Exploratory Data Analysis

A 360° tool for Exploratory Data Analysis

Findability.EDA is a no-code, data exploration and statistical analysis tool designed to help you save significant time and effort during the pre-modeling phase of AI solution development. Exploratory Data Analysis or EDA is an important step towards deciphering patterns and features of the input data. It is also a time-consuming process, taking up anywhere from 30 to 40% of the total project time. With our tool, the EDA-related tasks that could potentially take weeks to complete, may be accomplished within a few hours.

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Industry Use Cases

Enterprises / Businesses

  • Identify trends and anomalies for leading indicators such as sales, revenue, inventory, no. of users, etc.
  • Visualize and analyze the relationship between business KPIs and various internal & external factors.
  • Gain quick insights into business performance, such as ‘highest selling SKU’ or ‘fastest growing category’ or ‘number of users added last month’.

Universities/ Educational Institutes

  • Demonstrate multiple techniques for statistical analysis.
  • Perform rapid hypothesis testing (Z-test, t-test) for experiment data.
  • Supplement research and teaching activities by conducting exploratory studies on data from a variety of industries – finance, actuarial sciences, marketing, supply chain, and so on.

Government Agencies

  • Analyze census data for patterns and anomalies.
  • Gain valuable insights for policymaking by analyzing historical data.
  • Rapid analysis of incoming information during exigencies such as natural disasters or epidemic outbreaks.

Health Care

  • Streamline healthcare delivery in hospitals and clinics by identifying resource utilization trends for outpatient and in-patient departments.
  • Exploratory analysis and hypothesis testing for data obtained from clinical trials, drug efficacy and treatment outcome studies.

Key Features

  • Cutting-edge tool that empowers organizations to analyze their data with ease and efficiency
  • Eliminates the need to code during EDA, resulting in quick turnaround on end-to-end data exploration
  • In-memory processing results in low latency even for voluminous datasets
  • Highly visual representation of analysis out comes generates valuable insights
  • Rapid hypothesis testing allows users to quickly test and validate their assumptions

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Implementation Process

STEP 01

STEP 02

STEP 03

STEP 04

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SANJAY DESHMUKH

HEAD – GLOBAL PRESALES AND PARTNERSHIPS

Sanjay literally lives the term ‘Applying the power of AI’ when it comes to solving complex and consequential business challenges, which also forms a cornerstone of his digital transformation advocacy mantra, #FindabilitySolves!
As a highly experienced Fintech executive, Sanjay uses the predictive and prescriptive power of Findability.ai solutions to deliver significant financial ROI to global businesses.

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EDA Exploratory Data Analysis & Pre-processing by Machine Learning

Exploratory Data Analysis (EDA) is a critical aspect of data exploration and pre-processing in machine learning and data science. EDA is the process of analyzing and visualizing data to gain insight into its characteristics, patterns, and relationships. It involves identifying outliers, handling missing or incomplete data, and understanding the distribution and summary statistics of the data.

There are many tools available for conducting EDA, including Python EDA libraries, exploratory data analysis in R, and Azure ADX. These tools provide a range of techniques for exploring and analyzing data, including data visualization, correlation analysis, and hypothesis testing. EDA is an essential step in preparing data for further analysis, such as predictive modeling or hypothesis testing. It helps identify potential issues and errors in the data, as well as highlight patterns and relationships that may not be immediately apparent.

One of the challenges of EDA is handling large amounts of data. This is where data pre-processing comes in. Data pre-processing is the process of cleaning, transforming, and preparing data for analysis. It involves techniques such as data normalization, feature scaling, and handling missing data.

In conclusion, EDA is a crucial step in the data analysis process. It provides insights into the data and identifies potential issues that need to be addressed before further analysis can be performed. By using tools such as exploratory data analysis in Python or R, data scientists and analysts can efficiently conduct EDA and prepare the data for further analysis.

FAQs

What is exploratory data analysis?

Exploratory Data Analysis (EDA) is the process of exploring and analyzing data to understand its characteristics and patterns. It is an important step in data exploration and data pre-processing in machine learning and data science.

What are the key objectives of EDA?

The key objectives of EDA are to identify patterns, relationships, and outliers in the data, understand the distribution and summary statistics of the data, and identify potential issues and errors in the data.

What are some common techniques used in EDA?

Some common techniques used in EDA include data visualization, summary statistics, correlation analysis, hypothesis testing, and clustering.

What types of data are suitable for EDA?

EDA is suitable for all types of data, including numerical, categorical, and text data.

What are some potential challenges when performing EDA?

Potential challenges when performing EDA include missing or incomplete data, outliers, and the need to handle large amounts of data.

How does EDA differ from other types of data analysis?

EDA differs from other types of data analysis, such as predictive modeling and hypothesis testing, in that it is primarily focused on exploring and understanding the data rather than making predictions or testing hypotheses.

Can EDA be automated?

EDA can be automated to some extent using tools such as Python EDA libraries and Azure ADX, but it still requires human interpretation and decision-making.