چالش دیتاست مسکن بوستون با استفاده از رگرسیون خطی

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

# Importing Data
from sklearn.datasets import load_boston
boston = load_boston()

boston.data.shape

boston.feature_names

data = pd.DataFrame(boston.data)
data.columns = boston.feature_names

data.head(10)

# Adding 'Price' (target) column to the data 
boston.target.shape

data['Price'] = boston.target
data.head()

data.describe()

data.info()

# Input Data
x = boston.data

# Output Data
y = boston.target


# splitting data to training and testing dataset. 

#from sklearn.cross_validation import train_test_split
#the submodule cross_validation is renamed and deprecated to model_selection
from sklearn.model_selection import train_test_split

xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size =0.2,
													random_state = 0)

print("xtrain shape : ", xtrain.shape)
print("xtest shape : ", xtest.shape)
print("ytrain shape : ", ytrain.shape)
print("ytest shape : ", ytest.shape)

# Fitting Multi Linear regression model to training model
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(xtrain, ytrain)

# predicting the test set results
y_pred = regressor.predict(xtest)

# Plotting Scatter graph to show the prediction 
# results - 'ytrue' value vs 'y_pred' value
plt.scatter(ytest, y_pred, c = 'green')
plt.xlabel("Price: in $1000's")
plt.ylabel("Predicted value")
plt.title("True value vs predicted value : Linear Regression")
plt.show()

from sklearn.metrics import mean_squared_error, mean_absolute_error
mse = mean_squared_error(ytest, y_pred)
mae = mean_absolute_error(ytest,y_pred)
print("Mean Square Error : ", mse)
print("Mean Absolute Error : ", mae)