Data Analyst Course in Kolkata

This project will help you predict a flower’s class. By analyzing the data about the petals and their dimensions, we will train a machine-learning model that can accurately predict a flower class by analyzing each petal.

The project uses machine learning techniques, such as supervised and unsupervised algorithms, to analyze and predict whether an individual's annual income is more than INR 50,000 or less based on various input parameters.

The project will develop a model that will assist in detecting credit card fraud by applying machine learning algorithms, such as supervised, unsupervised, reinforcement learning techniques, neural networks, and random forests.

This project aims to analyze the factors that have the maximum impact on the preferences of pre-paid billing types and identify the highly correlated variables of these impacting factors.

Choose a Pokemon whose primary type is “grass" and whose secondary type is “poison” with the maximum speed. Using machine learning and data analysis techniques, develop a model that predicts whether the selected Pokemon is legendary or not.

This project will use machine learning to develop a predictive data model for predicting future stock prices. It will also use k-nearest neighbors and linear regression algorithms to analyze current stock market trends.

Work on the Yelp comment dataset to deploy a multi-headed model to identify and categorize toxic comments (obscenity, insults, threats, etc.). It must enable the user to find and choose the necessary type.

This project aims to develop a book recommendation engine based on an individual's past reading and buying history.

The project seeks to determine the variables correlated with impacting factors to analyze the likelihood of the loan application being approved or denied. Methods like random forests, decision trees, and logistic regression are used to forecast it.

The recommendation engine is developed using the Netflix prize dataset. It collects data about a user from numerous sources, anticipates their requirement, and recommends movies based on the user’s behavior and choices made earlier.