Solved end-to-end Data Science Projects in Retail & Ecommerce

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Data Science Projects in Retail & Ecommerce

Machine Learning project for Retail Price Optimization

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

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Recommender System Machine Learning Project for Beginners-2

Recommender System Machine Learning Project for Beginners Part 2- Learn how to build a recommender system for market basket analysis using association rule mining.

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Recommender System Machine Learning Project for Beginners-1

Recommender System Machine Learning Project for Beginners - Learn how to design, implement and train a rule-based recommender system in Python

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Build Customer Propensity to Purchase Model in Python

In this machine learning project, you will learn to build a machine learning model to estimate customer propensity to purchase.

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Deploying Machine Learning Models with Flask for Beginners

In this MLOps on GCP project you will learn to deploy a sales forecasting ML Model using Flask.

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Walmart Sales Forecasting Data Science Project

Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

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Machine Learning Project to Forecast Rossmann Store Sales

In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.

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Avocado Machine Learning Project Python for Price Prediction

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.

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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.

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Customer Churn Prediction Analysis using Ensemble Techniques

In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

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Customer Market Basket Analysis using Apriori and Fpgrowth algorithms

In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

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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.

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Create Your First Chatbot with RASA NLU Model and Python

Learn the basic aspects of chatbot development and open source conversational AI RASA to create a simple AI powered chatbot on your own.

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PyCaret Project to Build and Deploy an ML App using Streamlit

In this PyCaret Project, you will build a customer segmentation model with PyCaret and deploy the machine learning application using Streamlit.

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Build a Multi Touch Attribution Machine Learning Model in Python

Identifying the ROI on marketing campaigns is an essential KPI for any business. In this ML project, you will learn to build a Multi Touch Attribution Model in Python to identify the ROI of various marketing efforts and their impact on conversions or sales..

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ML Model Deployment on AWS for Customer Churn Prediction

MLOps Project-Deploy Machine Learning Model to Production Python on AWS for Customer Churn Prediction

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Recommender System Machine Learning Project for Beginners-3

Content Based Recommender System Project - Building a Content-Based Product Recommender App with Streamlit

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Build a Graph Based Recommendation System in Python -Part 1

Python Recommender Systems Project - Learn to build a graph based recommendation system in eCommerce to recommend products.

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Recommender System Machine Learning Project for Beginners-4

Collaborative Filtering Recommender System Project - Comparison of different model based and memory based methods to build recommendation system using collaborative filtering.

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Build a Review Classification Model using Gated Recurrent Unit

In this Machine Learning project, you will build a classification model in python to classify the reviews of an app on a scale of 1 to 5 using Gated Recurrent Unit.

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Build a Hybrid Recommender System in Python using LightFM

In this Recommender System project, you will build a hybrid recommender system in Python using LightFM .

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PyTorch Project to Build a LSTM Text Classification Model

In this PyTorch Project you will learn how to build an LSTM Text Classification model for Classifying the Reviews of an App .

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Build a Graph Based Recommendation System in Python-Part 2

In this Graph Based Recommender System Project, you will build a recommender system project for eCommerce platforms and learn to use FAISS for efficient similarity search.

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BigMart Sales Prediction ML Project in Python

The goal of the BigMart Sales Prediction ML project is to build and evaluate different predictive models and determine the sales of each product at a store.

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Inventory Demand Forecasting using Machine Learning in R

In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

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Identifying Product Bundles from Sales Data Using R Language

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.

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Machine learning for Retail Price Recommendation with R

Use the Mercari Dataset with dynamic pricing to build a price recommendation algorithm using machine learning in R to automatically suggest the right product prices.

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Data Science Project - Instacart Market Basket Analysis

Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

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ETL (Abintio) developer at IBM

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Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data. In each learning path, there are many customized projects with all the details from the beginner to the expert. As a new data science learner, you can just follow these projects to master the important techniques quickly. It is really helpful for both my research and job searching. Hope you can come and join ProjectPro to win a great future for yourself.

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Graduate Research assistance at Stony Brook University

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills in Data Engineering/Science and hoping to find real-world projects fortunately, I came across Project Pro. Project Pro helped me by providing an in-depth explanation of the end-to-end real-world data engineering projects. From data extraction, transformation, and storage up to data visualization. I learned more about Kafka, AWS, NI-FI, and Spark. Thru the help of the knowledge I gained from Project Pro, I was able to do well in the coding exams, interview and helped me land a job at EY. I will recommend every aspiring data professional as well as existing data science/engineer expert to try Project Pro to enhance their knowledge.

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Director Data Analytics at EY / EY Tech

As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of them too, and that's when I came across ProjectPro while watching one of the SQL videos on the E-Learning Bridge YouTube channel. One of the standout features was that it featured real projects on topics I just read about, across different job descriptions at the time. The main issue was the right path to guide us in using these tools and adding to the resume, and that's exactly what ProjectPro got me through. The fact that I can have a reliable route and videos explaining each tool in detail really motivated me to continue with the platform. Another thing we all struggle with is how to really connect with someone if we're stuck somewhere because there are so many solutions. But this has also been solved by experts we can chat with and believe me when I say this they will do whatever it takes to solve your problem even if it takes longer than expected. In my sophomore year of college and getting hands-on exposure to technologies like PySpark, NLP, Kafka, etc, and being able to really apply the theory and work on a project from start to finish really boosted my confidence in general!

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Data Science Intern, Capgemini

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop Admin, Hadoop projects. I have been happy with every project. They have really brought me into the forefront of Data Science and Big data. I would recommend this to everyone. It is more than worth the price. After working with them I feel so much more employable for current projects.

Ray han

Tech Leader | Stanford / Yale University

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic was "Credit Risk Modelling". To understand other domains, it is important to wear a thinking cap and that's where ProjectPro helped me. I also got a chance to talk to experts who have worked on these domains - they helped me by walking through the project. Kudos to the ProjectPro team!

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Data Consultant at Confidential

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Abhinav Agarwal

Graduate Student at Northwestern University

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Solved end-to-end Data Science Projects in Retail & Ecommerce

Get ready to use Data Science Projects in Retail & Ecommerce for solving real-world business problems

Data Science Projects in Retail & Ecommerce

Machine Learning project for Retail Price Optimization

Recommender System Machine Learning Project for Beginners-2

Recommender System Machine Learning Project for Beginners-1

Build Customer Propensity to Purchase Model in Python

Deploying Machine Learning Models with Flask for Beginners

Walmart Sales Forecasting Data Science Project

Machine Learning Project to Forecast Rossmann Store Sales

Avocado Machine Learning Project Python for Price Prediction

Ecommerce product reviews - Pairwise ranking and sentiment analysis

Customer Churn Prediction Analysis using Ensemble Techniques

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms

Time Series Python Project using Greykite and Neural Prophet

Create Your First Chatbot with RASA NLU Model and Python

PyCaret Project to Build and Deploy an ML App using Streamlit

Build a Multi Touch Attribution Machine Learning Model in Python

ML Model Deployment on AWS for Customer Churn Prediction

Recommender System Machine Learning Project for Beginners-3

Build a Graph Based Recommendation System in Python -Part 1

Recommender System Machine Learning Project for Beginners-4

Build a Review Classification Model using Gated Recurrent Unit

Build a Hybrid Recommender System in Python using LightFM

PyTorch Project to Build a LSTM Text Classification Model

Build a Graph Based Recommendation System in Python-Part 2

BigMart Sales Prediction ML Project in Python

Inventory Demand Forecasting using Machine Learning in R

Identifying Product Bundles from Sales Data Using R Language

Machine learning for Retail Price Recommendation with R

Data Science Project - Instacart Market Basket Analysis

Customer Love

Latest Blogs

We power Data Science & Data Engineering projects at

Join more than 115,000+ developers worldwide

We power Data Science & Data Engineering
projects at

Join more than
115,000+ developers worldwide