kiaanonthehub/Classification-BreastCancerMalignancy

This project developed a machine learning solution to predict breast cancer malignancy using the Wisconsin Breast Cancer Dataset, aiming to support a South African medical scheme in expediting benefit approvals for timely patient care. # Classification Project: PDAN8411 Part 2 Welcome! This repository contains a Jupyter Notebook for a classification task in PDAN8411 Part 2. ## What You Need ### Python Kernel - Use a **Python 3.8+** kernel in Jupyter Notebook. - To start Jupyter: ```bash jupyter notebook ``` - Select a `Python 3` kernel when opening the notebook. ### Install Packages Install these Python packages using `pip`: ```bash pip install numpy pandas matplotlib seaborn scikit-learn ``` ### Imports in the Notebook The notebook uses these imports: ```python import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score, classification_report, confusion_matrix from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier ``` ## Getting Started 1. **Clone this repository** and download the dataset if required. 2. **Install the required packages** (see above). 3. **Open the notebook** in Jupyter and follow along with the code and explanations. ## Contents - **Data Loading & Exploration:** Import and explore the dataset. - **Preprocessing:** Clean and prepare the data for modeling. - **Model Training:** Train classification models (e.g., Logistic Regression, Decision Trees). - **Evaluation:** Assess model performance using metrics and visualizations. - **Conclusion:** Summarize findings and suggest improvements. ## Running the Notebook 1. Ensure your dataset is in the same folder as the notebook. 2. Run all cells in order (`Cell` > `Run All` or `Shift+Enter`). 3. Outputs include: - Cleaned dataset files (if generated). - Trained model files (if saved). - Evaluation metrics and plots (e.g., confusion matrix, classification report). ## Troubleshooting - **Dataset Missing:** Make sure the dataset file is in the notebook folder. - **Package Errors:** Run `pip install` again or c …