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class DataScientist:
def __init__(self):
self.name = "Sharanya Thirumoorthi"
self.role = "AI & Data Science Graduate"
self.specialization = "Data Science & Machine Learning"
self.current_focus = [
"Data Analytics",
"Machine Learning",
"Explainable AI",
"LLM Fundamentals",
"Prompt Engineering",
]
self.learning = [
"FastAPI",
"PostgreSQL",
"Generative AI",
]
self.interests = [
"Exploratory Data Analysis",
"Predictive Modeling",
"Time Series Forecasting",
"AI Applications",
]
def say_hi(self):
print("Let's turn data into something meaningful!")
me = DataScientist()
me.say_hi()Programming Languages
|
Python █████████░ Strong
|
Java ███████░░░ Proficient
|
SQL ████████░░ Strong
|
Data Science & Machine Learning
|
Pandas █████████░ Strong
|
NumPy ████████░░ Strong
|
Scikit-Learn ████████░░ Strong
|
SHAP / XAI ███████░░░ Proficient
|
Data Analytics & Visualization
|
Matplotlib ████████░░ Strong
|
Seaborn ███████░░░ Proficient
|
Plotly ███████░░░ Proficient
|
Excel ██████░░░░ Familiar
|
Jupyter █████████░ Strong
|
Generative AI
|
LLM Basics ██████░░░░ Learning
|
Prompt Engineering ███████░░░ Proficient
|
Hugging Face ██████░░░░ Learning
|
LangChain ██████░░░░ Learning
|
Generative AI ██████░░░░ Learning
|
Backend & APIs
|
FastAPI ███████░░░ Proficient
|
Databases
|
PostgreSQL ██████░░░░ Learning
|
MySQL ███████░░░ Proficient
|
Tools & Platforms
|
Git ████████░░ Strong
|
GitHub ████████░░ Strong
|
VS Code ████████░░ Strong
|
Google Colab ████████░░ Strong
|
flowchart LR
A[📊 Data]
A --> B[🔬 Data Science]
A --> C[📈 Data Analysis]
A --> D[🤖 AI]
B --> B1[Python]
B --> B2[Machine Learning]
B --> B3[Scikit-Learn]
B --> B4[SHAP / XAI]
C --> C1[SQL]
C --> C2[Pandas]
C --> C3[EDA]
C --> C4[Visualization]
D --> D1[LLM Basics]
D --> D2[Prompt Engineering]
D --> D3[Hugging Face]
D --> D4[LangChain]
style A fill:#2B0D14,stroke:#D4A5A5,color:#F3D9DA,stroke-width:3px
style B fill:#5C1A2E,stroke:#D4A5A5,color:#F3D9DA,stroke-width:2px
style C fill:#5C1A2E,stroke:#D4A5A5,color:#F3D9DA,stroke-width:2px
style D fill:#5C1A2E,stroke:#D4A5A5,color:#F3D9DA,stroke-width:2px
style B1 fill:#D4A5A5,color:#2B0D14
style B2 fill:#D4A5A5,color:#2B0D14
style B3 fill:#D4A5A5,color:#2B0D14
style B4 fill:#D4A5A5,color:#2B0D14
style C1 fill:#D4A5A5,color:#2B0D14
style C2 fill:#D4A5A5,color:#2B0D14
style C3 fill:#D4A5A5,color:#2B0D14
style C4 fill:#D4A5A5,color:#2B0D14
style D1 fill:#D4A5A5,color:#2B0D14
style D2 fill:#D4A5A5,color:#2B0D14
style D3 fill:#D4A5A5,color:#2B0D14
style D4 fill:#D4A5A5,color:#2B0D14
| Project | Description | Tech Stack |
|---|---|---|
| 🌊 Flood Probability Prediction | Random Forest model for flood probability prediction with explainable AI to understand model decisions. | Python Pandas Scikit-learn Random Forest SHAP |
| 🌾 Agri-Horticulture Commodities Price Prediction | Time-series based analysis and forecasting of agricultural commodity prices and trends. | Python Pandas Time Series Forecasting |
| 🎙️ Voice Coding Assistant | Voice-driven Python learning assistant designed to make coding more accessible. | Python AI Voice Interaction |
| 🏅 Olympic 2024 Analysis | Exploratory analysis of Paris 2024 Olympic data to uncover medal and performance trends. | Python Pandas EDA Matplotlib Seaborn |
| 🚲 Bike Purchase Dashboard | Interactive dashboard analysing customer demographics and purchasing behaviour. | Power BI Excel Data Analytics |
| 🌍 Forbes Global 2000 | Analysis of revenue, profit and market trends across major global companies. | Python Pandas EDA Visualization |
⭐ From Sharanya Thirumoorthi with 🤍



