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PROJECT
Develop Naïve-Bayes Classifier from Scratch
Learn how to implement the Naïve-Bayes classifier from scratch in Python.
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
Relevant Courses
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