class AlicanKaya:
def __init__(self):
self.role = "AI & Data Scientist | CS Student"
self.focus = ["Machine Learning", "Time Series Analysis", "Recommender Systems"]
self.languages = ["Python", "SQL", "C++", "C#", ".NET", "JavaScript"]
self.ml_stack = ["Scikit-learn", "Pandas", "NumPy", "Statsmodels"]
self.databases = ["PostgreSQL", "MySQL", "MSSQL", "MongoDB"]
self.tools = ["Git", "GitHub", "VS Code", "Figma", "Linux", "Jupyter"]
self.goal = "Build scalable, measurable, high-impact AI-driven solutions 🚀"
def currently(self):
return [
"📚 Deepening ML fundamentals & statistical modeling",
"🔨 Building end-to-end data pipelines & predictive systems",
"🤝 Open to internships & research collaborations",
]I combine analytical thinking with engineering discipline to design data-driven solutions that are both technically sound and practically impactful. My expertise spans end-to-end data workflows — from preprocessing and feature engineering to model development and evaluation.
📌 Data Science RoadMap — A structured learning path from statistics to advanced ML
📌 21 Farklı Python Projeleri — 21 hands-on Python projects covering real-world use cases
📌 Improving end-to-end ML pipeline quality & reproducibility
📌 Exploring advanced feature engineering & model explainability techniques
- The Uncomfortable Truth About AI Salaries (From 1,500 Real Job Postings)
- I Analyzed the 2025 World Happiness Report — Here’s What the Data Actually Shows
- Prompt Injection: The Invisible Threat in the Age of Artificial Intelligence
