Predictive Modeling with Machine Learning

Josip Novak

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About this service

Summary

This service involves predictive modeling with machine learning algorithms. It encompasses both solutions with single learners and ensemble learning. Though the service is not limited to a particular domain, my domain expertise in psychology and psychometrics make me a suitable choice for predictive modeling in the context of human behavior.

Process

Data Preparation: cleaning (e.g., duplicate removal, missing values and outlier treatment), formatting (e.g., converting data types, string operations, date operations, filtering, sorting, aggregation, discretization), reshaping (long to wide, wide to long), merging several sources, transformation (e.g., normalization, standardization, Box-Cox, Johnson), feature engineering/creating new variables, dimension reduction

Predictive Modeling: feature selection, model selection, model training, model testing and evaluation

Documentation: code and dependencies, model configuration (model type, input/output specifications, training details, and performance metrics)

Final Delivery (listed deliverables)

FAQs

What advantages does your domain expertise in psychology and psychometrics provide for predictive modeling?
My domain expertise in psychology and psychometrics offers a significant advantage in predictive modeling by providing a deeper understanding of human behavior and its measurement. This expertise specifically enhances feature selection, feature engineering, and result interpretation.
What if my project requirements do not exactly match the offered service?
I am flexible, so feel welcome to message me and we can discuss the specific requirements of your project.
Do I need to provide my own data?
Typically, it is assumed that you will provide the dataset for the model training. However, if the project requires it, I can assist with obtaining data through web scraping or other methods to gather data from specific sources.
How will my data be handled in terms of confidentiality and data security?
I am committed to data ethics and understand the importance of protecting sensitive information. Your data will be used solely for the purpose of completing your requested analysis. It will not be shared with any third parties and will be deleted upon completion of the task.
Which tools do you use for predictive modeling with machine learning algorithms?
R and Python. The choice depends on the task at hand.

What's included

The Model
The trained model that is ready for deployment will be delivered in the requested format (e.g., TensorFlow SavedModel, Pickle, PMML, RDS, RData).
The Code (RNotebook, Jupyter) and Documentation
The code that was used for the analysis will be delivered along with the documentation. The documentation will include justification for every decision during the data preparation.
The Prepared Dataset (.csv, .xlsx, etc.)
If needed, the prepared form of the dataset can be delivered along with the report.
The Model Configuration File
For model deployment, the delivery will include the following configuration details: Model Type: - Specifies the type of model being deployed (e.g., classification, regression). Input/Output Specifications: - Input Features: The expected input features used by the model. - Output Label: The name of the output prediction or target variable. Training Details: - Training Data Source: Source of the training dataset. - Training Data Split: Method used to split the data for training and validation (e.g., "80/20"). Performance Metrics: - Key metrics used to evaluate the model’s performance, including accuracy, precision, recall, AUC, RMSE, and any other relevant metrics. Example Format: model type: "classification" input_features: - feature1 - feature2 - feature3 output_label: "prediction" training_details: training_data_source: "data_source_name" training_data_split: "80/20" performance_metrics: accuracy: 0.95 precision: 0.92 recall: 0.94 AUC: 0.92

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