An overview of the format of the Technical Test is shown for your reference.

1. A problem statement paired with a given dataset, and requires a ML solution. Some examples include predict amount of future sales, forecast air quality around a city, predict likelihood of machine fault.
2. Perform three (3) tasks related to building a ML model and pipeline:
   1. Data extraction. Write an SQL query in python to perform data extraction from the given dataset.
   2. Exploratory data analysis. Create an interactive notebook in Python with appropriate visualisations and clear explanations.
   3. End-to-end ML pipeline. Create a ML pipeline to ingest the dataset, feed into the appropriate algorithm and return suitable metrics as outputs.
3. Package and submit your solution in according to the test instruction, which include the following:
   1. A folder named `mlp` containing Python modules/classes.
   2. An executable bash script `run.sh` at the base folder of submission.
   3. A `requirements.txt` file at the base folder of submission.
   4. A `README.md` file that includes the pipeline design and its usage, the choice of model(s) and the evaluation of the model(s) developed.

Applicants will receive the technical test via their registered email.  The test must be completed with five (5) days and submitted according to test instructions or considered invalid.

Applicants will be assessed on quality of the code in terms of clean separation of functionality, creativity, and ease of use. Code reusability between the tasks will be viewed favorably.

Those subsequently shortlisted for assessment interview will be assessed on the clarity of visualizations, depth of insights, presentation flow and structure of his or her analysis.