ML Olympiad - TFUG Hajipur hosts Kaggle Competition

[**Download assets for participants**](https://drive.google.com/file/d/1cn-iZM_fTmU9bXnGNTpaZwICP4N8UPEa/view?usp=drive_link)

#Welcome to the 2024 edition of the ML Olympiad! 🎉

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11473972%2Fa197579d70a15e6c305d279ed410cc8e%2FCompetition%20Listing%20Image.png?generation=1707897309368170&alt=media)

##Problem Title:
Zero Waste Eats

##Problem Overview:
In a world witnessing a gradual increase in the number of people facing hunger since 2014, the imperative of reducing food losses and waste becomes evident. Each day, vast quantities of edible food are lost or wasted, presenting a critical challenge. Globally, approximately 13 percent of food produced faces losses between harvest and retail, with an additional 17 percent wasted across households, food service, and retail combined. The impact of food waste extends across the environment, food security, and the economy. However, existing solutions offer a diverse array of approaches, ranging from breakthrough innovations to fundamental best practices, many of which hold considerable potential for investment returns. The repercussions of food loss and waste are extensive, affecting the sustainability of our food systems. When food is lost or wasted, the resources invested in its production—such as water, land, energy, labor, and capital—are squandered. Furthermore, the disposal of food loss and waste in landfills contributes to greenhouse gas emissions, exacerbating climate change. Additionally, food loss and waste have adverse effects on food security, and food availability, and contribute to the rising cost of food.

##Problem Statement:
- To reduce food wastage all over the world, we need to work on some sustainable solutions that can help us analyze the overall food wastage.   Participants are tasked with designing and implementing solutions that contribute to the reduction of food waste. 
- To complete this challenge, participants need to perform the following tasks:

**Task 1:** Identifying significant trends and deep understanding of major factors contributing to food wastage.
**Task 2:** Conduct comprehensive data analysis to explore potential correlations with various socioeconomic and environmental factors.
**Task 3:** Visualize your findings with some charts to show your analysis based on different factors causing food wastage
**Task 4:** Use the above analysis to show some data-driven recommendations for reducing food wastage and promoting sustainable practices for food industries.

###Expected Results:
This challenge encourages participants to think critically about the food waste problem and apply their skills to develop impactful solutions. The participants are requested to build some innovative and well-designed solutions that demonstrate the potential to contribute to reducing food waste.

### Dataset
The original dataset is from [Kaggle User Josh Houlding](https://www.kaggle.com/code/joshhoulding/exploring-food-waste-data/input?select=Food+Waste+data+and+research+-+by+country.csv). We have modified the original dataset.

### Acknowledgments:
Thanks to Google Developers and the ML Developer Programs team (Nari Yoon, Hee Jung, and Soonson Kwaon) for their guidance and support.

### Organizer:
- **TensorFlow User Group Hajipur**

`#MLOlympiad` `#TFUGHajipur` `#TFUG`

## Show some excitement on socials
Share your progress using this hashtag: #MLOlympiad #TFUGHajipur