Build a Collaborative Filtering Recommender System in Python

Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

Predict Churn for a Telecom company using Logistic Regression

Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Census Income Data Set Project - Predict Adult Census Income

Use the Adult Income dataset to predict whether income exceeds 50K yr based on census data.

Ecommerce product reviews - Pairwise ranking and sentiment analysis

This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

PySpark Tutorial - Learn to use Apache Spark with Python

PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Time Series Python Project using Greykite and Neural Prophet

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.

AWS Project-Website Monitoring using AWS Lambda and Aurora

In this AWS Project, you will learn the best practices for website monitoring using AWS services like Lambda, Aurora MySQL, Amazon Dynamo DB and Kinesis.

Slow Changing Dimensions for Warehouse

One of the broadest uses of Snowflake is building a data warehouse platform or enhancing the existing data lake. It offers all sorts of services to build an efficient Data warehouse with ETL capability and support for various external data partners. Slowly Changing dimensions are a common database modeling technique used to capture data in a table and show how it changes over time. The slowly changing dimension of the warehouse dimension is said to rarely change. However, when they change, there should be a systematic approach to capturing that change. Examples of SCDs are customer and products information. This project explains how to build a Slowly Changing Dimension (SCD) using Snowflake’s Stream functionality and how to automate the process using Snowflake’s Task functionality.

Predict Macro Economic Trends using Kaggle Financial Dataset

In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Creating your own embeddings using Glove and Word2vec

We all at some point in time wished to create our own language as a child! But what if certain words always cooccur with another in a corpus? Thus you can make your own model which will understand which word goes with which one, which words are often coming together etc. This all can be done by building a custom embeddings model which we create in this project