Machine-Learning-Pipeline-Step4-Model-Building.ipynb

{
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   "cell_type": "markdown",
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    "## Machine Learning Model Building Pipeline: Machine Learning Model Build\n",
    "\n",
    "In the following videos, we will take you through a practical example of each one of the steps in the Machine Learning model building pipeline, which we described in the previous lectures. There will be a notebook for each one of the Machine Learning Pipeline steps:\n",
    "\n",
    "1. Data Analysis\n",
    "2. Feature Engineering\n",
    "3. Feature Selection\n",
    "4. Model Building\n",
    "\n",
    "**This is the notebook for step 4: Building the Final Machine Learning Model**\n",
    "\n",
    "We will use the house price dataset available on [Kaggle.com](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data). See below for more details.\n",
    "\n",
    "===================================================================================================\n",
    "\n",
    "## Predicting Sale Price of Houses\n",
    "\n",
    "The aim of the project is to build a machine learning model to predict the sale price of homes based on different explanatory variables describing aspects of residential houses. \n",
    "\n",
    "### Why is this important? \n",
    "\n",
    "Predicting house prices is useful to identify fruitful investments, or to determine whether the price advertised for a house is over or under-estimated.\n",
    "\n",
    "### What is the objective of the machine learning model?\n",
    "\n",
    "We aim to minimise the difference between the real price and the price estimated by our model. We will evaluate model performance using the mean squared error (mse) and the root squared of the mean squared error (rmse).\n",
    "\n",
    "### How do I download the dataset?\n",
    "\n",
    "To download the House Price dataset go this website:\n",
    "https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data\n",
    "\n",
    "Scroll down to the bottom of the page, and click on the link 'train.csv', and then click the 'download' blue button towards the right of the screen, to download the dataset. Rename the file as 'houseprice.csv' and save it to a directory of your choice.\n",
    "\n",
    "**Note the following:**\n",
    "-  You need to be logged in to Kaggle in order to download the datasets.\n",
    "-  You need to accept the terms and conditions of the competition to download the dataset\n",
    "-  If you save the file to the same directory where you saved this jupyter notebook, then you can run the code as it is written here.\n",
    "\n",
    "===================================================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## House Prices dataset: Model building\n",
    "\n",
    "In the following cells, we will finally build our machine learning model, utilising the engineered data and the pre-selected features. \n",
    "\n",
    "\n",
    "### Setting the seed\n",
    "\n",
    "It is important to note, that we are engineering variables and pre-processing data with the idea of deploying the model. Therefore, from now on, for each step that includes some element of randomness, it is extremely important that we **set the seed**. This way, we can obtain reproducibility between our research and our development code.\n",
    "\n",
    "This is perhaps one of the most important lessons that you need to take away from this course: **Always set the seeds**.\n",
    "\n",
    "Let's go ahead and load the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to handle datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# to build the model\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# to evaluate the model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from math import sqrt\n",
    "\n",
    "# to visualise al the columns in the dataframe\n",
    "pd.pandas.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>Id</th>\n",
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       "      <td>0.00000</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.559760</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.142807</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.172340</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.434539</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.333333</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
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       "      <td>0.394699</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.954545</td>\n",
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       "      <td>1.00</td>\n",
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       "      <td>0.888889</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.036765</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080794</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.601951</td>\n",
       "      <td>0.286743</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
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       "      <td>0.50</td>\n",
       "      <td>0.406206</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.228705</td>\n",
       "      <td>0.149909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>12.675764</td>\n",
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       "      <td>0.388581</td>\n",
       "      <td>0.493677</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.066176</td>\n",
       "      <td>0.163934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255670</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018114</td>\n",
       "      <td>0.242553</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.566920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.529943</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.084112</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.362482</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.469078</td>\n",
       "      <td>0.045704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>12.278393</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.577658</td>\n",
       "      <td>0.402702</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.737705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.086818</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.434278</td>\n",
       "      <td>0.233224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.549026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.513216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.411215</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.406206</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.801181</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>12.103486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass  MSZoning  LotFrontage   LotArea  Street  Alley  LotShape  \\\n",
       "0   931    0.000000      0.75     0.461171  0.377048     1.0    1.0  0.333333   \n",
       "1   657    0.000000      0.75     0.456066  0.399443     1.0    1.0  0.333333   \n",
       "2    46    0.588235      0.75     0.394699  0.347082     1.0    1.0  0.000000   \n",
       "3  1349    0.000000      0.75     0.388581  0.493677     1.0    1.0  0.666667   \n",
       "4    56    0.000000      0.75     0.577658  0.402702     1.0    1.0  0.333333   \n",
       "\n",
       "   LandContour  Utilities  LotConfig  LandSlope  Neighborhood  Condition1  \\\n",
       "0     1.000000        1.0        0.0        0.0      0.863636         0.4   \n",
       "1     0.333333        1.0        0.0        0.0      0.363636         0.4   \n",
       "2     0.333333        1.0        0.0        0.0      0.954545         0.4   \n",
       "3     0.666667        1.0        0.0        0.0      0.454545         0.4   \n",
       "4     0.333333        1.0        0.0        0.0      0.363636         0.4   \n",
       "\n",
       "   Condition2  BldgType  HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0         1.0      0.75         0.6     0.777778         0.50   0.014706   \n",
       "1         1.0      0.75         0.6     0.444444         0.75   0.360294   \n",
       "2         1.0      1.00         0.6     0.888889         0.50   0.036765   \n",
       "3         1.0      0.75         0.6     0.666667         0.50   0.066176   \n",
       "4         1.0      0.75         0.6     0.555556         0.50   0.323529   \n",
       "\n",
       "   YearRemodAdd  RoofStyle  RoofMatl  Exterior1st  Exterior2nd  MasVnrType  \\\n",
       "0      0.049180        0.0       0.0          1.0          1.0    0.000000   \n",
       "1      0.049180        0.0       0.0          0.6          0.6    0.666667   \n",
       "2      0.098361        1.0       0.0          0.3          0.2    0.666667   \n",
       "3      0.163934        0.0       0.0          1.0          1.0    0.000000   \n",
       "4      0.737705        0.0       0.0          0.6          0.7    0.666667   \n",
       "\n",
       "   MasVnrArea  ExterQual  ExterCond  Foundation  BsmtQual  BsmtCond  \\\n",
       "0     0.00000   0.666667        1.0         1.0      0.75      0.75   \n",
       "1     0.03375   0.666667        1.0         0.5      0.50      0.75   \n",
       "2     0.25750   1.000000        1.0         1.0      1.00      0.75   \n",
       "3     0.00000   0.666667        1.0         1.0      0.75      0.75   \n",
       "4     0.17000   0.333333        1.0         0.5      0.50      0.75   \n",
       "\n",
       "   BsmtExposure  BsmtFinType1  BsmtFinSF1  BsmtFinType2  BsmtFinSF2  \\\n",
       "0          0.75      1.000000    0.002835      0.666667         0.0   \n",
       "1          0.25      0.666667    0.142807      0.666667         0.0   \n",
       "2          0.25      1.000000    0.080794      0.666667         0.0   \n",
       "3          1.00      1.000000    0.255670      0.666667         0.0   \n",
       "4          0.25      0.333333    0.086818      0.666667         0.0   \n",
       "\n",
       "   BsmtUnfSF  TotalBsmtSF  Heating  HeatingQC  CentralAir  Electrical  \\\n",
       "0   0.673479     0.239935      1.0       1.00         1.0         1.0   \n",
       "1   0.114724     0.172340      1.0       1.00         1.0         1.0   \n",
       "2   0.601951     0.286743      1.0       1.00         1.0         1.0   \n",
       "3   0.018114     0.242553      1.0       1.00         1.0         1.0   \n",
       "4   0.434278     0.233224      1.0       0.75         1.0         1.0   \n",
       "\n",
       "   1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  \\\n",
       "0  0.559760       0.0           0.0   0.523250      0.000000           0.0   \n",
       "1  0.434539       0.0           0.0   0.406196      0.333333           0.0   \n",
       "2  0.627205       0.0           0.0   0.586296      0.333333           0.0   \n",
       "3  0.566920       0.0           0.0   0.529943      0.333333           0.0   \n",
       "4  0.549026       0.0           0.0   0.513216      0.000000           0.0   \n",
       "\n",
       "   FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  KitchenQual  TotRmsAbvGrd  \\\n",
       "0  0.666667       0.0         0.375      0.333333     0.666667      0.416667   \n",
       "1  0.333333       0.5         0.375      0.333333     0.666667      0.250000   \n",
       "2  0.666667       0.0         0.250      0.333333     1.000000      0.333333   \n",
       "3  0.666667       0.0         0.375      0.333333     0.666667      0.250000   \n",
       "4  0.666667       0.0         0.375      0.333333     0.333333      0.416667   \n",
       "\n",
       "   Functional  Fireplaces  FireplaceQu  GarageType  GarageYrBlt  GarageFinish  \\\n",
       "0         1.0    0.000000          0.2         0.8     0.018692      1.000000   \n",
       "1         1.0    0.000000          0.2         0.8     0.457944      0.666667   \n",
       "2         1.0    0.333333          0.8         0.8     0.046729      0.666667   \n",
       "3         1.0    0.333333          0.4         0.8     0.084112      0.666667   \n",
       "4         1.0    0.333333          0.8         0.8     0.411215      0.666667   \n",
       "\n",
       "   GarageCars  GarageArea  GarageQual  GarageCond  PavedDrive  WoodDeckSF  \\\n",
       "0        0.75    0.430183    0.666667         1.0         1.0    0.116686   \n",
       "1        0.25    0.220028    0.666667         1.0         1.0    0.000000   \n",
       "2        0.50    0.406206    0.666667         1.0         1.0    0.228705   \n",
       "3        0.50    0.362482    0.666667         1.0         1.0    0.469078   \n",
       "4        0.50    0.406206    0.666667         1.0         1.0    0.000000   \n",
       "\n",
       "   OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  PoolQC  \\\n",
       "0     0.032907            0.0   0.000000          0.0       0.0     0.0   \n",
       "1     0.000000            0.0   0.000000          0.0       0.0     0.0   \n",
       "2     0.149909            0.0   0.000000          0.0       0.0     0.0   \n",
       "3     0.045704            0.0   0.000000          0.0       0.0     0.0   \n",
       "4     0.000000            0.0   0.801181          0.0       0.0     0.0   \n",
       "\n",
       "   Fence  MiscFeature  MiscVal    MoSold  YrSold  SaleType  SaleCondition  \\\n",
       "0   0.75          1.0      0.0  0.545455    0.75  0.666667           0.75   \n",
       "1   0.50          1.0      0.0  0.636364    0.50  0.666667           0.75   \n",
       "2   0.75          1.0      0.0  0.090909    1.00  0.666667           0.75   \n",
       "3   0.75          1.0      0.0  0.636364    0.25  0.666667           0.75   \n",
       "4   0.75          1.0      0.0  0.545455    0.50  0.666667           0.75   \n",
       "\n",
       "   SalePrice  LotFrontage_na  MasVnrArea_na  GarageYrBlt_na  \n",
       "0  12.211060             0.0            0.0             0.0  \n",
       "1  11.887931             0.0            0.0             0.0  \n",
       "2  12.675764             0.0            0.0             0.0  \n",
       "3  12.278393             1.0            0.0             0.0  \n",
       "4  12.103486             0.0            0.0             0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the train and test set with the engineered variables\n",
    "\n",
    "# we built and saved these datasets in a previous notebook.\n",
    "# If you haven't done so, go ahead and check the previous notebooks (step 2)\n",
    "# to find out how to create these datasets\n",
    "\n",
    "X_train = pd.read_csv('xtrain.csv')\n",
    "X_test = pd.read_csv('xtest.csv')\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# capture the target (remember that is log transformed)\n",
    "\n",
    "y_train = X_train['SalePrice']\n",
    "y_test = X_test['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSSubClass',\n",
       " 'MSZoning',\n",
       " 'Neighborhood',\n",
       " 'OverallQual',\n",
       " 'OverallCond',\n",
       " 'YearRemodAdd',\n",
       " 'RoofStyle',\n",
       " 'MasVnrType',\n",
       " 'BsmtQual',\n",
       " 'BsmtExposure',\n",
       " 'HeatingQC',\n",
       " 'CentralAir',\n",
       " '1stFlrSF',\n",
       " 'GrLivArea',\n",
       " 'BsmtFullBath',\n",
       " 'KitchenQual',\n",
       " 'Fireplaces',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageFinish',\n",
       " 'GarageCars',\n",
       " 'PavedDrive',\n",
       " 'LotFrontage']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the pre-selected features\n",
    "# ==============================\n",
    "\n",
    "# we selected the features in the previous notebook (step 3)\n",
    "\n",
    "# if you haven't done so, go ahead and visit the previous notebook\n",
    "# to find out how to select the features\n",
    "\n",
    "features = pd.read_csv('selected_features.csv')\n",
    "features = features['0'].to_list() \n",
    "\n",
    "# We will add one additional feature to the ones we selected in the\n",
    "# previous notebook: LotFrontage\n",
    "\n",
    "# why?\n",
    "#=====\n",
    "\n",
    "# because it needs key feature engineering steps that we want to\n",
    "# discuss further during the deployment part of the course. \n",
    "\n",
    "features = features + ['LotFrontage'] \n",
    "\n",
    "# display final feature set\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reduce the train and test set to the selected features\n",
    "\n",
    "X_train = X_train[features]\n",
    "X_test = X_test[features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularised linear regression: Lasso\n",
    "\n",
    "Remember to set the seed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.005, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "      normalize=False, positive=False, precompute=False, random_state=0,\n",
       "      selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up the model\n",
    "# remember to set the random_state / seed\n",
    "\n",
    "lin_model = Lasso(alpha=0.005, random_state=0)\n",
    "\n",
    "# train the model\n",
    "\n",
    "lin_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train mse: 1087435415\n",
      "train rmse: 32976\n",
      "train r2: 0.8258384535431156\n",
      "\n",
      "test mse: 1405259552\n",
      "test rmse: 37486\n",
      "test r2: 0.7955128088451101\n",
      "\n",
      "Average house price:  163000\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model:\n",
    "# ====================\n",
    "\n",
    "# remember that we log transformed the output (SalePrice)\n",
    "# in our feature engineering notebook (step 2).\n",
    "\n",
    "# In order to get the true performance of the Lasso\n",
    "# we need to transform both the target and the predictions\n",
    "# back to the original house prices values.\n",
    "\n",
    "# We will evaluate performance using the mean squared error and\n",
    "# the root of the mean squared error and r2\n",
    "\n",
    "# make predictions for train set\n",
    "pred = lin_model.predict(X_train)\n",
    "\n",
    "# determine mse and rmse\n",
    "print('train mse: {}'.format(int(\n",
    "    mean_squared_error(np.exp(y_train), np.exp(pred)))))\n",
    "print('train rmse: {}'.format(int(\n",
    "    sqrt(mean_squared_error(np.exp(y_train), np.exp(pred))))))\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 = lin_model.predict(X_test)\n",
    "\n",
    "# determine mse and rmse\n",
    "print('test mse: {}'.format(int(\n",
    "    mean_squared_error(np.exp(y_test), np.exp(pred)))))\n",
    "print('test rmse: {}'.format(int(\n",
    "    sqrt(mean_squared_error(np.exp(y_test), np.exp(pred))))))\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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Evaluation of Lasso Predictions')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 463,
       "width": 757
      },
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's evaluate our predictions respect to the real sale price\n",
    "plt.scatter(y_test, lin_model.predict(X_test))\n",
    "plt.xlabel('True House Price')\n",
    "plt.ylabel('Predicted House Price')\n",
    "plt.title('Evaluation of Lasso Predictions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that our model is doing a pretty good job at estimating house prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2171ae01f48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 414,
       "width": 707
      },
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's evaluate the distribution of the errors: \n",
    "# they should be fairly normally distributed\n",
    "\n",
    "errors = y_test - lin_model.predict(X_test)\n",
    "errors.hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the errors follows quite closely a gaussian distribution. That suggests that our model is doing a good job as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
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       "width": 1759
      },
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   ],
   "source": [
    "# Finally, just for fun, let's look at the feature importance\n",
    "\n",
    "importance = pd.Series(np.abs(lin_model.coef_.ravel()))\n",
    "importance.index = features\n",
    "importance.sort_values(inplace=True, ascending=False)\n",
    "importance.plot.bar(figsize=(18,6))\n",
    "plt.ylabel('Lasso Coefficients')\n",
    "plt.title('Feature Importance')"
   ]
  },
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   "metadata": {
    "collapsed": true
   },
   "source": [
    "And that is all! Now we have our entire pipeline ready for deployment. \n",
    "\n",
    "In the next video, we will summarise which steps from the pipeline we will deploy to production."
   ]
  }
 ],
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