“Data Expedition: An Innovative Approaches to Preprocessing and EDA”

Abstract

The present invention relates to a comprehensive system and method for data preprocessing and exploratory data analysis (EDA), designed to automate and streamline the preparation of data for machine learning, artificial intelligence, and advanced analytics. The system integrates a series of modules that facilitate data cleaning, transformation, integration, and reduction, including sophisticated techniques for missing value imputation, outlier detection, scaling, and encoding. It also supports advanced exploratory data analysis tools, such as statistical summaries, correlation analysis, and visualization techniques including histograms, box plots, heat maps, and pair plots. The system features an intuitive, interactive user interface that enables real-time data manipulation, drag-and-drop import, and customizable workflows. Additionally, it provides automated report generation and collaborative features, enhancing usability and efficiency. The invention offers a robust, scalable solution that simplifies and accelerates the data preparation and analysis process, making it accessible to both technical and non-technical users and applicable to a wide range of industries where data-driven decision-making is essential.

Specification

Description:





Field of the Invention:
[001] The present invention pertains to the field of data science and analytics, focusing specifically on the preprocessing and exploratory data analysis (EDA) stages essential for machine learning, artificial intelligence, and advanced analytics applications. It introduces a comprehensive system designed to streamline and enhance data preparation and exploration processes. This innovation addresses the need for efficient and accurate data preprocessing, encompassing data cleaning, transformation, integration, and visualization techniques. It leverages automated and semi-automated methods to improve data quality and usability, facilitating data-driven decision-making across diverse sectors, including banking, healthcare, marketing, and research. By simplifying and refining these processes, the invention aims to make data preparation more accessible, customizable, and effective, ultimately advancing the capabilities of modern data analytics.

Background of the invention and related prior art:
[002] The background of the invention addresses the growing complexity and volume of data in various industries, which has made data preprocessing and exploratory data analysis (EDA) increasingly critical. Traditionally, preparing raw data for analysis has been a time-consuming and error-prone task, often requiring extensive manual intervention and domain-specific expertise. As data-driven decision-making becomes more integral to fields such as finance, healthcare, marketing, and research, the demand for efficient, automated tools to clean, transform, and explore large datasets has surged. Existing solutions often lack the necessary automation, flexibility, and user-friendliness, making it difficult for users without deep technical expertise to effectively prepare and analyze data. This invention seeks to address these challenges by providing an end-to-end solution that combines advanced algorithms for data preprocessing with intuitive, interactive tools for EDA, thus improving accuracy, reducing manual effort, and enabling faster insights from complex data.
[003] In a patent document US20240078271A1 discloses towards systems and methods for finding documents that are similar to a reference text. The inventive systems and methods examine a set of collected documents to determine the facts present in those documents by, for example, extracting triplets and expanding them. A user's input reference text is similarly examined to extract and expand triplets therein and the facts identified with respect to the reference text are used as a basis to find documents having similar facts. The present disclosure is also related to systems and methods for mining facts from documents relating to a primary source such as a piece of legislation and using the mined facts to improve the results of subsequent searches.
[004] Another patent document US11080295B2 discloses techniques for organizing knowledge about a dataset storing data from or about multiple sources. The data can be accessed from the multiple sources and categorized based on the data type. For each data type, a triple extraction technique specific to that data type may be invoked. One set of techniques can allow the extraction of triples from the data based on natural language-based rules. Another set of techniques can allow a similar extraction based on logical or structural-based rules. A triple may store a relationship between elements of the data. The extracted triples can be stored with corresponding identifiers in a list. Further, dictionaries storing associations between elements of the data and the triples can be updated. The list and the dictionaries can be used to return triples in response to a query that specifies one or more elements.
[005] A document RU2605077C2 relates to automatic processing of natural language, particularly to a method and a device for storing and searching information extracted from text documents. Method for storage, search and update of data extracted from text documents, comprises extracting information from a text document and forming a triplet of type <subject, predicate, object>. Access is facilitated to storage of extracted information containing RDF graph, including a plurality of triplets of type <subject, predicate, object> for a plurality of objects. Searching in storage of extracted information of a second information object, which is same object of real world as first object, where any two objects are identified, if they have a common object parameter, and where search involves selecting and searching identifiers in a table. If second data object is found, state of storage of extracted information is updated by adding triplet <subject, predicate, object> on first information object to main RDF graph of storage and updating index in table.
[006] Another document NZ794252A discloses systems and methods for finding documents that are similar to a reference text. The inventive systems and methods examine a set of collected documents to determine the facts present in those documents by, for example, extracting triplets and expanding them. A user's input reference text is similarly examined to extract and expand triplets therein and the facts identified with respect to the reference text are used as a basis to find documents having similar facts. The present disclosure is also related to systems and methods for mining facts from documents relating to a primary source such as a piece of legislation and using the mined facts to improve the results of subsequent searches. A user's input reference text is similarly examined to extract and expand triplets therein and the facts identified with respect to the reference text are used as a basis to find documents having similar facts. The present disclosure is also related to systems and methods for mining facts from documents relating to a primary source such as a piece of legislation and using the mined facts to improve the results of subsequent searches.
[007] A patent document NZ794000A discloses systems and methods for finding documents that are similar to a reference text. The inventive systems and methods examine a set of collected documents to determine the facts present in those documents by, for example, extracting triplets and expanding them. A user's input reference text is similarly examined to extract and expand triplets therein and the facts identified with respect to the reference text are used as a basis to find documents having similar facts. The present disclosure is also related to systems and methods for mining facts from documents relating to a primary source such as a piece of legislation and using the mined facts to improve the results of subsequent searches. A user's input reference text is similarly examined to extract and expand triplets therein and the facts identified with respect to the reference text are used as a basis to find documents having similar facts. The present disclosure is also related to systems and methods for mining facts from documents relating to a primary source such as a piece of legislation and using the mined facts to improve the results of subsequent searches.
[008] None of these above patents, however alone or in combination, disclose the present invention. The invention consists of certain novel features and a combination of parts hereinafter fully described, illustrated in the accompanying drawings, and particularly pointed out in the appended claims, it being understood that various changes in the details may be made without departing from the spirit, or sacrificing any of the advantages of the present invention.

Summary of the invention:
[009] The invention provides an innovative, end-to-end solution for data preprocessing and exploratory data analysis (EDA), designed to enhance the efficiency and accuracy of data preparation for machine learning, artificial intelligence, and advanced analytics. It includes automated and customizable modules for data cleaning, transformation, integration, and visualization. The system offers advanced techniques such as feature engineering, missing value imputation, and outlier detection, alongside robust statistical and visualization tools for in-depth data exploration. The user-friendly interface allows for interactive, real-time data manipulation, drag-and-drop functionality, and customizable workflows, making it accessible even to non-technical users. Additionally, the system supports automated reporting and collaborative features, streamlining the entire data preparation process and facilitating faster, data-driven decision-making.

Detailed description of the invention with accompanying drawings:
[010] For the purpose of facilitating an understanding of the invention, there is illustrated in the accompanying drawing a preferred embodiment thereof, from an inspection of which, when considered in connection with the following description, the invention, its preparation, and many of its advantages should be readily understood and appreciated.
[011] The principal object of the invention is to develop Data Expedition: an innovative approaches to preprocessing and EDA. This invention presents an advanced system for data preprocessing and exploratory data analysis (EDA), designed to automate and streamline the processes of preparing and exploring data for machine learning, artificial intelligence, and analytics applications. The invention integrates various techniques and algorithms to improve data quality, usability, and insights extraction while providing a user-friendly interface for seamless interaction. The system is modular and customizable, making it adaptable to various data types and use cases across different industries such as finance, healthcare, marketing, and research.
1. Data Preprocessing: Data preprocessing is a critical step in preparing raw data for analysis, ensuring it is clean, consistent, and properly structured. The system's preprocessing module includes the following key processes:
Data Cleaning: Handling Missing Values: Missing or incomplete data is a common issue in datasets. The invention utilizes various imputation techniques such as mean, median, mode, k-nearest neighbors (KNN), or predictive modeling to fill in the missing values. Alternatively, rows or columns with too many missing values can be removed.
Outlier Detection and Management: Outliers can distort analysis results. The system includes algorithms for detecting outliers using statistical methods (e.g., Z-scores, IQR) and offers options for handling them through removal, transformation, or capping.
Data Transformation: Normalization and Scaling: The system provides methods to scale data to a standard range or normalize it, including techniques like Min-Max scaling and Z-score standardization. These transformations ensure that variables with different scales do not unduly influence model outcomes.

Categorical Encoding: To handle categorical data, the system supports encoding techniques such as label encoding (converting labels to numerical values) and one-hot encoding (creating binary variables for each category), allowing categorical variables to be used in machine learning models.
Data Integration: Merging Datasets: The system facilitates the integration of data from multiple sources, merging them into a single, cohesive dataset. This can include joining tables based on common keys or combining datasets with similar structures.
Data Aggregation: The system can aggregate data to summarize information at a higher level, making it more manageable and insightful for analysis.
Data Reduction: Feature Selection: The system automatically selects the most relevant features (variables) for analysis, removing redundant or irrelevant features that could introduce noise or overcomplicate the model.
Dimensionality Reduction: Advanced techniques like Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE) are implemented to reduce the number of dimensions in the dataset while retaining essential information.
Data Splitting: Training and Test Set Division: The system automatically splits the data into training and test sets, ensuring that the model's performance can be accurately evaluated on unseen data.
2. Exploratory Data Analysis (EDA): EDA is a vital step in understanding the patterns, relationships, and structure of data before applying complex models. The invention offers a suite of powerful tools for conducting EDA, including:
Descriptive Statistics:
The system computes fundamental statistical measures such as the mean, median, mode, standard deviation, and range to summarize the central tendency and dispersion of the data. It also calculates skewness (asymmetry) and kurtosis (peakedness) to evaluate the shape of data distributions.
Distribution Analysis:
Histograms: The system generates histograms to visualize the frequency distribution of numerical variables.
Box Plots: Box plots are used to visualize the spread of data, including quartiles and potential outliers.
Density Plots: The system offers kernel density plots to provide a smooth, continuous estimate of the data distribution.

Correlation Analysis:
Correlation Matrices: The system calculates and visualizes the correlation between variables using correlation matrices, highlighting the strength and direction of relationships between variables.
Heat maps: The invention generates heat maps to visually represent the correlation matrix using color gradients.
Pair Plots: Pair plots are used to examine pair wise relationships between numerical variables, identifying patterns or correlations.
Visualization:
Scatter Plots: The system provides scatter plots to explore relationships between two numerical variables, helping to identify trends, clusters, or outliers.
Bar and Line Charts: For categorical or time-series data, bar charts and line plots are available to visualize trends and comparisons.
Advanced Visualization: The system includes advanced visualizations like 3D plots, swarm plots, and violin plots to gain deeper insights into the data, especially for more complex datasets.
3. User Interface: The user interface (UI) of the invention is designed to be intuitive, interactive, and highly customizable to accommodate both novice and expert users. Key features of the UI include:
Interactive Exploration: Users can manipulate visualizations in real-time, adjusting parameters and zooming into specific data points for a deeper understanding. Drag-and-drop functionality makes it easy to import and organize data for analysis.
Customizable Workflows: The system supports customizable workflows for both data preprocessing and EDA. Users can configure preprocessing pipelines, select relevant features, and design their own sequence of analytical steps using a modular approach.
Automated Reporting: Once the analysis is complete, the system can automatically generate detailed reports summarizing the findings, including statistical summaries, visualizations, and key insights. These reports can be customized and exported in various formats (e.g., PDF, HTML, CSV).
Visualization Tools: The UI offers a comprehensive suite of visualization tools, enabling users to create, customize, and explore a wide range of charts and graphs to represent data effectively.
Collaborative Features: The system supports collaboration, allowing multiple users to work together on the same project. Features like version control, real-time comments, shared workspaces, and customizable access levels promote team collaboration and data sharing.
4. Advanced Algorithms and Automation: The invention leverages machine learning and statistical algorithms to automate key processes such as missing value imputation, outlier detection, and feature selection. These algorithms are designed to minimize human error and improve the consistency of data preprocessing.
The system adapts to the specific needs of users by allowing customization at various stages of the analysis pipeline, making it flexible enough to handle a wide range of datasets, from simple tabular data to complex, multi-dimensional datasets.
5. Applications: This system is designed to be used in various industries where data-driven decision-making is critical. Key applications include:
Banking and Finance: For analyzing transaction data, customer behavior, and market trends to drive investment strategies or detect fraudulent activities.
Healthcare: For processing patient data, medical records, and clinical trials to derive actionable insights for personalized medicine or operational improvements.
Marketing: To analyze consumer behavior, optimize campaigns, and predict market trends based on customer data.
Research: For processing scientific datasets, exploring relationships, and visualizing complex research findings.
[012] The invention provides a comprehensive, efficient, and user-friendly solution for data preprocessing and exploratory data analysis. By integrating advanced algorithms with an intuitive interface, the system simplifies the process of preparing and analyzing data, enabling faster insights, improved decision-making, and reduced manual effort. It is a versatile tool that can be applied across a wide range of fields, making it a valuable asset for any organization or individual working with data.
Figure 1. Data analysis according to the embodiment of the present invention.
[013] Without further elaboration, the foregoing will so fully illustrate my invention, that others may, by applying current of future knowledge, readily adapt the same for use under various conditions of service. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention.



Advantages over the prior art
[014] Data Expedition: an innovative approaches to preprocessing and EDA proposed by the present invention has the following advantages over the prior art:
1. Automation and Efficiency:
The system automates key data preprocessing tasks such as handling missing values, detecting outliers, and performing feature selection. This significantly reduces the manual effort involved, accelerates the data preparation process, and minimizes human error, allowing users to focus more on analysis and decision-making.
2. Comprehensive Data Handling:
The invention supports a wide range of data preprocessing techniques, including data cleaning, transformation, integration, and reduction. This comprehensive approach ensures that all aspects of data preparation are covered, enabling the system to handle datasets of varying complexity and structure.
3. Advanced Analytical Tools:
The system incorporates sophisticated algorithms for statistical analysis, missing value imputation, outlier detection, and dimensionality reduction, allowing users to gain deeper insights from the data. It also offers powerful visualization tools to help uncover trends, relationships, and anomalies within the data through interactive, real-time exploration.
4. User-Friendly Interface:
The intuitive, interactive user interface makes the system accessible to both technical and non-technical users. Features like drag-and-drop data import, customizable workflows, and interactive visualizations ensure that users can engage with the data in a seamless and efficient manner, without needing deep technical expertise.
5. Customization and Flexibility:
The system is highly customizable, allowing users to tailor data preprocessing and EDA workflows to suit their specific needs. Whether working with small or large datasets, users can configure the system to focus on the most relevant aspects of the analysis, ensuring a personalized approach to data exploration.
6. Improved Data Quality:
By incorporating advanced techniques for outlier detection, feature engineering, and missing value imputation, the system helps improve the quality and consistency of data. This results in more accurate and reliable datasets, leading to better outcomes in downstream machine learning and analytics tasks.
7. Time and Cost Savings:
The automation of data preprocessing tasks reduces the time and cost associated with manual data preparation, enabling faster insights and decision-making. It also allows organizations to make more efficient use of resources, as data scientists and analysts can focus on higher-level tasks rather than repetitive data wrangling.
8. Collaboration and Sharing:
The system's collaborative features, including version control, shared workspaces, and real-time commenting, foster teamwork and facilitate data sharing. This is particularly beneficial for organizations where multiple team members need to work on the same dataset or analysis project, ensuring consistency and improving productivity.
9. Scalability:
The system is designed to handle datasets of varying sizes, from small files to large, complex datasets. Its scalability makes it suitable for organizations of all sizes, from small startups to large enterprises, and ensures it can grow with the increasing volume of data.
10. Enhanced Decision-Making:
By providing both statistical and visual tools for data exploration, the system enables users to gain valuable insights quickly. The ability to explore data interactively, identify patterns, and visualize results empowers better, data-driven decision-making across different industries, including finance, healthcare, marketing, and research.
11. Seamless Integration with Existing Workflows:
The system can be easily integrated into existing data analysis pipelines and workflows. With support for common data formats and compatibility with other tools, users can incorporate it into their data ecosystems without significant disruption or additional overhead.
12. Accessibility for Non-Experts:
The system's simplicity and ease of use make it accessible to individuals without a deep technical background in data science. This democratizes the process of data analysis, allowing a broader range of professionals to work with complex datasets and leverage data-driven insights for their decision-making.
[015] In the preceding specification, the invention has been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications and changes may be made thereunto without departing from the broader spirit and scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense.Therefore, the aim in the appended claims is to cover all such changes and modifications as fall within the true spirit and scope of the invention. The matter set forth in the foregoing description and accompanying drawings is offered by way of illustration only and not as a limitation. The actual scope of the invention is intended to be defined in the following claims when viewed in their proper perspective based on the prior art.



, Claims:We claim:

1. A method for preprocessing and exploratory data analysis (EDA) of datasets, comprising:
- Receiving raw data from one or more sources;
- Performing data cleaning operations including missing value imputation using one or more of mean, median, mode, k-nearest neighbors (KNN), or predictive modeling techniques;
- Detecting and managing outliers using statistical methods to identify and handle outliers;
- Applying data transformation techniques including scaling, normalization, and encoding categorical variables using label encoding or one-hot encoding;
- Merging and integrating data from multiple sources into a single dataset for further analysis;
- Reducing dimensionality of the data using feature selection or dimensionality reduction methods such as Principal Component Analysis (PCA);
- Splitting the data into training and test sets for model evaluation.
2. A system for data preprocessing and exploratory data analysis (EDA), comprising:
- A data input module configured to receive datasets from one or more data sources;
- A data cleaning module for handling missing values, detecting outliers, and cleaning data using advanced imputation and outlier detection techniques;
- A data transformation module for scaling, normalization, and encoding categorical variables;
- A data integration module for merging datasets and aggregating data from multiple sources;
- A data reduction module for feature selection and dimensionality reduction;
- An exploratory data analysis module for generating statistical summaries, visualizations, and performing correlation analysis.
3. The system of claim 2, wherein the system includes an interactive user interface (UI) that allows users to manipulate data visualizations in real-time, and configure preprocessing and EDA workflows using a drag-and-drop interface.
4. The method of claim 1, wherein the data cleaning operation further includes removing rows or columns with excessive missing values based on predefined thresholds.
5. The method of claim 1, wherein the exploratory data analysis step further includes generating visualizations, comprising:
- Histograms for visualizing frequency distributions;
- Box plots for summarizing data spread and detecting outliers;
- Pair plots to explore relationships between numerical variables;
- Heat maps to visualize correlations between variables.
6. The system of claim 2, wherein the system provides automated reporting functionality to generate a customizable report summarizing the preprocessing steps, statistical analyses, and visualizations performed on the data.
7. The method of claim 1, wherein the system automatically splits data into training and test sets for model evaluation, ensuring proper validation of machine learning models.
8. A non-transitory computer-readable medium having stored thereon instructions that, when executed by a processor, perform the method of claim 1.
9. The system of claim 2, wherein the data cleaning module further includes an outlier detection mechanism that uses one or more of Z-scores, interquartile range (IQR), or statistical tests to identify and handle outliers.
10. The method of claim 1, wherein the system is configured to handle large datasets efficiently, supporting parallel processing and distributed computing capabilities.

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