
In the case of this, try to split into training and test set and crosvalidate to avoid over/underfitting
Kurt divide into qualitative and quantitative

Qualitative: Try to make better intuition, visualize, so we can better understanding the data, and ask a quality question to ask for our data. In doing so doing dimensionality reduction(PCA) to reduce dimension of data so we can better visualize it, so long in the clustering(Kmeans)

Quantitave: Not Â just throwing a bunch of data, but rather selecting the features that causing the model, not based on instinct, but with analysis
Kurt's Advice
Kurt's give 3Â spesific fields, with particular interest

Building Model. WeÂ like to model our system, data, for example recommender system. Then increase our coding skill,

Data Analysis, increase statistical and machine learning, as well as mathematical analysis

Communication, increase communication skill, subtract highanalysis data, and make conclusion to the company

It's important to know what our spesific interest among the three (or mixed) and increase the skill of particular field

Assignment is create ttest to know the subway rider, are more people into subway(raining/not raining/weekend)

We have talked about modeling existing data analysis, as well ass predicting the data in the future

We also have talked about statistic(Welch's ttest) and machine learning(linear regression)

Now we want to analyze and draw conlusion based on rider subway data

After that, we want to project our findings to family/friends. Doing so we will need data visualization