Скачать или смотреть End-to-End Student Marks Prediction Project with Code | Machine Learning Tutorial

In this video, we dive into an end-to-end machine learning project: predicting student marks based on input features like study hours and other academic indicators. This beginner-friendly tutorial covers every step of the process, from data preparation to building and evaluating the prediction model.

Whether you're new to machine learning or looking to refine your project workflow, this comprehensive guide will help you understand how to approach real-world ML problems.

📌 What You’ll Learn in This Video:
✅ Understanding the project objective: Predicting student marks.
✅ Data collection and preprocessing techniques.
✅ Exploratory Data Analysis (EDA) to uncover insights.
✅ Building a regression model for marks prediction.
✅ Evaluating the model's performance using metrics like RMSE and R².
✅ Writing Python code for every step of the pipeline.


Chapters:
00:00 Getting Started with Student Marks Prediction Using Machine Learning
05:20 Loading the Dataset for Student Performance Analysis
10:35 Data Visualization for Student Marks Prediction
15:26 Feature Engineering and Training Machine Learning Models
30:03 Model Evaluation and Performance Metrics
34:40 Making Predictions: Student Marks Estimation
39:44 Need Help with AI & ML Projects? Contact CodersArts

🚀 Technologies Used:
-Python
-Pandas and NumPy (for data handling)
-Matplotlib and Seaborn (for visualization)
-Scikit-learn (for model building and evaluation)


💬 Have Questions?
Drop your queries in the comments, and we’ll be happy to help!



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Keywords:
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