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Subject: DSCI521-Analysis-Interpretation-project

Team members:

1. Name: Madhu Bandru
2. Name: Sudharshan Rao B
3. Name: Spandana Bendi
4. Name : Divya Lakshmi Narayana

INTRODUCTION

  • We have taken a dataset from Kaggle.com which has used car informations
  • We used this data to predict the price based on relevant features available in the dataset
  • The database has been created with data on their sales upto previous year
  • We are using machine learning techniques for price prediction
  • This dataset is going to provide the estimation quote of price based on data features entered by the user
  • It will help to guide the users to estimate the price for the used cars

SYSTEM REQUIREMENTS AND PRE-REQUISITES

  • OS: Mac, Linux or Windows
  • Software: Python 3.0 or later
  • OS :Mac,Linux or Windows
  • Python3.0 or above
  • Jupyter Lab
  • Django

ABOUT THE DATASET

Fields:

  • dateCrawled- series object
  • name-series object
  • seller-series object
  • offerType-series object
  • price-series int
  • abtest-series object
  • vehicleType - series object
  • yearOfRegistration- series int
  • gearbox- series object
  • powerPS- series int
  • model- series object
  • kilometer- series int
  • monthOfRegistration- series int
  • fuelType - series object
  • brand- series object
  • notRepairedDamage - series object
  • dateCreated - series object
  • nrOfPictures- series int
  • postalCode- series int
  • lastSeen - series object

Models implemented:

  • Linear Regression
  • K-Nearest Neighbors
  • MLP Regressor
  • Decision tree
  • Random Forest

Attributes used:

  • Price
  • vehicleType
  • gearbox
  • powerPS
  • model
  • kilometer
  • fuelType
  • brand
  • postalCode
  • vehicleAge

DATA CLEANSING

  • Reformatted existing columns for easier analysis.
  • NaN, space values filled with mean, median and mode.
  • Conversion of few columns to date-time and integer values as needed for analysis.

STEPS TO EXECUTE THE CODE

  • pip install below packages:
    • Pandas
    • Numpy
    • Sklearn
    • matplotlib
    • seaborn
  • Simply place the data in a separate directory and name it as 'Data'
  • run the .ipynb file and execute the code

UI

  • We have designed the UI based on the above model in a clickable format and used it to predict the price

Conclusion

  • We evaluated used-car price prediction using Kaggle dataset which gives an accuracy of 93% with RandomForest Model.
  • The most relevant features used for this prediction are price, year, Power_ps, and vehicleAge by filtering out outliers and irrelevant features of the dataset.
  • This model gives us good accuracy when compared to the other models.

FUTURE WORK

  • To collect data for more cars
  • More information of cars in the current dataset
  • To adjust model hyper-parameters, by using RandomizedSearchCV