What AI has to say about what makes a song a smash hit

Table of Contents

  1. Introduction
  2. Data Source
  3. Setup
  4. Data Preprocessing
  5. Training the Model
  6. Results

Introduction

Songs released by popular singers tend to be chart-toppers, but what about the occasional track put out by a lesser known artist that becomes a smash hit seemingly at random?

My goal is to create a machine learning model that can predict a song’s popularity.

To do this, I’ll look at features like danceability, energy, and even speechiness.

Data Source

This is the dataset that I’ll be using:

Note: the original source is now a dead link; the above link is the new source. As the dataset is updated overtime, there may…


How we can use data science to discover the latest trends in the music industry

Table of Contents

  1. Introduction
  2. Data Source
  3. Setup
  4. Exploratory Data Analysis
  5. Conclusion

Introduction

As the world’s largest music streaming service provider, Spotify’s data can provide valuable insight into past, current, and possibly even future trends.

In this article, I’ll be analyzing Spotify data on 160k+ tracks released in the past century.

To do this, I’ll be using Pandas, matplotlib, and NumPy.

Data Source

This is the dataset that I’ll be using:

Note: the original source is now a dead link; the above link is the new source. …


A multiclass classification walkthrough with code

Table of Contents

  1. Introduction
  2. Data Source
  3. Setup
  4. Data Preprocessing
  5. Training the Model
  6. Results

Introduction

What’s the visual difference between Braeburn and Crimson Snow apples? What about Pink Lady and Granny Smith? If you’re like me and don’t know off the top of your head, here’s where machine learning can help.

My goal is to build a classifier that can predict what fruit or vegetable variety a food item is based on its image.

As with my previous articles on Pokémon and waste classification, I’ll do this using a convolutional neural network.

Data Source

I’ll be using this dataset from Kaggle:

It contains 90483 images of 131…


Extra! Extra! Read All About It!

Table of Contents

  1. Introduction
  2. Data Source
  3. Setup
  4. Exploratory Data Analysis
  5. Training the Model
  6. Results
  7. Conclusion

Introduction

We can build classifiers to detect sarcasm in news headlines, but what about generating news headlines from scratch?

My goal is to build a machine learning model that can do just that.

To achieve this, I’ll use textgenrnn, a neural network architecture that allows you to easily train a text-generating neural network of any size and complexity on any text dataset. You can read more about textgenrnn here:

Data Source

I’ll be using this dataset from Kaggle:

It contains the publish date and headline text of news headlines from the…


Why did the machine learning algorithm cross the road?

Table of Contents


The key to stepping up your social media engagement

Table of Contents

  1. Introduction
  2. Data Source
  3. Results
  4. Conclusion
  5. References

Introduction

In this fourth and last stop of our Tweet success journey, we’ll tie everything together for the ultimate guide to the perfect Tweet.

Throughout the series, our goal has been to predict the number of Retweets a STEM-related Tweet would receive.

We’ve scrapped data, explored the dataset, and built a machine learning model, and in doing so, we’ve discovered correlations between Tweet features and Tweet success. But we were focusing on our dataset as a whole. Here, we’ll look at the characteristics of only the best!

Data Source

We’ll continue to use the data collected…


When you can’t catch em’ all, you generate em’ all

Table of Contents

  1. Introduction
  2. Data Source
  3. Setup
  4. Data Preprocessing
  5. Training the Model
  6. Results
  7. Conclusion
  8. References

Introduction

What is a Pokémon GO fan to do in the midst of a pandemic where leaving the house to catch Pokémon is a no go? Generate their own Pokémon of course!

In this article, I’ll be going over how to create a fake Pokémon image generator using a generative adversarial network, or GAN.

GANs are generative models that create new data based on given training data. They do this by pitting 2 models against each other, one generative model and one discriminator model. …


A machine learning novice’s crack at creating a Tweet success predictor

Table of Contents

  1. Introduction
  2. Data Source
  3. Setup
  4. Exploratory Data Analysis
  5. Data Preprocessing
  6. Training the Model
  7. Results
  8. Conclusion
  9. References

Introduction

When I first had the idea of building a machine learning model to predict STEM-related Tweet success, I knew it would be tough. But it ended up even more difficult than I had expected.

My goal was to predict the number of Retweets a STEM-related Tweet would receive.

In this article, I’ll go over how I created my best (but still terrible) Tweet success predictor and what I learned along the way. …


What the data has to say about Retweets, likes, and replies

Table of Contents

  1. Introduction
  2. Data Source
  3. Setup
  4. Exploratory Data Analysis
  5. Conclusion
  6. References

Introduction

Searching high and low for what makes a Tweet popular, we’ve scoured Twitter, gathering data on thousands of posts. But finding the secret to Tweet success by scrolling through data entries in an Excel spreadsheet is looking for a needle in a haystack. This is where data analysis comes in.

With data collected, we are one step closer to our goal of predicting the number of Retweets a STEM-related Tweet would receive.

Here in part 2, we’ll dive into just enough data exploration to build our final machine learning model.

Data Source


What I learned while failing to create a Tweet success predictor

Table of Contents

  1. Introduction
  2. Data Collection
  3. Conclusion

Introduction

Whether you are growing a business or wanting your ideas to reach as many people as possible, increasing your social media engagement is the key to victory. But what factors influence engagement? Time of post? Sentiment? Emojis? To a machine learning novice, this was the perfect challenge.

My goal was to predict the number of Retweets a STEM-related Tweet would receive.

So what’s step 1, you may ask? Data collection, of course! Data collection is one of the most vital steps in any machine learning project. …

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