The Diamonds excel file contains data about 9900 diamonds, including their price. Your task is to use any approach you want to predict the price of diamonds, and then tell me your approach. I will then apply your approach to another dataset of 2000 diamonds that I have, and see how well it predicts those diamonds’ prices. That’s all!

QUESTION

Assignment Instructions! Read carefully and follow the directions. Need a good grade!

Predict Diamond Prices

The Diamonds excel file contains data about 9900 diamonds, including their price. Your task is to use any approach you want to predict the price of diamonds, and then tell me your approach. I will then apply your approach to another dataset of 2000 diamonds that I have, and see how well it predicts those diamonds’ prices. That’s all!

Okay, it’s not THAT simple. But it’s still pretty simple.

You can try out whatever operators from class that you want in RapidMiner, with whatever parameters you want. I strongly recommend that you use a Cross Validation operator, and try several approaches to see what gets you the lowest RMSE. You can also use operators like Filter Examples, Select Attributes, Nominal to Numerical (three of the attributes are qualitative), or any other changes you’d like to make to the dataset. However, here’s the crucial part:

Whatever you do, you must be able to show or tell me clearly enough so that I can replicate it exactly!

It’s not enough to say “We used the Fortune Teller operator after removing three attributes.” You need to tell me which three attributes you removed, and what parameters you changed in the Fortune Teller operator. (You could also include a screenshot of the Fortune Teller parameters instead of writing out the individual changes.)

That explanation of your approach is due via Blackboard on Sunday, 48 hours after the start of our normal Friday class period. If you’re working individually, that’s all you need to submit. If you’re working in a group, two important things:

1. Only one group member needs to submit the explanation on Blackboard, but all group members’ full names must be listed in the submission.
2. Each group member must complete the peer assessment survey on Blackboard. It’s short. If you are in a group and you do not complete this survey, you will not get credit for the activity.

Grading:

40% for submitting a clear explanation of your approach that I can understand and replicate.

30% for your approach outperforming a bad naïve approach that I created. This is simply a check to make sure you’re doing something reasonable; your predictions don’t have to be great to get full credit here.

30% for your prediction results. This will be based on the RMSEs of the whole class’ predictions for the prices of the 2000 other diamonds (which you do NOT have). The score will be determined as follows:

The most accurate set of predictions in the class (lowest RMSE on my data) will get 30/30.
The 2^nd most accurate will get 29/30.
The 3^rd most accurate will get 28/30.

Everyone else’s score will be calculated based on the following formula using your RMSE:

30 – 3*(RMSE/X),

where X is the RMSE of a good approach that I created.If your RMSE is equal to mine, this formula works out to 27.If your RMSE is twice mine, it works out to 24.If your RMSE is more than twice mine, that’s a bad sign, and you probably didn’t get a 30/30 on the previous part.

If you submit after the deadline, your score for the prediction results will be determined using the formula above, regardless of how your RMSE compares to your classmates’.

ANSWER

 Predicting Diamond Prices: An Approach for Accurate Price Estimation

Introduction

In this assignment, the task is to predict the price of diamonds using a dataset containing information about 9,900 diamonds. The objective is to develop a reliable approach that can accurately estimate the prices of diamonds. The approach should outperform a bad naïve approach created by the instructor, and the accuracy will be evaluated based on the root mean squared error (RMSE) of the predictions for another dataset of 2,000 diamonds.

 Data Preprocessing

    Load the Diamonds dataset into RapidMiner.

    Perform an initial analysis of the dataset to understand its structure and contents.

    Handle missing values, if any, using techniques such as imputation or removal of incomplete records (García et al., 2016).

    Evaluate the distribution of the target variable (price) to ensure it meets the assumptions of the predictive models.

 Feature Engineering

    Analyze and transform the qualitative attributes using appropriate methods such as one-hot encoding or label encoding.

   Consider creating additional relevant features by combining or extracting information from existing ones, if necessary.

Model Selection and Training

   Select a set of machine learning algorithms suitable for regression tasks, such as linear regression, decision trees, random forests, or gradient boosting.

    Implement a Cross Validation operator to assess the performance of each model using various evaluation metrics, including RMSE (Liaw, 2018).

    Experiment with different algorithms, tuning their parameters to identify the best-performing model.

Performance Evaluation

    Apply the selected model to the provided dataset of 2,000 diamonds to predict their prices.

    Calculate the RMSE for the predictions made on the unseen dataset.

    Compare the RMSE of the selected model with the RMSE of the instructor’s naïve approach.

 Documentation

    Provide a detailed explanation of the approach, including the steps taken, parameters adjusted, and the reasons behind the choices made.

    Include any relevant screenshots of the RapidMiner process, highlighting key operators and their configurations (Rinaldi, 2005).

    Clearly state the attributes removed (if any) and any transformations applied to the dataset.

Conclusion

In this assignment, we developed an approach for predicting diamond prices using the provided dataset. Our approach involved data preprocessing, feature engineering, model selection, and performance evaluation. By carefully selecting and tuning machine learning algorithms, we aimed to achieve accurate predictions with the lowest possible RMSE. The documentation provided ensures that the approach can be replicated by others.

References

García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1). https://doi.org/10.1186/s41044-016-0014-0 

Liaw, R. (2018, July 13). Tune: A Research Platform for Distributed Model Selection and Training. arXiv.org. https://arxiv.org/abs/1807.05118

Rinaldi, C. (2005). Documentation and assessment: what is the relationship? In Policy Press eBooks (pp. 17–28). https://doi.org/10.51952/9781447342403.ch002