In Part 1(you can read it here), I discussed the Business Case for Predicting Visitor-to-Customer Conversion for an Online Store and covered Exploratory Data Analysis of the training dataset.
In this part, I will cover Data Preprocessing and the Application of Supervised Learning Algorithms, namely RandomForest and XGBoost to the prepared training dataset.
So without further ado, let’s go to Data Preprocessing!
- Data Preprocessing
“What you sow, so you reap”. This proverb, so true for life in general is also very much true for Data Science ! We cannot feed crappy data to our algorithms and expect them to magically give us accurate predictions.
Getting the data ready in a form that can be fed into a learning algorithm is a vital task that a Data Scientist does.
As I mentioned in Part 1, the attributes in this data challenge were discrete and continuous with a widely varying ranges as well as categorical, with widely varying class sizes. Take a look at the short document that I have created to describe the attributes here.
The key elements of the Data Preprocessing Strategy that I used are the following:
- Do One-Hot-Encoding of all categorical attributes
- Convert continuous and discrete attributes that present in the string form in the dataset to float form
- Scale the all numerical (continuous and discrete) attributes
- Combine all the attributes of a data point into one list with each value in the range 0 and 1
- Create a Panda Dataframe for the Training and Test Datasets
The Panda Dataframe is then used for feeding data to the machine learning algorithms.
#digital-marketing #machine-learning #kaggle #analytics #data-science
