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An End-to-End Machine Learning Project On Early Breast Cancer Detection Using Support Vector Machine and K-Nearest Neighbor. All The Exploratory Data Analysis, Data Visualization and Model Building is in breastCancerDetection.ipynb File.

Kaggle Data Repository

Attribute Information:

1. Sample code number: id number
2. Clump Thickness: 1 - 10
3. Uniformity of Cell Size: 1 - 10
4. Uniformity of Cell Shape: 1 - 10
5. Marginal Adhesion: 1 - 10
6. Single Epithelial Cell Size: 1 - 10
7. Bare Nuclei: 1 - 10
8. Bland Chromatin: 1 - 10
9. Normal Nucleoli: 1 - 10
10. Mitoses: 1 - 10

Class: (2 for benign, 4 for malignant) Malignant==> Cancerous

Benign==> Not Cancerous (Healthy)

Techniques Used

1. Data Cleaning
2. Data Visualization
3. Machine Learning Modeling

Algortihms Used

1. Logistic Regression
2. Support Vector Machine
3. KNN
4. Naivye Bayes
5. Random Forest Classifier

Model Evaluation Methods Used

1. Accuracy Score
2. Confusion Matrix

Packages and Tools Required:

1. Pandas
2. Matplotlib
3. Seaborn
4. Scikit Learn
5. Jupyter Notebook

Package Installation

1. pip install numpy
2. pip install pandas
3. pip install seaborn
4. pip install scikit-learn
5. pip install matplotlib
6. pip install plotly
7. pip install streamlit