Lung Cancer Prediction using Decision Tree

This project was developed as part of the BSc (Hons) in Data Science program at NSBM Green University.
The objective is to build a machine learning model for early detection of lung cancer risk levels using patient data.

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📌 Project Overview

Lung cancer remains one of the most critical health challenges worldwide.
This project leverages a Decision Tree Classifier to analyze medical and lifestyle factors such as:

• Demographics (age, gender, etc.)
• Medical history
• Smoking habits
• Lifestyle and occupational hazards
• Genetic markers

By preprocessing the dataset, performing Exploratory Data Analysis (EDA), and applying Decision Tree classification, we aim to predict lung cancer risk levels (Low, Medium, High) with high accuracy.

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📂 Repository Contents

• Lung_Cancer_Prediction.ipynb → Jupyter Notebook (data preprocessing, EDA, model training, evaluation)
• Final_Report_Group_E.pdf → Final written report of the project
• cancer patient data sets dataset.csv → Dataset

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⚙️ Installation & Setup

1. Clone the repository:

   git clone https://github.com/YourUsername/Lung_Cancer_Prediction.git
   cd Lung_Cancer_Prediction