Pipelines .ipynb

{
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  {
   "cell_type": "markdown",
   "id": "f9cde901",
   "metadata": {},
   "source": [
    "# Introduction to pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9510d0c7",
   "metadata": {},
   "source": [
    "Pipelines are a simple way to keep your data preprocessing and modeling code organized."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0442b0b1",
   "metadata": {},
   "source": [
    "Specifically, a pipeline bundles preprocessing and modeling steps so you can use the whole bundle as if it were a single step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24074a98",
   "metadata": {},
   "source": [
    "Benefits of pipeline:\n",
    "\n",
    "1. Cleaner Code: Accounting for data at each step of preprocessing can get messy. With a pipeline, you won't need to manually keep track of your training and validation data at each step.\n",
    "2. Fewer Bugs: There are fewer opportunities to misapply a step or forget a preprocessing step.\n",
    "3. Easier to Productionize: It can be surprisingly hard to transition a model from a prototype to something deployable at scale. We won't go into the many related concerns here, but pipelines can help.\n",
    "4. More Options for Model Validation: You will see an example in the next tutorial, which covers cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64382261",
   "metadata": {},
   "source": [
    "With a pipeline, it is easy to deal with both categorical and numerical data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "936dc065",
   "metadata": {},
   "source": [
    "We construct a full pipeline in three steps:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c8094b",
   "metadata": {},
   "source": [
    "## Purpose of using a pipeline: \n",
    "\n",
    "1. If you want to do different preprocessing of different columns of your same dataset then you can do that in one step by creating pipelines.\n",
    "\n",
    "\n",
    "2. It also helps to cross-validate the entire pipeline i.e., the model along with the preprocessing involved. This way our cross-validation results are more reliable.\n",
    "\n",
    "\n",
    "3. If you're predicting on some non-insample data then you do not need to repeat those preprocessing steps that you did on the training data before feeding it to the model to predict, as the pipeline does that for you.\n",
    "\n",
    "\n",
    "4. If your test contains any categories that is not present in the training data (say your training data contained categories like 'B', 'C' and your test data contain categories like 'B', 'C' & 'D', then the pipeline can handle that too.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c19748e",
   "metadata": {},
   "source": [
    "We construct a pipeline in three steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b4f0290",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>4/06/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
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      "text/plain": [
       "       Suburb           Address  Rooms Type      Price Method SellerG  \\\n",
       "0  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "1  Abbotsford   25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "2  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "3  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
       "4  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "1  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "2  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "3  4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0           NaN   \n",
       "4  4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
       "\n",
       "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN        Yarra  -37.7996    144.9984  Northern Metropolitan   \n",
       "1     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
       "2     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
       "3        NaN        Yarra  -37.7969    144.9969  Northern Metropolitan   \n",
       "4     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "0        4019.0  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "3        4019.0  \n",
       "4        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing dataset to work on\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('datasets/melb_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec1d98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13580 entries, 0 to 13579\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Suburb         13580 non-null  object \n",
      " 1   Address        13580 non-null  object \n",
      " 2   Rooms          13580 non-null  int64  \n",
      " 3   Type           13580 non-null  object \n",
      " 4   Price          13580 non-null  float64\n",
      " 5   Method         13580 non-null  object \n",
      " 6   SellerG        13580 non-null  object \n",
      " 7   Date           13580 non-null  object \n",
      " 8   Distance       13580 non-null  float64\n",
      " 9   Postcode       13580 non-null  float64\n",
      " 10  Bedroom2       13580 non-null  float64\n",
      " 11  Bathroom       13580 non-null  float64\n",
      " 12  Car            13518 non-null  float64\n",
      " 13  Landsize       13580 non-null  float64\n",
      " 14  BuildingArea   7130 non-null   float64\n",
      " 15  YearBuilt      8205 non-null   float64\n",
      " 16  CouncilArea    12211 non-null  object \n",
      " 17  Lattitude      13580 non-null  float64\n",
      " 18  Longtitude     13580 non-null  float64\n",
      " 19  Regionname     13580 non-null  object \n",
      " 20  Propertycount  13580 non-null  float64\n",
      "dtypes: float64(12), int64(1), object(8)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c3a279c",
   "metadata": {},
   "source": [
    "As we can see we have both categorical and numerical columns in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2f0b782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb              0\n",
       "Address             0\n",
       "Rooms               0\n",
       "Type                0\n",
       "Price               0\n",
       "Method              0\n",
       "SellerG             0\n",
       "Date                0\n",
       "Distance            0\n",
       "Postcode            0\n",
       "Bedroom2            0\n",
       "Bathroom            0\n",
       "Car                62\n",
       "Landsize            0\n",
       "BuildingArea     6450\n",
       "YearBuilt        5375\n",
       "CouncilArea      1369\n",
       "Lattitude           0\n",
       "Longtitude          0\n",
       "Regionname          0\n",
       "Propertycount       0\n",
       "dtype: int64"
      ]
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chaecking null values\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "496432c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating training and testing data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop(['Price'], axis=1)\n",
    "y = df.Price\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "761a5935",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12167</th>\n",
       "      <td>St Kilda</td>\n",
       "      <td>11/22 Charnwood Cr</td>\n",
       "      <td>1</td>\n",
       "      <td>u</td>\n",
       "      <td>S</td>\n",
       "      <td>hockingstuart</td>\n",
       "      <td>29/07/2017</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3182.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>Port Phillip</td>\n",
       "      <td>-37.85984</td>\n",
       "      <td>144.9867</td>\n",
       "      <td>Southern Metropolitan</td>\n",
       "      <td>13240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6524</th>\n",
       "      <td>Williamstown</td>\n",
       "      <td>18 James St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>SA</td>\n",
       "      <td>Hunter</td>\n",
       "      <td>17/09/2016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hobsons Bay</td>\n",
       "      <td>-37.85800</td>\n",
       "      <td>144.9005</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8413</th>\n",
       "      <td>Sunshine</td>\n",
       "      <td>10 Dundalk St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>S</td>\n",
       "      <td>Barry</td>\n",
       "      <td>8/04/2017</td>\n",
       "      <td>12.6</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brimbank</td>\n",
       "      <td>-37.79880</td>\n",
       "      <td>144.8220</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>3755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>Glenroy</td>\n",
       "      <td>1/2 Prospect St</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>SP</td>\n",
       "      <td>Brad</td>\n",
       "      <td>18/06/2016</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3046.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.70830</td>\n",
       "      <td>144.9158</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>8870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6043</th>\n",
       "      <td>Sunshine North</td>\n",
       "      <td>35 Furlong Rd</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>22/05/2016</td>\n",
       "      <td>13.3</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>Brimbank</td>\n",
       "      <td>-37.76230</td>\n",
       "      <td>144.8272</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>4217.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Suburb             Address  Rooms Type Method        SellerG  \\\n",
       "12167        St Kilda  11/22 Charnwood Cr      1    u      S  hockingstuart   \n",
       "6524     Williamstown         18 James St      2    h     SA         Hunter   \n",
       "8413         Sunshine       10 Dundalk St      3    h      S          Barry   \n",
       "2919          Glenroy     1/2 Prospect St      3    u     SP           Brad   \n",
       "6043   Sunshine North       35 Furlong Rd      3    h      S          First   \n",
       "\n",
       "             Date  Distance  Postcode  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "12167  29/07/2017       5.0    3182.0       1.0       1.0  1.0       0.0   \n",
       "6524   17/09/2016       8.0    3016.0       2.0       2.0  1.0     193.0   \n",
       "8413    8/04/2017      12.6    3020.0       3.0       1.0  1.0     555.0   \n",
       "2919   18/06/2016      13.0    3046.0       3.0       1.0  1.0     265.0   \n",
       "6043   22/05/2016      13.3    3020.0       3.0       1.0  2.0     673.0   \n",
       "\n",
       "       BuildingArea  YearBuilt   CouncilArea  Lattitude  Longtitude  \\\n",
       "12167           NaN     1940.0  Port Phillip  -37.85984    144.9867   \n",
       "6524            NaN        NaN   Hobsons Bay  -37.85800    144.9005   \n",
       "8413            NaN        NaN      Brimbank  -37.79880    144.8220   \n",
       "2919            NaN     1995.0      Moreland  -37.70830    144.9158   \n",
       "6043          673.0     1970.0      Brimbank  -37.76230    144.8272   \n",
       "\n",
       "                  Regionname  Propertycount  \n",
       "12167  Southern Metropolitan        13240.0  \n",
       "6524    Western Metropolitan         6380.0  \n",
       "8413    Western Metropolitan         3755.0  \n",
       "2919   Northern Metropolitan         8870.0  \n",
       "6043    Western Metropolitan         4217.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "320c2dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12167    481000.0\n",
       "6524     895000.0\n",
       "8413     651500.0\n",
       "2919     482500.0\n",
       "6043     591000.0\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bf37b764",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from data preprocessing to pipeline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b8edd9",
   "metadata": {},
   "source": [
    "# Step 1: Define Preprocessing Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27815a5d",
   "metadata": {},
   "source": [
    "Similar to how a pipeline bundles together preprocessing and modeling steps, we use the ColumnTransformer class to bundle together different preprocessing steps. The code below:\n",
    "\n",
    "1. imputes missing values in numerical data, and\n",
    "\n",
    "\n",
    "2. imputes missing values and applies a one-hot encoding to categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2bb662c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f86ad760",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing for numerical data\n",
    "\n",
    "# defining how to preprocess\n",
    "\n",
    "# start for numerical\n",
    "numerical_transformer = SimpleImputer(strategy='constant')\n",
    "# end for numerical\n",
    "\n",
    "# strategy : str, default='mean'\n",
    "#     The imputation strategy.\n",
    "\n",
    "#     - If \"mean\", then replace missing values using the mean along\n",
    "#       each column. Can only be used with numeric data.\n",
    "#     - If \"median\", then replace missing values using the median along\n",
    "#       each column. Can only be used with numeric data.\n",
    "#     - If \"most_frequent\", then replace missing using the most frequent\n",
    "#       value along each column. Can be used with strings or numeric data.\n",
    "#       If there is more than one such value, only the smallest is returned.\n",
    "#     - If \"constant\", then replace missing values with fill_value. Can be\n",
    "#       used with strings or numeric data.\n",
    "\n",
    "#     .. versionadded:: 0.20\n",
    "#        strategy=\"constant\" for fixed value imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "104e98c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing for categorical data\n",
    "\n",
    "# defining how to preprocess\n",
    "\n",
    "# start for categorical\n",
    "categorical_transformer = Pipeline(\n",
    "    steps=[\n",
    "        ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "        ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "    ]\n",
    ")\n",
    "# end for categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8a0552f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combining the above two ===> Bundle preprocessing for numerical and categorical data\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numerical_transformer, ['Car', 'BuildingArea', 'YearBuilt']),\n",
    "        ('onehot', categorical_transformer, ['CouncilArea'])\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9a7f0f",
   "metadata": {},
   "source": [
    "# Step 2: Define the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2a29faaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the model to train and predict\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa7d9f0",
   "metadata": {},
   "source": [
    "# Step 3: Create and Evaluate the Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd242167",
   "metadata": {},
   "source": [
    "Finally, we use the Pipeline class to define a pipeline that bundles the preprocessing and modeling steps. There are a few important things to notice:\n",
    "\n",
    "1. With the pipeline, we preprocess the training data and fit the model in a single line of code. (In contrast, without a pipeline, we have to do imputation, one-hot encoding, and model training in separate steps. This becomes especially messy if we have to deal with both numerical and categorical variables!)\n",
    "\n",
    "\n",
    "2. With the pipeline, we supply the unprocessed features in X_valid to the predict() command, and the pipeline automatically preprocesses the features before generating predictions. (However, without a pipeline, we have to remember to preprocess the validation data before making predictions.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1bbd584d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# code\n",
    "# Bundle preprocessing and modeling code in pipeline\n",
    "\n",
    "my_pipeline = Pipeline(\n",
    "    steps=[\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('model', model)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c6c1b5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing of training data and fitting model\n",
    "\n",
    "my_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# preprocessing of test data and get predictions\n",
    "\n",
    "preds = my_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ece9fe92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  287656.91673759103\n"
     ]
    }
   ],
   "source": [
    "# Evaluation of model\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "score= mean_absolute_error(y_test, preds)\n",
    "print(\"MAE: \", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7fef63",
   "metadata": {},
   "source": [
    "Pipelines are valuable for cleaning up machine learning code and avoiding errors, and are especially useful for workflows with sophisticated data preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db4f4b96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8d69d855",
   "metadata": {},
   "source": [
    "#### Author: Piyush Kumar\n",
    "[Github](https://github.com/styles3544)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b559ccd5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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