text: # -*- coding: utf-8 -*-
  """
  Created on Sun Apr 12 10:30:10 2020
  
  @author: Ken
  """
  
  import pandas as pd 
  import matplotlib.pyplot as plt 
  import numpy as np 
  
  df = pd.read_csv('eda_data.csv')
  
  # choose relevant columns 
  df.columns
  
  df_model = df[['avg_salary','Rating','Size','Type of ownership','Industry','Sector','Revenue','num_comp','hourly','employer_provided',
               'job_state','same_state','age','python_yn','spark','aws','excel','job_simp','seniority','desc_len']]
  
  # get dummy data 
  df_dum = pd.get_dummies(df_model)
  
  # train test split 
  from sklearn.model_selection import train_test_split
  
  X = df_dum.drop('avg_salary', axis =1)
  y = df_dum.avg_salary.values
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
  # multiple linear regression 
  import statsmodels.api as sm
  
  X_sm = X = sm.add_constant(X)
  model = sm.OLS(y,X_sm)
  model.fit().summary()
  
  from sklearn.linear_model import LinearRegression, Lasso
  from sklearn.model_selection import cross_val_score
  
  lm = LinearRegression()
  lm.fit(X_train, y_train)
  
  np.mean(cross_val_score(lm,X_train,y_train, scoring = 'neg_mean_absolute_error', cv= 3))
  
  # lasso regression 
  lm_l = Lasso(alpha=.13)
  lm_l.fit(X_train,y_train)
  np.mean(cross_val_score(lm_l,X_train,y_train, scoring = 'neg_mean_absolute_error', cv= 3))
  
  alpha = []
  error = []
  
  for i in range(1,100):
      alpha.append(i/100)
      lml = Lasso(alpha=(i/100))
      error.append(np.mean(cross_val_score(lml,X_train,y_train, scoring = 'neg_mean_absolute_error', cv= 3)))
      
  plt.plot(alpha,error)
  
  err = tuple(zip(alpha,error))
  df_err = pd.DataFrame(err, columns = ['alpha','error'])
  df_err[df_err.error == max(df_err.error)]
  
  # random forest 
  from sklearn.ensemble import RandomForestRegressor
  rf = RandomForestRegressor()
  
  np.mean(cross_val_score(rf,X_train,y_train,scoring = 'neg_mean_absolute_error', cv= 3))
  
  # tune models GridsearchCV 
  from sklearn.model_selection import GridSearchCV
  parameters = {'n_estimators':range(10,300,10), 'criterion':('mse','mae'), 'max_features':('auto','sqrt','log2')}
  
  gs = GridSearchCV(rf,parameters,scoring='neg_mean_absolute_error',cv=3)
  gs.fit(X_train,y_train)
  
  gs.best_score_
  gs.best_estimator_
  
  # test ensembles 
  tpred_lm = lm.predict(X_test)
  tpred_lml = lm_l.predict(X_test)
  tpred_rf = gs.best_estimator_.predict(X_test)
  
  from sklearn.metrics import mean_absolute_error
  mean_absolute_error(y_test,tpred_lm)
  mean_absolute_error(y_test,tpred_lml)
  mean_absolute_error(y_test,tpred_rf)
  
  mean_absolute_error(y_test,(tpred_lm+tpred_rf)/2)
  
  import pickle
  pickl = {'model': gs.best_estimator_}
  pickle.dump( pickl, open( 'model_file' + ".p", "wb" ) )
  
  file_name = "model_file.p"
  with open(file_name, 'rb') as pickled:
      data = pickle.load(pickled)
      model = data['model']
  
  model.predict(np.array(list(X_test.iloc[1,:])).reshape(1,-1))[0]
  
  list(X_test.iloc[1,:])