# 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 Perceptron, Logistic Regression, SVM, Decision Trees, KNN, and Random Forest 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
- Competition deadline on this website is set for the whole semester. The deadline for code submission is described on Canvas page.
- Make sure to read the detailed instructions and guidelines provided in the competition pages for further information.
- You have 10 trials per day on Kaggle, and you can select 1 final submission for the leaderboard.

## **Additional Instructions:**
- Remember to submit your final notebook for peer-review and upload it to Canvas.
- Ensure that your code has comments in markdown to explain your work and choices for the peer-review process.
- You need to submit your predictions file to the competition and be listed on the leaderboard with your name as "FirstName LastName". If you are not listed on the leaderboard by the end of the competition, you will lose the entire grade.
- The peer-review period will start the day after the submission deadline, and you will have 3 days to complete the review.

Get ready to showcase your machine learning skills! Best of luck!