Question 4 Please figure out and describe one machine learning task, either clas

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Question 4 Please figure out and describe one machine learning task, either classification or regression, based on your own experiences and background. It should not be the demo tasks taught in class, e.g., hand digit recognition, fish classification etc. Please design your machine learning algorithm, and answer the following questions. Note that you only need to submit your plans. No source codes or results are required. Task. Please briefly explain the inputs, outputs, and your goal in general Data preparation. Please describe how to collect your training data. Please explain how to get the ground-truth label for all samples;

Explanation / Answer

The Machine Learning Task which can be performed using classification is Credit Card Fraud Detection where we will classify whether a particular transaction by the customer is a fraud transaction or not on the basis of the previous transactions which are done by the Customer.

In this task the inputs will provided by a particular dataset which is available very easily online at various platforms in the form of csv files.(e.g. creditcard.csv). Once we have the data, we can specifically tell about the inputs or the labels but some which are present are Time of the transactions, Valid Transactions, Fraud Cases etc.

The output of this task will be first to analyze the dataset using our Machine Learning algorithm and then predicting whether a given transaction from the test set is valid or fraud based upon the training set and the training which has taken place.

The goal will be to design our algorithm in such a way that we get high prediction accuracy or the classification accuracy of test dataset as valid or fraud transactions i.e. if a transaction is a fraud transaction it should be classified as fraud and valid transaction if it is actually a valid one.

For data preparation, we can do the data cleaning which involves filling up the missing values, removing noisy data, smoothing out data types inconsistencies and removing outliers.We can also go for data reduction in which we can remove data which is insignificant with respect to our result obtaining process.Data transformation is another technique which can help in data preparation, through this we can transform large values into smaller values or integers into float for obtaining precise and quick result.

For collecting the training data, what we can do is that dataset which we will use can be divided into two parts i.e. in the ratio of 70:30 where 70% of the dataset will be training set and 30% will be test set. The ratio can vary a bit and even we can try different proportions to obtain better accuracy. But it is recommended to use 70-80% of the dataset as training set otherwise we may suffer from the problem of underfitting or overfitting of the data.

The ground-truth label for all samples is obtained through the training which we will be providing to our machine learning model, in this our model will understand the various kinds of labels which are required in the output of the samples which are provided.

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