Q1. From the tasks listed below which one is a supervised learning problem?
a. Grouping related documents from an unannotated corpus.
b. Predicting credit approval based on historical data
c. Predicting rainfall based on historical data
d. Predicting if a customer is going to return or keep a particular product he/she purchased from e- commerce website based on the historical data about the customer purchases and the particular product. e. Fingerprint recognition of a particular person used in biometric attendance from the fingerprint data of various other people and that particular person

Q2. From the below, which is NOT a Classification problem?
a. Predicting the temperature of a room from other environmental features (such as atmospheric pressure, humidity etc.).
b. Predicting if a baseball player is a pitcher or catcher given his playing records.
c. Predicting the price of house based on the data consisting prices of other house and its features such as area, number of rooms, location etc.
d. Filtering of spam messages
e. Predicting the weather for tomorrow as “hot”, “cold”, or “rainy” based on the historical data wind speed, humidity, temperature, and precipitation.

Q3. From the below, which is a Regression task? (Multiple options may be correct)
a. Predicting the monthly sales of a cloth store in rupees.
b. Predicting if a user would like to listen to a newly released song or not based on historical data.
c. Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data.
d. Predicting if a patient has diabetes or not based on historical medical records.
e. Predicting if a customer is satisfied or unsatisfied from the product purchased from e-commerce website using the reviews he/she wrote for the purchased product.

Q4. From the below, which is an unsupervised task?
a. Predicting if a new edible item is sweet or spicy based on the information of the ingredients, their quantities, and labels (sweet or spicy) for many other similar dishes.
b. Grouping related documents from an unannotated corpus.
c. Grouping of hand-written digits from their image.
d. Predicting the time (in days) a PhD student will take to complete his/her thesis to earn a degree based on the historical data such as qualifications, department, institute, research area, and time taken by other scholars to earn the degree.
e. all of the above

Q5. From the below, which is a categorical feature?
a. Number of rooms in a hotel.
b. Minimum RAM requirement (in GB) of a system to play a game. c. Your weekly expenditure.
d. Ethnicity of a person.
e. Area (in sq. centimeter) of your laptop screen.
f. The color of the curtains in your room.