The data science project starts with the identification of various data sources, which may include web server logs, social media posts, data from digital libraries and databases, data accessed through sources on the internet via APIs, web scraping, or information that is already present in an excel spreadsheet.

This stage is also referred to as Data Cleaning or Data Wrangling. It entails steps such as selecting relevant data, combining it by mixing data sets, cleaning it, dealing with missing values by either removing them or imputing them with relevant data, dealing with incorrect data by removing it, and also checking for and dealing with outliers.

The Feature Engineering stage involves building features from the data,selecting the most important features via feature selection and generating new features via feature creation and construction. This can aid in identifying the optimal set of features, and the algorithm to use for model creation, and model construction.

Natural Language Processing is the branch of data science involved with processing human spoken and written language generated via text,audio and others. It involves NLP,NLU and NLG

Embedding Machine Learning Models into Web Applications (Flask & Express) and Ensuring Safe and Secure Data Apps

Build a predictive model and use it to find the flag