Machine Learning Random Forest with Python from Scratch - Dealing with Missing Values

The video tutorial covers data cleaning, focusing on handling missing values in a dataset. It begins with an introduction to data cleaning and the importance of addressing missing values. The instructor sets up a Jupyter Notebook environment, imports necessary libraries, and loads the Titanic dataset. Various methods for handling missing values are discussed, including deletion and imputation using mean, median, and mode. The tutorial concludes with finalizing and saving the cleaned dataset for future use.

What is the first step in data cleaning as discussed in the lecture?

• Scaling features

• Normalizing data

• Handling missing values

• Removing duplicates

What is a potential downside of deleting rows with missing values?

• It can lead to overfitting

• It can increase computation time

• It can reduce dataset size significantly

• It can introduce bias

Which library is NOT mentioned as being imported for data cleaning in Jupyter Notebook?

• Pandas

• NumPy

• Matplotlib

• SciPy

What method is used to identify missing values in a Pandas DataFrame?

• dropna()

• replace()

• fillna()

• isnull()

Which statistical measure is suggested as a better option for imputing missing age values?

• Mean

• Median

• Standard Deviation

• Mode

What does setting 'inplace=True' in a Pandas operation do?

• Displays the DataFrame

• Saves the DataFrame to a file

• Permanently changes the DataFrame

• Creates a copy of the DataFrame

Which method is used to fill missing values with a specific value in Pandas?

• dropna()

• fillna()

• replace()

• interpolate()

What is the total number of records in the Titanic dataset mentioned in the lecture?

• 500

• 1000

• 891

• 177

What is the purpose of setting 'index=False' when writing a DataFrame to a CSV file?

• To include row numbers

• To exclude row numbers

• To sort the data

• To compress the file

What is the next topic to be covered after handling missing values?

• Data normalization

• Feature engineering

• Data visualization

• Outlier removal