Instacart data exploration
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Updated
Dec 17, 2018 - Jupyter Notebook
Instacart data exploration
Exploratory Data Analysis of dataset published by Instacart
Estudo de caso para análise de regras de associação (InstaCart data)
Identifying customer preferences, recommend product and predict next order
Conducting EDA on Instacart orders
About The objective of this project is to analyze the 3 million grocery orders from more than 200,000 Instacart users and predict which previously purchased item will be in user's next order. Customer segmentation and affinity analysis are done to study customer purchase patterns and for better product marketing and cross-selling.
An e-commerce application inspired by InstaCart built using MERN stack
An analysis of orders placed on Instacart website to create a Recommendation Engine in R
Instacart user market basket analysis of organic produce purchasing behavior.
Skills: Python (Pandas, Numpy, Matplotlib, Seaborn)
Exploring and mining basic information from an anonymised retail transactions dataset given by the company Instacart, mainly using TypeScript and NodeJS.
SQL + Tableau Instacart Analysis
Insta-hour: Identifying Peak Instacart Order Times
In this Exploratory Data Analysis (EDA) project we'll clean up the data and prepare a report that gives insight into the shopping habits of Instacart customers.
This project will use the Instacart data provided for the Kaggle challenge. We will perform a deep EDA and we will build a recommender using Word2vec embeddings
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