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# Build a model to predict the total energy consumption of the house based on weather data

INSTRUCTIONS TO CANDIDATES

Use the posted datasets to answer the questions. You should clearly show your goals, steps, relevant SPSS/python result tables, validation tests, final equations for the models and most importantly your discussions about the results and findings. Answer the questions in as much detail as possible. Use graphs/figures/tables where appropriate.

(b) Submit your files on Moodle (emails are not accepted).

(c) You can use textbooks, class lectures, and internet to answer questions.

(d) You are not allowed to work or discuss with others (do not discuss your solutions on the forum).

Question 1: The dataset contains house temperature and humidity conditions monitored with a ZigBee wireless sensor network. There are 9 wireless nodes in the system that each node transmitted the temperature and humidity conditions in 80 randomly selected days of year 2016. These nodes are in kitchen (R1), living room (R2), laundry room (R3), office (R4), bathroom (R5), outside the building (R6), ironing room (R7), teenagers’ room (R8), and parents’ room (R9). The energy consumption data of appliance and lights for the same dates were logged with m-bus energy meters. The weather data including wind speed, pressure, Tdewpoint and temperature and humidity of the nearest airport weather station is also included in the dataset. The data also contains data record date and time information.

Part a) Build a model to predict the total energy consumption of the house based on weather data. You may need to implement preprocessing before fitting an appropriate model.

Part b) Which one of the following factors affects the total energy consumption of the lights and appliances?

Station humidity

Station temperature

Wind speed

Pressure

Tdewpoint

Date

Time

Part c) Use an appropriate technique to identify whether weekdays affect appliance energy consumption of the house or not.

Part b) Given the data collected from wireless sensors, build single metric for house temperature based on the data collected from temperature sensors. Discuss your results.

Question 2: Given the data collected for the following features of 20 different batteries:

Charge rate (Continuous)

Discharge rate (Continuous)

Depth of discharge (Continuous)

Temperature (Categorical)

End of charge volt (Continuous)

Failed or not failed (Binary)

Part a) Which technique can be applied to predict the probability of battery failure based on these features? Part b) Explain in detail model evaluation and validation steps of this technique.

Question 3: Compare clustering and classification and explain their similarities and differences.

Question 4: Assume that you have access to 1000 records of a population with the following variables: X1: Gender (F/M)

X2: Higher Education (Y/N) X3: Age (Numerical)

X4: Income (Numerical)

X5: Years of Work Experience (Numerical) X6: Organization (Categorical)

X7: City (Categorical) X8: Healthy Life (Y/N)

X9: Marriage Status (Categorical) X10: Exercise (Y/N)

X11: Diagnosed Cancer (Y/N) X12: Job (Categorical)

X13: Weight (Numerical) X14: Test Score (Numerical)

List three questions that you can investigate using this data. Identify an appropriate data analytic technique that can be applied to answer each question. List independent and dependent variables that you will select for this analysis.

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