"In a world overflowing with information, I seek the stories hidden within data."
class DataScholar:
def __init__(self):
self.name = "Ayushi Rai"
self.role = "Data Science Student"
self.location = "India"
self.interests = ["Machine Learning", "Data Analytics", "Artificial Intelligence"]
self.domains = ["Data Science", "Data Analytics", "Data Engineering"]
self.current_focus = ["Machine Learning", "Exploratory Data Analysis",
"Data Visualization", "Building Data Pipelines"]
self.tools = ["Python", "SQL", "Pandas", "Scikit-Learn", "Jupyter"]
self.philosophy = (
"Data is like a manuscript — chaotic at first, "
"but through analysis it reveals powerful stories."
)
📖 Behind every dataset lies a narrative waiting to be uncovered.
🪶 My goal is to transform raw data → insight → knowledge → impact.
| 📖 Category | 🛠️ Tools |
|---|---|
| 🧠 Languages |    |
| 📊 Data Science |      |
| 📈 Visualization |   |
| ⚙️ Data Engineering |    |
| 🔮 Customer Churn Prediction |  |
| Predicts whether a bank customer will churn using Neural Networks, deployed via Streamlit |  |
   |
 |
| 🧠 SENSE — Sentiment Extraction Natural Language Scoring Engine | |
| Dual-model NLP pipeline (VADER + RoBERTa) that extracts, scores, and explains sentiment in any text — with SHAP word-level explainability and batch CSV analysis, deployed via Streamlit | |
  |
 |
Statistics from the archives of my coding journey
If you wish to discuss data, ideas, or collaborations,
send a letter through one of the channels below.
class FinalChapter:
def __init__(self):
self.chapter = "End of Chapter"
self.message = [
"The library of data is infinite.",
"Many stories remain undiscovered."
]
self.next = "See you in the next chapter 📖"