This project performs Exploratory Data Analysis (EDA) on a dataset of roller coasters from around the world.
The goal is to clean the data, explore trends, and visualize interesting patterns in coaster design and performance.
The dataset (coaster.csv) contains information about over 1,000 roller coasters, including:
- Coaster_Name : name of the roller coaster
- Location : where it is located
- Status : whether it’s operating, closed, or under construction
- Manufacturer : company that built the coaster
- Year_Introduced : year the coaster opened
- Type : construction type (wood, steel, launched, etc.)
- Speed_mph : maximum speed
- Height_ft : maximum height
- Inversions : number of upside-down elements
- Gforce : maximum G-force experienced
- Dropped irrelevant columns (
Opening date) - Renamed columns for consistency
- Converted dates into proper datetime format
- Checked and handled missing values
- Removed duplicates based on coaster name, location, and opening date
- Counted missing values with
df.isna().sum() - Checked for duplicate coaster entries
- Queried specific coasters (e.g., Crystal Beach Cyclone)
- Top 10 years with the most coaster introductions (bar chart)
- Distribution of speeds (histogram & KDE plot)
- Speed vs Height comparison (scatter plot, colored by year introduced)
- Correlation analysis of numeric features (heatmap)
- Certain years saw large spikes in coaster introductions.
- Most coasters fall within a moderate speed range, with few extreme outliers.
- Taller coasters generally tend to be faster, as shown in the scatter plot.
- Correlation analysis shows relationships between height, speed, inversions, and G-force.
- Python (Pandas, NumPy) : data wrangling
- Matplotlib & Seaborn : data visualization
- Clone the repository or download the project files.
- Install dependencies:
pip install pandas numpy matplotlib seaborn