Inspiration
The project was very interesting to predict the requirements.
What it does
It helps in learning about the data by evaluating the conditions.
How we built it
We used one-hot encoding to convert the categorical variables to one-hot encoding. Random-forest classification to build feature importances and feature importance calculation to learn more about the prediction.
Challenges we ran into
We were not able to plot the graphs as it looks. It took time to be as it is.
Accomplishments that we're proud of
We could complete the code which meets all the requirements.
What we learned
We have learned the use of sklearn, seaborn, matplotlib and smote.
What's next for SmsquareN
Built With
- jupyternotebook
- python
Log in or sign up for Devpost to join the conversation.