Muhammad Abdul Rehman
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Analyst with less than a year in Python, SQL, and Power BI
Results-driven Data Analyst specializing in Python, SQL, and Power BI to transform complex datasets into actionable business insights. Proven track record of improving operational efficiency: reduced reporting time by 30%, optimized query performance by 40%, and enhanced data accuracy by 20%. Expertise in statistical analysis, machine learning (LSTM, NLP), predictive modeling, and ETL pipelines. Strong background in designing data-driven solutions that solve complex business problems and support strategic decision-making. Passionate about leveraging advanced analytics to deliver measurable business impact.
Arid Agriculture University Rawalpindi
Bachelor of Science · Software Engineering
August 1, 2021 – June 30, 2025
Virtuo Byte
Junior Data Analyst
January 1, 2025 – May 1, 2025
Rawalpindi, Punjab, Pakistan
Stock Price Forecasting System
June 17, 2026 – Present
Developed deep learning LSTM neural network for time-series forecasting of stock prices, implementing sequence generation, data normalization, and sliding window techniques for temporal pattern recognition. Preprocessed historical market data from multiple years, handling missing values, outliers, and feature scaling to ensure model robustness and prediction accuracy. Visualized trend patterns, model predictions, and performance metrics using Matplotlib and Seaborn, creating comprehensive analytical reports for stakeholder presentations.
Customer Churn Prediction Model
June 17, 2026 – Present
Designed comprehensive machine learning pipeline to predict customer churn with high accuracy, enabling proactive retention strategies and reducing customer attrition through predictive insights. Performed extensive exploratory data analysis (EDA) and feature engineering on 20+ variables, identifying key churn indicators through correlation analysis, statistical testing, and domain knowledge integration. Optimized model performance using GridSearchCV for hyperparameter tuning, k-fold cross-validation for robustness testing, and feature selection techniques to improve precision and reduce overfitting.
Customer Sentiment Analysis Platform
June 17, 2026 – Present
Built end-to-end NLP classification pipeline to analyze customer reviews and classify sentiment (positive, negative, neutral) using machine learning algorithms and natural language processing techniques. Implemented advanced text preprocessing including tokenization, stemming, lemmatization, stop-word removal, and regular expression-based cleaning to optimize feature extraction quality. Applied TF-IDF vectorization and word embeddings (Word2Vec), comparing multiple ML algorithms (Logistic Regression, Random Forest, SVM, Naive Bayes) to maximize classification accuracy and F1-score.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in diverse data analysis applications (finance, customer behavior, NLP), indicating adaptability and a broad technical curiosity. The professional experience, though brief, shows collaboration and a focus on business outcomes, which are positive indicators for cultural fit. However, the limited professional experience and lack of diverse team environments in projects make a comprehensive assessment challenging.
Soft Skills & Operational Fit
The candidate's project descriptions and professional experience highlight collaboration with cross-functional teams, problem-solving, and a results-driven approach. The focus on delivering measurable business impact suggests a good operational fit for roles requiring data-driven decision support. However, without specific soft skill assessments, this remains an inference.