Muhammad Huzaifa - Portfolio

📊 Nvidia Stock Price Intelligence Dashboard

📊 Bike Sharing Demand Intelligence Dashboard

💳 Advanced Credit Card Fraud Detection Using Machine Learning

🌾 Crop Yield Analysis and Interactive Insights using Python & Plotly

🧬 White Blood Cell Classification using Deep Learning & Computer Vision

🌱 PlantPulse: A Data Driven Solution for Predicting Plant Health Using Machine Learning

🏠 House Price Predictor: Regression Modeling Workflow with Python

📊 Sales Performance and Segment Insights Dashboard using Power BI

📊 Customer Spend Analysis and Membership Impact using Power BI

📄 DocuMind AI: RAG-Powered PDF Question Answering Assistant

❤️ CardioCare: Predicting Heart Disease Risk with Machine Learning & FastAPI

📊 Nvidia Stock Price Intelligence Dashboard

🔍 Objective

To analyze Nvidia's historical stock price behavior, identify performance trends, quantify investment risk through volatility analysis, and deliver dynamic year-on-year financial intelligence using Business Intelligence modeling and DAX-driven metrics. .

💡 Key Contributions

Conducted time-series analysis on Nvidia's daily closing price data to uncover yearly performance patterns and long-term price momentum
Developed DAX measures for statistical and time intelligence calculations including volatility quantification and annual return computation
Implemented 12-month moving average smoothing to eliminate short-term noise and reveal directional price trends
Built dynamic year-on-year comparison enabling benchmarking of closing price behavior across multiple years
Delivered KPI cards — Annual Return %, Average, Min and Max Closing Price — for instant financial decision context
Identified monthly volatility patterns using STDEV.P to quantify investment risk exposure across selected years

🛠️ Tools & Techniques

Power BI Desktop, DAX, Time Intelligence Functions, STDEV.P, FIRSTNONBLANK, LASTNONBLANK, Moving Average, KPI Cards, Interactive Slicers, Financial Data Modeling

🌐 Industry Relevance

FinTech, Investment Analysis, Asset Management, Financial Reporting, Stock Market Analytics

📊 Outcomes

Identified long-term closing price trends using moving average smoothing
Enabled comparative performance analysis across years
Provided interactive dashboard-based financial insight visualization
Demonstrated business intelligence storytelling using analytical measures

Business Impact:

Enables instant year-specific investment performance assessment — identifying return rates, risk periods, and price trends without requiring financial modeling expertise.

📎 View Report

📄 Nvidia Stock Price Intelligence Dashboard

Explore dynamic year-on-year price comparison, monthly volatility patterns, 12-month moving average trends, and key financial KPIs in this interactive Power BI report.

📊 Bike Sharing Demand Intelligence Dashboard

🔍 Objective

To analyze bike rental demand patterns across weather conditions, seasons, and time periods — uncovering the key environmental and temporal factors that drive or suppress rental activity using Business Intelligence modeling and DAX-driven metrics.

💡 Key Contributions

Conducted exploratory analysis on daily bike rental data across 16 features including weather, temperature, humidity, season and working day indicators
Engineered calculated columns converting normalized temperature and humidity values into real-world readable units (°C and %) for accurate visual representation
Developed DAX measures identifying Peak Month and Peak Season by dynamically ranking rental aggregations across time dimensions
Built year-on-year monthly comparison visual revealing seasonal demand patterns and growth trends across 2011 and 2012
Designed two-page dashboard structure — Overview page for high level KPIs and Detail page for weather and temporal deep dive analysis
Implemented dynamic slicers for Year and Season enabling fully interactive demand exploration across all visuals

🛠️ Tools & Techniques

Power BI Desktop, DAX, SUMMARIZE, TOPN, MAXX, FORMAT, Calculated Columns, KPI Cards, Interactive Slicers, Combo Charts, Demand Analysis

🌐 Industry Relevance

Urban Mobility, Smart City Planning, Transportation Analytics, Environmental Impact Analysis, Operational Demand Forecasting

Business Impact:

Enables operational teams to identify peak demand periods, weather-driven rental patterns, and seasonal trends — supporting smarter fleet allocation and resource planning decisions.

📎 View Report

📄 Bike Sharing Demand Intelligence Dashboard

Explore seasonal demand patterns, weather impact analysis, year-on-year rental comparisons, and key operational KPIs in this interactive Power BI report.

💳 Advanced Credit Card Fraud Detection Using Machine Learning

🔍 Objective
To analyze credit card transaction patterns, detect fraudulent activities using ensemble and linear machine learning models, and build predictive systems for fraud prevention while addressing class imbalance in real-world financial data.

💡 Key Contributions

Conducted comprehensive exploratory data analysis (EDA) on 80,000+ transactions across 16 features to uncover transaction behavior and fraud anomalies
Preprocessed and cleaned structured financial data with categorical encoding and normalization for modeling
Implemented supervised machine learning models (Random Forest with 99% accuracy, Logistic Regression) with balanced class weighting to address 7% fraud rate
Evaluated models using accuracy, precision, recall, ROC-AUC, and confusion matrix—Random Forest achieved 98% precision, 82% recall, and 0.992 ROC-AUC
Selected Random Forest as production model for superior fraud detection (1,505+ frauds identified out of 1,836) while minimizing false alarms (2% false positive rate)

🛠️ Tools & Techniques
Python, Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, Supervised Learning (Random Forest, Logistic Regression), Balanced Class Weighting, Classification Metrics, ROC-AUC Analysis, Cross-Validation

🌐 Industry Relevance
FinTech & Digital Payments, Banking & Credit Institutions, Risk Management, Fraud Prevention, Cybersecurity.

Business Impact: Detected 82% of fraudulent transactions, enabling effective identification of high-risk activities and significantly improving fraud prevention capability in transaction processing systems.

🔧 Project Repository
Explore the complete project, source code, and documentation on GitHub:
🔗 Github Repo

🌾 Crop Yield Analysis and Interactive Insights using Python & Plotly

🔍 Objective

To analyze global agricultural crop yield trends and evaluate the relationship between crop output, rainfall patterns, and pesticide use.

💡 Key Contributions

Cleaned and preprocessed large-scale structured agricultural data.
Performed multi-dimensional grouping by area, crop, and year.
Computed and visualized correlation matrices (e.g., pesticide use vs. yield).
Designed interactive dashboards using Plotly for policy-level decision insights.

🛠️ Tools & Techniques

Python, Pandas, Plotly, Time-Series Grouping, Correlation Analysis

🌐 Industry Relevance

AgriTech, Climate Intelligence, Government Agricultural Policy

🔧 Project Repository

Explore the complete project, source code, and documentation on GitHub:

🔗 GitHub Repository

🧬 White Blood Cell Classification using Deep Learning & Computer Vision

🔍 Objective

Develop a Convolutional Neural Network (CNN) to automate white blood cell classification using medical imaging.

💡 Key Contributions

Preprocessed image datasets (resizing, normalization with OpenCV).
Built CNN model using TensorFlow/Keras with layers like Conv2D, MaxPooling.
Trained and validated model performance; saved model for clinical deployment.

🛠️ Tools & Techniques

TensorFlow, Keras, OpenCV, CNN Architecture, Model Evaluation

🌐 Industry Relevance

Medical Diagnostics, Healthcare AI, Digital Pathology

🔧 Project Repository

Explore the complete project, source code, and documentation on GitHub:
🔗 GitHub Repository

🌱 PlantPulse: A Data Driven Solution for Predicting Plant Health Using Machine Learning

🔍 Objective

Leverage sensor data to predict plant health status and enable early intervention through machine learning.

💡 Key Contributions

Performed exploratory data analysis to uncover patterns in soil moisture and plant stress levels.
Visualized correlations and feature distributions using Seaborn and Matplotlib.
Applied Label Encoding and SMOTE to prepare and balance the dataset.
Trained and evaluated KNN and Random Forest classifiers to detect plant health categories (Healthy, Moderate, Stressed).

🛠️ Tools & Techniques

Python, Pandas, NumPy, Seaborn, Matplotlib
Scikit-learn, imbalanced-learn (SMOTE), Jupyter Notebook

🌐 Industry Relevance

Precision Agriculture, AgriTech, Smart Farming Automation

✅ Outcomes

Identified Soil_Moisture as a key predictor of plant stress.
Developed scalable predictive models with interpretable results.
Demonstrated a complete end-to-end data science pipeline with real-world agricultural impact.

🔧 Project Repository
Explore the complete project, source code, and documentation on GitHub:
🔗 GitHub Repository

🏠 House Price Predictor: Regression Modeling Workflow with Python

🔍 Objective

Develop and evaluate regression models to predict a continuous target variable using a full ML pipeline in Python.

💡 Key Contributions

Loaded and explored Excel-based dataset with .info(), .describe(), and null value checks.
Cleaned data by removing irrelevant columns.
Engineered and selected features based on correlation heatmap analysis.
Built and trained two regression models:
- ✅ Decision Tree Regressor
- ✅ Polynomial Linear Regression (using pipeline)
Evaluated performance using Mean Absolute Percentage Error (MAPE).
Visualized predictions, feature relationships, and model comparisons.

🛠️ Tools & Techniques

Python, Pandas, NumPy
Scikit-learn (Decision Tree, Polynomial Regression, Pipelines)
Seaborn, Matplotlib

🌐 Industry Relevance

Predictive Analytics, Business Intelligence, Data-Driven Decision Making

✅ Outcomes

Identified strong predictors through correlation analysis.
Compared model performances using visual and quantitative metrics.
Delivered interpretable insights from regression models.

🔧 Project Repository

Explore the complete project, source code, and documentation on GitHub:
🔗 GitHub Repository

📊 Sales Performance and Segment Insights Dashboard using Power BI

🔍 Objective

Analyze international product sales data segmented by country, product, and customer segment to evaluate profitability, trends, and market performance using interactive Power BI reports.

💡 Key Contributions

Interactive dashboards by segment, product, and geography
Monthly/yearly breakdowns of units sold, gross sales, discounts
Country-wise profitability comparison
Multi-level slicers and intuitive filters
Drill-down profit and trend insights

🛠️ Tools & Techniques

Power BI, Power Query, Visual Analytics

🌐 Industry Relevance

Retail Analytics, Market Performance, Sales Strategy

✅ Outcomes

2014 identified as the strongest year for sales
Key products and countries visualized for growth
Informed strategy via data-driven insights

📎 View Report

📄 Financial Data Analysis Report (PDF)

This report contains all dashboards, visual analyses, and insights mentioned above.

📊 Customer Spend Analysis and Membership Impact using Power BI

🔍 Objective

Analyze departmental and membership-based revenue and profit data across UK cities to identify performance trends and customer spending patterns.

💡 Key Contributions

Combined multiple datasets (membership, departments, location)
Analyzed Gold/Bronze member behavior and impact
Visualized revenue by product categories like Bikes, Clothing, Footwear
Location-wise sales insights for 30+ cities
Club Membership segmentation and department success indicators

🛠️ Tools & Techniques

Power BI, Data Modeling, Interactive Slicers

🌐 Industry Relevance

Customer Segmentation, Loyalty Analytics, Retail BI

✅ Outcomes

Gold members drove majority of high-profit sales
Departments like Outdoors & Cycle led profit margins
Enabled location-specific insights for operational decisions

📎 View Report

📄 Customer Spend & Membership Report (PDF)

Explore interactive visuals and detailed business insights in this Power BI report.

📄 DocuMind AI: RAG-Powered PDF Question Answering Assistant

🔍 Objective

To create an intelligent PDF assistant powered by Retrieval-Augmented Generation (RAG) that enables users to summarize, explore, and ask context-grounded questions about any uploaded PDF document.

💡 Key Contributions

Developed a Streamlit-based interactive UI for PDF upload and exploration.
Implemented document chunking with overlap for improved context retrieval.
Integrated HuggingFace sentence-transformers with FAISS for fast, in-memory vector similarity search.
Designed prompt engineering with predefined and custom instruction styles for tailored responses.
Automated concise document summaries and generated recommended exploratory questions.
Ensured context-aware answers by restricting responses to uploaded document content only.

🛠️ Tools & Techniques

Python, Streamlit, LangChain, FAISS, HuggingFace Embeddings, ChatGroq, PyPDFLoader,LLM

🌐 Industry Relevance

Document Intelligence, LegalTech, Research Assistance, Knowledge Management

🔧 Project Repository

Explore the complete project, source code, and documentation on GitHub:

🔗 Github Repo

❤️ CardioCare: Predicting Heart Disease Risk with Machine Learning & FastAPI

🔍 Objective
To predict heart disease risk by analyzing clinical and behavioral health indicators and deploying a machine learning model as a REST API using FastAPI.

💡 Key Contributions

Conducted exploratory data analysis (EDA) to uncover health risk patterns across factors such as age, gender, BMI, glucose, smoking, and diabetes.
Preprocessed and cleaned clinical datasets, handling missing values and balancing the data using SMOTE for improved model fairness.
Built and compared machine learning classification models, including Random Forest and K-Nearest Neighbors (KNN).
Evaluated model performance using accuracy, precision, recall, F1-score, and confusion matrix to ensure reliability.
Deployed the final trained model as a REST API using FastAPI, integrating Pydantic validation for robust input handling and enabling real-time predictions.

📊 Nvidia Stock Price Intelligence Dashboard

🔍 Objective

💡 Key Contributions

🛠️ Tools & Techniques

🌐 Industry Relevance

📊 Outcomes

Business Impact:

📎 View Report

📊 Bike Sharing Demand Intelligence Dashboard

🔍 Objective

💡 Key Contributions

🛠️ Tools & Techniques

🌐 Industry Relevance

Business Impact:

📎 View Report

💳 Advanced Credit Card Fraud Detection Using Machine Learning

🌾 Crop Yield Analysis and Interactive Insights using Python & Plotly

🔍 Objective

💡 Key Contributions

🛠️ Tools & Techniques

🌐 Industry Relevance

🔧 Project Repository

🧬 White Blood Cell Classification using Deep Learning & Computer Vision

🔍 Objective

💡 Key Contributions

🛠️ Tools & Techniques

🌐 Industry Relevance

🔧 Project Repository

🌱 PlantPulse: A Data Driven Solution for Predicting Plant Health Using Machine Learning

🏠 House Price Predictor: Regression Modeling Workflow with Python

📊 Sales Performance and Segment Insights Dashboard using Power BI

📊 Customer Spend Analysis and Membership Impact using Power BI

📄 DocuMind AI: RAG-Powered PDF Question Answering Assistant

🔍 Objective

💡 Key Contributions

🛠️ Tools & Techniques

🌐 Industry Relevance

🔧 Project Repository

❤️ CardioCare: Predicting Heart Disease Risk with Machine Learning & FastAPI

🔍 Objective To predict heart disease risk by analyzing clinical and behavioral health indicators and deploying a machine learning model as a REST API using FastAPI.

💡 Key Contributions

🛠️ Tools & Techniques Python, Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, imblearn (SMOTE), FastAPI, Pydantic, Pickle, requests

🌐 Industry Relevance Healthcare, Preventive Medicine, HealthTech, Risk Prediction Systems

🔧 Project Repository Explore the complete project, source code, and usage instructions on GitHub: 🔗 GitHub Repo

🔍 Objective
To predict heart disease risk by analyzing clinical and behavioral health indicators and deploying a machine learning model as a REST API using FastAPI.

🛠️ Tools & Techniques
Python, Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, imblearn (SMOTE), FastAPI, Pydantic, Pickle, requests

🌐 Industry Relevance
Healthcare, Preventive Medicine, HealthTech, Risk Prediction Systems

🔧 Project Repository
Explore the complete project, source code, and usage instructions on GitHub:
🔗 GitHub Repo