Rework machine learning (Random Forest) code with different data
Budget: $150 – $250 USD
I am looking for a freelancer to rework my machine learning code using different data. The original code was written in Python.
Dataset:
- I have specific datasets that I would like to use for this project.
Current Code:
- Current code ("Draft.ipynb") reads a dataset where the Result/Output contains 4 different categories (A, B, C, D)
- It also includes hyper parameter tuning function and performance results analysis
- After training the algorithm with the training dataset ("inputdataset"), the trained algorithm is executed on a test dataset ("newdatainput") and writes the result/outcome in the first column
Purpose:
- The purpose of reworking the code is to adapt from predicting categories (A, B, C, D) to predicting actual numbers in percentage (%).
- The purpose of reworking the code so that it works with the new datasets (columns/data/values). I cannot share the old datasets that were fully compatible with the draft code.
- Hyperparameter tuning and performance results analysis functions to keep on working
- Re-use as much as possible of the current code
Ideal Skills and Experience:
- Strong proficiency in Python programming language.
- Experience with machine learning algorithms, particularly Random Forest.
- Familiarity with data manipulation and data preprocessing techniques.
- Ability to work with large datasets and handle missing values.
- Knowledge of evaluation metrics for regression tasks.
- Strong problem-solving and analytical skills.
- Attention to detail and ability to follow coding best practices.
If you have the necessary skills and experience in machine learning and Python programming, please submit your proposal.
Dataset:
- I have specific datasets that I would like to use for this project.
Current Code:
- Current code ("Draft.ipynb") reads a dataset where the Result/Output contains 4 different categories (A, B, C, D)
- It also includes hyper parameter tuning function and performance results analysis
- After training the algorithm with the training dataset ("inputdataset"), the trained algorithm is executed on a test dataset ("newdatainput") and writes the result/outcome in the first column
Purpose:
- The purpose of reworking the code is to adapt from predicting categories (A, B, C, D) to predicting actual numbers in percentage (%).
- The purpose of reworking the code so that it works with the new datasets (columns/data/values). I cannot share the old datasets that were fully compatible with the draft code.
- Hyperparameter tuning and performance results analysis functions to keep on working
- Re-use as much as possible of the current code
Ideal Skills and Experience:
- Strong proficiency in Python programming language.
- Experience with machine learning algorithms, particularly Random Forest.
- Familiarity with data manipulation and data preprocessing techniques.
- Ability to work with large datasets and handle missing values.
- Knowledge of evaluation metrics for regression tasks.
- Strong problem-solving and analytical skills.
- Attention to detail and ability to follow coding best practices.
If you have the necessary skills and experience in machine learning and Python programming, please submit your proposal.