risenW

# Declare an optimizer: here i use gradient descent
my_opt = tf.train.GradientDescentOptimizer(learning_rate=0.02)

#Create the train step
train_step = my_opt.minimize(loss)

n_iterations = 100

loss_stochastic = []

risenW

#Load our libraries
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
sess = tf.Session()

# declare batch size
batch_size = 20

x_vals = np.random.normal(1, 0.1, 100)

risenW

loss = tf.reduce_mean(tf.square(Y_pred - Y_target))

# Declare the optimizer (G.D)
my_opt = tf.train.GradientDescentOptimizer(0.02)
train_step = my_opt.minimize(loss)

loss_batch = []

for i in range(100):
    #pick a random 20 data points

risenW

plt.plot(range(0, 100, 5), loss_batch, 'r--', label='Batch Loss')
plt.plot(range(0, 100, 5), loss_stochastic, 'b-', label='Stochastic Loss')
plt.legend(loc='upper right')
plt.title('Batch training vs Stochastic training')
plt.show()

risenW

risenW

import pandas as pd
import numpy as np
from statistics import mode

german_cred = pd.read_csv('credit_preped.csv')
german_cred.head()

risenW

#Import single models
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.svm import SVR, SVC
from sklearn.neighbors import KNeighborsRegressor,KNeighborsClassifier

#Classification models
log_cf = LogisticRegression(solver='lbfgs', random_state=rand_seed)
svc_cf = SVC(gamma='scale', random_state=rand_seed)
knn_cf = KNeighborsClassifier()

risenW

#Classification 
for model in classification_models:
    model_train(model, features=german_cred, target_name='bad_credit')
    
#Regression  
for model in regression_models:
    model_train(model, features=german_cred, target_name='age_yrs', task='reg')

risenW

#get the data sets
X_train, X_val, y_train, y_val = get_split_data(german_cred, target_name='age_yrs')

#fit base models
linear_reg.fit(X_train, y_train)
knn_reg.fit(X_train, y_train)
svr_reg.fit(X_train, y_train)

#make predictions with trained models
pred1 = linear_reg.predict(X_val)

risenW

print("Linear Regression Model")
print(get_mae(pred1, y_val))
print("KNN Regression Model")
print(get_mae(pred2, y_val))
print("SVR Regression Model")
print(get_mae(pred3, y_val))
print("Average Model")
print(get_mae(avgpred, y_val))