08-final-machine-learning-pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Machine Learning Pipeline\n",
    "\n",
    "The pipeline features\n",
    "\n",
    "- open source classes\n",
    "- in house package classes\n",
    "- only uses the selected features\n",
    "- we score new data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducibility: Setting the seed\n",
    "\n",
    "With the aim to ensure reproducibility between runs of the same notebook, but also between the research and production environment, for each step that includes some element of randomness, it is extremely important that we **set the seed**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data manipulation and plotting\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for saving the pipeline\n",
    "import joblib\n",
    "\n",
    "# from Scikit-learn\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler, Binarizer\n",
    "\n",
    "# from feature-engine\n",
    "from feature_engine.imputation import (\n",
    "    AddMissingIndicator,\n",
    "    MeanMedianImputer,\n",
    "    CategoricalImputer,\n",
    ")\n",
    "\n",
    "from feature_engine.encoding import (\n",
    "    RareLabelEncoder,\n",
    "    OrdinalEncoder,\n",
    ")\n",
    "\n",
    "from feature_engine.transformation import LogTransformer\n",
    "\n",
    "from feature_engine.selection import DropFeatures\n",
    "from feature_engine.wrappers import SklearnTransformerWrapper\n",
    "\n",
    "import preprocessors as pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataset\n",
    "data = pd.read_csv('train.csv')\n",
    "\n",
    "# rows and columns of the data\n",
    "print(data.shape)\n",
    "\n",
    "# visualise the dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cast MSSubClass as object\n",
    "\n",
    "data['MSSubClass'] = data['MSSubClass'].astype('O')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Separate dataset into train and test\n",
    "\n",
    "It is important to separate our data intro training and testing set. \n",
    "\n",
    "When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.\n",
    "\n",
    "Our feature engineering techniques will learn:\n",
    "\n",
    "- mean\n",
    "- mode\n",
    "- exponents for the yeo-johnson\n",
    "- category frequency\n",
    "- and category to number mappings\n",
    "\n",
    "from the train set.\n",
    "\n",
    "**Separating the data into train and test involves randomness, therefore, we need to set the seed.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1314, 79), (146, 79))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's separate into train and test set\n",
    "# Remember to set the seed (random_state for this sklearn function)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['Id', 'SalePrice'], axis=1), # predictive variables\n",
    "    data['SalePrice'], # target\n",
    "    test_size=0.1, # portion of dataset to allocate to test set\n",
    "    random_state=0, # we are setting the seed here\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target\n",
    "\n",
    "We apply the logarithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.log(y_train)\n",
    "y_test = np.log(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categorical variables with NA in train set\n",
    "CATEGORICAL_VARS_WITH_NA_FREQUENT = ['BsmtQual', 'BsmtExposure',\n",
    "                                     'BsmtFinType1', 'GarageFinish']\n",
    "\n",
    "\n",
    "CATEGORICAL_VARS_WITH_NA_MISSING = ['FireplaceQu']\n",
    "\n",
    "\n",
    "# numerical variables with NA in train set\n",
    "NUMERICAL_VARS_WITH_NA = ['LotFrontage']\n",
    "\n",
    "\n",
    "TEMPORAL_VARS = ['YearRemodAdd']\n",
    "REF_VAR = \"YrSold\"\n",
    "\n",
    "# this variable is to calculate the temporal variable,\n",
    "# can be dropped afterwards\n",
    "DROP_FEATURES = [\"YrSold\"]\n",
    "\n",
    "# variables to log transform\n",
    "NUMERICALS_LOG_VARS = [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n",
    "\n",
    "\n",
    "# variables to binarize\n",
    "BINARIZE_VARS = ['ScreenPorch']\n",
    "\n",
    "# variables to map\n",
    "QUAL_VARS = ['ExterQual', 'BsmtQual',\n",
    "             'HeatingQC', 'KitchenQual', 'FireplaceQu']\n",
    "\n",
    "EXPOSURE_VARS = ['BsmtExposure']\n",
    "\n",
    "FINISH_VARS = ['BsmtFinType1']\n",
    "\n",
    "GARAGE_VARS = ['GarageFinish']\n",
    "\n",
    "FENCE_VARS = ['Fence']\n",
    "\n",
    "\n",
    "# categorical variables to encode\n",
    "CATEGORICAL_VARS = ['MSSubClass',  'MSZoning',  'LotShape',  'LandContour',\n",
    "                    'LotConfig', 'Neighborhood', 'RoofStyle', 'Exterior1st',\n",
    "                    'Foundation', 'CentralAir', 'Functional', 'PavedDrive',\n",
    "                    'SaleCondition']\n",
    "\n",
    "\n",
    "# variable mappings\n",
    "QUAL_MAPPINGS = {'Po': 1, 'Fa': 2, 'TA': 3,\n",
    "                 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n",
    "\n",
    "EXPOSURE_MAPPINGS = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n",
    "\n",
    "FINISH_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1,\n",
    "                   'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n",
    "\n",
    "GARAGE_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n",
    "\n",
    "\n",
    "# the selected variables\n",
    "FEATURES = [\n",
    "    'MSSubClass',\n",
    "    'MSZoning',\n",
    "    'LotFrontage',\n",
    "    'LotShape',\n",
    "    'LandContour',\n",
    "    'LotConfig',\n",
    "    'Neighborhood',\n",
    "    'OverallQual',\n",
    "    'OverallCond',\n",
    "    'YearRemodAdd',\n",
    "    'RoofStyle',\n",
    "    'Exterior1st',\n",
    "    'ExterQual',\n",
    "    'Foundation',\n",
    "    'BsmtQual',\n",
    "    'BsmtExposure',\n",
    "    'BsmtFinType1',\n",
    "    'HeatingQC',\n",
    "    'CentralAir',\n",
    "    '1stFlrSF',\n",
    "    '2ndFlrSF',\n",
    "    'GrLivArea',\n",
    "    'BsmtFullBath',\n",
    "    'HalfBath',\n",
    "    'KitchenQual',\n",
    "    'TotRmsAbvGrd',\n",
    "    'Functional',\n",
    "    'Fireplaces',\n",
    "    'FireplaceQu',\n",
    "    'GarageFinish',\n",
    "    'GarageCars',\n",
    "    'GarageArea',\n",
    "    'PavedDrive',\n",
    "    'WoodDeckSF',\n",
    "    'ScreenPorch',\n",
    "    'SaleCondition',\n",
    "    # this one is only to calculate temporal variable:\n",
    "    \"YrSold\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1314, 37), (146, 37))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train[FEATURES]\n",
    "X_test = X_test[FEATURES]\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline - End-to-end\n",
    "\n",
    "We have 3 steps less, they are commented out. So the pipeline is also simpler:\n",
    "\n",
    "- the yeo-johnson transformation\n",
    "- 1 of the mappings\n",
    "- the selection procedure\n",
    "\n",
    "this makes the pipeline faster and easier to deploy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the pipeline\n",
    "price_pipe = Pipeline([\n",
    "\n",
    "    # ===== IMPUTATION =====\n",
    "    # impute categorical variables with string missing\n",
    "    ('missing_imputation', CategoricalImputer(\n",
    "        imputation_method='missing', variables=CATEGORICAL_VARS_WITH_NA_MISSING)),\n",
    "\n",
    "    ('frequent_imputation', CategoricalImputer(\n",
    "        imputation_method='frequent', variables=CATEGORICAL_VARS_WITH_NA_FREQUENT)),\n",
    "\n",
    "    # add missing indicator\n",
    "    ('missing_indicator', AddMissingIndicator(variables=NUMERICAL_VARS_WITH_NA)),\n",
    "\n",
    "    # impute numerical variables with the mean\n",
    "    ('mean_imputation', MeanMedianImputer(\n",
    "        imputation_method='mean', variables=NUMERICAL_VARS_WITH_NA\n",
    "    )),\n",
    "    \n",
    "    \n",
    "    # == TEMPORAL VARIABLES ====\n",
    "    ('elapsed_time', pp.TemporalVariableTransformer(\n",
    "        variables=TEMPORAL_VARS, reference_variable=REF_VAR)),\n",
    "\n",
    "    ('drop_features', DropFeatures(features_to_drop=[REF_VAR])),\n",
    "\n",
    "   \n",
    "\n",
    "    # ==== VARIABLE TRANSFORMATION =====\n",
    "    ('log', LogTransformer(variables=NUMERICALS_LOG_VARS)),\n",
    "    \n",
    "#     ('yeojohnson', YeoJohnsonTransformer(variables=NUMERICALS_YEO_VARS)),\n",
    "    \n",
    "    ('binarizer', SklearnTransformerWrapper(\n",
    "        transformer=Binarizer(threshold=0), variables=BINARIZE_VARS)),\n",
    "    \n",
    "\n",
    "    # === mappers ===\n",
    "    ('mapper_qual', pp.Mapper(\n",
    "        variables=QUAL_VARS, mappings=QUAL_MAPPINGS)),\n",
    "\n",
    "    ('mapper_exposure', pp.Mapper(\n",
    "        variables=EXPOSURE_VARS, mappings=EXPOSURE_MAPPINGS)),\n",
    "\n",
    "    ('mapper_finish', pp.Mapper(\n",
    "        variables=FINISH_VARS, mappings=FINISH_MAPPINGS)),\n",
    "\n",
    "    ('mapper_garage', pp.Mapper(\n",
    "        variables=GARAGE_VARS, mappings=GARAGE_MAPPINGS)),\n",
    "    \n",
    "#     ('mapper_fence', pp.Mapper(\n",
    "#         variables=FENCE_VARS, mappings=FENCE_MAPPINGS)),\n",
    "\n",
    "\n",
    "    # == CATEGORICAL ENCODING\n",
    "    ('rare_label_encoder', RareLabelEncoder(\n",
    "        tol=0.01, n_categories=1, variables=CATEGORICAL_VARS\n",
    "    )),\n",
    "\n",
    "    # encode categorical and discrete variables using the target mean\n",
    "    ('categorical_encoder', OrdinalEncoder(\n",
    "        encoding_method='ordered', variables=CATEGORICAL_VARS)),\n",
    "    \n",
    "    \n",
    "    ('scaler', MinMaxScaler()),\n",
    "#     ('selector', SelectFromModel(Lasso(alpha=0.001, random_state=0))),\n",
    "    ('Lasso', Lasso(alpha=0.001, random_state=0)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('missing_imputation',\n",
       "                 CategoricalImputer(variables=['FireplaceQu'])),\n",
       "                ('frequent_imputation',\n",
       "                 CategoricalImputer(imputation_method='frequent',\n",
       "                                    variables=['BsmtQual', 'BsmtExposure',\n",
       "                                               'BsmtFinType1',\n",
       "                                               'GarageFinish'])),\n",
       "                ('missing_indicator',\n",
       "                 AddMissingIndicator(variables=['LotFrontage'])),\n",
       "                ('mean_imputation',\n",
       "                 MeanMedianImputer(imputation_method=...\n",
       "                                             'Foundation', 'CentralAir',\n",
       "                                             'Functional', 'PavedDrive',\n",
       "                                             'SaleCondition'])),\n",
       "                ('categorical_encoder',\n",
       "                 OrdinalEncoder(variables=['MSSubClass', 'MSZoning', 'LotShape',\n",
       "                                           'LandContour', 'LotConfig',\n",
       "                                           'Neighborhood', 'RoofStyle',\n",
       "                                           'Exterior1st', 'Foundation',\n",
       "                                           'CentralAir', 'Functional',\n",
       "                                           'PavedDrive', 'SaleCondition'])),\n",
       "                ('scaler', MinMaxScaler()),\n",
       "                ('Lasso', Lasso(alpha=0.001, random_state=0))])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the pipeline\n",
    "price_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train mse: 781396630\n",
      "train rmse: 27953\n",
      "train r2: 0.8748530315439074\n",
      "\n",
      "test mse: 1060769014\n",
      "test rmse: 32569\n",
      "test r2: 0.845641557120844\n",
      "\n",
      "Average house price:  163000\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model:\n",
    "# ====================\n",
    "\n",
    "# make predictions for train set\n",
    "pred = price_pipe.predict(X_train)\n",
    "\n",
    "# determine mse, rmse and r2\n",
    "print('train mse: {}'.format(int(\n",
    "    mean_squared_error(np.exp(y_train), np.exp(pred)))))\n",
    "print('train rmse: {}'.format(int(\n",
    "    mean_squared_error(np.exp(y_train), np.exp(pred), squared=False))))\n",
    "print('train r2: {}'.format(\n",
    "    r2_score(np.exp(y_train), np.exp(pred))))\n",
    "print()\n",
    "\n",
    "# make predictions for test set\n",
    "pred = price_pipe.predict(X_test)\n",
    "\n",
    "# determine mse, rmse and r2\n",
    "print('test mse: {}'.format(int(\n",
    "    mean_squared_error(np.exp(y_test), np.exp(pred)))))\n",
    "print('test rmse: {}'.format(int(\n",
    "    mean_squared_error(np.exp(y_test), np.exp(pred), squared=False))))\n",
    "print('test r2: {}'.format(\n",
    "    r2_score(np.exp(y_test), np.exp(pred))))\n",
    "print()\n",
    "\n",
    "print('Average house price: ', int(np.exp(y_train).median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identical results to when we did all the engineering manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Evaluation of Lasso Predictions')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's evaluate our predictions respect to the real sale price\n",
    "plt.scatter(y_test, price_pipe.predict(X_test))\n",
    "plt.xlabel('True House Price')\n",
    "plt.ylabel('Predicted House Price')\n",
    "plt.title('Evaluation of Lasso Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAATC0lEQVR4nO3df4zkdX3H8ee7/IgHi4A9HelBu9paGnuLlhstVWN3RZsTjGhiUglaaGk2ttHS5kjFmNY0jSm1xf5Im5iLXqCRsG0RlYJtpciWNAK6h8iCh6L2onfiXSlyuHgVz777x87VY25n57sz35n9fo7nI9nczHe+8/285ns7r/3ud2c+E5mJJKk8P7beASRJg7HAJalQFrgkFcoCl6RCWeCSVKjjxznYxo0bc3JycqRjPPnkk5x88skjHWNQTc4Gzc7X5GzQ7HxNzgbNzteUbDt37nw0M5971A2ZObavLVu25KjdcccdIx9jUE3OltnsfE3OltnsfE3OltnsfE3JBizkCp3qKRRJKpQFLkmFssAlqVAWuCQVygKXpEJZ4JJUqL4FHhE7ImJ/RDzQtfxdEfHliHgwIj4wuoiSpJVUOQK/Fth65IKImAEuAs7JzJ8H/rz+aJKk1fQt8My8E3isa/FvAVdn5vc76+wfQTZJ0ioiK3ygQ0RMArdk5ubO9fuAT7J8ZP4/wJWZ+fke950FZgFardaWubm5WoL3srS0xMTExEjHGFSTs8Fo8y3uPVBpvalNp664/Jm874bV5GzQ7HxNyTYzM7MzM9vdywedC+V44HTgPOBlwD9ExAtzhZ8Gmbkd2A7Qbrdzenp6wCGrmZ+fZ9RjDKrJ2WC0+S676tZK6+2+ZOXxn8n7blhNzgbNztfkbDD4q1D2ADd13qb/OeB/gY31xZIk9TNogX8CeA1ARPwscCLwaE2ZJEkV9D2FEhE3ANPAxojYA7wP2AHs6Ly08Cng0pVOn0iSRqdvgWfmxT1uelvNWSRJa+A7MSWpUBa4JBXKApekQlngklQoC1ySCmWBS1KhLHBJKtSgc6FIz0iTPeZ02TZ16Kj5XnZffeE4IukZzCNwSSqUBS5JhbLAJalQFrgkFcoCl6RCWeCSVCgLXJIKZYFLUqH6FnhE7IiI/Z1P3+m+7cqIyIjw8zAlacyqHIFfC2ztXhgRZwGvA75RcyZJUgV9Czwz7wQeW+GmvwB+H/CzMCVpHQx0Djwi3gjszcwv1pxHklRRVPkw+YiYBG7JzM0RcRJwB/ArmXkgInYD7cx8tMd9Z4FZgFartWVubq6u7CtaWlpiYmJipGMMqsnZYLB8i3sP1JphatOpKy5vyr7r9XhbG2Dfwacv6/VYxq0p+66XJudrSraZmZmdmdnuXj5IgU8BtwPf69x8JvAt4OWZ+e3VttNut3NhYWGt2ddkfn6e6enpkY4xqCZng8Hy9Zqdb1C9ZvBryr5bbTbCaxafPrlnU2YjbMq+66XJ+ZqSLSJWLPA1TyebmYvA847Y8G5WOQKXJI1GlZcR3gDcBZwdEXsi4vLRx5Ik9dP3CDwzL+5z+2RtaSRJlflOTEkqlAUuSYWywCWpUBa4JBXKApekQlngklQoC1ySCmWBS1KhLHBJKpQFLkmFssAlqVAWuCQVygKXpEJZ4JJUKAtckgplgUtSoSxwSSpUlY9U2xER+yPigSOW/VlEPBQR90fExyPitJGmlCQdpcoR+LXA1q5ltwGbM/Mc4CvAe2rOJUnqo2+BZ+adwGNdyz6dmYc6V+8GzhxBNknSKiIz+68UMQnckpmbV7jtn4C/z8yP9rjvLDAL0Gq1tszNzQ0VuJ+lpSUmJiZGOsagmpwNBsu3uPdArRmmNp264vKm7Ltej7e1AfYdfPqyXo9l3Jqy73ppcr6mZJuZmdmZme3u5X0/lX41EfFe4BBwfa91MnM7sB2g3W7n9PT0MEP2NT8/z6jHGFSTs8Fg+S676tZaM+y+ZOXxm7Lvej3ebVOHuGbx6U+nXo9l3Jqy73ppcr4mZ4MhCjwiLgXeAJyfVQ7jJUm1GqjAI2Ir8G7glzPze/VGkiRVUeVlhDcAdwFnR8SeiLgc+BvgFOC2iLgvIj404pySpC59j8Az8+IVFn9kBFkkSWvgOzElqVAWuCQVygKXpEJZ4JJUKAtckgplgUtSoSxwSSrUUHOhSOtlsuIcLLuvvrDW7UlN4hG4JBXKApekQlngklQoC1ySCmWBS1KhLHBJKpQFLkmFssAlqVAWuCQVqspHqu2IiP0R8cARy54TEbdFxMOdf08fbUxJUrcqR+DXAlu7ll0F3J6ZLwJu71yXJI1R3wLPzDuBx7oWXwRc17l8HfCmemNJkvqJzOy/UsQkcEtmbu5cfzwzTzvi9u9k5oqnUSJiFpgFaLVaW+bm5mqI3dvS0hITExMjHWNQTc4Gg+Vb3Hug1gxTm05dcXl3tqrj9tpet2EfR2sD7Ds42Nijdix+341LU7LNzMzszMx29/KRz0aYmduB7QDtdjunp6dHOt78/DyjHmNQTc4Gg+W7rOZZ/HZfsvL43dmqjttre92GfRzbpg5xzeLTn05Vxx61Y/H7blyanA0GfxXKvog4A6Dz7/76IkmSqhi0wG8GLu1cvhT4ZD1xJElVVXkZ4Q3AXcDZEbEnIi4HrgZeFxEPA6/rXJckjVHfc+CZeXGPm86vOYskaQ18J6YkFcoCl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYUa+Vwo0lpM9piTZNvUoYHmK+m1vXGoOvbuqy8ccRIdqzwCl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYWywCWpUBa4JBXKApekQg1V4BHxexHxYEQ8EBE3RMSz6gomSVrdwAUeEZuA3wHambkZOA54a13BJEmrG/YUyvHAhog4HjgJ+NbwkSRJVURmDn7niCuA9wMHgU9n5iUrrDMLzAK0Wq0tc3NzA49XxdLSEhMTEyMdY1DrnW1x74FVb29tgH0HxxRmjZqcDYbLN7Xp1HrDdFnv77t+mpyvKdlmZmZ2Zma7e/nABR4RpwMfA34VeBz4R+DGzPxor/u02+1cWFgYaLyq5ufnmZ6eHukYg1rvbP1mx9s2dYhrFps5QWWTs8Fw+UY9G+F6f9/10+R8TckWESsW+DCnUF4L/Gdm/ldm/gC4CXjFENuTJK3BMAX+DeC8iDgpIgI4H9hVTyxJUj8DF3hm3gPcCNwLLHa2tb2mXJKkPoY6qZiZ7wPeV1MWSdIa+E5MSSqUBS5JhbLAJalQFrgkFcoCl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYWywCWpUBa4JBXKApekQlngklQoC1ySCmWBS1KhLHBJKtRQBR4Rp0XEjRHxUETsiohfqiuYJGl1Q32kGvBXwL9k5lsi4kTgpBoySZIqGLjAI+LZwKuBywAy8yngqXpiSZL6icwc7I4RL2X5U+i/BLwE2AlckZlPdq03C8wCtFqtLXNzc8Pk7WtpaYmJiYmRjjGoUWVb3Huglu20NsC+g7VsqnZNzgbD5ZvadGq9Ybo0+TkBzc7XlGwzMzM7M7PdvXyYAm8DdwOvzMx7IuKvgCcy8w963afdbufCwsJA41U1Pz/P9PT0SMcY1KiyTV51ay3b2TZ1iGsWhz2rNhpNzgbD5dt99YU1p3m6Jj8noNn5mpItIlYs8GH+iLkH2JOZ93Su3wicO8T2JElrMHCBZ+a3gW9GxNmdReezfDpFkjQGw/5O+i7g+s4rUL4O/PrwkSRJVQxV4Jl5H3DUeRlJ0uj5TkxJKpQFLkmFssAlqVAWuCQVygKXpEJZ4JJUKAtckgrV3MkljmFV5y4Z9RwZksrmEbgkFcoCl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYWywCWpUBa4JBVq6AKPiOMi4gsRcUsdgSRJ1dRxBH4FsKuG7UiS1mCoAo+IM4ELgQ/XE0eSVFVk5uB3jrgR+BPgFODKzHzDCuvMArMArVZry9zc3MDjVbG0tMTExMRIxxjU4WyLew+sd5QVtTbAvoPrnWJlTc4Gw+Wb2nRqpfWqft90b6/Jzwlodr6mZJuZmdmZmUd9gPzAsxFGxBuA/Zm5MyKme62XmduB7QDtdjunp3uuWov5+XlGPcagDme7rOJshOO2beoQ1yw2c4LKJmeD4fLtvmS60npVv2+6t9fk5wQ0O1+Ts8Fwp1BeCbwxInYDc8BrIuKjtaSSJPU1cIFn5nsy88zMnATeCnwmM99WWzJJ0qp8HbgkFaqWk4qZOQ/M17EtSVI1HoFLUqEscEkqlAUuSYWywCWpUBa4JBXKApekQlngklSo5k4uIT1DTK7T3DhVx9199YUjTqJBeQQuSYWywCWpUBa4JBXKApekQlngklQoC1ySCmWBS1KhLHBJKpQFLkmFGrjAI+KsiLgjInZFxIMRcUWdwSRJqxvmrfSHgG2ZeW9EnALsjIjbMvNLNWWTJK1imE+lfyQz7+1c/i6wC9hUVzBJ0uoiM4ffSMQkcCewOTOf6LptFpgFaLVaW+bm5oYebzVLS0tMTEz0XW9x74HK25zadOowkf7f4WxrGXucWhtg38H1TrGyJmeDZucbZ7ZBnitVn7ProSnZZmZmdmZmu3v50AUeERPAvwPvz8ybVlu33W7nwsLCUOP1Mz8/z/T0dN/11jIDXF2zsR3Otl6zz/WzbeoQ1yw2c4LKJmeDZucbZ7ZBnitVn7ProSnZImLFAh/qVSgRcQLwMeD6fuUtSarXMK9CCeAjwK7M/GB9kSRJVQxzBP5K4O3AayLivs7XBTXlkiT1MfCJscz8DyBqzCJJWgPfiSlJhbLAJalQFrgkFcoCl6RCWeCSVCgLXJIKZYFLUqGaOXnDCqrOH3Lt1pNHnKS3fhm3TR3isobOgyKN05HPldWeF3XNQ7RWh/PV+ZwdxWPxCFySCmWBS1KhLHBJKpQFLkmFssAlqVAWuCQVygKXpEJZ4JJUKAtckgo17Icab42IL0fEVyPiqrpCSZL6G+ZDjY8D/hZ4PfBi4OKIeHFdwSRJqxvmCPzlwFcz8+uZ+RQwB1xUTyxJUj+RmYPdMeItwNbM/M3O9bcDv5iZ7+xabxaY7Vw9G/jy4HEr2Qg8OuIxBtXkbNDsfE3OBs3O1+Rs0Ox8Tcn2U5n53O6Fw8xGuNIn0h/10yAztwPbhxhnTSJiITPb4xpvLZqcDZqdr8nZoNn5mpwNmp2vydlguFMoe4Czjrh+JvCt4eJIkqoapsA/D7woIl4QEScCbwVurieWJKmfgU+hZOahiHgn8K/AccCOzHywtmSDG9vpmgE0ORs0O1+Ts0Gz8zU5GzQ7X5OzDf5HTEnS+vKdmJJUKAtckgpVfIFHxHMi4raIeLjz7+krrPOsiPhcRHwxIh6MiD9qULazIuKOiNjVyXbFOLJVzddZb0dE7I+IB8aQadXpGWLZX3duvz8izh11pjVk+7mIuCsivh8RV44r1xryXdLZZ/dHxGcj4iUNynZRJ9d9EbEQEa8aV7Yq+Y5Y72UR8cPO+2DWX2YW/QV8ALiqc/kq4E9XWCeAic7lE4B7gPMaku0M4NzO5VOArwAvbsq+69z2auBc4IER5zkO+BrwQuBE4Ivd+wK4APjnzv/pecA9Y9pXVbI9D3gZ8H7gynHkWmO+VwCndy6/vmH7boIf/U3uHOChJu27I9b7DPAp4C3j/P/t9VX8ETjLb9+/rnP5OuBN3SvksqXO1RM6X+P4622VbI9k5r2dy98FdgGbxpCtUj6AzLwTeGwMeapMz3AR8Hed/9O7gdMi4owmZMvM/Zn5eeAHY8gzSL7PZuZ3OlfvZvm9G03JtpSdlgROZjzPz8r5Ot4FfAzYP8ZsqzoWCryVmY/AchmyfBR0lIg4LiLuY3nn35aZ9zQl22ERMQn8Asu/IYzDmvKNwSbgm0dc38PRP8yqrDMK6zVuVWvNdznLv8mMQ6VsEfHmiHgIuBX4jTFlgwr5ImIT8GbgQ2PM1dcwb6Ufm4j4N+D5K9z03qrbyMwfAi+NiNOAj0fE5swc+pxuHdk625lg+af772bmE8PmOmK7teQbkyrTM1SawmEE1mvcqirni4gZlgt8XOeZq0678XGWn5uvBv4YeO2og3VUyfeXwLsz84cRK62+Pooo8Mzs+R8ZEfsi4ozMfKTzq/Sqv95k5uMRMQ9sBYYu8DqyRcQJLJf39Zl507CZ6s43RlWmZ1ivKRyaPnVEpXwRcQ7wYeD1mfnfTcp2WGbeGRE/HREbM3McE0lVydcG5jrlvRG4ICIOZeYnxpCvp2PhFMrNwKWdy5cCn+xeISKe2znyJiI2sPyT/aGGZAvgI8CuzPzgGDIdqW++MasyPcPNwK91Xo1yHnDg8GmgBmRbT33zRcRPAjcBb8/MrzQs2890ngt0Xll0IjCuHzB982XmCzJzMjMngRuB317v8gaOiVeh/DhwO/Bw59/ndJb/BPCp/NFftb8A3M/yUfcfNijbq1j+de1+4L7O1wVNyde5fgPwCMt/nNsDXD7CTBew/EqcrwHv7Sx7B/COzuVg+YNEvgYsAu0xfq/1y/b8zv55Ani8c/nZDcr3YeA7R3yfLTQo27uBBzu57gJeNa5sVfJ1rXstDXkVim+ll6RCHQunUCTpGckCl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYX6P/KEn1VbO70FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's evaluate the distribution of the errors: \n",
    "# they should be fairly normally distributed\n",
    "\n",
    "y_test.reset_index(drop=True, inplace=True)\n",
    "\n",
    "preds = pd.Series(price_pipe.predict(X_test))\n",
    "\n",
    "errors = y_test - preds\n",
    "errors.hist(bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['price_pipe.joblib']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now let's save the scaler\n",
    "\n",
    "joblib.dump(price_pipe, 'price_pipe.joblib') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Score new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1459, 37)\n"
     ]
    }
   ],
   "source": [
    "# load the unseen / new dataset\n",
    "data = pd.read_csv('test.csv')\n",
    "\n",
    "data.drop('Id', axis=1, inplace=True)\n",
    "\n",
    "data['MSSubClass'] = data['MSSubClass'].astype('O')\n",
    "\n",
    "data = data[FEATURES]\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSZoning',\n",
       " 'Exterior1st',\n",
       " 'BsmtFullBath',\n",
       " 'KitchenQual',\n",
       " 'Functional',\n",
       " 'GarageCars',\n",
       " 'GarageArea']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_vars_with_na = [\n",
    "    var for var in FEATURES\n",
    "    if var not in CATEGORICAL_VARS_WITH_NA_FREQUENT +\n",
    "    CATEGORICAL_VARS_WITH_NA_MISSING +\n",
    "    NUMERICAL_VARS_WITH_NA\n",
    "    and data[var].isnull().sum() > 0]\n",
    "\n",
    "new_vars_with_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>Functional</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RH</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MSZoning Exterior1st  BsmtFullBath KitchenQual Functional  GarageCars  \\\n",
       "0       RH     VinylSd           0.0          TA        Typ         1.0   \n",
       "1       RL     Wd Sdng           0.0          Gd        Typ         1.0   \n",
       "2       RL     VinylSd           0.0          TA        Typ         2.0   \n",
       "3       RL     VinylSd           0.0          Gd        Typ         2.0   \n",
       "4       RL     HdBoard           0.0          Gd        Typ         2.0   \n",
       "\n",
       "   GarageArea  \n",
       "0       730.0  \n",
       "1       312.0  \n",
       "2       482.0  \n",
       "3       470.0  \n",
       "4       506.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[new_vars_with_na].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning        0.002742\n",
       "Exterior1st     0.000685\n",
       "BsmtFullBath    0.001371\n",
       "KitchenQual     0.000685\n",
       "Functional      0.001371\n",
       "GarageCars      0.000685\n",
       "GarageArea      0.000685\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[new_vars_with_na].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1449, 37)\n"
     ]
    }
   ],
   "source": [
    "data.dropna(subset=new_vars_with_na, inplace=True)\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_preds = price_pipe.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot the predicted sale prices\n",
    "pd.Series(np.exp(new_preds)).hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Conclusion\n",
    "\n",
    "Now we are ready for deployment!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "583px",
    "left": "0px",
    "right": "1324px",
    "top": "107px",
    "width": "212px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}