Aim of the Project

Predict if the patients have Breast Cancer or not. And also classify that it is Malignant / Benign using UCI dataset

Life Cycle of the Project

Collected the dataset from Kaggle open source. Performed Data Cleaning using Pandas and Seaborn. Used Pandas, matplotlib & Numpy for Data Pre-processing, to decrease the redundancy, by taking care of the missing values, and duplicates. Used Label Encoder to handle the imbalanced dataset, in order to avoid the OVERFITTING. And use Standard-Scaler, to regularize the dataset. Trained the data on 5 algorithms, in order to get the model with best accuracy as an Output. Trained the model using below mentioned algorithms:

• Logistic Regression
• K Nearest Neighbors Classifier
• Support Vector Classifier (SVC)
• Decision Tree Classifier
• Random Forest Classifier

Results from the Project

Check out the Detail Project Overview on GitHub Repository

Technologies Used | Python | Seaborn | Numpy | Pandas | Scikit Learn | Flask | Matplotlib | Numpy |

Model Performance

• Model Accuracy =
• Precision = 97.82
• Recall = 95.71
• F-1 Score = 96.77
• ROC-AUC Score = 97.14%

• scene