NN_Vs_TreeBasedModels.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"XQyE95EYY831"},"source":["##  Neural Networks vs. Tree-Based Models for Tabular Data\n","\n","\n","### Abstract: \n","Implementation of three different models including a simple neural network with a single hidden layer, XGBoost and a random forest. The data is taken from the Kaggle and can be found here: https://www.kaggle.com/c/house-prices-advanced-regression-techniques. The dataset comprises of about 1461 observations and 81 features in the train data and 1459 observations and 80 features in the test data. Will be predicting the SalePrice of the houses. The test data does not incldue the labels.  "]},{"cell_type":"markdown","source":["## Getting and Understanding the Data"],"metadata":{"id":"vuosK_bAbKf_"}},{"cell_type":"markdown","source":["Installing and importing required libraries"],"metadata":{"id":"6VP8WW1Uesoh"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"thfjtpLdXNKW","outputId":"9cbe0211-9cdd-4616-f06e-0bd210972597"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting matplotlib_inline\n","  Downloading matplotlib_inline-0.1.6-py3-none-any.whl (9.4 kB)\n","Requirement already satisfied: traitlets in /usr/local/lib/python3.7/dist-packages (from matplotlib_inline) (5.1.1)\n","Installing collected packages: matplotlib-inline\n","Successfully installed matplotlib-inline-0.1.6\n"]}],"source":["! pip install matplotlib_inline"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"PKIbIqS7uBdN","outputId":"d66185d7-3802-404c-c34d-91079e74e26c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting d2l\n","  Downloading d2l-0.17.5-py3-none-any.whl (82 kB)\n","\u001b[K     |████████████████████████████████| 82 kB 457 kB/s \n","\u001b[?25hCollecting jupyter==1.0.0\n","  Downloading jupyter-1.0.0-py2.py3-none-any.whl (2.7 kB)\n","Collecting pandas==1.2.4\n","  Downloading pandas-1.2.4-cp37-cp37m-manylinux1_x86_64.whl (9.9 MB)\n","\u001b[K     |████████████████████████████████| 9.9 MB 25.1 MB/s \n","\u001b[?25hCollecting numpy==1.21.5\n","  Downloading numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)\n","\u001b[K     |████████████████████████████████| 15.7 MB 50.0 MB/s 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bleach->nbconvert->jupyter==1.0.0->d2l) (0.5.1)\n","Collecting qtpy>=2.0.1\n","  Downloading QtPy-2.2.1-py3-none-any.whl (82 kB)\n","\u001b[K     |████████████████████████████████| 82 kB 768 kB/s \n","\u001b[?25hInstalling collected packages: jedi, qtpy, qtconsole, numpy, fonttools, requests, pandas, matplotlib, jupyter, d2l\n","  Attempting uninstall: numpy\n","    Found existing installation: numpy 1.21.6\n","    Uninstalling numpy-1.21.6:\n","      Successfully uninstalled numpy-1.21.6\n","  Attempting uninstall: requests\n","    Found existing installation: requests 2.23.0\n","    Uninstalling requests-2.23.0:\n","      Successfully uninstalled requests-2.23.0\n","  Attempting uninstall: pandas\n","    Found existing installation: pandas 1.3.5\n","    Uninstalling pandas-1.3.5:\n","      Successfully uninstalled pandas-1.3.5\n","  Attempting uninstall: matplotlib\n","    Found existing installation: matplotlib 3.2.2\n","    Uninstalling matplotlib-3.2.2:\n","      Successfully uninstalled matplotlib-3.2.2\n","Successfully installed d2l-0.17.5 fonttools-4.37.4 jedi-0.18.1 jupyter-1.0.0 matplotlib-3.5.1 numpy-1.21.5 pandas-1.2.4 qtconsole-5.3.2 qtpy-2.2.1 requests-2.25.1\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["matplotlib","mpl_toolkits","numpy","pandas"]}}},"metadata":{}}],"source":["!pip install -U d2l"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":653},"id":"YlATXF22jQE2","outputId":"f95f8688-5b02-4374-cd6b-02e5c5710faa"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found existing installation: matplotlib 3.5.3\n","Uninstalling matplotlib-3.5.3:\n","  Would remove:\n","    /usr/local/lib/python3.7/dist-packages/matplotlib-3.5.3-py3.7-nspkg.pth\n","    /usr/local/lib/python3.7/dist-packages/matplotlib-3.5.3.dist-info/*\n","    /usr/local/lib/python3.7/dist-packages/matplotlib/*\n","    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axes_grid/*\n","    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axes_grid1/*\n","    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/axisartist/*\n","    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/mplot3d/*\n","    /usr/local/lib/python3.7/dist-packages/mpl_toolkits/tests/*\n","    /usr/local/lib/python3.7/dist-packages/pylab.py\n","Proceed (y/n)?   Successfully uninstalled matplotlib-3.5.3\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting matplotlib\n","  Using cached matplotlib-3.5.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (11.2 MB)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (7.1.2)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.4)\n","Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.5)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (21.3)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (4.37.4)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.1.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7->matplotlib) (1.15.0)\n","Installing collected packages: matplotlib\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","d2l 0.17.5 requires matplotlib==3.5.1, but you have matplotlib 3.5.3 which is incompatible.\u001b[0m\n","Successfully installed matplotlib-3.5.3\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["matplotlib","mpl_toolkits"]}}},"metadata":{}}],"source":["!printf 'y\\n' | pip uninstall matplotlib\n","!pip install --upgrade matplotlib"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7-z6mdAvZAgO"},"outputs":[],"source":["import hashlib\n","import os\n","import tarfile\n","import zipfile\n","import requests\n","\n","%matplotlib inline\n","import numpy as np\n","import pandas as pd\n","import torch\n","from torch import nn\n","\n","pd.options.display.max_rows = 999\n","pd.options.display.max_columns = 999\n","\n","\n","DATA_HUB = dict()\n","DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UZNoLPD5XGYd"},"outputs":[],"source":["from d2l import torch as d2l"]},{"cell_type":"markdown","source":["Downloading housing price prediction data using a user-defined function."],"metadata":{"id":"7zB0u6kmfQtr"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"MW_rhz-KbDLE"},"outputs":[],"source":["def download(name, cache_dir=os.path.join('..', 'data')): \n","    \"\"\"Download a file inserted into DATA_HUB, return the local filename.\"\"\"\n","    # check to see if the file has been downloaded\n","    assert name in DATA_HUB, f\"{name} does not exist in {DATA_HUB}.\"\n","    # if name in DATA_HUB get the associated tuple\n","    url, sha1_hash = DATA_HUB[name]\n","    # make a new dir if none exists otherwise, do nothing\n","    os.makedirs(cache_dir, exist_ok=True)\n","    #grab the filename off the url, join to dir and assign to fname\n","    fname = os.path.join(cache_dir, url.split('/')[-1])\n","    # check to see if the file is there\n","    if os.path.exists(fname):\n","        # create a sha1 has object\n","        sha1 = hashlib.sha1()\n","        # open the file and read as bytes\n","        with open(fname, 'rb') as f:\n","            while True:\n","                # read in file upto 1mb\n","                data = f.read(1048576)\n","                # if no data then exit\n","                if not data:\n","                    break\n","                # creates a sha1 has\n","                sha1.update(data)\n","        # check to see if there's a match\n","        if sha1.hexdigest() == sha1_hash: \n","            return fname  # Hit cache\n","    # notify of download\n","    print(f'Downloading {fname} from {url}...')\n","    # get the data\n","    r = requests.get(url, stream=True, verify=True)\n","    with open(fname, 'wb') as f:\n","        f.write(r.content)\n","        \n","    return fname\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"r5SOguEjoGc_"},"outputs":[],"source":["def download_extract(name, folder=None):\n","    \"\"\"Download and extract a zip/tar file.\"\"\"\n","    # call the above download function\n","    fname = download(name)\n","    # get the directory where the file is located\n","    base_dir = os.path.dirname(fname)\n","    # get dir and fname without extension\n","    data_dir, ext = os.path.splitext(fname)\n","    # test if ext is zip\n","    if ext == '.zip':\n","        #if it is then unzip\n","        fp = zipfile.ZipFile(fname, 'r')\n","    # test if .tar or .gz\n","    elif ext in ('.tar', '.gz'):\n","        #if it is then open\n","        fp = tarfile.open(fname, 'r')\n","    else:\n","        assert False, 'Only zip/tar files can be extracted.'\n","    # extract all the files\n","    fp.extractall(base_dir)\n","    return os.path.join(base_dir, folder) if folder else data_dir\n","\n","def download_all(): \n","    \"\"\"Download all files in the DATA_HUB.\"\"\"\n","    for name in DATA_HUB:\n","        download(name)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BTACRlMkouTs"},"outputs":[],"source":["DATA_HUB['kaggle_house_train'] = (DATA_URL + 'kaggle_house_pred_train.csv',\n","'585e9cc93e70b39160e7921475f9bcd7d31219ce')\n","DATA_HUB['kaggle_house_test'] = ( DATA_URL + 'kaggle_house_pred_test.csv',\n","'fa19780a7b011d9b009e8bff8e99922a8ee2eb90')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WWqTn4zNxRWy"},"outputs":[],"source":["df_tr = pd.read_csv(download('kaggle_house_train'))\n","df_ts = pd.read_csv(download('kaggle_house_test'))"]},{"cell_type":"markdown","source":["Understanding the downloaded data."],"metadata":{"id":"WApSLG3azaHi"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"-09DeZncxVWq","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9b75533e-6abf-4084-f097-f7e4feb88c27"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1460, 81)"]},"metadata":{},"execution_count":7}],"source":["df_tr.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_trZ3kIRxXFG","colab":{"base_uri":"https://localhost:8080/"},"outputId":"7b11a510-bd8d-400d-8770-7c20c753754d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1459, 80)"]},"metadata":{},"execution_count":8}],"source":["df_ts.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pXojFK2pxYxb","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5e87a300-29be-4757-e24d-d3c6f22a9333"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n","       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n","       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n","       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n","       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n","       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n","       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n","       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n","       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n","       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n","       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n","       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n","       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n","       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n","       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n","       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n","       'SaleCondition', 'SalePrice'],\n","      dtype='object')"]},"metadata":{},"execution_count":9}],"source":["df_tr.columns"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sv0nn6uFKBi0","colab":{"base_uri":"https://localhost:8080/"},"outputId":"066ee8cf-1ab6-42e8-8573-430a7c04b5af"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n","       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n","       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n","       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n","       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n","       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n","       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n","       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n","       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n","       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n","       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n","       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n","       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n","       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n","       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n","       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n","       'SaleCondition'],\n","      dtype='object')"]},"metadata":{},"execution_count":10}],"source":["df_ts.columns"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"APBBFWAbD5XL","colab":{"base_uri":"https://localhost:8080/","height":145},"outputId":"0fffa0ca-7ea1-4e06-81de-4129475e08a2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n","0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n","\n","  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n","0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n","\n","  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n","0       Norm     1Fam     2Story            7            5       2003   \n","\n","   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n","0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n","\n","   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n","0       196.0        Gd        TA      PConc       Gd       TA           No   \n","\n","  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n","0          GLQ         706          Unf           0        150          856   \n","\n","  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n","0    GasA        Ex          Y      SBrkr       856       854             0   \n","\n","   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n","0       1710             1             0         2         1             3   \n","\n","   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n","0             1          Gd             8        Typ           0         NaN   \n","\n","  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n","0     Attchd       2003.0          RFn           2         548         TA   \n","\n","  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n","0         TA          Y           0           61              0          0   \n","\n","   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n","0            0         0    NaN   NaN         NaN        0       2    2008   \n","\n","  SaleType SaleCondition  SalePrice  \n","0       WD        Normal     208500  "],"text/html":["\n","  <div id=\"df-ac1fb633-af65-46b4-abf5-3f231b7f10d8\">\n","    <div class=\"colab-df-container\">\n","      <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","      <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>Ex</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","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac1fb633-af65-46b4-abf5-3f231b7f10d8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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       60       RL         65.0       WD        Normal     208500\n","1   2          20       RL         80.0       WD        Normal     181500\n","2   3          60       RL         68.0       WD        Normal     223500\n","3   4          70       RL         60.0       WD       Abnorml     140000"],"text/html":["\n","  <div id=\"df-1f24b567-8782-4099-9533-296328d320b9\">\n","    <div class=\"colab-df-container\">\n","      <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>SaleType</th>\n","      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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-1f24b567-8782-4099-9533-296328d320b9');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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The categorical features will be one-hot encoded using get_dummies method. This pre-processed data will be used for all the models built."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AtH9-ISPJYjo"},"outputs":[],"source":["# get all numeric features\n","index_num_features = df_all.dtypes[df_all.dtypes != 'object'].index"]},{"cell_type":"markdown","metadata":{"id":"CN1RVcyNK9a_"},"source":["We standardize the numerical data by subtracting the mean from the values and dividing by the standard deviation:  $\\frac{x - \\bar{x}}{\\sigma}$"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6yRVWgYvJqsA"},"outputs":[],"source":["# standardize the numeric data  \n","df_all[index_num_features] = df_all[index_num_features].apply(lambda x: (x - x.mean())/(x.std()))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c5mM4HD4Kf4o"},"outputs":[],"source":["# fill in the na's with 0\n","df_all[index_num_features] = df_all[index_num_features].fillna(0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0pz_Qft6Kh_S","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b999e352-46ca-483b-8bed-3e5d5072b4de"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2919, 79)"]},"metadata":{},"execution_count":17}],"source":["df_all.shape"]},{"cell_type":"markdown","source":["Encoding the categorical columns."],"metadata":{"id":"GHm24WYv69YO"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"Tzbl6TgEPwZ7"},"outputs":[],"source":["# convert categorical variables to dummies\n","df_all = pd.get_dummies(df_all, dummy_na=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xncnRNdgQMj0","colab":{"base_uri":"https://localhost:8080/"},"outputId":"bf62528f-c24d-4bc3-bc7f-6e3859ba2fcf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2919, 331)"]},"metadata":{},"execution_count":19}],"source":["df_all.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SYEwSKABQNwN"},"outputs":[],"source":["# get the training rows from the converted dataframe\n","int_train_rows = df_tr.shape[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"w85exkOWQwkl"},"outputs":[],"source":["# creates a torch matrix (1460, 331)\n","tensor_tr_features = (torch.tensor(df_all.iloc[:int_train_rows].values, dtype=torch.float32))\n","# creates a torch matrix (1459, 331)\n","tensor_ts_features = (torch.tensor(df_all.iloc[int_train_rows:].values, dtype=torch.float32))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xwHeUlOGRN5Q"},"outputs":[],"source":["# reshapes the training data salesprince from (1460,) to (1460,1)\n","tensor_tr_labels = torch.tensor(df_tr.SalePrice.values.reshape(-1,1), dtype=torch.float32)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RfDlqgCY-Sgc","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f3af7fd6-042b-447b-8d45-9395ac7290a2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1460, 331])"]},"metadata":{},"execution_count":23}],"source":["tensor_tr_features.shape"]},{"cell_type":"markdown","metadata":{"id":"TB6p4jKzT_Tk"},"source":["## Model 1: Neural Network\n","\n","Here we will be doing the following:\n","\n","-  Creating functions to define the neural net, the loss function, and train.\n","- We use a simple neural network with just one hidden layer and we use ReLu as the activation function. \n","- The loss that is used is the log of root mean squared error. "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x9bfP0zVSHBQ"},"outputs":[],"source":["# define the loss function\n","loss = nn.MSELoss()\n","# get the number of features in the training data\n","int_tr_features = tensor_tr_features.shape[1]\n","\n","def get_net():\n","    net = nn.Sequential(nn.Linear(int_tr_features, 64),\n","                        nn.ReLU(),\n","                        # output layer\n","                        nn.Linear(64, 1)\n","                        )\n","    return net\n","\n","# def get_net():\n","#     net = nn.Sequential(nn.Linear(int_tr_features, 64),\n","#                         nn.ReLU(),\n","#                         nn.Linear(64,  32),\n","#                         nn.ReLU(),\n","#                         # output layer\n","#                         nn.Linear(32, 1)\n","#                         )\n","#     return net\n","\n","\n","# def get_net():\n","#     net = nn.Sequential(nn.Linear(int_tr_features, 1))\n","#     return net"]},{"cell_type":"markdown","metadata":{"id":"jsNbnGwerebs"},"source":["https://github.com/christianversloot/machine-learning-articles/blob/main/how-to-create-a-neural-network-for-regression-with-pytorch.md"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LnLPg6XsT6Ce"},"outputs":[],"source":["def log_rmse(net, features, labels):\n","    # To further stablize the value when the logarithm is taken, set\n","    # the value less than 1 as 1\n","    clipped_preds = torch.clamp(net(features), 1, float('inf'))\n","    rmse = torch.sqrt(loss(torch.log(clipped_preds), torch.log(labels)))\n","    return rmse.item()"]},{"cell_type":"markdown","source":["Here the Adam optimizer method from the torch.optim class is used to optimize the weights and biases of the neural network. This function returns the train and the test losses."],"metadata":{"id":"61exz3uR3i_b"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"svHjXshKT8sz"},"outputs":[],"source":["def train(net, train_features, train_labels, test_features, test_labels,\n","          num_epochs, learning_rate, weight_deacy, batch_size):\n","    train_ls, test_ls = [], []\n","    train_iter = d2l.load_array((train_features, train_labels),batch_size)\n","    # the Adam optimization is used here\n","    optimizer = torch.optim.Adam(net.parameters(), lr= learning_rate,\n","                                 weight_decay=weight_deacy)\n","    \n","    # iterate through the epochs\n","    for epoch in range(num_epochs):\n","        for X, y in train_iter:\n","            optimizer.zero_grad()\n","            l = loss(net(X), y)\n","            l.backward()\n","            optimizer.step()\n","        train_ls.append(log_rmse(net, train_features, train_labels))\n","        if test_labels is not None:\n","            test_ls.append(log_rmse(net, test_features, test_labels))\n","    return train_ls, test_ls"]},{"cell_type":"markdown","metadata":{"id":"DaYhyJRYc9nD"},"source":["#### K-Fold Cross-Validation for the neural network model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rlZwyDmOYNSl"},"outputs":[],"source":["def get_k_fold_data(k, i, X, y):\n","    assert k > 1\n","    # calcualte the size of the fold\n","    fold_size = X.shape[0] // k\n","    X_train, y_train = None, None\n","    for j in range(k):\n","        idx = slice(j * fold_size, (j + 1) * fold_size)\n","        X_part, y_part = X[idx, :], y[idx]\n","        if j == i:\n","            X_valid, y_valid = X_part, y_part\n","        elif X_train is None:\n","            X_train, y_train = X_part, y_part\n","        else:\n","            X_train = torch.cat([X_train, X_part], 0)\n","            y_train = torch.cat([y_train, y_part], 0)\n","    return X_train, y_train, X_valid, y_valid"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9QsV3Paucu6W"},"outputs":[],"source":["def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay,\n","           batch_size):\n","    train_l_sum, valid_l_sum = 0, 0\n","    for i in range(k):\n","        data = get_k_fold_data(k, i, X_train, y_train)\n","        net = get_net()\n","        train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,\n","                                   weight_decay, batch_size)\n","        train_l_sum += train_ls[-1]\n","        valid_l_sum += valid_ls[-1]\n","        if i == 0:\n","            d2l.plot(list(range(1, num_epochs + 1)), [train_ls, valid_ls],\n","                     xlabel='epoch', ylabel='rmse', xlim=[1, num_epochs],\n","                     legend=['train', 'valid'], yscale='log')\n","        print(f'fold {i + 1}, train log rmse {float(train_ls[-1]):f}, '\n","              f'valid log rmse {float(valid_ls[-1]):f}'\n","              )\n","    return train_l_sum / k, valid_l_sum / k"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"O68seBV6dnNj","colab":{"base_uri":"https://localhost:8080/","height":371},"outputId":"863aad06-62c3-4421-df62-e90386304fa1"},"outputs":[{"output_type":"stream","name":"stdout","text":["fold 1, train log rmse 0.055959, valid log rmse 0.151505\n","fold 2, train log rmse 0.112516, valid log rmse 0.227065\n","fold 3, train log rmse 0.067134, valid log rmse 0.155686\n","fold 4, train log rmse 0.095724, valid log rmse 0.131398\n","fold 5, train log rmse 0.069944, valid log rmse 0.159429\n","5-fold validation: avg train log rmse: 0.080255, avg valid log rmse: 0.165016\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 252x180 with 1 Axes>"],"image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"263.421875pt\" height=\"180.65625pt\" viewBox=\"0 0 263.421875 180.65625\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2022-10-12T16:18:46.668629</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.5.3, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linejoin: round; 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We will be comparing this score with the log rmse scores from the other models.  "],"metadata":{"id":"dGJpuFVK395t"}},{"cell_type":"markdown","source":["## Model 2: XGBoost with Sci-kit Learn"],"metadata":{"id":"XPOcBUT31ttA"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"f2WSWi6rb620"},"outputs":[],"source":["# importing required libraries\n","import xgboost as xgb\n","from sklearn.metrics import auc, accuracy_score, confusion_matrix, mean_squared_error, mean_squared_log_error\n","from sklearn.model_selection import cross_val_score, GridSearchCV, KFold, RandomizedSearchCV, train_test_split"]},{"cell_type":"code","source":["type(df_all)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g3e6PGAIzY5b","outputId":"39f78652-7626-49bb-ad36-4ea5e25f70aa"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pandas.core.frame.DataFrame"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["n_train = df_tr.shape[0]\n","n_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VHVWEuVz2hP9","outputId":"821a8fed-17a0-47ae-a75b-2fc8b21edb03"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1460"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","source":["# Creating training and test data variables\n","X_train = df_all[:n_train]\n","X_train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_JSRtSc_2BxI","outputId":"1b394fdb-16e9-4796-c626-7fef96256fc0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1460, 331)"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","source":["X_test = df_all[n_train:]\n","X_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tf5YqqJZ2S1R","outputId":"f8b84024-a19a-4878-ee78-8f63c23e3b33"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1459, 331)"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","source":["Y_train = df_tr['SalePrice']\n","Y_train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"18eHHmvh4Tra","outputId":"92462dcf-9ff1-45c9-fcd4-c0004dd7d3a8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1460,)"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["Y_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"U-hHpQCk4Ysp","outputId":"04c80eec-df53-4a8f-b3d4-e1c55b1229a0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0       208500\n","1       181500\n","2       223500\n","3       140000\n","4       250000\n","         ...  \n","1455    175000\n","1456    210000\n","1457    266500\n","1458    142125\n","1459    147500\n","Name: SalePrice, Length: 1460, dtype: int64"]},"metadata":{},"execution_count":53}]},{"cell_type":"markdown","source":["Splitting the train data into train and validation sets as we do not have the labels for the test data here. \n"],"metadata":{"id":"T0YkVeufL_1A"}},{"cell_type":"code","source":["x_train, x_val, y_train, y_val = train_test_split(X_train, Y_train, test_size=0.20, random_state=42) "],"metadata":{"id":"5r20nB6PL_O2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def XGBoost(xtrain,ytrain,xtest):\n","  xgb_model = xgb.XGBRegressor(objective=\"reg:squarederror\", random_state=42)\n","  xgb_model.fit(xtrain, ytrain)\n","  y_pred = xgb_model.predict(xtest)\n","  return y_pred"],"metadata":{"id":"iaQ05vU64bc2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_pred_train = XGBoost(x_train, y_train, x_train)\n","y_pred_val = XGBoost(x_train,y_train, x_val)"],"metadata":{"id":"Hlj8NDjd9dJk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_pred_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"C8j5g8gB4iG_","outputId":"752662b9-1377-4ffe-892c-bd2489b8a0d5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([143299.06 , 176250.92 ,  97041.22 , ..., 123951.664, 171698.94 ,\n","       189654.45 ], dtype=float32)"]},"metadata":{},"execution_count":63}]},{"cell_type":"markdown","source":["Log root mean squared error is calculated using the mean_squared_log_error of scikit-learn metrics class."],"metadata":{"id":"u8tnNuHk_-Ul"}},{"cell_type":"code","source":["lmse_train = mean_squared_log_error(y_train, y_pred_train)\n","print(lmse_train)\n","lmse_val = mean_squared_log_error(y_val, y_pred_val)\n","print(lmse_val)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u__jnoEB_xM9","outputId":"552807bd-07ef-4462-df9a-377b6834f2f2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.00781888387407571\n","0.020498468539549463\n"]}]},{"cell_type":"markdown","source":["#### Cross-validation using the GridSearchCV method.\n","\n","The best model parameters are chosen and the best model is used to find the log mean squared error. "],"metadata":{"id":"_inBYSf7AegV"}},{"cell_type":"code","source":["xgb1 = xgb.XGBRegressor()\n","parameters = {'nthread':[4], #when use hyperthread, xgboost may become slower\n","              'objective':['reg:linear'],\n","              'learning_rate': [.03, 0.05, .07], #so called `eta` value\n","              'max_depth': [5, 6, 7],\n","              'min_child_weight': [4],\n","              'silent': [1],\n","              'subsample': [0.7],\n","              'colsample_bytree': [0.7],\n","              'n_estimators': [500]}\n","\n","xgb_grid = GridSearchCV(xgb1,\n","                        parameters,\n","                        cv = 2,\n","                        n_jobs = 5,\n","                        verbose=True, \n","                        )\n","#scoring = 'neg_mean_squared_log_error'\n","xgb_grid.fit(x_train,\n","         y_train)\n","\n","print(xgb_grid.best_score_)\n","print(xgb_grid.best_params_)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"niC5YnxQ45M6","outputId":"9a0fa9d7-e9e0-451f-c33c-6167f240ec06"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 2 folds for each of 9 candidates, totalling 18 fits\n","0.8562079131440541\n","{'colsample_bytree': 0.7, 'learning_rate': 0.03, 'max_depth': 7, 'min_child_weight': 4, 'n_estimators': 500, 'nthread': 4, 'objective': 'reg:linear', 'silent': 1, 'subsample': 0.7}\n"]}]},{"cell_type":"code","source":["lmse_train_best = mean_squared_log_error(y_train, xgb_grid.best_estimator_.predict(x_train))\n","lmse_val_best =  mean_squared_log_error(y_val, xgb_grid.best_estimator_.predict(x_val))\n","\n","print(\"Log RMSE score on the train data for the best model:\", round(lmse_train_best,5))\n","print(\"Log RMSE score on the validation data for the best model:\", round(lmse_val_best,5))"],"metadata":{"id":"g_BAZbAg5AV9","colab":{"base_uri":"https://localhost:8080/"},"outputId":"361830b5-3a25-4f43-cc1e-30f4e15d7084"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Log RMSE score on the train data for the best model: 0.00116\n","Log RMSE score on the validation data for the best model: 0.01854\n"]}]},{"cell_type":"markdown","source":["The log root mean squared error on the train data is 0.00116  and validation data is 0.01854 for XGBoost."],"metadata":{"id":"JmXdTvt_C3Ct"}},{"cell_type":"markdown","source":["## Model 3: Random Forest with Sci-kit Learn"],"metadata":{"id":"g2DZg8FR57NQ"}},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestRegressor\n","rf = RandomForestRegressor(random_state = 42)\n","rf = RandomForestRegressor()"],"metadata":{"id":"2JKLf7Bi5_F1"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We will use the same train and validation data used in the XGBoost case."],"metadata":{"id":"xDPUfyhUVOJr"}},{"cell_type":"code","source":["rf.fit(x_train, y_train)\n","y_pred_train = rf.predict(x_train)\n","lmse = mean_squared_log_error(y_train, y_pred_train)\n","lmse"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eZAhYUWz-jbs","outputId":"5470a0e1-b38d-4efa-88c4-47764b557777"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0035976425593957874"]},"metadata":{},"execution_count":74}]},{"cell_type":"markdown","source":["We perform the cross-validation using GridSearchCV method to find the best model parameters."],"metadata":{"id":"6pp822W9E71e"}},{"cell_type":"code","source":["# Create the parameter grid based on the results of random search \n","param_grid = {\n","    'bootstrap': [True],\n","    'max_depth': [100, 105, 110, 115,],\n","    'max_features': [2, 3],\n","    'min_samples_leaf': [3, 4, 5],\n","    'min_samples_split': [8, 10, 12],\n","    'n_estimators': [100, 200, 300, 1000]\n","}"],"metadata":{"id":"rpLDLz4A6KTo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["rf_grid = GridSearchCV(estimator = rf, \n","                           param_grid = param_grid, \n","                           cv = 3, \n","                           n_jobs = -1,\n","                           verbose = 2)"],"metadata":{"id":"2wlfy96X6Onr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Fit the grid search to the data\n","rf_grid.fit(x_train,y_train)\n","rf_grid.best_params_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"viNO9UXj6jnV","outputId":"0de9e0b3-6890-43e0-ac80-f8a49b9d2578"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 3 folds for each of 288 candidates, totalling 864 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["{'bootstrap': True,\n"," 'max_depth': 110,\n"," 'max_features': 3,\n"," 'min_samples_leaf': 3,\n"," 'min_samples_split': 12,\n"," 'n_estimators': 100}"]},"metadata":{},"execution_count":75}]},{"cell_type":"code","source":["lmse_best_train = mean_squared_log_error(y_train, rf_grid.best_estimator_.predict(x_train))\n","lmse_best_val = mean_squared_log_error(y_val, rf_grid.best_estimator_.predict(x_val))"],"metadata":{"id":"VXBkgza960sX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(\"Log RMSE score on the train data for the best model:\", round(lmse_best_train,6))\n","print(\"Log RMSE score on the validation data for the best model:\", round(lmse_best_val,6))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZXku9YZJYG4p","outputId":"65fcfd06-85c3-49ec-fb2b-009ecc2baff7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Log RMSE score on the train data for the best model: 0.049034\n","Log RMSE score on the validation data for the best model: 0.072599\n"]}]},{"cell_type":"markdown","source":["The log rmse score for the best random forest model is 0.043356 for the train data and 0.0726 for the validation data.\n"],"metadata":{"id":"RW7NcL_kLWsU"}},{"cell_type":"markdown","source":["The log rmse scores obtained from the tree models for the train data are:\n","\n","1. Neural network: 0.080255\n","2. XGBoost: 0.00116\n","3. Random Forest: 0.049034\n","\n","The log rmse scores obtained from the tree models for the validation data are:\n","\n","1. Neural network: 0.165016\n","2. XGBoost: 0.01854\n","3. Random Forest: 0.072599\n","\n","Based on the train and the validation scores, we can see that the tree-based models such as Random forest and XGBoost does better than a simple neural network with a single hidden layer. In this case, XGBoost performs the best with a validation log rmse score of 0.01854. "],"metadata":{"id":"7bQ-u8ktIVJX"}}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}