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() ```