PxPixel

You will get machine learning predictive models with analysis

Hanling W.
Hanling W.
4.8
This project $1500
  • Delivery Time 3 days
  • Number of Revisions 3
  • Number of Model Variations 6
    • Model Validation/Testing
    • Model Documentation
    • Data Source Connectivity
      -
    • Source Code
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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!  
Tool scikit-learn

Project steps

  • 1
    Outline how the project would go on

  • 2
    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.

  • 3
    Necessary improvements

  • 4
    Final deliverables

    Include Jupyter Notebook (with HTML version), trained model files.

Requirements

  • 1
    Dataset to work with
  • 2
    Goal (what to predict based on what)
  • 3
    Other requirements (e.g., data pre-processing, deliverables)

About Hanling

Hanling W.
Machine Learning / Deep Learning Algorithm Engineer
Machine Learning / Deep Learning Algorithm Engineer
82% Job Success
Shenzhen, China - 10:54 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:
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✓ 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:
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- Strong problem-solving skills
- Highly object/goal oriented
- Paying attention to details
- Easy to communicate

Your satisfaction is my drive!!!