The objective of this project is to stimulate and represent real-world e-commerce problems and present their solution on a feasible scale. Initial steps included collection and organization of Raw data by creating a web scraper and extracting information through various concerned marketplaces websites, converting the quasi structured data into a more structured and definite form by using various data cleaning tools, and processing the filtered data for building our model.
Project Details:
The project started with the collection of the datasets and cleaning.
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Data Collection : Collection and organization of Raw data by creating a web scraper and extracting information through various concerned marketplaces websites.
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Data Cleaning : Converting the quasi-structured data into a more structured and definite form by using various data cleaning tools.
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Data Preprocessing : Processing the filtered data for building our model.
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Exploratory Data Analysis : Making use of various tools for visualizing and analyzing the data and finding patterns and trends in our dataset.
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Applying Machine Learning/ Deep Learning Algorithms : Training our model using various advanced machine learning and deep learning algorithms for the achievement of higher accuracy of price prediction.
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Building Web Application : Making use of Python framework and libraries (Django/Flask) to project our model on a web application for proving a good user interface and reflecting the various trends in the e-commerce market.