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You will learn to:
Implement the Naïve-Bayes algorithm.
Compute the required probabilities using pandas.
Save and retrieve the probabilities using Python dictionaries.
Perform model evaluation using Scikit-learn.
Skills
Machine Learning
Data Science
Prerequisites
Good programming skills in Python.
Good understanding of Machine Learning theory.
Proficient in probability and statistics.
Technologies
NumPy
Python
Pandas
seaborn
Scikit-learn
Project Description
The Naïve-Bayes algorithm is known for its simplicity and efficiency in probabilistic classification. It uses the Bayes' rule to compute a target class's conditional probability of occurrence, given a set of input features. It is based on the naive assumption that classes of all features are mutually independent.
In this project, we'll implement the Naïve-Bayes classifier from scratch in Python, without using any external libraries. We will load the "US Census Dataset" and preprocess it. We’ll use the Scikit-learn library to evaluate the classifier. By implementing this classifier from scratch, we will gain a deeper understanding of its inner workings.
Project Tasks
1
Getting Started
Task 0: Introduction
Task 1: Import the Libraries
Task 2: Load the Dataset
Task 3: Preprocess the Data
2
Implement the Naïve-Bayes Classifier
Task 4: The Initialization Method
Task 5: Outlier Handler
Task 6: Convert Numeric Features to Categorical
Task 7: Prepare Data
Task 8: The Train Function
Task 9: The Predict Function
3
Use the Model
Task 10: Model Creation, Training, and Prediction
Task 11: The Confusion Matrix
Task 12: Model Evaluation
Congratulations
Atabek BEKENOV
Senior Software Engineer
Pradip Pariyar
Senior Software Engineer
Renzo Scriber
Senior Software Engineer
Vasiliki Nikolaidi
Senior Software Engineer
Juan Carlos Valerio Arrieta
Senior Software Engineer
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.