Investigate factors affecting bike-share usage using Weka

1. WEKA: A MODERN APPLICATION
OF DATA MINING TECHNIQUES
SEAN,ROB,PRATIK,RHODRI,AL, VASANTI,MINGHAO

2. What is WEKA?
• Desktop application for machine learning & data mining
• Open source Java based tool
• Offers commonly used algorithms to model data.
• University of Waikato, New Zealand

3. What is Data Mining & Machine Learning?
• Data Mining :
• Searching for patterns in data
• Finding value in data
• Machine Learning:
• Developing models which computational resources can use
• Using computational resources to model data to predict a likely outcome.

4. Features of WEKA
• Pre-process data
• Classification & Clustering
• Association rules
• 3D visualisation

5. Choosing the Dataset
• Public datasets:
•data.gov.uk
•kaggle.com: such as Titanic dataset
•UCI Machine Learning Repository
• Dataset which could provide insight to a real world scenario
• Would model effectively in WEKA: several properties

6. Capital Bikeshare
Picture: Alejandro Castro, flickr, creative commons
• Bike-share system in Washington DC and surrounding area
• https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset

7. The Objective
• Investigate factors affecting bike-share usage
• Could this data be used to predict how busy or quiet a bike share
system may be on a given day?

8. Dataset fields
• Record index
• Time information
•Date, day of the week, whether day is holiday, whether day is working day,
month, year, season (1-4 spring/summer/autumn/winter)
•Weather Information
•weather description (separated into four distinct results which are roughly good
to bad)
•normalized values for temperature, ‘feels like’ temperature, humidity and
windspeed
•Totals
•counts for bikes rented by registered and casual users
•total count for bikes registered that day

9. Pre-processing
• Remove fields which don’t help prediction
•indexes, sub-totals etc
• Filters
• Discretize - categorise into discrete values
• ClassBalancer - re-weights instances so more evenly spread

10. Data Visualisation

11. Basic terminology to understand evolution of
classifiers
•True positive(tp): An instance is correctly predicted to belong to the
given class
•True negative(tn): An instance is correctly predicted not to belong
to the given class
•False positive(fp): An instance is incorrectly predicted to belong to
the given class
•False negative(fn): An instance is incorrectly predicted not to belong
to the given class

12. Explanation of Statistics
• Precision:
• Recall:
• F-measure:

13. Algorithms explored
Graph based:
• J48 - This classifier uses a tree structure to make decisions.
•Performs very good for our dataset

14. Algorithms explored
Rule based :
• ZeroR - ZeroR is the simplest classification method which relies on the target
and ignores all predictors.
•Not good for our dataset

15. Algorithms explored
Naïve Bayes
•This is a probabilistic classifier based on Bayes Theorem which
analyses the relationship between features and class labels.
•. This classifier can handle missing values by ignoring them during
calculation of the conditional probabilities.

16. Testset Division
Training and Testing set:
-Training data is used for building a ML model
-Testing data is used for measuring performance of a ML model
Supplying testing set in WekaSeparate training and testing

17. Testset Division
Cross Validation:
-To overcome the problem of overfitting
-Makes the predictions more general
•Includes:
-Splitting the original dataset into k equal parts (folds)
-Takes out one fold aside, and performs training over the rest k-1
folds and measures the performance
-Repeats the process k times by taking different fold each time.
•10-fold cross-validation : k = 10

18. Testset Division
Percentage split
-Randomly split your dataset into a training and a testing partitions
each time you evaluate a model.
Dividing original dataset into testing and training
For example:
If we have a data of 100
instances and we would like
to split 66% as training and
34% as test set using
percentage split

19. What is Clustering?
• Finding the class labels and the number of classes directly from the
data (in contrast to classification).
• It is unsupervised learning:
We want to explore the data to find some structures in them.
What is clustering for?
● Grouping items of similar properties together into clusters.
● For example to apply machine learning approaches to make
decisions based on data e.g. for classifying : “small”, “medium” and
“large” T-Shirts.

20. Clustering types:

21. Clustering types:

22. Some popular Clustering Algorithms
•K- means clustering (disjoint sets)
•EM clustering (probabilistic)
•Cobweb clustering (hierarchical)

23. KMeans: Iterative distance-based clustering
(disjoint sets)
1. Specify k, the desired number of clusters
2. Choose k points at random as cluster centers
3. Assign all instances to their closest cluster center
4. Calculate the centroid (i.e., mean) of instances in each cluster
5. These centroids are the new cluster centers
6. Continue until the cluster centers don’t change
Minimizes the total squared distance from instances to their cluster
centers.

24. K-means in Weka
•Note parameters:
• numClusters
•distanceFunction
How can we tell the
right number of clusters?
In general, this is
an unsolved problem
Clustering is subjective

25. •Use the AddCluster
unsupervised attribute filter
•Hard to evaluate clustering

26. Trying to cluster into seasons
Using K-means clustering, with k=4, we wish to see if the data falls
into the clusters based on the seasons

27. Observations
• We found that winter and summer months have separated into two
distinct clusters.
• The autumn and spring months have not separated so well.
• From the visualisation we also see the overall trend of more users in
the summer months compared to winter ones.
• This is not surprising since these months are hotter and people are
more likely to choose to rent bikes.

28. Possible Improvements
• Data accuracy
• Uncontrollable outside factors e.g. road closures,cycle paths built,tube strikes etc.
• As popularity increases -> may affect results.
• Data precision
• Bad measurements, subjective opinions(weather): generalised - exact calculations needed.
• Variable factors e.g. “temperature or weather” is different depending on exact location.
• Data itself always changing: only an indicator of some relationships.
• Different people: e.g. tourists – different people may have different attitudes
• Different locations yield different results: weather is variable across continents.

29. Evaluation of best approach
• J48 - easy to visualise
• Zero R is a bad idea for our dataset
Overall : the best approach is to analyse several different WEKA modules
and compare results to focus efforts and find the best solution.
• Graphs of properties: can indicate most important factors to be classified
• Classification algorithms: to build a model
• Testing the model is also crucial.

30. Conclusions based on data
• Dataset suitability - probably more suited to classification than
clustering
• Some prediction was possible
• External factors - other changes in the transport network, cycling
for health, city events
• Other possible analysis: usage by hour, casual users
• Applications: Smart cities & planning - effective bikeshare provision