What are some methods for exploring and analyzing your data before applying machine learning?

I've done data exploration using excel when the data set is small enough and I needed quick insights. Ex, Pivot tables Pandas in Python: df.head() - Displays the first few rows of a DataFrame. df.info() - Provides information about the data types and missing values in each column. df.describe() - Generates summary statistics of numerical variables. df.value_counts() - Computes the frequency distribution of categorical variables. Matplotlib in Python: plt.boxplot() - Generates a box plot to identify outliers and understand the distribution of a variable. Seaborn in Python: sns.distplot() - Plots a distribution plot, including a histogram and a kernel density estimate.

One thing I’ve found helpful is visualisation in lower dimensions based on your interest. Also, correlation matrix shows correlation coefficients between features might be helpful in some cases if you have tabular data. Overall, domain knowledge and the type of data significantly impact on how these steps need to be done at this stage.

Get familiar with data you are going to play around with, understand the data, shape, pattern at very high level before doing deeper analysis. If the data set has serious and obvious issues you might notice early signs and then critically evaluate to even proceed further with the next step. This can save lot of time which can be lost later on with faulty, meaningless or incomplete data.

By familiarizing oneself with the data's structure, one can effectively plan their analytical approach and determine the most appropriate methods for manipulation and analysis. Understanding the distribution is particularly useful for selecting appropriate statistical measures and models for subsequent analysis. You can do this through visualization. Visualizations are powerful tools in data exploration, as they facilitate the understanding of complex data patterns through graphical representations. Data exploration is an iterative process that helps one gain insights and make informed decisions about subsequent steps in the data analysis pipeline.

Regardless of where and how you obtain your data (via .csv, SQL query, etc.), exploratory data analysis (EDA) should always be the first step. This can involve quickly glancing at the dataset in either tabular form, making quick histograms/plots of features of interest, quick summary statistics, and examining the dimensionality of your data. Python and Excel are two very common tools used to do EDA, but one should not limit themself to just these tools. The command line (via csvkit) can also be quite useful in quick EDA before data cleaning and further processing.

While there’s a lot of appeal to talking about how to use complex algorithms in data models, much of the value they bring is in the low hanging fruit. Things like fixing misspelled words or correcting date formats don’t seem glamorous but they put us on track to give more complex models a try later on in the project.

Data cleaning is very critical to enable the data set to be eligible to be processed further. Its very easy to confuse this step with advanced data quality checks like bias, feature importance , checking data imbalalce etc. In reality , data cleaning is just the first step to refine the data to even arrive at any further analysis.

Data cleaning can have two aspects: a technical and a business/domain perspective. From a technical perspective, we are cleaning data based on data analysis or machine learning preparation—for example, data correction or missing data handling. However, we also need to consider the business aspect. For example, data filtering based on the business strategy and objective or masking for legal purposes. Think from various point of view to get the most values from our machine learning project.

Data analysis is one of the most important steps of the Machine Learning modeling. 1) once the project goal was set up, we could use analysis to check whether the model objectives are aligned with the project goals. 2) once the model objective is fixed, the analysis could help feature selection. 3) analysis could also help simulate the model impacts in the future.

Drawing from personal experience, one potent example that brings this to life is when analyzing financial datasets. Before diving into machine learning models, it's crucial to examine variables like stock prices, trading volumes, or economic indicators. Using pandas in Python, I could visualize time series data, calculate rolling averages, or detect anomalies. Additionally, tools like pandas-profiling are game-changers. With just a few lines of code, it generates a comprehensive EDA report, capturing statistics, correlations, and missing value insights. This simplifies the preliminary analysis, ensuring we feed quality data into our ML algorithms.

Statistical analysis, such as hypothesis testing and pattern analysis is important for our machine learning project. However, don't forget to validate the technique you use and the result with the domain expert.

There’s a tendency to collect data points “just in case we need them.” In reality, though, in order to build an impactful model, we typically need to narrow this focus down through algorithms like the ones mentioned. Another part of data reduction is identifying anomalies and then dealing with them. In some cases, we might need to focus on these anomalies but in other cases (time series models for example), we can take them out so they don’t skew the model results.

Data reduction optimizes time and cost of an experimentation by removing duplicates, reducing data imbalance, removing redundant features etc. The reduction can be in dimensionality or actual size of data required to store and process it.

The curse of dimensionality could hinder our machine learning development, as does noise from our data. However, feature reduction also depends on what your machine-learning model aims for. If your focus is on the best prediction/best metrics, then experimenting with feature reduction, such as PCA, forward/backward features selection, etc., can be done to maximize your metrics. However, if you require to have an explanation of your features, then transforming features to reduce them, such as PCA, might not work the best. The features you transform would lose their meaning.

By transforming categorical variables into numerical equivalents, models can effectively utilize those information for pattern recognition and prediction. By scaling numerical variables, models can effectively compare and weigh different features during training. Normalizing and standardizing can help when certain assumptions are required by the machine learning algorithms or when comparing variables with different scales and distributions. Applying appropriate mathematical functions can uncover complex patterns and make them more accessible to machine learning models. Utilizing tools such as scikit-learn, NumPy, and pandas streamlines the data transformation process, enabling one to efficiently preprocess and transform their datasets.

Only do data transformation if you know the consequences. It's fine if your target is to maximize the machine learning model metrics, but data transformation would change your data meaning. For example, if you do a log transformation to your data, then your analysis report would be about the data's log instead of the original data. It might lose some of its meaning even if you transform the data back. The categorical encoding could also affect your machine-learning model. For example, OHE can contribute to the curse of dimensionality so other techniques might be more appropriate.

Adding my insights for the tools that I have used/read for data validation: - pandas-profiling: It provides an overview of the data's structure, distributions, missing values, and correlations. - great_expectations: It allows users to define and test expectations about their data. This library makes setting up validation rules and continuously monitoring data quality easier. - Deequ: Deequ is an open-source library developed by Amazon that provides data quality verification for large-scale data. - PyDQC: PyDQC (Python Data Quality Checker) is a Python library that offers data quality checks and profiling for tabular data.

Data validation is the critical step of verifying data quality and accuracy post-preprocessing. This process guarantees that your data remains consistent, complete, and error-free before it fuels your machine learning models. Consider employing tools like pandas-profiling for comprehensive data reports, great_expectations for defining and testing data expectations, and sklearn-validation for validating input and output of scikit-learn models. These tools bolster the reliability of your data, ensuring that your models make well-informed decisions.

Statistics - Compute summary stats like mean, variance to characterize features. Feature Analysis - Assess data types, transformations needed, missing values. Correlation Analysis - Identify relationships between features. Data Profiling - Evaluate completeness, validity, errors. Cluster Analysis - Segment data into groups to reveal patterns. Dimensionality Reduction - Project into lower dimensions while preserving structure. Hypothesis Testing - Formally test assumptions about data relationships. Quantitative techniques analyze properties, relationships, patterns. Data profiling assesses quality. Dimensionality reduction reveals complexity. Hypothesis testing examines assumptions. The analysis provides insight to guide ML modeling.