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Then took the top 3 models based on the accuracy score then blend the model by using pycaret blend_models function. \n\n\n### Top 3 Classifier Models\n![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Top%203%20models.PNG)\n\n### Voting Classifier ROC Plot\n![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/ROC%20curve%20for%20voting%20classifier.PNG)\n\n### Voting Classifier Precision-Recall Plot\n![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Precision-recall-curve%20for%20voting%20classifier.PNG)\n\n### Voting Classifier Prediction Error Plot\n![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Voting%20classifier%20prediction%20error%20plot.PNG)\n\n### Voting Classifier Confusion Matrix\n![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Voting%20classifier%20confusion%20matrix.PNG)\n\n### Validation Curve for Random Forest Classifier\n![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Validation%20curve%20for%20random%20forest%20classifier.PNG)\n\n### Random Forest Classifier Feature Importances\n![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Feature%20Importance%20plot%20random%20forest.PNG)\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhariprasath-v%2Ftechgig---data-science---2022","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhariprasath-v%2Ftechgig---data-science---2022","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhariprasath-v%2Ftechgig---data-science---2022/lists"}