Machine Learning
Smartphone Resale Price Prediction
Python · Random Forest · Regression
Developed a machine learning regression model to accurately predict resale prices for smartphones.
Result
Achieved approximately 0.905 R² using Random Forest.
Data Analyst & ML Engineer
Kneez Fatima
About
Data Analyst and AI & Machine Learning practitioner based in Lahore, Pakistan, specializing in predictive modeling, computer vision, and data preprocessing. Experienced in building machine learning regressors and classifiers, object detection models using YOLO26, and comprehensive exploratory data analyses.
Technical Skills
- Programming & Core: Python, Pandas, NumPy, Jupyter Notebook, Kaggle
- Data Science & ML: Data Cleaning, Exploratory Data Analysis, Feature Engineering, Model Evaluation, Scikit-learn, Random Forest, Logistic Regression, Decision Trees, Regression, Classification
- AI & Computer Vision: YOLO26, CNN, GRU, Transformers, Autoencoders, OpenCV, Object Detection
Soft Skills
- Interpersonal & Management: Analytical Thinking, Problem Solving, Teamwork, Leadership, Communication, Time Management
Languages
- Languages: English, Urdu
Professional Experience
- [Employment Dates Placeholder]Details
[Company Name Placeholder]
Data Analyst · Remote / Lahore, Pakistan
Data Analysis & Machine Learning
- Working as a Data Analyst alongside undergraduate studies, contributing to data cleaning, preprocessing, and exploratory data analysis.
- [Verified job responsibility placeholder to be updated upon final company review]
Education
- Expected December 2026
Lahore Garrison University
BS Information Technology · CGPA: 3.55/4.00 | 75% Scholarship recipient
Punjab College Kamoke
FSc Pre-Engineering · 87% Score | 100% Scholarship recipient
Matriculation
Secondary School Education · 91% Score
Other work
Real Estate Analytics
Lahore House Price Prediction
Compared Linear Regression and Random Forest models for predicting housing market prices in Lahore.
Real Estate Analytics
Lahore House Price Prediction
Python · Linear Regression · Random Forest
Compared Linear Regression and Random Forest models for predicting housing market prices in Lahore.
Solution
Compared Linear Regression and Random Forest architectures.
Result
Achieved approximately 0.807 R² with Random Forest.
Predictive Analytics
Telco Customer Churn Prediction
Developed a customer churn classification model to identify user retention risk factors.
Predictive Analytics
Telco Customer Churn Prediction
Python · Classification
Developed a customer churn classification model to identify user retention risk factors.
Solution
Built a robust classification pipeline.
Result
Achieved approximately 80.7% accuracy.
Computer Vision
Query-Based Video Summarization
Developing an intelligent video retrieval and summarization system integrating fighting and weapon detection models.
Computer Vision
Query-Based Video Summarization
YOLO26 · Transformers · OpenCV
Developing an intelligent video retrieval and summarization system integrating fighting and weapon detection models.
Solution
Integrating YOLO26 and Transformer-based approaches to extract relevant video segments based on user queries.
AI Security
Fighting and Violence Detection
Developed violence and non-violence classification models for automated surveillance assessment.
AI Security
Fighting and Violence Detection
YOLO26 · Computer Vision
Developed violence and non-violence classification models for automated surveillance assessment.
Solution
Trained custom detection architectures.
Result
Achieved approximately 94.1% top-1 accuracy with YOLO26 in experimental evaluations.
Object Detection
Pistol Detection
Trained a custom YOLO26 object detection model for weapon recognition.
Object Detection
Pistol Detection
YOLO26 · Dataset Preparation
Trained a custom YOLO26 object detection model for weapon recognition.
Solution
Prepared and annotated a custom dataset containing approximately 9,044 images.
Data Analysis
Retail Sales Analysis
Conducted comprehensive retail data exploration and trend discovery.
Data Analysis
Retail Sales Analysis
Pandas · NumPy · Data Cleaning
Conducted comprehensive retail data exploration and trend discovery.
Solution
Performed end-to-end data cleaning, exploratory analysis, and identified retail sales trends.
Scholarships & Awards
- LGU Scholarship: 75% Scholarship – Lahore Garrison University
- Punjab College Scholarship: 100% Scholarship – Punjab College Kamoke
- Board Scholarship: Gujranwala Board Scholarship
- Robotics Competition: LEGO Robotics Competition Winner
- Workshops & Events: NERC participation and Robotics Workshop