feature-engineering-for-house-price-prediction.ipynb

{"metadata":{"jupytext":{"cell_metadata_filter":"-all","formats":"ipynb"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":5407,"databundleVersionId":868283,"sourceType":"competition"}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Introduction #\n\nWelcome to the feature engineering project for the [House Prices - Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) competition! This competition uses nearly the same data you used in the exercises of the [Feature Engineering](https://www.kaggle.com/learn/feature-engineering) course. We'll collect together the work you did into a complete project which you can build off of with ideas of your own.\n\n<blockquote style=\"margin-right:auto; margin-left:auto; background-color: #ebf9ff; padding: 1em; margin:24px;\">\n    <strong>Fork This Notebook!</strong><br>\nCreate your own editable copy of this notebook by clicking on the <strong>Copy and Edit</strong> button in the top right corner.\n</blockquote>\n\n# Step 1 - Preliminaries #\n## Imports and Configuration ##\n\nWe'll start by importing the packages we used in the exercises and setting some notebook defaults. Unhide this cell if you'd like to see the libraries we'll use:","metadata":{}},{"cell_type":"code","source":"\nimport os\nimport warnings\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom IPython.display import display\nfrom pandas.api.types import CategoricalDtype\n\nfrom category_encoders import MEstimateEncoder\nfrom sklearn.cluster import KMeans\nfrom sklearn.decomposition import PCA\nfrom sklearn.feature_selection import mutual_info_regression\nfrom sklearn.model_selection import KFold, cross_val_score\nfrom xgboost import XGBRegressor\n\n\n# Set Matplotlib defaults\nplt.style.use(\"seaborn-whitegrid\")\nplt.rc(\"figure\", autolayout=True)\nplt.rc(\n    \"axes\",\n    labelweight=\"bold\",\n    labelsize=\"large\",\n    titleweight=\"bold\",\n    titlesize=14,\n    titlepad=10,\n)\n\n# Mute warnings\nwarnings.filterwarnings('ignore')\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-06-20T07:41:26.792482Z","iopub.execute_input":"2024-06-20T07:41:26.793169Z","iopub.status.idle":"2024-06-20T07:41:29.880163Z","shell.execute_reply.started":"2024-06-20T07:41:26.793139Z","shell.execute_reply":"2024-06-20T07:41:29.879163Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_34/676408276.py:21: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n  plt.style.use(\"seaborn-whitegrid\")\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Data Preprocessing ##\n\nBefore we can do any feature engineering, we need to *preprocess* the data to get it in a form suitable for analysis. The data we used in the course was a bit simpler than the competition data. For the *Ames* competition dataset, we'll need to:\n- **Load** the data from CSV files\n- **Clean** the data to fix any errors or inconsistencies\n- **Encode** the statistical data type (numeric, categorical)\n- **Impute** any missing values\n\nWe'll wrap all these steps up in a function, which will make easy for you to get a fresh dataframe whenever you need. After reading the CSV file, we'll apply three preprocessing steps, `clean`, `encode`, and `impute`, and then create the data splits: one (`df_train`) for training the model, and one (`df_test`) for making the predictions that you'll submit to the competition for scoring on the leaderboard.","metadata":{}},{"cell_type":"code","source":"def load_data():\n    # Read data\n    data_dir = Path(\"../input/house-prices-advanced-regression-techniques/\")\n    df_train = pd.read_csv(data_dir / \"train.csv\", index_col=\"Id\")\n    df_test = pd.read_csv(data_dir / \"test.csv\", index_col=\"Id\")\n    # Merge the splits so we can process them together\n    df = pd.concat([df_train, df_test])\n    # Preprocessing\n    df = clean(df)\n    df = encode(df)\n    df = impute(df)\n    # Reform splits\n    df_train = df.loc[df_train.index, :]\n    df_test = df.loc[df_test.index, :]\n    return df_train, df_test\n","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:29.882172Z","iopub.execute_input":"2024-06-20T07:41:29.883001Z","iopub.status.idle":"2024-06-20T07:41:29.888777Z","shell.execute_reply.started":"2024-06-20T07:41:29.882962Z","shell.execute_reply":"2024-06-20T07:41:29.887902Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"### Clean Data ###\n\nSome of the categorical features in this dataset have what are apparently typos in their categories:","metadata":{}},{"cell_type":"code","source":"data_dir = Path(\"../input/house-prices-advanced-regression-techniques/\")\ndf = pd.read_csv(data_dir / \"train.csv\", index_col=\"Id\")\n\ndf.Exterior2nd.unique()","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:29.889931Z","iopub.execute_input":"2024-06-20T07:41:29.890226Z","iopub.status.idle":"2024-06-20T07:41:29.953898Z","shell.execute_reply.started":"2024-06-20T07:41:29.890202Z","shell.execute_reply":"2024-06-20T07:41:29.952968Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"array(['VinylSd', 'MetalSd', 'Wd Shng', 'HdBoard', 'Plywood', 'Wd Sdng',\n       'CmentBd', 'BrkFace', 'Stucco', 'AsbShng', 'Brk Cmn', 'ImStucc',\n       'AsphShn', 'Stone', 'Other', 'CBlock'], dtype=object)"},"metadata":{}}]},{"cell_type":"markdown","source":"Comparing these to `data_description.txt` shows us what needs cleaning. We'll take care of a couple of issues here, but you might want to evaluate this data further.","metadata":{}},{"cell_type":"code","source":"def clean(df):\n    df[\"Exterior2nd\"] = df[\"Exterior2nd\"].replace({\"Brk Cmn\": \"BrkComm\"})\n    # Some values of GarageYrBlt are corrupt, so we'll replace them\n    # with the year the house was built\n    df[\"GarageYrBlt\"] = df[\"GarageYrBlt\"].where(df.GarageYrBlt <= 2010, df.YearBuilt)\n    # Names beginning with numbers are awkward to work with\n    df.rename(columns={\n        \"1stFlrSF\": \"FirstFlrSF\",\n        \"2ndFlrSF\": \"SecondFlrSF\",\n        \"3SsnPorch\": \"Threeseasonporch\",\n    }, inplace=True,\n    )\n    return df\n","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:29.956309Z","iopub.execute_input":"2024-06-20T07:41:29.956965Z","iopub.status.idle":"2024-06-20T07:41:29.962237Z","shell.execute_reply.started":"2024-06-20T07:41:29.956932Z","shell.execute_reply":"2024-06-20T07:41:29.961282Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"### Encode the Statistical Data Type ###\n\nPandas has Python types corresponding to the standard statistical types (numeric, categorical, etc.). Encoding each feature with its correct type helps ensure each feature is treated appropriately by whatever functions we use, and makes it easier for us to apply transformations consistently. This hidden cell defines the `encode` function:","metadata":{}},{"cell_type":"code","source":"\n# The numeric features are already encoded correctly (`float` for\n# continuous, `int` for discrete), but the categoricals we'll need to\n# do ourselves. Note in particular, that the `MSSubClass` feature is\n# read as an `int` type, but is actually a (nominative) categorical.\n\n# The nominative (unordered) categorical features\nfeatures_nom = [\"MSSubClass\", \"MSZoning\", \"Street\", \"Alley\", \"LandContour\", \"LotConfig\", \"Neighborhood\", \"Condition1\", \"Condition2\", \"BldgType\", \"HouseStyle\", \"RoofStyle\", \"RoofMatl\", \"Exterior1st\", \"Exterior2nd\", \"MasVnrType\", \"Foundation\", \"Heating\", \"CentralAir\", \"GarageType\", \"MiscFeature\", \"SaleType\", \"SaleCondition\"]\n\n\n# The ordinal (ordered) categorical features \n\n# Pandas calls the categories \"levels\"\nfive_levels = [\"Po\", \"Fa\", \"TA\", \"Gd\", \"Ex\"]\nten_levels = list(range(10))\n\nordered_levels = {\n    \"OverallQual\": ten_levels,\n    \"OverallCond\": ten_levels,\n    \"ExterQual\": five_levels,\n    \"ExterCond\": five_levels,\n    \"BsmtQual\": five_levels,\n    \"BsmtCond\": five_levels,\n    \"HeatingQC\": five_levels,\n    \"KitchenQual\": five_levels,\n    \"FireplaceQu\": five_levels,\n    \"GarageQual\": five_levels,\n    \"GarageCond\": five_levels,\n    \"PoolQC\": five_levels,\n    \"LotShape\": [\"Reg\", \"IR1\", \"IR2\", \"IR3\"],\n    \"LandSlope\": [\"Sev\", \"Mod\", \"Gtl\"],\n    \"BsmtExposure\": [\"No\", \"Mn\", \"Av\", \"Gd\"],\n    \"BsmtFinType1\": [\"Unf\", \"LwQ\", \"Rec\", \"BLQ\", \"ALQ\", \"GLQ\"],\n    \"BsmtFinType2\": [\"Unf\", \"LwQ\", \"Rec\", \"BLQ\", \"ALQ\", \"GLQ\"],\n    \"Functional\": [\"Sal\", \"Sev\", \"Maj1\", \"Maj2\", \"Mod\", \"Min2\", \"Min1\", \"Typ\"],\n    \"GarageFinish\": [\"Unf\", \"RFn\", \"Fin\"],\n    \"PavedDrive\": [\"N\", \"P\", \"Y\"],\n    \"Utilities\": [\"NoSeWa\", \"NoSewr\", \"AllPub\"],\n    \"CentralAir\": [\"N\", \"Y\"],\n    \"Electrical\": [\"Mix\", \"FuseP\", \"FuseF\", \"FuseA\", \"SBrkr\"],\n    \"Fence\": [\"MnWw\", \"GdWo\", \"MnPrv\", \"GdPrv\"],\n}\n\n# Add a None level for missing values\nordered_levels = {key: [\"None\"] + value for key, value in\n                  ordered_levels.items()}\n\n\ndef encode(df):\n    # Nominal categories\n    for name in features_nom:\n        df[name] = df[name].astype(\"category\")\n        # Add a None category for missing values\n        if \"None\" not in df[name].cat.categories:\n            df[name] = df[name].cat.add_categories(\"None\")\n    # Ordinal categories\n    for name, levels in ordered_levels.items():\n        df[name] = df[name].astype(CategoricalDtype(levels,\n                                                    ordered=True))\n    return df\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-06-20T07:41:29.963397Z","iopub.execute_input":"2024-06-20T07:41:29.963866Z","iopub.status.idle":"2024-06-20T07:41:29.976484Z","shell.execute_reply.started":"2024-06-20T07:41:29.963841Z","shell.execute_reply":"2024-06-20T07:41:29.975521Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"### Handle Missing Values ###\n\nHandling missing values now will make the feature engineering go more smoothly. We'll impute `0` for missing numeric values and `\"None\"` for missing categorical values. You might like to experiment with other imputation strategies. In particular, you could try creating \"missing value\" indicators: `1` whenever a value was imputed and `0` otherwise.","metadata":{}},{"cell_type":"code","source":"def impute(df):\n    for name in df.select_dtypes(\"number\"):\n        df[name] = df[name].fillna(0)\n    for name in df.select_dtypes(\"category\"):\n        df[name] = df[name].fillna(\"None\")\n    return df\n","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:29.977422Z","iopub.execute_input":"2024-06-20T07:41:29.977702Z","iopub.status.idle":"2024-06-20T07:41:29.989772Z","shell.execute_reply.started":"2024-06-20T07:41:29.977675Z","shell.execute_reply":"2024-06-20T07:41:29.988982Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"## Load Data ##\n\nAnd now we can call the data loader and get the processed data splits:","metadata":{}},{"cell_type":"code","source":"df_train, df_test = load_data()","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:29.990945Z","iopub.execute_input":"2024-06-20T07:41:29.991366Z","iopub.status.idle":"2024-06-20T07:41:30.131312Z","shell.execute_reply.started":"2024-06-20T07:41:29.991336Z","shell.execute_reply":"2024-06-20T07:41:30.130083Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"Uncomment and run this cell if you'd like to see what they contain. Notice that `df_test` is\nmissing values for `SalePrice`. (`NA`s were willed with 0's in the imputation step.)","metadata":{}},{"cell_type":"code","source":"# Peek at the values\ndisplay(df_train)\ndisplay(df_test)\n\n# Display information about dtypes and missing values\n# display(df_train.info())\n# display(df_test.info())","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:30.132744Z","iopub.execute_input":"2024-06-20T07:41:30.133387Z","iopub.status.idle":"2024-06-20T07:41:30.199313Z","shell.execute_reply.started":"2024-06-20T07:41:30.133349Z","shell.execute_reply":"2024-06-20T07:41:30.198373Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"     MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\nId                                                                     \n1            60       RL         65.0     8450   Pave  None      Reg   \n2            20       RL         80.0     9600   Pave  None      Reg   \n3            60       RL         68.0    11250   Pave  None      IR1   \n4            70       RL         60.0     9550   Pave  None      IR1   \n5            60       RL         84.0    14260   Pave  None      IR1   \n...         ...      ...          ...      ...    ...   ...      ...   \n1456         60       RL         62.0     7917   Pave  None      Reg   \n1457         20       RL         85.0    13175   Pave  None      Reg   \n1458         70       RL         66.0     9042   Pave  None      Reg   \n1459         20       RL         68.0     9717   Pave  None      Reg   \n1460         20       RL         75.0     9937   Pave  None      Reg   \n\n     LandContour Utilities LotConfig  ... PoolArea PoolQC  Fence MiscFeature  \\\nId                                    ...                                      \n1            Lvl    AllPub    Inside  ...        0   None   None        None   \n2            Lvl    AllPub       FR2  ...        0   None   None        None   \n3            Lvl    AllPub    Inside  ...        0   None   None        None   \n4            Lvl    AllPub    Corner  ...        0   None   None        None   \n5            Lvl    AllPub       FR2  ...        0   None   None        None   \n...          ...       ...       ...  ...      ...    ...    ...         ...   \n1456         Lvl    AllPub    Inside  ...        0   None   None        None   \n1457         Lvl    AllPub    Inside  ...        0   None  MnPrv        None   \n1458         Lvl    AllPub    Inside  ...        0   None  GdPrv        Shed   \n1459         Lvl    AllPub    Inside  ...        0   None   None        None   \n1460         Lvl    AllPub    Inside  ...        0   None   None        None   \n\n     MiscVal MoSold YrSold SaleType  SaleCondition  SalePrice  \nId                                                             \n1          0      2   2008       WD         Normal   208500.0  \n2          0      5   2007       WD         Normal   181500.0  \n3          0      9   2008       WD         Normal   223500.0  \n4          0      2   2006       WD        Abnorml   140000.0  \n5          0     12   2008       WD         Normal   250000.0  \n...      ...    ...    ...      ...            ...        ...  \n1456       0      8   2007       WD         Normal   175000.0  \n1457       0      2   2010       WD         Normal   210000.0  \n1458    2500      5   2010       WD         Normal   266500.0  \n1459       0      4   2010       WD         Normal   142125.0  \n1460       0      6   2008       WD         Normal   147500.0  \n\n[1460 rows x 80 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MSSubClass</th>\n      <th>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>...</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    <tr>\n      <th>Id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>60</td>\n      <td>RL</td>\n      <td>65.0</td>\n      <td>8450</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>208500.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>20</td>\n      <td>RL</td>\n      <td>80.0</td>\n      <td>9600</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>FR2</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>5</td>\n      <td>2007</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>181500.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>60</td>\n      <td>RL</td>\n      <td>68.0</td>\n      <td>11250</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>223500.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>70</td>\n      <td>RL</td>\n      <td>60.0</td>\n      <td>9550</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Corner</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Abnorml</td>\n      <td>140000.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>60</td>\n      <td>RL</td>\n      <td>84.0</td>\n      <td>14260</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>FR2</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>250000.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1456</th>\n      <td>60</td>\n      <td>RL</td>\n      <td>62.0</td>\n      <td>7917</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>8</td>\n      <td>2007</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>175000.0</td>\n    </tr>\n    <tr>\n      <th>1457</th>\n      <td>20</td>\n      <td>RL</td>\n      <td>85.0</td>\n      <td>13175</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>MnPrv</td>\n      <td>None</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>210000.0</td>\n    </tr>\n    <tr>\n      <th>1458</th>\n      <td>70</td>\n      <td>RL</td>\n      <td>66.0</td>\n      <td>9042</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>GdPrv</td>\n      <td>Shed</td>\n      <td>2500</td>\n      <td>5</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>266500.0</td>\n    </tr>\n    <tr>\n      <th>1459</th>\n      <td>20</td>\n      <td>RL</td>\n      <td>68.0</td>\n      <td>9717</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>4</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>142125.0</td>\n    </tr>\n    <tr>\n      <th>1460</th>\n      <td>20</td>\n      <td>RL</td>\n      <td>75.0</td>\n      <td>9937</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>147500.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1460 rows × 80 columns</p>\n</div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"     MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\nId                                                                     \n1461         20       RH         80.0    11622   Pave  None      Reg   \n1462         20       RL         81.0    14267   Pave  None      IR1   \n1463         60       RL         74.0    13830   Pave  None      IR1   \n1464         60       RL         78.0     9978   Pave  None      IR1   \n1465        120       RL         43.0     5005   Pave  None      IR1   \n...         ...      ...          ...      ...    ...   ...      ...   \n2915        160       RM         21.0     1936   Pave  None      Reg   \n2916        160       RM         21.0     1894   Pave  None      Reg   \n2917         20       RL        160.0    20000   Pave  None      Reg   \n2918         85       RL         62.0    10441   Pave  None      Reg   \n2919         60       RL         74.0     9627   Pave  None      Reg   \n\n     LandContour Utilities LotConfig  ... PoolArea PoolQC  Fence MiscFeature  \\\nId                                    ...                                      \n1461         Lvl    AllPub    Inside  ...        0   None  MnPrv        None   \n1462         Lvl    AllPub    Corner  ...        0   None   None        Gar2   \n1463         Lvl    AllPub    Inside  ...        0   None  MnPrv        None   \n1464         Lvl    AllPub    Inside  ...        0   None   None        None   \n1465         HLS    AllPub    Inside  ...        0   None   None        None   \n...          ...       ...       ...  ...      ...    ...    ...         ...   \n2915         Lvl    AllPub    Inside  ...        0   None   None        None   \n2916         Lvl    AllPub    Inside  ...        0   None   None        None   \n2917         Lvl    AllPub    Inside  ...        0   None   None        None   \n2918         Lvl    AllPub    Inside  ...        0   None  MnPrv        Shed   \n2919         Lvl    AllPub    Inside  ...        0   None   None        None   \n\n     MiscVal MoSold YrSold SaleType  SaleCondition  SalePrice  \nId                                                             \n1461       0      6   2010       WD         Normal        0.0  \n1462   12500      6   2010       WD         Normal        0.0  \n1463       0      3   2010       WD         Normal        0.0  \n1464       0      6   2010       WD         Normal        0.0  \n1465       0      1   2010       WD         Normal        0.0  \n...      ...    ...    ...      ...            ...        ...  \n2915       0      6   2006       WD         Normal        0.0  \n2916       0      4   2006       WD        Abnorml        0.0  \n2917       0      9   2006       WD        Abnorml        0.0  \n2918     700      7   2006       WD         Normal        0.0  \n2919       0     11   2006       WD         Normal        0.0  \n\n[1459 rows x 80 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MSSubClass</th>\n      <th>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>...</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    <tr>\n      <th>Id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1461</th>\n      <td>20</td>\n      <td>RH</td>\n      <td>80.0</td>\n      <td>11622</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>MnPrv</td>\n      <td>None</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1462</th>\n      <td>20</td>\n      <td>RL</td>\n      <td>81.0</td>\n      <td>14267</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Corner</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</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      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1463</th>\n      <td>60</td>\n      <td>RL</td>\n      <td>74.0</td>\n      <td>13830</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>MnPrv</td>\n      <td>None</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1464</th>\n      <td>60</td>\n      <td>RL</td>\n      <td>78.0</td>\n      <td>9978</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1465</th>\n      <td>120</td>\n      <td>RL</td>\n      <td>43.0</td>\n      <td>5005</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>IR1</td>\n      <td>HLS</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2915</th>\n      <td>160</td>\n      <td>RM</td>\n      <td>21.0</td>\n      <td>1936</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>6</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2916</th>\n      <td>160</td>\n      <td>RM</td>\n      <td>21.0</td>\n      <td>1894</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>4</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Abnorml</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2917</th>\n      <td>20</td>\n      <td>RL</td>\n      <td>160.0</td>\n      <td>20000</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Abnorml</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2918</th>\n      <td>85</td>\n      <td>RL</td>\n      <td>62.0</td>\n      <td>10441</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>MnPrv</td>\n      <td>Shed</td>\n      <td>700</td>\n      <td>7</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2919</th>\n      <td>60</td>\n      <td>RL</td>\n      <td>74.0</td>\n      <td>9627</td>\n      <td>Pave</td>\n      <td>None</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>Inside</td>\n      <td>...</td>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>0</td>\n      <td>11</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1459 rows × 80 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Establish Baseline ##\n\nFinally, let's establish a baseline score to judge our feature engineering against.\n\nHere is the function we created in Lesson 1 that will compute the cross-validated RMSLE score for a feature set. We've used XGBoost for our model, but you might want to experiment with other models.\n","metadata":{}},{"cell_type":"code","source":"\ndef score_dataset(X, y, model=XGBRegressor()):\n    # Label encoding for categoricals\n    #\n    # Label encoding is good for XGBoost and RandomForest, but one-hot\n    # would be better for models like Lasso or Ridge. The `cat.codes`\n    # attribute holds the category levels.\n    for colname in X.select_dtypes([\"category\"]):\n        X[colname] = X[colname].cat.codes\n    # Metric for Housing competition is RMSLE (Root Mean Squared Log Error)\n    log_y = np.log(y)\n    score = cross_val_score(\n        model, X, log_y, cv=5, scoring=\"neg_mean_squared_error\",\n    )\n    score = -1 * score.mean()\n    score = np.sqrt(score)\n    return score\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-06-20T07:41:30.200604Z","iopub.execute_input":"2024-06-20T07:41:30.200864Z","iopub.status.idle":"2024-06-20T07:41:30.207054Z","shell.execute_reply.started":"2024-06-20T07:41:30.200842Z","shell.execute_reply":"2024-06-20T07:41:30.206174Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"We can reuse this scoring function anytime we want to try out a new feature set. We'll run it now on the processed data with no additional features and get a baseline score:","metadata":{}},{"cell_type":"code","source":"X = df_train.copy()\ny = X.pop(\"SalePrice\")\n\nbaseline_score = score_dataset(X, y)\nprint(f\"Baseline score: {baseline_score:.5f} RMSLE\")","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:30.211681Z","iopub.execute_input":"2024-06-20T07:41:30.211939Z","iopub.status.idle":"2024-06-20T07:41:31.820797Z","shell.execute_reply.started":"2024-06-20T07:41:30.211908Z","shell.execute_reply":"2024-06-20T07:41:31.820006Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Baseline score: 0.14387 RMSLE\n","output_type":"stream"}]},{"cell_type":"markdown","source":"This baseline score helps us to know whether some set of features we've assembled has actually led to any improvement or not.\n\n# Step 2 - Feature Utility Scores #\n\nIn Lesson 2 we saw how to use mutual information to compute a *utility score* for a feature, giving you an indication of how much potential the feature has. This hidden cell defines the two utility functions we used, `make_mi_scores` and `plot_mi_scores`: ","metadata":{}},{"cell_type":"code","source":"\ndef make_mi_scores(X, y):\n    X = X.copy()\n    for colname in X.select_dtypes([\"object\", \"category\"]):\n        X[colname], _ = X[colname].factorize()\n    # All discrete features should now have integer dtypes\n    discrete_features = [pd.api.types.is_integer_dtype(t) for t in X.dtypes]\n    mi_scores = mutual_info_regression(X, y, discrete_features=discrete_features, random_state=0)\n    mi_scores = pd.Series(mi_scores, name=\"MI Scores\", index=X.columns)\n    mi_scores = mi_scores.sort_values(ascending=False)\n    return mi_scores\n\n\ndef plot_mi_scores(scores):\n    scores = scores.sort_values(ascending=True)\n    width = np.arange(len(scores))\n    ticks = list(scores.index)\n    plt.barh(width, scores)\n    plt.yticks(width, ticks)\n    plt.title(\"Mutual Information Scores\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-06-20T07:41:31.824182Z","iopub.execute_input":"2024-06-20T07:41:31.826209Z","iopub.status.idle":"2024-06-20T07:41:31.834543Z","shell.execute_reply.started":"2024-06-20T07:41:31.826178Z","shell.execute_reply":"2024-06-20T07:41:31.833892Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"Let's look at our feature scores again:","metadata":{}},{"cell_type":"code","source":"X = df_train.copy()\ny = X.pop(\"SalePrice\")\n\nmi_scores = make_mi_scores(X, y)\nmi_scores","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:31.836038Z","iopub.execute_input":"2024-06-20T07:41:31.836390Z","iopub.status.idle":"2024-06-20T07:41:33.922499Z","shell.execute_reply.started":"2024-06-20T07:41:31.836356Z","shell.execute_reply":"2024-06-20T07:41:33.921769Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"OverallQual     0.571457\nNeighborhood    0.526220\nGrLivArea       0.430395\nYearBuilt       0.407974\nLotArea         0.394468\n                  ...   \nPoolQC          0.000000\nMiscVal         0.000000\nMiscFeature     0.000000\nYrSold          0.000000\nMoSold          0.000000\nName: MI Scores, Length: 79, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"You can see that we have a number of features that are highly informative and also some that don't seem to be informative at all (at least by themselves). As we talked about in Tutorial 2, the top scoring features will usually pay-off the most during feature development, so it could be a good idea to focus your efforts on those. On the other hand, training on uninformative features can lead to overfitting. So, the features with 0.0 scores we'll drop entirely:","metadata":{}},{"cell_type":"code","source":"def drop_uninformative(df, mi_scores):\n    return df.loc[:, mi_scores > 0.0]\n","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:33.923546Z","iopub.execute_input":"2024-06-20T07:41:33.924007Z","iopub.status.idle":"2024-06-20T07:41:33.928422Z","shell.execute_reply.started":"2024-06-20T07:41:33.923983Z","shell.execute_reply":"2024-06-20T07:41:33.927303Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"Removing them does lead to a modest performance gain:","metadata":{}},{"cell_type":"code","source":"X = df_train.copy()\ny = X.pop(\"SalePrice\")\nX = drop_uninformative(X, mi_scores)\n\nscore_dataset(X, y)","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:33.929827Z","iopub.execute_input":"2024-06-20T07:41:33.930364Z","iopub.status.idle":"2024-06-20T07:41:35.443916Z","shell.execute_reply.started":"2024-06-20T07:41:33.930329Z","shell.execute_reply":"2024-06-20T07:41:35.442858Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"0.14415135885828517"},"metadata":{}}]},{"cell_type":"markdown","source":"Later, we'll add the `drop_uninformative` function to our feature-creation pipeline.\n\n# Step 3 - Create Features #\n\nNow we'll start developing our feature set.\n\nTo make our feature engineering workflow more modular, we'll define a function that will take a prepared dataframe and pass it through a pipeline of transformations to get the final feature set. It will look something like this:\n\n```\ndef create_features(df):\n    X = df.copy()\n    y = X.pop(\"SalePrice\")\n    X = X.join(create_features_1(X))\n    X = X.join(create_features_2(X))\n    X = X.join(create_features_3(X))\n    # ...\n    return X\n```\n\nLet's go ahead and define one transformation now, a [label encoding](https://www.kaggle.com/alexisbcook/categorical-variables) for the categorical features:","metadata":{}},{"cell_type":"code","source":"def label_encode(df):\n    X = df.copy()\n    for colname in X.select_dtypes([\"category\"]):\n        X[colname] = X[colname].cat.codes\n    return X\n","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:35.445430Z","iopub.execute_input":"2024-06-20T07:41:35.446068Z","iopub.status.idle":"2024-06-20T07:41:35.451466Z","shell.execute_reply.started":"2024-06-20T07:41:35.446032Z","shell.execute_reply":"2024-06-20T07:41:35.450581Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"A label encoding is okay for any kind of categorical feature when you're using a tree-ensemble like XGBoost, even for unordered categories. If you wanted to try a linear regression model (also popular in this competition), you would instead want to use a one-hot encoding, especially for the features with unordered categories.\n\n## Create Features with Pandas ##\n\nThis cell reproduces the work you did in Exercise 3, where you applied strategies for creating features in Pandas. Modify or add to these functions to try out other feature combinations.","metadata":{}},{"cell_type":"code","source":"\ndef mathematical_transforms(df):\n    X = pd.DataFrame()  # dataframe to hold new features\n    X[\"LivLotRatio\"] = df.GrLivArea / df.LotArea\n    X[\"Spaciousness\"] = (df.FirstFlrSF + df.SecondFlrSF) / df.TotRmsAbvGrd\n    # This feature ended up not helping performance\n    # X[\"TotalOutsideSF\"] = \\\n    #     df.WoodDeckSF + df.OpenPorchSF + df.EnclosedPorch + \\\n    #     df.Threeseasonporch + df.ScreenPorch\n    return X\n\n\ndef interactions(df):\n    X = pd.get_dummies(df.BldgType, prefix=\"Bldg\")\n    X = X.mul(df.GrLivArea, axis=0)\n    return X\n\n\ndef counts(df):\n    X = pd.DataFrame()\n    X[\"PorchTypes\"] = df[[\n        \"WoodDeckSF\",\n        \"OpenPorchSF\",\n        \"EnclosedPorch\",\n        \"Threeseasonporch\",\n        \"ScreenPorch\",\n    ]].gt(0.0).sum(axis=1)\n    return X\n\n\ndef break_down(df):\n    X = pd.DataFrame()\n    X[\"MSClass\"] = df.MSSubClass.str.split(\"_\", n=1, expand=True)[0]\n    return X\n\n\ndef group_transforms(df):\n    X = pd.DataFrame()\n    X[\"MedNhbdArea\"] = df.groupby(\"Neighborhood\")[\"GrLivArea\"].transform(\"median\")\n    return X\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-06-20T07:41:35.452716Z","iopub.execute_input":"2024-06-20T07:41:35.453256Z","iopub.status.idle":"2024-06-20T07:41:35.462011Z","shell.execute_reply.started":"2024-06-20T07:41:35.453229Z","shell.execute_reply":"2024-06-20T07:41:35.461317Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"Here are some ideas for other transforms you could explore:\n- Interactions between the quality `Qual` and condition `Cond` features. `OverallQual`, for instance, was a high-scoring feature. You could try combining it with `OverallCond` by converting both to integer type and taking a product.\n- Square roots of area features. This would convert units of square feet to just feet.\n- Logarithms of numeric features. If a feature has a skewed distribution, applying a logarithm can help normalize it.\n- Interactions between numeric and categorical features that describe the same thing. You could look at interactions between `BsmtQual` and `TotalBsmtSF`, for instance.\n- Other group statistics in `Neighboorhood`. We did the median of `GrLivArea`. Looking at `mean`, `std`, or `count` could be interesting. You could also try combining the group statistics with other features. Maybe the *difference* of `GrLivArea` and the median is important?\n\n## k-Means Clustering ##\n\nThe first unsupervised algorithm we used to create features was k-means clustering. We saw that you could either use the cluster labels as a feature (a column with `0, 1, 2, ...`) or you could use the *distance* of the observations to each cluster. We saw how these features can sometimes be effective at untangling complicated spatial relationships.","metadata":{}},{"cell_type":"code","source":"\ncluster_features = [\n    \"LotArea\",\n    \"TotalBsmtSF\",\n    \"FirstFlrSF\",\n    \"SecondFlrSF\",\n    \"GrLivArea\",\n]\n\n\ndef cluster_labels(df, features, n_clusters=20):\n    X = df.copy()\n    X_scaled = X.loc[:, features]\n    X_scaled = (X_scaled - X_scaled.mean(axis=0)) / X_scaled.std(axis=0)\n    kmeans = KMeans(n_clusters=n_clusters, n_init=50, random_state=0)\n    X_new = pd.DataFrame()\n    X_new[\"Cluster\"] = kmeans.fit_predict(X_scaled)\n    return X_new\n\n\ndef cluster_distance(df, features, n_clusters=20):\n    X = df.copy()\n    X_scaled = X.loc[:, features]\n    X_scaled = (X_scaled - X_scaled.mean(axis=0)) / X_scaled.std(axis=0)\n    kmeans = KMeans(n_clusters=20, n_init=50, random_state=0)\n    X_cd = kmeans.fit_transform(X_scaled)\n    # Label features and join to dataset\n    X_cd = pd.DataFrame(\n        X_cd, columns=[f\"Centroid_{i}\" for i in range(X_cd.shape[1])]\n    )\n    return X_cd\n","metadata":{"_kg_hide-input":true,"lines_to_next_cell":2,"execution":{"iopub.status.busy":"2024-06-20T07:41:35.463186Z","iopub.execute_input":"2024-06-20T07:41:35.463689Z","iopub.status.idle":"2024-06-20T07:41:35.475692Z","shell.execute_reply.started":"2024-06-20T07:41:35.463662Z","shell.execute_reply":"2024-06-20T07:41:35.475016Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"## Principal Component Analysis ##\n\nPCA was the second unsupervised model we used for feature creation. We saw how it could be used to decompose the variational structure in the data. The PCA algorithm gave us *loadings* which described each component of variation, and also the *components* which were the transformed datapoints. The loadings can suggest features to create and the components we can use as features directly.\n\nHere are the utility functions from the PCA lesson:","metadata":{}},{"cell_type":"code","source":"\ndef apply_pca(X, standardize=True):\n    # Standardize\n    if standardize:\n        X = (X - X.mean(axis=0)) / X.std(axis=0)\n    # Create principal components\n    pca = PCA()\n    X_pca = pca.fit_transform(X)\n    # Convert to dataframe\n    component_names = [f\"PC{i+1}\" for i in range(X_pca.shape[1])]\n    X_pca = pd.DataFrame(X_pca, columns=component_names)\n    # Create loadings\n    loadings = pd.DataFrame(\n        pca.components_.T,  # transpose the matrix of loadings\n        columns=component_names,  # so the columns are the principal components\n        index=X.columns,  # and the rows are the original features\n    )\n    return pca, X_pca, loadings\n\n\ndef plot_variance(pca, width=8, dpi=100):\n    # Create figure\n    fig, axs = plt.subplots(1, 2)\n    n = pca.n_components_\n    grid = np.arange(1, n + 1)\n    # Explained variance\n    evr = pca.explained_variance_ratio_\n    axs[0].bar(grid, evr)\n    axs[0].set(\n        xlabel=\"Component\", title=\"% Explained Variance\", ylim=(0.0, 1.0)\n    )\n    # Cumulative Variance\n    cv = np.cumsum(evr)\n    axs[1].plot(np.r_[0, grid], np.r_[0, cv], \"o-\")\n    axs[1].set(\n        xlabel=\"Component\", title=\"% Cumulative Variance\", ylim=(0.0, 1.0)\n    )\n    # Set up figure\n    fig.set(figwidth=8, dpi=100)\n    return axs\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-06-20T07:41:35.476945Z","iopub.execute_input":"2024-06-20T07:41:35.477288Z","iopub.status.idle":"2024-06-20T07:41:35.487949Z","shell.execute_reply.started":"2024-06-20T07:41:35.477241Z","shell.execute_reply":"2024-06-20T07:41:35.487081Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"And here are transforms that produce the features from the Exercise 5. You might want to change these if you came up with a different answer.\n","metadata":{}},{"cell_type":"code","source":"\ndef pca_inspired(df):\n    X = pd.DataFrame()\n    X[\"Feature1\"] = df.GrLivArea + df.TotalBsmtSF\n    X[\"Feature2\"] = df.YearRemodAdd * df.TotalBsmtSF\n    return X\n\n\ndef pca_components(df, features):\n    X = df.loc[:, features]\n    _, X_pca, _ = apply_pca(X)\n    return X_pca\n\n\npca_features = [\n    \"GarageArea\",\n    \"YearRemodAdd\",\n    \"TotalBsmtSF\",\n    \"GrLivArea\",\n]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-06-20T07:41:35.488955Z","iopub.execute_input":"2024-06-20T07:41:35.489259Z","iopub.status.idle":"2024-06-20T07:41:35.501359Z","shell.execute_reply.started":"2024-06-20T07:41:35.489234Z","shell.execute_reply":"2024-06-20T07:41:35.500516Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"These are only a couple ways you could use the principal components. You could also try clustering using one or more components. One thing to note is that PCA doesn't change the distance between points -- it's just like a rotation. So clustering with the full set of components is the same as clustering with the original features. Instead, pick some subset of components, maybe those with the most variance or the highest MI scores.\n\nFor further analysis, you might want to look at a correlation matrix for the dataset:","metadata":{}},{"cell_type":"code","source":"def corrplot(df, method=\"pearson\", annot=True, **kwargs):\n    sns.clustermap(\n        df.corr(method, numeric_only=True),\n        vmin=-1.0,\n        vmax=1.0,\n        cmap=\"icefire\",\n        method=\"complete\",\n        annot=annot,\n        **kwargs,\n    )\n\n\ncorrplot(df_train, annot=None)","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:35.502427Z","iopub.execute_input":"2024-06-20T07:41:35.502682Z","iopub.status.idle":"2024-06-20T07:41:36.875060Z","shell.execute_reply.started":"2024-06-20T07:41:35.502660Z","shell.execute_reply":"2024-06-20T07:41:36.874170Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 4 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"Groups of highly correlated features often yield interesting loadings.\n\n### PCA Application - Indicate Outliers ###\n\nIn Exercise 5, you applied PCA to determine houses that were **outliers**, that is, houses having values not well represented in the rest of the data. You saw that there was a group of houses in the `Edwards` neighborhood having a `SaleCondition` of `Partial` whose values were especially extreme.\n\nSome models can benefit from having these outliers indicated, which is what this next transform will do.","metadata":{}},{"cell_type":"code","source":"def indicate_outliers(df):\n    X_new = pd.DataFrame()\n    X_new[\"Outlier\"] = (df.Neighborhood == \"Edwards\") & (df.SaleCondition == \"Partial\")\n    return X_new\n","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:36.878324Z","iopub.execute_input":"2024-06-20T07:41:36.878605Z","iopub.status.idle":"2024-06-20T07:41:36.883408Z","shell.execute_reply.started":"2024-06-20T07:41:36.878579Z","shell.execute_reply":"2024-06-20T07:41:36.882561Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"You could also consider applying some sort of robust scaler from scikit-learn's `sklearn.preprocessing` module to the outlying values, especially those in `GrLivArea`. [Here](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html) is a tutorial illustrating some of them. Another option could be to create a feature of \"outlier scores\" using one of scikit-learn's [outlier detectors](https://scikit-learn.org/stable/modules/outlier_detection.html).","metadata":{}},{"cell_type":"markdown","source":"## Target Encoding ##\n\nNeeding a separate holdout set to create a target encoding is rather wasteful of data. In *Tutorial 6* we used 25% of our dataset just to encode a single feature, `Zipcode`. The data from the other features in that 25% we didn't get to use at all.\n\nThere is, however, a way you can use target encoding without having to use held-out encoding data. It's basically the same trick used in cross-validation:\n1. Split the data into folds, each fold having two splits of the dataset.\n2. Train the encoder on one split but transform the values of the other.\n3. Repeat for all the splits.\n\nThis way, training and transformation always take place on independent sets of data, just like when you use a holdout set but without any data going to waste.\n\nIn the next hidden cell is a wrapper you can use with any target encoder:","metadata":{}},{"cell_type":"code","source":"\nclass CrossFoldEncoder:\n    def __init__(self, encoder, **kwargs):\n        self.encoder_ = encoder\n        self.kwargs_ = kwargs  # keyword arguments for the encoder\n        self.cv_ = KFold(n_splits=5)\n\n    # Fit an encoder on one split and transform the feature on the\n    # other. Iterating over the splits in all folds gives a complete\n    # transformation. We also now have one trained encoder on each\n    # fold.\n    def fit_transform(self, X, y, cols):\n        self.fitted_encoders_ = []\n        self.cols_ = cols\n        X_encoded = []\n        for idx_encode, idx_train in self.cv_.split(X):\n            fitted_encoder = self.encoder_(cols=cols, **self.kwargs_)\n            fitted_encoder.fit(\n                X.iloc[idx_encode, :], y.iloc[idx_encode],\n            )\n            X_encoded.append(fitted_encoder.transform(X.iloc[idx_train, :])[cols])\n            self.fitted_encoders_.append(fitted_encoder)\n        X_encoded = pd.concat(X_encoded)\n        X_encoded.columns = [name + \"_encoded\" for name in X_encoded.columns]\n        return X_encoded\n\n    # To transform the test data, average the encodings learned from\n    # each fold.\n    def transform(self, X):\n        from functools import reduce\n\n        X_encoded_list = []\n        for fitted_encoder in self.fitted_encoders_:\n            X_encoded = fitted_encoder.transform(X)\n            X_encoded_list.append(X_encoded[self.cols_])\n        X_encoded = reduce(\n            lambda x, y: x.add(y, fill_value=0), X_encoded_list\n        ) / len(X_encoded_list)\n        X_encoded.columns = [name + \"_encoded\" for name in X_encoded.columns]\n        return X_encoded\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-06-20T07:41:36.884603Z","iopub.execute_input":"2024-06-20T07:41:36.884899Z","iopub.status.idle":"2024-06-20T07:41:36.896994Z","shell.execute_reply.started":"2024-06-20T07:41:36.884875Z","shell.execute_reply":"2024-06-20T07:41:36.896172Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"Use it like:\n\n```\nencoder = CrossFoldEncoder(MEstimateEncoder, m=1)\nX_encoded = encoder.fit_transform(X, y, cols=[\"MSSubClass\"]))\n```\n\nYou can turn any of the encoders from the [`category_encoders`](http://contrib.scikit-learn.org/category_encoders/) library into a cross-fold encoder. The [`CatBoostEncoder`](http://contrib.scikit-learn.org/category_encoders/catboost.html) would be worth trying. It's similar to `MEstimateEncoder` but uses some tricks to better prevent overfitting. Its smoothing parameter is called `a` instead of `m`.\n\n## Create Final Feature Set ##\n\nNow let's combine everything together. Putting the transformations into separate functions makes it easier to experiment with various combinations. The ones I left uncommented I found gave the best results. You should experiment with you own ideas though! Modify any of these transformations or come up with some of your own to add to the pipeline.","metadata":{}},{"cell_type":"code","source":"def create_features(df, df_test=None):\n    X = df.copy()\n    y = X.pop(\"SalePrice\")\n    mi_scores = make_mi_scores(X, y)\n\n    # Combine splits if test data is given\n    #\n    # If we're creating features for test set predictions, we should\n    # use all the data we have available. After creating our features,\n    # we'll recreate the splits.\n    if df_test is not None:\n        X_test = df_test.copy()\n        X_test.pop(\"SalePrice\")\n        X = pd.concat([X, X_test])\n\n    # Lesson 2 - Mutual Information\n    X = drop_uninformative(X, mi_scores)\n\n    # Lesson 3 - Transformations\n    X = X.join(mathematical_transforms(X))\n    X = X.join(interactions(X))\n    X = X.join(counts(X))\n    # X = X.join(break_down(X))\n    X = X.join(group_transforms(X))\n\n    # Lesson 4 - Clustering\n    # X = X.join(cluster_labels(X, cluster_features, n_clusters=20))\n    # X = X.join(cluster_distance(X, cluster_features, n_clusters=20))\n\n    # Lesson 5 - PCA\n    X = X.join(pca_inspired(X))\n    # X = X.join(pca_components(X, pca_features))\n    # X = X.join(indicate_outliers(X))\n\n    X = label_encode(X)\n\n    # Reform splits\n    if df_test is not None:\n        X_test = X.loc[df_test.index, :]\n        X.drop(df_test.index, inplace=True)\n\n    # Lesson 6 - Target Encoder\n    encoder = CrossFoldEncoder(MEstimateEncoder, m=1)\n    X = X.join(encoder.fit_transform(X, y, cols=[\"MSSubClass\"]))\n    if df_test is not None:\n        X_test = X_test.join(encoder.transform(X_test))\n\n    if df_test is not None:\n        return X, X_test\n    else:\n        return X\n\n\ndf_train, df_test = load_data()\nX_train = create_features(df_train)\ny_train = df_train.loc[:, \"SalePrice\"]\n\nscore_dataset(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:36.897995Z","iopub.execute_input":"2024-06-20T07:41:36.898284Z","iopub.status.idle":"2024-06-20T07:41:42.387923Z","shell.execute_reply.started":"2024-06-20T07:41:36.898261Z","shell.execute_reply":"2024-06-20T07:41:42.387044Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"0.1369423756646913"},"metadata":{}}]},{"cell_type":"markdown","source":"# Step 4 - Hyperparameter Tuning #\n\nAt this stage, you might like to do some hyperparameter tuning with XGBoost before creating your final submission.","metadata":{}},{"cell_type":"code","source":"X_train = create_features(df_train)\ny_train = df_train.loc[:, \"SalePrice\"]\n\nxgb_params = dict(\n    max_depth=6,           # maximum depth of each tree - try 2 to 10\n    learning_rate=0.01,    # effect of each tree - try 0.0001 to 0.1\n    n_estimators=1000,     # number of trees (that is, boosting rounds) - try 1000 to 8000\n    min_child_weight=1,    # minimum number of houses in a leaf - try 1 to 10\n    colsample_bytree=0.7,  # fraction of features (columns) per tree - try 0.2 to 1.0\n    subsample=0.7,         # fraction of instances (rows) per tree - try 0.2 to 1.0\n    reg_alpha=0.5,         # L1 regularization (like LASSO) - try 0.0 to 10.0\n    reg_lambda=1.0,        # L2 regularization (like Ridge) - try 0.0 to 10.0\n    num_parallel_tree=1,   # set > 1 for boosted random forests\n)\n\nxgb = XGBRegressor(**xgb_params)\nscore_dataset(X_train, y_train, xgb)","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:42.391707Z","iopub.execute_input":"2024-06-20T07:41:42.391984Z","iopub.status.idle":"2024-06-20T07:41:57.899718Z","shell.execute_reply.started":"2024-06-20T07:41:42.391960Z","shell.execute_reply":"2024-06-20T07:41:57.898921Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"0.1249121741317448"},"metadata":{}}]},{"cell_type":"code","source":"import optuna\n\ndef objective(trial):\n    xgb_params = dict(\n        max_depth=trial.suggest_int(\"max_depth\", 2, 10),\n        learning_rate=trial.suggest_float(\"learning_rate\", 1e-4, 1e-1, log=True),\n        n_estimators=trial.suggest_int(\"n_estimators\", 1000, 8000),\n        min_child_weight=trial.suggest_int(\"min_child_weight\", 1, 10),\n        colsample_bytree=trial.suggest_float(\"colsample_bytree\", 0.2, 1.0),\n        subsample=trial.suggest_float(\"subsample\", 0.2, 1.0),\n        reg_alpha=trial.suggest_float(\"reg_alpha\", 1e-4, 1e2, log=True),\n        reg_lambda=trial.suggest_float(\"reg_lambda\", 1e-4, 1e2, log=True),\n    )\n    xgb = XGBRegressor(**xgb_params)\n    return score_dataset(X_train, y_train, xgb)\n\nstudy = optuna.create_study(direction=\"minimize\")\nstudy.optimize(objective, n_trials=20)\nxgb_params = study.best_params","metadata":{"execution":{"iopub.status.busy":"2024-06-20T07:41:57.900756Z","iopub.execute_input":"2024-06-20T07:41:57.901620Z","iopub.status.idle":"2024-06-20T07:56:57.295263Z","shell.execute_reply.started":"2024-06-20T07:41:57.901587Z","shell.execute_reply":"2024-06-20T07:56:57.294515Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stderr","text":"[I 2024-06-20 07:41:58,030] A new study created in memory with name: no-name-8024450f-ebaf-4b28-9498-c3d4c4ed2373\n[I 2024-06-20 07:42:55,369] Trial 0 finished with value: 0.17383577595222746 and parameters: {'max_depth': 7, 'learning_rate': 0.0011352895035483261, 'n_estimators': 6343, 'min_child_weight': 3, 'colsample_bytree': 0.5908047637510403, 'subsample': 0.6740678344813122, 'reg_alpha': 13.211574852649257, 'reg_lambda': 0.005841584683688352}. Best is trial 0 with value: 0.17383577595222746.\n[I 2024-06-20 07:44:42,251] Trial 1 finished with value: 0.2514629014977577 and parameters: {'max_depth': 6, 'learning_rate': 0.00010193679295839901, 'n_estimators': 7116, 'min_child_weight': 5, 'colsample_bytree': 0.8038786313167723, 'subsample': 0.7420610014336297, 'reg_alpha': 0.1259371468805651, 'reg_lambda': 2.8979683351976844}. Best is trial 0 with value: 0.17383577595222746.\n[I 2024-06-20 07:46:12,989] Trial 2 finished with value: 0.20669851585177912 and parameters: {'max_depth': 6, 'learning_rate': 0.00012890082293749133, 'n_estimators': 7692, 'min_child_weight': 5, 'colsample_bytree': 0.47478150045937995, 'subsample': 0.5285082809077961, 'reg_alpha': 0.007085526816714086, 'reg_lambda': 0.0012074529899111738}. Best is trial 0 with value: 0.17383577595222746.\n[I 2024-06-20 07:47:57,455] Trial 3 finished with value: 0.16944288335954788 and parameters: {'max_depth': 9, 'learning_rate': 0.00027279779722385965, 'n_estimators': 6693, 'min_child_weight': 6, 'colsample_bytree': 0.6981510840620168, 'subsample': 0.4611167371546451, 'reg_alpha': 0.085637533160075, 'reg_lambda': 3.5943894676036447}. Best is trial 3 with value: 0.16944288335954788.\n[I 2024-06-20 07:48:47,420] Trial 4 finished with value: 0.14100104182637163 and parameters: {'max_depth': 7, 'learning_rate': 0.0004580947215128974, 'n_estimators': 5544, 'min_child_weight': 9, 'colsample_bytree': 0.2922476638352984, 'subsample': 0.2620809387891315, 'reg_alpha': 0.0157668020393247, 'reg_lambda': 0.0224859955903641}. Best is trial 4 with value: 0.14100104182637163.\n[I 2024-06-20 07:49:02,534] Trial 5 finished with value: 0.25382428628666687 and parameters: {'max_depth': 3, 'learning_rate': 0.002501360925575422, 'n_estimators': 2162, 'min_child_weight': 4, 'colsample_bytree': 0.985445839968141, 'subsample': 0.2773056678283042, 'reg_alpha': 19.162386406558003, 'reg_lambda': 17.080051279052032}. Best is trial 4 with value: 0.14100104182637163.\n[I 2024-06-20 07:49:47,266] Trial 6 finished with value: 0.1226668723637485 and parameters: {'max_depth': 3, 'learning_rate': 0.008608095794058218, 'n_estimators': 7201, 'min_child_weight': 7, 'colsample_bytree': 0.6190940542727315, 'subsample': 0.7757052570911307, 'reg_alpha': 0.5509025183409233, 'reg_lambda': 60.6245129679046}. Best is trial 6 with value: 0.1226668723637485.\n[I 2024-06-20 07:50:19,053] Trial 7 finished with value: 0.1245380969836173 and parameters: {'max_depth': 9, 'learning_rate': 0.0034180203287604285, 'n_estimators': 1746, 'min_child_weight': 5, 'colsample_bytree': 0.6270334151605974, 'subsample': 0.7132362172656455, 'reg_alpha': 0.00042774656452568364, 'reg_lambda': 3.732172081369319}. Best is trial 6 with value: 0.1226668723637485.\n[I 2024-06-20 07:51:26,161] Trial 8 finished with value: 0.12155952286389736 and parameters: {'max_depth': 7, 'learning_rate': 0.010989889047875541, 'n_estimators': 6449, 'min_child_weight': 9, 'colsample_bytree': 0.4671800334850688, 'subsample': 0.800810947288356, 'reg_alpha': 0.004887593630537396, 'reg_lambda': 0.0005690398115160347}. Best is trial 8 with value: 0.12155952286389736.\n[I 2024-06-20 07:51:52,412] Trial 9 finished with value: 0.26231103404135603 and parameters: {'max_depth': 5, 'learning_rate': 0.0755487040920226, 'n_estimators': 6370, 'min_child_weight': 2, 'colsample_bytree': 0.8148156818103336, 'subsample': 0.535016693047357, 'reg_alpha': 46.03276517821765, 'reg_lambda': 0.6951664184419057}. Best is trial 8 with value: 0.12155952286389736.\n[I 2024-06-20 07:52:20,645] Trial 10 finished with value: 0.124037836582526 and parameters: {'max_depth': 10, 'learning_rate': 0.03169823412860936, 'n_estimators': 3989, 'min_child_weight': 10, 'colsample_bytree': 0.212914112673395, 'subsample': 0.9959473372652324, 'reg_alpha': 0.0001214496285487107, 'reg_lambda': 0.0003578753174039653}. Best is trial 8 with value: 0.12155952286389736.\n[I 2024-06-20 07:52:42,668] Trial 11 finished with value: 0.125365667616973 and parameters: {'max_depth': 2, 'learning_rate': 0.01250869465171808, 'n_estimators': 4561, 'min_child_weight': 8, 'colsample_bytree': 0.43070248730772853, 'subsample': 0.8904932261865006, 'reg_alpha': 1.0222430657092465, 'reg_lambda': 72.41723686182561}. Best is trial 8 with value: 0.12155952286389736.\n[I 2024-06-20 07:53:16,465] Trial 12 finished with value: 0.11907802586852832 and parameters: {'max_depth': 4, 'learning_rate': 0.01049058052149516, 'n_estimators': 5019, 'min_child_weight': 7, 'colsample_bytree': 0.4276384605807066, 'subsample': 0.8697303218090773, 'reg_alpha': 0.002367760616142172, 'reg_lambda': 0.12159442277935509}. Best is trial 12 with value: 0.11907802586852832.\n[I 2024-06-20 07:53:47,245] Trial 13 finished with value: 0.12042590599912599 and parameters: {'max_depth': 4, 'learning_rate': 0.014009653822892987, 'n_estimators': 4642, 'min_child_weight': 8, 'colsample_bytree': 0.4102571326625484, 'subsample': 0.8937558195423893, 'reg_alpha': 0.0023428648917672504, 'reg_lambda': 0.13385656223339065}. Best is trial 12 with value: 0.11907802586852832.\n[I 2024-06-20 07:54:09,068] Trial 14 finished with value: 0.12051389347683204 and parameters: {'max_depth': 4, 'learning_rate': 0.03246228856673867, 'n_estimators': 3523, 'min_child_weight': 7, 'colsample_bytree': 0.37757315038829015, 'subsample': 0.9969809030244358, 'reg_alpha': 0.0011303330240530307, 'reg_lambda': 0.08305847368789844}. Best is trial 12 with value: 0.11907802586852832.\n[I 2024-06-20 07:54:39,800] Trial 15 finished with value: 0.11877847847721908 and parameters: {'max_depth': 4, 'learning_rate': 0.005651384861530117, 'n_estimators': 4894, 'min_child_weight': 8, 'colsample_bytree': 0.31900276457494536, 'subsample': 0.8875834011315716, 'reg_alpha': 0.0018563922672120252, 'reg_lambda': 0.14637845022632903}. Best is trial 15 with value: 0.11877847847721908.\n[I 2024-06-20 07:54:54,199] Trial 16 finished with value: 0.12642795296939724 and parameters: {'max_depth': 2, 'learning_rate': 0.003524078565082035, 'n_estimators': 2892, 'min_child_weight': 7, 'colsample_bytree': 0.29482870547793216, 'subsample': 0.9020308114408897, 'reg_alpha': 0.024229142497983323, 'reg_lambda': 0.24951801276361527}. Best is trial 15 with value: 0.11877847847721908.\n[I 2024-06-20 07:55:30,527] Trial 17 finished with value: 0.11858378632122468 and parameters: {'max_depth': 4, 'learning_rate': 0.0013794459366953898, 'n_estimators': 5331, 'min_child_weight': 1, 'colsample_bytree': 0.3074477737924936, 'subsample': 0.6204833864880572, 'reg_alpha': 0.00012976502127939809, 'reg_lambda': 0.012452013611824474}. Best is trial 17 with value: 0.11858378632122468.\n[I 2024-06-20 07:56:13,780] Trial 18 finished with value: 0.1180730276347259 and parameters: {'max_depth': 5, 'learning_rate': 0.001306916990977639, 'n_estimators': 5542, 'min_child_weight': 1, 'colsample_bytree': 0.2514470679914137, 'subsample': 0.4172314052068504, 'reg_alpha': 0.00015489840157348182, 'reg_lambda': 0.007327077867124266}. Best is trial 18 with value: 0.1180730276347259.\n[I 2024-06-20 07:56:57,289] Trial 19 finished with value: 0.12053619240218164 and parameters: {'max_depth': 5, 'learning_rate': 0.0010348249616898458, 'n_estimators': 5609, 'min_child_weight': 1, 'colsample_bytree': 0.20448746244625662, 'subsample': 0.3954153708732858, 'reg_alpha': 0.00016371534695222947, 'reg_lambda': 0.008389115355791257}. Best is trial 18 with value: 0.1180730276347259.\n","output_type":"stream"}]},{"cell_type":"code","source":"X_train = create_features(df_train)\ny_train = df_train.loc[:, \"SalePrice\"]\n\nxgb_params = dict(\n    max_depth=5,           # maximum depth of each tree - try 2 to 10\n    learning_rate=0.001306916990977639,    # effect of each tree - try 0.0001 to 0.1\n    n_estimators=5542,     # number of trees (that is, boosting rounds) - try 1000 to 8000\n    min_child_weight=1,    # minimum number of houses in a leaf - try 1 to 10\n    colsample_bytree=0.2514470679914137,  # fraction of features (columns) per tree - try 0.2 to 1.0\n    subsample=0.4172314052068504,         # fraction of instances (rows) per tree - try 0.2 to 1.0\n    reg_alpha=0.00015489840157348182,         # L1 regularization (like LASSO) - try 0.0 to 10.0\n    reg_lambda=0.007327077867124266,        # L2 regularization (like Ridge) - try 0.0 to 10.0\n    num_parallel_tree=1,   # set > 1 for boosted random forests\n)\n\nxgb = XGBRegressor(**xgb_params)\nscore_dataset(X_train, y_train, xgb)","metadata":{"execution":{"iopub.status.busy":"2024-06-20T08:03:08.422501Z","iopub.execute_input":"2024-06-20T08:03:08.422840Z","iopub.status.idle":"2024-06-20T08:03:53.597769Z","shell.execute_reply.started":"2024-06-20T08:03:08.422814Z","shell.execute_reply":"2024-06-20T08:03:53.596840Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"0.1180730276347259"},"metadata":{}}]},{"cell_type":"markdown","source":"Just tuning these by hand can give you great results. However, you might like to try using one of scikit-learn's automatic [hyperparameter tuners](https://scikit-learn.org/stable/modules/grid_search.html). Or you could explore more advanced tuning libraries like [Optuna](https://optuna.readthedocs.io/en/stable/index.html) or [scikit-optimize](https://scikit-optimize.github.io/stable/).\n\nHere is how you can use Optuna with XGBoost:\n\n```\nimport optuna\n\ndef objective(trial):\n    xgb_params = dict(\n        max_depth=trial.suggest_int(\"max_depth\", 2, 10),\n        learning_rate=trial.suggest_float(\"learning_rate\", 1e-4, 1e-1, log=True),\n        n_estimators=trial.suggest_int(\"n_estimators\", 1000, 8000),\n        min_child_weight=trial.suggest_int(\"min_child_weight\", 1, 10),\n        colsample_bytree=trial.suggest_float(\"colsample_bytree\", 0.2, 1.0),\n        subsample=trial.suggest_float(\"subsample\", 0.2, 1.0),\n        reg_alpha=trial.suggest_float(\"reg_alpha\", 1e-4, 1e2, log=True),\n        reg_lambda=trial.suggest_float(\"reg_lambda\", 1e-4, 1e2, log=True),\n    )\n    xgb = XGBRegressor(**xgb_params)\n    return score_dataset(X_train, y_train, xgb)\n\nstudy = optuna.create_study(direction=\"minimize\")\nstudy.optimize(objective, n_trials=20)\nxgb_params = study.best_params\n```\n\nCopy this into a code cell if you'd like to use it, but be aware that it will take quite a while to run. After it's done, you might enjoy using some of [Optuna's visualizations](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/005_visualization.html).\n\n# Step 5 - Train Model and Create Submissions #\n\nOnce you're satisfied with everything, it's time to create your final predictions! This cell will:\n- create your feature set from the original data\n- train XGBoost on the training data\n- use the trained model to make predictions from the test set\n- save the predictions to a CSV file","metadata":{}},{"cell_type":"code","source":"X_train, X_test = create_features(df_train, df_test)\ny_train = df_train.loc[:, \"SalePrice\"]\n\nxgb = XGBRegressor(**xgb_params)\n# XGB minimizes MSE, but competition loss is RMSLE\n# So, we need to log-transform y to train and exp-transform the predictions\nxgb.fit(X_train, np.log(y))\npredictions = np.exp(xgb.predict(X_test))\n\noutput = pd.DataFrame({'Id': X_test.index, 'SalePrice': predictions})\noutput.to_csv('my_submission.csv', index=False)\nprint(\"Your submission was successfully saved!\")","metadata":{"execution":{"iopub.status.busy":"2024-06-20T08:07:45.717091Z","iopub.execute_input":"2024-06-20T08:07:45.717468Z","iopub.status.idle":"2024-06-20T08:07:56.521081Z","shell.execute_reply.started":"2024-06-20T08:07:45.717438Z","shell.execute_reply":"2024-06-20T08:07:56.520365Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"Your submission was successfully saved!\n","output_type":"stream"}]},{"cell_type":"markdown","source":"To submit these predictions to the competition, follow these steps:\n\n1. Begin by clicking on the blue **Save Version** button in the top right corner of the window.  This will generate a pop-up window.\n2. Ensure that the **Save and Run All** option is selected, and then click on the blue **Save** button.\n3. This generates a window in the bottom left corner of the notebook.  After it has finished running, click on the number to the right of the **Save Version** button.  This pulls up a list of versions on the right of the screen.  Click on the ellipsis **(...)** to the right of the most recent version, and select **Open in Viewer**.  This brings you into view mode of the same page. You will need to scroll down to get back to these instructions.\n4. Click on the **Output** tab on the right of the screen.  Then, click on the file you would like to submit, and click on the blue **Submit** button to submit your results to the leaderboard.\n\nYou have now successfully submitted to the competition!\n\n# Next Steps #\n\nIf you want to keep working to improve your performance, select the blue **Edit** button in the top right of the screen. Then you can change your code and repeat the process. There's a lot of room to improve, and you will climb up the leaderboard as you work.\n\nBe sure to check out [other users' notebooks](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/notebooks) in this competition. You'll find lots of great ideas for new features and as well as other ways to discover more things about the dataset or make better predictions. There's also the [discussion forum](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/discussion), where you can share ideas with other Kagglers.\n\nHave fun Kaggling!","metadata":{}},{"cell_type":"markdown","source":"---\n\n\n\n\n*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/feature-engineering/discussion) to chat with other learners.*","metadata":{}}]}