Diamond Price Prediction Using ML

Diamond Price Prediction Using ML – Student Project

Are you a student looking for a practical and impressive machine learning project that you can add to your resume or final year submission?
Then the Diamond Price Prediction Using ML project is a very good choice.

This project teaches how to use machine learning to predict the price of diamonds based on different quality and size features. It is a complete end-to-end project that includes data analysis, model training, and a simple web application.

What is This Project About?

The Diamond Price Prediction project uses real data about diamonds such as carat, cut, color, clarity, depth, and size to predict how much a diamond will cost. Instead of manual estimation, the machine learning model learns from historical data and predicts the price for new diamond details.

Why Students Should Build This Project

This project is recommended for students because:

• It covers the complete machine learning pipeline
• Uses real-world data
• Teaches data cleaning and preprocessing
• Helps understand regression models
• Includes model evaluation techniques
• Has a web interface for prediction
• Is suitable for final year projects and resumes

You will not just train a model but also build a working system.

Project Workflow

1. Load the diamond dataset
2. Perform exploratory data analysis
3. Clean and preprocess the data
4. Select and engineer features
5. Train machine learning models
6. Evaluate model performance
7. Connect the model with a web application
8. Predict diamond prices

Features Used for Prediction

The model uses the following diamond features:

• Carat weight
• Cut quality
• Color grade
• Clarity
• Depth percentage
• Table percentage
• Length, width, and height of the diamond

These features help the model understand how price changes with diamond quality and size.

Machine Learning Models Used

This is a regression problem, so models such as the following are used:

• Linear Regression
• Decision Tree Regression
• Random Forest Regression
• Gradient Boosting Regression

You can try multiple models and compare which gives better accuracy.

Model Evaluation

The model is evaluated using metrics like:

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• Mean Absolute Error (MAE)
• R-Squared Score

These metrics show how close the predicted prices are to the real prices.

Web Application

The project includes a simple web interface where:

1. User enters diamond details
2. Data is sent to the trained model
3. The model predicts the price
4. The predicted price is displayed on the screen

This makes the project feel like a real application.

Technologies Used

Component	Technology
Programming Language	Python
Data Handling	Pandas, NumPy
Visualization	Matplotlib, Seaborn
Machine Learning	Scikit-learn
Web Framework	Flask

Steps to Run the Project

Step 1

• Install Python 3.14 on your system.

Step 2

• Download and extract the project files into a folder.

Step 3

• Open Command Prompt or Terminal and go to the project folder.

Step 4

pip install pandas numpy scikit-learn flask matplotlib seaborn

Step 5

python preprocess.py

Step 6

python train.py

Step 7

python app.py

Step 8

http://127.0.0.1:5000

What You Will Learn

By doing this project, you will learn:

• How regression models work
• How to handle real datasets
• How to preprocess and engineer features
• How to evaluate machine learning models
• How to connect ML with a web interface

Download : Click Here

These skills are very useful for data science and machine learning careers.

Final

The Diamond Price Prediction Using Machine Learning project is a strong and practical choice for students. It helps you understand real-world data, machine learning models, and how to turn them into a working application.

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