class AasimAnsari:
def __init__(self):
self.name = "Mohd Aasim Ansari"
self.role = "Aspiring Data Scientist & AI Engineer"
self.expertise = ["Data Science", "Machine Learning", "Deep Learning",
"NLP", "Computer Vision", "Gen AI", "RAG Pipelines",
"Agentic AI", "Multi-Agent Systems", "Full Stack Development"]
self.stack = {
"Data Science" : ["Pandas", "NumPy", "scikit-learn", "XGBoost",
"TensorFlow", "PyTorch", "Matplotlib", "Seaborn"],
"Gen AI" : ["LangChain", "LangGraph", "OpenAI API", "HuggingFace",
"RAG", "Groq", "Ollama", "LlamaIndex"],
"Agentic AI" : ["CrewAI", "AutoGen", "LangGraph", "Multi-Agent Systems"],
"Full Stack" : ["Python", "React", "FastAPI", "Node.js", "MongoDB",
"TailwindCSS", "Flask", "TypeScript"],
}
self.current_focus = "Full Stack Data Science with Gen AI and Agentic AI"
self.open_to = ["Full-Time Roles", "Internships", "Open Source Contributions", "Collaborations"]
def __repr__(self):
return "Always learning. Always building. Always delivering. π"| Project | What it does | Stack |
|---|---|---|
| π SalesCast AI | Sales forecasting Β· 5 models Β· anomaly detection Β· RFM segmentation | XGBoost Β· ARIMA Β· Prophet Β· LSTM |
| ποΈ Smart City Analytics | Traffic, pollution, transport & energy analytics with ML | Python Β· Pandas Β· scikit-learn |
| πΈ AI Job Salary Estimator | Instant salary predictions from job features | XGBoost Β· LightGBM Β· CatBoost Β· Streamlit |
| π° FakeShield | NLP fake-news detector Β· 95% accuracy Β· confidence scores | TF-IDF Β· scikit-learn Β· Flask |
| π€ AI Resume Screener | Rank 100s of resumes by job relevance | Python Β· spaCy Β· Flask |
| π¦ Smart Traffic System | Dynamic signal control Β· emergency override | YOLOv8 Β· OpenCV Β· Computer Vision |




