Naive Bayes Classifier to predict income

Data: The dataset contains over 32,000 instances of individuals, with 14 features describing demographic and employment information. The target variable is whether the individual earns over $50k per year. The features are assumed to be independent, making it a good fit for Naive Bayes classification. Link: https://archive.ics.uci.edu/ml/datasets/Adult

Task 1: Data Preprocessing

Task 2: Naive Bayes Classifier Implementation

1. Implementing a function to calculate the prior probability of each class (benign and malignant) in the training set.
2. Implementing a function to calculate the conditional probability of each feature given to each class in the training set.
3. Implementing a function to predict the class of a given instance using the Naive Bayes algorithm.
4. Implementing a function to calculate the accuracy of Naive Bayes classifier on the testing set.

Task 3: Evaluation and Improvement

1. Evaluating the performance of Naive Bayes classifier using accuracy, precision, recall, and F1-score.
2. Experimenting with Laplace smoothing technique to improve the performance of your classifier.
3. Comparing the performance of Naive Bayes classifier with other classification algorithms like logistic regression and k-nearest neighbors.