Project Title

Car Price Prediction Using Machine Learning with Python

Domain

Python | Artificial Intelligence (AI) | Machine Learning (ML) | Data Science | Predictive Analytics

Project Level

Beginner to Intermediate

Project Objective

The automobile industry generates a vast amount of data related to vehicle specifications, market demand, and pricing. Determining the correct price of a car is important for both buyers and sellers. The objective of this project is to build an intelligent Machine Learning model capable of predicting the selling price of a car based on various features such as brand, year, fuel type, transmission type, mileage, and engine specifications.

Students will learn the complete Machine Learning workflow, including data collection, preprocessing, exploratory data analysis, feature engineering, model training, testing, evaluation, and prediction. This project provides practical exposure to Artificial Intelligence and Predictive Analytics concepts used in the automotive industry.

Session Access Link

Project Training Session

Session Link:

Click here to Watch your uploaded session

Learning Outcomes

After successful completion of this project, students will be able to:

• Understand the fundamentals of Machine Learning.

• Learn how price prediction systems work.

• Understand regression algorithms and predictive analytics.

• Perform data cleaning and preprocessing.

• Conduct exploratory data analysis (EDA).

• Work with real-world automobile datasets.

• Handle categorical and numerical data.

• Train and evaluate machine learning models.

• Measure model performance using different evaluation metrics.

• Build a real-world car price prediction system.

• Improve analytical and problem-solving skills.

Programming Language & Technologies Used

Programming Language

• Python 3.x

Machine Learning Libraries

• Scikit-learn

• NumPy

• Pandas

Data Visualization Libraries

• Matplotlib

• Seaborn

Development Platforms

• Google Colab

• Jupyter Notebook

• VS Code (Optional)

Data Storage

• CSV Files

• Google Drive

Project Workflow

Phase 1: Dataset Collection

Students will collect a Car Price Dataset containing:

• Car Name

• Brand

• Manufacturing Year

• Present Price

• Selling Price

• Kilometers Driven

• Fuel Type

• Seller Type

• Transmission Type

• Number of Previous Owners

• Engine Capacity

• Mileage

Dataset Format:

• CSV File

Phase 2: Data Preprocessing

Students will perform:

• Removal of duplicate records

• Handling missing values

• Data cleaning

• Encoding categorical variables

• Feature selection

• Outlier detection and treatment

• Data normalization/scaling

Purpose:

To improve data quality and model performance.

Phase 3: Exploratory Data Analysis (EDA)

Students will analyze the dataset using:

• Summary statistics

• Correlation analysis

• Histograms

• Scatter plots

• Box plots

• Heatmaps

Benefits:

• Understand relationships between features and selling price.

• Identify important factors affecting car prices.

• Detect trends, patterns, and anomalies in data.

Phase 4: Feature Engineering

Students will prepare data for machine learning using:

• Label Encoding

• One-Hot Encoding

• Feature Scaling

• Feature Selection Techniques

Benefits:

• Improves prediction performance.

• Enhances model accuracy.

• Reduces irrelevant features.

Phase 5: Model Building

Students will train Machine Learning models such as:

Linear Regression

• Simple and widely used regression algorithm.

• Suitable for predicting continuous values.

Decision Tree Regressor

• Easy to interpret.

• Captures non-linear relationships in data.

Random Forest Regressor

• Ensemble learning method.

• Provides higher prediction accuracy.

Gradient Boosting Regressor (Optional)

• Improves performance through sequential learning.

• Effective for complex datasets.

XGBoost Regressor (Optional)

• Advanced boosting algorithm.

• Commonly used in industry-level prediction systems.

Phase 6: Model Evaluation

Students will evaluate model performance using:

• Mean Absolute Error (MAE)

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• R-Squared Score (R²)

Expected Accuracy:

80% – 95% R² Score (depending on dataset quality and feature engineering)

Phase 7: Prediction System

Students will create a system where users can:

• Enter car details.

• Click Predict.

• Receive output:

  • Estimated Car Selling Price

Example:

• Predicted Price: ₹5,75,000

Final Project Deliverables

Each student must submit:

Source Code

• Python Files (.py)

• Jupyter Notebook (.ipynb)

Dataset

• CSV Dataset Files

Documentation

• Project Report (PDF)

Screenshots

• Dataset Import

• Data Cleaning

• Exploratory Data Analysis

• Model Training

• Evaluation Results

• Price Prediction Results

Presentation

• PPT (Minimum 10 Slides)

Additional Files

• Trained Model (.pkl file)

• Output Results

Google Drive Submission Process

Step 1:

Create a Google Drive Folder.

Folder Name Format:

CarPricePrediction_StudentName

Step 2:

Upload all project files.

Step 3:

Share the folder with:

support@corporatewebsolutions.in

Permission:

Viewer Access

Step 4:

Copy the shared Google Drive link.

Step 5:

Submit the link through the Google Form.

Status Report Submission Form

Google Form Link:

https://forms.gle/RNLyVNgnsbeuffP27

Important Instructions

• All assignments must be completed and submitted on time.

• Students must submit original work only.

• Copying projects from online sources is strictly prohibited.

• Every student must maintain a project progress report.

• All project files must be uploaded to Google Drive before final submission.

• Ensure the shared folder is accessible before submitting the link.

• Late submissions may lead to reduced marks.

• Students should attend all project guidance sessions.