Click here for awesomness.
- Home:
- Introduction:
- EDA:
- Modelling:
- Model List
- Hyper Parameter Tuning for each models:
- Feature Importance & Deepdive:
- Model Observations:
- 3D Interactable Plots for Multiple parameters:
- Results:
This is little bit large file
From the expert, the time to diagnosis of cancer takes a lot of time as it includes new studies/papers, which makes this a time-consuming and exhaustive process. With machine learning, we can fast-track the majority of scenarios and help the expert get updated details.
I have used below classical machine learning algorithms for the problem.
1: Naive Bayes
2: K Nearest Neighbors
3: Logistic Regression
4: Support Vector Machine (SVM)
5: Random Forest Classifier
6: StackedClassifier (Ensemble)
7: MaxVoting Classifier (Ensemble)
As you might know, these algorithms have their limitation and advantages, I have tried to incorporate the best use of them by remediating the problems. Like
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The curse of dimensionality has been addressed by Response Coding.
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Class imbalance can be tuned with stratified splits and using the Class weight parameter whenever exploitable.
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Compute intensive Hyper-Tuning with parallelism when needed.
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At last, the beautiful interface & ton of integration of streamlit used.
1: Download package.
2: Give executable rights to setup.sh and run it.
Note:
- Change values in setup.sh if you find it necessary.
- For model generation, please run the Notebook to export data models. I have added here for (lightweight) streamlit runtime.
Once complete run, check host's 8080 (default) port.