First, for our instance, we have to develop a mannequin. Since this text focuses on mannequin deployment, we is not going to fear concerning the efficiency of the mannequin. As a substitute, we are going to construct a easy mannequin with restricted options to give attention to studying mannequin deployment.
On this instance, we are going to predict a knowledge skilled’s wage based mostly on a couple of options, resembling expertise, job title, firm dimension, and so on.
See information right here: https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries (CC0: Public Area). I barely modified the information to scale back the variety of choices for sure options.
#import packages for information manipulation
import pandas as pd
import numpy as np#import packages for machine studying
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from sklearn.metrics import mean_squared_error, r2_score
#import packages for information administration
import joblib
First, let’s check out the information.
Since all of our options are categorical, we are going to use encoding to rework our information to numerical. Under, we use ordinal encoders to encode expertise stage and firm dimension. These are ordinal as a result of they characterize some sort of development (1 = entry stage, 2 = mid-level, and so on.).
For job title and employment sort, we are going to create a dummy variables for every possibility (notice we drop the primary to keep away from multicollinearity).
#use ordinal encoder to encode expertise stage
encoder = OrdinalEncoder(classes=[['EN', 'MI', 'SE', 'EX']])
salary_data['experience_level_encoded'] = encoder.fit_transform(salary_data[['experience_level']])#use ordinal encoder to encode firm dimension
encoder = OrdinalEncoder(classes=[['S', 'M', 'L']])
salary_data['company_size_encoded'] = encoder.fit_transform(salary_data[['company_size']])
#encode employmeny sort and job title utilizing dummy columns
salary_data = pd.get_dummies(salary_data, columns = ['employment_type', 'job_title'], drop_first = True, dtype = int)
#drop authentic columns
salary_data = salary_data.drop(columns = ['experience_level', 'company_size'])
Now that we have now remodeled our mannequin inputs, we are able to create our coaching and take a look at units. We’ll enter these options right into a easy linear regression mannequin to foretell the worker’s wage.
#outline impartial and dependent options
X = salary_data.drop(columns = 'salary_in_usd')
y = salary_data['salary_in_usd']#cut up between coaching and testing units
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state = 104, test_size = 0.2, shuffle = True)
#match linear regression mannequin
regr = linear_model.LinearRegression()
regr.match(X_train, y_train)
#make predictions
y_pred = regr.predict(X_test)
#print the coefficients
print("Coefficients: n", regr.coef_)
#print the MSE
print("Imply squared error: %.2f" % mean_squared_error(y_test, y_pred))
#print the adjusted R2 worth
print("R2: %.2f" % r2_score(y_test, y_pred))
Let’s see how our mannequin did.
Appears like our R-squared is 0.27, yikes. Much more work would have to be performed with this mannequin. We might doubtless want extra information and extra data on the observations. However for the sake of this text, we are going to transfer ahead and save our mannequin.
#save mannequin utilizing joblib
joblib.dump(regr, 'lin_regress.sav')
