ANLY482 AY2016-17 T2 Group20 Findings

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PROJECT OVERVIEW

FINDINGS

PROJECT DOCUMENTATION

PROJECT MANAGEMENT

Working documents

• Example of analysis: File:RegressionBMW.xlsx
  

• Example of R code: File:RegressionBMW.txt

Data Exploration and Cleaning

Issues

Too large dataset

Due to the large dataset, it took too long to run the nodes in SAS Enterprise Miner which resulted in the inefficiency. It took approximately 6 hours to run a single node.

We reduced the dataset to BMW cars by filtering the dataset “makeName = BMW” and “used=1,2” to retrieve a dataset result of used BMW cars. We categorized the BMW cars based on the ‘modelName’, where the cleaning and categorization of ‘modelName’ will be explained below. Throughout the project, we will be using BMW cars as a model while keeping it generalised. Thereafter, we will be using the same model as a template for other car brands. This can be done by filtering the ‘makeName’ column which uses brand as the main filter.

This reduces the load on the system as we will only be using BMW cars for analysis.

Customers would already have a preferred car brand based on their buying capacity and personal preference for cars. From the general model, dealers will be able to select the car brands (“makeName”) of the customers’ preference, which will lead them to the specifications (“modelName”) of the car.

Unclean variable – modelName

When we looked at the summary statistics of modelName, we can see that there are a few categories which should belong in another. For example, in the screenshot above, you can see that there are many 3 Series trims such as ‘318’, ‘323’, ‘325i’ which should belong in the ‘3 Series’ category so should be relabeled to ‘3 Series’.

Missing Values

Column	Number Missing (before)	Percentage Missing (before)	Number Missing (after)	Percentage Missing (after)
year	0	0.00%	0	0.00%
minDate	0	0.00%	0	0.00%
maxDate	0	0.00%	0	0.00%
daysCount	0	0.00%	0	0.00%
currentMsrp	74357	53.31%	14127	26.69%
currentPriceOther	86550	62.05%	0	0.00%
minMsrp	65831	47.20%	9232	17.44%
maxMsrp	66243	47.49%	9312	17.59%
msrpCount	0	0.00%	0	0.00%
minPriceOther	78926	56.58%	154	0.29%
maxPriceOther	78937	56.59%	164	0.31%
priceOtherCount	0	0.00%	0	0.00%
sold	0	0.00%	0	0.00%
used	0	0.00%	0	0.00%
mileage	49438	35.44%	1924	3.63%
Sq Root[mileage]	49438	35.44%	1924	3.63%
Log[mileage]	54259	38.90%	4482	8.47%
Log10[mileage]	54259	38.90%	4482	8.47%
Cube Root[mileage]	49438	35.44%	1924	3.63%
vehicleAge	0	0.00%	0	0.00%
Square Root[vehicleAge]	9	0.01%	3	0.01%
Log10[vehicleAge]	4301	3.08%	2705	5.11%
Log[vehicleAge]	4301	3.08%	2705	5.11%

We chose to remove the rows which have missing values of ‘currentPriceOther’ since that is the target variable. Without data from the target variable, the row would be unable to be used in the model. After removing the rows with missing values of ‘currentPriceOther’, we can see that there is a significantly lower percentage of missing values.

Removing Outliers

Outliers have extreme values that can largely influence our statistical analysis. While it may represent natural variation in our data, we decided to set the standard in classifying a data point as an outlier by using the Quantile Range Outliers in JMP. We chose Quantile Range Outliers instead of the other 3 possible choices such as Robust Fit Options, Multivariate Robust outliers and Multivariate k-Nearest Neighbor Outliers because the Quantile Range Outliers does not assume distribution associated with the data. Extreme values are calculated using the multiplier of the interquartile range(IQR).

We used a value of 0.1 for the Tail Quantile, and 4 for Q, which means in identifying the outliers, values have to be 4 times for than the IQR. All the values in the low threshold is negative,which means only the high threshold is considered as a qualifier.

Variable	High Threshold	Max Value	No. of outliers	Explanation
daysCount	408	522	117	In building a pricing model, we are looking at used cars that have a reasonable days to market period. For these cars beyond more than a year, cases of such car existing could be due to: Vintage cars Small dealerships - “Some small dealerships could be storing it in a warehouse or empty space, these cars are not maintained” - Steve Greenfield, CEO of Automotive Ventures Hence it may not be a relevant indicator for our pricing model.
currentMsrp	193,572	1,056,454	3	Blanket rule for a generalised model.
currentPriceOther	196,506	955,719	4	Blanket rule for a generalised model.
mileage	287,275	962,525	23	Cars driven beyond 200,000 miles are considered the high mileage for used cars. https://www.reference.com/vehicles/considered-high-mileage-car-e661f36f0817e76b

We did not consider removing outliers for msrpCount, priceOtherCount and Age because these are calculated variables based on the initial variables in currentMsrp, currentPriceOther and for year variable, we are not removing the possibilities of vintage cars which can be manufactured in 1983.

Another advantage of Quantile Range Outliers is that it identifies the Nines. In most industries, a high value of 999999 is commonly used to fill in for a missing value. Though our analysis we found that the number of nines found in the dataset does not reflect that it has been used to recode for missing variables. The highest count was 3603, and it was found for Age, which reflects cars that have been produced in 2008. In addition, we consulted with the developers to how they have recoded missing values, where it is left as it is.

In conclusion, the total no. of outliers removed are insignificant compared to our dataset, hence we chose to remove the abovementioned outliers.

Is the dataset large enough?

One of the questions we are asking is if the dataset is large enough given the degrees of freedom that we play with.

In the scope of our project which aims to do predictive modelling mainly through regression and considering other modelling techniques such as randomForest decision trees, our sample size of 52,937 is large enough considering that this dataset is representative of the BMW cars in United States even after removing outliers and excluding missing values.

http://stackoverflow.com/questions/25665017/does-the-dataset-size-influence-a-machine-learning-algorithm

Data Exploration

Feature Engineering

Feature engineering is the process of manipulating and combining features to arrive at more informative ones. The process in which the features were engineered is outlined below.

carCategory

Due to the many car models in the column ‘cleanModelName’, we were unable to use a decision tree algorithm to classify the cars because there were too many categories. Therefore, we decide to reduce the number of categories to the following by creating a super group of ‘SUV’, ‘Sports car’, ‘MPV’, ‘Economy car’, ‘Sedan’. We also considered the type of vehicle in the column ‘vehicleType’, in the formula for the ‘carCategory’ column because some of the vehicles are motorbikes so we labeled them as ‘Motorbike’. The 10 missing values are null values.

vehicleType

While cleaning the model names, we have also realized that some of the vehicles are not models of cars. Instead, they are models of motorbikes. As such, we decided to create a new column called ‘vehicleType’ to record whether each row contains a car or a motorbike. After excluding the missing values in ‘currentPriceOther’, we do not need this variable because the dataset now only contains cars.

Square Root[vehicleAge]

We understand that consumers would care about the age of the car because even if the models are of the same trim, it could be slightly different based on the year of production. After taking 2017 subtracted by the ‘year’ column, we are able to create a new feature which is the age of the vehicle. However, the age of the vehicle is not normally distributed so we performed a square root, ln and log10 transformation on it. A normal distribution is helpful later to increase the accuracy of our model. We then settled on using the square root transformation since according to the Q-Q plot, it is the closest to normality.