All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
Enter the answer to 1 decimal point.
Check the dataset and initial analysis to check for missing values.
Enter the answer to 1 decimal point.
Rename the columns to have underscore instead of space. For example old name: fixed acidity to new name: fixed_acidity. Skip single-word columns. Set inplace=True.
Perform the modification and store in a new variable: df_first_last.
Locate and remove the row with the maximum value for total_sulfur_dioxide and store in a new variable: df_drop.
All the datatypes are float besides the quality column. Create a new column in the df DataFrame named quality_float which contains the values of quality, but with a float type.
Modify the dataframe by droping the three variables density, residual_sugar,chlorides and store your result as df_drop_three.
Get the percentage of alcohol content for each datapoint and store your result in a new column alcohol_perc.
Create a new column in the data frame that contains the sum of sulphates and citric_acid. Store your result in a new column: sulphate_citric_acid.
Modify the dataset accordingly and store your result in a new column deviation_alcohol
Convert the wine quality scores into categorical labels. Classify as low if values are 5 and below; medium if values are between 5 and 7; high if greater than 7. Store your result in a new column quality_label
Modify the DataFrame to obtain the ratio and store your result in a new column free_total_ratio.
