This project provides an in-depth analysis of global workout trends, keyword popularity, and geographical interest in various workout types. Using data visualization techniques, it uncovers insights into workout preferences over time and across regions.
- Data Loading and Cleaning: Imports datasets, inspects structure, checks for missing values, and ensures data integrity.
- Exploratory Data Analysis (EDA): Visualizes distributions, trends, and correlations in workout popularity.
- Trend Analysis: Examines global workout trends and geographical variations, with a focus on keyword interest (home workout, gym workout, and home gym).
- Conclusion and Key Insights: Summarizes findings on workout trends and highlights future optimization ideas.
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Data Preprocessing:
- Loads datasets and inspects structure.
- Cleans data by handling missing values and removing duplicates.
- Converts date columns to datetime format for time series analysis.
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Exploratory Data Analysis (EDA):
- Visualizes data distributions for workout trends globally and by keyword.
- Shows workout interest over time and explores correlations between workout types.
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Trend Analysis:
- Identifies peak interest times and examines workout trends before, during, and after COVID-19.
- Analyzes geographical interest in workouts, highlighting regions with high engagement.
workout.csv: Contains monthly global workout popularity data.three_keywords.csv: Tracks monthly global interest in specific keywords.workout_geo.csv: Shows workout interest across different countries.three_keywords_geo.csv: Details country-level interest in specific workout keywords.
Ensure you have the following libraries installed:
pip install pandas matplotlib seaborn