# Default Imports
from greyatomlib.linear_regression.q01_load_data.build import load_data
from greyatomlib.linear_regression.q02_data_splitter.build import data_splitter
from sklearn.linear_model import LinearRegression
# Load the package for linear regression and use load_data() and data_splitter() function
df = load_data('data/house_prices_multivariate.csv')
X, y = data_splitter(df)
def linear_regression(X, y):
model = LinearRegression()
return model.fit(X, y)
# %load q06_plot_residuals/build.py
# Default Imports
from greyatomlib.linear_regression.q01_load_data.build import load_data
from greyatomlib.linear_regression.q02_data_splitter.build import data_splitter
from greyatomlib.linear_regression.q03_linear_regression.build import linear_regression
from greyatomlib.linear_regression.q04_linear_predictor.build import linear_predictor
from greyatomlib.linear_regression.q05_residuals.build import residuals
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
dataframe = load_data('data/house_prices_multivariate.csv')
X, y = data_splitter(dataframe)
linear_model = linear_regression(X, y)
y_pred, _, __, ___ = linear_predictor(linear_model, X, y)
error_residuals = residuals(y, y_pred)
# Your code here
def plot_residuals(y,error_residuals):
plt.scatter(y,error_residuals)
plt.title('Residual plot')
plt.xlabel('Sales Price')
plt.ylabel('Error')
plt.show()
from greyatomlib.linear_regression.q01_load_data.build import load_data
from greyatomlib.linear_regression.q02_data_splitter.build import data_splitter
from sklearn.linear_model import LinearRegression
dataframe = load_data('data/house_prices_multivariate.csv')
res = data_splitter(dataframe)
xinp = res[0]
yinp = res[1]
def linear_regression(x, y):
regressor = LinearRegression()
lm = regressor.fit(x, y)
return lm
