I have a dataset in CSV format, 6 columns and 1877 rows. The full dataset can be viewed at ShareCSV.
The first five columns are characteristics and the final column is a binary result, I want to create a classification network to predict result using the five inputs as seen in the CSV above.
I use the following code to normalize the data with pandas, then split into testing and training groups.
from sklearn import preprocessing import pandas as pd df = pd.read_csv(r"D:\path\data.csv", sep=",") df=(df-df.min())/(df.max()-df.min()) X = df[["Title words", "Hour posted", "Author age", "Is Text", "Subreddit age"]] Y = df['Front'] X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
I now need to pass this data to scikit-learn and select a classification algorithm, however this is where I am unsure what would be optimal, if anyone could recommend the best algorithm for my data and a rough implementation that would be great.