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An analysis of 20,000 products of Flipkart. In this, we analyze the various factors on which the overall rating and product rating of any product depends. This analysis will help the manager to know the factors on which the rating of the Product depends, and with this, they can increase their market value.

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What is Flipkart product dataset?

This is a pre-crawled dataset, taken as subset of a bigger dataset (more than 5.8 million products) that was created by extracting data from Flipkart.com, a leading Indian eCommerce store. Analyzing the Flipkart Sales Dataset

Introduction Flipkart is an electronic commerce company headquartered in Bengaluru, India. It was founded in October 2007 by Sachin Bansal and Binny Bansal (no relation). Flipkart has launched its own product range under the name “DigiFlip” with products including tablets, USB flash drives, and laptop bags. As of April 2017, the company was valued at $11.6 billion. Flipkart has established itself as a renowned E-commerce company in India through TV ads, online branding, and through its presence on social media. Brand activities like the “Big billion day” have really increased the brand recall of the company. After its 2014 Big Billion Sale, Flipkart carried out a second Big Billion Sale. Where it is reported that they saw a business turnover of $300 million in gross merchandise volume.

Overview An analysis of 20,000 products of Flipkart. In this, we analyze the various factors on which the overall rating and product rating of any product depends. This analysis will help the manager to know the factors on which the rating of the Product depends, and with this, they can increase their market value.

Dataset Data has been collected from the Kaggle website. Kaggle is a platform for predictive modeling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users. This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modeling task, and it is impossible to know beforehand which technique or analyst will be most effective.

About Dataset Context This is a pre-crawled dataset, taken as subset of a bigger dataset (more than 5.8 million products) that was created by extracting data from Flipkart.com, a leading Indian eCommerce store.

Content

This dataset has following fields: • product_url • product_name • product_category_tree • pid • retail_price • discounted_price • image • is_FK_Advantage_product • description • product_rating • overall_rating • brand • product_specifications

Features

• 20K products

• With properly leveled catalog information

• Products come with their images! There is a list of urls for each product (Some of the URL's are broken tho)

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An analysis of 20,000 products of Flipkart. In this, we analyze the various factors on which the overall rating and product rating of any product depends. This analysis will help the manager to know the factors on which the rating of the Product depends, and with this, they can increase their market value.

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