# Lung Cancer Analysis Project

# Overview:

This project aims to analyze lung cancer data using linear regression. The dataset, named 'lung_cancer_data.csv', includes anonymized patient information, covering age, smoking history, genetic factors, and tumor size.

# Dataset Overview:

The dataset contains the following columns:

• patient_id: Unique identifier for each patient.
• age: Patient's age.
• smoking_history: Smoking history categorized as "Current smoker," "Former smoker," or "Non-smoker."
• genetic_factor: Presence of a genetic factor categorized as "Yes" or "No."
• tumor_size: Target variable representing tumor size.

Please note that the dataset is anonymized and de-identified to comply with privacy standards.

# Data Preprocessing:

The dataset undergoes preprocessing to handle missing values, encode categorical variables, and scale numerical features. Cleaning steps ensure data quality for the linear regression model.

# Feature Selection:

Features are selected based on their relevance to lung cancer analysis. This involves considering p-values, statistical tests, and domain knowledge to identify meaningful contributors to tumor size prediction.

# Train-Test Split:

The dataset is split into training and testing sets using an 80-20 ratio, with a random_state of 42 for reproducibility.

# Linear Regression Model:

A linear regression model is implemented using scikit-learn in Python. It's trained on the dataset, minimizing Mean Squared Error (MSE) as the loss function.

# Linear Regression Sample Code:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Assuming 'X' is your feature matrix and 'y' is your target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

# Evaluate the model:

Model performance is evaluated using Mean Squared Error (MSE) on the test set, indicating the average squared difference between predicted and actual tumor sizes.

# Interpret Results:

The results provide insights into the model's performance. Consideration of linear regression limitations and adherence to assumptions is crucial for reliable results.

# Contributing:

Contributions to this project are welcome. If you have suggestions, find issues, or want to add features, please follow the guidelines in the CONTRIBUTING.md file.

# License:

This project is licensed under the [MIT License] - see the LICENSE.md file for details.

# Contact:

For support or collaboration, contact Kishan Kumar Suresh Kumar at [email protected].