willkoehrsen / machine-learning-project-walkthrough

An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!

machine-learning-project-walkthrough

An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!

machine-learning-project-walkthrough's Issues

Unable to replace missing data

while running the following snippet of code given here :

# Replace all occurrences of Not Available with numpy not a number
data = data.replace({'Not Available': np.nan})

# Iterate through the columns
for col in list(data.columns):
    # Select columns that should be numeric
    if ('ft²' in col or 'kBtu' in col or 'Metric Tons CO2e' in col or 'kWh' in 
        col or 'therms' in col or 'gal' in col or 'Score' in col):
        # Convert the data type to float
        data[col] = data[col].astype(float)

Following error occurs:

TypeError: Type aliases cannot be used with isinstance().

residuals data normally distributed, which means model is predicting correctly or wrongly ?

Hi Will,

Really thanks a lot for the project sharing. Here I am having small doubt.

Please see the attached screen shot. in the part 2, If the residuals data normally distributed, which means model is predicting correctly or wrongly ?

typeerror in cell [9]

---

TypeError Traceback (most recent call last)
in ()
1
2 # Drop the columns# Drop
----> 3 data = data.drop(columns = list(missing_columns))
4
5 # For older versions of pandas (pandas-dev/pandas#19078)

TypeError: drop() got an unexpected keyword argument 'columns'