Music Popularity Prediction Using Data Science

Data Science is continuously thriving as a great career option for this generation. It is among the most promising & happening choices altogether. So, suppose you are an aspiring data scientist and want to practice skills to become an efficient professional in this field. In that case, you are advised that after grabbing some excellent theoretical knowledge of Data Science, now is the time to do some practical projects. You must do some technical & real-time data science project so that it helps you boost your knowledge, technical skills, and overall confidence.

Task
As a data scientist, you have been tasked to model trends and manage data on the topic 1. Predicting Music Popularity Using Streaming and Social Media Data - Using Machine Learning: A Data-Driven Analysis of Audio Features and Streaming Trends

You need to carry on the data science procedure and write a report at the end.

The following components highlight the most general architecture of a Data Science project, and hence your report should include:

Problem Statement: This is the fundamental component on which the project is based. It should define the problem that your model will solve and discuss the approach that your project will follow.

Dataset: This is a crucial component of your project and should be chosen carefully. Only large enough datasets from trusted sources should be used for the project.

Algorithm: This includes the algorithm you use to analyze your data and predict the results.

Training Models: This involves training your model against various inputs and predicting the output. This component decides the accuracy of your project. Note that using proper training techniques can produce better outcomes.

Conclusion: Write your observations and the trends found in the data. Also, predict the future trends, which can help in better decision making in your problem domain.

Your report should contain all the original figures and necessary references to authoritative sources to back up your claims and assertions. It should be written as a formal, technical report and contain a brief review of relevant literature.

Guidelines
Your submission should be 3000 words in length (+/- 10%).

The following points should be kept in mind while working on your project and compiling the report for submission:

Choose the programming language that you are comfortable with. However, the language chosen should be one of the in-demand languages such as Python, R, and Scala.
Use datasets from trusted sources. You can use Kaggle datasets. Moreover, ensure that the dataset you are using does not contain errors.
Find errors or outliers in your dataset and rectify them before training your model. You can use visualisation tools to find the errors in your dataset.
Your algorithm to model your data and reason to select that particular algorithm for modelling purposes.
Please make sure that you correctly cite and reference all secondary sources and include a reference list. The reference list will not be included in your final word count. Note that you can only submit either a PDF or a Microsoft Word document.

Grading
This activity will be graded, and you will receive feedback on it.