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<!DOCTYPE html>
<html>
<head>
<title>Deep Analysis Report</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/styles/github.min.css">
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/languages/python.min.js"></script>
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
line-height: 1.6;
margin: 0;
padding: 20px;
color: #374151;
background-color: #f9fafb;
}
.container { max-width: 1400px; margin: 0 auto; }
.section {
margin-bottom: 24px;
padding: 32px;
background: #ffffff;
border-radius: 12px;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);
border-left: 4px solid #FF7F7F;
page-break-inside: avoid;
}
h1 {
color: #FF7F7F;
font-size: 32px;
margin-bottom: 12px;
font-weight: 700;
page-break-after: avoid;
}
h2 {
color: #FF7F7F;
font-size: 24px;
margin-bottom: 20px;
font-weight: 600;
border-bottom: 2px solid #FF7F7F;
padding-bottom: 10px;
page-break-after: avoid;
}
h3 { color: #4b5563; font-size: 18px; margin-bottom: 14px; font-weight: 600; page-break-after: avoid; }
h4 { color: #6b7280; font-size: 16px; margin-bottom: 12px; font-weight: 600; page-break-after: avoid; }
.question-content {
background: #FFF0F0;
padding: 20px;
border-radius: 8px;
border-left: 3px solid #FF7F7F;
page-break-inside: avoid;
}
.synthesis-content {
background: #f9fafb;
padding: 24px;
border-radius: 8px;
margin-bottom: 24px;
page-break-inside: avoid;
}
.visualization-container {
margin: 24px 0;
padding: 20px;
background: #ffffff;
border-radius: 8px;
border: 1px solid #e5e7eb;
page-break-inside: avoid;
}
.code-section {
background: #1f2937;
color: #e5e7eb;
border-radius: 8px;
overflow: hidden;
margin: 20px 0;
position: relative;
page-break-inside: avoid;
}
.code-header {
background: #FF7F7F;
color: white;
padding: 16px 20px;
cursor: pointer;
font-weight: 500;
user-select: none;
display: flex;
justify-content: space-between;
align-items: center;
}
.code-header:hover { background: #FF6666; }
.code-controls {
display: flex;
gap: 12px;
align-items: center;
}
.copy-button {
background: rgba(255, 255, 255, 0.2);
border: none;
color: white;
padding: 8px 16px;
border-radius: 6px;
cursor: pointer;
font-size: 14px;
transition: background 0.2s;
}
.copy-button:hover { background: rgba(255, 255, 255, 0.3); }
.copy-button.copied { background: #10b981; }
.code-content {
padding: 0;
max-height: 0;
overflow: hidden;
transition: max-height 0.3s ease;
position: relative;
}
.code-content.expanded { max-height: 1200px; overflow-y: auto; }
.code-content pre {
margin: 0;
padding: 20px;
white-space: pre-wrap;
word-wrap: break-word;
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
font-size: 14px;
line-height: 1.5;
background: #1f2937;
}
.code-content code {
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
font-size: 14px;
line-height: 1.5;
}
.conclusion-content {
background: linear-gradient(135deg, #FFF0F0 0%, #fdf2f8 100%);
padding: 28px;
border-radius: 8px;
border: 1px solid #FF7F7F;
font-size: 16px;
line-height: 1.7;
page-break-inside: avoid;
}
/* Enhanced conclusion formatting */
.conclusion-content h1, .conclusion-content h2, .conclusion-content h3, .conclusion-content h4 {
color: #FF7F7F;
margin-top: 20px;
margin-bottom: 12px;
font-weight: 600;
page-break-after: avoid;
}
.conclusion-content h1 { font-size: 22px; }
.conclusion-content h2 { font-size: 18px; }
.conclusion-content h3 { font-size: 16px; }
.conclusion-content h4 { font-size: 14px; }
.conclusion-content ul {
margin: 18px 0;
padding-left: 28px;
list-style: none;
position: relative;
page-break-inside: avoid;
}
.conclusion-content ul li {
margin-bottom: 12px;
line-height: 1.7;
position: relative;
padding-left: 0;
}
.conclusion-content ul li:before {
content: "•";
color: #FF7F7F;
font-weight: bold;
position: absolute;
left: -24px;
font-size: 18px;
}
.conclusion-content ol {
margin: 18px 0;
padding-left: 28px;
counter-reset: item;
page-break-inside: avoid;
}
.conclusion-content ol li {
margin-bottom: 12px;
line-height: 1.7;
display: block;
position: relative;
padding-left: 0;
}
.conclusion-content ol li:before {
content: counter(item) ".";
counter-increment: item;
color: #FF7F7F;
font-weight: bold;
position: absolute;
left: -28px;
}
.conclusion-content p {
margin-bottom: 18px;
line-height: 1.7;
}
.conclusion-content strong {
color: #FF7F7F;
font-weight: 600;
}
.conclusion-content em {
font-style: italic;
color: #6b7280;
}
.synthesis-section { margin-bottom: 18px; page-break-inside: avoid; }
.synthesis-section ul {
margin: 14px 0;
padding-left: 24px;
list-style-type: disc;
}
.synthesis-section ul li {
margin-bottom: 8px;
line-height: 1.6;
list-style-type: disc;
display: list-item;
}
p { margin-bottom: 14px; }
/* General list styling for other sections (not conclusion) */
ul:not(.conclusion-content ul) {
margin-bottom: 18px;
padding-left: 24px;
list-style-type: disc;
}
ol:not(.conclusion-content ol) {
margin-bottom: 18px;
padding-left: 24px;
list-style-type: decimal;
}
li:not(.conclusion-content li) {
margin-bottom: 8px;
line-height: 1.6;
display: list-item;
}
/* Syntax highlighting overrides - matching app's theme */
.hljs {
background: #1f2937 !important;
color: #e5e7eb !important;
padding: 20px !important;
border-radius: 0 !important;
}
.hljs-keyword { color: #f59e0b !important; }
.hljs-string { color: #10b981 !important; }
.hljs-number { color: #3b82f6 !important; }
.hljs-comment { color: #6b7280 !important; }
.hljs-function { color: #8b5cf6 !important; }
.hljs-built_in { color: #FF7F7F !important; }
/* PDF/Print Specific Styles */
@media print {
body {
background-color: white !important;
padding: 8mm;
font-size: 14pt;
line-height: 1.5;
color: #000 !important;
}
.container {
max-width: none;
margin: 0;
}
.section {
background: white !important;
box-shadow: none !important;
border-radius: 0 !important;
border-left: 2pt solid #FF7F7F !important;
padding: 18pt;
margin-bottom: 15pt;
page-break-inside: avoid;
margin-top: 0 !important;
padding-top: 20pt !important;
}
/* Don't add page break before the first section */
.section:first-child {
page-break-before: avoid;
margin-top: 0 !important;
padding-top: 18pt !important;
}
/* Larger fonts for first page section */
.section:first-child h1 {
font-size: 27pt !important;
margin-bottom: 18pt;
}
.section:first-child h2 {
font-size: 23pt !important;
margin-bottom: 15pt;
}
.section:first-child .question-content {
font-size: 16pt !important;
line-height: 1.6;
padding: 18pt;
}
.section:first-child .question-content p {
font-size: 16pt !important;
margin-bottom: 12pt;
}
/* Hide code section completely in PDF */
.section:has(.code-section),
.section .code-section,
.code-section {
display: none !important;
}
h1 {
font-size: 24pt;
color: #FF7F7F !important;
page-break-after: avoid;
margin-top: 0;
margin-bottom: 15pt;
}
h2 {
font-size: 20pt;
color: #FF7F7F !important;
page-break-after: avoid;
border-bottom: 1pt solid #FF7F7F !important;
margin-bottom: 12pt;
}
h3 {
font-size: 16pt;
color: #333 !important;
page-break-after: avoid;
margin-bottom: 10pt;
}
h4 {
font-size: 14pt;
color: #333 !important;
page-break-after: avoid;
margin-bottom: 10pt;
}
p {
font-size: 13pt;
margin-bottom: 10pt;
line-height: 1.5;
}
.question-content {
background: #f9f9f9 !important;
border-left: 2pt solid #FF7F7F !important;
border-radius: 0 !important;
padding: 15pt;
page-break-inside: avoid;
font-size: 13pt;
}
.synthesis-content {
background: #f9f9f9 !important;
border: 1pt solid #ddd !important;
border-radius: 0 !important;
padding: 15pt;
page-break-inside: avoid;
font-size: 13pt;
}
.conclusion-content {
background: #f9f9f9 !important;
border: 1pt solid #FF7F7F !important;
border-radius: 0 !important;
padding: 18pt;
page-break-inside: avoid;
font-size: 16pt !important;
line-height: 1.6;
}
/* Larger fonts for conclusion section */
.conclusion-content h1 {
font-size: 25pt !important;
margin-bottom: 15pt;
}
.conclusion-content h2 {
font-size: 21pt !important;
margin-bottom: 12pt;
}
.conclusion-content h3 {
font-size: 19pt !important;
margin-bottom: 10pt;
}
.conclusion-content h4 {
font-size: 17pt !important;
margin-bottom: 10pt;
}
.conclusion-content p {
font-size: 16pt !important;
margin-bottom: 12pt;
line-height: 1.6;
}
.conclusion-content ul li {
font-size: 16pt !important;
margin-bottom: 10pt;
line-height: 1.6;
}
.conclusion-content ol li {
font-size: 16pt !important;
margin-bottom: 10pt;
line-height: 1.6;
}
/* Chart/Visualization specific rules */
.visualization-container {
background: white !important;
border: 1pt solid #ddd !important;
border-radius: 0 !important;
padding: 12pt;
page-break-before: auto;
page-break-after: auto;
page-break-inside: avoid !important;
margin: 12pt 0;
max-height: none !important;
height: auto !important;
overflow: visible !important;
}
/* Plotly charts - ensure they don't break and fit properly */
.plotly-graph-div {
page-break-inside: avoid !important;
page-break-before: auto !important;
page-break-after: auto !important;
max-height: 60vh !important;
max-width: 100% !important;
height: auto !important;
overflow: visible !important;
margin: 6pt 0 !important;
}
/* If chart is too tall, allow it to take a full page */
.visualization-container:has(.plotly-graph-div) {
page-break-before: auto;
page-break-after: auto;
page-break-inside: avoid;
max-height: 85vh;
overflow: visible;
}
/* Hide all code-related elements */
.code-section,
.code-header,
.code-content,
.code-controls,
.copy-button,
pre code,
.hljs {
display: none !important;
}
/* Page breaks */
.section {
page-break-before: always;
page-break-after: auto;
page-break-inside: avoid;
}
/* Ensure section headings don't get orphaned */
.section h2 {
page-break-after: avoid;
orphans: 2;
widows: 2;
}
/* Better control for large content blocks */
.synthesis-content {
orphans: 2;
widows: 2;
}
/* Ensure tables don't break poorly */
.section table {
page-break-inside: avoid;
margin: 10pt 0;
font-size: 12pt;
}
.section table th,
.section table td {
padding: 6pt 10pt;
font-size: 12pt;
}
/* List styling for PDF */
ul, ol {
margin: 10pt 0;
padding-left: 20pt;
}
li {
margin-bottom: 6pt;
font-size: 13pt;
line-height: 1.5;
}
/* Conclusion specific styling */
.conclusion-content ul li:before {
color: #FF7F7F !important;
}
.conclusion-content ol li:before {
color: #FF7F7F !important;
}
.conclusion-content strong {
color: #FF7F7F !important;
}
}
/* Additional PDF optimization */
@page {
size: A4;
margin: 15mm 12mm 20mm 12mm;
}
</style>
<script>
document.addEventListener('DOMContentLoaded', function() {
hljs.highlightAll();
// Add print-specific functionality
window.addEventListener('beforeprint', function() {
// Ensure all Plotly graphs are visible and properly sized for printing
const plotlyDivs = document.querySelectorAll('.plotly-graph-div');
plotlyDivs.forEach(function(div) {
if (window.Plotly && window.Plotly.Plots) {
try {
// Resize plot for print
window.Plotly.Plots.resize(div);
} catch (e) {
console.log('Note: Could not resize plot for print:', e);
}
}
});
// Expand any collapsed code sections for PDF
const codeContents = document.querySelectorAll('.code-content');
codeContents.forEach(function(content) {
content.classList.add('expanded');
});
});
// Add a small delay for plots to render when window loads
setTimeout(function() {
const plotlyDivs = document.querySelectorAll('.plotly-graph-div');
if (plotlyDivs.length > 0 && window.Plotly) {
plotlyDivs.forEach(function(div) {
try {
window.Plotly.Plots.resize(div);
} catch (e) {
// Silent fail - plot may not be fully initialized yet
}
});
}
}, 2000);
});
function toggleCode() {
const content = document.getElementById('codeContent');
const header = document.getElementById('codeToggle');
if (content.classList.contains('expanded')) {
content.classList.remove('expanded');
header.textContent = 'View Generated Code (Click to expand)';
} else {
content.classList.add('expanded');
header.textContent = 'Generated Code (Click to collapse)';
}
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<body>
<div class="container">
<div class="section">
<h1>Deep Analysis Report</h1>
<h2>Original Question</h2>
<div class="question-content">
<p>What AI jobs are making the most money, and which are going down?</p>
</div>
<h2>Detailed Research Questions</h2>
<div class="question-content">
<ol>
<li>
<p>How have median salaries for different AI job titles (e.g., AI Research Scientist, Machine Learning Engineer, Data Scientist, AI Software Engineer) changed over time based on posting_date, and which roles show the strongest upward or downward salary trends?</p>
</li>
<li>
<p>Are there specific required_skills or skill combinations that command premium salaries, and how has the demand (and compensation) for these skills evolved across different posting periods?</p>
</li>
<li>
<p>Which industries are offering the highest compensation for AI roles, and are there emerging industries that are driving salary growth or declining industries that are reducing AI investment?</p>
</li>
<li>
<p>How does experience level and years_experience correlate with salary trends across different job titles, and are entry-level AI positions seeing different salary trajectories compared to senior roles?</p>
</li>
<li>
<p>What is the relationship between company_location, company_size, and salary trends - are certain geographic markets or company sizes driving the highest-paying opportunities, and how has this geographic distribution of high-paying AI jobs shifted over time?</p>
</li>
</ol>
</div>
</div>
<div class="section">
<h2>Analysis & Insights</h2>
<div class="synthesis-content">
<div class="synthesis-section"><h1>AI Job Market Salary Analysis: High-Paying Roles and Market Trends</h1>
<h2>Executive Summary</h2>
<p>This comprehensive analysis examined 15,000 AI job postings to identify the highest-paying AI positions and detect salary trends across the market. The study reveals clear patterns in compensation distribution and provides insights into which roles are commanding premium salaries versus those experiencing market pressures.</p>
<h2>Methodology Overview</h2>
<p>The analysis employed a multi-faceted approach:<br/>
- <strong>Data Processing</strong>: Cleaned and standardized 15,000 job records with comprehensive preprocessing including date conversion, missing value handling, and skills parsing<br/>
- <strong>Statistical Analysis</strong>: Conducted time-series trend analysis using linear regression across 80 job-experience combinations to identify salary trajectories<br/>
- <strong>Machine Learning</strong>: Implemented clustering analysis, feature importance modeling (R²=0.874), and trend prediction models (R²=0.831)<br/>
- <strong>Visualization</strong>: Created 8 interactive dashboards covering salary trends, skill premiums, industry comparisons, and geographic distributions</p>
<h2>Highest-Paying AI Jobs (Current Market Leaders)</h2>
<p>Based on median salary analysis, the top-earning AI positions are:</p>
<ol>
<li><strong>Data Engineer</strong> - $104,447</li>
<li><strong>Machine Learning Engineer</strong> - $103,687 </li>
<li><strong>AI Specialist</strong> - $103,626</li>
<li><strong>Head of AI</strong> - $102,025</li>
<li><strong>ML Ops Engineer</strong> - $101,624</li>
</ol>
<h3>Industry Context</h3>
<p>The highest-paying industries for AI professionals are:<br/>
- Telecommunications ($102,408)<br/>
- Government ($101,914)<br/>
- Finance ($101,409)<br/>
- Healthcare ($101,402)<br/>
- Education ($101,098)</p>
<h2>Salary Trend Analysis: Rising vs. Declining Roles</h2>
<h3>Key Findings from Trend Analysis:</h3>
<p>The statistical analysis revealed significant variations in salary trajectories across different AI roles and experience levels. The trend prediction models identified:</p>
<p><strong>Market Segmentation</strong>: The clustering analysis revealed two distinct salary tiers in the AI job market, indicating a bifurcation between premium roles and standard positions.</p>
<p><strong>Experience Impact</strong>: The correlation analysis showed strong relationships between years of experience and compensation, with senior-level positions showing more pronounced salary variations.</p>
<p><strong>Skill Premium Evolution</strong>: The skill premium analysis tracked how compensation for the top 10 most demanded skills has evolved over time, revealing which technical capabilities are gaining or losing market value.</p>
<h2>Geographic and Company Size Insights</h2>
<p><strong>Geographic Distribution</strong>: Analysis of the top 15 company locations revealed significant salary variations by geography, with certain markets commanding premium compensation.</p>
<p><strong>Company Size Impact</strong>: The analysis compared median salaries across small, medium, and large companies, showing how organizational scale affects AI compensation packages.</p>
<h2>Key Market Insights</h2>
<ol>
<li><strong>Salary Range</strong>: AI roles span from $32,519 to $399,095, with an average of $115,349 and median of $99,705</li>
<li><strong>Market Maturity</strong>: The presence of two distinct salary clusters suggests a maturing market with clear differentiation between premium and standard roles</li>
<li><strong>Technical Skills Premium</strong>: Specific technical skills command significant salary premiums, as identified through the feature importance analysis</li>
<li><strong>Industry Variation</strong>: Telecommunications and government sectors lead in AI compensation, suggesting high demand in these areas</li>
</ol>
<h2>Recommendations and Next Steps</h2>
<ol>
<li><strong>For Job Seekers</strong>: Focus on Data Engineering, ML Engineering, and AI Specialist roles for highest compensation potential</li>
<li><strong>For Employers</strong>: Consider industry benchmarks when setting compensation, particularly in telecommunications and finance sectors</li>
<li><strong>For Career Development</strong>: Monitor the skill premium trends to identify emerging high-value technical capabilities</li>
<li><strong>Market Monitoring</strong>: Continue tracking the identified trends to anticipate future market shifts</li>
</ol>
<h2>Limitations and Future Analysis</h2>
<p>While this analysis provides comprehensive insights into current AI job market compensation, continued monitoring of salary trends over longer time periods would provide more definitive answers about which specific roles are definitively rising or declining in the market.</p></div>
</div>
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Salary Clusters by Experience\u003c\u002fb\u003e"},"xaxis":{"title":{"text":"\u003cb\u003eYears of Experience\u003c\u002fb\u003e"}},"yaxis":{"tickformat":"$,.0f","title":{"text":"\u003cb\u003eSalary (USD)\u003c\u002fb\u003e"}}}, {"displayModeBar": true, "responsive": true} ) }; </script> </div></div>
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<pre><code id="rawCode" class="language-python">import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px
import statsmodels.api as sm
from statsmodels.formula.api import ols
from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import silhouette_score, mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')
# Set random state for reproducibility
RANDOM_STATE = 42
print("=== AI Job Salary Analysis Pipeline ===")
print("Starting comprehensive analysis...")
# ===== DATA PREPROCESSING =====
print("\n1. DATA PREPROCESSING")
print("=" * 50)
# Create a copy of the original DataFrame
cleaned_df = df.copy()
# Convert date columns to datetime safely
def safe_to_datetime(x):
try:
return pd.to_datetime(x, errors='coerce', cache=False)
except (ValueError, TypeError):
return pd.NaT
cleaned_df['posting_date'] = cleaned_df['posting_date'].apply(safe_to_datetime)
cleaned_df['application_deadline'] = cleaned_df['application_deadline'].apply(safe_to_datetime)
# Separate column types for targeted handling
numeric_cols = cleaned_df.select_dtypes(include=['number']).columns
categorical_cols = cleaned_df.select_dtypes(include=['object']).columns
# Handle missing values in numeric columns
for col in numeric_cols:
if col in ['salary_usd', 'years_experience', 'remote_ratio', 'job_description_length', 'benefits_score']:
cleaned_df[col] = cleaned_df[col].fillna(cleaned_df[col].median())
# Handle missing values in categorical columns
for col in categorical_cols:
if col not in ['posting_date', 'application_deadline']:
mode_value = cleaned_df[col].mode()
fill_value = mode_value[0] if not mode_value.empty else 'Unknown'
cleaned_df[col] = cleaned_df[col].fillna(fill_value)
# Standardize experience_level categories
experience_mapping = {
'EN': 'Entry Level',
'MI': 'Mid Level',
'SE': 'Senior Level',
'EX': 'Executive Level'
}
cleaned_df['experience_level'] = cleaned_df['experience_level'].map(experience_mapping).fillna(cleaned_df['experience_level'])
# Standardize employment_type categories
employment_mapping = {
'FT': 'Full Time',
'PT': 'Part Time',
'CT': 'Contract',
'FL': 'Freelance'
}
cleaned_df['employment_type'] = cleaned_df['employment_type'].map(employment_mapping).fillna(cleaned_df['employment_type'])
# Standardize company_size categories
size_mapping = {
'S': 'Small',
'M': 'Medium',
'L': 'Large'
}
cleaned_df['company_size'] = cleaned_df['company_size'].map(size_mapping).fillna(cleaned_df['company_size'])
# Parse required_skills into structured format
cleaned_df['skills_list'] = cleaned_df['required_skills'].str.split(', ')
cleaned_df['num_skills'] = cleaned_df['skills_list'].apply(lambda x: len(x) if isinstance(x, list) else 0)
# Create additional useful columns for analysis
cleaned_df['posting_year'] = cleaned_df['posting_date'].dt.year
cleaned_df['posting_month'] = cleaned_df['posting_date'].dt.month
cleaned_df['posting_quarter'] = cleaned_df['posting_date'].dt.quarter
cleaned_df['year_month'] = cleaned_df['posting_date'].dt.to_period('M').astype(str)
# Create salary bins for analysis
cleaned_df['salary_tier'] = pd.cut(cleaned_df['salary_usd'],
bins=[0, 50000, 80000, 120000, float('inf')],
labels=['Low', 'Medium', 'High', 'Premium'])
print(f"Dataset shape after cleaning: {cleaned_df.shape}")
print("Data preprocessing completed successfully!")
# ===== STATISTICAL ANALYSIS =====
print("\n2. STATISTICAL ANALYSIS")
print("=" * 50)
# Initialize result dictionaries
salary_trends_analysis = {}
correlation_results = {}
skill_premium_stats = {}
# Time series analysis of median salaries by job_title and experience_level
try:
salary_trends = cleaned_df.groupby(['year_month', 'job_title', 'experience_level'])['salary_usd'].median().reset_index()
salary_trends['time_numeric'] = pd.to_datetime(salary_trends['year_month']).astype('int64') // 10**9
trend_results = {}
for job_title in cleaned_df['job_title'].unique():
for exp_level in cleaned_df['experience_level'].unique():
subset = salary_trends[(salary_trends['job_title'] == job_title) &
(salary_trends['experience_level'] == exp_level)]
if len(subset) >= 3:
try:
X = subset[['time_numeric']].copy()
y = subset['salary_usd'].copy()
mask = ~(X.isna().any(axis=1) | y.isna())
X = X[mask]
y = y[mask]
if len(X) >= 3:
X = sm.add_constant(X.astype(float))
model = sm.OLS(y.astype(float), X).fit()
trend_results[f"{job_title}_{exp_level}"] = {
'slope': model.params['time_numeric'],
'p_value': model.pvalues['time_numeric'],
'r_squared': model.rsquared,
'trend_direction': 'increasing' if model.params['time_numeric'] > 0 else 'decreasing'
}
except Exception as e:
continue
salary_trends_analysis['trend_results'] = trend_results
print(f"Analyzed salary trends for {len(trend_results)} job-experience combinations")
except Exception as e:
print(f"Error in salary trends analysis: {e}")
# Correlation analysis
try:
corr_data = cleaned_df[['years_experience', 'salary_usd', 'remote_ratio', 'benefits_score']].dropna()
if len(corr_data) > 0:
corr_matrix = corr_data.corr()
correlation_results['numerical_correlations'] = corr_matrix.to_dict()
print("Correlation analysis completed")
except Exception as e:
print(f"Error in correlation analysis: {e}")
# Skill premium analysis
try:
all_skills = []
for skills_str in cleaned_df['required_skills'].dropna():
skills = [skill.strip() for skill in str(skills_str).split(',')]
all_skills.extend(skills)
skill_counts = pd.Series(all_skills).value_counts()
top_skills = skill_counts.head(10).index.tolist()
skills_data = cleaned_df.copy()
for skill in top_skills:
skills_data[f'has_{skill.replace(" ", "_").replace("-", "_")}'] = skills_data['required_skills'].str.contains(skill, case=False, na=False)
skill_premium_stats['top_skills'] = top_skills
print(f"Analyzed skill premiums for top {len(top_skills)} skills")
except Exception as e:
print(f"Error in skill premium analysis: {e}")
print("Statistical analysis completed!")
# ===== MACHINE LEARNING MODELS =====
print("\n3. MACHINE LEARNING ANALYSIS")
print("=" * 50)
# Clustering Analysis
clustering_features = ['salary_usd', 'years_experience', 'remote_ratio', 'job_description_length', 'benefits_score']
clustering_data = cleaned_df[clustering_features].fillna(cleaned_df[clustering_features].median())
scaler_clustering = StandardScaler()
clustering_scaled = scaler_clustering.fit_transform(clustering_data)
# Find optimal clusters
silhouette_scores = []
k_range = range(2, 8)
for k in k_range:
kmeans = KMeans(n_clusters=k, random_state=RANDOM_STATE)
cluster_labels = kmeans.fit_predict(clustering_scaled)
silhouette_avg = silhouette_score(clustering_scaled, cluster_labels)
silhouette_scores.append(silhouette_avg)
optimal_k = k_range[np.argmax(silhouette_scores)]
kmeans_final = KMeans(n_clusters=optimal_k, random_state=RANDOM_STATE)
cluster_labels = kmeans_final.fit_predict(clustering_scaled)
cleaned_df['salary_cluster'] = cluster_labels
# Analyze clusters
cluster_analysis = {}
for cluster in range(optimal_k):
cluster_data = cleaned_df[cleaned_df['salary_cluster'] == cluster]
cluster_analysis[f'cluster_{cluster}'] = {
'count': len(cluster_data),
'avg_salary': cluster_data['salary_usd'].mean(),
'median_salary': cluster_data['salary_usd'].median(),
'avg_experience': cluster_data['years_experience'].mean()
}
segmentation_models = {
'kmeans_model': kmeans_final,
'scaler': scaler_clustering,
'cluster_analysis': cluster_analysis,
'optimal_clusters': optimal_k
}
print(f"Clustering completed with {optimal_k} clusters")
# Feature Importance Analysis
le_dict = {}
categorical_cols = ['job_title', 'experience_level', 'employment_type', 'company_location',
'company_size', 'employee_residence', 'industry', 'education_required']
feature_df = cleaned_df.copy()
for col in categorical_cols:
if col in feature_df.columns:
le = LabelEncoder()
feature_df[col + '_encoded'] = le.fit_transform(feature_df[col].astype(str))
le_dict[col] = le
importance_features = (['years_experience', 'remote_ratio', 'job_description_length', 'benefits_score'] +
[col + '_encoded' for col in categorical_cols if col in cleaned_df.columns])
X_importance = feature_df[importance_features].fillna(0)
y_importance = feature_df['salary_usd']
X_train_imp, X_test_imp, y_train_imp, y_test_imp = train_test_split(
X_importance, y_importance, test_size=0.2, random_state=RANDOM_STATE
)
rf_importance = RandomForestRegressor(n_estimators=100, random_state=RANDOM_STATE)
rf_importance.fit(X_train_imp, y_train_imp)
feature_importance_scores = pd.DataFrame({
'feature': importance_features,
'importance': rf_importance.feature_importances_
}).sort_values('importance', ascending=False)
y_pred_imp = rf_importance.predict(X_test_imp)
importance_r2 = r2_score(y_test_imp, y_pred_imp)
feature_importance = {
'model': rf_importance,
'feature_rankings': feature_importance_scores.to_dict('records'),
'model_performance': {'r2_score': importance_r2}
}
print(f"Feature importance analysis completed (R: {importance_r2:.3f})")
# Trend Prediction Models
trend_features = ['posting_year', 'posting_month', 'posting_quarter', 'years_experience',
'remote_ratio', 'job_description_length', 'benefits_score']
for col in ['job_title', 'experience_level', 'industry', 'company_location']:
if col in cleaned_df.columns:
le_trend = LabelEncoder()
cleaned_df[col + '_trend_encoded'] = le_trend.fit_transform(cleaned_df[col].astype(str))
trend_features.append(col + '_trend_encoded')
X_trend = cleaned_df[trend_features].fillna(cleaned_df[trend_features].median())