a
ai_ml_eng

Anii Rozzeq

@ai_ml_eng

Machine Learning , AI Engineer: Python, Data Analysis, Visualization Expert

Pakistán
Inglés, Español, Alemán, Hindi, Urdu
Parte de la información aparece en idioma inglés.
Sobre mí
As a dedicated Machine Learning and AI Engineer, I transform complex data into actionable insights and intelligent solutions. With strong expertise in Python, I specialize in data manipulation (NumPy, Pandas), insightful data visualization (Matplotlib, Seaborn), and building robust machine learning models (Scikit-learn). I deliver clean, efficient code and on-time results, empowering your projects with cutting-edge AI. Let's innovate together!... Lee más

Habilidades

a
ai_ml_eng
Anii Rozzeq
desconectado • 

Revisa mis servicios

Programación y tecnología
I will perform data cleaning, eda, and data visualization in python
DeUSD 30 / proyectoMás información

Experiencia laboral

Data_to Leads

Sentiment Analysis of Social Media Data for Brand Monitoring

Data to Leads • Tiempo completo

Jul 2026 - Present • 3 mos

Objective: To gauge public sentiment towards a brand by analyzing social media mentions and identifying key themes. • Approach: Collected and preprocessed unstructured text data from various social media platforms using Pandas. Applied natural language processing (NLP) techniques and used Scikit-learn to build a sentiment classification model. Visualized sentiment distribution and trending topics with Matplotlib and Seaborn. • Outcome: Delivered a comprehensive sentiment report and an automated system to monitor brand perception in real-time, allowing the client to quickly respond to negative feedback and leverage positive mentions, enhancing brand reputation.

Code_Maximus

AL ML Engineer

Code Maximus • Freelance

Feb 2026 - Jun 2026 • 4 mos

Project 1: Customer Churn Prediction for an E-commerce Business • Objective: To identify customers at high risk of churning and understand the factors contributing to their departure. • Approach: Utilized Pandas for data cleaning and feature engineering from transactional and demographic data. Developed and optimized a classification model using Scikit-learn to predict churn likelihood. Visualized key churn drivers with Matplotlib and Seaborn. • Outcome: Delivered a robust predictive model with 85% accuracy, enabling the client to proactively engage at-risk customers and implement targeted retention strategies, leading to a significant reduction in churn rate.