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Car price prediction using machine learning

Assignment Help Python Programming
Reference no: EM132833681

Question: Used Car Price Prediction using Machine Learning Approaches in Various Algorithms Models (Logistic Regression, Decision Tree, Random Forest, ANN, SVM, Naïve Bayes, KNN, K-Means) and deploy the model in a front-end software for users that Predicts the most accuracy.

Table of contents

Abstract

Chapter 01: Introduction (Why, What and How, a brief Introduction)
01. Project Aim and Objectives
02. Project rationale (in paragraphs)
03. Project Questions (3 or 4)
04. Chapter Organization (Brief explanation of all chapters)

Chapter 02:
Literature Review (Related Works)
(1) What is Machine Learning all about?
(2) At least 15 research paper references and their percentage of prediction accuracy should be explained with citations & references.

Chapter 03: Methodology (Which are the methodologies will be used in this study)
(1) Collecting data (from where and how, source and ethical consideration)
(2) Feature engineering
(3) Exploratory data analysis
(4) Numerical, categorical etc.
(5) Principal Component Analysis (PCA)
(6) How to handle missing data, cleaning data, shaping data etc
(7) How to train and test data
(8) Brief all the Title algorithms (with Mathematic how it functions)
(9) In fact, in this chapter dataset collection to result everything should be explained.

Chapter 04: Findings and critical evaluation
Performance, Parameters (accuracy, precision, F1 Score, correlation etc.
Graphs, picture, can be used in the body of the assignment

Chapter 05: Conclusion and recommendation

Attachment:- Machine Learning Project.rar

Reference no: EM132833681

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Reviews

len2833681

3/19/2021 10:55:02 PM

After going through the attached files please quote me your best discount price. - Please note that Jupyter file will be like that attached main.ipynb where you will be explaining every steps of this research. - Thank you

len2833681

3/19/2021 10:54:47 PM

Dear concern: Kindly read very carefully the guideline and also a complete project with deployment but only one Machine Learning Model i.e. Random Forest but we''ve to do many different algorithms however data processing and analysis are same for all. We can use the final binary dataset for prediction using various Machine Learning Models.

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