Question 9 - what am i missing?

M4. Linear Models M4. Wrap-up quiz 4
#1

Hi Team,

I ran the requirement from the question, first with numerical data and compared with dummy. None of my answer matched, and thus i got the answer wrong. No Problem …

I went through the solution and then ran the solution which your team have provided and i got nan for all 10 folds(expected as we have string, non-numerical data?).

# ur team solution 
from sklearn.linear_model import LogisticRegression
import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import cross_validate

ames_housing = pd.read_csv("../datasets/ames_housing_no_missing.csv")
target_name = "SalePrice"
data = ames_housing.drop(columns=target_name)
target = ames_housing[target_name]

model = make_pipeline(StandardScaler(), LogisticRegression())
cv_results = cross_validate(
    model, data, target, cv=10, return_estimator=True
)
test_score_lr = cv_results["test_score"]
test_score_lr
# my first attempt, no option matched
numerical_features = [
    "LotFrontage", "LotArea", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2",
    "BsmtUnfSF", "TotalBsmtSF", "1stFlrSF", "2ndFlrSF", "LowQualFinSF",
    "GrLivArea", "BedroomAbvGr", "KitchenAbvGr", "TotRmsAbvGrd", "Fireplaces",
    "GarageCars", "GarageArea", "WoodDeckSF", "OpenPorchSF", "EnclosedPorch",
    "3SsnPorch", "ScreenPorch", "PoolArea", "MiscVal",
]

data_numerical = data[numerical_features]
cv_results = cross_validate(
    model, data_numerical, target, cv=10, return_estimator=True
)
test_score_lr = cv_results["test_score"]
test_score_lr
#2

It seems that you are still using the "ames_housing_no_missing.csv", whereas questions 8 to 13 use the "adult-census.csv" dataset. Just above question 8 such change is indicated as follows:

Now, we will tackle a classification problem instead of a regression problem. Load the Adult Census dataset with the following snippet of code and we will work only with numerical features.

adult_census = pd.read_csv("../datasets/adult-census.csv")
target = adult_census["class"]
data = adult_census.select_dtypes(["integer", "floating"])
data = data.drop(columns=["education-num"])