Welcome to the "Mini-Datathon"! Gear yourselves up for an exciting real-world application challenge that we have in store for you. This is where you will have to give your brain a workout and use your skills, to make a Classification Model for the given situation!

## **About the data**
The apparel industry houses a huge amount and variety of data. At every step of the supply
chain, data is collected and stored by each supply chain actor. This data, when used intelligently,
can help with solving a good deal of problems for the industry.

Data mining and machine learning have been at the forefront of research, helping to solve
analytical problems and overcoming business problems. The power of data mining in analyzing big data has been proven in various studies.

This dataset includes various attributes of the apparel industry which may include the logistics of the department, incentives, amount of workers, time given for a target etc. The dataset comprises of over 1000 instances which need to be analyzed and used to **predict the productivity performance of the teams.**

## **Tasks**

**(a) Convert the target column i.e. `actual_productivity` column into three classes (0, 1, 2) using the given thresholds:**
- 0 (low productivity) for `actual_productivity` <= 0.5
- 1 (moderate productivity) for 0.5 < `actual_productivity` <= 0.8
- 2 (high productivity) for `actual_productivity` > 0.8

**(b) Create a classification model to classify productivity performance into defined classes (0, 1, 2).**

### Now that you have come this far, head to the data section and show us what you got!
![check](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSKRx5vM1A-WYg8cxu0FFYOU6HKOcHE7YRmww&usqp=CAU)