from preprocessing import Preprocessing
import regressor as reg
import visualizer as vis
#preprocssing
dataset = pd.read_csv('diamonds.csv')
prepro = Preprocessing()
prepro.handleMissing(dataset)
x = dataset.drop(['price','cut','color','clarity'],axis = 1)
y = dataset['price']
x = prepro.scale(x)
encode_col = dataset[['cut','color','clarity']]
encode_col = prepro.encode(encode_col)
x = np.concatenate((x,encode_col),axis=1)
X_train, X_test, y_train, y_test = train_test_split(x, y,random_state=0,test_size=0.33)
vis.Visualizer().scatterplot(X_test[:,0],y_test.iloc[:])
# Linear Regression
regressor = reg.Regressor(type=reg.LINEAR_REGRESSION)
regressor.fit(X_train, y_train)
print("******************Linear Regression******************")
print(regressor.score(X_test,y_test))
#vis.Visualizer().scatterplot(X_test[:,0],y_test.iloc[:],regressor)
print("*************************************************")
