E-Books →THE APPLIED DATA SCIENCE WORKSHOP

The project began with data exploration, where the prostate cancer dataset was examined to understand its structure and content. Various statistical techniques were employed to gain insights into the data, such as checking the dimensions, identifying missing values, and examining the distribution of the target variable.
The next step involved exploring the distribution of features in the dataset. Visualizations were created to analyze the characteristics and relationships between different features. Histograms, scatter Descriptions, and correlation matrices were used to uncover patterns and identify potential variables that may contribute to the classification of prostate cancer.
Machine learning models were then developed to classify prostate cancer based on the available features. Several algorithms, including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), were implemented. Each model was trained and evaluated using appropriate techniques such as cross-validation and grid search for hyperparameter tuning.