The Road Accident Analysis project focuses on understanding the causes, trends, and impact of traffic accidents through comprehensive data analysis. The goal is to identify risk factors, evaluate road safety measures, and recommend preventive strategies to reduce casualties. Using secondary data collected from online sources, the project examines several key metrics, including total casualties, accident severity, vehicle type, location-based analysis, and monthly trends. The dataset was cleaned, transformed, and visualized using Power BI to derive actionable insights.
Descriptive Analysis: Studied accident characteristics such as time, location, and severity. Statistical Analysis: Identified patterns, trends, and correlations within accident data. Spatial Analysis: Leveraged GIS-based mapping to visualize accident hotspots and high-risk zones.
The findings from this project highlight the key factors contributing to road accidents and provide insights useful for policymakers, urban planners, and law enforcement to enhance road safety initiatives and reduce accident-related casualties. Tools & Technologies: Power BI, Microsoft Excel, GIS (for spatial mapping, Power Bi Map ), Data Cleaning & Visualization techniques.
This project analyzes the Top 100 most streamed songs on Spotify to identify patterns, trends, and correlations between various musical attributes. The dataset contains 100 unique songs with 14 columns describing song characteristics, including basic information like name, artist, genre, and year, as well as technical details such as beats per minute, energy, danceability, loudness, acousticness, valence, and more.
Music Composer / Singer → Understanding what makes a song popular.
Data Analyst → Identifying similarities, correlations, and patterns within the dataset.
Dataset Features
Basic Information: Song Name, Artist, Genre, Release Year, Popularity, Length.
BPM → Beats per minute
Energy → Intensity of the song (0–100)
Danceability → Suitability for dancing (0–100)
Loudness → Measured in decibels
Liveness → Audience presence during recording (0–100)
Valence → Positiveness of the song (0–100)
Acousticness → Use of acoustic vs. electric instruments (0–100)
Speechiness → Ratio of vocals to music (0–100)
Loudness Trends: Recent songs tend to have higher loudness compared to older tracks.
Danceability & BPM: Danceability is positively correlated with beats per minute.
Liveness & Genre: Genres like Dance Pop and Pop tend to have higher liveness.
Valence (Positivity): While valence doesn’t strictly depend on genre, Dance Pop shows the highest positivity on average.
Energy vs. Loudness: Surprisingly, energy and loudness are not strongly related.
Acousticness by Genre:
Highest Acousticness: Dfw Pop
Lowest Acousticness: Sia Australian Dance
Genre Popularity Over Time: Dance Pop (2018) dominates in popularity.
Electro-Pop Insight: Despite its name, Electro Pop songs often use fewer electric instruments (e.g., Billie Eilish’s “When The Party’s Over” has 98% acousticness).
Danceability & Popularity: Highly danceable songs tend to be more popular, but exceptions exist (e.g., “Blinding Lights” has low danceability but high popularity).
A song’s popularity is not determined by any single attribute like genre, BPM, or loudness. Instead, it depends on a combination of factors—notably, danceability, positivity, and catchiness. While technical attributes influence trends, the artist’s creativity and listener preferences play the most crucial role.