This repository contains a breast cancer prediction model developed using logistic regression. The model aims to predict the likelihood of a breast cancer diagnosis based on input features. This project utilizes a dataset containing various features.

Breast cancer is a significant health issue worldwide, and early detection is crucial for effective treatment. This model leverages logistic regression, a powerful statistical method, to classify whether a tumor is benign or malignant based on a set of features derived from breast cancer biopsy data.

The dataset used in this project is the Breast Cancer Data Set referred from the Kaggle site.

• Number of Instances: 569
• Number of Attributes: 32
• Attribute Information:
• Mean radius
• Mean texture
• Mean perimeter
• Mean area
• Mean smoothness ... (and other relevant features)
• Target Variable: Diagnosis (M = malignant, B = benign)

The logistic regression model is used to estimate the probability that a given instance (tumor) is malignant. Logistic regression is well-suited for binary classification tasks.

• The dataset is split into training and testing sets.
• Feature scaling is performed to standardize the features.
• The logistic regression algorithm is applied to the training data.

The model's performance is evaluated using metrics such as accuracy and precision.

The model's performance is assessed using the following metrics:

• Accuracy: The proportion of true results (both true positives and true negatives) among the total number of cases examined.
• Precision: The proportion of true positive results among the total predicted positives.

The logistic regression model achieves an accuracy of 97.9% on the test dataset. The results demonstrate the model's effectiveness in predicting breast cancer diagnoses.