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   "source": [
    "# Regression Example With Boston Dataset: Standardized and Wider\n",
    "from pandas import read_csv\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "# load dataset\n",
    "file=\"https://raw.githubusercontent.com/masterfloss/data/main/housing.csv\"\n",
    "dataframe = read_csv(file, delim_whitespace=True, header=None)\n",
    "dataset = dataframe.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into input (X) and output (Y) variables\n",
    "X = dataset[:,0:13]\n",
    "Y = dataset[:,13]\n",
    "# define wider model\n",
    "def wider_model():\n",
    "        # create model\n",
    "        model = Sequential()\n",
    "        model.add(Dense(20, input_dim=13, kernel_initializer='normal', activation='relu'))\n",
    "        model.add(Dense(1, kernel_initializer='normal'))\n",
    "        # Compile model\n",
    "        model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "        return model\n",
    "# evaluate model with standardized dataset\n",
    "estimators = []\n",
    "estimators.append(('standardize', StandardScaler()))\n",
    "estimators.append(('mlp', KerasRegressor(build_fn=wider_model, epochs=100, batch_size=5, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "kfold = KFold(n_splits=10)\n",
    "results = cross_val_score(pipeline, X, Y, cv=kfold)\n",
    "print(\"Wider: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
   ]
  }
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