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Taranpreet10451
@SrijanShovit Solved the issue.
Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early detection and accurate diagnosis are crucial for improving survival rates and outcomes for patients. However, current diagnostic methods, including mammograms and biopsies, can be time-consuming, expensive, and sometimes yield false positives or negatives. There is a need for a more efficient, accurate, and accessible method for breast cancer detection.
Solution Proposed: Data Collection and Preparation: Collect and preprocess a dataset of breast cancer diagnostic data, including features such as tumor size, cell shape, and other clinical attributes. Feature Selection: Identify and select the most relevant features for the model. Machine Learning Model: Develop a basic machine learning model (e.g., logistic regression, decision tree) to classify the data as benign or malignant. Model Evaluation and Validation: Evaluate the model's performance using appropriate metrics and validate its effectiveness on unseen data.