import warnings

warnings.filterwarnings('ignore')

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LinearRegression

from sklearn.linear_model import Ridge, Lasso,ElasticNet

from sklearn.model_selection import GridSearchCV

from sklearn import metrics

from sklearn import ensemble

from sklearn.metrics import r2_score

from sklearn.metrics import mean_squared_error

from sklearn.model_selection import cross_val_score

from sklearn.model_selection import RepeatedKFold

# read the csv file

Data = pd.read_csv(r'C:\Users\hp\Desktop\Sathya\ml\kc_house_data.csv')

print("*********************************DATASET*******************************************")

print(Data.head())

print("*********************************INFORMATION*******************************************")

print(Data.info())

print("*********************************ROWS AND COLUMNS******************************************")

print(Data.shape)

#print("**************************** Columns having null values *****************************")

#print(list(Data.columns.values.tolist()) )

#for col in list(Data.columns.values.tolist()):

# Data[col]=Data[col].fillna('None')

if True in Data.isnull().any():

print('There is null value in the dataset')

else:

print('There is no null value in the dataset')

print("**************************** DESCRIBE*****************************")

print(Data.describe().transpose().to_string())

Data = Data.drop('id',axis=1)

Data = Data.drop('zipcode',axis=1)

Data['date'] = pd.to_datetime(Data['date'])

Data['month'] = Data['date'].apply(lambda date:date.month)

Data['year'] = Data['date'].apply(lambda date:date.year)

# check correlation

print("**************************** CORRELATION*****************************")

print(Data.corr()['price'].sort_values(ascending=False))

print("**************************** CORRELATION BETWEEN FEATURES*****************************")

correlations=Data.corr()

attrs = correlations.iloc[:-1,:-1] # all except target

threshold = 0.5

important_corrs = (attrs[abs(attrs) > threshold][attrs != 1.0]) \

.unstack().dropna().to_dict()

unique_important_corrs = pd.DataFrame(

list(set([(tuple(sorted(key)), important_corrs[key]) \

for key in important_corrs])),

columns=['Attribute Pair', 'Correlation'])

# sorted by absolute value

unique_important_corrs = unique_important_corrs.iloc[

abs(unique_important_corrs['Correlation']).argsort()[::-1]]

print(unique_important_corrs)

Data = Data.drop('date',axis=1)

X = Data.drop('price',axis =1).values

y = Data['price'].values

print("*********************************FEATURES*******************************************")

print(Data.drop('price',axis =1))

print("*********************************TARGET*******************************************")

print(Data['price'])

#splitting Train and Test

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=101)

#print(X_train, X_test)

#standardization scaler - fit&transform on train, fit only on test

s_scaler = StandardScaler()

X_train = s_scaler.fit_transform(X_train.astype(np.float))

X_test = s_scaler.transform(X_test.astype(np.float))

#print(X_train, X_test)

regressor = LinearRegression()

regressor.fit(X_train, y_train)

y_predd = regressor.predict(X_test)

df = pd.DataFrame({'Actual': y_test, 'Predicted': y_predd})

df1 = df.head(10)

print("****************************************************************************")

print(' MULTIPLE LINEAR REGRESSION')

print(df1)

rr = Ridge(alpha=0.01,normalize=True)

rr.fit(X_train, y_train)

pred_train_rr= rr.predict(X_train)

pred_test_rr= rr.predict(X_test)

df = pd.DataFrame({'Actual': y_test, 'Predicted': pred_test_rr})

df1 = df.head(10)

print("****************************************************************************")

print('RIDGE REGRESSION')

print(df1)

model_lasso = Lasso(alpha=10)

model_lasso.fit(X_train, y_train)

pred_train_lasso= model_lasso.predict(X_train)

pred_test_lasso= model_lasso.predict(X_test)

df = pd.DataFrame({'Actual': y_test, 'Predicted': pred_test_lasso})

df1 = df.head(10)

print("****************************************************************************")

print('LASSO REGRESSION')

print(df1)

model_enet = ElasticNet(alpha = 0.01)

model_enet.fit(X_train, y_train)

pred_train_enet= model_enet.predict(X_train)

pred_test_enet= model_enet.predict(X_test)

df = pd.DataFrame({'Actual': y_test, 'Predicted': pred_test_enet})

df1 = df.head(10)

print("****************************************************************************")

print('ELASTICNET REGRESSION')

print(df1)

clf = ensemble.GradientBoostingRegressor(n_estimators = 400, max_depth = 5, min_samples_split = 2,learning_rate = 0.1, loss = 'ls')

clf.fit(X_train,y_train)

y_pred=clf.predict(X_test)

df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})

df1 = df.head(10)

print("****************************************************************************")

print('GRADIENTBOOST REGRESSION')

print(df1)

x=[regressor.score(X_train,y_train)*100,rr.score(X_train,y_train)*100,model_lasso.score(X_train,y_train)*100,model_enet.score(X_train,y_train)*100,clf.score(X_train,y_train)*100]

y=[regressor.score(X_test, y_test)*100,rr.score(X_test, y_test)*100,model_lasso.score(X_test, y_test)*100,model_enet.score(X_test, y_test)*100,clf.score(X_test, y_test)*100]

z=[metrics.mean_absolute_error(y_test, y_predd),metrics.mean_absolute_error(y_test,pred_test_rr),metrics.mean_absolute_error(y_test,pred_test_lasso),metrics.mean_absolute_error(y_test,pred_test_enet),metrics.mean_absolute_error(y_test, y_pred)]

h=[metrics.mean_squared_error(y_test, y_predd),metrics.mean_squared_error(y_test,pred_test_rr),metrics.mean_squared_error(y_test,pred_test_lasso),metrics.mean_squared_error(y_test,pred_test_enet),metrics.mean_squared_error(y_test, y_pred)]

g=[np.sqrt(metrics.mean_squared_error(y_test, y_predd)),np.sqrt(metrics.mean_squared_error(y_test,pred_test_rr)), np.sqrt(metrics.mean_squared_error(y_test,pred_test_lasso)),np.sqrt(metrics.mean_squared_error(y_test,pred_test_enet)),np.sqrt(metrics.mean_squared_error(y_test, y_pred))]

v=[metrics.explained_variance_score(y_test,y_predd),metrics.explained_variance_score(y_test,pred_test_rr),metrics.explained_variance_score(y_test,pred_test_lasso),metrics.explained_variance_score(y_test,pred_test_enet),metrics.explained_variance_score(y_test, y_pred)]

print("****************************************************************************")

data = pd.DataFrame(np.column_stack([x,y,z,h,g,v]),columns=['Train Score','Test Score','Mean Absolute Error','Mean Squared Error','Root Mean Squared Error','Variance'],index= ['Linear Regression Model:','Ridge Regression Model:','Lasso Regression Model:','ElasticNet Regression Model:','GradientBoosting Regression Model:'])

print(data.to_string())

print("****************************************************************************")

fig = plt.figure(figsize=(10,5))

fig.add_subplot(3,2,1)

plt.scatter(y_test,y_predd)

plt.title(" MULTIPLE LINEAR REGRESSION ")

plt.ylabel('predicted value')

plt.xlabel('Actual price')

fig.add_subplot(3,2,2)

plt.scatter(y_test,pred_test_rr,color='purple')

plt.title("RIDGE REGRESSION ")

plt.ylabel('predicted value')

plt.xlabel('Actual price');

fig.add_subplot(3,2,3)

plt.scatter(y_test,pred_test_lasso,color='red')

plt.title("LASSO REGRESSION ")

plt.ylabel('predicted value')

plt.xlabel('Actual price');

fig.add_subplot(3,2,4)

plt.scatter(y_test,pred_test_enet,color='gold')

plt.title("ELASTICNET REGRESSION ")

plt.ylabel('predicted value')

plt.xlabel('Actual price');

fig.add_subplot(3,2,5)

plt.scatter(y_test,y_pred,color='green')

plt.title("GRADIENTBOOST REGRESSION' ")

plt.ylabel('predicted value')

plt.xlabel('Actual price');

plt.tight_layout()

fig.tight_layout(pad=1.5,w_pad=1.5,h_pad=10.0)

plt.show()