Birth Chart Similarity and Career Outcomes #

Research Question #

Do individuals with similar birth charts tend to have similar career trajectories? Can chart similarity predict career path better than demographic factors alone?

Hypothesis #

Birth chart similarity will not predict career similarity beyond demographic factors, but cluster analysis may reveal interesting patterns in the data.

Background #

This research approaches the astrology-career question from a similarity perspective: rather than testing specific rules, we ask whether overall chart similarity correlates with career similarity.

Data Sources #

• LinkedIn/Kaggle: Employment history datasets
• Census data: Occupation information with birth dates
• Astro-Databank: Charts with profession tags
• Public records: Professional registries

Mathematical Methods #

1. Cluster analysis: Group similar charts together
2. Logistic regression: Predict career categories
3. Survival analysis: Career longevity modeling
4. Similarity metrics: Define chart distance measures

Implementation Plan #

Step 1: Data Collection #

• Gather birth data with career information
• Define career categories
• Create employment timeline where available

Step 2: Chart Similarity Calculation #

• Define chart distance metric:
  • Planetary position similarity
  • Aspect pattern similarity
  • House placement similarity
• Create pairwise similarity matrix

Step 3: Cluster Analysis #

• Apply hierarchical clustering
• Identify optimal number of clusters
• Characterize clusters by chart features
• Compare cluster membership to career categories

Step 4: Predictive Modeling #

• Build logistic regression for career prediction
• Include chart similarity features
• Compare to demographic-only baseline
• Apply survival analysis for career duration

Step 5: Validation #

• Cross-validate all models
• Test stability of clusters
• Compare across different career definitions

Expected Outputs #

• Chart clustering results
• Career prediction model performance
• Visualization of chart-career relationships
• Assessment of similarity hypothesis

Required Python Libraries #

pyswisseph
pandas
numpy
scikit-learn
scipy
lifelines
matplotlib

seaborn

Astrological Methods #

This project now provides both Tropical (Western) and Vedic (sidereal, Lahiri ayanamsha) planetary placements for all similarity analyses.

Tropical (Western) #

• Standard planetary positions (Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn) calculated using the tropical zodiac.

Vedic (Sidereal, Lahiri) #

• Planetary positions calculated using the sidereal zodiac with Lahiri ayanamsha.
• Results are provided in separate output files and visualizations.

Files Generated #

• analysis_results_tropical.csv - Tropical similarity metrics
• career_similarity_tropical.png - Tropical visualization
• analysis_results_vedic_lahiri.csv - Vedic (Lahiri) similarity metrics
• career_similarity_vedic_lahiri.png - Vedic (Lahiri) visualization

Ethical Considerations #

• Privacy protection for career data
• Avoid occupational stereotyping
• Report limitations clearly

Data Provenance #

Career Data #

• Source: Wikidata / O*NET.
• Method: Clustering of occupation codes with birth charts.