[FIXED] SKLEARN Bestfeatures.fit (X,Y), what does it mean? How do I define X and Y?

Issue

my code doesn't work and I think it is because X and Y are not defined. I got the code from a book and it doesn't actually tell me how they are defined.

import pandas as pd
from matplotlib import pyplot
import seaborn as sns
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.datasets import load_digits
from pandas import read_csv
from pandas.plotting import scatter_matrix


filename = '/Users/rahulparmeshwar/Documents/Algo Bots/Data/Live Data/Tester.csv'
data = read_csv(filename)

correlation = data.corr()

bestfeatures = SelectKBest(k=5)
fit = bestfeatures.fit(X,Y)

dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X.columns)
featurescores = pd.concat([dfcolumns,dfscores],axis=1)

pd.set_option('display.width',100)
data.head(1)
print(data)

scatter_matrix(data)
pyplot.show()

print(featurescores.nlargest('2,score'))

I've checked the documentation for SkLearn but it is not very helpful. Any Help will be greatly appreciated

Solution

X and y should be the feature set and target variable that you loaded from your data file. This is one typical way to define them:

data = read_csv(filename)
y = data['target variable name']
X = data.drop('target variable name', axis=1)

Answered By - Bill the Lizard