# Forest Fire Prediction Challenge
With rising global temperatures, forest fires are becoming more common and larger and present an increasing threat to the environment. If we can predict them in advance we will be able to prevent them and thus save lives and the environment.
## Problem Statement:
You will be given various parameters such as relative humidity, temperature, wind speed, etc. and your task will be to develop a program to predict whether or not there will be a forest fire.

## Dataset:
You are provided two splits of data - train, and test. Navigate to the Data Section for more details regarding these.

## Duration:
The coding challenge will be open to submissions from the 7th to the 19th of February (EOD).

## Submission:
You will have to submit the solution in the form of a .csv file, with the predicted values for each test case (using the ID column to denote the case). A sample submission file (sample_submission.csv) has been provided along with the dataset, you may simply fill the predictions column in the same before uploading it as the solution. But you are not done yet!

You also need to submit the link to your Colab Notebook (https://colab.research.google.com/) containing the code you used for generating the submission file. Kindly set view access to 'Everyone' while generating the shareable link.

These details must be furnished at: https://forms.gle/E6uHUaU8Q86XWVaR7
Kindly note that submitting the solution description and Colab Notebook on the aforementioned Google Form is mandatory, and the code submitted must be able to replicate your final submission file.

**Important:** In case you are using Kaggle notebooks, then strictly keep your notebook private. Sharing your Kaggle notebook publicly will make you liable for disqualification. Also, even if you are using Kaggle notebooks for writing your code, you need to first import it to Google Colaboratory and then share the link of the same, as mentioned above.

In case you are new to Kaggle, here is a handy link to Kaggle's [competition documentation](https://www.kaggle.com/docs/competitions), which includes, among other things, instructions on [submitting predictions](https://www.kaggle.com/docs/competitions#making-a-submission).

## Acknowledgements:
This challenge was created with the help of Yash Vashistha and Rupal Shah.