In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.
Use the Adult Income dataset to predict whether income exceeds 50K yr based on
In this project, we will cover in detail the architecture of a transformer used in natural language processing use cases. We will go through the key nlp areas in the pre-transformer stage like bow, word2vec...and then the origin and gradual refinement of transformers. Finally, we will study one of the most popular state of the art transformer models, called BERT and use it for text classification on a large dataset.
In this project, we will use time-series forecasting to predict the values of a sensor using multiple dependent variables. A variety of machine learning models are applied in this task of time series forecasting. We will see a comparison between the LSTM, ARIMA and Regression models. Classical forecasting methods like ARIMA are still popular and powerful but they lack the overall generalizability that memory-based models like LSTM offer. Every model has its own advantages and disadvantages and that will be discussed. The main objective of this article is to lead you through building a working LSTM model and it's different variants such as Vanilla, Stacked, Bidirectional, etc. There will be special focus on customized data preparation for LSTM.
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
Classification is one of the basic things in ML and most of us jump to Neural networks or boosting to predict classes. But more often than not, to make the other person understand how the classification is happening, we need to use basic models like Logistic, decision trees etc. In this project we talk about you can apply various basic techniques, the maths and intuition behind them and how they paved way to bagging and boosting of the world