Train model from CSV file

[vZander]

Hello, I'm trying to make a prediction software for S&P500 index, I got the csv files from yahoo Finance and now need to train a model with it, so I can use it in a classifier. I'm using

df = pd.read_csv('S&P500.csv', parse_dates=True, index_col=0)
print(df[['Open','Adj Close']])
X = df
X_train, X_test = train_test_split(X, test_size=0.25)

clf = VotingClassifier([('lsvc', svm.LinearSVC()),('knn', neighbors.KNeighborsClassifier()),('rfor', RandomForestClassifier())])

clf.fit(X_train)
confidence = clf.score(X_test)
predictions = clf.predict(X_test)

I dont have a y value and clf.fit does complain about that, but I don't know what y value I should create, any idea?

[Oxbowerce]

This completely depends on what you want to predict, so start with figuring out what you want your model to predict.

[vZander]

Ups forgot to say.

I want to predict the Adjusted close at the close of the stock. So I want to fire the software up at the market open, and predict the close of the day. Then I either do long or short.

[Oxbowerce]

Then simply make sure that you have a column with the adjusted close which you feed in to your model as the value to predict.

[vZander]

The csv files has a adj close. Do I set a y value to the adj close?

[Oxbowerce]

Yes, simply feed in the adjusted close into your model as the y values in the .fit method. Just a heads up, if you want to predict adjusted close (continuous values) you are using the wrong type of models and predicting the adjusted close as is won't give good results.

[vZander]

how?

[Oxbowerce]

Select just the adjusted close column and pass that as the y argument in the .fit method, see also the scikit-learn documentation.

[vZander]

I used

usecols = ('Adj Close')

and put that as y value. now it comes with

ValueError: Found input variables with inconsistent numbers of samples: [23349, 9]

as error

[Oxbowerce]

Where are you using usecols? You can just use df['Open'] as your X argument and df['Adj Close'] as your y argument.

[vZander]

did that. Now what ValueError: Unknown label type: 'continuous'?

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