A web app that includes Data Analytics. Tableau is used for Visualizations.
HOW TO RUN ON VS CODE??
https://drive.google.com/file/d/1cwXDr9jvZlIkStsqDqJ_5eyd2LPRG3FM/view?usp=drivesdk
Team Member - A project submitted by
AKSHAT SINGH (20BCG10017 - BHOPAL)
INSHA FATIMA (20BEE0372 - VELLORE)
ADEEBA RASHID (20BAI10107 - BHOPAL)
DESHMUKH NIRANJAN KHUSHAVANTRAO (20BCT0220 - VELLORE)
Another section of the project is at this Github Repository: https://github.com/CallmeAk/Amazon-mobile-phone-Reviews
Summary- The Amazon Mobile Phone Reviews project analyzes customer reviews of mobile phones on Amazon to extract valuable insights. It collects and preprocesses a dataset of reviews, applies data analytics techniques like sentiment analysis and text mining to understand customer feedback and preferences. The project also utilizes data visualization to present the findings effectively. The output includes a comprehensive report with analysis results, customer sentiments, prevalent themes, and recommendations. This information helps businesses make data-driven decisions to enhance their product offerings and improve customer satisfaction in the competitive mobile phone market.
In the Amazon Mobile Phone Reviews data analytics project, Flask is used as a web framework to develop and deploy the project's user interface and API. Flask is a popular and lightweight web framework written in Python that allows developers to build web applications quickly and efficiently.
Flask also facilitates the integration of data visualization components into the web interface. With Flask, developers can render interactive charts, graphs, and visual representations of the analysis results using libraries such as Matplotlib or Plotly. Users can then view these visualizations directly in their web browsers, making it easier to understand and interpret the insights derived from the mobile phone reviews.
Analysis Of Amazon Cell Phone Reviews Category: Deep Learning
Skills Required: Python,Python Web Frame Works,NLP
Project Description:
Mobile phones have revolutionized the way we purchase products online, making all the information available at our fingertips. Reviews and ratings submitted by consumers became an integral part of the customer’s buying decision process. The review and rating platform provided by eCommerce players creates a transparent system for consumers to take decisions and feel confident about it. However, it is difficult to read all the feedback for a particular item especially for the popular items with many comments. In this project, we will attempt to understand the factors that contribute to classifying reviews as positive or negative We will be using Natural language processing to analyze the sentiment (positive or a negative) of the given review. A sample web application is integrated to the model built.
