Stress-detection-using-machine-learningTrain a Machine learning model to detect stress using Bernoulili Naïve Bayes Algorithm.
Open source data as dataset is labelled as 0 & 1 ,where 0 indicates no stress else 1 is stress collected from Internet , Facebook comments etc.
Libraries used: Pandas Numpy Matlabplotlib Scikit-learn :(Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib.
Following command can be used to install scikit-learn via conda −
conda install scikit-learn
Mathematics needed for this project: Probability
STAGES OF PROJECT: Data collection Data preparation and cleaning Data cleaning covers in Python pandas ,mathematics topics like mean ,median mode also covers within data cleaning. Model evaluation or Finding the Right ML algorithm for the problem
Algorithm : Bernoulili Naïve Bayes Algorithm ( type of supervised learning (labeled data)) Train the model using data set Splitting the data occurs in this phase (80% data is to train for model whereas 20% is test (which model doesn't know before))
Note: •Accuracy is the Major criteria for Machine learning Project. If Data set is increased , Accuracy increases,vice-versa. Count Vetoctorsier:Countvectorizer makes it easy for text data to be used directly in machine learning and deep learning models such as text classification.
Metrics:Every machine learning task can be broken down to either Regression or Classification, just like the performance metrics. There are dozens of metrics for both problems, but we’re gonna discuss popular ones along with what information they provide about model performance. It’s important to know how your model sees your data!
If you ever participated in a Kaggle competition, you probably noticed the evaluation section. More often than not, there’s a metric on which they judge your performance.
Metrics are different from loss functions. Loss functions show a measure of model performance. They’re used to train a machine learning model (using some kind of optimization like Gradient Descent), and they’re usually differentiable in the model’s parameters.
Metrics are used to monitor and measure the performance of a model (during training and testing), and don’t need to be differentiable.