_id":"e288df1346a84da691d4bc87e81d9c39","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":958,"user_tz":300,"timestamp":1637194657612},"deepnote_cell_type":"code"},"outputs":[],"execution_count":42},{"cell_type":"code","source":"X.head()","metadata":{"id":"nkWUYPvL5We4","colab":{"height":226,"base_uri":"https://localhost:8080/"},"cell_id":"50a88880b06d42eabd6a503f468f3442","outputId":"b20e16e9-c54a-4547-b5f5-a02411d57f1d","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637194659152},"deepnote_cell_type":"code"},"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>Grvl</th>\n      <th>Pave</th>\n      <th>AllPub</th>\n      <th>NoSeWa</th>\n      <th>N</th>\n      <th>Y</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>-0.220799</td>\n      <td>8450</td>\n      <td>7</td>\n      <td>5</td>\n      <td>2003</td>\n      <td>2003</td>\n      <td>0.513928</td>\n      <td>706</td>\n      <td>0</td>\n      <td>150</td>\n      <td>856</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>8</td>\n      <td>0</td>\n      <td>1.017250</td>\n      <td>2</td>\n      <td>548</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>0</td>\n      <td>2</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0.460162</td>\n      <td>9600</td>\n      <td>6</td>\n      <td>8</td>\n      <td>1976</td>\n      <td>1976</td>\n      <td>-0.570555</td>\n      <td>978</td>\n      <td>0</td>\n      <td>284</td>\n      <td>1262</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>6</td>\n      <td>1</td>\n      <td>-0.107890</td>\n      <td>2</td>\n      <td>460</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>0</td>\n      <td>5</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>-0.084607</td>\n      <td>11250</td>\n      <td>7</td>\n      <td>5</td>\n      <td>2001</td>\n      <td>2002</td>\n      <td>0.325803</td>\n      <td>486</td>\n      <td>0</td>\n      <td>434</td>\n      <td>920</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>6</td>\n      <td>1</td>\n      <td>0.933906</td>\n      <td>2</td>\n      <td>608</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>0</td>\n      <td>9</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>70</td>\n      <td>-0.447787</td>\n      <td>9550</td>\n      <td>7</td>\n      <td>5</td>\n      <td>1915</td>\n      <td>1970</td>\n      <td>-0.570555</td>\n      <td>216</td>\n      <td>0</td>\n      <td>540</td>\n      <td>756</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>7</td>\n      <td>1</td>\n      <td>0.808890</td>\n      <td>3</td>\n      <td>642</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>0</td>\n      <td>2</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>0.641752</td>\n      <td>14260</td>\n      <td>8</td>\n      <td>5</td>\n      <td>2000</td>\n      <td>2000</td>\n      <td>1.366021</td>\n      <td>655</td>\n      <td>0</td>\n      <td>490</td>\n      <td>1145</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>9</td>\n      <td>1</td>\n      <td>0.892234</td>\n      <td>3</td>\n      <td>836</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>0</td>\n      <td>12</td>\n      <td>2008</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   Grvl  Pave  AllPub  NoSeWa  ...  PoolArea  MiscVal  MoSold  YrSold\n0     0     1       1       0  ...         0        0       2    2008\n1     0     1       1       0  ...         0        0       5    2007\n2     0     1       1       0  ...         0        0       9    2008\n3     0     1       1       0  ...         0        0       2    2006\n4     0     1       1       0  ...         0        0      12    2008\n\n[5 rows x 42 columns]"},"metadata":{},"execution_count":43}],"execution_count":43},{"cell_type":"code","source":"#Separación en Train y Test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) # 95% training and 5% test porque tengo un dataset de test aparte","metadata":{"id":"Tcw71NkE5eZ6","cell_id":"99a43b8b765e4af8a9d4dfd2d74856d5","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":373,"user_tz":300,"timestamp":1637194672328},"deepnote_cell_type":"code"},"outputs":[],"execution_count":44},{"cell_type":"markdown","source":"# Modelo 1: AdaBoost","metadata":{"id":"NCrm4Wd7_ODw","cell_id":"c41c0fb719a143f78a9036375b6ba2fc","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.ensemble import AdaBoostRegressor\nada=AdaBoostRegressor(n_estimators=12,learning_rate=0.1, loss='linear',random_state=42)","metadata":{"id":"AhH47uIf5h54","cell_id":"77468b54b48649d3b700fc200f20801b","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":406,"user_tz":300,"timestamp":1637194682393},"deepnote_cell_type":"code"},"outputs":[],"execution_count":45},{"cell_type":"code","source":"#Fiteamos el modelo\nmodel = ada.fit(X_train, y_train)","metadata":{"id":"OBVofsZg6IU8","cell_id":"f75a2bd1d6d5429e94291431e93bcdc9","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":526,"user_tz":300,"timestamp":1637194685171},"deepnote_cell_type":"code"},"outputs":[],"execution_count":46},{"cell_type":"code","source":"X_train.head()","metadata":{"id":"R1eAFbGbaNKO","colab":{"height":226,"base_uri":"https://localhost:8080/"},"cell_id":"0f9737940d254973b5a84c7181694253","outputId":"af54dcc1-53d0-47b6-9165-17914b283e6d","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":446,"user_tz":300,"timestamp":1637194735536},"deepnote_cell_type":"code"},"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>Grvl</th>\n      <th>Pave</th>\n      <th>AllPub</th>\n      <th>NoSeWa</th>\n      <th>N</th>\n      <th>Y</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>176</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>1.231918</td>\n      <td>10029</td>\n      <td>6</td>\n      <td>5</td>\n      <td>1988</td>\n      <td>1989</td>\n      <td>0.912309</td>\n      <td>831</td>\n      <td>0</td>\n      <td>320</td>\n      <td>1151</td>\n      <td>1164</td>\n      <td>896</td>\n      <td>0</td>\n      <td>2060</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.392172</td>\n      <td>2</td>\n      <td>521</td>\n      <td>0</td>\n      <td>228</td>\n      <td>0</td>\n      <td>0</td>\n      <td>192</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>221</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>-0.039210</td>\n      <td>8068</td>\n      <td>6</td>\n      <td>5</td>\n      <td>2002</td>\n      <td>2002</td>\n      <td>-0.570555</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1010</td>\n      <td>1010</td>\n      <td>1010</td>\n      <td>1257</td>\n      <td>0</td>\n      <td>2267</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.975578</td>\n      <td>2</td>\n      <td>390</td>\n      <td>120</td>\n      <td>46</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>294</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0.460162</td>\n      <td>9600</td>\n      <td>6</td>\n      <td>5</td>\n      <td>1953</td>\n      <td>1953</td>\n      <td>0.746317</td>\n      <td>1285</td>\n      <td>0</td>\n      <td>131</td>\n      <td>1416</td>\n      <td>1644</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1644</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>7</td>\n      <td>2</td>\n      <td>-1.066342</td>\n      <td>2</td>\n      <td>418</td>\n      <td>110</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>10</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>813</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0.233175</td>\n      <td>9750</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1958</td>\n      <td>1958</td>\n      <td>0.773982</td>\n      <td>608</td>\n      <td>0</td>\n      <td>834</td>\n      <td>1442</td>\n      <td>1442</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1442</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>7</td>\n      <td>0</td>\n      <td>-0.857983</td>\n      <td>1</td>\n      <td>301</td>\n      <td>0</td>\n      <td>0</td>\n      <td>275</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>500</td>\n      <td>4</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>2.321457</td>\n      <td>16059</td>\n      <td>8</td>\n      <td>5</td>\n      <td>1991</td>\n      <td>1992</td>\n      <td>1.000838</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1288</td>\n      <td>1288</td>\n      <td>1301</td>\n      <td>1116</td>\n      <td>0</td>\n      <td>2417</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>9</td>\n      <td>1</td>\n      <td>0.517187</td>\n      <td>2</td>\n      <td>462</td>\n      <td>127</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>2006</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"     Grvl  Pave  AllPub  NoSeWa  ...  PoolArea  MiscVal  MoSold  YrSold\n176     0     1       1       0  ...         0        0       9    2007\n221     0     1       1       0  ...         0        0      12    2009\n294     0     1       1       0  ...         0        0      10    2009\n813     0     1       1       0  ...         0      500       4    2007\n85      0     1       1       0  ...         0        0       4    2006\n\n[5 rows x 42 columns]"},"metadata":{},"execution_count":48}],"execution_count":48},{"cell_type":"code","source":"X_test.head()","metadata":{"id":"RfT2numoaSjD","colab":{"height":226,"base_uri":"https://localhost:8080/"},"cell_id":"3a770928631d4f4e81ed1741cd621525","outputId":"1efbd03c-b9e6-4298-c14a-984801ff9207","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":364,"user_tz":300,"timestamp":1637194744305},"deepnote_cell_type":"code"},"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>Grvl</th>\n      <th>Pave</th>\n      <th>AllPub</th>\n      <th>NoSeWa</th>\n      <th>N</th>\n      <th>Y</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1092</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>50</td>\n      <td>-0.447787</td>\n      <td>8400</td>\n      <td>6</td>\n      <td>5</td>\n      <td>1925</td>\n      <td>1950</td>\n      <td>-0.570555</td>\n      <td>423</td>\n      <td>0</td>\n      <td>758</td>\n      <td>1181</td>\n      <td>1390</td>\n      <td>304</td>\n      <td>0</td>\n      <td>1694</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>-2.233154</td>\n      <td>2</td>\n      <td>576</td>\n      <td>342</td>\n      <td>0</td>\n      <td>128</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>748</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>-0.493184</td>\n      <td>10593</td>\n      <td>7</td>\n      <td>5</td>\n      <td>1996</td>\n      <td>1996</td>\n      <td>1.299624</td>\n      <td>919</td>\n      <td>0</td>\n      <td>801</td>\n      <td>1720</td>\n      <td>1720</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1720</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.725547</td>\n      <td>2</td>\n      <td>527</td>\n      <td>240</td>\n      <td>56</td>\n      <td>154</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>430</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>160</td>\n      <td>-2.218286</td>\n      <td>1680</td>\n      <td>6</td>\n      <td>5</td>\n      <td>1971</td>\n      <td>1971</td>\n      <td>0.713118</td>\n      <td>387</td>\n      <td>0</td>\n      <td>96</td>\n      <td>483</td>\n      <td>483</td>\n      <td>504</td>\n      <td>0</td>\n      <td>987</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>-0.316249</td>\n      <td>1</td>\n      <td>264</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>7</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>965</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>-0.220799</td>\n      <td>10237</td>\n      <td>6</td>\n      <td>5</td>\n      <td>2005</td>\n      <td>2007</td>\n      <td>-0.570555</td>\n      <td>0</td>\n      <td>0</td>\n      <td>783</td>\n      <td>783</td>\n      <td>783</td>\n      <td>701</td>\n      <td>0</td>\n      <td>1484</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>8</td>\n      <td>1</td>\n      <td>1.100593</td>\n      <td>2</td>\n      <td>393</td>\n      <td>0</td>\n      <td>72</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>349</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>-0.629376</td>\n      <td>20431</td>\n      <td>9</td>\n      <td>5</td>\n      <td>2005</td>\n      <td>2006</td>\n      <td>4.243219</td>\n      <td>1410</td>\n      <td>0</td>\n      <td>438</td>\n      <td>1848</td>\n      <td>1848</td>\n      <td>880</td>\n      <td>0</td>\n      <td>2728</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>10</td>\n      <td>2</td>\n      <td>1.142265</td>\n      <td>3</td>\n      <td>706</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>4</td>\n      <td>2006</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"      Grvl  Pave  AllPub  NoSeWa  ...  PoolArea  MiscVal  MoSold  YrSold\n1092     0     1       1       0  ...         0        0       6    2008\n748      0     1       1       0  ...         0        0       3    2010\n430      0     1       1       0  ...         0        0       7    2008\n965      0     1       1       0  ...         0        0       7    2007\n349      0     1       1       0  ...         0        0       4    2006\n\n[5 rows x 42 columns]"},"metadata":{},"execution_count":49}],"execution_count":49},{"cell_type":"code","source":"#Predicción!\ny_pred = model.predict(X_test) # Sacar predicciones con test","metadata":{"id":"k1l2iCVq6J8p","cell_id":"6b93a6bdf7e34bc483eb7472c3cac230","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":391,"user_tz":300,"timestamp":1637194902131},"deepnote_cell_type":"code"},"outputs":[],"execution_count":54},{"cell_type":"code","source":"from sklearn.metrics import r2_score\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import median_absolute_error\nr2=r2_score(y_true=y_test, y_pred=y_pred)\nprint('r2: ', r2)\nmse=mean_squared_error(y_true=y_test, y_pred=y_pred)\nprint('MSE: ',mse)\nmae=median_absolute_error(y_true=y_test, y_pred=y_pred)\nprint('MAE: ',mae)","metadata":{"id":"WOwdMHP17bx0","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"82dbcd4b8d764ba1b3331086919216f9","outputId":"00394f18-0fbc-45ca-a7ae-326cf94a94be","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":415,"user_tz":300,"timestamp":1637194948538},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"r2:  0.7367893518145634\nMSE:  1648289558.8257012\nMAE:  21582.873793242165\n"}],"execution_count":56},{"cell_type":"markdown","source":"# Modelo 2: GradientBoosting","metadata":{"id":"QqcNx3TB_Jy_","cell_id":"a8ad9bf60ec04c4a955b2d11fb031328","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"#Ahora comparemos con GBM\nfrom sklearn.ensemble import GradientBoostingRegressor\ngbrt=GradientBoostingRegressor(loss='ls',learning_rate=0.1, n_estimators=50, subsample=0.8,max_depth=4,criterion='friedman_mse',\n                               random_state=42)","metadata":{"id":"Ox2jwNWL9BYk","cell_id":"b1197edb8f0d4247a88825dd3b903e6c","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":505,"user_tz":300,"timestamp":1637194954474},"deepnote_cell_type":"code"},"outputs":[],"execution_count":57},{"cell_type":"code","source":"#Fiteamos el modelo\nmodel1 = gbrt.fit(X_train, y_train)","metadata":{"id":"uEyVI0Bd9g0K","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"828e19a9d9444a56bce51533717f9cac","outputId":"4af6cb1c-9ea7-4cf0-c947-db5114720265","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":426,"user_tz":300,"timestamp":1637194961553},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_gb.py:290: FutureWarning: The loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. Use 'squared_error' which is equivalent.\n  FutureWarning,\n"}],"execution_count":58},{"cell_type":"code","source":"y_pred = model1.predict(X_test)","metadata":{"id":"FKtg8EsS9q1s","cell_id":"897506023b8b49a59caa26087bedca99","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":398,"user_tz":300,"timestamp":1637194971153},"deepnote_cell_type":"code"},"outputs":[],"execution_count":59},{"cell_type":"code","source":"from sklearn.metrics import r2_score\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import median_absolute_error\nr2=r2_score(y_true=y_test, y_pred=y_pred)\nprint('r2: ', r2)\nmse=mean_squared_error(y_true=y_test, y_pred=y_pred)\nprint('MSE: ',mse)\nmae=median_absolute_error(y_true=y_test, y_pred=y_pred)\nprint('MAE: ',mae)","metadata":{"id":"73hGRMzk9si5","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"a436839690ba4cbfa9422beff9d98e87","outputId":"7b865fd2-7b21-4806-aa19-fee2d898b4e2","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":383,"user_tz":300,"timestamp":1637194989558},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"r2:  0.911749740054561\nMSE:  552644746.838815\nMAE:  9685.009492501253\n"}],"execution_count":60},{"cell_type":"markdown","source":"# Modelo 3: LightGBM","metadata":{"id":"_h-KGC1F_Ryq","cell_id":"f09a694dc8cc428baf67139db3110164","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"import lightgbm as lgb #pip install lightgbm\nclf = lgb.LGBMRegressor(boosting_type='gbdt',max_depth=4,num_leaves=20,learning_rate=0.01,n_estimators=100)\nclf.fit(X_train, y_train)\ny_pred=clf.predict(X_test)","metadata":{"id":"-EJq4Pb69yPd","cell_id":"85bb2ab4145a4bbf861682a8775771d9","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":385,"user_tz":300,"timestamp":1637195018902},"deepnote_cell_type":"code"},"outputs":[],"execution_count":61},{"cell_type":"code","source":"from sklearn.metrics import r2_score\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import median_absolute_error\nr2=r2_score(y_true=y_test, y_pred=y_pred)\nprint('r2: ', r2)\nmse=mean_squared_error(y_true=y_test, y_pred=y_pred)\nprint('MSE: ',mse)\nmae=median_absolute_error(y_true=y_test, y_pred=y_pred)\nprint('MAE: ',mae)","metadata":{"id":"qCy5id5o-PfO","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"93b746ddb3d446c9bf946648f03bbcec","outputId":"b6d66a1d-54a6-4300-af02-d22989590c17","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":446,"user_tz":300,"timestamp":1637195039261},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"r2:  0.70613934811082\nMSE:  1840227390.4868917\nMAE:  20513.164854036193\n"}],"execution_count":62},{"cell_type":"markdown","source":"# Modelo 4: XGBoost","metadata":{"id":"N4eXG5Zl_Fvi","cell_id":"e2fdb13844f5420ab065a813e81d3c75","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"import pandas as pd\nimport xgboost as xgb\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nregressor = xgb.XGBRegressor(\n    n_estimators=100,\n    reg_lambda=1,\n    gamma=0,\n    max_depth=3\n)","metadata":{"id":"nyImc9M6-TkT","cell_id":"2c8f6e427b454cf8822a64993fb48ce3","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":379,"user_tz":300,"timestamp":1637195055662},"deepnote_cell_type":"code"},"outputs":[],"execution_count":63},{"cell_type":"code","source":"#Fiteamos\nregressor.fit(X_train, y_train)","metadata":{"id":"AZzGWYQz-nCL","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"fa926623fd014beea7766fe4c8060ef4","outputId":"463eac90-7f7d-4d95-d9d6-a8e04099b2fa","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":458,"user_tz":300,"timestamp":1637195058103},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"[00:24:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"},{"output_type":"execute_result","data":{"text/plain":"XGBRegressor()"},"metadata":{},"execution_count":64}],"execution_count":64},{"cell_type":"code","source":"#Predecimos\ny_pred = regressor.predict(X_test)","metadata":{"id":"ZxrWykJr-sm0","cell_id":"58b4c290ee3743caa368291cc6ecf1de","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":449,"user_tz":300,"timestamp":1637195065988},"deepnote_cell_type":"code"},"outputs":[],"execution_count":65},{"cell_type":"code","source":"from sklearn.metrics import r2_score\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import median_absolute_error\nr2=r2_score(y_true=y_test, y_pred=y_pred)\nprint('r2: ', r2)\nmse=mean_squared_error(y_true=y_test, y_pred=y_pred)\nprint('MSE: ',mse)\nmae=median_absolute_error(y_true=y_test, y_pred=y_pred)\nprint('MAE: ',mae)","metadata":{"id":"jKg3YE3X-xnD","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"4a6159d5cd57415db724186ddd583404","outputId":"388e3708-217a-492b-bac4-b5e2e29424c1","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":410,"user_tz":300,"timestamp":1637195083605},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"r2:  0.891122802621978\nMSE:  681815682.1146731\nMAE:  9738.4609375\n"}],"execution_count":66},{"cell_type":"markdown","source":"# Ahora miremos que tan bien lo hace con el dataset que nos dieron","metadata":{"id":"axkbr4D5bntV","cell_id":"490a6fdeebd6484b8fae88a155a6a7f8","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"test.head()","metadata":{"id":"iNcdWw4AbrmF","colab":{"height":226,"base_uri":"https://localhost:8080/"},"cell_id":"c0ebfc720c34451984894c60c56769b7","outputId":"584c72e9-4c28-4fce-9288-32cad5206606","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":473,"user_tz":300,"timestamp":1637196021160},"deepnote_cell_type":"code"},"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>HeatingQC</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1461</td>\n      <td>20</td>\n      <td>RH</td>\n      <td>80.0</td>\n      <td>11622</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>NAmes</td>\n      <td>Feedr</td>\n      <td>Norm</td>\n      <td>1Fam</td>\n      <td>1Story</td>\n      <td>5</td>\n      <td>6</td>\n      <td>1961</td>\n      <td>1961</td>\n      <td>Gable</td>\n      <td>CompShg</td>\n      <td>VinylSd</td>\n      <td>VinylSd</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>CBlock</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>No</td>\n      <td>Rec</td>\n      <td>468.0</td>\n      <td>LwQ</td>\n      <td>144.0</td>\n      <td>270.0</td>\n      <td>882.0</td>\n      <td>GasA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>896</td>\n      <td>0</td>\n      <td>0</td>\n      <td>896</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>TA</td>\n      <td>5</td>\n      <td>Typ</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>Attchd</td>\n      <td>1961.0</td>\n      <td>Unf</td>\n      <td>1.0</td>\n      <td>730.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>140</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>120</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>MnPrv</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1462</td>\n      <td>20</td>\n      <td>RL</td>\n      <td>81.0</td>\n      <td>14267</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>NAmes</td>\n      <td>Norm</td>\n      <td>Norm</td>\n      <td>1Fam</td>\n      <td>1Story</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1958</td>\n      <td>1958</td>\n      <td>Hip</td>\n      <td>CompShg</td>\n      <td>Wd Sdng</td>\n      <td>Wd Sdng</td>\n      <td>BrkFace</td>\n      <td>108.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>CBlock</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>No</td>\n      <td>ALQ</td>\n      <td>923.0</td>\n      <td>Unf</td>\n      <td>0.0</td>\n      <td>406.0</td>\n      <td>1329.0</td>\n      <td>GasA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>1329</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1329</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</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>0</td>\n      <td>NaN</td>\n      <td>Attchd</td>\n      <td>1958.0</td>\n      <td>Unf</td>\n      <td>1.0</td>\n      <td>312.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>393</td>\n      <td>36</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>Gar2</td>\n      <td>12500</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1463</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>74.0</td>\n      <td>13830</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>Gilbert</td>\n      <td>Norm</td>\n      <td>Norm</td>\n      <td>1Fam</td>\n      <td>2Story</td>\n      <td>5</td>\n      <td>5</td>\n      <td>1997</td>\n      <td>1998</td>\n      <td>Gable</td>\n      <td>CompShg</td>\n      <td>VinylSd</td>\n      <td>VinylSd</td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>TA</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>791.0</td>\n      <td>Unf</td>\n      <td>0.0</td>\n      <td>137.0</td>\n      <td>928.0</td>\n      <td>GasA</td>\n      <td>Gd</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>928</td>\n      <td>701</td>\n      <td>0</td>\n      <td>1629</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>1</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>1997.0</td>\n      <td>Fin</td>\n      <td>2.0</td>\n      <td>482.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>212</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>MnPrv</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1464</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>78.0</td>\n      <td>9978</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>Gilbert</td>\n      <td>Norm</td>\n      <td>Norm</td>\n      <td>1Fam</td>\n      <td>2Story</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1998</td>\n      <td>1998</td>\n      <td>Gable</td>\n      <td>CompShg</td>\n      <td>VinylSd</td>\n      <td>VinylSd</td>\n      <td>BrkFace</td>\n      <td>20.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>PConc</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>No</td>\n      <td>GLQ</td>\n      <td>602.0</td>\n      <td>Unf</td>\n      <td>0.0</td>\n      <td>324.0</td>\n      <td>926.0</td>\n      <td>GasA</td>\n      <td>Ex</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>926</td>\n      <td>678</td>\n      <td>0</td>\n      <td>1604</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>1</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>Attchd</td>\n      <td>1998.0</td>\n      <td>Fin</td>\n      <td>2.0</td>\n      <td>470.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>360</td>\n      <td>36</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>6</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1465</td>\n      <td>120</td>\n      <td>RL</td>\n      <td>43.0</td>\n      <td>5005</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>HLS</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>Gtl</td>\n      <td>StoneBr</td>\n      <td>Norm</td>\n      <td>Norm</td>\n      <td>TwnhsE</td>\n      <td>1Story</td>\n      <td>8</td>\n      <td>5</td>\n      <td>1992</td>\n      <td>1992</td>\n      <td>Gable</td>\n      <td>CompShg</td>\n      <td>HdBoard</td>\n      <td>HdBoard</td>\n      <td>None</td>\n      <td>0.0</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>ALQ</td>\n      <td>263.0</td>\n      <td>Unf</td>\n      <td>0.0</td>\n      <td>1017.0</td>\n      <td>1280.0</td>\n      <td>GasA</td>\n      <td>Ex</td>\n      <td>Y</td>\n      <td>SBrkr</td>\n      <td>1280</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1280</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>Gd</td>\n      <td>5</td>\n      <td>Typ</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>Attchd</td>\n      <td>1992.0</td>\n      <td>RFn</td>\n      <td>2.0</td>\n      <td>506.0</td>\n      <td>TA</td>\n      <td>TA</td>\n      <td>Y</td>\n      <td>0</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>144</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"     Id  MSSubClass MSZoning  ...  YrSold  SaleType SaleCondition\n0  1461          20       RH  ...    2010        WD        Normal\n1  1462          20       RL  ...    2010        WD        Normal\n2  1463          60       RL  ...    2010        WD        Normal\n3  1464          60       RL  ...    2010        WD        Normal\n4  1465         120       RL  ...    2010        WD        Normal\n\n[5 rows x 80 columns]"},"metadata":{},"execution_count":105}],"execution_count":105},{"cell_type":"markdown","source":"Necesitamos aplicar la misma metodologia de limpieza que antes","metadata":{"id":"Mga2UpR1b1YW","cell_id":"5189df95777b48949b0debd511285dbd","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"col_del=['PoolQC','MiscFeature','Alley','Fence','Id']\ncol_inter= ['LotFrontage','GarageYrBlt','MasVnrArea','BsmtFinSF1','BsmtFinSF2','BsmtUnfSF','TotalBsmtSF','BsmtFullBath','BsmtHalfBath',\\\n            'GarageCars','GarageArea']\ncol_cat= ['FireplaceQu','GarageFinish','GarageCond','GarageType','BsmtExposure','BsmtFinType2','BsmtFinType1','BsmtCond',\n          'BsmtQual','MasVnrType','Electrical']\ntest_x=test.drop(labels=col_del, axis=1)\nfor i in col_inter:\n  test_x[i] = test_x[i].fillna(test_x[i].median())\nfor i in col_cat:\n  test_x[i] = test_x[i].fillna('Desconocido')\nalldata=[]\nalldata1=[]\nvariables=[]\nfor i in test_x.columns:\n  if test_x[i].dtype == 'O':\n    if len(test_x[i].unique()) ==2:\n      print('Variable 2 categorias:',i)\n      y= pd.get_dummies(test_x[i])\n      alldata.append(y)\n      variables.append(i)\n    else:\n      pass\n    print('--------------------')\n    if len(test_x[i].unique()) >2:\n      print('Variable >2 categorias:',i)\n      z= pd.DataFrame()\n      z[i]=test_x[i].astype('category').cat.codes\n      alldata1.append(z)\n      variables.append(i)\n  if test_x[i].dtype == 'float64':\n    test_x[i]=(test_x[i]-test_x[i].mean())/(test_x[i].std())\ndata_y=pd.concat(alldata, axis=1)\ndata_z=pd.concat(alldata1, axis=1)\ntest_y= pd.concat([data_y,data_z,test_x],axis=1) # Unir todos los datasets\ntest_y=test_y.drop(labels=variables, axis=1) # Borrar las columnas ya recodificadas\ntest_y.head()","metadata":{"id":"1gILREg46O-C","colab":{"height":1000,"base_uri":"https://localhost:8080/"},"cell_id":"f7cca23ddfd44ec0a7e0c6a3829a8aee","outputId":"9b31cc90-0386-4f88-f9fc-556b55f0fa4e","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":478,"user_tz":300,"timestamp":1637196023567},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"--------------------\nVariable >2 categorias: MSZoning\nVariable 2 categorias: Street\n--------------------\n--------------------\nVariable >2 categorias: LotShape\n--------------------\nVariable >2 categorias: LandContour\nVariable 2 categorias: Utilities\n--------------------\n--------------------\nVariable >2 categorias: LotConfig\n--------------------\nVariable >2 categorias: LandSlope\n--------------------\nVariable >2 categorias: Neighborhood\n--------------------\nVariable >2 categorias: Condition1\n--------------------\nVariable >2 categorias: Condition2\n--------------------\nVariable >2 categorias: BldgType\n--------------------\nVariable >2 categorias: HouseStyle\n--------------------\nVariable >2 categorias: RoofStyle\n--------------------\nVariable >2 categorias: RoofMatl\n--------------------\nVariable >2 categorias: Exterior1st\n--------------------\nVariable >2 categorias: Exterior2nd\n--------------------\nVariable >2 categorias: MasVnrType\n--------------------\nVariable >2 categorias: ExterQual\n--------------------\nVariable >2 categorias: ExterCond\n--------------------\nVariable >2 categorias: Foundation\n--------------------\nVariable >2 categorias: BsmtQual\n--------------------\nVariable >2 categorias: BsmtCond\n--------------------\nVariable >2 categorias: BsmtExposure\n--------------------\nVariable >2 categorias: BsmtFinType1\n--------------------\nVariable >2 categorias: BsmtFinType2\n--------------------\nVariable >2 categorias: Heating\n--------------------\nVariable >2 categorias: HeatingQC\nVariable 2 categorias: CentralAir\n--------------------\n--------------------\nVariable >2 categorias: Electrical\n--------------------\nVariable >2 categorias: KitchenQual\n--------------------\nVariable >2 categorias: Functional\n--------------------\nVariable >2 categorias: FireplaceQu\n--------------------\nVariable >2 categorias: GarageType\n--------------------\nVariable >2 categorias: GarageFinish\n--------------------\nVariable >2 categorias: GarageQual\n--------------------\nVariable >2 categorias: GarageCond\n--------------------\nVariable >2 categorias: PavedDrive\n--------------------\nVariable >2 categorias: SaleType\n--------------------\nVariable >2 categorias: SaleCondition\n"},{"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>Grvl</th>\n      <th>Pave</th>\n      <th>AllPub</th>\n      <th>N</th>\n      <th>Y</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0.567135</td>\n      <td>11622</td>\n      <td>5</td>\n      <td>6</td>\n      <td>1961</td>\n      <td>1961</td>\n      <td>-0.563123</td>\n      <td>0.063406</td>\n      <td>0.517359</td>\n      <td>-0.650238</td>\n      <td>-0.370589</td>\n      <td>896</td>\n      <td>0</td>\n      <td>0</td>\n      <td>896</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>-0.652883</td>\n      <td>-0.987850</td>\n      <td>1.185515</td>\n      <td>140</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>120</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0.615752</td>\n      <td>14267</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1958</td>\n      <td>1958</td>\n      <td>0.047040</td>\n      <td>1.063147</td>\n      <td>-0.297587</td>\n      <td>-0.339109</td>\n      <td>0.639011</td>\n      <td>1329</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1329</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>6</td>\n      <td>0</td>\n      <td>-0.769542</td>\n      <td>-0.987850</td>\n      <td>-0.740981</td>\n      <td>393</td>\n      <td>36</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12500</td>\n      <td>6</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>0.275437</td>\n      <td>13830</td>\n      <td>5</td>\n      <td>5</td>\n      <td>1997</td>\n      <td>1998</td>\n      <td>-0.563123</td>\n      <td>0.773112</td>\n      <td>-0.297587</td>\n      <td>-0.954504</td>\n      <td>-0.266693</td>\n      <td>928</td>\n      <td>701</td>\n      <td>0</td>\n      <td>1629</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1</td>\n      <td>0.747020</td>\n      <td>0.301303</td>\n      <td>0.042522</td>\n      <td>212</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>0.469903</td>\n      <td>9978</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1998</td>\n      <td>1998</td>\n      <td>-0.450130</td>\n      <td>0.357835</td>\n      <td>-0.297587</td>\n      <td>-0.526701</td>\n      <td>-0.271210</td>\n      <td>926</td>\n      <td>678</td>\n      <td>0</td>\n      <td>1604</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.785906</td>\n      <td>0.301303</td>\n      <td>-0.012784</td>\n      <td>360</td>\n      <td>36</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>120</td>\n      <td>-1.231670</td>\n      <td>5005</td>\n      <td>8</td>\n      <td>5</td>\n      <td>1992</td>\n      <td>1992</td>\n      <td>-0.563123</td>\n      <td>-0.387027</td>\n      <td>-0.297587</td>\n      <td>1.058685</td>\n      <td>0.528339</td>\n      <td>1280</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1280</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0.552589</td>\n      <td>0.301303</td>\n      <td>0.153134</td>\n      <td>0</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>144</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2010</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   Grvl  Pave  AllPub  N  Y  ...  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold\n0     0     1       1  0  1  ...          120         0        0       6    2010\n1     0     1       1  0  1  ...            0         0    12500       6    2010\n2     0     1       1  0  1  ...            0         0        0       3    2010\n3     0     1       1  0  1  ...            0         0        0       6    2010\n4     0     1       1  0  1  ...          144         0        0       1    2010\n\n[5 rows x 41 columns]"},"metadata":{},"execution_count":106}],"execution_count":106},{"cell_type":"code","source":"test_y.isna().sum()","metadata":{"id":"U6cRlT8bcaoq","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"5a16708587c44d76ae239025459f07bf","outputId":"855fd02a-9883-4e49-8d54-bc449e8c7252","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":469,"user_tz":300,"timestamp":1637196028047},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Grvl             0\nPave             0\nAllPub           0\nN                0\nY                0\nMSSubClass       0\nLotFrontage      0\nLotArea          0\nOverallQual      0\nOverallCond      0\nYearBuilt        0\nYearRemodAdd     0\nMasVnrArea       0\nBsmtFinSF1       0\nBsmtFinSF2       0\nBsmtUnfSF        0\nTotalBsmtSF      0\n1stFlrSF         0\n2ndFlrSF         0\nLowQualFinSF     0\nGrLivArea        0\nBsmtFullBath     0\nBsmtHalfBath     0\nFullBath         0\nHalfBath         0\nBedroomAbvGr     0\nKitchenAbvGr     0\nTotRmsAbvGrd     0\nFireplaces       0\nGarageYrBlt      0\nGarageCars       0\nGarageArea       0\nWoodDeckSF       0\nOpenPorchSF      0\nEnclosedPorch    0\n3SsnPorch        0\nScreenPorch      0\nPoolArea         0\nMiscVal          0\nMoSold           0\nYrSold           0\ndtype: int64"},"metadata":{},"execution_count":107}],"execution_count":107},{"cell_type":"code","source":"len(test_y.columns)","metadata":{"id":"LOZb8NqPdpUP","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"97ebfca934124b87af5850eb6db9911b","outputId":"66c00d0c-d596-4b88-f0d9-2f28000b4d09","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":397,"user_tz":300,"timestamp":1637196031735},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"41"},"metadata":{},"execution_count":108}],"execution_count":108},{"cell_type":"code","source":"len(X_test.columns)","metadata":{"id":"DrGx25ZHd02H","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"f06d031ed80d464c921dde6a17941557","outputId":"babe54e7-348d-48d4-838e-0abb6534f161","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":565,"user_tz":300,"timestamp":1637196051501},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"42"},"metadata":{},"execution_count":111}],"execution_count":111},{"cell_type":"code","source":"test_y.columns","metadata":{"id":"nyA8GowifOiB","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"472a3104b4c24a04ae1f095b38a15fac","outputId":"465a9878-9114-4d80-e750-d602aa7a8047","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":365,"user_tz":300,"timestamp":1637196042335},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Index(['Grvl', 'Pave', 'AllPub', 'N', 'Y', 'MSSubClass', 'LotFrontage',\n       'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n       'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF',\n       '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',\n       'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\n       'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea',\n       'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\n       'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold'],\n      dtype='object')"},"metadata":{},"execution_count":110}],"execution_count":110},{"cell_type":"code","source":"X_test.columns","metadata":{"id":"mdZ1IFW-fTYI","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"66cb510b4a124543ae8a6ee8f08a7af9","outputId":"f4ba5a0f-05fb-49c4-d19e-0c981cf370e8","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":528,"user_tz":300,"timestamp":1637196059090},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Index(['Grvl', 'Pave', 'AllPub', 'NoSeWa', 'N', 'Y', 'MSSubClass',\n       'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',\n       'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',\n       'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea',\n       'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr',\n       'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt',\n       'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n       'MoSold', 'YrSold'],\n      dtype='object')"},"metadata":{},"execution_count":112}],"execution_count":112},{"cell_type":"code","source":"X_test.NoSeWa.value_counts() # Todo es cero entonces podemos crear una igual","metadata":{"id":"QPOAQTUPgI8O","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"0aebfb3e89354041a24330e138f58fdb","outputId":"b0ec303b-1862-4d87-ef39-263f5984e55b","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":6,"user_tz":300,"timestamp":1637196295994},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0    292\nName: NoSeWa, dtype: int64"},"metadata":{},"execution_count":115}],"execution_count":115},{"cell_type":"code","source":"test_y['NoSeWa']=0\ntest_y.head()","metadata":{"id":"CECo3RYOgTUV","colab":{"height":226,"base_uri":"https://localhost:8080/"},"cell_id":"9027f356f6064c8880a1a069efa05c55","outputId":"4d0545c2-77ef-4f61-f904-28c79152e98d","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":423,"user_tz":300,"timestamp":1637196339249},"deepnote_cell_type":"code"},"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>Grvl</th>\n      <th>Pave</th>\n      <th>AllPub</th>\n      <th>N</th>\n      <th>Y</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>NoSeWa</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0.567135</td>\n      <td>11622</td>\n      <td>5</td>\n      <td>6</td>\n      <td>1961</td>\n      <td>1961</td>\n      <td>-0.563123</td>\n      <td>0.063406</td>\n      <td>0.517359</td>\n      <td>-0.650238</td>\n      <td>-0.370589</td>\n      <td>896</td>\n      <td>0</td>\n      <td>0</td>\n      <td>896</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>-0.652883</td>\n      <td>-0.987850</td>\n      <td>1.185515</td>\n      <td>140</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>120</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0.615752</td>\n      <td>14267</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1958</td>\n      <td>1958</td>\n      <td>0.047040</td>\n      <td>1.063147</td>\n      <td>-0.297587</td>\n      <td>-0.339109</td>\n      <td>0.639011</td>\n      <td>1329</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1329</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>6</td>\n      <td>0</td>\n      <td>-0.769542</td>\n      <td>-0.987850</td>\n      <td>-0.740981</td>\n      <td>393</td>\n      <td>36</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12500</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>0.275437</td>\n      <td>13830</td>\n      <td>5</td>\n      <td>5</td>\n      <td>1997</td>\n      <td>1998</td>\n      <td>-0.563123</td>\n      <td>0.773112</td>\n      <td>-0.297587</td>\n      <td>-0.954504</td>\n      <td>-0.266693</td>\n      <td>928</td>\n      <td>701</td>\n      <td>0</td>\n      <td>1629</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1</td>\n      <td>0.747020</td>\n      <td>0.301303</td>\n      <td>0.042522</td>\n      <td>212</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2010</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>60</td>\n      <td>0.469903</td>\n      <td>9978</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1998</td>\n      <td>1998</td>\n      <td>-0.450130</td>\n      <td>0.357835</td>\n      <td>-0.297587</td>\n      <td>-0.526701</td>\n      <td>-0.271210</td>\n      <td>926</td>\n      <td>678</td>\n      <td>0</td>\n      <td>1604</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.785906</td>\n      <td>0.301303</td>\n      <td>-0.012784</td>\n      <td>360</td>\n      <td>36</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>120</td>\n      <td>-1.231670</td>\n      <td>5005</td>\n      <td>8</td>\n      <td>5</td>\n      <td>1992</td>\n      <td>1992</td>\n      <td>-0.563123</td>\n      <td>-0.387027</td>\n      <td>-0.297587</td>\n      <td>1.058685</td>\n      <td>0.528339</td>\n      <td>1280</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1280</td>\n      <td>-0.817788</td>\n      <td>-0.258071</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0.552589</td>\n      <td>0.301303</td>\n      <td>0.153134</td>\n      <td>0</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>144</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   Grvl  Pave  AllPub  N  Y  ...  PoolArea  MiscVal  MoSold  YrSold  NoSeWa\n0     0     1       1  0  1  ...         0        0       6    2010       0\n1     0     1       1  0  1  ...         0    12500       6    2010       0\n2     0     1       1  0  1  ...         0        0       3    2010       0\n3     0     1       1  0  1  ...         0        0       6    2010       0\n4     0     1       1  0  1  ...         0        0       1    2010       0\n\n[5 rows x 42 columns]"},"metadata":{},"execution_count":117}],"execution_count":117},{"cell_type":"code","source":"interseccion = list(set(test_y.columns) & set(X_test.columns))\nlen(interseccion)\nunion = list(set(test_y.columns) | set(X_test.columns))\nset(test_y.columns) ^ set(X_test.columns) # Esta columna tiene un problema y nos cambia la dimension del dataset original","metadata":{"id":"iaKwg1cReGub","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"929aedee6aa4475fba5948ea990e5d32","outputId":"47eb62b5-5416-4a47-c7c0-f36c9f40ff43","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":408,"user_tz":300,"timestamp":1637196344262},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"set()"},"metadata":{},"execution_count":118}],"execution_count":118},{"cell_type":"code","source":"# Predecimos con GradientBoosting\ny_pred = model.predict(test_y)\ny_pred\ndf= pd.DataFrame()\ndf['Id']= test_y.index\ndf['Prediccion_Adaboost']= y_pred\ny_pred1=gbrt.predict(test_y)\ndf['Prediccion_GradientBoosting']= y_pred1\ny_pred2=clf.predict(test_y)\ndf['Prediccion_LightGBM']= y_pred2\ndf.head()","metadata":{"id":"x0gVBP8KcAKz","colab":{"height":365,"base_uri":"https://localhost:8080/"},"cell_id":"4a4ff38bce054e5095190163bb6a6e3e","outputId":"ac51e436-4f74-4af9-b3e8-b6ff08333e5c","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":519,"user_tz":300,"timestamp":1637196650958},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\nFeature names must be in the same order as they were in fit.\n\n  warnings.warn(message, FutureWarning)\n/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\nFeature names must be in the same order as they were in fit.\n\n  warnings.warn(message, FutureWarning)\n"},{"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>Prediccion_Adaboost</th>\n      <th>Prediccion_GradientBoosting</th>\n      <th>Prediccion_LightGBM</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>124989.680288</td>\n      <td>102220.681189</td>\n      <td>139929.143521</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>124989.680288</td>\n      <td>127633.610369</td>\n      <td>153849.447869</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>124989.680288</td>\n      <td>117111.617785</td>\n      <td>141688.350363</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>124989.680288</td>\n      <td>120649.428563</td>\n      <td>145765.584356</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>124989.680288</td>\n      <td>131458.061514</td>\n      <td>151059.186036</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   Id  Prediccion_Adaboost  Prediccion_GradientBoosting  Prediccion_LightGBM\n0   0        124989.680288                102220.681189        139929.143521\n1   1        124989.680288                127633.610369        153849.447869\n2   2        124989.680288                117111.617785        141688.350363\n3   3        124989.680288                120649.428563        145765.584356\n4   4        124989.680288                131458.061514        151059.186036"},"metadata":{},"execution_count":125}],"execution_count":125},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=4f9037d8-f2e9-4683-9ce3-67576f784ec7' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Colab_final_datathon_David.ipynb","provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyMFt+SLl2js5FMzlFDmCgbo","collapsed_sections":[]},"deepnote":{},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"deepnote_notebook_id":"ccacc3597efd4405ba1b132b929a5af4","deepnote_execution_queue":[]}}