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

# In[ ]:


import numpy as np
import pandas as pd 
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import RFE
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
print((check_output(["ls", "../input"]).decode("utf8")))


# In[ ]:


house = pd.read_csv("../input/kc_house_data.csv")
del house["id"]
del house["date"]


# In[ ]:


X = house[house.columns[1:19]]
Y = house["price"]
colnames = X.columns


# In[ ]:


ranks = {}
# Create our function which stores the feature rankings to the ranks dictionary
def ranking(ranks, names, order=1):
    minmax = MinMaxScaler()
    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]
    ranks = [round(x,2) for x in ranks]
    return dict(list(zip(names, ranks)))


# In[ ]:


lr = LinearRegression(normalize=True)
lr.fit(X,Y)
rfe = RFE(lr, n_features_to_select=1, verbose =3 )
rfe.fit(X,Y)
ranks["RFE"] = ranking(list(map(float, rfe.ranking_)), colnames)


# In[ ]:


rf = RandomForestRegressor(n_jobs=-1, n_estimators=50, verbose=3)
rf.fit(X,Y)
ranks["RF"] = ranking(rf.feature_importances_, colnames)


# In[ ]:


ranks


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

# In[ ]:


import numpy as np
import pandas as pd 
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import RFE
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestRegressor
from subprocess import check_output
print((check_output(["ls", "../input"]).decode("utf8")))


# In[ ]:


house = pd.read_csv("../input/kc_house_data.csv")
del house["id"]
del house["date"]


# In[ ]:


X = house[house.columns[1:19]]
Y = house["price"]
colnames = X.columns


# In[ ]:


ranks = {}
# Create our function which stores the feature rankings to the ranks dictionary
def ranking(ranks, names, order=1):
    minmax = MinMaxScaler()
    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]
    ranks = [round(x,2) for x in ranks]
    return dict(list(zip(names, ranks)))


# In[ ]:


lr = LinearRegression(normalize=True)
lr.fit(X,Y)
rfe = RFE(lr, n_features_to_select=1, verbose =3 )
rfe.fit(X,Y)
ranks["RFE"] = ranking(list(map(float, rfe.ranking_)), colnames)


# In[ ]:


rf = RandomForestRegressor(n_jobs=-1, n_estimators=50, verbose=3)
rf.fit(X,Y)
ranks["RF"] = ranking(rf.feature_importances_, colnames)


# In[ ]:


ranks
#!/usr/bin/env python # coding: utf-8 # In[ ]: # In[ ]: # In[ ]: # In[ ]: # Create our function which stores the feature rankings to the ranks dictionary # In[ ]: # In[ ]: # In[ ]: