        1. Roles & Responsibilities: 2/10  
        2. Years of Experience: 4/10  
        3. Education Qualification: 8/10  
        4. Skills/Knowledge: 4/10  
        5. Domain/Industry: 4/10  
        6. Key Competencies: 4/10  
        7. Certifications: 4/10  

```html
<h5 style="font-family:Roboto; font-size:0.88rem; font-weight:bold;">Overall Summary</h5>
<ol style="font-family:Roboto; font-size:0.88rem;">
    <li>The candidate's resume shows limited alignment with the roles and responsibilities outlined in the job requirements. While the candidate has experience in machine learning and AI projects, there is no direct mention of designing, developing, or maintaining data pipelines, data cleaning, or creating data visualizations and dashboards. This results in a low score for this section.</li>
    <li>The candidate has less than 1 year of relevant experience, which aligns partially with the job requirement of 0 to 1 year. However, the experience is more focused on machine learning rather than data engineering, leading to an average score.</li>
    <li>The candidate holds a Bachelor of Technology in Computer Science with a specialization in AI & ML, which is closely related to the required qualification. This earns a high score for education qualification.</li>
    <li>The resume lists skills such as Python, MySQL, Pandas, Matplotlib, and Seaborn, which are relevant to the job requirements. However, there is no evidence of proficiency in Power BI or Excel, which are critical for the role. This results in a below-average score for skills/knowledge.</li>
    <li>The candidate's experience and projects are primarily in the machine learning domain, with limited exposure to data engineering or IT-specific tasks. This results in a low score for domain/industry knowledge.</li>
    <li>The resume demonstrates some key competencies such as problem-solving, teamwork, and communication skills. However, there is no evidence of writing reusable and modular code, attention to detail, or collaboration with cross-functional teams in the context of data engineering. This leads to a below-average score for key competencies.</li>
    <li>The certifications listed are primarily focused on machine learning and deep learning, with no mention of certifications in Python, MySQL, Power BI, or Excel. This results in a low score for certifications.</li>
</ol>
```