A small machine learning package
Project description
This is a Python ML librabry like scikit-learn
You can create your ML model or use some ML algorithms on your project
Example: Logistic Regression
Read csv file and slip data into training and test data
import pandas as pd df = pd.read_csv('Data_for_UCI_named.csv', header=0) df['stabf'] = df['stabf'].map({'unstable': 0, 'stable': 1}) Y = df['stabf'].values # sometimes it's needed to reshape data X = df.drop(['stabf'], axis=1).values X_train = X[:9000] Y_train = Y[:9000] X_test = X[9000:] Y_test = Y[9000:]
Let’s use our library ```` # call the LogisticRegression class from nista_learn.regressions import LinearRegression, LogisticRegression
log_reg = LogisticRegression() # fitting data log_reg.fit(X_train, Y_train, iterations=200000, learning_rate=0.25, show=True) # predict a small dataset y_pred = log_reg.predict(X_test[20:28]) print(’— small value —’) print(Y_test[20:28]) print(’— predicted data —‘) print(y_pred) # plotting the cost function log_reg.plot_cost() ```
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