The problem Project_H2O solves

Now a days the worsening water crisis occurring in many regions has pushed them
to the brink of many conflicts. It has became a need of
the hour to take appropriate measures for the efficient management of
the limited water resources available.

So to reduce the inefficient management of water resources, we have
built a website which with the help of a ML model that predicts the water
required by a particular house considering the data provided by the
user

Here in our website, we have provided a login and register option
where the user can sign up and login to provide the details. On the
basis of the data provided, the ML model will allocate the required
water to the user along with considering the various factors like
weather, season, topography, whether it is a festival
day or not, availability of water in the source etc.

Impacts of the website

• Efficient water management.
• One stop location for payment, knowing water usage, demand and supply etc.
• Provides a transparent mechanism to the user and the authorities. Also the user can know any information in a faster way.
• This can benefit a particular region/community where water availability is scarce.

Challenges we ran into

• Getting Appropriate Data for ML Model
• Deploying and integrating the ML model with the website.
• Due to Version Conflicts of some Python Packages we weren't able to deploy our ML Integrated Website on Heroku
• While Integrating the Payment Option we faced some issues

As for getting the appropriate data, we did not find anything over the internet, so we decided to generate random data to train our Ml Model
We were able to save the ml model using pickle and then integrate into our website with help of it.
we weren't able to solve the version conflicting issues so our idea of deploying it on heroku failed.