panicpotatoe

# -*- coding: utf-8 -*-
"""
Created on Fri Dec 21 18:59:49 2018

@author: Nhan Tran
"""

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

panicpotatoe

# -*- coding: utf-8 -*-
"""
Created on Mon Nov 12 18:19:23 2018

@author: Nhan Tran
"""

"""
y = b0 + b1*x1
y: dependent variable

panicpotatoe

# Visualizing the Training set results
viz_train = plt
viz_train.scatter(X_train, y_train, color='red')
viz_train.plot(X_train, regressor.predict(X_train), color='blue')
viz_train.title('Salary VS Experience (Training set)')
viz_train.xlabel('Year of Experience')
viz_train.ylabel('Salary')
viz_train.show()

# Visualizing the Test set results

panicpotatoe

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('salary_data.csv')
X = dataset.iloc[:, :-1].values #get a copy of dataset exclude last column
y = dataset.iloc[:, 1].values #get array of dataset in column 1st

panicpotatoe

# Predicting the result of 5 Years Experience
y_pred = regressor.predict(5)

panicpotatoe

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
pol_reg = LinearRegression()
pol_reg.fit(X_poly, y)

# Visualizing the Polymonial Regression results
def viz_polymonial():
    plt.scatter(X, y, color='red')

panicpotatoe

panicpotatoe

panicpotatoe

panicpotatoe