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Arfa NaazAN
Looking for a job

Arfa Naaz

@arfanaaz

Aspiring analyst with IT and business analytics experience, including a machine-learning project to detect credit card fraud.

India
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What I'm looking for

I’m keen to explore opportunities where I can apply my strengths, gain hands-on experience, and contribute to meaningful business outcomes.

At SkillForge, I worked on a Credit Card Fraud Detection project using Machine Learning. I cleaned and preprocessed data, identified fraudulent transactions, and evaluated model performance to distinguish fraudulent and genuine transactions.

At JAIN College, I worked on an academic project called “Personalized Recommendation System for E-Commerce Platforms.” It explored TF-IDF and cosine similarity to identify similar products and generate recommendations based on user preferences and product information.

The recommendation project also gave me exposure to building a web-based application using Flask and MongoDB. It helped me understand how recommendation systems can help shoppers discover relevant products.

Alongside my studies, I worked on the Leadzin platform, promoting educational courses, generating leads, and engaging with potential learners. I’m keen to explore opportunities where I can apply my strengths, gain hands-on experience, and contribute to meaningful business outcomes.

Experience

Work history, roles, and key accomplishments

SkillForge logoSK

Intern

SkillForge

Jan 2025 - Feb 2025 (1 month)

Worked on a Credit Card Fraud Detection project using Machine Learning. Gained experience in data cleaning, preprocessing, identifying fraudulent transactions, and evaluating model performance to distinguish fraudulent and genuine transactions.

Education

Degrees, certifications, and relevant coursework

SS

Sree Mahaveer jain school

SSLC

JAIN College logoJC

JAIN College

BCA, computer application

2023 - 2026

Worked on an academic project titled “Personalized Recommendation System for E-Commerce Platforms,” focused on providing relevant product recommendations based on user preferences and product information.

The project explored the use of data science techniques, including TF-IDF and cosine similarity, to identify similarities between products and generate recommendations. It also provided exposure

Tech stack

Software and tools used professionally

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