Machine Learning with Jupyter notebbok and SHAP ´s explainer libraries
Budget: $10 – $30 USD
I usually do "mini" Machine Learning projects for my students (2 or 3 per month).
My skills are not strong enough to develop some of these projects. So I'm looking for someone to help me to develop these small projects, when my other helpers are busy.
I like creative freelancers who apply not only my suggested models but other tools and models, plots , etc.
Since the projects are for academic lectures, budget is VERY LOW !
THIS PROJECT:
I have already created a model of 48 categorical variables (already created and tested) (21 variables "before diet" and 21 "after diet". (the model includes some other features and an output variable that summarizes the success.)
My initial approach is to use BOTH Recomendation engines PLUS Machine Learning Techniques.
Initially I found "similar" patients who were successful on their diet, then I used Cosine similarity with Sklearn. This initial approach was already developed and tested .
Seconfdly ]I aleady applyed a Machine Learning clasificator with SHAP libraries to explain which features are more relevant for each person who makes diet
I already have an "almost done" notebook for this project, but I need to look at different approaches.
Please note that I'm not just looking for a one time freelancer , a I look for a long term small academic basic jobs...
I usually ask to apply K-means, random forest, xgboost, lightGBM, or flask. All of them with descriptive statistics with plots and charts. Projectrs Usually has tiny datasets
PLEASE READ: the average time paid to develop each project is approximately 2 or 2.5 hours, (obviously sometimes more than that) but the datasets are usually ready to use. So the total amount of the project will be accordingly,
I can share my notebook with really interest freelancers, that are in agree with this LOW BUDGET projects
My skills are not strong enough to develop some of these projects. So I'm looking for someone to help me to develop these small projects, when my other helpers are busy.
I like creative freelancers who apply not only my suggested models but other tools and models, plots , etc.
Since the projects are for academic lectures, budget is VERY LOW !
THIS PROJECT:
I have already created a model of 48 categorical variables (already created and tested) (21 variables "before diet" and 21 "after diet". (the model includes some other features and an output variable that summarizes the success.)
My initial approach is to use BOTH Recomendation engines PLUS Machine Learning Techniques.
Initially I found "similar" patients who were successful on their diet, then I used Cosine similarity with Sklearn. This initial approach was already developed and tested .
Seconfdly ]I aleady applyed a Machine Learning clasificator with SHAP libraries to explain which features are more relevant for each person who makes diet
I already have an "almost done" notebook for this project, but I need to look at different approaches.
Please note that I'm not just looking for a one time freelancer , a I look for a long term small academic basic jobs...
I usually ask to apply K-means, random forest, xgboost, lightGBM, or flask. All of them with descriptive statistics with plots and charts. Projectrs Usually has tiny datasets
PLEASE READ: the average time paid to develop each project is approximately 2 or 2.5 hours, (obviously sometimes more than that) but the datasets are usually ready to use. So the total amount of the project will be accordingly,
I can share my notebook with really interest freelancers, that are in agree with this LOW BUDGET projects