# Mini-Kaggle Project 1: Breast Cancer Classification
Welcome to the mini-Kaggle project on breast cancer classification! In this project, you will have the opportunity to apply your machine learning skills and techniques to predict the diagnosis of breast cancer based on various features.

## About the Dataset
The dataset used in this project is the Wisconsin Breast Cancer dataset, which consists of various features extracted from breast mass samples. The goal is to classify the samples as either malignant (M) or benign (B) based on these features.

## Project Tasks
1. **Data Exploration**: Start by exploring the dataset, understanding the features, and identifying any missing or erroneous values.
2. **Preprocessing and Feature Engineering**: Perform necessary preprocessing steps such as handling missing values, scaling features, and encoding the target variable.
3. **Model Training and Evaluation**: Train different classifiers such as Logistic Regression, Decision Trees, Random Forests, and SVM on the preprocessed dataset. Evaluate their performance using appropriate metrics and select the best-performing model.
4. **Model Deployment and Submission**: Deploy the selected model on the test dataset and generate predictions. Create a submission file in the required format for evaluation.
5. **Peer Review and Discussion**: Engage in the peer review process to evaluate and provide feedback on other participants' submissions. Discuss your approaches, findings, and challenges faced during the project.

## Competition Guidelines
The competition submission deadline is **TBC**.
The evaluation metric used for this competition is accuracy.
Make sure to read the detailed instructions and guidelines provided in the competition page for further information.
Get ready to showcase your machine learning skills and contribute to the field of breast cancer classification! Best of luck!