I’m a Third-year Information Technology student at Pune Institute of Computer Technology, Pune, India.
- 🌱 Learning: Next.js, Three.js, advanced React patterns, and scalable backend architectures with Python Flask.
- ⚡ In my free time: I enjoy competitive programming, reading tech blogs, and scripting YouTube Shorts.
• Built with HTML, CSS & JavaScript; migrating to Vite-React with Three.js, GSAP & Glassmorphism for enhanced UI/UX.
- Cloud Minds Team: With Nawaz Sayyad & Mayuresh Muluk, secured 3rd prize at the AWS Ideathon for an LLM fine-tuning prototype (SFLLM).
- Diploma, VAPM Latur – Completed 2024
- Cisco Introduction to Data Science – Data Analytics & AI/ML fundamentals
- INFOSYS-SPRINGBOARD: Basics of Python
- LinkedIn Learning: Project Management Foundations
- Multiple hackathon & workshop participations (Innovation Foundation, RAILMIT, DEVTOWN, INNOTECH)
“Striving to build solutions that blend innovation with impact.”
import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression
data = pd.read_csv("Advertising.csv")
X = data[['TV']] # feature must be 2D y = data['Sales'] # target
model = LinearRegression() model.fit(X, y)
print("Intercept:", model.intercept_) print("Slope:", model.coef_[0]) print(f"R² score: {model.score(X, y):.3f}")
plt.figure(figsize=(7,5)) plt.scatter(X, y, color='blue', label='Data Points') plt.plot(X, model.predict(X), color='red', linewidth=2, label='Regression Line') plt.xlabel('TV Advertising Budget ($1000s)') plt.ylabel('Sales (in 1000s units)') plt.title('Simple Linear Regression: TV vs Sales') plt.legend() plt.show()