Analyze Data For Insights

Analyze Data For Insights

What do you need to analyze data?

What do you need to analyze data?

We'll use the Body Fat Extended Dataset from Kaggle, a dataset that perfectly bridges the gap between theoretical learning and practical application. As you load and explore the data, you'll gain hands-on experience with essential tools and techniques used by data science professionals. The tutorial is structured to provide a comprehensive understanding of exploratory data analysis (EDA), allowing you to uncover insights, visualize trends, and formulate hypotheses like a pro. Each step is an opportunity to ignite your curiosity and propel you further into the fascinating world of machine learning.

Doing Exploratory Data Analysis (EDA)

When I do Exploratory Data Analysis (EDA) on a new dataset I like to follow the following steps:

  1. Initial Assessment

    • Begin by loading the dataset and perform a basic examination - look at the first few rows to get a feel for the data, checking the number of rows and columns, and understanding the data types (numerical, categorical, etc.).
    • Perform a quick check for missing values and duplicate entries.
  2. Descriptive Statistics and Quality Check

    • Generate summary statistics for numerical features to understand their central tendencies and dispersion.
    • For categorical features, examine the frequency of different categories.
    • Do we have missing values? Why?
  3. Visualization

    • Plot numerical features to understand the distribution and spot any outliers
    • For categorical data, bar charts can be useful to visualize the frequency of different categories
    • Create correlation matrices to identify trends, patterns, and potential dependencies
  4. Feature Engineering

    • Can we create new features that might be useful for the model?
    • Do you have some domain knowledge or expertise? Use it to come up with ideas for new features. If not, you can always search for research papers or articles that might help you come up with some ideas.
  5. Feature Importance

    • Use a machine learning model to estimate the importance of each feature.
    • Can you use some of the less important features to create new features?

Let's see how we can apply these steps to a real-world dataset.

Load Data

Let's start by adding all existing imports and configuring the plotting colors/style:

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  1. Body Fat Extended Dataset (opens in a new tab)