5.modelling.py

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
df=pd.read_csv('data_eda.csv')
print(df.head())

# Model Building

# 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','Tableau','Power_BI','SQL','job_simp','seniority','desc_len']]
   
    
# get dummy variables
df_dum=pd.get_dummies(df_model)   

# train_test_splits
X = df_dum.drop('avg_salary', axis =1)
y = df_dum.avg_salary.values #.values gives a series instead of array
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 = sm.add_constant(X)
model= sm.OLS(y, X_sm)
model.fit().summary() # has multi-collinearity

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=0.17)
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)]

#best value for alpha= 0.17

#Random Forests
from sklearn.ensemble import RandomForestRegressor

rf= RandomForestRegressor()
np.mean(cross_val_score(rf, X_train, y_train, scoring='neg_mean_absolute_error'))

#Tune the models using 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.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)

#Random Forests performs the best with an avg error of 11.69