This repository is focused on predicting the price of diamonds using machine learning techniques. The dataset is collected from Kaggle which includes various attributes of diamonds, such as cut, color, clarity, carat weight, and more, providing a comprehensive basis for analysis and prediction.
The primary objective is to predict the total sales price of a diamond based on its characteristics. This prediction is crucial for buyers, sellers, and investors to make informed decisions, set competitive prices, and assess the potential return on investment.
The project utilizes Python as the primary programming language. Key libraries employed include Pandas for data manipulation, NumPy for numerical operations, Matplotlib and Seaborn for data visualization, and Scikit-learn for machine learning tasks such as regression modeling. TensorFlow or PyTorch may also be utilized for advanced machine learning algorithms.
Gaining insights into the dataset through visualization techniques like pair plots, scatter plots, and pie charts to understand relationships and patterns.
Cleaning and organizing the data to make it suitable for analysis, followed by transforming it to meet the requirements of machine learning algorithms.
Utilizing various regression models, such as Linear Regression, Polynomial Regression, Decision Tree, and Random Forest. Hyperparameter tuning techniques like grid search are employed to optimize model performance.
Assessing the model's performance using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
By leveraging these machine learning techniques, the project aims to provide accurate price predictions that reflect the true value of diamonds based on their attributes.
For detailed code and analysis, refer to the Google Colab notebook.