Car Price Prediction

Hello Everyone,

Here is My Regression Project based on Predicting Price of Car using Linear Regression.

Dataset

I used Honda Used Car Selling Dataset which is one of my own Dataset uploaded on Kaggle.

Link to the Dataset : Car Price Dataset

Problem Statement

• To develop a Machine Learning Model that can accurately predict the prices of used cars based on various features and attributes.

• The predicted prices will assist both buyers and sellers in making informed decisions, ensuring fair transactions in the used car market.

Streamlit Web App

• For my Car Price Prediction Project, I have created a Streamlit Web App for predicting the prices of cars in more interactive and user friendly way.

• This Web App allows you to predict the price of a car by just selecting some of its features and fill in some details.

• These all are the features you need to select or enter before Pressing the Predict Button :

  • 𝗬𝗲𝗮𝗿 : Select the manufacturing Year of the Car.
  • 𝗸𝗺𝘀 𝗗𝗿𝗶𝘃𝗲𝗻 : Input the Total Distance the Car has traveled.
  • 𝗙𝘂𝗲𝗹 𝗧𝘆𝗽𝗲 : Choose the Fuel Type of the Car.
  • 𝗦𝘂𝘀𝗽𝗲𝗻𝘀𝗶𝗼𝗻 : Pick the Type of Suspension the Car has.
  • 𝗖𝗮𝗿 𝗠𝗼𝗱𝗲𝗹 : Select Your Car Model from the available options.

• After selecting all these features, Just hit the '𝗣𝗿𝗲𝗱𝗶𝗰𝘁' Button.

• This Web App also has multiple constraints in the Input Feilds.

• I have named it AutoValuate.

Link to the Web App : Car Price Prediction App

Table of Contents

• Setting up the Enviroment
• Libraries required for the Project
• Getting started with Repository
• Steps involved in the Project
• Conclusion
• Link to the Notebook

Setting up the Enviroment

Jupyter Notebook is required for this project and you can install and set it up in the terminal.

• Install the Notebook - pip install notebook

• Run the Notebook - jupyter notebook

Libraries required for the Project

NumPy

• Go to Terminal and run this code - pip install numpy

• Go to Jupyter Notebook and run this code from a cell - !pip install numpy

Pandas

• Go to Terminal and run this code - pip install pandas

• Go to Jupyter Notebook and run this code from a cell - !pip install pandas

Matplotlib

• Go to Terminal and run this code - pip install matplotlib

• Go to Jupyter Notebook and run this code from a cell - !pip install matplotlib

Seaborn

• Go to Terminal and run this code - pip install seaborn

• Go to Jupyter Notebook and run this code from a cell - !pip install seaborn

Sklearn

• Go to Terminal and run this code - pip install sklearn

• Go to Jupyter Notebook and run this code from a cell - !pip install sklearn

Getting Started

• Clone the repository to your local machine using the following command :

git clone https://github.com/TheMrityunjayPathak/CarPricePrediction.git

Steps involved in the Project

Data Cleaning

• Fuel Type, Suspension and Car Model has extra whitespaces which is removed by str.strip() Method.

• Removing kms Suffix from kms Driven Column by using str.split() Method and keeping only Numeric Part of the String and removing kms Suffix.

• After that we can convert kms Driven Column to int DataType.

• Modifying Price Column from 6.45 Lakh to 645000 and convering it into Integer by using a Custom Made Function.

• From Car Model Column we will keep only First 3 Words of Cae Model and removing the rest of the Words for better Model Traning.

Data Visualization

• Visualizing Year with Price by using sns.swarmplot().

• Visualizing kms Driven with Price by using sns.relplot().

• Visualizing Car Model with Price by using sns.relplot() and Suspension as Hue Parameter.

Dummy Variable

• We first Create Dummy Variable Column based on the Text Column.

• Then we change it into a DataFrame.

• After that we will Merge the Dummies DataFrame and our Orignal DataFrame.

• Finally we will drop the Text Column from our Dataset.

Outlier Removal

• After describing the Dataset I noticed that in our kms Driven Column, 75% of Cars has driven 85000 kms and our Maximum Value in kms Driven is 11 Lakh kms which is an Outlier.

• And Similarly In our Price Column, 75% of Cars has Price 7 Lakh and our Maximum Price is 26 Lakh which is an Outlier.

Model Building

• Firstly I have definied Dependent and Independent Variables for our Traning and Testing.

• I have splitted data into Traning and Testing Set by using Train Test Split.

• Then I fitted the Model with X_train and y_train and checked the Score.

• After that I used KFold Cross Validation for Measuring Accuracy of our Model.

• So I cheked Cross_Val_Score of our Model for Measuring the Best Score of Model and then I have taken Mean of All that Scores.

• And Finally I predicted the Result from our Trained Model.

Conclusion

• Developed a highly accurate Linear Regression Model using various features and attributes to predict used car prices, achieving an average prediction accuracy of 82%.

• Further Model showcased its robustness by undergoing rigorous k-fold cross-validation, resulting in a mean cross-validation score of 83%.

Link to the Notebook

Car Price Prediction

Scroll to Top ⬆️