I will build custom machine learning predictive models in python
Python Developer AI Workflow Specialist
About this Gig
Creation of machine learning models by employing TensorFlow and Python, which was used to predict intricate outcomes (for example, wildfire propagation) using various environmental data sets. This project required a complete range of activities such as data pre-processing, modeling, and tuning the precision of the machine learning model.
Programming language:
Python
•
SQL
•
Colab
Frameworks:
Scikit-learn
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Keras
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Panda
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
Colab
FAQ
What kind of data do you need from me to start?
I can work with most standard data formats, including CSV files, Excel spreadsheets, or SQL database exports. The more historical data you have, the more accurate the predictive model will be! If your data is messy, don't worry—data cleaning and preprocessing with Pandas are included in my workflow.
How can I use the model once it's finished? Can it connect to my app?
Absolutely. For the Premium package (or as a Gig Extra), I can wrap the machine learning model in a custom REST API using Flask. This allows you to easily connect the AI predictions to your existing website, software, or frontend interface.
What types of predictive models do you specialize in?
I specialize in classification and predictive analytics using Python, Scikit-learn, and TensorFlow. My focus ranges from business-oriented solutions, like Customer Churn prediction to retain users, to analyzing complex environmental or operational datasets to forecast risks.
Will I receive the source code?
Yes! Transparency is key. All packages include the fully commented Python source code. Depending on the project, this will be delivered as an interactive Jupyter Notebook (great for reviewing the data analysis step-by-step) or a complete, organized project folder.
Can you host the model online for me?
Yes! If you select the "Cloud Deployment" Extra, I will deploy your API or model to a reliable cloud platform so it runs 24/7 in the cloud without you needing to set up local servers.
