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Mind map

Used Car value estimation using Data Analytics

Firahm Afandi FYP English

Map content

Used Car value estimation using Data Analytics

  1. Methods

    • Sentiment Analysis
      • Data preprocessing
        • Tokenization
      • Scrapping data
        • Platform
          • Mudah.com
          • Youtube
          • Carbase
        • Techniques
          • Text analysis
          • Audio analysis
          • Predictive analysis
      • Method
        • Lexicon based
    • Machine learning
      • Techniques
        • Multiple linear Regression
        • Naive Bayes
        • Support Vector Machine
  2. Previous Research

    • Factors affecting used car prices
    • How to increase sale for second hand car
    • Impact on regression method on forecast resale prices of used car
  3. Motivation

    • To help dealer to analyse the customer review on the car significant features from media
    • To help dealer to reduce the inventory cost of the used car
    • To help dealer to increase the sale for used car based on the significant features of the car
    • To help new drive or anyone who just learn to buy used car that commonly lack of knowledge on used car price and their characteristics
  4. Theoritical Framework

    • Premilinary study
      • Used website such as www.paultan to dig knowledge and get deeper understand ing
    • Knowledge Acquisition
    • Development
      • System that can help user to buy used car
  5. Used Car

    • Types of car
      • Continental car
        • BMW
        • Mercedes Benz
      • Local Car
        • Perodua
        • Proton
      • Other Asian car
        • Honda
        • Toyota
        • Hyundai
    • Variables of car
      • Car brand
      • Model
      • Year Manufactured
      • Engine capacity
      • Variant (Series)

Information

Description
FYP Mind Map on Used Car value estimation using Data Analytics, created by Firahm Afandi on 08/11/2019.
Created by
Firahm Afandi
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History
  • Created by Firahm Afandi · Oct 9, 2019
  • Copied by Firahm Afandi · Nov 8, 2019
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