In my college semester, I built a Machine Learning Project, where I faced many challenges👩‍🎓

• didn't get the desired features for our datasets
• inaccurate model evaluation - RMS, MSE, MAE, MAPE, R2 Score, precision, recall, and accuracy

Well, I know these challenges I faced are common for a data scientist but what if a beginner builds a project and faces the same challenges, so for this in this section I am going to share my experience of how I dealt with these challenges and what things you should avoid be cautious while building a model.

Let me give you a glimpse of datasets if you are a beginner in Data Analytics or Data Science😄

Insights of the dataset

Well, a dataset is a structured collection of data points or observations typically stored in a file or a database. It is fundamental in various fields, enabling data-driven decision-making and statistical analysis📊.

Few things to be taken care of when one is dealing with the dataset❗

• ✅While performing data analysis, one must make sure that the dataset is fetched from a reliable website- Kaggle, GitHub, Data World, and many more, in order to achieve accurate results.

• make sure the size of your dataset is not too small as it would affect the prediction- might not give you an accurate result i.e. false positive result

• ❌never generate a dataset with a Chat GPT or any other Open AI source

About Open AI

• 🤖With the current technology each and every person is aware of Open AI- an artificial intelligence research lab consisting of the for-profit OpenAI LP and the non-profit OpenAI Inc.

• With Open AI- Chat GPT, Bard, and many more sources, it can help the developers in many ways such as Innovation, Research & also efficiency, and Time-Saving

Analysts dealing with data generation

A Data Analyst might think of getting the dataset by using Open AI- Chat GPT as it's easy, time-consuming, and can provide the dataset based on our requirements. Suppose an analyst requires Electronic Health Record Data to predict Hospital Associated Infections (HAI) but couldn't find any relatable dataset from the websites. For a viable alternate option, she generated a dataset from Chat GPT & provided the features required for further analysis.

Do you think the dataset generated by Open AI will provide accurate results?🤔

So the answer to the above question is NO. Well a dataset generated by Open AI is feasible for testing purposes such as for building commercial websites. If you are using it for Machine Learning, it might affect your model evaluation- RMS, MSE, MAE, MAPE, R2 Score, precision, recall, and accuracy.

👉Click- To learn more about Model evaluation

A few points about how open AI affects the final results of the model

• Machine Learning is a tool or a technique within the broader field of //artificial intelligence**, that allows AI systems to learn and adapt from data, enhancing their ability to perform tasks intelligently.

• generating a dataset from Chat GPT, might give you accurate results but when you regenerate the features for your respective dataset, the values may alter and might not be as same as the actual researched dataset.

• So the generated dataset will give you false positive results no matter what ML algorithms you apply for model evaluations.

What to do if we don't find the dataset with the desired features?🤔

The answer is simple- being a data scientist, your job is to do proper research about the datasets and what other features are required to train a model. If you're performing predictions on infections, you can visit hospitals or university surveys, can visit universities in order to get a valuable dataset. So continuous research is needed if you're working as a data scientist. Generating datasets from Chat GPT will only affect your predictions resulting in False Positive.

Lastly- steps after generating the dataset

• cleaning and preprocessing the data are the essential steps for ensuring accuracy and reliability.
• having a dataset with desired features employs statistical techniques and machine learning algorithms to extract meaningful insights, uncover patterns, and develop predictive models
• can continuously iterate, refining their approaches based on results, to extract valuable information and drive data-driven decision-making.

Thank you so much for reading this blog!!.......... Happy Learning!!😄