You will get to know how feature selection affects machine learning model's performance
You will get to know how feature selection affects machine learning model's performance
Project details
This project gives you insights into the importance of feature selection that can highly affect the performance of various machine learning models. You will also see how you can tune the feature selection algorithm and machine learning models to further boosting the performance of the model.
Machine Learning Tools
NumPy, pandas, Python Scikit-Learn, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$30
|
Advanced
$40
|
|---|---|---|---|
| Delivery Time | 1 day | 1 day | 1 day |
Number of Revisions | 1 | 1 | 1 |
Number of Model Variations | 3 | 5 | 6 |
Number of Scenarios | 6 | 15 | 30 |
Number of Graphs/Charts | 8 | 17 | 32 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code |
About Zhenji
Data Scientist
Cheras, Malaysia - 11:59 am local time
I am a data-lover which enjoy exploring insights from data and data story telling. I also builds machine learning models and deep learning networks depends on the business problems. For example, linear regression or ARIMA for stock price prediction, manpower prediction, disaster prediction and food stock prediction; classification algorithms for real disaster post prediction; LLM for topic modeling and sentiment analysis.
Besides, just a little introduction with my background. I studied Biomedical Science for my degree and Master in Business Analytics and Data Science for master. Hence, I always hope for handling projects from different fields.
Tools: Python, R, SQL, SAS, Excel, Tableau, PowerBI
Steps for completing your project
After purchasing the project, send requirements so Zhenji can start the project.
Delivery time starts when Zhenji receives requirements from you.
Zhenji works on your project following the steps below.
Revisions may occur after the delivery date.
EDA
Understanding the dataset is important before building any models. Some charts or graphs will be built to analyse the dataset that can help in data cleaning step later.
Data Cleaning
Preprocessing of data to remove noises, steps including but not limited to : - missing data - outlier - class imbalance