You will get a Machine Learning Classifier for your tabular data
Project details
In today's data-focused world, the paramount requirement is to extract valuable insights and automate decision-making. Harness the power of your data to improve decision-making, predictive modelling, and process automation.
Whether you are a business aiming to optimize your operations, or an individual seeking to streamline your digital life, our service is the solution.
We realize every problem is inherently unique. The service will deliver a customized classifier that employs state-of-the-art algorithms to address your specific needs.
Optimize your processes and unleash untapped potential in your data.
Whether you are a business aiming to optimize your operations, or an individual seeking to streamline your digital life, our service is the solution.
We realize every problem is inherently unique. The service will deliver a customized classifier that employs state-of-the-art algorithms to address your specific needs.
Optimize your processes and unleash untapped potential in your data.
Machine Learning Tools
Keras, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, SciPy, TensorFlow, XGBoostWhat's included $900
These options are included with the project scope.
$900
- Delivery Time 15 days
- Number of Revisions 1
- Number of Model Variations 3
- Number of Scenarios 1
- Number of Graphs/Charts 3
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Fast 5 Days Delivery
+$500
Additional Revision
+$200
Additional Model Variation
(+ 1 Day)
+$200
Additional Scenario
(+ 2 Days)
+$200
Additional Graph/Chart
(+ 1 Day)
+$40Frequently asked questions
About Tomoe
Data Science | Data Analysis | Python|R
Porto Alegre, Brazil - 1:45 pm local time
Some of my Data Science skills comprise:
🌟 Data Analysis in Python, R, SPSS, Minitab, Amos, SmartPLS, etc.
🌟 Matched groups comparison analysis (ex. propensity scores) for quasi-experiments, when perfect experiments are not viable;
🌟 Time-series analysis (Gradient boosting regression, multiseasonal time series using TBATS, Prohpet, etc.);
🌟 Development of machine-learning models for classification and prediction (time series, multi-label data)
🌟 Data mining (depict hidden structures from data with graph/network analysis, factor analysis, cluster analysis, etc.);
🌟 Cluster analysis (market/customer/user segmentation, for example, including time-series clustering);
🌟 Design of experiments (and data analysis of experimental data);
🌟 Alongside traditional methods like regression and ANOVA, parametric and non-parametric tests;
🌟 Structural equation modelling - SEM (Covariance-based - CB-SEM, or partial least squared-SEM - PSL-SEM, longitudinal SEM, etc.);
🌟 Data scrapping (web, PDF scrapping or acquisition of data using API);
🌟 Modelling and finding patterns to monitor for anomalies (malfunctions in machinery, equipment wears, track frauds, non-compliance events, or even changes in the market or users' behaviour and/or mindset).
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With my interdisciplinary background in both the industrial and scientific fields, I have trained myself beyond my formal education by experiencing several problems and projects in my professional trajectory. With this wide and diversified experience, I have the expertise in quickly understanding the problems, by an initial broad view of the problem, before scoping into specificities of the questions to be answered by specific analyses applied to specific conjoint of variables/data.
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Some of Data Science applied projects I have working experience are:
🌟 Development of machine-learning models for multi-label data
🌟 Graph/network analysis to generate a patent map for technology assessment;
🌟 Analysis of association among diseases and molecular targets, based on bibliographical (academic publications, molecular databases, clinical study) data.
🌟 Analysis of perception-based survey data analysis (to assess customer satisfaction, workplace, or labour-related surveys, the effectiveness of health, sustainability, or even brand awareness campaigns focused on attitude, intention and/or behaviour, for example).
🌟 SEM analysis to assess and model interrelated data (the effectiveness and safety of pharmacological, medical or other therapeutic interventions).
🌟 SEM analysis monitor campaigns’ effectiveness (health, promotional, sustainability, etc.) awareness campaigns.
🌟 Mediation and moderations (SEM) studies enable further assessment of complex causal relationships, like the interplay of intermediate outcomes, and phenotypical variations in the population, for example.
🌟 Demand forecast for pharmaceutical market, using time series analysis.
🌟 Market research and financial plan for startup;
🌟 Mixed method studies to diagnose local technological transference culture;
🌟 A Web and PDF scrapping with data wrangling and text mining service to extract and convert complex textual and unstructured data into workable data.
🌟 Interactive web app development using R Shiny to showcase the analysis results;
🌟 Interactive web app (R shiny) development for student exercises.
🌟 Gathering data from several databases like protein, clinical trial, patent, academic publication, public service databases for a variety of data analysis/applications.
🌟 The projects were devoted to a variety of industrial fields, from biotech to engineering, including startup companies. And applied to areas like innovation, technology management, marketing, Human Resource Management, supply chain management, etc.
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If the above sounds like something you are looking for, please send me a message, or invite me to your project. I would love to hear from you!
Steps for completing your project
After purchasing the project, send requirements so Tomoe can start the project.
Delivery time starts when Tomoe receives requirements from you.
Tomoe works on your project following the steps below.
Revisions may occur after the delivery date.
Definitions of the algorithms to be used
Definition of the best type of algorithms to use, considering the type of classifier required. The dataset provided is also inspected for adequacy to train the dataset.
Trainning of the machine learning model
Machine learning models are trained, considering the algorithms selected previously, and compared regarding their performance.