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faridghattas/README.md

Hi there, I'm Farid Ghattas 👋

Aspiring Data Analyst | Automotive Industry Specialist 🚗📊

I bridge the gap between complex datasets and strategic business decisions. With a deep passion for the automotive sector, I leverage Python, SQL, and predictive analytics to uncover hidden operational efficiencies, optimize fleet performance, and analyze market pricing.

🛠️ Technical Toolbox

  • Data Analysis: Python (Pandas, NumPy, SciPy), SQL
  • Machine Learning & Deep Learning: Scikit-Learn (Random Forest, SVM, KNN), TensorFlow, Keras (LSTM)
  • Data Visualization: Power BI, Tableau, Matplotlib, Seaborn
  • Platforms & Tools: Git, GitHub, Jupyter Notebook, Kaggle

🚗 Featured Data Portfolio

🧠 Predictive Analytics & Machine Learning & Deep Learning (Advanced Projects)

  • CWRU Bearing Vibration Fault Detection: DSP & Physics-Informed AI 📊🛠️ Developed an industrial-grade mechanical health monitoring pipeline using the benchmark CWRU Dataset. Implemented Fast Fourier Transform (FFT) and Envelope Analysis (Hilbert Transform Demodulation) to isolate high-frequency mechanical shock signatures (BPFI/BPFO). Built a physics-informed classifier optimized through systematic time-window scaling ($\Delta f$ contraction from $5.85\text{ Hz}$ to $1.46\text{ Hz}$), achieving 94% accuracy in deterministic defect isolation.

  • NASA Jet Engine RUL Prediction: From Baseline to Deep Learning ✈️🛠️ Designed a 3-stage optimization engine using the benchmark NASA CMAPSS dataset to predict the Remaining Useful Life (RUL) of turbofan engines. Conducted empirical threshold sweeping, optimized data structures via RobustScaler, and engineered a temporal 3D sliding window for a Deep Learning LSTM Recurrent Neural Network. Successfully reduced the baseline prediction error (RMSE) by 47.3%.

  • Sonar Signal Classification 🗺️ Implementing Logistic Regression for binary classification of acoustic signals—highly applicable to automotive predictive maintenance.

  • Predictive Quality Control (Random Forest) 🧪 Utilizing Random Forest Classifier to predict product quality ratings based on multi-variable chemical balances.

  • Diagnostic Risk Prediction (SVM) 🩺 Leveraging Support Vector Machines (SVM) for complex, distance-based classification pipelines.

📊 Data Analysis & Insights

  • Olympic History Data Analysis 🏅 Exploratory Data Analysis (EDA) and cleaning on a 120-year historical dataset to uncover long-term demographic and performance trends.

  • Supermarket Sales Insights 🛒 Transactional data analysis using Python to optimize business shift-staffing, peak hours, and customer purchasing patterns.

📈 Automotive Focus Area

I actively apply my analytical frameworks to automotive use cases, focusing on:

  • Predictive Maintenance: Reducing downtime by analyzing sensor frequencies, temporal degradation sequences (LSTM), and component wear.
  • Market Pricing & Depreciation: Modeling vehicle value degradation patterns over time.
  • Fleet & Sales Optimization: Streamlining parts inventory and operational performance.

🤝 Connect With Me

Pinned Loading

  1. faridghattas faridghattas Public

  2. nasa-cmapss-rul-prediction nasa-cmapss-rul-prediction Public

    A 3-stage optimization engine for predicting the Remaining Useful Life (RUL) of turbofan engines using Random Forest and Deep Learning (LSTM) on NASA CMAPSS dataset.

    Jupyter Notebook

  3. Diabetes-Risk-Prediction-SVM Diabetes-Risk-Prediction-SVM Public

    Predicting diabetes risk using Support Vector Machines (SVM) based on clinical and diagnostic features in Python.

    Jupyter Notebook

  4. Chemical-Quality-Classification Chemical-Quality-Classification Public

    Predicting product quality ratings based on chemical features using Random Forest Classifier in Python.

    Jupyter Notebook

  5. Sonar-Signal-Classification Sonar-Signal-Classification Public

    Binary classification of sonar signals to differentiate between rocks and mines using Logistic Regression in Python.

    Jupyter Notebook

  6. Olympic-History-Data-Analysis Olympic-History-Data-Analysis Public

    Exploratory Data Analysis (EDA) and data cleaning on a 120-year historical Olympics dataset using Python, Pandas, and Seaborn.

    Jupyter Notebook