This repository is designed to showcase various machine learning projects and algorithms. It aims to discover patterns in data and make predictions to answer business questions, detect trends, and solve problems. The project will demonstate the varibales which are dependent on the income level for the each variable in the dataseet.
The experiment shows as a decent result.
The repository includes multiple Jupyter notebooks, such as a Naive Bayes classifier, demonstrating practical applications of machine learning techniques.
About my project:
In this project, I build a Gaussian Naïve Bayes Classifier model to predict whether a person makes over 50K a year. The model yields a very good performance as indicated by the model accuracy which was found to be 0.8083.
The training-set accuracy score is 0.8067 while the test-set accuracy to be 0.8083. These two values are quite comparable. So, there is no sign of overfitting. I have compared the model accuracy score which is 0.8083 with null accuracy score which is 0.7582. So, we can conclude that our Gaussian Naïve Bayes classifier model is doing a very good job in predicting the class labels.
ROC AUC of our model approaches towards 1. So, we can conclude that our classifier does a very good job in predicting whether a person makes over 50K a year. Using the mean cross-validation, we can conclude that we expect the model to be around 80.63% accurate on average.
If we look at all the 10 scores produced by the 10-fold cross-validation, we can also conclude that there is a relatively small variance in the accuracy between folds, ranging from 81.35% accuracy to 79.64% accuracy. So, we can conclude that the model is independent of the particular folds used for training.
Our original model accuracy is 0.8083, but the mean cross-validation accuracy is 0.8063. So, the 10-fold cross-validation accuracy does not result in performance improvement for this model.