https://github.com/hariprasath-v/doceree_machine-learning-hackathon_round_1

Create a model that can accurately predict whether a user belongs to the HCP(Healthcare Professional) category or not. Based on server logs.
https://github.com/hariprasath-v/doceree_machine-learning-hackathon_round_1

accuracy binaryclassification catboost exploratory-data-analysis machine-learning optuna python shap

Last synced: 10 months ago
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Create a model that can accurately predict whether a user belongs to the HCP(Healthcare Professional) category or not. Based on server logs.

• Host: GitHub
• URL: https://github.com/hariprasath-v/doceree_machine-learning-hackathon_round_1
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2023-07-03T16:08:16.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-07-04T07:17:31.000Z (over 2 years ago)
• Last Synced: 2025-01-13T01:45:02.673Z (11 months ago)
• Topics: accuracy, binaryclassification, catboost, exploratory-data-analysis, machine-learning, optuna, python, shap
• Language: HTML
• Homepage:
• Size: 4.59 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# DOCEREE_machine-learning-hackathon_round_1

##  Leaderboard

* Rank:4

* Score:99.8824

### Competition hosted on Techgig

### Problem

Create a model that can accurately predict whether a user belongs to the HCP(Healthcare Professional) category or not. Based on server logs.

### Evaluation

#### Evaluation metric for this competition is Accuracy.

### Solution:

### Exploratory Data Analysis

#### The basic exploratory data analysis of the data,

* Target distribution

* Categorical column level count

#### The above analysis had done by using,

* pandas 

* numpy

* seaborn

* matplotlib

### Model

#### Created catboost classifier model and tuned hyperparameters by using optuna framework. The model was evaluated by Accuracy.

#### Packages Used,

      * Sklearn

      * Pandas

      * Numpy

      * Matplotlib

      * catboost

      * optuna

      * shap

      

#### [For more detailed information about the model.](https://github.com/hariprasath-v/DOCEREE_machine-learning-hackathon_round_1/blob/main/Approach_Doceree_Machine_Learning_Hackathon.pdf)     

### Catboost Model Feature Importances

![Alt text](https://github.com/hariprasath-v/DOCEREE_machine-learning-hackathon_round_1/blob/main/Model%20interpretation%20visualization/Catboost%20Model%20Top%20Feature%20Importances.png)

### SHAP Catboost Model Feature Importances

![Alt text](https://github.com/hariprasath-v/DOCEREE_machine-learning-hackathon_round_1/blob/main/Model%20interpretation%20visualization/Catboost%20SHAP%20feature%20importance%E2%80%99s.png)

### Catboost Model train and validation accuracy

![Alt text](https://github.com/hariprasath-v/DOCEREE_machine-learning-hackathon_round_1/blob/main/Model%20interpretation%20visualization/Overall%20Train%20and%20Validation%20Accuracy.png)

### Catboost Model validation data confusion matrix

![Alt text](https://github.com/hariprasath-v/DOCEREE_machine-learning-hackathon_round_1/blob/main/Model%20interpretation%20visualization/Validation%20data%20Confusion%20matrix.png)

### File information

doceree-machine-learning-hackathon-1-eda.ipynb[![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/code/hari141v/doceree-machine-learning-hackathon-1-eda/notebook)

 

doceree-machine-learning-hackathon-1-model.ipynb[![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/hari141v/doceree-machine-learning-hackathon-1-model)