What are the most effective ways to visualize and explore cleaned data for ML?

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Data cleaning is an essential step in any machine learning (ML) project, but it is not enough to just remove outliers, handle missing values, and normalize features. You also need to visualize and explore your cleaned data to understand its patterns, distributions, relationships, and potential problems. In this article, you will learn some of the most effective ways to do that using various tools and techniques.

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Sanjay Kumar MBA,MS,PhD

1 Scatter plots

One of the simplest and most powerful ways to visualize and explore cleaned data for ML is to use scatter plots. Scatter plots show how two or more variables are related by plotting them as points on a Cartesian plane. You can use scatter plots to identify clusters, trends, outliers, correlations, and linear or nonlinear relationships in your data. For example, you can use scatter plots to see how different features affect the target variable in a regression problem, or how different classes are separated in a classification problem. You can also use scatter plots to compare different subsets of your data, such as train and test sets, or different groups based on categorical variables. You can create scatter plots using libraries like matplotlib, seaborn, or plotly in Python.

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Sanjay Kumar MBA,MS,PhD
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Scatter plots are a fundamental and effective way to visualize cleaned data for machine learning purposes. They represent the relationship between two or more variables by plotting data points on a Cartesian plane. Scatter plots help identify clusters, trends, outliers, correlations, and both linear and nonlinear relationships within the data. They are valuable for understanding how various features impact the target variable in regression or how different classes are separated in classification tasks. Scatter plots can also be used to compare subsets of data, such as training and testing sets or groups defined by categorical variables.

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2 Histograms and box plots

Another way to visualize and explore cleaned data for ML is to use histograms and box plots. Histograms show the frequency distribution of a single variable by dividing it into bins and counting the number of observations in each bin. Box plots show the summary statistics of a single variable by displaying its minimum, maximum, median, first quartile, and third quartile values, as well as any outliers. You can use histograms and box plots to examine the shape, spread, skewness, and outliers of your data. For example, you can use histograms and box plots to see if your data is normally distributed, symmetric, or skewed, and how that affects your ML model performance. You can also use histograms and box plots to compare the distribution of different variables or groups in your data. You can create histograms and box plots using libraries like pandas, matplotlib, seaborn, or plotly in Python.

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Sanjay Kumar MBA,MS,PhD
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Histograms and box plots are useful tools for visualizing and exploring cleaned data for machine learning: Histograms: These display the frequency distribution of a single variable by dividing it into bins and counting the number of observations in each bin. Histograms help assess the shape, spread, skewness, and presence of outliers in the data. They are valuable for understanding the distribution characteristics of a variable. Box Plots: Box plots summarize the statistics of a single variable, including minimum, maximum, median, first quartile, and third quartile values. They also highlight any outliers. Box plots provide a concise view of data distribution and are useful for identifying central tendencies.

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3 Heat maps and correlation matrices

A third way to visualize and explore cleaned data for ML is to use heat maps and correlation matrices. Heat maps show the intensity of a value by using a color scale. Correlation matrices show the correlation coefficients between pairs of variables by using a heat map. You can use heat maps and correlation matrices to analyze the multivariate relationships in your data. For example, you can use heat maps and correlation matrices to see how strongly each feature is correlated with the target variable or with other features in a ML problem. You can also use heat maps and correlation matrices to detect multicollinearity, redundancy, or independence in your data. You can create heat maps and correlation matrices using libraries like numpy, pandas, seaborn, or plotly in Python.

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Ir. Muhammad R.
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Heat maps and correlation matrices are powerful tools to visualize the relations between the variables in the data. Using these tools, we can select the appropriate variables or features to be used in training the machine-learning model.

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4 Dimensionality reduction and clustering

A fourth way to visualize and explore cleaned data for ML is to use dimensionality reduction and clustering. Dimensionality reduction is a technique that reduces the number of features in your data by transforming them into a lower-dimensional space that preserves the most important information. Clustering is a technique that groups similar observations in your data based on some distance or similarity measure. You can use dimensionality reduction and clustering to discover the underlying structure, patterns, and categories in your data. For example, you can use dimensionality reduction and clustering to see how your data is organized in a high-dimensional space, or to find hidden segments or outliers in your data. You can also use dimensionality reduction and clustering to reduce the complexity and noise in your data, or to create new features for your ML model. You can use dimensionality reduction and clustering methods like principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), k-means, or hierarchical clustering using libraries like scikit-learn, matplotlib, seaborn, or plotly in Python.

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5 Interactive and dynamic visualization

A fifth way to visualize and explore cleaned data for ML is to use interactive and dynamic visualization. Interactive and dynamic visualization allows you to manipulate, filter, zoom, or animate your data using widgets, sliders, buttons, or menus. You can use interactive and dynamic visualization to enhance your understanding, exploration, and communication of your data. For example, you can use interactive and dynamic visualization to see how your data changes over time, or how different parameters affect your ML model results. You can also use interactive and dynamic visualization to create engaging and interactive dashboards or reports that showcase your data analysis and ML insights. You can create interactive and dynamic visualization using libraries like plotly, bokeh, dash, or streamlit in Python.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most effective ways to visualize and explore cleaned data for ML?

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2

3

4

5

6

1 Scatter plots

2 Histograms and box plots

3 Heat maps and correlation matrices

4 Dimensionality reduction and clustering

5 Interactive and dynamic visualization

6 Here’s what else to consider

Machine Learning

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What are the most effective ways to visualize and explore cleaned data for ML?

1

2

3

4

5

6

1 Scatter plots

2 Histograms and box plots

3 Heat maps and correlation matrices

4 Dimensionality reduction and clustering

5 Interactive and dynamic visualization

6 Here’s what else to consider

Machine Learning

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