https://github.com/hariprasath-v/techgig---data-science---2022

create a model to classify the raisin based 7 morphological features are extracted from images
https://github.com/hariprasath-v/techgig---data-science---2022

classification-model exploratory-data-analysis jupyter-notebook machine-learning matplotlib pandas pycaret python seaborn techgig

Last synced: about 2 months ago
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create a model to classify the raisin based 7 morphological features are extracted from images

• Host: GitHub
• URL: https://github.com/hariprasath-v/techgig---data-science---2022
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2022-09-29T05:32:35.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-09-29T06:00:22.000Z (almost 4 years ago)
• Last Synced: 2025-06-07T20:43:12.706Z (about 1 year ago)
• Topics: classification-model, exploratory-data-analysis, jupyter-notebook, machine-learning, matplotlib, pandas, pycaret, python, seaborn, techgig
• Language: Jupyter Notebook
• Homepage:
• Size: 743 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# TechGig---Data-Science---2022

# About

### create a model to classify the raisin based 7 morphological features are extracted from images.

### Final Score is 87.46 (not a competition score)

### Evaluation Metric is Accuracy.

### File information

 * techgig-data-science-2022-eda.ipynb

    #### Basic Exploratory Data Analysis

    #### Packages Used,

        * seaborn

        * Pandas

        * Numpy

        * Matplotlib

* techgig-data-science-2022-model.ipynb

    #### Data Pre-processing and model. 

    #### Packages Used,

        * Sklearn

        * Pandas

        * Numpy

        * Matplotlib

        * pycaret    

     #### Compared multiple classification models using pycaret’s compare_models function. Then took the top 3 models based on the accuracy score then blend the model by using pycaret blend_models function. 

### Top 3 Classifier Models

![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Top%203%20models.PNG)

### Voting Classifier ROC Plot

![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/ROC%20curve%20for%20voting%20classifier.PNG)

### Voting Classifier Precision-Recall Plot

![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Precision-recall-curve%20for%20voting%20classifier.PNG)

### Voting Classifier Prediction Error Plot

![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Voting%20classifier%20prediction%20error%20plot.PNG)

### Voting Classifier Confusion Matrix

![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Voting%20classifier%20confusion%20matrix.PNG)

### Validation Curve for Random Forest Classifier

![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Validation%20curve%20for%20random%20forest%20classifier.PNG)

### Random Forest Classifier Feature Importances

![Alt text](https://github.com/hariprasath-v/TechGig---Data-Science---2022/blob/main/Visualization%20Plots/Feature%20Importance%20plot%20random%20forest.PNG)