panicpotatoe

panicpotatoe

panicpotatoe

panicpotatoe

panicpotatoe

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

# Predicting a new result with Linear Regression
lin_reg.predict([[5.5]])
#output should be 249500

# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
#output should be 132148.43750003

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

# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)

# Visualizing the Linear Regression results
def viz_linear():
    plt.scatter(X, y, color='red')
    plt.plot(X, lin_reg.predict(X), color='blue')
    plt.title('Truth or Bluff (Linear Regression)')

panicpotatoe

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)