help me with these please:

i$.$ What is the trade$-$off between bias and variance in Machine Learning? $[5]$

ii$.$ How can a dataset without the target variable be utilized in supervised learning

algorithms? $[5]$

iii. Why accuracy is not always the ideal metric for model evaluation $[5]$

iv$.$ Given a dataset and a variety of Machine learning algorithms, how do you decide

which algorithm to use. $[5]$

v$.$ You are a data scientist at a real estate company. Your task is to build a model to

predict house prices based on various features such as location, size, number of

bedrooms, and age of the house.

a$)$ Describe the steps you would take to clean and preprocess the dataset. $[5]$

b$)$ Explain using example how you would encode categorical variables. $[5]$

c$)$ Justify which machine learning algorithms would you consider for this problem. $[5]$

d$)$ Describe the process of hyperparameter tuning. Which method would you use in

this context and why $[5]$