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Box Office Revenue Prediction Using Linear Regression in ML
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Analyzing Selling Price of used Cars using Python

Last Updated : 28 Mar, 2025
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Analyzing the selling price of used cars is essential for making informed decisions in the automotive market. Using Python, we can efficiently process and visualize data to uncover key factors influencing car prices. This analysis not only aids buyers and sellers but also enables predictive modeling for future price estimation. This article will explore how to analyze the selling price of used cars using Python.

Step 1: Understanding the Dataset

The dataset contains various attributes of used cars, including price, brand, color, horsepower and more. Our goal is to analyze these factors and determine their impact on selling price. To download the file used in this example, click here.

Problem Statement: Our friend Otis wants to sell his car but isn't sure about the price. He wants to maximize profit while ensuring a reasonable deal for buyers. To help Otis we will analyze the dataset and determine the factors affecting car prices.

Step 2: Converting .data File to .csv

If the dataset is in .data format, follow these steps to convert it to .csv:

  1. Open MS Excel.
  2. Go to Data > From Text.
  3. Select Comma Delimiter.
  4. Save the file as .csv.

Now we can proceed with loading the dataset into Python.

Step 3: Install and Import Required Python Libraries

To analyze the data install the following Python libraries using the command below:

pip install pandas numpy matplotlib seaborn scipy

Import the following python libraries: numpy, pandas, matplotlib, seaborn and scipy.

Python 
import pandas as pd  import numpy as np import matplotlib.pyplot as plt import seaborn as sns import scipy as sp

Step 4: Load the Dataset

Now, we load the dataset into a Pandas DataFrame and preview the first few rows. Let's check the first five entries of dataset.

Python 
df = pd.read_csv('output.csv')  df = df.iloc[: , 1:]  df.head()

Output:

dataset
Dataset

Step 5: Assign Column Headers

To make our dataset more readable we assign column headers:

Python 
headers = ["symboling", "normalized-losses", "make",             "fuel-type", "aspiration","num-of-doors",            "body-style","drive-wheels", "engine-location",            "wheel-base","length", "width","height", "curb-weight",            "engine-type","num-of-cylinders", "engine-size",             "fuel-system","bore","stroke", "compression-ratio",            "horsepower", "peak-rpm","city-mpg","highway-mpg","price"]  df.columns=headers df.head()

Output:

column-header
Column Header

Step 6: Check for Missing Values

Missing values can impact our analysis. Let's check if any columns contain missing values.

Python 
data = df  data.isna().any()  data.isnull().any()

Output:

Screenshot-2025-03-15-184537
Missing Values

Step 7: Convert MPG to L/100km

Since fuel consumption is measured differently in different regions, we convert miles per gallon (MPG) to liters per 100 kilometers (L/100km)

Python 
data['city-mpg'] = 235 / df['city-mpg'] data.rename(columns = {'city_mpg': "city-L / 100km"}, inplace = True)  print(data.columns)  data.dtypes

Output:

MPG
MPG

Step 8: Convert Price Column to Integer

The price column should be numerical, but it may contain string values like ?. We need to clean and convert it:

Python 
data.price.unique()  data = data[data.price != '?']  data['price'] = data['price'].astype(int)  data.dtypes

Output:

convert-column-to-integer

Step 9: Normalize Features

To ensure fair comparisons between different features, we normalize numerical columns. To categorize cars based on their price we divide the price range into three categories: Low, Medium and High.

Python 
data['length'] = data['length']/data['length'].max() data['width'] = data['width']/data['width'].max() data['height'] = data['height']/data['height'].max()  # binning- grouping values bins = np.linspace(min(data['price']), max(data['price']), 4)  group_names = ['Low', 'Medium', 'High'] data['price-binned'] = pd.cut(data['price'], bins,                                labels = group_names,                                include_lowest = True)  print(data['price-binned']) plt.hist(data['price-binned']) plt.show()

Output:

normalization-features
Normalization Features

Step 10: Convert Categorical Data to Numerical

Machine learning models require numerical data. We convert categorical variables into numerical ones using one-hot encoding:

Python 
pd.get_dummies(data['fuel-type']).head()  data.describe()

Output:

convert-categorical-to-numerical
Convert Categorical Data to Numerical

Step 11: Data Visualization

Python 
plt.boxplot(data['price'])  sns.boxplot(x ='drive-wheels', y ='price', data = data)  plt.scatter(data['engine-size'], data['price']) plt.title('Scatterplot of Enginesize vs Price') plt.xlabel('Engine size') plt.ylabel('Price') plt.grid() plt.show()

Output:

engine-size
Engine Size

Step 12: Grouping Data by Drive-Wheels and Body-Style

Grouping data helps identify trends based on key variables:

Python 
test = data[['drive-wheels', 'body-style', 'price']] data_grp = test.groupby(['drive-wheels', 'body-style'],                           as_index = False).mean()  data_grp

Output:

grouping-data
Grouping Data

Step 13: Create a Pivot Table & Heatmap

Python 
data_pivot = data_grp.pivot(index = 'drive-wheels',                             columns = 'body-style') data_pivot  plt.pcolor(data_pivot, cmap ='RdBu') plt.colorbar() plt.show()

Output:

pivot-table
Pivot Table

Step 14: Perform ANOVA Test

The Analysis of Variance (ANOVA) test helps determine if different groups have significantly different means.

Python 
data_annova = data[['make', 'price']] grouped_annova = data_annova.groupby(['make']) annova_results_l = sp.stats.f_oneway(                              grouped_annova.get_group('honda')['price'],                              grouped_annova.get_group('subaru')['price']                                     ) print(annova_results_l)  sns.regplot(x ='engine-size', y ='price', data = data) plt.ylim(0, )

Output:

anova-test
ANOVA Test

This step-by-step analysis helps in understanding the key factors influencing the selling price of used cars. Proper data cleaning, visualization and statistical tests ensure that our findings are accurate and insightful.


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    Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro
    11 min read
    Residual Networks (ResNet) - Deep Learning
    After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble
    9 min read

    Natural Language Processing Projects

    Twitter Sentiment Analysis using Python
    This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
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    Facebook Sentiment Analysis using python
    This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
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    Next Sentence Prediction using BERT
    Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
    4 min read
    Hate Speech Detection using Deep Learning
    There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
    5 min read
    Image Caption Generator using Deep Learning on Flickr8K dataset
    Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
    12 min read
    Movie recommendation based on emotion in Python
    Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their
    4 min read
    Speech Recognition in Python using Google Speech API
    Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require
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    Voice Assistant using python
    Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required
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    Human Activity Recognition - Using Deep Learning Model
    Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
    6 min read
    Fine-tuning BERT model for Sentiment Analysis
    Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
    6 min read
    Sentiment Classification Using BERT
    BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
    12 min read
    Sentiment Analysis with an Recurrent Neural Networks (RNN)
    Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews
    5 min read
    Building an Autocorrector Using NLP in Python
    Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
    4 min read
    Python | NLP analysis of Restaurant reviews
    Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach
    7 min read
    Restaurant Review Analysis Using NLP and SQLite
    Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei
    9 min read
    Twitter Sentiment Analysis using Python
    This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
    10 min read

    Clustering Projects

    Customer Segmentation using Unsupervised Machine Learning in Python
    Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning
    5 min read
    Music Recommendation System Using Machine Learning
    When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona
    4 min read
    K means Clustering – Introduction
    K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ
    4 min read
    Image Segmentation using K Means Clustering
    Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.  Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im
    2 min read

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