Samaksh
Third-year Computer Science student working with Python and backend development. Actively learning machine learning by implementing algorithms, building practical projects, evaluating models, and understanding the concepts behind them.
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I had an interview with Prachi where she asked questions about my two projects: NumDuel — Backend Development Used Car Price Prediction — Machine Learning I was able to answer most of her questions. The main feedback I received was to improve the structure of my answers by stating the most important points first and then going into further details. 1. NumDuel — Backend Project Questions Asked What is NumDuel? Explain the project and its functionality. What technical decisions did you make while building the project? How did you approach testing your backend? Why did you initially use JSON for storing data? Why did you later switch from JSON to PostgreSQL? How does FastAPI work? What are GET and POST requests? What do GET and POST do in your project? Write a SQL query to retrieve the top 5 players from NumDuel. Key Areas to Revise Project architecture and workflow Backend development using FastAPI REST API fundamentals GET and POST requests Testing strategies and implementation JSON vs PostgreSQL: advantages, limitations, and reasons for migration SQL queries, sorting, and retrieving top players 2. Used Car Price Prediction — Machine Learning Project Questions Asked Explain the Used Car Price Prediction project. What different machine learning models did you use? Why did you choose those particular models? What parameters did you select for the models? What is the difference between R² score and RMSE? What is residual analysis? Explain other concepts related to model evaluation and performance. Key Areas to Revise Project workflow and data preprocessing Different machine learning algorithms used Model selection and reasoning Hyperparameter tuning R² score and RMSE Residual analysis and interpretation Model evaluation and performance improvement 3. Interview Feedback What Went Well I was able to answer most of the interviewer's questions. I had a good understanding of my projects. I was able to explain my technical decision
No public reading from this one.
No public reading from this one.
Nobody is scored in these. What you are reading is what they chose to write about their own hour. Sit one →