car price predication using python for beginners 🚘🚗

Cracking the Code: Predicting Used Car Prices for Beginners

Embarking on the exciting journey of predicting second-hand car prices is like solving a puzzle filled with different pieces. Imagine being able to understand why a used car is priced the way it is. In this blog post, we'll take a beginner-friendly dive into my project where I explored the world of second-hand car price prediction using a straightforward tool called Linear Regression.

Getting to the Core:

At the heart of this adventure is a quest to understand the various things that affect a car's resale value. Armed with a dataset featuring 14 carefully chosen details, picked because they can influence pricing, the goal was clear: to build a simple predictive model that could provide insights into the world of used car prices.

Why Linear Regression?

Among the many tools available, I chose Linear Regression because it's simple and easy to understand, making it perfect for beginners. In this blog post, we'll explore why I chose Linear Regression and how this model shows the connection between different factors and the final car price in a way that everyone can understand.

Preparing the Data:

Our journey starts with a crucial step — getting the data ready. Think of it as cleaning and preparing the data for our model to learn. Throughout this post, we'll talk about the challenges we faced during this step and the choices we made to solve them. It's like making sure our data is ready for a smooth journey into the world of predictions.

Training the Model:

Once our data is ready, the next step is training our model. This section will break down the process, talking about any changes we made to the model settings and why they matter. We'll also discuss splitting the data into parts for training and testing, setting the stage for the part where we see how well our model works.

As we journey through the basics of second-hand car price prediction, let's unravel the mysteries of Linear Regression together. We'll understand the importance of each detail and explore how this project can be a useful tool for those stepping into the world of used car markets. So, get ready as we start this adventure into the world of predicting used car prices for beginners!

Live Demonstration

Discover the secret to build car price Predictor Using Python! Watch our easy-to-follow video tutorial and download the source code today.

Significance of Predicting Used Car Prices for a Smarter Tomorrow

In today's fast-paced world, where cars play a crucial role in our daily lives, understanding the ins and outs of second-hand car prices is more important than ever. The project to predict used car prices comes at a time when the demand for affordable and reliable transportation is soaring. Let's delve into the reasons behind developing such a project and understand its importance, along with its pros and cons in straightforward terms that even school students can grasp.

Why Develop a Used Car Price Prediction Project:

In this era, where sustainability and economic choices are at the forefront, predicting used car prices becomes a valuable tool. Many families and individuals opt for second-hand cars due to budget constraints, making it crucial to have a way to estimate a fair and reasonable price. This project serves as a guide to make informed decisions when buying or selling a used car.

Importance of the Project:

The project holds significance as it empowers people with information. For buyers, it ensures they don't overpay, and for sellers, it helps in setting a fair price. This transparency in pricing fosters a healthier and more trustworthy used car market, benefiting both parties involved.

Pros of the Project:

• Affordability: Predicting used car prices aids in making cars more accessible to a wider audience, promoting affordability.
• Informed Decisions: Buyers and sellers can make informed decisions based on accurate predictions, reducing the likelihood of unfair transactions.
• Market Efficiency: The project contributes to the efficiency of the used car market, creating a win-win situation for buyers and sellers.

Cons of the Project:

• Data Dependence: The accuracy of predictions relies heavily on the quality and relevance of the data used. Inaccurate or outdated data may affect the reliability of the model.
• Dynamic Market: The used car market is subject to constant changes influenced by various factors, making it challenging to predict prices accurately at all times.

Project Description

The goal of a car price prediction project is to use machine learning algorithms to analyze historical data on car prices and make predictions about future prices. This involves collecting large amounts of data on different car models, their features, and their prices over time.

Once the data has been collected and organized, the next step is to choose an appropriate machine learning model and train it on the data. This involves feeding the model the historical data on car prices, allowing it to learn the patterns and relationships between the different features and their impact on the price.

After the model has been trained, it can then be used to predict future car prices based on new data inputs. For example, if a new car model is released, the model can analyze its features and predict its price based on the historical data.

Overall, a car price prediction project can be a valuable tool for consumers, car dealerships, and manufacturers to better understand and anticipate changes in the automotive market.

Download this project from GitHub : click here

Download Dataset from here

Importing Required Modules

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
import seaborn as sns
      

Reading Dataset and Displaying first 5 rows of dataset

data = pd.read_excel("car_prediction.xlsx")
data.head()
    

Concise summary of dataset

data.info()
    

Checking size of dataset

data.shape
    

(205, 26)

Generating heat map using sns package

plt.figure(figsize=(12,8))
sns.heatmap(data.corr(),annot=True)
plt.title("Correlation between the columns")
    

storing independant features in x variable and dependant feature in y variable

x = data.drop(['price','CarName','fueltype','car_ID','aspiration','doornumber','carbody','drivewheel','enginelocation','enginetype','cylindernumber','fuelsystem'], axis=1)
y = data['price'].values
print(f"x shape : {x.shape}  y shape : {y.shape}")
    

x shape : (205, 14) y shape : (205,)

Spliting data for building model

from sklearn.model_selection import train_test_split as tts
x_train,x_test,y_train,y_test = tts(x,y,test_size=0.25,random_state=42)
print("X-train : ",x_train.shape)
print("X-test : ",x_test.shape)
print("y-train : ",y_train.shape)
print("y-test : ",y_test.shape)
    

X-train : (153, 14)
X-test : (52, 14)
y-train : (153,)
y-test : (52,)

Building Linear Regression model

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x_train,y_train)
    

LinearRegression()

Predicting results for x_test input

pred = model.predict(x_test)
    

Checking Accuracy of model using mean square error, mean absolute error and r2 score

import sklearn.metrics as metrics
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
print("MSE : ",(metrics.mean_squared_error(pred,y_test)))
print("MAE : ",(metrics.mean_absolute_error(pred,y_test)))
print("R2 Score : ",(metrics.r2_score(pred,y_test)))
    

MSE : 12778023.312240602
MAE : 2539.7416115618225
R2 Score : 0.7961095379581425

Predicting Output for User Defined Input

pred2 = model.predict([[3,88.6,168.8,64.1,48.8,2548,130,3.47,2.68,9,111,5000,21,27]])
pred2.round(2)
    

array([13579.49])

Conclusion

In this captivating journey through the realm of predicting second-hand car prices, we've uncovered the secrets behind making informed decisions in the world of used cars. Our adventure began by carefully selecting 14 key details about cars – essential factors like mileage, age, and brand – forming the foundation for our predictive model. We embraced the power of Linear Regression, a simple yet powerful tool that acted as our guide in understanding the relationships between these details and the prices of used cars.

As we delved deeper into the intricacies of our project, we engaged in the crucial process of data cleaning. This step ensured our information was neat and organized, laying the groundwork for a reliable and efficient model. The subsequent training of our model was akin to teaching a computer a super-smart way to predict the value of a car based on the selected details. This process equipped our digital companion with the ability to guide us through the complex landscape of the used car market.

What we've learned in this journey extends beyond the technicalities of creating a predictive model. We've empowered ourselves with a tool that goes beyond mere numbers – it's a compass for fairness in the world of used cars. Predicting prices enables us to make wise choices, ensuring we don't overspend as buyers or unintentionally sell our cars for less than they're worth. This project, in essence, contributes to fostering a transparent and equitable environment in the used car market.

As we conclude this adventure, let's remember that this is just the beginning. The road ahead is filled with opportunities to enhance our understanding and refine our predictions. Whether you're a student intrigued by the world of data or a future car buyer seeking a fair deal, the doors to comprehending and navigating the roads of used car prices are wide open. Happy predicting, and may your future journeys be filled with informed decisions and exciting discoveries!

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