The chatbot about diabetes was developed using Natural Language Processing (NLP). Scraped over 1000 relevant information about diabetes such as symptoms, medications, and nutrition was web scraped from reliable sources. Used the RASA Framework to build the chatbot and train it using the scraped data. Implemented Natural Language Understanding (NLU) was to help the chatbot understand the user’s intent and respond appropriately. The chatbot can improve patient care, increase efficiency, and lead to cost savings for patients and healthcare providers.

The project aimed to predict energy production using time series models, which are statistical models used to analyze data over time to make forecasts or predictions. Collected and analyzed energy production data over time, which was then cleaned and preprocessed to prepare it for modeling. Preprocessed the data by removing trends i.e., subtracting the rolling mean and decomposition. Autocorrelation analysis was also done to detect the patterns and check for randomness.

Created a tool that predicted the survival of passengers based on various features such as age, gender, and ticket class using machine learning models. Cleaned and preprocessed data using methods such as, imputing missing values, encoding categorical features, and scaling numeric features. Engineered features from the data to model using logistic regression, decision trees, random forests, and support vector machines (SVMs) algorithms. Optimized the algorithms using GridSearchCV to reach best model.