You will get machine learning predictive models with analysis
Project details
You will get an analysis of how different machine learning predictive models perform on your dataset. With more than three years' experience in data science and predictive modeling, I purse to mine the gold from data and provide insights about how machine learning can help to boost your business. High quality work and 100% satisfaction!
What's included $1,500
These options are included with the project scope.
$1,500
- Delivery Time 3 days
- Number of Revisions 3
- Number of Model Variations 6
- Model Validation/Testing
- Model Documentation
- Source Code
About Hanling
Machine Learning / Deep Learning Algorithm Researcher
Shenzhen, China - 6:21 am local time
-------------------------
I love discovering the myth under data! If you need any analysis on your data, feel free to let me know! My fields of interests: 1) Data Mining: Classification/Regression Tasks, Pattern Recognition, Data Analysis; 2) Computer Vision: Image Classification, Object Detection, Image Recognition, Deep Learning.
Skills:
------
✓ Solid background in Mathematics and statistics
✓ Skilled with data science tools, including Python, R, Matlab, Linux, MongoDB
✓ Skilled with deep learning framework, including Caffe, Tensorflow, Keras, PyTorch
✓ Familiar with machine learning algorithms, including LightGBM, XGBoost, RF, LR, FFM
✓ Familiar with various of NN architecture, including CNN (AlexNet, ResNet, GAN), RNN, DNN
Characteristics:
------------------
- Strong problem-solving skills
- Highly object/goal oriented
- Paying attention to details
- Easy to communicate
Your satisfaction is my drive!!!
Steps for completing your project
After purchasing the project, send requirements so Hanling can start the project.
Delivery time starts when Hanling receives requirements from you.
Hanling works on your project following the steps below.
Revisions may occur after the delivery date.
Outline how the project would go on
Deliver models and analysis results
Pre-process the dataset and provide ncessary data visualizations. Compare results from 6 models in scikit-learn (LR, SVM, DT, RF, GBDT, LightGBM), and show the feature importances. Analyze each model based on metrics and make a conclusion.