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https://github.com/arif-miad/-lung-cancer-prediction-analysis-using-machine-learning
https://github.com/arif-miad/-lung-cancer-prediction-analysis-using-machine-learning
data data-science data-visualization keras-tensorflow machine-learning-algorithms matplotlib python sklearn
Last synced: 3 days ago
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- Host: GitHub
- URL: https://github.com/arif-miad/-lung-cancer-prediction-analysis-using-machine-learning
- Owner: Arif-miad
- License: apache-2.0
- Created: 2025-02-07T17:43:47.000Z (8 days ago)
- Default Branch: main
- Last Pushed: 2025-02-07T18:30:00.000Z (8 days ago)
- Last Synced: 2025-02-07T18:36:36.493Z (8 days ago)
- Topics: data, data-science, data-visualization, keras-tensorflow, machine-learning-algorithms, matplotlib, python, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🩺 Lung Cancer Prediction & Analysis using Machine Learning
## 📌 Project Overview
Lung cancer is one of the deadliest diseases globally, and early detection is crucial for improving survival rates. This project involves **advanced Exploratory Data Analysis (EDA)** and **machine learning classification models** to analyze lung cancer cases, risk factors, smoking trends, and healthcare accessibility across **25 of the world's most populated countries**.
## 📊 Dataset Details
- **Total Records:** 220,632 individuals
- **Features:** 21 columns
- **Key Variables:**
- `Age`, `Gender`, `Smoker`, `Years_of_Smoking`, `Family_History`
- `Lung_Cancer_Diagnosis`, `Air_Pollution_Exposure`, `Occupational_Exposure`
- `Healthcare_Access`, `Annual_Lung_Cancer_Deaths`, `Lung_Cancer_Prevalence_Rate`
- **Target Variable:** `Lung_Cancer_Diagnosis` (Binary Classification: Yes/No)
## 🔍 Exploratory Data Analysis (EDA)
We performed **50+ visualizations**, including:
### 📌 **Univariate Analysis**
- **Histogram & KDE:** Distribution of numerical features
- **Box Plot & Violin Plot:** Outlier detection & spread
- **Count Plot & Bar Chart:** Categorical feature analysis
### 📌 **Bivariate Analysis**
- **Scatter Plot & Pair Plot:** Relationship between numerical features
- **Heatmap & Correlation Matrix:** Feature correlations
### 📌 **Geospatial Analysis**
- **World Map Visualization**: Shows lung cancer cases by country
## 🤖 Machine Learning Models
We implemented **Top 10 Classification Models** for prediction:
1. Logistic Regression
2. Decision Tree
3. Random Forest
4. Gradient Boosting (XGBoost, CatBoost, LightGBM)
5. Support Vector Machine (SVM)
6. k-Nearest Neighbors (KNN)
7. Naive Bayes
8. Multi-layer Perceptron (MLP Neural Network)
9. AdaBoost Classifier
10. Stacking Classifier
## 📏 Model Evaluation
We used multiple metrics for model evaluation:
- **Accuracy, Precision, Recall, F1-score**
- **Confusion Matrix** (Heatmap)
- **AUC-ROC Curve** (Performance visualization)
- **Feature Importance Plot** (For tree-based models)
## 📂 Project Structure
```
📦 Lung_Cancer_Analysis
├── 📄 README.md # Project documentation
├── 📂 dataset # Dataset files (CSV)
├── 📂 notebooks # Jupyter Notebooks for analysis
├── 📂 models # Trained ML models
└── 📂 visualizations # Saved plots & charts
```
## 🚀 Results & Insights
- **Key Risk Factors Identified**: Smoking, Air Pollution, Family History
- **Random Forest & XGBoost** provided the highest accuracy (~95%)
- **Healthcare Access** plays a crucial role in survival rates
- **Countries with high pollution levels** show increased lung cancer prevalence
## 🔗 Links
- 📌 **Kaggle Notebook**: [View Full Notebook](www.linkedin.com/in/arif-miah-8751bb217)
- 🔗 **LinkedIn Profile**: [Connect with me](https://www.linkedin.com/in/arifmia)
## 💡 Future Scope
- Improving deep learning models for enhanced prediction accuracy
- Analyzing genetic & environmental factors in greater detail
- Building a web app for lung cancer risk assessment
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### 📢 Feel free to ⭐ this repository if you find it useful! 🚀