WEBVTT

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Machine Learning Project, predictive analysis.

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In this video, we will learn to plan and build a simple machine learning project.

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Collect, clean, and use data for predictions.

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Test and improve your model.

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Work in Teams and present your results.

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Introduction and Planning

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Hi everyone, today we're going to talk about how to build a machine learning project using predictive analysis.

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First, we begin with a project introduction.

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The goal is to predict student grades using data like attendance, study hours, and test scores.

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Then we go into Project Planning, where objectives are defined and the needed data is collected, such as report cards, attendance records, and study habits.

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Working with data and building the model.

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Next is data preprocessing, which means cleaning the data set, fixing errors, filling missing values so everything is fair.

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After that, we move to model building.

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Different models, like linear regression, decision trees, or random forests, are trained to find patterns in the data and predict final grades.

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Evaluating and improving the model.

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Once the model is ready, we check its accuracy through model evaluation.

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Metrics like mean absolute error, mean squared error, and R squared show how close the predictions are to real results.

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If accuracy is low, model optimization is done.

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By tuning parameters, testing more models, or improving the data set.

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Presenting and collaborating, now we're ready for presentation preparation.

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We write down everything we did, the problems we faced, and how we solve them.

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We also create charts or graphs to show our results.

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Then comes the project presentation.

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We share our findings with the class and talk about what we learned and how we could improve.

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Finally, we focus on collaboration, working in teams, and giving helpful feedback to others.

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Teamwork and sharing ideas make the project even better.

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By following these steps, you can build your own machine learning project from start to finish.

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It's a great way to solve real-world problems using data and technology.