In these machine learning projects, various algorithms were applied to solve healthcare-related problems. For breast cancer prediction, models like linear regression, decision trees, and random forest were used to classify tumors as benign or malignant, with performance comparisons. In diabetes prediction, a Support Vector Machine model was employed to determine whether a person has diabetes. Additionally, linear regression was applied to predict medical insurance costs using a dataset from Kaggle. These projects showcase the versatility of machine learning in addressing healthcare challenges.

Neural networks were applied to solve diverse classification in these deep learning projects. For breast cancer classification, a simple neural network was used to predict whether cancer is benign or malignant, employing ReLU and sigmoid activations for feature extraction and binary classification. Similarly, in the MNIST Handwritten Digit Classification project, a neural network was built to classify digits, using a similar structure with ReLU activations and a sigmoid layer for multi-class classification. Both projects showcase the power of neural networks in tackling medical diagnosis and image recognition challenges.

In these NLP projects, a Fake News Classifier was developed using LSTM to classify news articles as fake or real by leveraging the power of sequence-based learning on textual data. Additionally, sentiment analysis was performed on the Amazon Fine Food Reviews dataset, utilizing NLP techniques to analyze customer reviews and classify sentiments as either positive or negative. Both projects demonstrate the application of deep learning and natural language processing in addressing text classification challenges.

Across these exploratory data analysis (EDA) projects, various datasets were explored to uncover meaningful insights and implement machine learning techniques. The Credit Card Fraud Detection project focused on analyzing a highly imbalanced dataset, using features derived from PCA to identify fraudulent transactions. The Zomato dataset involved both EDA and feature engineering to explore patterns in restaurant data. With the Irish dataset, univariate, bivariate, and multivariate analyses were conducted to understand relationships between variables. Lastly, the Titanic dataset combined EDA with logistic regression to predict passenger survival. These projects showcase the practical use of EDA across diverse datasets.