Capstone Project- The Song Stats App featuring Predictive Modelling

Welcome to my Capstone Project, The Song Stats App Featuring Predictive Modelling! This project combines data analysis, machine learning, and integration with Spotify's API to create an interactive app using Streamlit. The objective is to analyze The Song Popularity Dataset, ustilize Machine Learning Modelling to predict song popularity, and to combine those into a Steamlit app that allows users to interact with the data.

• Predictive Modeling Presentation 📽️:

  • In the presentation folder, you'll find a PDF showcasing my predictive modeling presentation. This includes insights, analysis steps, and an overview of the Streamlit app.
  • The presentation details the three machine learning models used: k-Nearest Neighbors, Logistic Regression, and Random Forest.

• Data 🔢:

  • The data folder contains both the raw and cleaned datasets obtained from Kaggle.com, named 'The Song Popularity Dataset'.

• Code 📙:

  • The code folder hosts my Jupyter notebook, providing insights into the data cleaning, exploratory data analysis, and model fitting/testing processes.
  • Explore the steps I took in analyzing The Song Popularity Dataset.

• Images 📷:

  • The images folder stores visuals generated by my Streamlit app. More specifically, these photos are mainly album music covers generated from the Spotify API, which are then stored into this folder.

• Streamlit App 📱:

  • The app.py file is the main script using Visual Studio Code, running the Streamlit app. Interact with the app to explore musical attributes, visualize and explore data, get song recommendations, and predict song popularity.

• Additional Scripts 🖥️:

  • polarplot.py: Contains code for generating polar plots.
  • songrecommendations.py: Utilizes Spotify's API to provide song recommendations based on user input.

1. Predictive Modeling Presentation: 🗣️

   • Explore the presentation folder to view the PDF of the predictive modeling presentation.

2. Data Analysis and Modeling 📊:

   • Delve into the code folder to find my Jupyter notebook detailing data cleaning, exploratory data analysis, and machine learning model implementations.

3. Raw and Cleaned Data 🔢:

   • Access the data folder to obtain both the raw and cleaned versions of 'The Song Popularity Dataset'.

4. Streamlit App 📲:

   • Run the Streamlit app by executing app.py in Visual Studio Code.

5. Additional Scripts 📝:

   • Check out polarplot.py for polar plot code and songrecommendations.py for Spotify API-based recommendations.

Ensure you have the necessary requirements by referencing the requirements.txt file.

Feel free to explore, contribute, and enjoy The Song Popularity App! 🎵