Red Hat has a large amount of data about the behavior of individuals over time. They wanted to use this data to predict which individual they should approach.
- Predicted the potential business value of a person based on its characteristics and activities.
- Done Data Cleaning, Feature Engineering, and Feature Selection
- Build the Machine Learning model (Random Forest Classifier) for predicting the business value
Data Source: Kaggle
The dataset contains 3 files:
| File Name | Contains | Description of file |
|---|---|---|
| act_train.zip | act_train.csv | Contains all the activities that each person had performed, characteristic of these activities, and the outcome (whether or not the company approach them or not) |
| act_test.zip | act_test.csv | Contains all the activities that each person had performed and characteristics of these activities |
| people.zip | people.csv | Contains all the unique people who have performed activities and their characteristics |
- Covert the string elements to int or float elements
- Change the formate of dates
In act_train.csv, we have a column named "date" which gives the date on which the person does an activity. There is also a column labeled "date" in the people.csv file which gives you the date at which people characteristics is been noted. When we merge these two files we have columns named "date_x" (representing date column from act_train.csv file) and "date_y" (representing date column from people.csv file). Both of these features can be useful for our prediction. Thus we will introduce a new feature "date" which will be the difference of date_x and date_y.
date = date_x - date_y
Lets have a look in training datset. Here we can observe that almost 92% of the elements in columns (from char_1 to char_9) are non-null. Thus we can eliminate these features.
Build RandomForestClassifier model to predict the outcome
The model had been evaluated using AUC Metric. Got an accuracy of 99%