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: 3 months 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 (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-08-11T12:13:43.000Z (11 months ago)
• Last Synced: 2025-08-11T14:19:33.921Z (11 months ago)
• Topics: linear-regression, matplotlib, numpy, pandas, python, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage: https://car-price-prediction-model-mpr-project.streamlit.app/
• Size: 889 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,  

This is my **Regression Project** aimed at predicting used car prices using **Linear Regression**.  

It demonstrates my skills in **data cleaning, visualization, feature engineering, and model building**.  

---

## 📊 Dataset

**Source:** [Honda Used Car Selling](https://www.kaggle.com/datasets/themrityunjaypathak/honda-car-selling)  

The dataset contains various attributes of used cars, such as **model, fuel type, kilometers driven, suspension, and selling price**.

---

## 🎯 Problem Statement

The goal is to develop a **Machine Learning model** that can predict the price of a used car based on its features.  

This helps buyers and sellers make **data-driven** pricing decisions.

---

## 🛠 Tech Stack & Libraries

```python

import numpy as np 

import pandas as pd

from matplotlib import pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split, KFold, cross_val_score

from sklearn.linear_model import LinearRegression

%matplotlib inline

📂 Project Workflow

1️⃣ Data Loading & Exploration

df = pd.read_csv("honda_car_selling.csv")

df.head()

df.info()

df.shape

2️⃣ Data Cleaning

Removed extra whitespaces from Fuel Type, Suspension, and Car Model.

Converted kms driven into integers after stripping "kms".

Converted price from "6.45 Lakh" to 645000 using a custom function.

df['Fuel_Type'] = df['Fuel_Type'].str.strip()

df['Suspension'] = df['Suspension'].str.strip()

df['Car_Model'] = df['Car_Model'].str.strip()

df['kms_driven'] = df['kms_driven'].str.split().str[0].astype(int)

def convert_price(price_str):

    return int(float(price_str.split()[0]) * 100000)

df['Price'] = df['Price'].apply(convert_price)

3️⃣ Data Visualization

sns.swarmplot(x='Year', y='Price', data=df)

sns.relplot(x='kms_driven', y='Price', data=df)

sns.relplot(x='Car_Model', y='Price', hue='Suspension', data=df)

4️⃣ Feature Engineering

df = pd.get_dummies(df, columns=['Fuel_Type', 'Suspension'], drop_first=True)

5️⃣ Model Building & Evaluation

X = df.drop('Price', axis=1)

y = df['Price']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()

model.fit(X_train, y_train)

cv = KFold(n_splits=10)

scores = cross_val_score(model, X, y, cv=cv, scoring='r2')

print("Cross-validation scores:", scores)

print("Mean R² score:", scores.mean())

📌 Conclusion

Developed a Linear Regression Model to predict car prices based on multiple attributes.

Achieved an average prediction accuracy of ~82%.

Validated model performance using K-Fold Cross Validation with a mean R² score of ~83%.