<gh_stars>0
#!/usr/bin/env python
# coding: utf-8

# In[ ]:


# Importing the libraries
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.preprocessing import StandardScaler
from matplotlib import pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split


# In[ ]:


# Importing the dataset
df = pd.read_csv('../input/kc_house_data.csv')
df.head()


# In[ ]:


# Getting the dependent variables and independent variables
df['date'] = df['date'].apply(lambda x: int(x[:8]))
X = df[['date', 'bedrooms', 'bathrooms', 'sqft_living',
       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',
       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',
       'lat', 'long', 'sqft_living15', 'sqft_lot15']].values
y = df['price'].values


# In[ ]:


# Splitting the training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 0)
# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)


# In[ ]:


# define base model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(19, input_dim=19, kernel_initializer='normal', activation='relu'))
    model.add(Dense(output_dim = 19, init = 'uniform', activation = 'relu'))
    model.add(Dense(output_dim = 19, init = 'uniform', activation = 'relu'))
    model.add(Dense(1, kernel_initializer='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model
# Fitting to the training set
estimator = KerasRegressor(build_fn=baseline_model, epochs=150, batch_size=10, verbose=False)
estimator.fit(X_train, y_train)
# Predicting the results
prediction = estimator.predict(X_test)


# In[ ]:


# Visualization the results and evaluation
n = 12
length = len(prediction)
sns.set_style('darkgrid', {'axis.facecolor':'black'})
f, axes = plt.subplots(n, 1, figsize=(25,30))
times = 0
for i in range(n):
    if i == 0:
        plt.sca(axes[0])
        plt.plot(y_test[:round(length/n)], color = 'red', label = 'Real Price')
        plt.plot(prediction[:round(length/n)], color = 'blue', label = 'Predicted Price')
        plt.title('House Price Predicitionin King County', fontsize=30)
        plt.ylabel('Price', fontsize=20)
        plt.legend(loc=1, prop={'size': 10})
    else:
        if i == n-1:
            plt.sca(axes[n-1])
            plt.plot(y_test[round(length/n*(n-1)):], color = 'red', label = 'Real Price')
            plt.plot(prediction[round(length/n*(n-1)):], color = 'blue', label = 'Predicted Price')
            plt.ylabel('Price', fontsize=20)
            plt.legend(loc=1, prop={'size': 10})
        else:
            plt.sca(axes[i])
            plt.plot(y_test[round(length/n*i):round(length/n*(i+1))], color = 'red', label = 'Real Price')
            plt.plot(prediction[round(length/n*i):round(length/n*(i+1))], color = 'blue', label = 'Predicted Price')
            plt.ylabel('Price', fontsize=20)
            plt.legend(loc=1, prop={'size': 10})
plt.show()


#!/usr/bin/env python
# coding: utf-8

# In[ ]:


# Importing the libraries
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.preprocessing import StandardScaler
from matplotlib import pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split


# In[ ]:


# Importing the dataset
df = pd.read_csv('../input/kc_house_data.csv')
df.head()


# In[ ]:


# Getting the dependent variables and independent variables
df['date'] = df['date'].apply(lambda x: int(x[:8]))
X = df[['date', 'bedrooms', 'bathrooms', 'sqft_living',
       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',
       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',
       'lat', 'long', 'sqft_living15', 'sqft_lot15']].values
y = df['price'].values


# In[ ]:


# Splitting the training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 0)
# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)


# In[ ]:


# define base model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(19, input_dim=19, kernel_initializer='normal', activation='relu'))
    model.add(Dense(output_dim = 19, init = 'uniform', activation = 'relu'))
    model.add(Dense(output_dim = 19, init = 'uniform', activation = 'relu'))
    model.add(Dense(1, kernel_initializer='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model
# Fitting to the training set
estimator = KerasRegressor(build_fn=baseline_model, epochs=150, batch_size=10, verbose=False)
estimator.fit(X_train, y_train)
# Predicting the results
prediction = estimator.predict(X_test)


# In[ ]:


# Visualization the results and evaluation
n = 12
length = len(prediction)
sns.set_style('darkgrid', {'axis.facecolor':'black'})
f, axes = plt.subplots(n, 1, figsize=(25,30))
times = 0
for i in range(n):
    if i == 0:
        plt.sca(axes[0])
        plt.plot(y_test[:round(length/n)], color = 'red', label = 'Real Price')
        plt.plot(prediction[:round(length/n)], color = 'blue', label = 'Predicted Price')
        plt.title('House Price Predicitionin King County', fontsize=30)
        plt.ylabel('Price', fontsize=20)
        plt.legend(loc=1, prop={'size': 10})
    else:
        if i == n-1:
            plt.sca(axes[n-1])
            plt.plot(y_test[round(length/n*(n-1)):], color = 'red', label = 'Real Price')
            plt.plot(prediction[round(length/n*(n-1)):], color = 'blue', label = 'Predicted Price')
            plt.ylabel('Price', fontsize=20)
            plt.legend(loc=1, prop={'size': 10})
        else:
            plt.sca(axes[i])
            plt.plot(y_test[round(length/n*i):round(length/n*(i+1))], color = 'red', label = 'Real Price')
            plt.plot(prediction[round(length/n*i):round(length/n*(i+1))], color = 'blue', label = 'Predicted Price')
            plt.ylabel('Price', fontsize=20)
            plt.legend(loc=1, prop={'size': 10})
plt.show()
#!/usr/bin/env python # coding: utf-8 # In[ ]: # Importing the libraries # In[ ]: # Importing the dataset # In[ ]: # Getting the dependent variables and independent variables # In[ ]: # Splitting the training set and test set # Feature Scaling # In[ ]: # define base model # create model # Compile model # Fitting to the training set # Predicting the results # In[ ]: # Visualization the results and evaluation