You will get Tabular Predictive Modeling (ML + Neural Net)
Project details
This project implements an end-to-end binary classification pipeline, focusing on reproducible training, evaluation and comparison of ML models and a compact feed‑forward neural network (Keras). Ingest, cleans and encodes the diagnosis label, splits data with stratification, and preserves preprocessing for reproducible inference. The ML model is trained as a calibrated classifier inside a Pipeline; the neural network is trained on scaled inputs with a small architecture and binary cross‑entropy loss, recording training history. Evaluation is handled consistently: classification reports, confusion matrices, ROC/AUC, and standard metrics (accuracy, precision, recall, F1) are computed and stored in a results table. The project produces visual artifacts (ROC comparison plot, confusion matrices, NN loss curves) and exposes trained artifacts (models, scaler, history) suitable for delivery as scripts or a notebook. This setup serves as a fast freelance baseline deliverable that can be extended with hyperparameter tuning, feature engineering, or deployment packaging (Docker/REST) per client needs.
Machine Learning Tools
Apache Spark, Azure Machine Learning, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, R, scikit-learn, SQL, TensorFlow, XGBoostWhat's included $20
These options are included with the project scope.
$20
- Delivery Time 3 days
- Number of Revisions 2
- Number of Model Variations 2
- Number of Scenarios 3
- Number of Graphs/Charts 4
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Additional Revision
+$5
Additional Model Variation
(+ 1 Day)
+$5
Additional Scenario
(+ 1 Day)
+$5
Additional Graph/Chart
+$5About Omar
Data Analyst Specialist
Alexandria, Egypt - 7:12 pm local time
Steps for completing your project
After purchasing the project, send requirements so Omar can start the project.
Delivery time starts when Omar receives requirements from you.
Omar works on your project following the steps below.
Revisions may occur after the delivery date.
Data & Baseline Model
Tasks: ingest dataset, clean & encode, quick EDA, create preprocessing pipeline (scaler), compute metrics. Deliverables: cleaned sample, preprocessing pipeline, baseline metrics table + confusion matrix, short notes on data issues.
Neural Network & Model Comparison
Tasks: scale features, build/train small feed‑forward NN (early stopping / simple tuning), evaluate (accuracy, precision, recall, F1, AUC), plot ROC and compare with SVM, investigate improvements if needed.