1. FLOWCHART:
A workflow diagram is a visual layout of a process, project or job in the form of a flow
chart. It’s a highly effective way to impart the steps more easily in a business process,
how each one will be completed, by whom and in what sequence.
Workflow diagrams make work easier and your organization more secure overall.
Workflow diagrams create greater efficiency by streamlining business processes. Not
only do workflow charts serve as a helpful visual representation, but they provide the
necessary documentation for areas like legal, compliance or audit requirements.
2. IMPORTING MODULES
A file is considered as a module in python. To use the module, you have to import it
using the import keyword. The function or variables present inside the file can be used
in another file by importing the module. This functionality is available in other h,,a and
sometimes derivationally related forms of a word to a common base form. Eg: A
stemmer for English should identify the strings "cats", "catlike", "catty" as based on
the root "cat".
3.COUNT VECTORIZER:
Count Vectorizer tokenizes (tokenization means breaking down a sentence or
paragraph or any text into words) the text along with performing very basic
preprocessing like removing the punctuation marks, converting all the words to
lowercase, etc. The vocabulary of known words is formed which is also used for
encoding unseen text later. An encoded vector is returned with a length of the entire
vocabulary and an integer count for the number of times each word appeared in the
document.
4.LOGISTIC REGRESSION
Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is used for predicting the categorical dependent variable using a given set of independent variables. Logistic regression predicts the output of a categorical dependent variable. Therefore, the outcome must be a categorical or discrete value. It can be either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact value as 0 and 1, it gives the probabilistic values which lie between 0 and 1. Logistic Regression is much similar to the Linear Regression except that how they are used. Linear Regression is used for solving Regression problems, whereas Logistic regression is used for solving the classification problems. In Logistic regression, instead of fitting a regression line, we fit an "S" shaped logistic function, which predicts two maximum values (0 or 1). The curve from the logistic function indicates the likelihood of something such as whether the cells are cancerous or not, a mouse is obese or not based on its weight, etc. Logistic Regression is a significant machine learning algorithm because it has the ability to provide probabilities and classify new data using continuous and discrete datasets.
5 EVALUATION MEASURES
Whenever we build Machine Learning models, we need some form of metric to
measure the goodness of the model. Bear in mind that the “goodness” of the model
could have multiple interpretations, but generally when we speak of it in a Machine
Learning context, we are talking of the measure of a model's performance on new
instances that weren’t a part of the training data. Determining whether the model being
used for a specific task is successful depends on 2 key factors:
a. Whether the evaluation metric we have selected is the correct one for our problem
b. If we are following the correct evaluation process
6 Deployment of the model
Deployment of an ML-model simply means the integration of the model into an
existing production environment which can take in an input and return an output that
can be used in making practical business decisions.
Streamlit is an open-source Python library that makes it easy to create and share
beautiful, custom web apps for machine learning and data science. In just a few minutes
you can build and deploy powerful data apps. Using streamlit we have deployed the
ml model in a responsive webpage