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

The video tutorial covers data cleaning, focusing on handling missing values. It explains two main approaches: deleting rows with missing data or imputing values using mean, median, or mode. The tutorial demonstrates these techniques using a Jupyter Notebook with the Titanic dataset. It concludes by saving the cleaned dataset to a CSV file.

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

• Handling missing values

• Removing duplicates

• Scaling features

• Normalizing data

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

• It can lead to overfitting

• It can increase computational cost

• It can reduce the dataset size significantly

• It can introduce bias

Which libraries are imported for data cleaning in the Jupyter Notebook?

• Pandas and Seaborn

• NumPy and Pandas

• NumPy and Matplotlib

• Scikit-learn and Pandas

What method is used to identify missing values in the dataset?

• replace

• fillna

• dropna

• isnull

How many rows in the Titanic dataset have missing age values?

• 177

• 891

• 150

• 200

Which method is suggested as the best option for imputing missing age values?

• Mean

• Median

• Mode

• Random

What does setting 'inplace=True' do when filling missing values?

• Deletes the dataset

• Creates a new dataset

• Permanently changes the dataset

• Temporarily changes the dataset

What is the purpose of setting 'index=False' when saving the dataset to CSV?

• To create a new index

• To sort the data by index

• To exclude index from the CSV

• To include index in the CSV

What will be the focus of the next lecture as mentioned in the video?

• Handling missing values

• Removing outliers

• Feature engineering

• Data visualization

What is the main advantage of using median over mean for imputing age values?

• Median is faster to compute

• Median is less affected by outliers

• Median is easier to calculate

• Median is always a whole number