Find a dataset[minimum 500 rows] and a problem that you would like to solve using one of the supervised or unsupervised machine learning algorithms: Linear Regression, Logistic Regression, Decision Tree, K-Nearest Neighbor, K-Means Clustering, or DBSCAN.

Describe the business problem: whether it is explanatory (causal) or predictive; discuss the dependent/target variable; discuss independent/predictor variables. Discuss how knowledge generated from this model will help in decision making. If possible, discuss the various costs it may save or additional revenue it may generate.

Read in the data and follow:

Make sure all columns to be used in the analysis are set to correct data types.[eg: if you want to use a date column, it is set as "date" type.]

Handle missing values appropriately by either deleting them or imputing them

Do any other ETL and data clean-up tasks as required for the given algorithm.

Print the data types of each column (e.g., use the str() function in RStudio).

Show summary of the columns (e.g., use the summary() function in RStudio).

Split the data into train and test datasets, if youre interested in making a predictive model.

Run your model/algorithm.

Report model performance. For explanatory models report p-value for the independent variable(s) of interest and for predictive models report confusion matrix and other metrics such as sensitivity, specificity, precision, recall, R-squared or any other as desired based on the business problem. For a clustering algorithm, discuss the clusters that are created.

Write a summary that includes the following: business problem, description and justification of the ML algorithm chosen, comments on the model performance, and insights from the model results [250 - 500 words.]