📊 Spotify Data Analysis

This project performs an end-to-end exploratory data analysis (EDA) on a Spotify tracks dataset. It focuses on cleaning the data, understanding feature distributions, and uncovering insights about track popularity, audio characteristics, and trends within the dataset.

🔍 Key Features

Data Cleaning & Preprocessing Handles missing values, fixes data types, and prepares the dataset for analysis.

Exploratory Data Analysis (EDA)

Distribution analysis of Spotify audio features (danceability, energy, tempo, etc.)

Visualizations to understand trends, patterns, and outliers

Correlation analysis to identify which factors impact track popularity

Feature Insights Examines how different musical attributes relate to popularity and listening patterns.

Visualizations Includes histograms, boxplots, pairplots, correlation heatmaps, and more to derive meaningful insights.

📁 Contents

Spotify Analysis Code.ipynb – Jupyter Notebook with complete code and visualizations.

🛠️ Technologies Used

Python (Pandas, NumPy)

Matplotlib & Seaborn

Machine Learning (linear Regression, Decision Tree, Random Forest)

Jupyter Notebook

🎯 Purpose

This project is ideal for anyone exploring data analysis on music datasets, learning EDA techniques, or understanding the relationships between Spotify audio features and track success.