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Abdulsamad

Abdulsamad

Data Labeling, Text Classification, AI response evaluation

Nigeria flagN/A, Nigeria
$15.00/hrIntermediateOther

Key Skills

Software

Other

Top Subject Matter

SMS Spam Detection
Iris Flower Classification

Top Data Types

TextText
ImageImage
DocumentDocument

Top Task Types

Classification
Transcription
Question Answering

Freelancer Overview

I used the sms spam detection dataset to train a machine learning classifier that detects spam and legitimate messages using Scikit-learn, Pandas and NumPy in Python. I trained a machine learning classifier to identify flower species from measurements. It tests predictions by changing input values. The result was approximately 97 percent accuracy on test data.

IntermediateEnglishChinese MandarinSpanishKorean

Labeling Experience

Iris AI - Data Classification

OtherClassification
I trained a machine learning classifier to identify iris flower species using a structured, labeled dataset. The process utilized Python along with libraries such as Scikit-learn and NumPy for data handling and model training. The project achieved approximately 97% accuracy on test data. • Worked with the Iris dataset to classify flower species. • Preprocessed and validated machine learning features. • Automated some labeling and evaluation steps using Python. • Assessed results to ensure reliability and model robustness.

I trained a machine learning classifier to identify iris flower species using a structured, labeled dataset. The process utilized Python along with libraries such as Scikit-learn and NumPy for data handling and model training. The project achieved approximately 97% accuracy on test data. • Worked with the Iris dataset to classify flower species. • Preprocessed and validated machine learning features. • Automated some labeling and evaluation steps using Python. • Assessed results to ensure reliability and model robustness.

Not specified

SMS Spam Detection App - Data Classification

OtherTextClassification
I trained a machine learning classifier to detect spam messages versus legitimate messages. This involved using a labeled SMS spam collection dataset and evaluating the model's performance. The work was implemented using Python libraries such as Scikit-learn, Pandas, and NumPy. • Labeled and processed SMS messages for spam classification. • Utilized supervised learning techniques to improve accuracy. • Evaluated effectiveness with standard metrics and test data. • Automated labeling workflow using Python tools.

I trained a machine learning classifier to detect spam messages versus legitimate messages. This involved using a labeled SMS spam collection dataset and evaluating the model's performance. The work was implemented using Python libraries such as Scikit-learn, Pandas, and NumPy. • Labeled and processed SMS messages for spam classification. • Utilized supervised learning techniques to improve accuracy. • Evaluated effectiveness with standard metrics and test data. • Automated labeling workflow using Python tools.

Not specified

Education

F

Federal University of Technology, Minna

Bachelor of Engineering, Civil Engineering

Bachelor of Engineering
2023

Work History

F

Fluence Network

Cloud Ambassador

F.C.T Abuja
2025 - 2026