NITW'25 | Data Science Aspirant | ML & AI Enthusiastic | Road Safety Audit Intern | Third Rock Consultants 💫💫
Hey guys, excited to share my latest project accomplishment! 🚀 I recently developed an end-to-end machine learning model to predict diamond prices, and I'm thrilled to walk you through the entire process, from initial development to the final deployment as a web application. 1. Data Collection and Preprocessing 📒: I began by collecting a comprehensive dataset of diamond features and prices. Using Python libraries such as pandas and numpy, I cleaned and preprocessed the data, handling missing values, encoding categorical features, and normalizing numerical attributes to ensure the dataset was ready for model training. 2. Model Development 👨💻: To predict diamond prices, I explored a range of machine learning algorithms, including Linear Regression, Decision Tree Regressor, Ridge Regression, Lasso Regression, Elastic Net Regression. I implemented these models using scikit-learn and identified the best-performing model. 3. Model Evaluation ✅: After training the models, I evaluated their performance using metrics such as R-squared (R²) score. This step ensured that the selected model provided accurate and reliable predictions. 4. Model Integration with Pipelines and Transformers 🔁: To streamline the process, I used pipelines and transformers, which allowed me to automate the sequence of data preprocessing, model training, and evaluation. This approach not only made the process more efficient but also enhanced the maintainability of the code. 5. Project Environment Setup 🌐: Using Anaconda and VS Code, I created a dedicated environment for the project. I organized the project structure with folders such as 'venv' and 'setup .py' files to manage dependencies and maintain a clean workspace. 6. Version Control via GitHub 🆚: I maintained version control and collaborated on the project using GitHub. By frequently committing changes and pushing updates, I ensured the project's progress was well-documented and easily accessible. 7. Web Application Development 📌: To make the model accessible to users, I developed a web application using HTML and CSS for the front-end and Flask for the back-end. The web app allows users to input diamond characteristics and instantly receive price predictions. The user-friendly interface and seamless integration between front-end and back-end components made the application both functional and interactive. This project was a fantastic learning experience, encompassing various aspects of data science, machine learning, and web development. I'm proud to have combined these technologies to deliver a practical solution. 🔗 GitHub Repository: https://lnkd.in/gKEaaxvG Credits: A huge thanks to Krish Naik Sir, for helping me out in making this amazing project!! 🙇🙇🙇 #MachineLearning #DataScience #WebDevelopment #Python #Flask #HTML #CSS #DiamondPricePrediction
Data Science Aspirant | Data Analysis | Data Wrangling | Feature Engineering | Data Visualization | Data Enthusiast | Python | SQL | Statistics | Tableau | Excel | Passionate about Turning Data into Insights
3wWhy are you doing data engineering after civil engineering, what was your thought process behind this?