Polynomial Regression Template for Machine learning in Python programming language. Prediction calculation.

PolynomialRegressionTemplate.py

# Regression template

# Importing the Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

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

# Feature Scaling
""""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)""""

# Fitting the Regression Model to the dataset
# Create your regressor here

# Predicting a new result
y_pred = regressor.predict(6.5)

# Visualising the Regression results
plt.scatter(X, y, color = 'red')
plt.plot(X, regressor.predict(X), color = 'blue')
plt.title('Truth or Bluff (Regression Model)')
plt.xlabel('Independence variable (X)')
plt.ylabel('Dependence variable (y)')
plt.show()

# Visualising the Regression results (for higher resolution and smooth curve)
X_grid = np.arange(min(X), max(X), 0.1) # It a Numpy matrice
X_grid = X_grid.reshape((len(X_grid), 1)) # Reshape
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (Regression Model)')
plt.xlabel('Independence variable (X)')
plt.ylabel('Dependence variable (y)')
plt.show()