Getting the right data for the perfect segmentation! We will be going through all the steps necessary for transforming our raw dataset to the format we need for training and testing our segmentation algorithms.
Data driven customer targeting or product bundling are critical for businesses to stay relevant against the intense competition they face. Consumers are now spoilt for choice and prefer personalized product offerings. With the coming of the fourth industrial revolution in the form of the immense growth of artificial intelligence and big data technologies, there is no better time to leverage segmentation models to perform such analysis. But before we do a deep dive into these models, we should be aware of what kind of data is needed for these models. This is the focus of my blog as we will be going through all the steps necessary for transforming our raw dataset to the format we need for training and testing our segmentation algorithms.
For this exercise, we will be working with clickstream data from an online store offering clothing for pregnant women. It has data are from April 2008 to August 2008 and includes variables like product category, location of the photo on the webpage, country of origin of the IP address and product price in US dollars. The reason I chose this dataset is that clickstream data is becoming a very important source of providing fine-grained information about customer behaviour. It also provides us a dataset with typical challenges like high dimensionality, need for feature engineering, presence of categorical variables and different scales of fields.
We will try to prepare the data for product segmentation by performing the following steps:
The applications of reinforcement learning are growing across the globe with each day passing by. Especially, AI engineers are expected to be trained at it.
Why Feature Engineering is important? The features in your data will directly influence the accuracy of your model. The better features gives good accuracy on your test data.
Today we will look into unsupervised learning techniques, we will go into details of: What is Unsupervised Learning? Types Of Unsupervised Learning; Understanding clustering & its types; Hands-on on K-Means & hierarchical clustering
Detailed understanding of the concepts of unsupervised learning with the help of clustering algorithms. Clustering and association are two of the most important types of unsupervised learning algorithms. Today, we will be focusing only on Clustering.
This series of articles is my attempt to illustrate some of my key learning points and thought process when carrying out the project. Hopefully, it will serve as a motivation for experts in domains outside of machine learning (or data science) to consider using machine learning as a tool when solving novel problems in their respective fields.