Your First Machine Learning Model

This notebook is an exercise in the Introduction to Machine Learning course. You can reference the tutorial at this link.

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Recap

So far, you have loaded your data and reviewed it with the following code. Run this cell to set up your coding environment where the previous step left off.

# Code you have previously used to load data
import pandas as pd

# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'

home_data = pd.read_csv(iowa_file_path)

# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex3 import *

print("Setup Complete")

Setup Complete

Exercises

Step 1: Specify Prediction Target

Select the target variable, which corresponds to the sales price. Save this to a new variable called y. You’ll need to print a list of the columns to find the name of the column you need.

# print the list of columns in the dataset to find the name of the prediction target
home_data.columns

Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',
       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',
       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',
       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',
       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',
       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',
       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',
       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',
       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',
       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',
       'SaleCondition', 'SalePrice'],
      dtype='object')

y = home_data.SalePrice

# Check your answer
step_1.check()

<IPython.core.display.Javascript object>

Correct

# The lines below will show you a hint or the solution.
# step_1.hint() 
# step_1.solution()

Step 2: Create X

Now you will create a DataFrame called X holding the predictive features.

Since you want only some columns from the original data, you’ll first create a list with the names of the columns you want in X.

You’ll use just the following columns in the list (you can copy and paste the whole list to save some typing, though you’ll still need to add quotes):

• LotArea
• YearBuilt
• 1stFlrSF
• 2ndFlrSF
• FullBath
• BedroomAbvGr
• TotRmsAbvGrd

After you’ve created that list of features, use it to create the DataFrame that you’ll use to fit the model.

# Create the list of features below
feature_names = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']

# Select data corresponding to features in feature_names
X = home_data[feature_names]

# Check your answer
step_2.check()

<IPython.core.display.Javascript object>

Correct

# step_2.hint()
# step_2.solution()

Review Data

Before building a model, take a quick look at X to verify it looks sensible

# Review data
# print description or statistics from X
print(X.describe())

# print the top few lines
print(X.head())

             LotArea    YearBuilt     1stFlrSF     2ndFlrSF     FullBath  \
count    1460.000000  1460.000000  1460.000000  1460.000000  1460.000000   
mean    10516.828082  1971.267808  1162.626712   346.992466     1.565068   
std      9981.264932    30.202904   386.587738   436.528436     0.550916   
min      1300.000000  1872.000000   334.000000     0.000000     0.000000   
25%      7553.500000  1954.000000   882.000000     0.000000     1.000000   
50%      9478.500000  1973.000000  1087.000000     0.000000     2.000000   
75%     11601.500000  2000.000000  1391.250000   728.000000     2.000000   
max    215245.000000  2010.000000  4692.000000  2065.000000     3.000000   

       BedroomAbvGr  TotRmsAbvGrd  
count   1460.000000   1460.000000  
mean       2.866438      6.517808  
std        0.815778      1.625393  
min        0.000000      2.000000  
25%        2.000000      5.000000  
50%        3.000000      6.000000  
75%        3.000000      7.000000  
max        8.000000     14.000000  
   LotArea  YearBuilt  1stFlrSF  2ndFlrSF  FullBath  BedroomAbvGr  \
0     8450       2003       856       854         2             3   
1     9600       1976      1262         0         2             3   
2    11250       2001       920       866         2             3   
3     9550       1915       961       756         1             3   
4    14260       2000      1145      1053         2             4   

   TotRmsAbvGrd  
0             8  
1             6  
2             6  
3             7  
4             9  

Step 3: Specify and Fit Model

Create a DecisionTreeRegressor and save it iowa_model. Ensure you’ve done the relevant import from sklearn to run this command.

Then fit the model you just created using the data in X and y that you saved above.

from sklearn.tree import DecisionTreeRegressor
#specify the model. 
#For model reproducibility, set a numeric value for random_state when specifying the model
iowa_model = DecisionTreeRegressor(random_state = 1)

# Fit the model
iowa_model.fit(X, y)

# Check your answer
step_3.check()

<IPython.core.display.Javascript object>

Correct

# step_3.hint()
# step_3.solution()

Step 4: Make Predictions

Make predictions with the model’s predict command using X as the data. Save the results to a variable called predictions.

predictions = iowa_model.predict(X)
print(predictions)

# Check your answer
step_4.check()

[208500. 181500. 223500. ... 266500. 142125. 147500.]



<IPython.core.display.Javascript object>

Correct

# step_4.hint()
# step_4.solution()

Think About Your Results

Use the head method to compare the top few predictions to the actual home values (in y) for those same homes. Anything surprising?

# You can write code in this cell
print("Iowa House's Predictions are...")
print(iowa_model.predict(X.head()))

Iowa House's Predictions are...
[208500. 181500. 223500. 140000. 250000.]

It’s natural to ask how accurate the model’s predictions will be and how you can improve that. That will be you’re next step.

Keep Going

You are ready for Model Validation.

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