You will get forecast time-series failures with LSTM for predictive maintenance
Top Rated
Project details
I build time-series and deep-learning pipelines that predict equipment failures from high-frequency sensor data (HVAC, IoT, manufacturing-style telemetry). I use LSTM with attention, strong preprocessing, and class-imbalance handling to produce a deployable model plus clear metrics (e.g., RMSE / MAE / AUC-ROC where applicable) and a short handover so your team can run or extend the pipeline.
Machine Learning Tools
Apache Spark, Google AutoML, Google Data Studio, Google Sheets, Keras, MATLAB, Microsoft Excel, Microsoft Power BI, MLflow, OpenCV, PyTorch, R, SAS, SQL, Tableau, TensorFlow, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$90
|
Standard
$180
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 4 days | 6 days | 10 days |
Number of Revisions | 0 | 0 | 0 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
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About Tayyab
Data Scientist | Python & R | Machine Learning & Deep Learning Models
100%
Job Success
Sahiwal, Pakistan - 4:10 am local time
I'm a Data Scientist with 2+ years of experience in Data Scraping, Data Analysis, Data Visualization, Machine Learning, Deep Learning, Computer Vision, and NLP. I work primarily in Python and R/RStudio.
What I Do:
📊 Statistical Analysis, Time Series Analysis, Quantitative & Predictive Analysis
🤖 Designing, developing, and fine-tuning ML/DL models that deliver real impact
🚀 Building models from scratch or optimizing existing ones
Background:
Currently pursuing a Master's in Data Science with hands-on experience across Machine Learning, Deep Learning, and Advanced Analytics.
Technical Skills:
Languages: Python 🐍, R 📈
Frameworks: TensorFlow, Keras, PyTorch, Scikit-learn, Hugging Face Transformers, XGBoost, LightGBM, CatBoost, OpenCV, FastAPI, Flask, Streamlit
Libraries: NumPy, Pandas, Matplotlib, Seaborn, Plotly, Statsmodels, SciPy
Data Tools: Selenium, Scrapy, SQL, Power BI, Tableau, AWS (S3, Glue, Redshift), GCP, Azure ML
Others: MATLAB, Git, Docker, Google Earth Engine
Project Experience:
I've delivered projects across healthcare, finance, computer vision, and geospatial analytics. Some highlights:
Built deep learning models for drought prediction using 20 years of satellite data
Developed medical image classification systems for cervical cancer detection using DenseNet, ResNet, and EfficientNet architectures
Designed sales forecasting pipelines and stock market analysis dashboards with interactive visualizations
Created an AI voice assistant for paramedics
Built a real-time multi-camera object tracking system
Developed a student pressure dashboard powered by D3.js
Explored XAI methods (GradCam, SHAP, LIME) for model interpretability
Implemented models for churn prediction and match outcome forecasting
These projects combine machine learning, deep learning, and data science to deliver practical, impactful solutions.
What I Offer:
✅ Machine Learning Models: Build, fine-tune & deploy high-performance ML solutions
✅ Deep Learning Architectures: Custom DL models tailored to your needs
✅ Python Programming: Clean, optimized code for ML & data science tasks
✅ Data Analytics & Visualization: Insightful analysis and compelling visuals
✅ Data Scraping & Parsing: Data extraction using BeautifulSoup, Scrapy, and Selenium
Why Work With Me:
✨ Strong expertise in both ML and DL
✨ Clear communication throughout projects
✨ Fast delivery with professional support
✨ Focus on quality and client satisfaction
I take pride in delivering solutions that work. My approach is straightforward: build powerful models, provide actionable insights, and ensure clients are happy with the results.
Let's Work Together:
If you need someone who can take your machine learning projects to the next level, I'm here to help turn your ideas into reality. 🚀
Steps for completing your project
After purchasing the project, send requirements so Tayyab can start the project.
Delivery time starts when Tayyab receives requirements from you.
Tayyab works on your project following the steps below.
Revisions may occur after the delivery date.
What specific time-series models do you use?
I primarily implement LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units). For complex multi-sensor data, I incorporate Attention Mechanisms to help the model focus on the most critical sensor fluctuations before a failure occurs.