Tumor Size Prediction using Linear Regression

Overview

This project implements a tumor size prediction model using linear regression. Given a set of test samples with multiple features, the model predicts the tumor size in centimeters. The model is trained on a dataset and normalizes the test samples using the mean (mu) and standard deviation (sigma) from the training set before making predictions.

Features

• Feature Normalization: Ensures proper scaling of test data.
• Bias Term Inclusion: Uses a bias term in each test sample for correct prediction.
• Linear Regression Model: Utilizes a pre-trained theta vector for prediction.
• Prediction Clipping: Ensures tumor size remains within a valid range (0 to 100 cm).

Requirements

This project requires Python 3.x and the following libraries:

pip install numpy

File Structure

📂 Tumor-Size-Prediction
│── 📜 a.ipynb              # Jupyter Notebook containing the model
│── 📜 README.md            # Project Documentation
│── 📜 requirements.txt     # List of dependencies

Installation & Usage

1. Clone the Repository

git clone https://github.com/yourusername/tumor-size-prediction.git
cd tumor-size-prediction

2. Install Dependencies

pip install -r requirements.txt

3. Run the Model

Run the Jupyter Notebook (a.ipynb) to test the tumor size predictions:

# Import necessary libraries
import numpy as np
 
# Load the test dataset
X_test = np.array([...])
 
# Normalize the dataset using training mean (mu) and standard deviation (sigma)
X_test[:, 1:] = (X_test[:, 1:] - mu[1:]) / sigma[1:]
 
# Perform predictions
price = X_test @ theta
price = np.clip(price, 0, 100)
 
# Display results
for i, pred in enumerate(price):
    print(f"Sample {i+1} - Predicted Tumor Size: {pred:.2f} cm")

Troubleshooting

• Dimension Mismatch Error: Ensure mu, sigma, and theta are correctly shaped to match X_test.
• Invalid Normalization: Exclude the bias term while normalizing features (mu[1:] and sigma[1:]).
• Incorrect Predictions: Ensure theta was obtained from a properly trained model.

Contributing

Feel free to submit pull requests to improve the project. For major changes, open an issue first to discuss the proposal.

License

This project is licensed under the MIT License.