Periods usually arrive every month in a woman’s life. But we all are so busy with the mundane work that we tend to forget our period dates. Moreover, most of the women have such an inconsistent cycle that it is worthless for them to remember their previous dates. Also due to a lack of awareness and hesitation in society, many women don’t know the reason and what exactly one should do during menstruation.
What it does
To solve this problem faced by almost every woman we made a period tracking web app. This will help in recording the changes in her cycle which might be the sign of a potentially dangerous health issue. Furthermore, it will also help her to know more about her body, mood swings and to avoid sudden and severe period cramps. We also provide the estimated day of ovulation so that a woman could know when we can conceive.
Most of the pre-existing websites or apps lack accuracy so women generally get more confused about their dates and moreover these apps take into account very few features to determine the dates. We take more data into account to provide much more accurate and efficient results. Our other features include specialized WeCare forums where women can freely and anonymously discuss their queries. We also have our 24x7 WeCare chatbot where women can get answers to their queries anytime.
Challenges we ran into
It was very difficult to find the dataset for a project like this. We had to go through lots of websites and research papers to finally get this dataset. Also, creating and deploying using Flask was a very big challenge.
Accomplishments that we're proud of
Even though using Flask the first time, we built a really good website. Also, the same goes for streamlit. Also, the project was really big with all these features we provide, so teamwork and time management were very much crucial for us, and we did manage it really well.
What we learned
We learned to deploy the website using Flask, and ML models using Streamlit. Time management and team management are also two very important lessons we learned.
What's next for WeCare
Next, we wanna make our interface have multi-language support so that more women can access it. Also, more training data for the Machine Learning model is also a crucial part so we can get better accuracy of the model. Also, we aim to collaborate with researchers for creating more features, and also how some scientific features can be measured in day-to-day life.