I created dataset with five folders namely Animals, Birds, Fruits, Trees, Flowers each containing 50 images and I did K means Clustering, Hierarchical clustering, NN Model for the same dataset.

This dataset contains information about patients who underwent breast cancer surgery, including their age, year of operation, number of positive axillary nodes, and survival status. We implemented an Artificial Neural Network to predict survival in breast cancer patients using the Haberman's dataset, which contains 306 cases with three input features (age, operation year, and number of positive nodes) and a binary survival outcome. The data was preprocessed by scaling features and split into 70% training and 30% testing sets. The neural network architecture consisted of two hidden layers (5 and 3 neurons respectively) with sigmoid activation functions. The model achieved moderate performance with approximately 73% accuracy, 70% precision, 65% recall, and an F1 score of 67%. The number of positive lymph nodes emerged as the strongest predictor of survival, while age and operation year had less influence. This historical dataset (1958-1970) provided valuable insights, though the model's performance suggests that additional modern medical factors would be needed for more accurate predictions. The implementation demonstrates the potential of machine learning in medical prognosis while highlighting the importance of comprehensive clinical assessment beyond just the available features.

Fresh water Swim Beach dataset provides a comprehensive overview of water quality at various freshwater swim beaches, highlighting the need for continuous monitoring and analysis. The results from the K-means clustering will support efforts to maintain safe recreational water conditions and promote public health. K-means clustering was employed on this dataset to categorize beaches based on their water quality metrics. By grouping similar observations, the analysis aims to: Identify patterns and trends in water quality across different beaches. Determine the relationship between water temperature and contamination levels. Provide actionable insights for beach management authorities to enhance public safety measures.

The Beach Safety Classification dataset is likely focused on providing information about various beaches and their safety conditions. Based on typical beach safety datasets, it might include data related to water quality, environmental factors, beach location, and classification of safety levels. Here’s a general description of what such a dataset might contain: