Document Type
Event
Start Date
1-12-2022 5:00 PM
Description
Mental health issue are the most common issue facing in human society. These issues are mostly in impact negatively on working peoples, on the individual, his/her family, workplace, community, and the economy. Our project is based on mental health chat bot of natural language processing with deep learning models .We have a structure data set of Mental Health. In the data set we have nine columns. We use neural networks to create our models .We use another method call scratch in which we create a model by our self and use it so our project we create a layer with the help of scratch .There is a 3 script of code first on is for chat bot second one is for deep learning models with neural networks and third one is from scratch. We use NLTK preprocessing methods that helps us to clean text data. Then we pass the data into chat bot class that we have created. The chat bot class is helps us to test that our data set is perfectly clean or not and we take predictions of that data set. We select two columns in our data set and extract them know we apply some method to extract features on it the data set. These features helps us to find a better accuracy result for the models. So we convert our data set into training part or test part. We convert it randomly and through library so we sure that our data set is perfectly clean for our deep learning models. We create our own different deep learning models and compile that model after that we fit data into these models and take predictions. We draw a graph and confusion matrix that help us to understand who our models works and what is the predictions of the models. We use python version 3.9 and tools is Jupiter Notebook in Anaconda.
Included in
GR-283 Chatbot
Mental health issue are the most common issue facing in human society. These issues are mostly in impact negatively on working peoples, on the individual, his/her family, workplace, community, and the economy. Our project is based on mental health chat bot of natural language processing with deep learning models .We have a structure data set of Mental Health. In the data set we have nine columns. We use neural networks to create our models .We use another method call scratch in which we create a model by our self and use it so our project we create a layer with the help of scratch .There is a 3 script of code first on is for chat bot second one is for deep learning models with neural networks and third one is from scratch. We use NLTK preprocessing methods that helps us to clean text data. Then we pass the data into chat bot class that we have created. The chat bot class is helps us to test that our data set is perfectly clean or not and we take predictions of that data set. We select two columns in our data set and extract them know we apply some method to extract features on it the data set. These features helps us to find a better accuracy result for the models. So we convert our data set into training part or test part. We convert it randomly and through library so we sure that our data set is perfectly clean for our deep learning models. We create our own different deep learning models and compile that model after that we fit data into these models and take predictions. We draw a graph and confusion matrix that help us to understand who our models works and what is the predictions of the models. We use python version 3.9 and tools is Jupiter Notebook in Anaconda.