https://github.com/anas436/exploratory-data-analysis-of-car-price-using-python

https://github.com/anas436/exploratory-data-analysis-of-car-price-using-python

jupyter-notebook matplotlib numpy pandas python3 scipy seaborn

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/anas436/exploratory-data-analysis-of-car-price-using-python
• Owner: Anas436
• Created: 2022-07-08T16:21:20.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-07-08T16:38:26.000Z (almost 3 years ago)
• Last Synced: 2025-02-01T15:30:30.092Z (3 months ago)
• Topics: jupyter-notebook, matplotlib, numpy, pandas, python3, scipy, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 145 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Exploratory-Data-Analysis-of-Car-Price-Using-Python

In this NoteBooks I have shown Exploratory Data Analysis using various techniques to find out proper impact of Car price. Also I have used different statistical operations base on this question __"What are the main characteristics that have the most impact on the car price?"__

Important Variables


We now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:


Continuous numerical variables:



    
Length

    
Width

    
Curb-weight

    
Engine-size

    
Horsepower

    
City-mpg

    
Highway-mpg

    
Wheel-base

    
Bore



Categorical variables:



    
Drive-wheels



As we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance.