Hepatitis C Prediction Project

A statistical analysis and machine learning project using a real-world Hepatitis C dataset. The project applies hypothesis testing, probability analysis, and logistic regression to understand key blood markers in liver disease.

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📋 Project Overview

This project uses a dataset of 615 blood donors to analyze clinical markers associated with Hepatitis C. It was designed as an applied statistics project to answer specific clinical questions using inferential statistics and predictive modeling.

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❓ Research Questions

1. What is the probability of Albumin (ALB) being more than 30 mg/L?
2. Is the mean of Alkaline Phosphatase (ALP) greater than the mean of Protein? (α = 0.075)
3. Is the mean of Cholesterol (CHOL) greater than the mean of Alanine Transaminase (ALT)? (α = 0.05)
4. Are the test statistics for CHOL and ALP within the not-reject region?
5. Run a logistic regression on the dataset and report confidence intervals.

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📊 Key Findings

• Q1 — Albumin Probability: Only 24 out of 615 donors had ALB < 30 mg/L. The probability of ALB > 30 mg/L is approximately 78.23%, suggesting most donors have healthy albumin levels.
• Q2 — ALP vs. Protein (z-test): With z = −3.459 and a critical value of 1.440 at α = 0.075, the null hypothesis is rejected.
• Q3 — CHOL vs. ALT (z-test): With z = −22.43 and a critical value of 1.645 at α = 0.05, the null hypothesis is rejected.
• Q4 — Hypothesis Summary: Both CHOL and ALP test statistics fall outside the non-rejection region, confirming predominantly healthy donors.
• Q5 — Logistic Regression: See ProjectPY.ipynb (Python) and Project 1.Rmd (R) for full results with confidence intervals.

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📁 Repository Structure

File	Description
HepatitisCdata.csv	Raw dataset (615 blood donor records)
ProjectPY.ipynb	Python analysis: probability, z-tests, logistic regression
Project 1.Rmd	R analysis: confidence intervals and logistic regression
Final Project.docx	Written report with full findings

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🛠️ Technologies Used

• Python — pandas, numpy, scipy, statsmodels, sklearn
• R — glm(), confint(), base statistics
• Jupyter Notebook & R Markdown

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🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/janmejoykar1807/HepatitisC_Project.git

2. Open ProjectPY.ipynb in Jupyter Notebook for the Python analysis.

3. Open Project 1.Rmd in RStudio for the R analysis.

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👤 Author

Janmejoy Kar Data Science learner — applying Python, R, and SQL for data analysis and predictive modeling. GitHub Profile