Regression

# Simple Linear Regression
## Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
 
## Importing the dataset
 
dataset = pd.read_csv('Salary_Data.csv')
years_of_exp = dataset.iloc[:, :-1].values
salary = dataset.iloc[:, -1].values
 
## Splitting the dataset into the Training set and Test set
 
from sklearn.model_selection import train_test_split
exp_train_data, exp_test_data, salary_train_data, salary_test_data = train_test_split(years_of_exp, salary, test_size = 1/3, random_state = 0)
 
## Training the Simple Linear Regression model on the Training set
 
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(exp_train_data, salary_train_data)
 
## Predicting the Test set results
 
salary_pred = regressor.predict(exp_test_data)
 
## Visualising the Training set results
 
plt.scatter(exp_train_data, salary_train_data, color = 'red')
plt.plot(exp_train_data, regressor.predict(exp_train_data), color = 'blue')
plt.title('Salary vs Experience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()
 
## Visualising the Test set results
 
plt.scatter(exp_test_data, salary_test_data, color = 'red')
plt.plot(exp_train_data, regressor.predict(exp_train_data), color = 'blue')
plt.title('Salary vs Experience (Test set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

# Multiple Linear Regression
 
## Importing the libraries
 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
 
## Importing the dataset
 
dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
 
print(X)
 
## Encoding categorical data
 
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')
X = np.array(ct.fit_transform(X))
 
print(X)
 
## 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)
 
## Training the Multiple Linear Regression model on the Training set
 
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
 
## Predicting the Test set results
 
y_pred = regressor.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

# Polynomial Regression
## Importing the libraries
 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
 
## Importing the dataset
 
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
 
## Training the Linear Regression model on the whole dataset
 
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)
 
## Training the Polynomial Regression model on the whole dataset
 
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
 
## Visualising the Linear Regression results
 
plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg.predict(X), color = 'blue')
plt.title('Truth or Bluff (Linear Regression)')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()
 
## Visualising the Polynomial Regression results
 
plt.scatter(X, y, color = 'red')
plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
 
## Visualising the Polynomial Regression results (for higher resolution and smoother curve)
 
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
 
## Predicting a new result with Linear Regression
 
lin_reg.predict([[6.5]])
 
## Predicting a new result with Polynomial Regression
 
lin_reg_2.predict(poly_reg.fit_transform([[6.5]]))

# Support Vector Regression (SVR)
 
## Importing the libraries
 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
 
## Importing the dataset
 
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
 
print(X)
 
print(y)
 
y = y.reshape(len(y),1)
 
print(y)
 
## Feature Scaling
 
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)
 
print(X)
print(y)
 
## Training the SVR model on the whole dataset
 
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)
 
## Predicting a new result
 
sc_y.inverse_transform(regressor.predict(sc_X.transform([[6.5]])).reshape(-1,1))
 
## Visualising the SVR results
 
plt.scatter(sc_X.inverse_transform(X), sc_y.inverse_transform(y), color = 'red')
plt.plot(sc_X.inverse_transform(X), sc_y.inverse_transform(regressor.predict(X)), color = 'blue')
plt.title('Truth or Bluff (SVR)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
 
## Visualising the SVR results (for higher resolution and smoother curve)
 
X_grid = np.arange(min(sc_X.inverse_transform(X)), max(sc_X.inverse_transform(X)), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(sc_X.inverse_transform(X), sc_y.inverse_transform(y), color = 'red')
plt.plot(X_grid, sc_y.inverse_transform(regressor.predict(sc_X.transform(X_grid))), color = 'blue')
plt.title('Truth or Bluff (SVR)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

# Decision Tree Regression
 
## Importing the libraries
 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
 
## Importing the dataset
 
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
 
## Training the Decision Tree Regression model on the whole dataset
 
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = 0)
regressor.fit(X, y)
 
## Predicting a new result
 
regressor.predict([[6.5]])
 
## Visualising the Decision Tree Regression results (higher resolution)
 
X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (Decision Tree Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

# Random Forest Regression
 
## Importing the libraries
 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
 
## Importing the dataset
 
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
 
## Training the Random Forest Regression model on the whole dataset
 
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)
regressor.fit(X, y)
 
## Predicting a new result
 
regressor.predict([[6.5]])
 
## Visualising the Random Forest Regression results (higher resolution)
 
X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (Random Forest Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()