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Introduction to Model Development and Deployment

In this section, we will delve into the foundational aspects of model development, specifically focusing on the nuances of model deployment. It is essential to highlight that our prime objective is not to optimize the model’s performance at this stage. Instead, our goal is to construct a straightforward model that incorporates limited features. This will allow us to concentrate effectively on the vital process of deploying the model.

Objective of the Model

In our illustrative example, we aim to predict the salary of data professionals based on various factors. These features interface with our prediction model, incorporating crucial variables such as experience level, job title, company size, and possibly others that can affect salary outcomes. The understanding we gain from this example will serve as a robust foundation for later, more complex models requiring enhanced performance tuning.

Data Source

For this project, we are leveraging a public dataset to inform our model. You can access the data here: https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries. The dataset, attributed to CC0, is in the public domain. For simplification purposes, minor modifications have been made to the dataset to limit the number of options available for specific features.

Required Libraries for Data Manipulation

To commence our project, we will import a series of libraries pivotal for efficient data manipulation and model building:

#import packages for data manipulation
import pandas as pd
import numpy as np

#import packages for machine learning
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 data management
import joblib

Data Inspection

Before diving into the modeling process, it is essential to familiarize ourselves with the dataset. Understanding the nature and structure of the data will guide us in choosing the best modeling approaches and features to include.

Data Visualization

Here is a visual representation to assist in gathering insights from the data that we will be working with:

Image by Author

Data Preprocessing: Encoding Features

Given that our features are predominantly categorical, we will need to encode our data into a numerical format to make it suitable for the model. This step is crucial because machine learning algorithms typically require numeric input for computations.

Encoding Techniques

Below, we will utilize ordinal encoders to convert experience levels and company sizes into numerical representations. It’s important to recognize that these encodings reflect an implicit order (for example, 1 = entry level, 2 = mid-level, etc.).

For job titles and employment types, we will create dummy variables for each option while ensuring that the first category is dropped to prevent multicollinearity issues.

# Use ordinal encoder to encode experience level
encoder = OrdinalEncoder(categories=[['EN', 'MI', 'SE', 'EX']])
salary_data['experience_level_encoded'] = encoder.fit_transform(salary_data[['experience_level']])

# Use ordinal encoder to encode company size
encoder = OrdinalEncoder(categories=[['S', 'M', 'L']])
salary_data['company_size_encoded'] = encoder.fit_transform(salary_data[['company_size']])

# Encode employment type and job title using dummy columns
salary_data = pd.get_dummies(salary_data, columns=['employment_type', 'job_title'], drop_first=True, dtype=int)

# Drop original columns
salary_data = salary_data.drop(columns=['experience_level', 'company_size'])

Preparing for Modeling

Once our model inputs have been transformed adequately, we can now segregate our data into training and testing sets. The primary objective here is to allow our model to learn from training data while leaving some data unseen for testing its predictive capabilities.

Implementing the Linear Regression Model

We will implement a linear regression model to predict employee salaries using the prepared features:

# Define independent and dependent features
X = salary_data.drop(columns='salary_in_usd')
y = salary_data['salary_in_usd']

# Split between training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, random_state=104, test_size=0.2, shuffle=True)

# Fit linear regression model
regr = linear_model.LinearRegression()
regr.fit(X_train, y_train)

# Make predictions
y_pred = regr.predict(X_test)

# Print the coefficients
print("Coefficients: \n", regr.coef_)

# Print the MSE
print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred))

# Print the adjusted R2 value
print("R2: %.2f" % r2_score(y_test, y_pred))

Evaluating Model Performance

Let’s take a moment to assess how our model performs with the data at hand. We examine key metrics such as the R-squared value and mean squared error to gauge the effectiveness of our prediction:

Image by Author

The observed R-squared value is approximately 0.27, which indicates that there is substantial room for improvement. This suggests that we require more comprehensive data and potentially a richer set of features to construct a robust predictive model.

Model Persistence

Despite the need for enhancements, we will proceed to save our model for future use. Utilizing joblib for model persistence will ensure that the efforts made thus far are not lost:

# Save model using joblib
joblib.dump(regr, 'lin_regress.sav')

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