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Introduction to Artificial Intelligence
创建日期：2024-06-20 16:42:58
标签： ai
Introduction to Artificial Intelligence

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Introduction to Artificial Intelligence

Assignment 

AI for HealthCare
Description
A hotel has plans to enter new markets, and in their existing market, the sales team classified customers into 4 segments (A,B,C,D), also collected customer opinions with two opinion orientations( positive, negative). Then, they can perform segmented outreach and communication for different segment of customers.
You are required to develop an AI application for this hotel to segment customers into four segments and analyse the opinion orientation of customers in each segment. In this case, you will apply AI techniques to predict segment of customers and analyse their opinions. You are given a list of customers (identified by their IDs) with their segment level in the labels_record.csv file. The hotel also provides two sets of features which can be used to detect levels of customers, and the third feature which can be used to analyse their opinion orientation.
The first set of features are in the Customer-PI_record.csv file. The features are about personal information of the customers, including ID, Gender, Ever_Married, Age, Graduated, Profession, Work_Experience and Family_Size. The ID refers to the customer ID in the labels_record.csv.
The second set of features are in the Payment_record.csv file. The features consist of IDs (for the customers in labels_record.csv), and amount of previous payment in last 12 months. Pay_AMT1 to Pay_AMT12 are recorded a series of payment information as below:
PAY_AMT1: Amount of previous payment in January, 2015 (AUS dollar)
PAY_AMT2: Amount of previous payment in February, 2015 (AUS dollar)
PAY_AMT3: Amount of previous payment in March, 2015 (AUS dollar)
PAY_AMT4: Amount of previous payment in April, 2015 (AUS dollar)
PAY_AMT5: Amount of previous payment in May, 2015 (AUS dollar)
PAY_AMT6: Amount of previous payment in June, 2015 (AUS dollar)
PAY_AMT7: Amount of previous payment in July, 2015 (AUS dollar)
PAY_AMT8: Amount of previous payment in August, 2015 (AUS dollar)
PAY_AMT9: Amount of previous payment in September, 2015 (AUS dollar)
PAY_AMT10: Amount of previous payment in October, 2015 (AUS dollar)
PAY_AMT11: Amount of previous payment in November, 2015 (AUS dollar)
PAY_AMT12: Amount of previous payment in December, 2015 (AUS dollar)

The third set of features are in the descriptive_opinion.csv file. The features consist of a list of opinions with polarity (positive and negative).

The hotel also has a list of unsegmented customers (unsegmented_customer.csv) whose results (level of segment and polarity of opinion) have not been revealed yet. They already recorded their features and store them in three files (Customer-PI_predict.csv, Payment_predict.csv, unsegmented_customer_opinion.csv ). The hotel wants to check the usefulness of your AI model later when the results available. You are asked to select a model that you have evidence that it is the best and apply that model to predict the segment levels and identify opinion polarity for these customers.
Part A: Modelling (80%)
You will follow the steps below for the task.
1.Data Exploration
a.Does the data balance? (10%)
b.Use the visualisation to show how payment history in the last 12 months (Pay_AMT1 to Pay_AMT12) looks like? (5%)
2.Data Preparation
a.Data Selection/Data Integration/Data Cleaning. (5% - do not need to apply all, choose which deemed relevant)
b.Data transformation/Normalization (5%-- do not need to apply all, choose which deemed relevant)
3.Modelling and Evaluation
a.Which types of features (personal information or payment information) give better performance? You should try several AI/ML methods (at least 3) on each type of features to make a case for your claim. (20%)
b.Which AI/ML methods can be used to predict opinion polarity based on collected opinion feature? Show the results (5%)
c.Can we apply CNN to the payment information feature to predict segment level of customers? If Yes, what the results are? If No, explain why? (5%)
d.Can combining two feature results in better performance? Show empirical evidence. (5%)
e.What is the best approach to predict the segment of customer? Provide an explanation using the results in 3.a, 3.c, 3.d.(10%)

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