Breast cancer is a prevalent form of cancer that affects millions of individuals worldwide. Early detection plays a critical role in improving the chances of successful treatment and survival. In this project, I developed a breast cancer detection model using machine learning techniques.

The primary goals of this project are as follows:

1. Build a machine learning model capable of accurately detecting breast cancer.
2. Get good accuracy in Breast Cancer Prediction.

• For this project, we will use a publicly available breast cancer dataset, such as the Breast Cancer Wisconsin (Diagnostic) Data Set.
• This dataset contains various features extracted from digitized images of fine needle aspirates (FNA) of breast mass.
• The dataset includes measurements such as mean radius, texture, smoothness, and more, along with the corresponding diagnosis (benign or malignant). This information will serve as the basis for training our model.

The breast cancer detection process involves the following steps:

1. Exploratory Data Analysis(EDA): First of all we will do EDA.
2. Model Development: Employ machine learning algorithms, such as logistic regression, Decision Tree, and Random Forest.
3. Model Evaluation: Assess the performance of the trained model using appropriate evaluation metrics such as accuracy, precision, recall, and F1 score.

Achieved outstanding accuracy of 97.36% by Random Forest.

Breast cancer detection is a critical task that can significantly impact patient outcomes. By developing an accurate and reliable machine learning model, we aim to contribute to the early detection and diagnosis of breast cancer.