Solution to Kaggle Intro to Machine Learning: Your First Machine Learning Model
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()
# 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()
# 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()
# step_3.hint()
# step_3.solution()
random_state when specifying the model. Data is specified when fitting it.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.]
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
y.head()
0 208500 1 181500 2 223500 3 140000 4 250000 Name: SalePrice, dtype: int64
