How to import csv data file into scikit-learn?
python, scikit-learn
Solution
This is not a CSV file; this is just a space separated file. Assuming there are no missing values, you can easily load this into a Numpy array called `data` with
import numpy as np
f = open("filename.txt")
f.readline() # skip the header
data = np.loadtxt(f)
If the stock price is what you want to predict (your `y` value, in scikit-learn terms), then you should split `data` using
X = data[:, 1:] # select columns 1 through end
y = data[:, 0] # select column 0, the stock price
Alternatively, you might be able to massage the standard Python `csv` module into handling this type of file.
Problem
From my understanding, the scikit-learn accepts data in (n-sample, n-feature) format which is a 2D array. Assuming I have data in the form ... ``` Stock prices indicator1 indicator2 2.0 123 1252 1.0 .. .. .. . . . ``` How do I import this?