Regimenate

Inspiration

We were motivated to explore the everyday routines of ordinary individuals with the aim of enhancing their quality of life.

What it does

We will conduct an analysis and exploration of the daily routines of the average person by inputting their routine data and leveraging machine learning algorithms to evaluate and quantify their efficiency.

How we built it

By gathering data provided by users themselves and creating various datasets for comparison, we train the machine learning model to evaluate and offer efficiency comparisons.

Challenges we ran into

While generating multiple datasets during the machine learning process, we faced difficulties in maintaining consistent accuracy across these datasets, especially when introducing variations. Consequently, the efficiency results displayed variations, Yet we successfully overcame those challenges.

Accomplishments that we're proud of

Even though we observed variations in machine learning results and efficiency, we enhanced performance by training the model with various deviations and making algorithmic adjustments to attain improved outcomes.

What we learned

We've obtained valuable insights into creating a well-balanced daily routine for the typical person. Furthermore, we've honed our skills in training machine learning models to calculate routine efficiency based on a variety of datasets.

What's next for Regimenate

We have developed an automated regimen that caters to both individual users and a diverse user base. Our future plans involve expanding into health and habit analysis, as well as implementing these features into apps and websites for open-source accessibility

Built With

• jupyter
• numpy
• pandas
• python
• scikit-learn
• sql
• tableau