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User Acceptance Testing (UAT) – Meaning, How-to guide, Process Template and Agile Quality

user acceptance testing - uat testing

User Acceptance Testing (UAT) stands as a pivotal phase in the realm of software development, ensuring that software solutions align perfectly with user needs and expectations. As organizations strive for seamless and reliable software, UAT emerges as an indispensable process that bridges the gap between development and user satisfaction. In this article, we delve into the significance of User Acceptance Testing, exploring its definition, role in software development, and a glimpse into the content that follows.

Definition of User Acceptance Testing (UAT)

User Acceptance Testing, commonly referred to as UAT, is the final testing phase before software is released to its intended users. It involves evaluating the software’s functionality and performance to ensure that it meets predefined acceptance criteria. UAT is primarily executed by end-users, validating whether the software fulfills their requirements and expectations. This testing phase extends beyond technical validation, focusing on the software’s user-friendliness, usability, and alignment with real-world scenarios.

Importance of UAT in Software Development

UAT holds immense importance in the software development lifecycle for several reasons. It serves as the ultimate litmus test, determining if the software is ready for its intended users. While previous testing phases uncover technical glitches, UAT ensures that the software makes sense from an end-user perspective. It safeguards against releasing software that might be functionally accurate but lacks practical usability. UAT serves as a direct feedback loop from users to developers, highlighting any deviations from the intended user experience.

Brief overview of the article content

In this article, we embark on a journey to explore the facets of User Acceptance Testing. We delve into a comprehensive guide on the UAT process, providing insights into each step from planning to execution. Discover how meticulous planning and thorough execution of UAT scenarios contribute to the overall software quality.

Unveil the numerous benefits UAT brings to the table – from ensuring software meets user requirements to enhancing user satisfaction and minimizing post-release surprises. Understand the key considerations that lead to effective UAT implementation, from defining clear acceptance criteria to addressing potential security concerns.

Explore real-world challenges that UAT endeavors to overcome, and the strategies employed to conquer them. Additionally, learn about the automation tools that amplify UAT efficiency and delve into a compelling UAT success story that underscores the impact of a meticulous testing approach.

As we journey through the various dimensions of User Acceptance Testing, one thing becomes clear: UAT is not just a phase; it’s a commitment to delivering software that aligns with user needs, enriching both the software experience and user satisfaction.

Business Analysts (BA) are expected to perform UAT testing. Become a great BA with the Business Analyst Work Experience Program

UAT Process: A Step-by-Step Guide

User Acceptance Testing (UAT) stands as the ultimate checkpoint in software development, where the rubber meets the road for end-user satisfaction. This comprehensive guide sheds light on the intricate process of UAT, unveiling its stages and essential steps that pave the way for flawless software delivery.

Explanation of the UAT process stages

The UAT process comprises distinct stages that collectively contribute to delivering software excellence.

User acceptance testing (UAT) process
User acceptance testing (UAT) process

Steps involved in planning UAT

User acceptance testing begins with understanding the software's objectives and scope, followed by devising test cases that mirror real-world user scenarios. Subsequently, executing these scenarios illuminates potential discrepancies between user expectations and the software's performance. Capturing and addressing defects that emerge during testing lead us to the final stages: reviewing and approving UAT results.

  1. Planning UAT: Laying the Foundation

    Effective UAT begins with meticulous planning. It involves collaborating with stakeholders to define clear acceptance criteria that the software must meet. This phase also necessitates identifying and involving the right participants – the end-users whose feedback will determine the software's readiness. The planning stage sets the tone for a structured UAT execution, ensuring every critical aspect is addressed.

  2. Executing UAT Scenarios and Test Cases

    With the groundwork laid, the execution phase commences. End-users embark on a journey to simulate real-life scenarios, testing the software's functionalities in various contexts. This stage is marked by the deliberate exploration of the software, evaluating its performance, ease of use, and alignment with user expectations. Each scenario and test case scrutinizes different aspects of the software, contributing to a holistic understanding of its capabilities.

  3. Capturing and Reporting UAT Defects

    UAT thrives on transparency, and defects are part of that reality. As end-users traverse the software landscape, any deviations from the expected user experience are noted and documented. This phase isn't about blame but improvement. It's an opportunity to refine the software based on real user interactions, fostering a user-centric approach to development.

  4. Review and Approval of UAT Results

    The journey concludes with a meticulous review of UAT results. Stakeholders and end-users collaboratively assess the software's performance against acceptance criteria. The insights garnered during testing guide the decision-making process. Upon approval, the software is deemed ready for release, backed by the confidence that it meets user needs and expectations.

Tools
Materials

Business Analysts (BA) are expected to perform UAT testing. Become a great BA with the Business Analyst Work Experience Program

Benefits of User Acceptance Testing

In the intricate realm of software development, one pivotal phase emerges as the lighthouse of assurance – User Acceptance Testing (UAT). This process not only bridges the gap between developer intentions and user expectations but also showers a multitude of benefits that elevate the entire software experience.

Ensuring software meets user requirements

The heart of UAT beats to the rhythm of user needs. It serves as the ultimate validation that software aligns with the intricate requirements of its intended users. As end-users meticulously navigate through the software, their interactions unveil the extent to which the software caters to their needs and aspirations. This process instills a profound sense of alignment, where every code and feature resonates with the essence of user expectations.

Minimizing post-release issues and user dissatisfaction

Imagine a scenario where a software release triggers an array of user grievances. User Acceptance Testing is the sentinel against such possibilities. By simulating real-world scenarios, UAT uncovers issues that might have remained dormant in the developmental shadows. By addressing these concerns pre-release, it becomes a guardian against the ripple effect of post-release dissatisfaction.

Increasing confidence in the software’s reliability

Software users seek reliability, an unwavering trust that the solution will deliver as promised. UAT emerges as a catalyst in cultivating this trust. As end-users meticulously validate the software’s functionalities, their experiences shape a robust belief in the software’s reliability. This phase doesn’t merely test; it builds an unshakable bridge of faith between the software and its users.

Enhancing user experience and satisfaction

User experience reigns supreme, and UAT serves as its advocate. Every test, scenario, and interaction contributes to refining the user journey. Flaws are ironed out, processes streamlined, and user-friendliness optimized. As end-users traverse the software landscape seamlessly, they’re greeted with an experience that mirrors their desires and aspirations. This harmonious user experience becomes the cornerstone of ultimate satisfaction.

Key Considerations for Effective UAT

Imagine a world where software meets not only functional standards but user aspirations. This world is within reach through User Acceptance Testing (UAT), a crucial phase that transforms software dreams into user realities. To harness the power of UAT, several key considerations come into play, ensuring the perfect blend of user satisfaction and software excellence.

Defining clear acceptance criteria for UAT

UAT doesn’t thrive in ambiguity; it flourishes with clarity. Defining crystal-clear acceptance criteria is akin to setting the compass for a successful UAT journey. These criteria outline the boundaries of excellence that the software must meet. With these boundaries set, UAT becomes a guided exploration, ensuring that every step aligns with user needs and expectations.

Involving end-users and stakeholders

End-users aren’t just passengers on this UAT journey; they are its navigators. Involving end-users and stakeholders isn’t a mere formality; it’s the essence of UAT’s success. Their insights, feedback, and experiences paint a vivid picture of what the software needs to be. With their fingerprints on the process, UAT evolves from a technical test to a user-centric voyage.

Realistic scenario creation for testing

UAT isn’t a robotic repetition of steps; it’s an intricate dance of real-life scenarios. Creating scenarios that mimic actual user interactions is the heartbeat of UAT’s effectiveness. This process delves into the essence of user journeys, simulating the highs and lows they encounter. These scenarios become the canvas on which UAT paints a masterpiece of user-friendliness and functionality.

Addressing security and data privacy concerns

In a digitized world, security and data privacy are non-negotiable. UAT doesn’t merely ensure software functionality; it safeguards user trust. Addressing security concerns means fortifying the software against vulnerabilities. It’s a commitment to building a fortress of reliability where user data is protected and user confidence is upheld.

Business Analysts (BA) are expected to perform UAT testing. Become a great BA with the Business Analyst Work Experience Program

UAT Best Practices

User Acceptance Testing (UAT) isn’t just a phase; it’s a gateway to software excellence that resonates with end-users. To harness the full potential of UAT, a set of best practices emerge as guiding lights, ensuring a user-centric and flawless software journey.

Collaborative approach between development and testing teams

The synergy between development and testing teams isn’t just essential; it’s the backbone of UAT success. A collaborative approach fosters a shared understanding of objectives, challenges, and solutions. Development teams provide insight into technical intricacies, while testing teams offer user perspective. This alliance ensures that UAT isn’t a standalone event but a harmonious symphony of expertise.

Examples of creating comprehensive UAT test cases

UAT isn’t guesswork; it’s a systematic exploration. Crafting comprehensive test cases paves the way for this exploration. These test cases are more than mere steps; they’re roadmaps that guide end-users through the software landscape. Each test case reflects a user scenario, ensuring that no corner of the software remains untested. This comprehensive approach eradicates guesswork and ensures that user experiences mirror the intended outcomes.

Test Case IDTest ScenarioTest StepsExpected OutcomePass/Fail
UAT_TC01User Registration1. Navigate to the registration page.Successful registration with a unique username and password.
2. Fill in valid user information.A confirmation message and email are received.
3. Submit the registration form.User is registered and can log in.
UAT_TC02Product Purchase1. Log in using valid credentials.Successful login.
2. Browse the product catalog.Products are displayed accurately.
3. Add a product to the cart.Product is added to the cart.
4. Proceed to checkout.Checkout process is smooth and error-free.
5. Complete the payment process.Payment is successful, and a confirmation is received.
UAT_TC03Account Settings Update1. Log in using valid credentials.Successful login.
2. Navigate to account settings.Account settings page is accessible.
3. Update email address or password.Changes are saved and confirmed.
4. Save the changes.User receives a notification of successful update.
UAT_TC04Content Publishing1. Log in with appropriate credentials.Successful login.
2. Navigate to content creation section.Content creation interface is accessible.
3. Create a new article or post.Content is created and saved without errors.
4. Add relevant media (images or videos).Media is added and displayed correctly within the content.
5. Publish the content.Content is published and visible to users.
UAT_TC05Search Functionality1. Access the search feature on the website.Search bar is present and functional.
2. Enter relevant keywords.Search results match the entered keywords.
3. Review displayed search results.Results include relevant content and are organized logically.
4. Click on a search result.User is directed to the selected content.

Real-world scenario simulation

UAT isn’t confined to sterile labs; it thrives in the real world. Simulating real-world scenarios elevates UAT from a technical process to a user-centric adventure. The software isn’t tested in isolation; it’s evaluated as users would engage with it. This simulation injects authenticity into the UAT process, addressing potential hiccups and ensuring a seamless user journey.

Incorporating end-user feedback

End-users aren’t passive recipients; they’re active participants in UAT’s success. Their feedback isn’t a footnote; it’s a cornerstone. Incorporating end-user feedback polishes the software, ironing out wrinkles that only users can uncover. This practice transforms UAT from a one-time event to an iterative process, driving continuous improvement and fine-tuning user experiences.

Business Analysts (BA) are expected to perform UAT testing. Become a great BA with the Business Analyst Work Experience Program

Real-Life UAT Success Story

Many companies across various industries have successfully implemented User Acceptance Testing (UAT) as a crucial step in their software development process. Here are a few notable examples:

  1. Apple: Apple extensively uses UAT for testing new software releases, ensuring that their products meet the high standards expected by their users. This includes both macOS and iOS updates.
  2. Facebook: Social media giant Facebook employs UAT to validate new features and changes to their platform before they are rolled out to millions of users, ensuring a smooth user experience.
  3. Microsoft: Microsoft incorporates UAT in the development of its software products, such as the Windows operating system and Office suite. This helps them identify and address issues before widespread release.
  4. Amazon: E-commerce giant Amazon utilizes UAT to test new features and enhancements on their website and mobile apps. This helps them maintain a seamless shopping experience for their customers.
  5. Google: Google employs UAT to test updates and new features for their suite of products, including Google Workspace (formerly G Suite) and Android operating system.
  6. Salesforce: As a leading customer relationship management (CRM) platform, Salesforce implements UAT to validate new features and customizations before they are available to their users.
  7. Netflix: Streaming giant Netflix uses UAT to ensure a glitch-free experience for their subscribers when rolling out new app versions and features.
  8. Uber: Ride-sharing company Uber employs UAT to thoroughly test updates and new features in their app to provide a reliable and user-friendly service.
  9. Airbnb: Airbnb utilizes UAT to validate changes to their platform, ensuring that hosts and guests have a smooth experience when using the website and app.
  10. Adobe: Adobe employs UAT to test updates and enhancements to their creative software products like Photoshop, Illustrator, and Premiere Pro.

These companies, among many others, recognize the importance of UAT in delivering software and services that meet user expectations, enhance user satisfaction, and maintain their reputation for quality and reliability.

UAT and Agile Development

In the dynamic landscape of software development, agility has emerged as the guiding principle for innovation. The integration of User Acceptance Testing (UAT) within Agile methodologies has given rise to a symbiotic relationship that propels the development process towards excellence. This fusion not only accelerates software delivery but also enhances user satisfaction through a continuous cycle of testing and refinement.

Integrating UAT within Agile methodologies

Agile methodologies, characterized by their iterative and incremental approach, emphasize adaptability and collaboration. Integrating UAT seamlessly aligns with these principles, infusing the development cycle with user-centricity. In Agile, UAT is not an isolated event at the end of development but an ongoing process. As each iteration progresses, UAT becomes a checkpoint where user feedback is sought and incorporated, steering the software towards alignment with user needs. The use of acceptance criteria in the agile software development process makes for the inclusion of UAT like verifications.

There are two choices to integrate UAT as part of Agile:

  1. You treat it as “release to production” and the Product Owner contacts the users or Business Analyst to test the functionality in UAT.
  2. You treat it as part of the development. Then it should be in Definition of Done, and it should be part of the Product Backlog Item’s flow to “Done” i.e. To Do -> In Progress -> UAT -> Done.

UAT’s role in continuous delivery and frequent releases

Agile’s hallmark is continuous delivery and frequent releases. UAT plays a pivotal role in ensuring that these releases are not just swift but also polished. With UAT as a recurring step, each release undergoes meticulous user scrutiny. This process is a buffer against the introduction of defects and glitches, safeguarding the user experience. As Agile embraces change, UAT steps in to validate changes, making certain that they resonate positively with users.

The team’s Definition of Done should be such that downstream activities, such as integration or user acceptance testing, complete successfully. If the result of user acceptance testing is that the product is not acceptable, the team should understand why and make changes to their way of working to regularly create Increments that are likely to be acceptable. Any other feedback from UAT can be treated like customer or user feedback and ordered with the rest of the Product Backlog.

By removing external dependencies, you no longer need to worry about how to estimate or plan for these external dependencies during refinement or Sprint Planning events.

Moreover, UAT’s involvement in the Agile cycle nurtures a culture of collaboration. Developers, testers, and end-users converge, where user feedback shapes the software’s evolution. This real-time engagement refines the software, nurturing a product that evolves organically with user needs.

Business Analysts (BA) are expected to perform UAT testing. Become a great BA with the Business Analyst Work Experience Program

Frequently asked questions about User Acceptance Testing UAT

  1. What is user acceptance testing UAT and how it works?

    User Acceptance Testing (UAT), or software testing from the point of view of the users, is usually the final stage of any software development lifecycle (SDLC) before going live. UAT is the final stage of the development process to determine that the software does what it was designed to do according to the requirements originally stated.

  2. What is UAT vs QA testing?

    UAT and QA both involve testing. However, they have different objectives. The difference is that the QA teams work to ensure error-free software whereas UAT ensures that end users get the product they want. QA teams generally perform system integration testing while business analysts perform UAT.

  3. What is UAT in agile?

    UAT, or user acceptance testing, is the final stage in the software testing process. In Agile as well as waterfall projects, it is typically performed by the end-users, clients or business analysts to determine whether an application or feature fulfills its purpose. UAT must be completed before the software can be released to the market. UAT can be performed within a sprint or before a production release.

  4. What tool is used for UAT?

    With the help of Selenium, testers can automate the acceptance tests, ensuring that the application meets the requirements of the end users. However, it's important to note that Selenium alone may not be sufficient for all aspects of UAT and may need to be combined with other tools for a complete UAT solution. JIRA is also typically used to manage and maintain test cases.

  5. What is UAT in DevOps?

    User acceptance testing (UAT) is the last phase of the software testing process. During UAT, actual software users test the software to make sure it works as per real-world scenarios, and according to the requirements. DevOps incorporates the practice of UAT to allow for seamless delivery of high quality software products.

  6. Who prepares UAT?

    User acceptance testing is performed by business analysts, clients or the end-users. They will write a complete UAT test plan, prepare a UAT environment that mirrors production, write corresponding UAT test cases, execute these test cases, report defects if any, verify the fixes to the defects raised and finally confirm that the software is fit for use.

  7. Is UAT part of Agile?

    UAT is included in the agile framework, and should be part of the sub tasks for each use story in the product backlog. A user story describes a user, the feature they want to use, and how it helps them achieve their goal, and the UAT tests should describe an explain the acceptance criteria.

  8. Who manages UAT in Agile?

    This could be the Business Analyst or Product Owner. But because the ability to produce a “Done” increment would be so tightly coupled to this process, a Development Team should certainly take an interest in making sure UAT takes place at the right time and in the right way to maximize what they are able to achieve.

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Gap Analysis for Business Analysts – How to perform a gap analysis – format, template and techniques

gap analysis performed by business analysts - templates, format and guidelines

A gap analysis is a strategic planning tool used to identify the difference (“gap”) between the current state and the desired future state of a business or project. It helps organizations understand where they are currently, where they want to be, and what steps are needed to bridge the gap between the two.

Overview of the Gap Analysis

Gap analysis - Look for gaps in processes and technologies
Gap analysis – Look for gaps in processes and technologies

Gap analysis is a systematic approach to assess the current state of the organization or project and compare it to the desired future state. The analysis helps identify discrepancies or “gaps” between the two states, enabling the organization to plan and strategize for improvement.

Download the Gap Analysis Template

Purpose of the gap analysis

The purpose is to understand the current performance, capabilities, or status of the organization or project in relation to its desired goals. The main objectives of the gap analysis may include:

  1. Identifying areas of improvement: Determine which aspects of the organization or project require enhancement to meet the desired objectives and performance levels.
  2. Setting realistic targets: Establish specific, measurable, achievable, relevant, and time-bound (SMART) targets to bridge the identified gaps.
  3. Formulating actionable strategies: Develop strategies and action plans to address the identified gaps and improve the overall performance.
  4. Aligning with strategic goals: Ensure that the organization or project is aligned with its strategic objectives and long-term vision.

The gap analysis is usually performed by the business analyst or product manager. Learn more about the role of the business analyst here.

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Gap Analysis in 5 steps

  1. Identify Goals and Criteria: Clearly define the organization’s goals and objectives. Establish measurable criteria or key performance indicators (KPIs) that will be used to assess the current state and measure progress towards the desired future state.
  2. Assess Current State: Gather data and information about the organization’s current performance and capabilities. Compare the current state against the predefined criteria to identify gaps and areas where the organization falls short of its goals.
  3. Define Future State: Envision the desired future state of the organization. Set specific, achievable, and time-bound targets aligned with the organization’s strategic vision. This step serves as the benchmark for assessing progress during the analysis.
  4. Analyze and Interpret Gaps: Analyze the gaps between the current state and the future state. Identify the root causes and contributing factors to the gaps, considering both internal and external factors that influence performance.
  5. Develop Action Plan: Create an action plan to bridge the identified gaps. Propose strategies, initiatives, and solutions to address weaknesses and capitalize on opportunities. Establish a timeline, allocate resources, and assign responsibilities for implementing the action plan. Regularly monitor progress and adjust strategies as needed to achieve the desired future state.

Download the Gap Analysis Template

The need to perform gap analysis / application of gap analysis / types of gap analysis

  1. Goal Alignment: Gap analysis helps align an organization’s objectives with its actual performance. It ensures that the organization’s goals are realistic, achievable, and grounded in the current capabilities and resources.
  2. Performance Evaluation: It provides an objective evaluation of an organization’s current state, including strengths, weaknesses, and areas for improvement. This evaluation is crucial for understanding where the organization stands in comparison to its desired future state.
  3. Strategic Planning: Gap analysis is an essential component of strategic planning. It helps organizations identify the gaps between their current position and their strategic vision. This information is critical for formulating effective strategies to bridge those gaps and achieve long-term success.
  4. Resource Optimization: By identifying gaps, organizations can optimize the allocation of resources. It allows them to prioritize areas that require immediate attention and allocate resources efficiently for the most impactful outcomes.
  5. Decision-Making: Gap analysis provides a data-driven basis for decision-making. It helps leaders and stakeholders make informed choices about resource allocation, investments, and strategic initiatives.
  6. Risk Management: Understanding gaps and weaknesses helps organizations identify potential risks and vulnerabilities. Addressing these gaps proactively can minimize risks and prevent potential issues from escalating.
  7. Continuous Improvement: Gap analysis fosters a culture of continuous improvement within the organization. It encourages regular assessment and adjustment of strategies to adapt to changing circumstances and remain competitive.
  8. Customer-Centric Approach: For businesses, gap analysis helps in understanding customer needs and expectations. By identifying gaps in customer satisfaction and experience, organizations can tailor their products and services to meet customer demands effectively.
  9. Performance Measurement: Gap analysis provides a benchmark for measuring progress and success. Organizations can track their improvements over time and evaluate the effectiveness of their initiatives.
  10. Compliance and Regulatory Requirements: In regulated industries, gap analysis helps organizations ensure compliance with industry standards, laws, and regulations. It allows them to identify and address gaps in meeting these requirements.

Scope of the gap analysis

The gap analysis will have its scope defined, including what aspects of the organization or project will be covered and what will be excluded. The scope may include specific departments, processes, systems, or functions. Be sure to clarify the boundaries and limitations of the analysis to manage expectations.

  1. Inclusions: Clearly state what will be covered in the gap analysis, such as financial performance, operational efficiency, customer satisfaction, or specific project deliverables.
  2. Exclusions: Specify what will not be part of the analysis to avoid any misunderstandings. For instance, it might be necessary to exclude certain factors that are not within the scope of the current project.
  3. Timeframe: Mention the time period for which the analysis will be conducted. It could be the current fiscal year, a specific quarter, or a certain phase of the project.
  4. Data Sources: Describe the data sources that will be used to gather information for the analysis. These may include internal reports, interviews, surveys, or external benchmarks.
  5. Constraints: Highlight any constraints or limitations that may affect the analysis, such as resource availability, time constraints, or data accessibility.

Download the Gap Analysis Template

Benefits of performing a gap analysis

Gap analysis offers several benefits to organizations and projects:

  1. Identifies Opportunities for Improvement: Gap analysis helps organizations identify areas where they are falling short of their goals or desired outcomes. By understanding the gaps between the current state and the future state, organizations can identify specific areas for improvement and growth.
  2. Sets Clear Objectives: Gap analysis sets clear and measurable objectives for the organization or project. It defines the target outcomes and provides a roadmap for achieving them, enabling better focus and direction for the team.
  3. Optimizes Resource Allocation: By identifying areas with significant gaps, gap analysis allows organizations to prioritize resource allocation. It ensures that resources such as time, budget, and manpower are allocated to the most critical areas for improvement.
  4. Enhances Decision-Making: Gap analysis provides a data-driven basis for decision-making. It helps leaders and stakeholders understand the potential risks, benefits, and impacts of various choices and strategies.
  5. Encourages Continuous Improvement: Gap analysis is a continuous process, and organizations can regularly assess their progress and adjust strategies accordingly. It fosters a culture of continuous improvement and adaptation to changing circumstances.
  6. Aligns Objectives with Strategy: By defining the future state and comparing it with the current state, gap analysis ensures that objectives are closely aligned with the organization’s strategic vision. It helps ensure that efforts are directed towards achieving the organization’s long-term goals.
  7. Promotes Accountability: Gap analysis assigns responsibilities and accountabilities for bridging the identified gaps. It clarifies who is responsible for what tasks, improving accountability and ownership among team members.
  8. Increases Efficiency and Productivity: Addressing identified gaps often involves streamlining processes and eliminating inefficiencies. This leads to increased overall efficiency and productivity in the organization.
  9. Mitigates Risks: Gap analysis helps identify potential risks and challenges that may hinder progress. By addressing these risks proactively, organizations can reduce the likelihood of negative outcomes.
  10. Boosts Competitive Advantage: By identifying and addressing gaps, organizations can gain a competitive advantage in the market. They can differentiate themselves by offering superior products, services, or processes compared to their competitors.

Techniques used to perform gap analysis

Several techniques are used to perform gap analysis, depending on the context and the specific requirements of the analysis. Some commonly used techniques include:

  • SWOT Analysis: SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis is a widely used technique to assess the internal strengths and weaknesses of an organization and external opportunities and threats it faces. By comparing strengths and weaknesses to opportunities and threats, gaps can be identified, and strategies can be developed to address them.
SWOT Analysis template for use during gap analysis
SWOT Analysis template
  • Benchmarking: Benchmarking involves comparing an organization’s performance metrics with those of industry peers or best-in-class companies. It helps identify performance gaps and highlights areas where the organization lags behind or excels, providing insights for improvement.
  • Performance Metrics Analysis: This technique involves analyzing key performance indicators (KPIs) and other relevant metrics to assess an organization’s current performance against predefined targets or industry benchmarks. Any gaps between the current and desired performance levels can be identified and addressed.
  • Customer Feedback and Surveys: Collecting feedback from customers through surveys, interviews, or focus groups can help identify gaps in customer expectations and experiences. Customer feedback is crucial for understanding areas where the organization needs to improve to better meet customer needs.
  • Process Mapping: Process mapping visually represents the current processes within an organization, helping to identify inefficiencies, bottlenecks, and areas of improvement. Comparing the current process with the desired future state can reveal gaps that need to be addressed.
  • Capability Maturity Model (CMM): CMM is a framework used to assess and improve the maturity level of an organization’s processes. By comparing the organization’s current maturity level to the desired level, gaps in process maturity can be identified.
  • Gap Analysis Surveys and Questionnaires: Specific surveys and questionnaires can be designed to gather targeted information about various aspects of the organization’s operations. The results can then be compared to ideal or desired conditions to uncover gaps.
  • Cost-Benefit Analysis: Cost-benefit analysis helps evaluate the financial impact of different strategies and initiatives. It can be used to compare the cost of implementing improvements against the potential benefits to identify the most cost-effective solutions.
  • Risk Analysis: Analyzing potential risks and vulnerabilities can help identify gaps in risk management practices. This analysis enables organizations to develop risk mitigation strategies and improve their resilience.
  • Employee Feedback and Stakeholder Interviews: Gathering feedback from employees and stakeholders within the organization can provide valuable insights into operational challenges and potential gaps that need to be addressed.

The choice of technique(s) for gap analysis depends on the organization’s goals, available data, and the complexity of the analysis. Often, a combination of these techniques is used to gain a comprehensive understanding of the gaps and develop effective strategies for improvement.

Download the Gap Analysis Template

Current State Assessment

Be sure to provide a comprehensive description of the current state of the organization or project. Include details about its current structure, processes, systems, and overall performance. Describe the organization’s current position in the market, its products or services, and any recent developments or changes that have taken place.

Define Key Performance Indicators (KPIs) and Metrics, Current State and Issues

Identify and present the key performance indicators (KPIs) and metrics that are used to measure the current state. KPIs may vary based on the organization’s goals and objectives, but they should be relevant to the specific scope of the gap analysis. Common KPIs may include financial metrics (e.g., revenue, profitability), operational metrics (e.g., efficiency, productivity), customer metrics (e.g., satisfaction, retention), and quality metrics (e.g., defects, errors).

Assess and outline the strengths and weaknesses of the organization or project’s current state. Consider both internal and external factors that influence its performance. Strengths may include areas where the organization excels, such as strong brand reputation, efficient processes, or a talented workforce. Weaknesses may include areas of concern, such as outdated technology, inefficient workflows, or limited market share.

Identify and highlight any significant issues or challenges that are affecting the current state. These may include obstacles that hinder progress, obstacles that prevent the organization from reaching its goals, or issues that have the potential to cause significant impact. It’s essential to be specific and provide evidence or data to support the identified issues and challenges.

Future State Definition

Ensure that describe the desired future state of the organization or project. Paint a detailed picture of what the organization aims to achieve in terms of its structure, processes, capabilities, and overall performance. Explain how the future state aligns with the organization’s long-term vision and strategic objectives.

Outline the specific goals, objectives, and targets that the organization aims to accomplish in the future state. Goals are broad, high-level statements of what the organization wants to achieve. Objectives are more specific and measurable outcomes that contribute to the achievement of the goals. Targets are quantifiable metrics or milestones used to track progress toward the objectives.

For example:

  • Goal: Increase customer satisfaction and loyalty.
  • Objective: Improve customer service response time by 30%.
  • Target: Achieve a customer satisfaction rating of 90% by the end of the next quarter.

Explain the organization’s vision for the future state and how it fits into the broader strategic direction. The vision should be a clear and inspiring statement of the organization’s long-term aspirations and what it aims to become. Describe how the future state aligns with the organization’s overall strategy and how it supports growth, innovation, or market expansion.

Download the Gap Analysis Template

Gap Identification

With the above done, you will not be able to conduct a detailed comparison between the current state (as described in Section II) and the desired future state (as outlined in Section III). Identify the gaps or differences between the two states in terms of processes, capabilities, performance, and any other relevant aspects. Use visual aids such as tables or diagrams to present the comparison clearly.

If feasible, quantify the gaps between the current and future states using the key performance indicators (KPIs) and metrics identified in Section II. Provide numerical values to represent the differences and demonstrate the extent of improvement required to reach the future state targets. Quantifying the gap helps in prioritizing areas for improvement and sets a clear target for each identified gap.

For example:

  • Current State: Customer satisfaction rating of 75%.
  • Future State Target: Customer satisfaction rating of 90%.
  • Gap: 15 percentage points.

Subsequently, delve into the root causes behind each identified gap between the current and future states. Use various analytical techniques, such as brainstorming, cause-and-effect diagrams (Ishikawa or Fishbone diagrams), or 5 Whys analysis, to identify the underlying reasons for the gaps. Understanding the root causes is critical for devising effective solutions and action plans.

Gap analysis - Fishbone diagram for root cause analysis
Fishbone diagram for root cause analysis

For example:

  • Gap: Customer service response time not meeting the future state target.
  • Root Causes: Insufficient staff training, outdated technology, and lack of automated response systems.

Factors Contributing to the Gap

Internal Factors (e.g., Processes, Systems, Resources, Skills):

Identify and analyze the internal factors within the organization that contribute to the gaps between the current and future states. These factors are within the organization’s control and can be influenced through strategic decisions and actions. Some examples of internal factors include:

  1. Processes: Assess the efficiency and effectiveness of existing processes. Identify any bottlenecks, redundancies, or gaps in the workflows that hinder progress towards the future state.
  2. Systems and Technology: Evaluate the organization’s current technological infrastructure and tools. Determine whether the existing systems support the desired future state requirements or if upgrades are necessary.
  3. Resources: Examine the availability and allocation of resources, including human resources, financial capital, and equipment. Determine whether the organization has the necessary resources to achieve the future state objectives.
  4. Skills and Training: Assess the skill sets and capabilities of the workforce. Identify any gaps in skills and knowledge that may hinder the organization from reaching the future state targets.

Download the Gap Analysis Template

External Factors (e.g., Market Trends, Competitors, Regulatory Changes):

Identify and analyze the external factors that contribute to the gaps between the current and future states. These factors are outside the direct control of the organization but can significantly influence its performance and success. Some examples of external factors include:

  1. Market Trends: Analyze current and emerging market trends, consumer preferences, and industry developments. Identify how these trends impact the organization’s ability to achieve its future state objectives.
  2. Competitor Analysis: Evaluate the strengths and weaknesses of competitors and how they compare to the organization’s capabilities. Identify areas where the organization lags behind or can gain a competitive advantage.
  3. Regulatory Changes: Assess how changes in laws, regulations, or industry standards may impact the organization’s operations and ability to meet the future state requirements.
  4. Economic Factors: Consider economic conditions, such as inflation, interest rates, and market stability, that can influence the organization’s financial performance and ability to invest in future state initiatives.

Risks of not addressing the gap and Opportunities of having addressed the gap

Risks of not addressing the gap

Identify and assess the potential risks and negative consequences that the organization may face if the gaps between the current and future states are not addressed. Failure to bridge the gaps could lead to various challenges, setbacks, and missed opportunities.

Some common risks associated with not addressing the gap include:

  1. Loss of Competitive Advantage: Not achieving the desired future state may result in the organization losing its competitive edge and market position.
  2. Customer Dissatisfaction: Failure to meet customer expectations and demands may lead to decreased customer satisfaction and loyalty.
  3. Inefficient Processes: Unaddressed gaps in processes may lead to inefficiencies, increased costs, and operational inefficiencies.
  4. Financial Losses: Failure to achieve the future state objectives may lead to financial losses, missed revenue opportunities, and increased costs.
  5. Employee Disengagement: Lack of progress towards the desired future state may impact employee morale and engagement.
  6. Compliance and Legal Issues: Failure to meet regulatory requirements or address changes in compliance standards could lead to legal or reputational risks.

Opportunities Gained from Addressing the Gap

Highlight the potential opportunities and positive outcomes that the organization can gain by addressing the identified gaps. Successfully bridging the gaps can lead to several advantages and benefits. Some opportunities gained from addressing the gap include:

  1. Increased Market Share: Achieving the desired future state may lead to increased market share and a larger customer base.
  2. Enhanced Customer Experience: Meeting customer expectations and delivering on the desired future state can lead to improved customer experience and loyalty.
  3. Improved Efficiency and Productivity: Addressing process gaps can lead to streamlined workflows and increased efficiency.
  4. Cost Savings: Closing gaps in operations can lead to cost savings and better resource allocation.
  5. Innovation and Differentiation: Successfully implementing future state initiatives can lead to innovation and differentiation from competitors.
  6. Attracting Talent: Progressing towards the desired future state can enhance the organization’s reputation and attractiveness to potential employees.

Download the Gap Analysis Template

Recommendations and Solutions for Gap Analysis

Proposed Strategies to Bridge the Gap

In this section, present the recommended strategies and approaches to bridge the gaps between the current state and the desired future state. Each strategy should directly address the identified gaps and align with the organization’s goals and objectives. Consider both short-term and long-term strategies that will lead to sustainable improvements. Clearly explain the rationale behind each proposed strategy and how it contributes to achieving the future state.

Action Plan with Specific Steps and Milestones

Outline a detailed action plan that lays out the specific steps and milestones required to implement the recommended strategies. The action plan should be well-structured, sequential, and time-bound. Include responsible parties or teams for each action, along with expected completion dates for each milestone. This ensures clear accountability and helps track progress throughout the implementation process.

Resource Requirements (Financial, Human, Technological)

Identify the resource requirements needed to execute the action plan effectively. These resources may include financial investments, human resources, technological upgrades, or external expertise. Quantify the estimated costs associated with each strategy and provide a budget for the entire implementation process. Ensure that the organization has the necessary resources to support the gap-closing initiatives.

Risk Mitigation Plan for Implementing Solutions

Outline the risk mitigation plan to address potential challenges and obstacles that may arise during the implementation of the recommended solutions. Identify key risks and uncertainties, along with their potential impact on the success of the gap-closing initiatives. For each risk, propose specific mitigation strategies to reduce or eliminate its negative effects. The risk mitigation plan helps ensure a smoother implementation process and minimizes disruptions.

Implementation Plan of the Gap Analysis

Timeline and Sequence of Activities

Provide a detailed timeline and sequence of activities for the implementation of the proposed strategies and action plan. Break down the action plan into smaller tasks or phases, and assign estimated start and end dates for each activity. Ensure that the timeline is realistic and considers any dependencies or interrelationships between tasks. Include milestones to track progress and celebrate achievements.

Roles and Responsibilities in performing a Gap Analysis

Identify and assign specific roles and responsibilities to individuals or teams involved in the implementation process. Clearly define who will be accountable for each task, who will be responsible for executing it, and who will be consulted or informed. Ensuring clear roles and responsibilities helps streamline communication and decision-making during the implementation phase.

For example:

  • Project Manager: Overall coordination and management of the implementation plan.
  • Department A Team: Responsible for implementing Strategy 1 and Strategy 2.
  • Department B Team: Responsible for implementing Strategy 3 and Strategy 4.
  • Finance Department: Responsible for budget allocation and financial oversight.
  • Senior Management: Decision-makers and sponsors for the implementation process.

Communication and Stakeholder Engagement Plan

Outline a communication and stakeholder engagement plan to ensure effective communication with all relevant stakeholders throughout the implementation process. Identify key stakeholders, such as employees, management, customers, suppliers, or external partners, and determine the appropriate communication channels and frequency of updates.

The communication plan should include:

  • Regular progress updates to stakeholders on the status of implementation.
  • Channels of communication (e.g., meetings, emails, progress reports, presentations).
  • Stakeholder engagement activities to involve them in the process and address any concerns.
  • A feedback mechanism to capture suggestions or concerns from stakeholders.

Download the Gap Analysis Template

Monitoring and Evaluation of the Gap Analysis project

Key Performance Indicators to Measure Progress

Identify the key performance indicators (KPIs) that will be used to monitor and measure the progress of the implementation plan. These KPIs should be aligned with the objectives and targets set in Section III and should reflect the organization’s priorities. The selected KPIs should be specific, measurable, achievable, relevant, and time-bound (SMART).

For example:

  • KPI: Customer satisfaction rating.
  • Target: Achieve a customer satisfaction rating of 90% by the end of the next quarter.
  • Progress: Monitor customer satisfaction scores on a monthly basis and compare them against the target.

Evaluation Criteria for Success:

Define the criteria that will be used to determine the success of the implementation plan. These criteria should be based on the achievement of the desired future state and the objectives set in Section III. The evaluation criteria should be clear, objective, and aligned with the organization’s overall goals.

For example:

  • Criterion: Increase in market share.
  • Success: Achieving a market share growth of 5% within the next six months.

Review Mechanisms and Frequency

Outline the review mechanisms and the frequency of evaluation to assess the progress of the implementation plan. Determine when and how progress will be reviewed, who will be involved in the review process, and the format of the review meetings or reports.

For example:

  • Monthly Progress Review: Hold monthly meetings with the project team to review the progress, discuss challenges, and make necessary adjustments to the implementation plan.
  • Quarterly Performance Review: Conduct quarterly evaluations to assess the achievement of targets and alignment with the desired future state.

Conclusion

Gap analysis is a valuable tool that supports decision-making, goal-setting, and continuous improvement efforts. It provides organizations with a systematic approach to identify and address challenges, maximize opportunities, and ultimately drive success and growth. It is a valuable tool for organizations seeking to make informed decisions, align their strategies with their objectives, and continuously improve their performance. It enables organizations to bridge the gap between their current state and their desired future state, driving growth, efficiency, and competitiveness.

Download the Gap Analysis Template

Frequently asked questions about gap analysis

  1. What do you mean by gap analysis?

    A gap analysis is performed to recognize an organization's current state—by mapping processes, activities and measuring time, money, and labor—and comparing it with its desired state. By defining and analyzing these gaps between the desired state and the current state, the project team can create an action plan to move the organization forward and fill in the gaps.

  2. Why is gap analysis important?

    Gap analysis helps organizations set clear objectives, optimize resource allocation, and make informed decisions. It promotes continuous improvement and ensures alignment with strategic goals.

  3. What is a gap analysis also known as?

    A gap analysis is also called a needs analysis and is important for ongoing improvement of the performance of any organization.

  4. How do you write a gap analysis example?

    1. Identify the organizational area to be analyzed.
    2. Identify the goals to be accomplished.
    3. State the ideal future state.
    4. Analyze the current state.
    5. Compare the current state with the ideal future state.
    6. Describe the gap and quantify the difference.
    7. Create a plan of action (project) to bridge the gap.

  5. What are the techniques used in gap analysis?

    Techniques include SWOT analysis, benchmarking, performance metrics analysis, customer feedback, process mapping, CMM, cost-benefit analysis, risk analysis, and surveys.

  6. Is a SWOT analysis a gap analysis?

    SWOT analysis is a technique used while performing a gap analysis. Using a SWOT analysis diagram is one of the ways to take understanding where an organization stands, its current state position in the competitive landscape, what it is doing well, and what it could be doing better.

  7. What is the value of gap analysis?

    A gap analysis is a good way to determine and move to a higher state of organizational productivity. By evaluating ongoing performance, inputs and outputs, and comparing these to desired higher states, one is able to determine the difference and work out ways to navigate that gap.

  8. Who should perform gap analysis?

    Business analysts are usually the ones who undertake gap analyses to determine how to make improvements. The gap analysis can be applied to performance of a department or team, an individual, or the entire company. Whenever there are growth goals, or existing objectives are not met, it is an indicator to discover what may be getting in the way through a gap analysis.

  9. How does gap analysis benefit decision-making?

    Gap analysis provides data-driven insights that assist in making informed decisions about resource allocation, investments, and strategic initiatives.

  10. What are the three 3 fundamental components of a gap analysis?

    The three fundamental components of a gap analysis are the current state, desired state, and the gap. A gap analysis is used in organizations to help them understand the differences between their current and desired state. By understanding this, they can work on strategies to help close the gaps.

  11. What role does gap analysis play in strategic planning?

    Gap analysis helps identify gaps between the current state and the strategic vision, enabling the development of effective strategies to bridge those gaps.

  12. How does gap analysis support continuous improvement?

    By regularly assessing progress and adapting strategies, gap analysis fosters a culture of continuous improvement and adaptation to changing circumstances.

  13. How does gap analysis help organizations prioritize improvements?

    Gap analysis prioritizes areas requiring immediate attention, optimizing the allocation of resources for the most impactful outcomes.

  14. What are the potential risks of not conducting gap analysis?

    Without gap analysis, organizations may lack direction, miss growth opportunities, and face operational inefficiencies due to a lack of focus on key improvement areas.

  15. Can gap analysis be used in various industries?

    Yes, gap analysis is applicable across industries, from business and healthcare to education and technology, as it provides a universal framework for improvement.

  16. What is the outcome of gap analysis?

    The outcome of gap analysis is a comprehensive report highlighting identified gaps, recommended solutions, and a roadmap for achieving the desired future state.

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Project Manager Salary packs a punch in the US

project manager salaries in the US savio education global

Project management is an excellent career option for individuals who enjoy leading teams, organizing tasks, and driving successful outcomes. It involves planning, executing, and controlling projects to achieve specific goals within defined constraints such as time, budget, and resources. 

Project management jobs in the United States are available across various industries and sectors. The demand for skilled project managers remains consistently high as organizations strive to execute projects efficiently and achieve their strategic objectives.

INDUSTRIES WITH THE HIGHEST LEVELS OF EMPLOYMENT IN THIS OCCUPATION

While project management is a versatile skill that is applicable across various industries, here are some industries known to have high levels of employment in project management roles:

  1. Information Technology (IT): IT companies often have a significant number of projects, ranging from software development to infrastructure upgrades, which require project management expertise to ensure successful execution.
  2. Construction: Construction projects, such as building infrastructure, residential and commercial buildings, and civil engineering projects, require project managers to oversee planning, coordination, and execution.
  3. Engineering: Engineering firms involved in sectors like civil, mechanical, electrical, and industrial engineering rely on project managers to lead and manage complex engineering projects.
  4. Healthcare: The healthcare industry, including hospitals, clinics, and medical research organizations, employs project managers to oversee the implementation of new systems, process improvements, and regulatory compliance projects.
  5. Financial Services: Banks, insurance companies, and financial institutions often undertake projects related to new product launches, system upgrades, regulatory changes, and process improvements, all requiring project management expertise.
  6. Manufacturing: Manufacturing companies often undertake projects for process optimization, new product development, equipment upgrades, and facility expansions, necessitating project management skills.
  7. Consulting: Project management consulting firms provide project management services to clients across various industries, enabling them to effectively execute projects and achieve their objectives.
  8. Government: Government organizations at different levels (local, state, and federal) undertake projects related to infrastructure development, public services, policy implementation, and more, all requiring project management expertise.

It’s worth noting that project management skills are in demand in many other industries as well, including telecommunications, energy, marketing and advertising, retail, and nonprofit organizations, among others. The specific industries with the highest levels of employment in project management can vary depending on geographic location and economic factors.

INDUSTRYEMPLOYMENT PERCENTAGE OF INDUSTRY EMPLOYMENTHOURLY MEAN WAGEANNUAL MEAN WAGE
Federal Executive Branch (OES Designation)173,8508.65$42.09$87,550
Management of Companies and Enterprises77,3303.14$40.50$84,250
Colleges, Universities, and Professional Schools60,2901.95$32.56$67,720
Management, Scientific, and Technical Consulting Services59,7404.01$40.20$83,610
Computer Systems Design and Related Services55,6902.56$46.80$97,340

It is worth noting that project managers in computer technologies and software development get paid the most.

PROJECT MANAGER OPPORTUNITIES ACROSS THE STATES IN THE US

StateEmployment Employment per thousand jobsLocation quotient Hourly mean wageAnnual mean wage 
California209,16012.031.38$40.09$83,390
Texas117,9909.491.09$39.78$82,750
Florida86,8009.871.13$33.16$68,970
Illinois52,0308.630.99$38.45$79,970
Colorado50,93019.012.18$42.00$87,360
California209,16012.031.38$40.09$83,390
Image courtesy: BLS

ANNUAL SALARIES OF PROJECT MANAGERS AND SPECIALISTS ACROSS THE STATES IN THE US

Image: BLS

US State wise division of annual wages

Area NameAnnual mean wage
Alabama(0100000)      102460
Alaska(0200000)      103030
Arizona(0400000)      88690
Arkansas(0500000)      82180
California(0600000)      119130
Colorado(0800000)      102360
Connecticut(0900000)      102800
Delaware(1000000)      108620
District of Columbia(1100000)      106950
Florida(1200000)      95120
Georgia(1300000)      104990
Guam(6600000)      63900
Hawaii(1500000)      80850
Idaho(1600000)      82200
Illinois(1700000)      99210
Indiana(1800000)      84070
Iowa(1900000)      85770
Kansas(2000000)      90240
Kentucky(2100000)      84600
Louisiana(2200000)      80460
Maine(2300000)      86440
Maryland(2400000)      102250
Massachusetts(2500000)      106590
Michigan(2600000)      94570
Minnesota(2700000)      95440
Mississippi(2800000)      84310
Missouri(2900000)      90110
Montana(3000000)      79150
Nebraska(3100000)      84110
Nevada(3200000)      91290
New Hampshire(3300000)      92100
New Jersey(3400000)      145790
New Mexico(3500000)      102290
New York(3600000)      117020
North Carolina(3700000)      99770
North Dakota(3800000)      83180
Ohio(3900000)      90250
Oklahoma(4000000)      88980
Oregon(4100000)      92730
Pennsylvania(4200000)      92910
Puerto Rico(7200000)      53740
Rhode Island(4400000)      101620
South Carolina(4500000)      92320
South Dakota(4600000)      74310
Tennessee(4700000)      78920
Texas(4800000)      94390
Utah(4900000)      90850
Vermont(5000000)      79020
Virgin Islands(7800000)      68490
Virginia(5100000)      110960
Washington(5300000)      113140
West Virginia(5400000)      81730
Wisconsin(5500000)      95020
Wyoming(5600000)      100050
OCCUPATIONJOB SUMMARYENTRY-LEVEL EDUCATION 2021 MEDIAN PAY 
Advertising, Promotions, and Marketing ManagersAdvertising, promotions, and marketing managers plan programs to generate interest in products or services.Bachelor’s degree$133,380
Architectural and Engineering ManagersArchitectural and engineering managers plan, direct, and coordinate activities in the fields of architecture and engineering.Bachelor’s degree$152,350
Compensation and Benefits ManagersCompensation and benefits managers plan, develop, and oversee programs to pay employees.Bachelor’s degree$127,530
Computer and Information Systems ManagersComputer and information systems managers plan, coordinate, and direct computer-related activities in an organization.Bachelor’s degree$159,010
Construction ManagersConstruction managers plan, coordinate, budget, and supervise construction projects from start to finish.Bachelor’s degree$98,890
Emergency Management DirectorsEmergency management directors prepare plans and procedures for responding to natural disasters or other emergencies. They also help lead the response during and after emergencies.Bachelor’s degree$76,730
Financial ManagersFinancial managers create financial reports, direct investment activities, and develop plans for the long-term financial goals of their organization.Bachelor’s degree$131,710
Industrial Production ManagersIndustrial production managers oversee the operations of manufacturing and related plants.Bachelor’s degree$103,150
Medical and Health Services ManagersMedical and health services managers plan, direct, and coordinate the business activities of healthcare providers.Bachelor’s degree$101,340
Natural Sciences ManagersNatural sciences managers supervise the work of scientists, including chemists, physicists, and biologists.Bachelor’s degree$137,900
Postsecondary Education AdministratorsPostsecondary education administrators oversee student services, academics, and faculty research at colleges and universities.Master’s degree$96,910
Property, Real Estate, and Community Association ManagersProperty, real estate, and community association managers oversee many aspects of residential, commercial, or industrial properties.High school diploma or equivalent$59,230
Public Relations and Fundraising ManagersPublic relations managers direct the creation of materials that will enhance the public image of their employer or client. Fundraising managers coordinate campaigns that bring in donations for their organization.Bachelor’s degree$119,860
Social and Community Service ManagersSocial and community service managers coordinate and supervise programs and organizations that support public well-being.Bachelor’s degree$74,000
Top ExecutivesTop executives plan strategies and policies to ensure that an organization meets its goals.Bachelor’s degree$98,980
Training and Development ManagersTraining and development managers plan, coordinate, and direct skills- and knowledge-enhancement programs for an organization’s staff.Bachelor’s degree$120,130

MOST COMMON BENEFITS IN A PROJECT MANAGEMENT JOB

  1. Competitive Salary
  2. Health Insurance
  3. Retirement Plans
  4. Paid Time Off (PTO)
  5. Professional Development
  6. Performance Bonuses
  7. Flexible Work Hours
  8. Work-Life Balance
  9. Employee Assistance Programs (EAP)
  10. Maternity/Paternity Leave
  11. Wellness Programs
  12. Employee Recognition Programs
  13. Travel Opportunities
  14. Remote Work Options
  15. Team Building Activities

Frequently Asked Questions about project management jobs in the United States

  1. What is the role of a project manager?

    The project manager is the individual accountable for delivering the project. They lead and manage the project team, with authority and responsibility vested in them by the organization through the project charter, to run the project on a day-to-day basis and utilize organization resources.

  2. Is project manager an IT job?

    The project manager role exists in information technology (IT) and in other sectors as well. An IT project manager helps organizations achieve their IT goals by planning and executing projects. They lead projects to introduce new software solutions, improve efficiency, scale business processes, and more.

  3. Who can be project manager?

    Professionals with skills and experience in a project management, people management and business management can be a project manager. As you gain experience, the scope of your work and responsibilities may increase in terms of project size and complexity.

  4. Do project managers need IT skills?

    Project managers need IT skills irrespective of the sector they work in. Most project today are planned, executed and monitored with the use of a software system like MS Projects, Atlassian JIRA or Asana. Managers in the IT sector need greater domain understanding and technology comprehension to ensure that their project deliver the IT needs of the organization. Those working in IT project management have a thorough knowledge of IT, possess a well-rounded skill set and are aware of current trends.

  5. Is project manager job difficult?

    Project management is a challenging career as no day will be the same, and you will need all your project management skills to solve problems. Also, you'll be the first person your team goes to when a problem occurs. They might expect you to hold the answers to any inquiry.

  6. Can a fresher become project manager?

    With the right qualifications, skills, and mindset, it is certainly possible for a fresher to become a project manager. Become a project manager by mastering technical project management techniques, business management and leadership skills.

  7. What is the job outlook for project management roles in the United States?

    The job outlook for project management roles in the United States is promising, with steady demand across various industries.

  8. What are the typical entry-level positions in project management?

    Common entry-level positions in project management include project coordinator, assistant project manager, or project analyst.

  9. How can I gain relevant experience in project management?

    Gaining relevant experience in project management can be achieved through internships, volunteering, taking on project-based roles within organizations like the project management work experience program.

  10. Are there any specific industries in the United States that offer strong project management career opportunities?

    Industries such as IT, construction, healthcare, and finance offer strong project management career opportunities in the United States.

  11. What are the average salaries for project management professionals in the United States?

    Average salaries for project management professionals in the United States range from around $70,000 to $120,000 per year, depending on factors such as experience, location, and industry.

  12. What are the key skills that employers look for in project management candidates?

    Employers often seek project management candidates with strong communication, leadership, technical project management, domain specific knowledge, problem-solving, and organizational skills.

  13. Is the Project Manager Work Experience certification beneficial for project management jobs in the United States?

    Having a Project Manager Work Experience certification is highly beneficial for project management jobs in the United States, as it demonstrates expertise, experience and conveys your proven capabilities to manage projects.

  14. What are some popular project management software tools used?

    Popular project management software tools used include Microsoft Project, JIRA, Asana, and Trello.

  15. How can I advance my project management career in the United States?

    Advancing a project management career in the United States can be achieved through continuous learning, obtaining advanced certifications, networking, and taking on increasingly complex projects.

  16. Are there any specific educational requirements for project management jobs?

    While a bachelor's degree is often preferred for project management roles, there is no specific educational requirement, and relevant work experience and certifications are valuable.

  17. What are the typical responsibilities of a project manager?

    Typical responsibilities of a project manager include developing project plans, managing budgets and resources, coordinating project teams, monitoring progress, and ensuring project goals are met.

  18. What are the common job titles associated with project management?

    Common job titles associated with project management include project manager, program manager, project coordinator, project analyst, scrum master and agile coach.

  19. Are there any industry-specific skills or knowledge that are highly sought after in project management jobs?

    Certain industries, such as IT, engineering, and healthcare, may require specific technical skills or domain knowledge relevant to their respective fields in addition to project management expertise.

  20. Is it common for project managers to work with cross-functional or remote teams?

    Yes, it is common for project managers to work with cross-functional teams and remote teams, particularly with the rise of virtual collaboration tools and remote work practices.

  21. What are the typical career advancement opportunities for project management professionals?

    Career advancement opportunities for project management professionals may include progressing to senior project management roles, becoming a project management office (PMO) director, or transitioning into executive leadership positions.

  22. How important is professional networking in the project management job market?

    Professional networking is highly important in the project management job market, as it can lead to job opportunities, collaborations, and access to valuable industry contacts.

  23. What are the typical interview questions for project management roles?

    Typical interview questions for project management roles may include inquiries about previous project experiences, problem-solving skills, conflict resolution abilities, and leadership approaches.

  24. How can I stay updated with the latest trends and developments in project management?

    Staying updated with the latest trends and developments in project management can be achieved through attending industry conferences, participating in webinars, reading industry publications, and engaging in professional development activities offered by organizations like the PMI and review articles on the library page.

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Become a machine learning engineer for free

guide to machine learning engineering free - savio education global

Machine learning is a subset of artificial intelligence that focuses on developing algorithms and statistical models that enable computers to learn from data without being explicitly programmed. The goal of the machine learning engineer is to create intelligent systems that can make predictions, recognize patterns, and make decisions based on data.

It is important career-wise to become an expert in machine learning because it is a rapidly growing field with high demand for skilled professionals. Companies across industries are using machine learning to develop new products, optimize processes, and improve customer experience. As a result, there are many opportunities for those with expertise in this area to work on interesting and challenging projects and earn competitive salaries. Additionally, machine learning has the potential to transform industries and solve some of the world’s most pressing problems, making it an exciting and rewarding field to be a part of.

In this article we offer you a clear guide to become a machine learning engineer on your own, with additional resources.

Typical job description of a machine learning engineer

A typical job description of a machine learning engineer may include responsibilities like:

  • Develop and implement machine learning algorithms and models
  • Design and implement data processing systems and pipelines
  • Collaborate with cross-functional teams to develop and implement machine learning solutions
  • Build and deploy machine learning models into production environments
  • Perform exploratory data analysis and model selection
  • Evaluate and improve the performance of machine learning models
  • Stay up-to-date with the latest advancements in machine learning and related technologies

Academic requirements may include:

  • Bachelor’s or Master’s degree in Computer Science, Statistics, or related field
  • Experience with machine learning algorithms and techniques (such as deep learning, supervised and unsupervised learning, and reinforcement learning)
  • Proficiency in programming languages such as Python, R, or Java
  • Experience with big data technologies such as Hadoop, Spark
  • Strong analytical and problem-solving skills
  • Excellent communication and collaboration skills
  • Ability to work in a fast-paced, dynamic environment

Preferred qualifications may include responsibilities around software development and / or data engineering:

  • Experience with natural language processing (NLP) and computer vision
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud
  • Knowledge of software engineering best practices such as Agile development and DevOps

Recipe to become a machine learning engineer

Take the following steps to realize your career as a machine learning engineer:

  1. Learn the basics of programming: It’s important to have a solid foundation in programming languages such as Python, Java, or C++.
  2. Develop a strong foundation in math and statistics: Understanding calculus, linear algebra, and statistics will help you in developing a deep understanding of machine learning algorithms.
  3. Learn machine learning fundamentals: Start with supervised and unsupervised learning techniques and then move to advanced techniques like deep learning, natural language processing, and computer vision.
    guide to machine learning engineering free - savio education global
  4. Work on projects: Work on projects and build a portfolio. This will demonstrate your skills to potential employers and help you stand out. Practice projects we’ve listed out here: Popular Sectors for the Application of Machine Learning: Projects, examples and datasets – Savio Education Global (savioglobal.com)
  5. Participate in online communities: Join online communities such as Kaggle, GitHub, and Stack Overflow to learn from experts, connect with like-minded individuals, and work on real-world problems.
  6. Gain experience: Consider gaining experience through our pioneering machine learning engineer work experience simulations or applying for internships / entry-level positions to gain practical experience and learn from experienced professionals.
  7. Keep learning: Stay updated with the latest research and advancements in the field by reading research papers, attending conferences, and taking courses.

Paid options:

  1. Obtain relevant education: Consider earning a certification in machine learning, or a related field.
  2. Attend conferences and workshops: Attend conferences and workshops to learn about the latest trends and techniques in the field like Google Developer Events.

Skills needed to become a machine learning engineer

To be and succeed as a machine learning engineer, you will need to sharpen your skills around:

  1. Programming: Proficiency in at least one programming language such as Python, R, or Java is necessary. You should be able to write clean, efficient, and well-documented code.
  2. Classical machine learning: Knowledge of machine learning algorithms, data preprocessing, feature engineering, model selection, and evaluation is essential.
  3. Statistics and probability: You should have a strong understanding of probability theory, statistical inference, and regression analysis.
  4. Deep learning: Familiarity with deep learning frameworks like PyTorch, TensorFlow or Keras is important for developing and deploying deep learning models.
  5. ML design patterns: Familiarity with common design patterns like ensembling and transfer learning is much needed in today machine learning landscape.
  6. Problem-solving and critical thinking: Machine learning engineers should be able to think critically and solve complex problems.
  7. Communication and collaboration: Good communication skills are important for working with cross-functional teams and stakeholders.
  8. Continuous learning: The field of machine learning is constantly evolving, and it’s important to stay up-to-date with the latest advancements and techniques.

Gain all the skills you need in our machine learning work experience program along with demonstrable experience and stellar portfolio of your work.

You will learn:

  • Acquire data from file and API data sources
  • Perform exploratory data analysis and visualization
  • Create and setup data processing pipelines
  • Understand and select appropriate machine learning models for different business situations
  • Train machine learning models and measure model performance
  • Optimize machine learning models to deliver the best performance
  • Train unsupervised learning models
  • Train deep learning models
  • Create multiple machine learning apps!
  • Use multiple deployment strategies to serve these machine learning models in the cloud
  • Bonus: perform ML engineering with Google Cloud Platform (GCP) Vertex AI and Cloud Run!
  • Perform advanced natural language processing and understanding
  • Utilize large language (LLM) generative AI models: text to text and text to image
  • Perform computer vision tasks like object recognition

Frequently Asked Questions about Machine Learning Engineering

  1. What is machine learning?

    Machine learning is a subset of artificial intelligence that focuses on developing algorithms and statistical models that enable computers to learn from data without being explicitly programmed.

  2. How long does it take to become a machine learning engineer?

    Becoming a machine learning engineer can take anywhere between 6 months to a year depending on your ability to devote consistent learning hours, guidance and mentoring that you receive and the tools you learn.

  3. Is it difficult to become a machine learning engineer?

    Yes, becoming a machine learning engineer requires knowledge and skills in statistical learning algorithms, computer programming, data management, API development, and cloud / software hosting infrastructure management. While not impossible to master, the learning curve to becoming a machine learning engineer is quite steep.

  4. How do I get into machine learning in the UK?

    Get into machine learning by mastering statistical learning algorithms, computer programming, data management, API development, and cloud / software hosting infrastructure management. Upskilling in these areas will offer you ample opportunities to get into machine learning as a data scientist, or a machine learning engineer. Become a Certified Machine Learning Engineer with Experience.

  5. How to become a machine learning engineer in 6 months?

    Become a machine learning engineer by mastering statistical learning algorithms, computer programming, data management, API development, and cloud / software hosting infrastructure management. Upskilling in these areas will offer you ample opportunities to get into machine learning as a data scientist, or a machine learning engineer. Become a Certified Machine Learning Engineer with Experience.

  6. Why is there a craze to become an expert in machine learning?

    Machine learning is a rapidly growing field with high demand for skilled professionals. It has the potential to transform industries and solve some of the world's most pressing problems. It also offers interesting and challenging projects and competitive salaries.

  7. What is the typical job description of a machine learning engineer?

    A machine learning engineer is responsible for developing and implementing machine learning algorithms and models, designing data processing systems and pipelines, collaborating with cross-functional teams to develop and implement machine learning solutions, building and deploying machine learning models into production environments, performing exploratory data analysis and model selection, evaluating and improving the performance of machine learning models, staying up-to-date with the latest advancements in machine learning and related technologies.

  8. What are the academic requirements for a machine learning engineer?

    A bachelor's or master's degree in Computer Science, Statistics, or a related field is usually required. Experience with machine learning algorithms and techniques, proficiency in programming languages such as Python, R, or Java, and experience with big data technologies such as Hadoop, Spark are also sometime required. Strong analytical and problem-solving skills, excellent communication and collaboration skills, and the ability to work in a fast-paced, dynamic environment are also essential.

  9. How can I become a machine learning engineer?

    To become a machine learning engineer, you can start by learning the basics of programming, developing a strong foundation in math and statistics, learning machine learning fundamentals, working on projects, participating in online communities, gaining experience, and staying updated with the latest research and advancements in the field. You can also consider obtaining relevant education, attending conferences and workshops, and enrolling in the machine learning work experience program.

  10. What skills do I need to become a machine learning engineer?

    You will need to sharpen your skills around programming, classical machine learning, statistics and probability, deep learning, ML design patterns, problem-solving and critical thinking, communication and collaboration, and continuous learning. Our machine learning work experience program will provide you with all the skills you need, along with demonstrable experience and a stellar portfolio of your work.

  11. What are some common applications of machine learning?

    Machine learning is used in a variety of industries and applications, including:
    Healthcare: for predicting diseases and personalized treatment plans
    Finance: for fraud detection and risk assessment
    Retail: for personalized marketing and product recommendations
    Manufacturing: for predictive maintenance and quality control
    Transportation: for optimizing logistics and route planning
    Natural language processing: for chatbots and virtual assistants

  12. What are some common challenges faced by machine learning engineers?

    Some common challenges faced by machine learning engineers include:
    Data quality and availability: getting access to high-quality, relevant data can be a challenge
    Overfitting: building models that perform well on training data but not on new, unseen data
    Interpretability: understanding why a model makes certain decisions can be difficult, especially with complex models like deep neural networks
    Scalability: building models that can handle large amounts of data and scale to production environments can be challenging
    Ethical considerations: ensuring that machine learning models are fair, unbiased, and respect privacy and security concerns

  13. What are some popular machine learning libraries and frameworks?

    There are many popular machine learning libraries and frameworks available, including:
    Scikit-learn: a library for classical machine learning in Python
    TensorFlow: an open-source framework for building and deploying deep learning models
    PyTorch: a popular deep learning framework developed by Facebook
    Keras: a high-level deep learning API that runs on top of TensorFlow and Theano
    XGBoost: a library for gradient boosting algorithms
    Apache Spark MLlib: a distributed machine learning library for big data processing

  14. What is the difference between supervised and unsupervised learning?

    Supervised learning is a type of machine learning where the model is trained on labeled data, meaning the target variable is known. The goal is to learn a function that maps inputs to outputs, such as predicting the price of a house based on its features.
    Unsupervised learning, on the other hand, is a type of machine learning where the model is trained on unlabeled data, meaning the target variable is unknown. The goal is to learn the underlying structure of the data, such as identifying clusters of similar data points or finding patterns in the data.

  15. What is deep learning?

    Deep learning is a subfield of machine learning that uses artificial neural networks, which are inspired by the structure and function of the human brain. Deep learning models are capable of learning from large amounts of data and can be used to solve complex problems such as image and speech recognition, natural language processing, and autonomous driving.

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Decision Tree Algorithm in Machine Learning: Concepts, Techniques, and Python Scikit Learn Example

decision tree algorithm concepts using scikit-learn in python

Machine learning is a subfield of artificial intelligence that involves the development of algorithms that can learn from data and make predictions or decisions based on patterns learned from the data. Decision trees are one of the most widely used and interpretable machine learning algorithms that can be used for both classification and regression tasks. They are particularly popular in fields such as finance, healthcare, marketing, and customer analytics due to their ability to provide understandable and transparent models.

In this article, we will provide a comprehensive overview of decision trees, covering their concepts, techniques, and practical implementation using Python. We will start by explaining the basic concepts of decision trees, including tree structure, node types, and decision rules. We will then delve into the techniques for constructing decision trees, such as entropy, information gain, and Gini impurity, as well as tree pruning methods for improving model performance. Next, we will discuss feature selection techniques in decision trees, including splitting rules, attribute selection measures, and handling missing values. Finally, we will explore methods for interpreting decision tree models, including model visualization, feature importance analysis, and model explanation.

Important decision tree concepts

Decision trees are tree-like structures that represent decision-making processes or decisions based on the input features. They consist of nodes, edges, and leaves, where nodes represent decision points, edges represent decisions or outcomes, and leaves represent the final prediction or decision. Each node in a decision tree corresponds to a feature or attribute, and the tree is constructed recursively by splitting the data based on the values of the features until a decision or prediction is reached.

Elements of a Decision Tree Algorithm
Elements of a Decision Tree

There are several important concepts to understand in decision trees:

  1. Root Node: The topmost node in a decision tree, also known as the root node, represents the feature that provides the best split of the data based on a selected splitting criterion.
  2. Internal Nodes: Internal nodes in a decision tree represent decision points where the data is split into different branches based on the feature values. Internal nodes contain decision rules that determine the splitting criterion and the branching direction.
  3. Leaf Nodes: Leaf nodes in a decision tree represent the final decision or prediction. They do not have any outgoing edges and provide the output or prediction for the input data based on the majority class or mean/median value, depending on whether it’s a classification or regression problem.
  4. Decision Rules: Decision rules in a decision tree are determined based on the selected splitting criterion, which measures the impurity or randomness of the data. The decision rule at each node determines the feature value that is used to split the data into different branches.
  5. Impurity Measures: Impurity measures are used to determine the splitting criterion in decision trees. Common impurity measures include entropy, information gain, and Gini impurity. These measures quantify the randomness or impurity of the data at each node, and the split that minimizes the impurity is selected as the splitting criterion.

Become a Machine Learning Engineer with Experience and implement decision trees in production environments

Decision Tree Construction Techniques

The process of constructing a decision tree involves recursively splitting the data based on the values of the features until a stopping criterion is met. There are several techniques for constructing decision trees, including entropy, information gain, and Gini impurity.

Entropy

Entropy is a measure of the randomness or impurity of the data at a node in a decision tree. It is defined as the sum of the negative logarithm of the probabilities of all classes in the data, multiplied by their probabilities. The formula for entropy is given as:

Entropy = – Σ p(i) * log2(p(i))

where p(i) is the probability of class i in the data at a node. The goal of entropy-based decision tree construction is to minimize the entropy or maximize the information gain at each split, which leads to a more pure and accurate decision tree.

Information Gain

Information gain is another commonly used criterion for decision tree construction. It measures the reduction in entropy or increase in information at a node after a particular split. Information gain is calculated as the difference between the entropy of the parent node and the weighted average of the entropies of the child nodes after the split. The formula for information gain is given as:

Information Gain = Entropy(parent) – Σ (|Sv|/|S|) * Entropy(Sv)

where Sv is the subset of data after the split based on a particular feature value, and |S| and |Sv| are the total number of samples in the parent node and the subset Sv, respectively. The decision rule that leads to the highest information gain is selected as the splitting criterion.

Gini Impurity

Gini impurity is another impurity measure used in decision tree construction. It measures the probability of misclassification of a randomly chosen sample at a node. The formula for Gini impurity is given as:

Gini Impurity = 1 – Σ p(i)^2

where p(i) is the probability of class i in the data at a node. Similar to entropy and information gain, the goal of Gini impurity-based decision tree construction is to minimize the Gini impurity or maximize the Gini gain at each split.

Become a Machine Learning Engineer with Experience and implement decision trees in production environments

Decision Trees in Python Scikit-Learn (sklearn)

Python provides several libraries for implementing decision trees, such as scikit-learn, XGBoost, and LightGBM. Here, we will illustrate an example of decision tree classifier implementation using scikit-learn, one of the most popular machine learning libraries in Python.

Download the dataset here: Iris dataset uci | Kaggle

# Import the required libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load the dataset
data = pd.read_csv('iris.csv')  # Load the iris dataset

# Split the dataset into features and labels
X = data.iloc[:, :-1]  # Features
y = data.iloc[:, -1]  # Labels

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the decision tree classifier
clf = DecisionTreeClassifier()

# Train the decision tree classifier
clf.fit(X_train, y_train)

# Make predictions on the test set
y_pred = clf.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy * 100))

In this example, we load the popular Iris dataset, split it into features (X) and labels (y), and then split it into training and testing sets using the train_test_split function from scikit-learn. We then initialize a decision tree classifier using the DecisionTreeClassifier class from scikit-learn, fit the classifier to the training data using the fit method, and make predictions on the test data using the predict method. Finally, we calculate the accuracy of the decision tree classifier using the accuracy_score function from scikit-learn.

Become a Machine Learning Engineer with Experience and implement decision trees in production environments

Overfitting in Decision Trees and how to prevent overfitting

Overfitting is a common problem in decision trees where the model becomes too complex and captures noise instead of the underlying patterns in the data. As a result, the tree performs well on the training data but poorly on new, unseen data.

To prevent overfitting in decision trees, we can use the following techniques:

Use more data to prevent overfitting

Overfitting can occur when a model is trained on a limited amount of data, causing it to capture noise rather than the underlying patterns. Collecting more data can help the model generalize better, reducing the likelihood of overfitting.

  • Collect more data from various sources
  • Use data augmentation techniques to create synthetic data

Set a minimum number of samples for each leaf node

A leaf node is a terminal node in a decision tree that contains the final classification decision. Setting a minimum number of samples for each leaf node can help prevent the model from splitting the data too finely, which can lead to overfitting.

from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(min_samples_leaf=5)

Prune and visualize the decision tree

Decision trees are prone to overfitting, which means they can become too complex and fit the training data too closely, resulting in poor generalization performance on unseen data. Pruning is a technique used to prevent overfitting by removing unnecessary branches or nodes from a decision tree.

Pre-pruning

Pre-pruning is a pruning technique that involves stopping the tree construction process before it reaches its maximum depth or minimum number of samples per leaf. This prevents the tree from becoming too deep or too complex, and helps in creating a simpler and more interpretable decision tree. Pre-pruning can be done by setting a maximum depth for the tree, a minimum number of samples per leaf, or a maximum number of leaf nodes.

from sklearn.tree import DecisionTreeClassifier

# Set the maximum depth for the tree
max_depth = 5

# Set the minimum number of samples per leaf
min_samples_leaf = 10

# Create a decision tree classifier with pre-pruning
clf = DecisionTreeClassifier(max_depth=max_depth, min_samples_leaf=min_samples_leaf)

# Fit the model on the training data
clf.fit(X_train, y_train)

# Evaluate the model on the test data
y_pred = clf.predict(X_test)

Post-pruning

Post-pruning is a pruning technique that involves constructing the decision tree to its maximum depth or allowing it to overfit the training data, and then pruning back the unnecessary branches or nodes. This is done by evaluating the performance of the tree on a validation set or using a pruning criterion such as cost-complexity pruning. Cost-complexity pruning involves calculating the cost of adding a new node or branch to the tree, and pruning back the nodes or branches that do not improve the performance significantly.

from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_text

# Create a decision tree classifier without pruning
clf = DecisionTreeClassifier()

# Fit the model on the training data
clf.fit(X_train, y_train)

# Evaluate the model on the validation data. This is the baseline score
score = clf.score(X_val, y_val)

# Print the decision tree before pruning
print(export_text(clf))

# Prune the decision tree using cost-complexity pruning
ccp_alphas = clf.cost_complexity_pruning_path(X_train, y_train).ccp_alphas
for ccp_alpha in ccp_alphas:
    pruned_clf = DecisionTreeClassifier(ccp_alpha=ccp_alpha)
    pruned_clf.fit(X_train, y_train)
    pruned_score = pruned_clf.score(X_val, y_val)
    if pruned_score > score:
        score = pruned_score
        clf = pruned_clf

# Print the decision tree after pruning
print(export_text(clf))

Use cross-validation to evaluate model performance

Cross-validation is a technique for evaluating the performance of a model by training and testing it on different subsets of the data. This can help prevent overfitting by testing the model’s ability to generalize to new data.

In this example we use cross_val_score from scikit liearn.

from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
scores = cross_val_score(dtc, X, y, cv=10)
print("Cross-validation scores: {}".format(scores))

Limit the depth of the tree

Limiting the depth of the tree can prevent the model from becoming too complex and overfitting to the training data. This can be done by setting a maximum depth or a minimum number of samples required for a node to be split.

from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(max_depth=5)

Use ensemble methods like random forests or boosting

Ensemble methods combine multiple decision trees to improve the model’s accuracy and prevent overfitting. Random forests create a collection of decision trees by randomly sampling the data and features for each tree, while boosting iteratively trains decision trees on the residual errors of the previous trees.

Here is an example of using the GradientBoostingClassifier from scikit learn.

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Generate synthetic dataset
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_classes=2, random_state=42)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# Fit gradient boosting classifier to training data
gb = GradientBoostingClassifier(n_estimators=100, max_depth=5, learning_rate=0.1, random_state=42)
gb.fit(X_train, y_train)

# Evaluate performance on test data
print("Accuracy: {:.2f}".format(gb.score(X_test, y_test)))

Feature selection and engineering to reduce noise in the data

Feature selection involves selecting the most relevant features for the model, while feature engineering involves creating new features or transforming existing ones to better capture the underlying patterns in the data. This can help reduce noise in the data and prevent the model from overfitting to irrelevant or noisy features.

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
X_new = SelectKBest(chi2, k=10).fit_transform(X, y)

Feature Selection Techniques in Decision Trees

Feature selection is an important step in machine learning to identify the most relevant features or attributes that contribute the most to the prediction or decision-making process. In decision trees, feature selection is typically done during the tree construction process when determining the splitting criterion. There are several techniques for feature selection in decision trees:

Feature Importance

Decision trees can also provide a measure of feature importance, which indicates the relative importance of each feature in the decision-making process. Feature importance is calculated based on the number of times a feature is used for splitting across all nodes in the tree and the improvement in the impurity measure (such as entropy or Gini impurity) achieved by each split. Features with higher importance values are considered more relevant and contribute more to the decision-making process.

Recursive Feature Elimination

Recursive feature elimination is a technique that recursively removes less important features from the decision tree based on their importance values. The decision tree is repeatedly trained with the remaining features, and the feature with the lowest importance value is removed at each iteration. This process is repeated until a desired number of features or a desired level of feature importance is achieved.

Become a Machine Learning Engineer with Experience and implement decision trees in production environments

Sources

  1. Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106. Link: https://link.springer.com/article/10.1007/BF00116251
  2. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media. Link: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
  3. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Link: https://www.springer.com/gp/book/9780387310732
  4. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830. Link: https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
  5. Kohavi, R., & Quinlan, J. R. (2002). Data mining tasks and methods: Classification: decision-tree discovery. Handbook of data mining and knowledge discovery, 267-276. Link: https://dl.acm.org/doi/abs/10.1007/978-1-4615-0943-3_19
  6. W. Loh, (2014). Fifty Years of Classification and Regression Trees 1. Link: https://www.semanticscholar.org/paper/Fifty-Years-of-Classification-and-Regression-Trees-Loh/f1c3683cacc3dc7898f3603753af87565f8ad677?p2df

Frequently asked questions about decision trees in machine learning

  1. What is a decision tree in machine learning?

    A decision tree is a graphical representation of a decision-making process or decision rules, where each internal node represents a decision based on a feature or attribute, and each leaf node represents an outcome or decision class.

  2. What are the advantages of using decision trees?

    Decision trees are easy to understand and interpret, can handle both categorical and numerical data, require minimal data preparation, can handle missing values, and are capable of handling both classification and regression tasks.

  3. What are the common splitting criteria used in decision tree algorithms?

    Some common splitting criteria used in decision tree algorithms include Gini impurity, entropy, and information gain, which are used to determine the best attribute for splitting the data at each node.

  4. How can decision trees be used for feature selection?

    Decision trees can be used for feature selection by analyzing the feature importance or feature ranking obtained from the decision tree, which can help identify the most important features for making accurate predictions.

  5. What are the methods to avoid overfitting in decision trees?

    Some methods to avoid overfitting in decision trees include pruning techniques such as pre-pruning (e.g., limiting the depth of the tree) and post-pruning (e.g., pruning the tree after it is fully grown and then removing less important nodes), and using ensemble methods such as random forests and boosting.

  6. What are the limitations of decision trees?

    Some limitations of decision trees include their susceptibility to overfitting, sensitivity to small changes in the data, lack of robustness to noise and outliers, and difficulty in handling continuous or large-scale datasets.

  7. What are the common applications of decision trees in real-world problems?

    Decision trees are commonly used in various real-world problems, including classification tasks such as spam detection, medical diagnosis, and credit risk assessment, as well as regression tasks such as housing price prediction, demand forecasting, and customer churn prediction.

  8. Can decision trees handle missing values in the data?

    Yes, decision trees can handle missing values in the data by using techniques such as surrogate splitting, where an alternative splitting rule is used when the value of a certain attribute is missing for a data point.

  9. Can decision trees be used for multi-class classification problems?

    Yes, decision trees can be used for multi-class classification problems by extending the binary splitting criteria to handle multiple classes, such as one-vs-rest or one-vs-one approaches.

  10. How can I implement decision trees in Python?

    Decision trees can be implemented in Python using popular machine learning libraries such as scikit-learn, TensorFlow, and PyTorch, which provide built-in functions and classes for training and evaluating decision tree models.

  11. Is decision tree a supervised or unsupervised algorithm?

    A decision tree is a supervised learning algorithm that is used for classification and regression modeling.

  12. What is pruning in decision trees?

    Pruning is a technique used in decision tree algorithms to reduce the size of the tree by removing nodes or branches that do not contribute significantly to the accuracy of the model. This helps to avoid overfitting and improve the generalization performance of the model.

  13. What are the benefits of pruning?

    Pruning helps to simplify and interpret the decision tree model by reducing its size and complexity. It also improves the generalization performance of the model by reducing overfitting and increasing accuracy on new, unseen data.

  14. What are the different types of pruning for decision trees?

    There are two main types of pruning: pre-pruning and post-pruning. Pre-pruning involves stopping the tree construction process before it reaches its maximum depth or minimum number of samples per leaf, while post-pruning involves constructing the decision tree to its maximum depth and then pruning back unnecessary branches or nodes.

  15. How is pruning performed in decision trees?

    Pruning can be performed by setting a maximum depth for the tree, a minimum number of samples per leaf, or a maximum number of leaf nodes for pre-pruning. For post-pruning, the model is trained on the training data, evaluated on a validation set, and then unnecessary branches or nodes are pruned based on a pruning criterion such as cost-complexity pruning.

  16. When should decision trees be pruned?

    Pruning should be used when the decision tree model is too complex or overfits the training data. It should also be used when the size of the decision tree becomes impractical for interpretation or implementation.

  17. Are there any drawbacks to pruning?

    One potential drawback of pruning is that it can result in a loss of information or accuracy if too many nodes or branches are pruned. Additionally, pruning can be computationally expensive, especially for large datasets or complex decision trees.

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Agile User Stories: 40+ user story examples, formats and templates for product triumph!

user story in agile software development

A user story is a description of a feature / functionality described from the perspective of the end-user. User stories describe the users’ expectations of the system. User stories are described in Agile projects, and are organized in a product backlog, which is an ordered list of product functions. It is a concise, simple description of a feature or functionality that is written from the perspective of an end-user or customer. User stories are commonly used in agile software development as a way to capture requirements and guide the development process.

A typical user story includes three main components:

  • the user,
  • the action, and
  • the benefit.

A typical user story is written like this:

As a <type of user>, I want to <achieve / perform some task> so that I can <get some value>.

Example of a user story for an e-commerce website might look like this:

As a customer, I want to add products to the cart so that I can checkout.

Another example:
As a customer, I want to be able to view my order history, so I can track my purchases and see when they will be delivered.

In this example, the user is the customer, the action is to view the order history, and the benefit is the ability to track purchases and delivery dates. User stories are usually short and simple, and they are written in a way that is easy for both developers and non-technical stakeholders to understand. They are designed to be flexible and open to negotiation, allowing the development team and stakeholders to collaborate and refine the requirements over time as new information becomes available.

When are user stories created?

User stories are typically created during the planning and requirements gathering phase of a project, which is usually done at the beginning of each development cycle in agile software development. This process involves working closely with stakeholders, including end-users, customers, and product owners, to identify the key features and functionalities that are needed in the software.

During this process, user stories are used as a way to capture and communicate requirements in a simple, easy-to-understand format. The development team works with stakeholders to identify the key user roles and personas, and to define the actions and benefits that are needed to meet their needs.

Once the initial set of user stories has been created, they are typically prioritized based on their value to the end-user and their impact on the overall project goals. This allows the development team to focus on the most important stories first, and to deliver incremental improvements to the software in each development cycle.

Throughout the development process, user stories may be refined and updated as new information becomes available or requirements change. This allows the development team to remain flexible and responsive to changing needs, while still delivering software that meets the needs of the end-users. Business analysts are usually the professionals who create user stories and capture requirements.

Learn to create brilliant user stories and become a business analyst with work experience!

Analytics User Stories Examples – Agile requirements template and format

The following user stories are examples in the analytics domain. These include those for business intelligence like charts, and machine learning like sentiment analysis.

  1. As a strategy consultant, I would like to review KPIs related to my domain, because that would help me understand the status of the business.
  2. As a business manager, I would like to review progress over a period of time as a line chart, so that I can make necessary corrective adjustments.
  3. As a SEO copywriter, I would like to generate positive-negative-neutral sentiments of my copy, so that I can write better effective and catchy articles.
  4. As the president of the department, I would like to review charts of income and expenses, because I can determine the profitability of the department (and the security of my job?)
  5. As the chief investment officer, I would like to have an aggregation of all spends and ROI, so that I can determine investment areas of greater return on investment.

Business intelligence user stories examples – Agile requirements template and format

Here are some examples of business intelligence user stories:

  1. As a marketing manager, I want to view real-time dashboards of customer behavior and engagement, so I can optimize marketing campaigns and improve customer retention.
  2. As a sales representative, I want to access detailed reports on customer interactions and sales performance, so I can identify sales trends and opportunities to improve performance.
  3. As a finance analyst, I want to generate ad-hoc reports on financial metrics and KPIs, so I can analyze financial performance and identify areas for cost reduction and optimization.
  4. As an operations manager, I want to monitor key performance indicators for operational efficiency, such as cycle time, throughput, and inventory levels, so I can identify opportunities to improve operational performance.
  5. As a product manager, I want to track customer feedback and sentiment data, so I can identify customer needs and preferences and make data-driven decisions about product development and marketing.

E-commerce user stories examples – Agile requirements template and format

The following user stories capture various aspects of an e-commerce website from the perspective of the end-users (customers) and the store owner. They focus on the functionalities and features that are important for a seamless and convenient online shopping experience, while also addressing the needs of the business owner for effective store management and data analysis.

  1. As a customer, I want to be able to search for products by category or keyword, so I can easily find and purchase the items I am interested in.
  2. As a customer, I want to be able to add products to my shopping cart, view the contents of my cart, and proceed to checkout, so I can complete my purchase quickly and easily.
  3. As a customer, I want to be able to create an account, save my payment information, and view my order history, so I can have a personalized shopping experience and easily track my purchases.
  4. As a customer, I want to be able to view product details, including images, descriptions, prices, and customer reviews, so I can make informed purchasing decisions.
  5. As a customer, I want to be able to apply discount codes, promotions, and gift cards to my purchase, so I can take advantage of special offers and discounts.
  6. As a customer, I want to receive email notifications about my order status, including order confirmation, shipping updates, and delivery notifications, so I can stay informed about my purchases.
  7. As a customer, I want to be able to provide feedback and reviews on products, so I can share my experiences and help other customers make informed purchasing decisions.
  8. As a store owner, I want to be able to manage my product inventory, update product details, and track sales and revenue, so I can effectively manage my online store and make data-driven decisions about my business.

Create ecommerce recommendation engines as a machine learning engineer to drive greater sales through intelligent and timely suggestions

Media advertising technology user stories examples – Agile requirements template and format

These user stories highlight the key functionalities and features that are important for a media ad tech tool, covering the needs of different user roles such as media buyers, marketing managers, creative designers, publishers, data analysts, account managers, advertisers, and campaign optimizers. These stories focus on the capabilities that enable effective campaign management, performance tracking, ad creation, targeting, audience management, and optimization, among others.

  1. As a media buyer, I want to be able to create and manage advertising campaigns, including setting campaign budgets, targeting criteria, and ad creatives, so I can effectively reach my target audience and achieve my marketing goals.
  2. As a marketing manager, I want to be able to track and analyze the performance of my advertising campaigns in real-time, including impressions, clicks, conversions, and return on investment (ROI), so I can optimize my ad spend and make data-driven decisions to improve campaign performance.
  3. As a creative designer, I want to be able to upload and manage ad creatives, including images, videos, and ad copy, in various formats and sizes, so I can easily create and update ads for different platforms and placements.
  4. As a publisher, I want to be able to monetize my website or app by displaying ads from different advertisers, and to have control over the types of ads that are displayed, the frequency, and the placement, so I can generate revenue and provide a positive user experience.
  5. As a data analyst, I want to be able to access and analyze ad performance data, including impressions, clicks, conversions, and audience demographics, in a visual and customizable way, so I can generate insights and reports to inform marketing strategies and optimizations.
  6. As an account manager, I want to be able to manage multiple client accounts within the ad tech tool, including creating and managing campaigns, setting budgets, and providing performance reports, so I can effectively serve my clients and track their advertising performance.
  7. As an advertiser, I want to be able to define and manage custom audiences, including demographic, geographic, and behavioral criteria, so I can target my ads to the most relevant audience and maximize my ad effectiveness.
  8. As a campaign optimizer, I want to be able to use machine learning algorithms and predictive analytics to automatically optimize my advertising campaigns based on performance data, so I can improve campaign efficiency and achieve better results over time.

Optimize campaign performance as a machine learning engineer and generate create returns on ad spends (ROAS).

Customer Relationship Management (CRM) user stories examples – Agile requirements template and format

These user stories cover various user roles within a CRM tool, including sales representatives, sales managers, customer service representatives, marketing managers, executives, product managers, system administrators, and mobile salespeople. They address the functionalities and features that are important for managing customer relationships, sales activities, marketing campaigns, customer feedback, and overall business performance.

  1. As a sales representative, I want to be able to track my leads, opportunities, and deals in a centralized CRM system, so I can easily manage my sales pipeline, prioritize my tasks, and close deals effectively.
  2. As a sales manager, I want to be able to monitor the performance of my sales team, including their sales activities, deal progress, and revenue targets, so I can provide coaching, feedback, and support to improve their performance and achieve team goals.
  3. As a customer service representative, I want to be able to access customer information and interaction history in the CRM system, so I can provide personalized and efficient support, resolve issues, and deliver a positive customer experience.
  4. As a marketing manager, I want to be able to segment and target my customers and prospects in the CRM system, based on criteria such as demographics, behaviors, and engagement levels, so I can deliver relevant and personalized marketing campaigns to drive customer engagement and retention.
  5. As an executive, I want to be able to access high-level dashboards and reports in the CRM system, so I can monitor overall sales performance, customer acquisition, retention, and lifetime value, and make data-driven decisions to drive business growth.
  6. As a product manager, I want to be able to gather and analyze customer feedback and product usage data in the CRM system, so I can identify customer needs, preferences, and pain points, and incorporate them into product development and improvement strategies.
  7. As a system administrator, I want to be able to configure and customize the CRM system to match our organization’s sales, marketing, and customer service processes, so I can ensure that the CRM tool is aligned with our specific business requirements and workflows.
  8. As a mobile salesperson, I want to be able to access and update customer and prospect information in the CRM system on my mobile device, so I can manage my sales activities and update customer interactions on the go.

Analyze customer needs and create stunning products as a business analyst / product manager

Enterprise Resource Planning (ERP) tool user stories examples – Agile requirements template and format

These user stories cover different functional areas within an ERP tool, including procurement, production planning, finance, human resources, sales, warehouse management, business analysis, and system administration. They highlight the key functionalities and features that are important for managing various aspects of an organization’s operations, such as procurement, production, finance, human resources, sales, inventory, and data analysis.

  1. As a procurement manager, I want to be able to create and manage purchase orders in the ERP system, including selecting vendors, defining quantities, and tracking order status, so I can effectively manage the procurement process and ensure timely delivery of goods and services.
  2. As a production planner, I want to be able to create and manage production schedules in the ERP system, including defining production orders, allocating resources, and tracking progress, so I can optimize production capacity and meet customer demand.
  3. As a finance manager, I want to be able to manage financial transactions and records in the ERP system, including recording invoices, payments, and expenses, reconciling accounts, and generating financial reports, so I can accurately track and report on the financial health of the organization.
  4. As a human resources manager, I want to be able to manage employee information, including hiring, onboarding, performance evaluations, and benefits administration, in the ERP system, so I can effectively manage the workforce and ensure compliance with company policies and regulations.
  5. As a salesperson, I want to be able to create and manage sales orders, track customer orders, and view inventory availability in the ERP system, so I can efficiently process customer orders, manage order fulfillment, and provide accurate order status updates.
  6. As a warehouse manager, I want to be able to manage inventory levels, including receiving, stocking, and picking inventory items, in the ERP system, so I can maintain accurate inventory records, optimize warehouse space, and ensure timely order fulfillment.
  7. As a business analyst, I want to be able to access and analyze data from various modules in the ERP system, including sales, inventory, procurement, and finance, so I can generate insights, trends, and reports to inform decision-making and strategic planning.
  8. As an IT administrator, I want to be able to configure and customize the ERP system, including setting up user permissions, defining workflows, and integrating with other systems, so I can ensure that the ERP tool is aligned with our organization’s business processes and requirements.

Create your own user story online

Savio Education Global User Story Generator · Streamlit (user-story-generator.streamlit.app)

INVEST in User Stories

Finally, all user stories must fit the INVEST quality model:

  •         I – Independent
  •         N – Negotiable
  •         V – Valuable
  •         E – Estimable
  •         S – Small
  •         T – Testable
  1. Independent. This means that you can schedule and implement each user story separately. This is very helpful if you implement continuous integration processes.
  2. Negotiable. This means that all parties agree to prioritize negotiations over specification. This also means that details will be created constantly during development.
  3. Valuable. A story must be valuable to the customer.  You should ask yourself from the customer’s perspective “why” you need to implement a given feature.
  4. Estimable. A quality user story can be estimated. This will help a team schedule and prioritize the implementation. The bigger the story is, the harder it is to estimate it.
  5. Small. Good user stories tend to be small enough to plan for short production releases. Small stories allow for more specific estimates.
  6. Testable. If a story can be tested, it’s clear enough and good enough. Tested stories mean that requirements are done and ready for use.

Best practices to write good user stories

Consider the following best practices when writing user stories for agile requirements:

  1. Involve stakeholders: Involve stakeholders such as the product owner, end-users, and development team members in the process of creating user stories. This helps ensure that everyone has a shared understanding of the goals and requirements.
  2. Focus on end-users: User stories should focus on the needs and goals of the end-users. It’s important to avoid writing stories that are too technical or feature-focused.
  3. Use a consistent format: User stories should be written in a consistent format that includes the user, action, and benefit. This helps to ensure clarity and consistency across the stories.
  4. Keep stories small: Keep user stories small and focused on a specific goal or outcome. This makes it easier to estimate, prioritize, and complete the stories within a single iteration.
  5. Prioritize stories: Prioritize user stories based on their value to the end-user and their impact on the overall project goals. This helps to ensure that the most important stories are completed first.
  6. Make stories testable: User stories should include clear acceptance criteria that can be used to verify that the story has been completed successfully. This helps to ensure that the resulting software meets the needs of the end-users.
  7. Refine stories over time: User stories should be refined and updated over time as new information becomes available or requirements change. This helps to ensure that the stories remain relevant and useful throughout the development process.

By following these best practices, development teams can create effective user stories that help to guide the development process and ensure that the resulting software meets the needs of the end-users.

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Prioritizing Agile User Stories

User stories are typically prioritized based on their value to the end-user and their impact on the overall project goals. Here are some common factors that are considered when prioritizing user stories:

  1. User value: User stories that provide the greatest value to the end-users are typically given higher priority. For example, a user story that improves the user experience or solves a critical user problem may be considered more important than a story that adds a new feature.
  2. Business value: User stories that have the greatest impact on the business goals and objectives are typically given higher priority. For example, a user story that increases revenue or reduces costs may be considered more important than a story that provides a minor improvement to the software.
  3. Technical feasibility: User stories that are technically feasible and can be implemented easily are typically given higher priority. For example, a user story that can be completed quickly with minimal effort may be considered more important than a story that requires significant development effort.
  4. Dependencies: User stories that have dependencies on other stories or features may be given higher priority to ensure that they are completed in the appropriate order.
  5. Risks: User stories that address high-risk areas of the project or software may be given higher priority to mitigate potential issues.

The prioritization of user stories is usually done in collaboration with stakeholders, including product owners, end-users, and development team members. By considering these factors and working collaboratively, the team can ensure that they are delivering software that meets the needs of the end-users and achieves the project goals.

User Story – Acceptance Criteria Example and Template

User stories must be accompanied by acceptance criteria.  It is important to have descriptive summaries and detailed acceptance criteria to help the team know when a user story is considered complete or “done.” These are the conditions that the product must satisfy to be accepted by users, stakeholders, or a product owner. Each user story must have at least one acceptance criterion. Effective acceptance criteria are testable, concise, and clearly understood by all stakeholders. They can be written as checklists, plain text, or by using Given/When/Then format.

Example:

Here’s an example of the acceptance criteria checklist for a user story describing a search feature:

  • A search field is available on the top-bar.
  • A search is started when the user clicks Submit.
  • The default placeholder is a grey text Type the name.
  • The placeholder disappears when the user starts typing.
  • The search language is English.
  • The user can type no more than 200 symbols.
  • It doesn’t support special symbols. If the user has typed a special symbol in the search input, it displays the warning message: Search input cannot contain special symbols.
user stories acceptance criteria format
Acceptance Criteria for Scenario Tests

Acceptance Criteria Formatted as Given-When-Then

According to the Agile Alliance, the Given-When-Then format is a template intended to guide the writing of acceptance criteria / tests for a User Story. The template is as follows:

(Given) some context
(When) some action is carried out
(Then) a particular set of observable consequences should obtain
An example:

Given my bank account is in credit, and I made no withdrawals recently,
When I attempt to withdraw an amount less than my card’s limit,
Then the withdrawal should complete without errors or warnings

The usual practice is the have the acceptance criteria written after the requirements have been specified and before development sprint begins. The acceptance criteria are often utilized during the user acceptance testing (UAT) of the product.

What are user story points?

User story points are a unit of measure used in agile software development to estimate the relative effort required to implement a user story. They are assigned to each user story based on the amount of effort and complexity involved in completing it, and help teams to prioritize and plan their work. Points are typically assigned using a scale such as Fibonacci numbers (1, 2, 3, 5, 8, 13, 21, etc.), where each number represents a larger increment of effort than the previous one. The purpose of using story points is to provide a rough, relative estimate of effort, rather than an exact estimate in terms of hours or days.

Fibonacci series used for user stories point estimation
Fibonacci series

User story points are determined through a process called estimation. This is typically done as part of a team-based effort, with representatives from all relevant departments, such as development, testing, and product management.

Estimation is done by comparing each user story to others that have already been completed and assigned points, and by considering various factors that impact the effort required to implement the story, such as complexity, size, and uncertainty. The team then agrees on a point value for each story, usually using the Fibonacci scale. The way to think about estimating these points is similar to the way gap analysis is performed.

It’s important to note that the goal of user story points is to provide a rough, relative estimate of effort. The actual points assigned to each story are less important than the consistency in the way they are assigned and the fact that they allow the team to prioritize and plan their work. Over time, the team will gain a better understanding of what different point values represent and will become more accurate in their estimations.

If you’re using JIRA, then you see these points in the image as follows:

Points in JIRA scrum board for user stories
Points in JIRA scrum board for user stories

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Techniques to estimate points and size user stories

There are several different techniques that can be used to size (estimate the effort required for) user stories in agile software development. Some of the most common techniques include:

  1. Planning Poker: A consensus-based technique where team members hold cards with values from a predetermined scale (such as Fibonacci numbers) and simultaneously reveal their estimates for each story. Discussions ensue until the team reaches a consensus on the story’s point value.
  2. T-Shirt Sizing: A quick and simple technique where team members use descriptive terms such as XS, S, M, L, XL, etc. to size stories, based on their complexity and effort required.
  3. Affinity Mapping: A technique where team members write down their estimates for each story on sticky notes, and then group similar stories together based on their estimates. The resulting clusters of stories can then be assigned point values based on the average of the estimates within each cluster.
  4. Expert Judgment: A technique where an individual with expertise in the relevant domain (e.g. a senior developer) provides estimates for each story based on their experience and knowledge.
  5. Analogous Estimation: A technique where the team estimates the effort required for a new story based on similar stories that have been completed in the past, taking into account any differences or additional complexities.
planning poker cards template for user stories point estimations
Planning poker cards template for user stories point estimations. Get these cards here: redbooth/scrum-poker-cards (github.com)

These are some of the most common techniques used in agile software development to estimate the effort required for user stories. The choice of technique will depend on various factors such as the team’s experience, the size and complexity of the project, and the culture and preferences of the organization.

Steps to measure the team’s velocity with user story estimations

The velocity of an agile team is a measure of the amount of work the team can complete in a given period of time, usually a sprint. The velocity of a team can be determined by tracking the number of points completed in each sprint and taking an average over several sprints.

To determine the team’s velocity, follow these steps:

  1. Assign story points to each user story: Use a sizing estimation technique, such as planning poker or T-shirt sizing, to estimate the effort required to complete each story.
  2. Track completed story points in each sprint: At the end of each sprint, tally the number of points assigned to each story that was completed and accepted by the customer.
  3. Calculate the average velocity: Divide the total number of completed story points by the number of sprints to calculate the average velocity. For example, if a team completed 40 story points in the first sprint and 50 story points in the second sprint, its average velocity would be 45 story points.
  4. Use the velocity to plan future sprints: The team’s velocity can be used to plan future sprints, by taking into account the number of story points the team is capable of completing in a given sprint.

It’s important to note that the velocity of a team can change over time, based on various factors such as changes in team composition, the complexity of the work, and the team’s level of experience. As such, the velocity should be re-evaluated regularly to ensure that it accurately reflects the team’s current capabilities.

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Elements of Agile Requirements

In addition to user stories, there are several other elements of agile requirements that are important to consider when developing software using agile methodologies. Some of these elements include:

  • Epics: These are large-scale user stories that describe a high-level goal or feature. Epics are usually broken down into smaller user stories or tasks that can be completed in shorter iterations.
  • Acceptance criteria: These are the specific conditions or requirements that must be met for a user story to be considered complete. Acceptance criteria are typically defined in collaboration with the product owner and the development team.
  • User personas: These are fictional characters or archetypes that represent the different types of users who will be using the software system. User personas help the development team to understand the needs, goals, and behaviors of the end-users.
  • Backlog: This is a prioritized list of user stories and tasks that need to be completed in the current iteration or sprint. The backlog is continuously updated and reprioritized based on feedback from the product owner, the development team, and other stakeholders.
  • Iterations/sprints: These are short, time-boxed periods (usually 1-4 weeks) during which the development team works on a specific set of user stories and tasks. At the end of each iteration/sprint, the team delivers a working increment of the software system that can be reviewed and tested by stakeholders.

Frequently Asked Questions about Agile User Stories

  1. What are user stories in Scrum?

    A user story in agile scrum is a structure that is used in software development and product management to represent a unit of work. It provides an informal, natural language description of a product feature from the user's perspective and the value to them.

  2. What is in a user story?

    A user story is an informal explanation of a software feature written from the end user's perspective. Its purpose is to articulate how a software feature will provide value to the customer. A user story looks like: “As [a user persona], I want [to perform this action] so that [I can accomplish this goal].”

  3. What is a user story example?

    A user story is a small, self-contained unit of development work designed to accomplish a specific goal within a product. A user story is usually written from the user's perspective and follows the format: “As [a user persona], I want [to perform this action] so that [I can accomplish this goal].”

  4. Who writes a user story in agile?

    The Business Analyst or the Product Owner usually writes User Stories. Most of the times, these are developed by the BA in conjunction and consultation with the development team and other relevant stakeholders.

  5. What is Jira user story?

    A Jira user story helps the development team determine what they're working on, why they're working on it, and what value this work creates for the user. The JIRA user story can contain sub-tasks, the size in terms of story points, the acceptance criteria, the EPIC to which it belongs, and the sprint in which it must be completed.

  6. What is epic and user story?

    User stories are requirements or requests written from the perspective of an end user. Epics are large parts of work that are broken down into a number of smaller tasks (called user stories). Think of Epics as the logical grouping of features and work.

  7. What are the 3 C's of user stories?

    These 3 C's are Cards, Conversation, and Confirmation. These are essential components for writing a good User Story. The Card, Conversation, and Confirmation model was introduced by Ron Jefferies in 2001 for Extreme Programming (XP) and is suitable even today.

  8. What is the format of a user story? Which 3 elements should a user story have?

    The format of a user story includes three elements of the standard user story template: Who wants the functionality? What it is they want? Why do they want it?

  9. What is the template syntax of a user story?

    A user story is usually written from the user's perspective and follows the format: “As [a type of user], I want [to perform an action] so that [I can accomplish this goal].”

  10. How does and epic relate to a user story?

    An epic is a portion of work which is too big to fit into a sprint. This can be a high-level story that is usually split into smaller user stories, each of which can be completed within a sprint. An epic can be considered as a logically grouped collection of user stories.

  11. What are acceptance criteria?

    Acceptance Criteria is defined as a set of conditions that a product must satisfy to be accepted by a user, customer or other stakeholder. It is also understood as a set of standards or requirements a product or project must meet. These criteria are set in advance i.e. before development work begins.

  12. When are acceptance criteria written?

    Acceptance criteria are documented before the development of the user story starts. This way, the team will likely capture all customer needs in advance. It's usually enough to set the acceptance criteria of user stories across the next two sprints in the agile Scrum methodology.

  13. What is INVEST in a user story?

    The acronym INVEST stands for Independent, Negotiable, Valuable, Estimable, Small and Testable. Business analysts should design user stories that exhibit these six attributes.

  14. How do you calculate story points?

    It's the total completed story points divided by the total number of sprints. For example, let's say that your team finishes 50 story points in 2 sprints. Then, their sprint velocity will be (50/2) = 25 points per sprint.

  15. What is the velocity of the team in Agile?

    Velocity ​​in agile terms means the average amount of work a team can complete in one “delivery cycle” – typically a sprint or a release for Scrum teams or a time period such as a Week or a month for Kanban teams. (It is also referred to by many as the Throughput, especially by Kanban teams).

  16. What does team velocity mean?

    According to Scrum, Inc., team velocity is a “measure of the amount of work a team can tackle during a single sprint and is the key metric in Scrum”. When you complete a sprint, you'll total the points for all fully completed user stories and over time find the average number of points you complete per sprint.

  17. How do you calculate your team's velocity?

    Teams calculate velocity at the end of each Sprint. Simply take the number of story points for each completed user story during your Sprint and add them up. Your velocity metric will be the absolute number of story points your team completed.

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Solutions to key challenges in machine learning and data science

Data scientists and machine learning engineers face challenges in machine learning (ML) due to various reasons, such as the complexity of the data, the unavailability of data, the need to balance model performance and interpretability, the difficulty of selecting the right algorithms and hyperparameters, and the need to keep up with the rapidly evolving field of ML.

Dealing with the challenges in ML requires a combination of technical skills, domain expertise, and problem-solving skills, as well as a willingness to learn and experiment with new approaches and techniques.

Data Preparation and Preprocessing

  1. Pre-processing and cleaning of raw data: This involves identifying and removing or correcting errors, inconsistencies, or irrelevant data in the raw data before using it for modeling. This step can include tasks such as removing duplicates, handling missing values, and removing irrelevant columns.
  2. Selecting appropriate features for the model: This involves selecting the subset of features that are most relevant for the model’s performance. This step can involve techniques such as feature selection, dimensionality reduction, and domain expertise.
  3. Handling missing or noisy data: This involves dealing with data points that are missing or noisy, which can negatively impact the performance of the model. Techniques such as imputation, smoothing, and outlier detection can be used to handle missing or noisy data.
  4. Dealing with imbalanced datasets: This involves handling datasets where one class is much more prevalent than the other(s), which can lead to biased models. Techniques such as oversampling, undersampling, and cost-sensitive learning can be used to address this issue.
  5. Handling categorical and ordinal data: This involves dealing with data that is not numerical, such as categorical or ordinal data. Techniques such as one-hot encoding, label encoding, and ordinal encoding can be used to transform this data into a numerical form that can be used in the model.
  6. Dealing with outliers in the data: This involves handling data points that are significantly different from the rest of the data and may be the result of measurement errors or other anomalies. Techniques such as removing outliers, winsorizing, and transformation can be used to address this issue.
  7. Implementing appropriate techniques for feature scaling and normalization: This involves scaling or normalizing the features to ensure that they are on the same scale and have the same variance. Techniques such as min-max scaling, z-score normalization, and robust scaling can be used for this purpose.
  8. Implementing data augmentation techniques for image and text data: This involves generating new data samples from the existing ones to improve the performance of the model. Techniques such as rotation, flipping, and cropping can be used for image data, while techniques such as random insertion and deletion can be used for text data.
  9. Dealing with time-series data: This involves handling data that is ordered in time, such as stock prices or weather data. Techniques such as lagging, differencing, and rolling window analysis can be used for time-series data.
  10. Implementing appropriate techniques for data imputation: This involves filling in missing values in the data using various techniques, such as mean imputation, median imputation, and regression imputation.
  11. Dealing with collinearity in the data: This involves handling features that are highly correlated with each other, which can lead to unstable model estimates. Techniques such as principal component analysis (PCA), ridge regression, and elastic net regularization can be used to handle collinearity.
  12. Implementing appropriate data encoding techniques for categorical data: This involves transforming categorical data into a numerical form that can be used in the model. Techniques such as one-hot encoding, label encoding, and binary encoding can be used for this purpose.
  13. Dealing with biased data or sampling errors: This involves handling datasets that are biased or have sampling errors, which can lead to biased models. Techniques such as stratified sampling, random oversampling, and weighted loss functions can be used to address this issue.

Model Selection and Evaluation

  1. Understanding the underlying mathematical concepts and algorithms used in machine learning: This involves understanding the mathematical and statistical concepts used in machine learning, such as linear algebra, calculus, probability, and optimization.
  2. Determining the optimal model architecture and parameters: This involves choosing the appropriate model architecture and hyperparameters that best fit the data and achieve the desired performance.
  3. Choosing the appropriate evaluation metrics for the model: This involves selecting the appropriate metrics to evaluate the performance of the model, such as accuracy, precision, recall, F1-score, and ROC-AUC.
  4. Overfitting or underfitting of the model: This involves addressing the issue of overfitting, where the model fits too closely to the training data and does not generalize well to new data, or underfitting, where the model is too simple to capture the underlying patterns in the data.
  5. Evaluating the model’s performance on new, unseen data: This involves assessing the performance of the model on data that it has not seen before, to ensure that it generalizes well and does not suffer from overfitting.
  6. Understanding the bias-variance trade-off: This involves understanding the trade-off between bias and variance in the model, where bias refers to the error due to underfitting and variance refers to the error due to overfitting.
  7. Optimizing hyperparameters for the model: This involves tuning the hyperparameters of the model to improve its performance, such as the learning rate, regularization strength, and number of hidden layers.
  8. Choosing the right cross-validation strategy: This involves selecting the appropriate cross-validation technique to assess the performance of the model, such as k-fold cross-validation, stratified cross-validation, or leave-one-out cross-validation.
  9. Applying appropriate techniques for feature scaling and normalization: This involves scaling or normalizing the features to ensure that they are on the same scale and have the same variance, to improve the performance of the model.
  10. Handling the curse of dimensionality: This involves addressing the issue of the curse of dimensionality, where the performance of the model decreases as the number of features or dimensions increases, due to the sparsity of the data.
  11. Understanding the different types of ensembling techniques: This involves understanding the concept of ensembling, where multiple models are combined to improve the performance of the overall model, and the different types of ensembling techniques, such as bagging, boosting, and stacking.
  12. Applying transfer learning techniques for pre-trained models: This involves using pre-trained models on large datasets to improve the performance of the model on smaller datasets, by fine-tuning the pre-trained model on the new data.
  13. Understanding the concept of backpropagation and gradient computation in neural networks: This involves understanding how neural networks are trained using backpropagation and how gradients are computed using the chain rule of calculus.
  14. Understanding the trade-offs between model complexity and interpretability: This involves balancing the trade-off between the complexity of the model and its interpretability, where a more complex model may have better performance but may be more difficult to interpret.
  15. Choosing the right evaluation metric for clustering algorithms: This involves selecting the appropriate metric to evaluate the performance of clustering algorithms, such as silhouette score, Davies-Bouldin index, or purity.
  16. Understanding the impact of batch size and learning rate on model convergence: This involves understanding how the choice of batch size and learning rate can impact the convergence and performance of the model during training.

Algorithm Selection and Implementation

  1. Choosing appropriate algorithms for classification or regression problems: This involves selecting the appropriate machine learning algorithm for a given task, such as logistic regression, decision trees, random forests, or support vector machines (SVMs) for classification, or linear regression, polynomial regression, or neural networks for regression.
  2. Understanding the different types of gradient descent algorithms: This involves understanding the concept of gradient descent and its variants, such as batch gradient descent, stochastic gradient descent (SGD), mini-batch SGD, or Adam optimizer, and choosing the appropriate variant for the task.
  3. Implementing regularization techniques for deep learning models: This involves applying regularization techniques, such as L1 or L2 regularization, dropout, or early stopping, to prevent overfitting in deep learning models.
  4. Dealing with multi-label classification problems: This involves addressing the issue of multi-label classification, where each sample can belong to multiple classes simultaneously, and applying appropriate techniques, such as binary relevance, label powerset, or classifier chains.
  5. Applying appropriate techniques for handling non-linear data: This involves applying appropriate techniques, such as polynomial regression, decision trees, or neural networks, to handle non-linear data and capture the underlying patterns in the data.
  6. Dealing with class imbalance in binary classification problems: This involves addressing the issue of class imbalance, where the number of samples in each class is uneven, and applying appropriate techniques, such as oversampling, undersampling, or class weighting.
  7. Applying appropriate techniques for handling skewness in the data: This involves addressing the issue of skewness in the data, where the distribution of the data is skewed, and applying appropriate techniques, such as log transformation, box-cox transformation, or power transformation.
  8. Dealing with heteroscedasticity in the data: This involves addressing the issue of heteroscedasticity in the data, where the variance of the data is not constant across the range of values, and applying appropriate techniques, such as weighted regression, generalized least squares, or robust regression.
  9. Choosing the right activation function for non-linear data: This involves selecting the appropriate activation function for neural networks to capture the non-linear patterns in the data, such as sigmoid, tanh, ReLU, or softmax.

Solution approaches to key challenges in machine learning

To deal with these challenges, data scientists and ML engineers use various techniques and approaches, such as:

  1. Preprocessing and cleaning of data: They preprocess and clean the raw data to remove any noise, outliers, or missing values that can negatively impact model performance.
  2. Exploratory data analysis (EDA): They perform EDA to gain insights into the data, such as its distribution, correlations, and patterns, which can help them select the appropriate algorithms and hyperparameters.
  3. Feature engineering: They use feature engineering techniques to extract relevant features from the data and transform them into a format that can be easily understood by the model.
  4. Model selection and hyperparameter tuning: They carefully select the appropriate ML algorithm and tune its hyperparameters to obtain the best model performance.
  5. Regularization: They use regularization techniques to prevent overfitting and ensure the model generalizes well on new, unseen data.
  6. Ensemble learning: They use ensemble learning techniques to combine the predictions of multiple models and improve the overall model performance.
  7. Transfer learning: They use transfer learning techniques to leverage pre-trained models and fine-tune them for a specific task, which can save time and computational resources.
  8. Continuous learning and experimentation: They continuously learn and experiment with new ML techniques and approaches to keep up with the rapidly evolving field of ML.
  9. Collaborative problem-solving: They collaborate with other data scientists and ML engineers to solve complex problems and share knowledge and expertise.

Frequently asked questions of challenges in machine learning

  1. What is pre-processing in machine learning?

    Pre-processing is the process of cleaning, transforming, and preparing raw data before it can be used for machine learning tasks.

  2. What are some common techniques used for pre-processing data?

    Some common techniques used for pre-processing data include data cleaning, feature scaling, normalization, handling missing data, and handling outliers.

  3. What is the curse of dimensionality and how does it affect machine learning models?

    The curse of dimensionality refers to the difficulty of dealing with high-dimensional data, where the number of features is much larger than the number of samples. This can lead to overfitting, increased computational complexity, and decreased model performance.

  4. What is overfitting in machine learning and how can it be prevented?

    Overfitting occurs when a model is too complex and fits the training data too well, but does not generalize well on new, unseen data. It can be prevented by using regularization techniques, such as L1 or L2 regularization, or by using simpler models with fewer features.

  5. What is underfitting in machine learning and how can it be prevented?

    Underfitting occurs when a model is too simple and does not capture the underlying patterns in the data, resulting in poor model performance. It can be prevented by using more complex models or by adding more features to the model.

  6. What is the bias-variance trade-off in machine learning?

    The bias-variance trade-off refers to the trade-off between model complexity (variance) and model bias, where a complex model may fit the data well but have high variance, while a simpler model may have low variance but high bias.

  7. What is regularization in machine learning and why is it important?

    Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function that encourages the model to have smaller weights. It is important to prevent overfitting and ensure the model generalizes well on new, unseen data.

  8. What is cross-validation in machine learning and why is it important?

    Cross-validation is a technique used to evaluate the performance of a model on new, unseen data by splitting the data into training and validation sets multiple times. It is important to ensure the model generalizes well on new, unseen data.

  9. What is feature scaling in machine learning and why is it important?

    Feature scaling is the process of scaling the features to a similar range, which can improve model performance and convergence. It is important because some machine learning algorithms are sensitive to the scale of the features.

  10. What is the impact of learning rate on model convergence in machine learning?

    Learning rate is a hyperparameter that controls the step size of the optimization algorithm during training. A too high or too low learning rate can negatively impact model convergence and performance.

  11. What is transfer learning in machine learning and how is it used?

    Transfer learning is a technique used to leverage pre-trained models for a specific task by fine-tuning the model on new, related data. It is used to save time and computational resources and improve model performance.

  12. What is the impact of batch size on model convergence in machine learning?

    Batch size is a hyperparameter that determines the number of samples used in each iteration of the optimization algorithm during training. A too large or too small batch size can negatively impact model convergence and performance.

  13. How do I handle missing data in my dataset?

    There are several techniques you can use, such as imputation, deletion, or prediction-based methods. The best approach depends on the amount and pattern of missing data, as well as the nature of the problem you are trying to solve.

  14. What is overfitting, and how can I prevent it?

    Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new, unseen data. To prevent it, you can use techniques such as regularization, early stopping, or cross-validation to ensure that your model generalizes well.

  15. What are some common techniques for feature selection?

    Some common techniques include filter methods (e.g., correlation-based feature selection), wrapper methods (e.g., recursive feature elimination), and embedded methods (e.g., Lasso or Ridge regression).

  16. What is transfer learning, and when should I use it?

    Transfer learning is a technique where a model trained on one task is reused or adapted for another related task. It can be useful when you have limited labeled data for your target task or when you want to leverage the knowledge and features learned from a pre-trained model.

  17. How do I choose the right evaluation metric for my model?

    The choice of evaluation metric depends on the problem you are trying to solve and the specific requirements or constraints of the application. Some common metrics for classification include accuracy, precision, recall, F1 score, and ROC AUC, while common metrics for regression include mean squared error, mean absolute error, and R-squared.

  18. How do I deal with imbalanced datasets in classification problems?

    There are several techniques you can use, such as resampling (e.g., oversampling the minority class or undersampling the majority class), modifying the loss function or decision threshold, or using cost-sensitive learning.

  19. What is gradient descent, and how does it work?

    Gradient descent is a popular optimization algorithm used in machine learning to minimize a loss function. It works by iteratively adjusting the model parameters in the direction of steepest descent of the loss function gradient until a minimum is reached.

  20. How do I choose the right hyperparameters for my model?

    Hyperparameters control the behavior of the learning algorithm and can have a significant impact on the performance of the model. You can use techniques such as grid search, random search, or Bayesian optimization to search the hyperparameter space and find the optimal values.

  21. What is ensemble learning, and how does it work?

    Ensemble learning is a technique where multiple models are combined to improve the overall performance and reduce the risk of overfitting. Some common ensemble methods include bagging, boosting, and stacking.

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SKLEARN LOGISTIC REGRESSION multiclass (more than 2) classification with Python scikit-learn

multiclass logistic regression with sklearn python

Logistic Regression is a commonly used machine learning algorithm for binary classification problems, where the goal is to predict one of two possible outcomes. However, in some cases, the target variable has more than two classes. In such cases, a multiclass classification problem is encountered. In this article, we will see how to create a logistic regression model using the scikit-learn library for multiclass classification problems.

Multinomial classification

Multinomial logistic regression is used when the dependent variable in question is nominal (equivalently categorical, meaning that it falls into any one of a set of categories that cannot be ordered in any meaningful way) and for which there are more than two categories. Some examples would be:

  • Which major will a college student choose, given their grades, stated likes and dislikes, etc.? 
  • Which blood type does a person have, given the results of various diagnostic tests? 
  • In a hands-free mobile phone dialing application, which person’s name was spoken, given various properties of the speech signal? 
  • Which candidate will a person vote for, given particular demographic characteristics? 
  • Which country will a firm locate an office in, given the characteristics of the firm and of the various candidate countries? 

These are all statistical classification problems. They all have in common a dependent variable to be predicted that comes from one of a limited set of items that cannot be meaningfully ordered, as well as a set of independent variables (also known as features, explanators, etc.), which are used to predict the dependent variable. Multinomial logistic regression is a particular solution to classification problems that use a linear combination of the observed features and some problem-specific parameters to estimate the probability of each particular value of the dependent variable. The best values of the parameters for a given problem are usually determined from some training data (e.g. some people for whom both the diagnostic test results and blood types are known, or some examples of known words being spoken).

Common Approaches

  • One-vs-Rest (OvR)
  • Softmax Regression (Multinomial Logistic Regression)
  • One vs One(OvO)

Multiclass classification problems are usually tackled in two ways – One-vs-Rest (OvR), One-vs-One (OvO) and using the softmax function. In the OvA / OvR approach, a separate binary classifier is trained for each class, where one class is considered positive and all other classes are considered negative. In the OvO approach, a separate binary classifier is trained for each pair of classes. For example, if there are k classes, then k(k-1)/2 classifiers will be trained in the OvO approach.

In this article, we will be using the OvR and softmax approach to create a logistic regression model for multiclass classification.

One-vs-Rest (OvR)

One-vs-rest (OvR for short, also referred to as One-vs-All or OvA) is a heuristic method for using binary classification algorithms for multi-class classification.

It involves splitting the multi-class dataset into multiple binary classification problems. A binary classifier is then trained on each binary classification problem and predictions are made using the model that is the most confident.

For example, given a multi-class classification problem with examples for each class ‘red,’ ‘blue,’ and ‘green‘. This could be divided into three binary classification datasets as follows:

  • Binary Classification Problem 1: red vs [blue, green]
  • Binary Classification Problem 2: blue vs [red, green]
  • Binary Classification Problem 3: green vs [red, blue]

A possible downside of this approach is that it requires one model to be created for each class. For example, three classes require three models. This could be an issue for large datasets (e.g. millions of rows), slow models (e.g. neural networks), or very large numbers of classes (e.g. hundreds of classes).

This approach requires that each model predicts a class membership probability or a probability-like score. The argmax of these scores (class index with the largest score) is then used to predict a class.

As such, the implementation of these algorithms in the scikit-learn library implements the OvR strategy by default when using these algorithms for multi-class classification.

Multi class logistic regression using one vs rest (OVR) strategy

The strategy for handling multi-class classification can be set via the “multi_class” argument and can be set to “ovr” for the one-vs-rest strategy when using sklearn’s LogisticRegression class from linear_model.

To start, we need to import the required libraries:

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

Next, we will load the load_iris dataset from the sklearn.datasets library, which is a commonly used dataset for multiclass classification problems:

iris = load_iris()
X = iris.data
y = iris.target

The load_iris dataset contains information about the sepal length, sepal width, petal length, and petal width of 150 iris flowers. The target variable is the species of the iris flower, which has three classes – 0, 1, and 2.

Next, we will split the data into training and testing sets. 80%-20% split:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Training the multiclass logistic regression model

Now, we can create a logistic regression model and train it on the training data:

model = LogisticRegression(solver='lbfgs', multi_class='ovr')
model.fit(X_train, y_train)

The multi_class parameter is set to ‘ovr’ to indicate that we are using the OvA approach for multiclass classification. The solver parameter is set to ‘lbfgs’ which is a suitable solver for small datasets like the load_iris dataset.

Next, we can evaluate the performance of the model on the test data:

y_pred = model.predict(X_test)
accuracy = np.mean(y_pred == y_test)
print("Accuracy:", accuracy)

The predict method is used to make predictions on the test data, and the accuracy of the predictions is calculated by comparing the predicted values with the actual values.

Finally, we can use the trained model to make predictions on new data:

new_data = np.array([[5.1, 3.5, 1.4, 0.2]])
y_pred = model.predict(new_data)
print("Prediction:", y_pred)

In this example, we have taken a single new data point with sepal length 5.1, sepal width 3.5, petal length 1.4, and petal width 0.2. The model will return the predicted class for this data point.

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Softmax Regression (Multinomial Logistic Regression)

The inputs to the multinomial logistic regression are the features we have in the dataset. Suppose if we are going to predict the Iris flower species type, the features will be the flower sepal length, width and petal length and width parameters will be our features. These features will be treated as the inputs for the multinomial logistic regression.

The keynote to remember here is the features values are always numerical. If the features are not numerical, we need to convert them into numerical values using the proper categorical data analysis techniques.

Linear Model

The linear model equation is the same as the linear equation in the linear regression model. You can see this linear equation in the image. Where the X is the set of inputs, Suppose from the image we can say X is a matrix. Which contains all the feature( numerical values) X = [x1,x2,x3]. Where W is another matrix includes the same input number of coefficients W = [w1,w2,w3].

In this example, the linear model output will be the w1x1, w2x2, w3*x3

Softmax Function 

The softmax function is a mathematical function that takes a vector of real numbers as input and outputs a probability distribution over the classes. It is often used in machine learning for multiclass classification problems, including neural networks and logistic regression models.

The softmax function is defined as:

softmax function used for multi class / multinomial logistic regression

The softmax function transforms the input vector into a probability distribution over the classes, where each class is assigned a probability between 0 and 1, and the sum of the probabilities is 1. The class with the highest probability is then selected as the predicted class.

The softmax function is a generalization of the logistic function used in binary classification. In binary classification, the logistic function is used to output a single probability value between 0 and 1, representing the probability of the input belonging to the positive class.

The softmax function is different from the sigmoid function, which is another function used in machine learning for binary classification. The sigmoid function outputs a value between 0 and 1, which can be interpreted as the probability of the input belonging to the positive class.

Cross Entropy

The cross-entropy is the last stage of multinomial logistic regression. Uses the cross-entropy function to find the similarity distance between the probabilities calculated from the softmax function and the target one-hot-encoding matrix.

Cross-entropy is a distance calculation function which takes the calculated probabilities from softmax function and the created one-hot-encoding matrix to calculate the distance. For the right target class, the distance value will be smaller, and the distance values will be larger for the wrong target class.

Multi class logistic regression using sklearn multinomial parameter

Multiclass logistic regression using softmax function (multinomial)

In the previous example, we created a logistic regression model for multiclass classification using the One-vs-All approach. In the softmax approach, the output of the logistic regression model is a vector of probabilities for each class. The class with the highest probability is then selected as the predicted class.

To use the softmax approach with logistic regression in scikit-learn, we need to set the multi_class parameter to ‘multinomial’ and the solver parameter to a solver that supports the multinomial loss function, such as ‘lbfgs’, ‘newton-cg’, or ‘sag’. Here’s an example of how to create a logistic regression model with multi_class set to ‘multinomial’:

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
accuracy = np.mean(y_pred == y_test)
print("Accuracy:", accuracy)

new_data = np.array([[5.1, 3.5, 1.4, 0.2]])
y_pred = model.predict(new_data)
print("Prediction:", y_pred)

In this example, we have set the multi_class parameter to ‘multinomial’ and the solver parameter to ‘lbfgs’. The lbfgs solver is suitable for small datasets like the load_iris dataset. We then train the logistic regression model on the training data and evaluate its performance on the test data.

We can also use the predict_proba method to get the probability estimates for each class for a given input. Here’s an example:

probabilities = model.predict_proba(new_data)
print("Probabilities:", probabilities)

In this example, we have used the predict_proba method to get the probability estimates for each class for the new data point. The output is a vector of probabilities for each class.

It’s important to note that the logistic regression model is a linear model and may not perform well on complex non-linear datasets. In such cases, other algorithms like decision trees, random forests, and support vector machines may perform better.

Conclusion

In conclusion, we have seen how to create a logistic regression model using the scikit-learn library for multiclass classification problems using the OvA and softmax approach. The softmax approach can be more accurate than the One-vs-All approach but can also be more computationally expensive. We have used the load_iris dataset for demonstration purposes but the same steps can be applied to any multiclass classification problem. It’s important to choose the right algorithm based on the characteristics of the dataset and the problem requirements.

  1. Can logistic regression be used for multiclass classification?

    Logistic regression is a binary classification model. To support multi-class classification problems, we would need to split the classification problem into multiple steps i.e. classify pairs of classes.

  2. Can you use logistic regression for a classification problem with three classes?

    Yes, we can apply logistic regression on 3 class classification problem. Use One Vs rest method for 3 class classification in logistic regression.

  3. When do I use predict_proba() instead of predict()?

    The predict() method is used to predict the actual class while predict_proba() method can be used to infer the class probabilities (i.e. the probability that a particular data point falls into the underlying classes). It is usually sufficient to use the predict() method to obtain the class labels directly. However, if you wish to futher fine tune your classification model e.g. threshold tuning, then you would need to use predict_proba()

  4. What is softmax function?

    The softmax function is a function that turns a vector of K real values into a vector of K real values that sum to 1. The input values can be positive, negative, zero, or greater than one, but the softmax transforms them into values between 0 and 1, so that they can be interpreted as probabilities. Learn more in this article.

  5. Why and when is Softmax used in logistic regression?

    The softmax function is used in classification algorithms where there is a need to obtain probability or probability distribution as the output. Some of these algorithms are the following: Neural networks. Multinomial logistic regression (Softmax regression)

  6. Why use softmax for classification?

    Softmax classifiers give you probabilities for each class label. It's much easier for us as humans to interpret probabilities to infer the class labels.

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Logistic regression – sklearn (sci-kit learn) machine learning – easy examples in Python – tutorial

logistic regression sklearn machine learning with python

Logistic Regression is a widely used machine learning algorithm for solving binary classification problems like medical diagnosis, churn or fraud detection, intent classification and more. In this article, we’ll be covering how to implement a logistic regression model in Python using the scikit-learn (sklearn) library. In this article you will get started with logistic regression and familiarize yourself with the sklearn library.

Before diving into the implementation, let’s quickly understand what logistic regression is and what it’s used for.

What is Logistic Regression?

Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes). It is used to predict a binary outcome (1/0, Yes/No, True/False) given a set of independent variables.

Applications of logistic regression for classification (binary)

Logistic Regression is a widely used machine learning algorithm for binary classification. It is used in many applications where the goal is to predict a binary outcome, such as:

  1. Medical Diagnosis: Logistic Regression can be used to diagnose a medical condition based on patient symptoms and other relevant factors.
  2. Customer Churn Prediction: Logistic Regression can be used to predict whether a customer is likely to leave a company based on their past behavior and other factors.
  3. Fraud Detection: Logistic Regression can be used to detect fraudulent transactions by identifying unusual patterns in transaction data.
  4. Credit Approval: Logistic Regression can be used to approve or reject loan applications based on a customer’s credit score, income, and other financial information.
  5. Marketing Campaigns: Logistic Regression can be used to predict the response to a marketing campaign based on customer demographics, past behavior, and other relevant factors.
  6. Image Classification: Logistic Regression can be used to classify images into different categories, such as animals, people, or objects.
  7. Natural Language Processing (NLP): Logistic Regression can be used for sentiment analysis in NLP, where the goal is to classify a text as positive, negative, or neutral.

These are some of the common applications of Logistic Regression for binary classification. The algorithm is simple to implement and can provide good results in many cases, making it a popular choice for binary classification problems.

Prerequisites

Before getting started, make sure you have the following libraries installed in your environment:

  • Numpy
  • Pandas
  • Sklearn

You can install them by running the following command in your terminal/command prompt:

pip install numpy pandas scikit-learn

Importing the Libraries

The first step is to import the necessary libraries that we’ll be using in our implementation.

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

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Loading the Dataset

Next, we’ll load the dataset using pandas. We’ll be using the load_breast_cancer dataset from the sklearn.datasets library. This dataset contains information about the cancer diagnosis of patients. The dataset includes features such as the mean radius, mean texture, mean perimeter, mean area, mean smoothness, mean compactness, mean concavity, mean concave points, mean symmetry, mean fractal dimension, radius error, texture error, perimeter error, area error, smoothness error, compactness error, concavity error, concave points error, symmetry error, and fractal dimension error. The target variable is a binary variable indicating whether the patient has a malignant tumor (represented by 0) or a benign tumor (represented by 1).

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()

We’ll create a dataframe from the dataset and have a look at the first 5 rows to get a feel for the data.

df = pd.DataFrame(data.data, columns=data.feature_names)
df.head()

Preprocessing the Data

Before we start building the model, we need to preprocess the data. We’ll be splitting the data into two parts: training data and testing data. The training data will be used to train the model and the testing data will be used to evaluate the performance of the model. We’ll use the train_test_split function from the sklearn.model_selection library to split the data.

X = df
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Next, we’ll normalize the data. Normalization is a crucial step in preprocessing the data as it ensures that all the features have the same scale, which is important for logistic regression. We’ll use the StandardScaler function from the sklearn.preprocessing library to normalize the data.

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

Why do we need to scale data?

Scaling the data is important in many machine learning algorithms, including logistic regression, because the algorithms can be sensitive to the scale of the features. If one feature has a much larger scale than the other features, it can dominate the model and negatively affect its performance.

Scaling the data ensures that all the features are on a similar scale, which can help the model to better capture the relationship between the features and the target variable. By scaling the data, we can avoid issues such as domination of one feature over others, and reduce the computational cost and training time for the model.

In the example, we used the StandardScaler class from the sklearn.preprocessing library to scale the data. This class scales the data by subtracting the mean and dividing by the standard deviation, which ensures that the data has a mean of 0 and a standard deviation of 1. This is a commonly used method for scaling data in machine learning.

NOTE: In the interest of preventing information about the distribution of the test set leaking into your model, you should fit the scaler on your training data only, then standardize both training and test sets with that scaler. By fitting the scaler on the full dataset prior to splitting, information about the test set is used to transform the training set, which in turn is passed downstream. As an example, knowing the distribution of the whole dataset might influence how you detect and process outliers, as well as how you parameterize your model. Although the data itself is not exposed, information about the distribution of the data is. As a result, your test set performance is not a true estimate of performance on unseen data.

Building the Logistic Regression Model

Now that the data is preprocessed, we can build the logistic regression model. We’ll use the LogisticRegression function from the sklearn.linear_model library to build the model. The same package is also used to import and train the linear regression model. Know more here.

model = LogisticRegression()
model.fit(X_train, y_train)

Evaluating the Model

We’ll evaluate the performance of the model by calculating its accuracy. Accuracy is defined as the ratio of correctly predicted observations to the total observations. We’ll use the score method from the model to calculate the accuracy.

accuracy = model.score(X_test, y_test)
print("Accuracy:", accuracy)

Making Predictions

Now that the model is trained and evaluated, we can use it to make predictions on data that the model has not been trained on. We’ll use the predict method from the model to make predictions.

y_pred = model.predict(X_test)

Conclusion

In this article, we covered how to build a logistic regression model using the sklearn library in Python. We preprocessed the data, built the model, evaluated its performance, and made predictions on new data. This should serve as a good starting point for anyone looking to get started with logistic regression and the sklearn library.

Frequently asked questions (FAQ) about logistic regression

  1. What is logistic regression in simple terms?

    Logistic regression is a statistical analysis method to predict a binary outcome, such as yes or no, based on prior observations of a data set. A logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.

  2. What is logistic regression vs linear regression?

    Linear regression is utilized for regression tasks, while logistic regression helps accomplish classification tasks. Supervised machine learning is a widely used machine learning technique that predicts future outcomes or events. It uses labeled datasets i.e. datasets with a dependent variable, to learn and generate accurate predictions.

  3. Which type of problem does logistic regression solve?

    Logistic regression is the most widely used machine learning algorithm for classification problems. In its original form, it is used for binary classification problem which has only two classes to predict.

  4. Why is logistic regression used in machine learning?

    Logistic regression is applied to predict binary categorical dependent variable. In other words, it's used when the prediction is categorical, for example, yes or no, true or false, 0 or 1. The predicted probability or output of logistic regression can be either one of them.

  5. How to evaluate the performance of a logistic regression model?

    Logistic regression like classification models can be evaluated on several metrics including accuracy score, precision, recall, F1 score, and the ROC AUC.

  6. What kind of model is logistic regression?

    Logistic regression, despite its name, is a classification model. Logistic regression is a simple method for binary classification problems.

  7. What type of variables is used in logistic regression?

    There must be one or more independent variables, for a logistic regression, and one dependent variable. The independent variables can be continuous or categorical (ordinal/nominal) while the dependent variable must be categorical.

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sklearn Linear Regression in Python with sci-kit learn and easy examples

linear regression sklearn in python

Linear regression is a statistical method used for analyzing the relationship between a dependent variable and one or more independent variables. It is widely used in various fields, such as finance, economics, and engineering, to model the relationship between variables and make predictions. In this article, we will learn how to create a linear regression model using the scikit-learn library in Python.

Scikit-learn (also known as sklearn) is a popular Python library for machine learning that provides simple and efficient tools for data mining and data analysis. It provides a wide range of algorithms and models, including linear regression. In this article, we will use the sklearn library to create a linear regression model to predict the relationship between two variables.

Before we dive into the code, let’s first understand the basic concepts of linear regression.

Understanding Linear Regression

Linear regression is a method that models the relationship between a dependent variable (also known as the response variable or target variable) and one or more independent variables (also known as predictor variables or features). The goal of linear regression is to find the line of best fit that best predicts the dependent variable based on the independent variables.

In a simple linear regression, the relationship between the dependent variable and the independent variable is represented by the equation:

y = b0 + b1x

where y is the dependent variable, x is the independent variable, b0 is the intercept, and b1 is the slope.

The intercept b0 is the value of y when x is equal to zero, and the slope b1 represents the change in y for every unit change in x.

In multiple linear regression, the relationship between the dependent variable and multiple independent variables is represented by the equation:

y = b0 + b1x1 + b2x2 + ... + bnxn

where y is the dependent variable, x1, x2, …, xn are the independent variables, b0 is the intercept, and b1, b2, …, bn are the slopes.

Creating a Linear Regression Model in Python

Now that we have a basic understanding of linear regression, let’s dive into the code to create a linear regression model using the sklearn library in Python.

The first step is to import the necessary libraries and load the data. We will use the pandas library to load the data and the scikit-learn library to create the linear regression model.

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import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression

Next, we will load the data into a pandas DataFrame. In this example, we will use a simple dataset that contains the height and weight of a group of individuals. The data consists of two columns, the height in inches and the weight in pounds. The goal is to fit a linear regression model to this data to find the relationship between the height and weight of individuals. The data can be represented in a 2-dimensional array, where each row represents a sample (an individual), and each column represents a feature (height and weight). The X data is the height of individuals and the y data is their corresponding weight.

height (inches)weight (pounds)
65150
70170
72175
68160
71170
Heights and Weights of Individuals for a Linear Regression Model Exercise
# Load the data
df = pd.read_excel('data.xlsx')

Next, we will split the data into two arrays: X and y. X contains the independent variable (height) and y contains the dependent variable (weight).

# Split the data into X (independent variable) and y (dependent variable)
X = df['height'].values.reshape(-1, 1)
y = df['weight'].values

It’s always a good idea to check the shape of the data to ensure that it has been loaded correctly. We can use the shape attribute to check the shape of the arrays X and y.

# Check the shape of the data
print(X.shape)
print(y.shape)

The output should show that X has n rows and 1 column and y has n rows, where n is the number of samples in the dataset.

Perform simple cross validation

One common method for performing cross-validation on the data is to split the data into training and testing sets using the train_test_split function from the model_selection module of scikit-learn.

In this example, the data is first split into the X data, which is the height of individuals, and the y data, which is their corresponding weight. Then, the train_test_split function is used to split the data into training and testing sets. The test_size argument specifies the proportion of the data to use for testing, and the random_state argument sets the seed for the random number generator used to split the data.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Train the linear regression model

Now that we have split the data into X and y, we can create a linear regression model using the LinearRegression class from the scikit-learn library. This same package is used to load and train the logistic regression model for classification. Learn more here.

# Create a linear regression model
reg = LinearRegression()

Next, we will fit the linear regression model to the data using the fit method.

# Fit the model to the data
reg.fit(X_train, y_train)

After fitting the model, we can access the intercept and coefficients using the intercept_ and coef_ attributes, respectively.

# Print the intercept and coefficients
print(reg.intercept_)
print(reg.coef_)

The intercept and coefficients represent the parameters b0 and b1 in the equation y = b0 + b1x, respectively.

Finally, we can use the predict method to make predictions for new data.

# Make predictions for new data
new_data = np.array([[65]]) # Height of 65 inches
prediction = reg.predict(new_data)
print(prediction)

This will output the predicted weight for a person with a height of 65 inches.

Cost functions for linear regression models

There are several cost functions that can be used to evaluate the linear regression model. Here are a few common ones:

  1. Mean Squared Error (MSE): MSE is the average of the squared differences between the predicted values and the actual values. The lower the MSE, the better the fit of the model. MSE is expressed as:
MSE = 1/n * Σ(y_i - y_i_pred)^2

where n is the number of samples, y_i is the actual value, and y_i_pred is the predicted value.

  1. Root Mean Squared Error (RMSE): RMSE is the square root of MSE. It is expressed as:
RMSE = √(1/n * Σ(y_i - y_i_pred)^2)
  1. Mean Absolute Error (MAE): MAE is the average of the absolute differences between the predicted values and the actual values. The lower the MAE, the better the fit of the model. MAE is expressed as:
MAE = 1/n * Σ|y_i - y_i_pred|
  1. R-Squared (R^2) a.k.a the coefficient of determination: R^2 is a measure of the goodness of fit of the linear regression model. It is the proportion of the variance in the dependent variable that is predictable from the independent variable. The R^2 value ranges from 0 to 1, where a value of 1 indicates a perfect fit and a value of 0 indicates a poor fit.

In scikit-learn, these cost functions can be easily computed using the mean_squared_error, mean_absolute_error, and r2_score functions from the metrics module. For example:

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

y_pred = model.predict(X_test)

# Mean Squared Error
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)

# Root Mean Squared Error
rmse = mean_squared_error(y_test, y_pred, squared = False)
print("Root Mean Squared Error:", rmse)

# Mean Absolute Error
mae = mean_absolute_error(y_test, y_pred)
print("Mean Absolute Error:", mae)

# R-Squared
r2 = r2_score(y_test, y_pred)
print("R-Squared:", r2)

These cost functions provide different perspectives on the performance of the linear regression model and can be used to choose the best model for a given problem.

Conclusion

In this article, we learned how to create a linear regression model using the scikit-learn library in Python. We first split the data into X and y, created a linear regression model, fit the model to the data, and finally made predictions for new data.

Linear regression is a simple and powerful method for analyzing the relationship between variables. By using the scikit-learn library in Python, we can easily create and fit linear regression models to our data and make predictions.

Frequently Asked Questions about Linear Regression with Sklearn in Python

  1. Which Python library is best for linear regression?

    scikit-learn (sklearn) is one of the best Python libraries for statistical analysis and machine learning and it is adapted for training models and making predictions. It offers several options for numerical calculations and statistical modelling. LinearRegression is an important sub-module to perform linear regression modelling.

  2. What is linear regression used for?

    Linear regression analysis is used to predict the value of a target variable based on the value of one or more independent variables. The variable you want to predict / explain is called the dependent or target variable. The variable you are using to predict the dependent variable's value is called the independent or feature variable.

  3. What are the 2 most common models of regression analysis?

    The most common models are simple linear and multiple linear. Nonlinear regression analysis is commonly used for more complicated data sets in which the dependent and independent variables show a nonlinear relationship. Regression analysis offers numerous applications in various disciplines.

  4. What are the advantages of linear regression?

    The biggest advantage of linear regression models is linearity: It makes the estimation procedure simple and, most importantly, these linear equations have an easy to understand interpretation on a modular level (i.e. the weights).

  5. What is the difference between correlation and linear regression?

    Correlation quantifies the strength of the linear relationship between a pair of variables, whereas regression expresses the relationship in the form of an equation.

  6. What is LinearRegression in Sklearn?

    LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.

  7. What is the full form of sklearn?

    scikit-learn (also known as sklearn) is a free software machine learning library for the Python programming language.

  8. What is the syntax for linear regression model in Python?

    from sklearn.linear_model import LinearRegression
    lr = LinearRegression()
    lr.fit(X,y)
    lr.score()
    lr.predict(new_data)