Data exploration and visualization are crucial steps in any machine learning project. They help you understand the characteristics, patterns, and relationships in your data, as well as identify potential issues and opportunities for improvement. In this article, you will learn some practical tips and techniques to explore and visualize your data before modeling.
The first step is to get familiar with your data sources, formats, and dimensions. You should check the data types, ranges, and distributions of your variables, as well as the number of observations, missing values, and outliers. You should also look for any inconsistencies, errors, or anomalies in your data that might affect your analysis. You can use various tools and libraries, such as pandas, numpy, and scipy, to perform basic data manipulation and summary statistics in Python.
#1 Rule for a Data Scientist: Know your data outside of the numbers. Beginners often overlook the meaning of their data, and not exactly what the data is representing. Are you dealing with financial data? contract purchases? account numbers? qualitative data? It is important to look beyond the numbers, and know what your data means. Do spot checks, sample records! Does the data make sense? Does it pass common sense checks? Check with stakeholders, those who create and work with the data from a non technical standpoint, and get a firm understanding of what these pieces of information mean. Knowing you data helps you anticipate outcomes, and ultimately makes you a better Data Scientist.
The next step is to define your machine learning problem and goal. You should ask yourself what are you trying to achieve, what are the inputs and outputs, and what are the evaluation metrics. You should also consider the assumptions, constraints, and limitations of your problem domain and data. You can use a framework, such as CRISP-DM or OSEMN, to guide your problem definition and analysis process.
The first step is getting to know your data by calculating simple statistics like means and distributions. This gives you a broad sense of the data landscape. Next, I like to create various plots and graphs to visualize the data - scatter plots to see relationships between variables, histograms to see data distributions, box plots to detect outliers. Automated tools can help surface interesting patterns. My goal is to poke around the data from various angles until I really understand its quirks. This upfront investment pays dividends later when building and evaluating models. In my opinion, treating data exploration as a dialogue between the data and yourself leads to the best outcomes.
The third step is to explore your variables individually and in relation to each other. You should examine the descriptive statistics, such as mean, median, mode, standard deviation, skewness, and kurtosis, of your numerical variables, and the frequency counts, proportions, and modes of your categorical variables. You should also look for any correlations, dependencies, or associations among your variables, as well as any relevant features or groups. You can use various tools and libraries, such as matplotlib, seaborn, and plotly, to create histograms, box plots, bar charts, scatter plots, and heat maps in Python.
When analyzing a new dataset, I like to explore each attribute individually first. Calculating statistics like mean, variance, histogram distributions, uniqueness etc. gives me a solid baseline to understand the nature of that variable. I also spot check raw values to ensure data quality. I find interactive visualization tools best facilitate this exploration. Being able to dynamically filter, zoom, and highlight interesting variable relationships enables deeper analysis. No model can compensate for a lack of understanding of the core components that serve as its inputs.
The fourth step is to transform your data to make it more suitable for modeling. You should apply appropriate data preprocessing and feature engineering techniques, such as scaling, normalization, encoding, imputation, binning, aggregation, selection, extraction, and generation. You should also test different transformations and compare their effects on your data quality and performance. You can use various tools and libraries, such as scikit-learn, keras, and tensorflow, to perform data transformation and feature engineering in Python.
The fifth step is to visualize your results and insights from your data exploration and transformation. You should use clear, concise, and informative visualizations that convey your main findings and recommendations. You should also use appropriate colors, shapes, sizes, labels, legends, titles, and annotations to enhance your visualizations. You can use various tools and libraries, such as matplotlib, seaborn, plotly, and dash, to create interactive and dynamic visualizations in Python.
The final step is to communicate your story and value proposition to your stakeholders and audience. You should use a logical and coherent structure that guides your audience through your data exploration and visualization process. You should also use a compelling and persuasive narrative that highlights your key messages and actions. You can use various tools and platforms, such as Jupyter Notebook, Google Colab, or Medium, to create and share your data story in Python.