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https://github.com/mastermindromii/car-price-prediction-model
Here is My Regression Project based on Predicting Price of Car using Linear Regression.
https://github.com/mastermindromii/car-price-prediction-model
linear-regression matplotlib numpy pandas python scikit-learn seaborn
Last synced: 8 days ago
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Here is My Regression Project based on Predicting Price of Car using Linear Regression.
- Host: GitHub
- URL: https://github.com/mastermindromii/car-price-prediction-model
- Owner: MasterMindRomii
- License: mit
- Created: 2024-03-13T15:08:56.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-10-30T04:07:33.000Z (20 days ago)
- Last Synced: 2024-10-30T07:15:59.437Z (20 days ago)
- Topics: linear-regression, matplotlib, numpy, pandas, python, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 884 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## 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 Dataset uploaded on Kaggle.
**Link to the Dataset :** [Honda Used Car Selling](https://www.kaggle.com/datasets/themrityunjaypathak/honda-car-selling)
## Problem Statement
- The objective of this Project is 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.
## Table of Contents
- [Setting up the Enviroment](#setting-up-the-enviroment)
- [Libraries required for the Project](#libraries-required-for-the-project)
- [Getting started with Repository](#getting-started)
- [Steps involved in the Project](#steps-involved-in-the-project)
- [Conclusion](#conclusion)
- [Link to the Notebook](#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/MasterMindRomii/Car-Price-Prediction-Model.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%.
## Thank you :)