Started to learn machinelearning with Python.
First step to ML is data collection and preparation, from what I understand so far, clean and meaningful data is the key to success when teaching a model to a program. I selected my data from Kaggle, house pricing with parameters such as, "YearBuild", "Neighborhood", "TotalBasement" etc.
In this exercise, I tried to prepare data for a train model for Sale Price and followed these steps below.
- Understand the problem.
- Univariable study.
- Multivariate study.
- Basic cleaning.
- Test assumptions.
I used numpy, pandas, matplotlib, seaborn as usual and also exprienced scipy and sklearn modules for the first time.