Descriptive Statistics

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In the Goodlife_Fitness_Solutions.ipynb file, I perform a descriptive analysis to create customer profiles for each GoodLife Fitness membership option. This analysis explores key statistical insights and visualizations, covering the following topics:

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Importing Libraries and Loading the Dataset: Import the necessary Python packages and load the GoodlifeFitness.csv dataset.
Basic Data Exploration: Print basic information about the dataset, check for null values, and review the data's structure.
Univariate Analysis:
- Examine the five-number summary statistics.
- Analyze the dataset's summary, including descriptive stats for categorical data.
Visualization of Numerical Distributions:
- Plot the distribution of numerical columns such as Age.
- Create boxplots to identify outliers in Age.
Categorical Data Analysis:
- Determine value counts for each categorical column, including Type.
- Calculate the percentage distribution of user types using normalization.
Bivariate Analysis: Visualize relationships, such as plotting a boxplot to compare Income with Membership Type.
Multivariate Analysis:
- Use pd.crosstab to analyze data across Gender and Type.
- Apply pd.pivot_table to explore data by Income and Type.
- Create scatter plots using Pandas for further insights.

This case study demonstrates the application of descriptive statistics and visualization techniques to uncover actionable insights regarding fitness memberships.