Decision tree model

Title: Hypothyroidism Prediction using Machine Learning

Overview:

This project focuses on applying machine learning algorithms to medical datasets related to hypothyroidism. The aim was to build a predictive model that can assist in early detection and classification of patients based on clinical and laboratory features.

Key Steps:

Data Preprocessing: Cleaning the dataset, handling missing values, and encoding categorical variables.

Feature Selection: Identifying the most relevant medical attributes (TSH, T4, symptoms, etc.) to improve accuracy.

Model Training: Implemented algorithms such as Naive Bayes, Logistic Regression, and Decision Trees to classify patient outcomes.

Evaluation: Compared models using metrics like accuracy, precision, recall, and F1-score.

Visualization: Generated clear plots to show patient distribution, feature importance, and classification performance.

Outcome:

The project demonstrated how machine learning can support medical decision-making by providing reliable predictions for hypothyroidism. It highlights skills in Python (pandas, scikit-learn, seaborn), data analysis, and algorithm implementation.