Breast Cancer detection and prediction

Summary
Developed a predictive analytics solution to detect and predict breast cancer using machine learning and deep learning techniques. This project leveraged machine learning models and TensorFlow for neural network-based predictions, providing a robust framework for accurate early detection. The Breast Cancer Wisconsin (Diagnostic) Dataset was utilized, containing detailed medical attributes. The workflow covered data preprocessing, exploratory data analysis (EDA), feature engineering, model development, evaluation, and visualization, showcasing the integration of traditional and deep learning approaches.

What I Did
Data Preprocessing:

Cleaned the dataset by handling missing values and anomalies.
Scaled numerical features to normalize data distribution for improved model accuracy.
Exploratory Data Analysis (EDA):

Identified key patterns and feature relationships using visualizations created with Matplotlib and Seaborn.
Analyzed the differences between malignant and benign cases.
Feature Engineering:

Applied Principal Component Analysis (PCA) for dimensionality reduction, focusing on key features contributing to diagnosis.
Selected the most relevant features for both traditional machine learning and deep learning models.
Model Development (Machine Learning):

Implemented algorithms such as Logistic Regression, Random Forest, and Support Vector Machines (SVM) using Scikit-learn.
Optimized hyperparameters using GridSearchCV to improve performance.
Achieved an accuracy of 96% with the Random Forest Classifier, the best-performing model in the traditional approach.
Model Development (Deep Learning):

Built and trained a neural network model using TensorFlow and Keras.
Neural network architecture included input, hidden, and output layers, with activation functions like ReLU and Sigmoid for binary classification.
Enhanced the model with dropout layers to prevent overfitting.
Achieved comparable accuracy to machine learning models, with added robustness in predictions.
Model Evaluation:

Used metrics such as accuracy, precision, recall, F1-score, and ROC-AUC for evaluation.
TensorFlow-based neural network performed consistently across test data, reinforcing its reliability for deployment.
Deployment Readiness:

Prepared both machine learning and deep learning models for deployment on platforms like Snowflake or AWS SageMaker for real-time scalability and integration into healthcare systems.
Tools and Techniques Used
Programming Language: Python.
Libraries:
Pandas, NumPy for data manipulation and preprocessing.
Matplotlib, Seaborn for visualization.
Scikit-learn for traditional machine learning models.
TensorFlow and Keras for building and training deep learning models.
Machine Learning Techniques: Classification, PCA, and hyperparameter tuning.
Deep Learning Techniques: Neural networks with ReLU and Sigmoid activation functions, dropout for regularization.
Dataset
Name: Breast Cancer Wisconsin (Diagnostic) Dataset.
Source: OpenML/UCI Machine Learning Repository.
Key Features:
Diagnosis (malignant or benign).
Attributes such as mean radius, mean texture, and fractal dimensions.
Size: 569 records with 30 features and a target variable (diagnosis).
Conclusion and Recommendation
The project successfully integrated both traditional machine learning and deep learning methods to predict breast cancer with high accuracy.
TensorFlow's neural network model provided an advanced framework for prediction, complementing traditional methods and enhancing robustness.
PCA identified critical features for diagnosis, enabling domain experts to focus on the most significant attributes.
Recommended implementing these models in healthcare systems for early detection, potentially improving diagnostic accuracy and patient outcomes.
Future work includes expanding the dataset, exploring transfer learning techniques with TensorFlow, and deploying the models on cloud platforms like Snowflake for scalable and real-time analytics.