Introduction

Bayesian classification is used to predict the probability of a class membership. Despite the simplicity of the Nave Bayes assumption of independence among explanatory variables, these classifiers have been found to perform well with relatively small amount of training data. Nave Bayes classifiers can use continuous and categorical independent variables. Examples of the use of Bayesian classification, among many others, include those in spam detection and sentiment analysis.

1. General steps:

• Review theoretical background and implementation examples, resources are provided on the course content page.
• Your dataset will be the Kaggle Dataset - Water Quality
• Perform your analysis and fit a Nave Bayes model using python.
• Run an analysis, perform evaluation, and capture the results.
• Document your findings and analysis in a technical report using the template that accompanies these instructions.

1. Deliverables with critical areas:

Overview: areas to address:

• Problem Domain: give some background and context about the problem domain (application area). For instance, if you are doing the analysis for predicting heart disease, provide some context about the disease and include some interesting statistics about it. Also, discuss how the method is relevant for the chosen problem.
• Objective: clearly state the objective of the analysis in relation to the kind of algorithm you are employing. Use specific language as to what question(s) you are trying to answer using the specific analysis/modeling type.

Analysis: areas to address:

• Exploratory Analysis: describe the data including the source, the collection method and variables. Perform exploratory analysis. Also, select few key variables (including the target variable for supervised learning) and study their distributions using plots such as histograms, box plot, bar chart, etc.
• Preprocessing: armed with the exploratory analysis, perform the necessary preprocessing, both general and specific types appropriate for the modeling type being employed.
• Model Fitting: explain the key steps and activities you perform to fit the model. Experiment (as appropriate) with parameters tuning. This is key, what separates highly accurate model from a less accurate ones is the amount of performance tuning performed.

Results: areas to address:

• Model Properties: explain the components of the fitted model and their characteristics. Leverage functions to summarize the model properties. Also, leverage visualization as required.
• Output Interpretation: explain the result and interpret the final model output using terms that reflect the application area and in relation to the stated objective. This is where you check whether or not the stated objective is met.
• Evaluation: employ appropriate metrics to quantitatively evaluate the performance of the fitted model. For supervised classification, this includes simple accuracy, precision & recall (or sensitivity & specificity), all of which can be generated from a confusion matrix, or ROC.

Conclusion: areas to address:

• Summary: highlight the main findings in relation to the stated objective. You don't need to discuss the details of the analysis and the model such as accuracy here, just focus on the key findings.
• Limitations & Improvement areas: discuss the limitations of the analysis and identify potential improvement areas for future work. This could be related to the data, algorithm or a combination of the two.