I'm sharing an Exploratory Data Analysis (EDA) and Data Visualization of the data from using Python - A Data Analysis Project performed in my journey into Data Science.
is a Swedish audio and media services provider founded in April 2006. It is the world's largest music service provider and has over 381 million monthly active users, which also includes 172 million paid subscribers.
Here, l have explored and quantified data about music and drawn valuable insights.
Conducted data cleaning to perform exploratory data analysis (EDA) and data visualization of the dataset using Python (Pandas, NumPy, Matplotlib and Seaborn).
Data analysis - Exploring the relationship between the audio features of a song and how positive or negative its lyrics are, involving sentiment analysisand manyuy more.
Data Analysis makes use of secondary data from . Use data to identify patterns and relationships between different characteristics. The activity will support in developing ability to review and interpret a dataset.
Prerequisite: Data Analyst Roadmap ⌛ , Python Lessons 📑 & Python Libraries for Data Science 🗂️
- Pandas
| NumPy 
| Matplotlib 
| Seaborn
Data Analysis with Python - by IBM
Data Visualization with Python - by IBM
Pandas - by Kaggle
Numpy & Matplotlib - by Great Learning
Kaggle Project: Data Analysis 
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Kaggle Datasets: Tracks & Features
Top 10 most popular songs on
Top 10 least popular songs on
Correlation Heatmap between Variable
- Regression plot - Correlation between Loudness and Energy
- Regression plot - Correlation between Popularity and Acousticness
- Distibution plot - Visualize total number of songs on since 1992
- Change in Duration of songs wrt Years
- Duration of songs in different Genres
- Top 5 Genres by Popularity
Sales Insights - Data Analysis using Tableau & SQL 📊
Statistics for Data Science using Python 📊
Kaggle - Pandas Solved Exercises 📊
Python Libraries for Data Science 🗂️
👉 Nominate Me for Stars ⭐ ✨
