MACHINE LEARNING END-TO-END PROJECT

DIAMOND PRICE PREDICTION

This repository contains an end-to-end machine learning project, deployed using AWS services. The project utilizes various Python libraries and tools to create, train, and deploy a machine learning model for predictive analysis.

Overview

The primary goal of this project is to build a robust machine learning model and make it accessible through a web interface. The following libraries and frameworks are used in this project:

• Pandas: For data manipulation and analysis.
• NumPy: For numerical computations.
• scikit-learn: For building and evaluating machine learning models.
• ipykernel: For interactive computing in Jupyter notebooks.
• Seaborn: For statistical data visualization.
• Matplotlib: For plotting graphs and visualizations.
• Flask: For creating the web application and serving the model.

Features

• Data Preprocessing: Efficient handling and preprocessing of data using Pandas and NumPy.
• Model Training: Implementation of machine learning algorithms with scikit-learn.
• Visualization: Detailed data and result visualizations with Matplotlib and Seaborn.
• Deployment: The model is deployed on AWS, making it accessible via a web interface powered by Flask.