Note: Pdf file is well formatted and easy to read.

• Jupyter Notebook Support
• Run pip install -r requirements.txt to install all the dependencies in your environment.

• Run jupyter notebook in your terminal to open the notebook in your browser.
• Open the Project1.ipynb file and run the cells in the notebook.

• Open the Project1.pdf file to see the results of the notebook.

Github: lurkingryuu

1. Classify breast tumor dataset into 2 classes, i.e., benign or malignant using machine learning models like Logistic Regression, SVM, Neural Network.
2. Evaluate the model accuracy.

The given dataset is made up with fine needle aspiration biopsy reports where a thin needle is inserted into abnormal tissue or fluid, aiding in diagnosis or excluding conditions like cancer. The dataset contains a total of 569 biopsy reports out of which 357 are benign, and 212 are malignant. The "diagnosis" column contains the report, with "M" indicating Malignant and "B" indicating Benign.

Ten real-valued features are computed for each cell nucleus:

• a) radius (mean of distances from center to points on the perimeter)
• b) texture (standard deviation of gray-scale values)
• c) perimeter
• d) area
• e) smoothness (local variation in radius lengths)
• f) compactness (perimeter * 2 / area - 1.0)
• g) concavity (severity of concave portions of the contour)
• h) concave points (number of concave portions of the contour)
• i) symmetry
• j) fractal dimension

The mean, standard error, and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.

Create a folder with the name <YourRoll>_A1. Copy your code and all your supporting files into this folder, including one README file on how to execute the code.

Do not use the logistic regression function directly. Write the code for logistic regression from scratch.