Machine_Learning

Basic machine learning technique in Ruby. This is for pure understanding of the techniques

Techniques

1. Decision Trees [On Progress] How DT is build:

• Check the model for the base case
• Iterate through all the attributes
• Get the normalized information gain from splitting the attributes
• Let best_attr be the attribute with the highest information gain
• Create a decision node that splits on the best_attr
• Work on the sublists that are obtained by splitting on best_attr and add those nodes a child nodes.

Training Data Attributes [data/customer_purcharse_training.csv]

Placement:

What type of stand the CD is displayed on: an end rack,special offer bucket
, or a standard rack

Prominence:

What percentage of the CDs on display are from that author CDs.

Pricing:

What percentage of the full price was the CD at the time of purchase,
unless it is an old, back catalog title.

Eye Level:

Was the product displayed at eye level position? The majority of sales
will happen when a product is displayed at the eye level.

Customer purchase:

What was the outcome? Did the customer purchase?

2. Bayesian Networks [On Progress]
3. Artificial Neural Networks [On Progress]
4. Association Rules learning [On Progress]
5. Support Vector Machines [On Progress]
6. Clustering [On Progress]