Academic portfolio and course documentation for Probability and Statistics at KLE Technological University.
This repository reorganizes the original visual portfolio into a readable academic record. The repository contains chapter-wise notes, the mini project, laboratory documentation, and the original portfolio website.
- Name: Kartik Shivshetty
- Program: Computer Science and Engineering
- Roll Number: 632
- USN: 01FE25BCS387
- University: KLE Technological University
- Course: Probability and Statistics
- Track: Embedded + AI + Robotics
| Section | Description |
|---|---|
| Course Overview | Course and portfolio context |
| Chapter 1 — Description of Data | Data, descriptive statistics, visualization, and formulas |
| Chapter 2 — Probability | Probability laws, conditional probability, Bayes theorem, and applications |
| Chapter 3 — Random Variables & Probability Distributions | Random variables, distributions, statistical parameters, and applications |
| Chapter 4 — Statistical Inference | Sampling, hypothesis testing, confidence intervals, and inference formulas |
| Mini Project — AI Impact on Job Market | Statistical analysis of AI and employment data |
| Laboratory | Four documented Python/Jupyter experiments |
| Original Portfolio | Original visual portfolio website source |
The portfolio documents learning in probability and statistics through four chapters, practical laboratory work, and a mini project. The academic material covers descriptive statistics, probability, random variables and probability distributions, and statistical inference.
The practical work uses Python and Jupyter Notebook together with Pandas, NumPy, Matplotlib, Seaborn, and SciPy where documented by the portfolio.
The portfolio's mini project is AI Impact on Job Market. It applies Probability and Statistics concepts to employment-related data, including salary, automation risk, remote work, and employment status. The documented analysis includes descriptive statistics, visualization, probability, hypothesis testing, correlation and regression, and chi-square testing.
View the complete project documentation →
Four laboratory experiments are documented:
- Python Basics & Control Structures
- Statistical Analysis using E-Commerce Dataset
- Probability Distribution & Binomial Analysis
- Python Data Structures & Tuple Operations
View laboratory documentation →
The original interactive portfolio is preserved under web/. It remains separate from the academic Markdown documentation so the visual presentation and the academic record serve different purposes.
The original portfolio contains external Google Drive notebook links for laboratory work and a Google Docs link for the mini-project report. Those links are preserved in the corresponding documentation instead of being replaced with fabricated local files.
This repository is derived from the supplied portfolio source. Content has been reorganized for GitHub Markdown without inventing additional experiments, datasets, results, references, or academic sections.
Mathematical expressions have been normalized into GitHub-compatible LaTeX where the original HTML contained character-encoding artifacts.