The problem ML Simulator solves

Many data scientist, ML enthusiasts spend a lot of time just visualizing data in order to find the best possible model to get the best accuract result using hyper-parameter tuning. Since, nowadays a lot of enthusiasts knows sklearn library without getting deep into the exact implementation thus they take a lot of time, to devised the most optimum model rather than solving the actual business problem by using the dataset.

Challenges we ran into

Going through every models we started writing code which exactly implemented the models. But on later investigation we got to know that sometimes best of the best algorithm boils to a simple model such as taking an average. Lots of blogs just paved path to official research papers which took a lot of time.