In this project, we will build a Naïve Bayes Classifier to predict whether a given resume text is flagged or not. Our training data consist of 125 resumes with 33 flagged resumes and 92 non flagged resumes. This project could be practically used to screen resumes in companies.
Naive Bayes 101: Resume Selection with Machine Learning
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4.5
(13 reviews)
Intermediate level
Recommended experience
2 hours
Learn at your own pace
Hands-on learning
What you'll learn
Create a pipeline to remove stop-words, punctuation, and perform tokenization
Understand the theory and intuition behind Naive Bayes classifiers
Train a Naive Bayes Classifier and assess its performance
Skills you'll practice
Details to know
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Taught in English
No downloads or installation required
Only available on desktop
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Task 1: Understand the Problem Statement and Business Case
Task 2: Import libraries and datasets
Task 3: Perform exploratory data analysis
Task 4: Perform data cleaning
Task 5: Visualize cleaned datasets
Task 6: Prepare the data by applying count vectorization
Task 7: Understand the intuition behind Naive Bayes Classifier - Part #1
Task 8: Understand the intuition behind Naive Bayes Classifier - Part #2
Task 9: Train a Naive Bayes classifier model
Task 10: Assess trained model performance
Recommended experience
Basic python programming knowledge
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Available only on desktop
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