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https://github.com/odeyiany2/ecx-4.0-21-days-data-science-challenge

Last synced: 7 days ago
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Host: GitHub
URL: https://github.com/odeyiany2/ecx-4.0-21-days-data-science-challenge
Owner: Odeyiany2
Created: 2024-04-15T17:37:41.000Z (7 months ago)
Default Branch: main
Last Pushed: 2024-05-02T07:08:46.000Z (7 months ago)
Last Synced: 2024-05-02T13:25:30.962Z (7 months ago)
Language: Jupyter Notebook
Size: 3.45 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

        ![pic](https://github.com/Odeyiany2/ECX-4.0-21-Days-Data-Science-Challenge/blob/main/dataset-cover.jpg)

# ECX 4.0 21 Days Data Science Challenge: Iris Classification

This is a challenge organized by the Engineering Career Expo Unilag. A challenge to test our data science skills for 21 days. 

In this challenge, I dealt with the Iris dataset and built a model that predicts the specie of an Iris by its measurements. 

## Tools 

The following tools were used for different areas of the project:

* Python Libraries:

  - `Pandas`: for data analysis and manipulation

  - `Seaborn`: a library based on matplotlib and it provides a high-level interface for data visualization

  - `matplotlib`: for data visualization

  - `Joblib`: Saving our model for deployment

* Scikit Learn (Python Machine Learning Library):

  - `GridSearchCV and RandomSearchCV`: Hyperparameter tuning

  - `StandardScaler`: for standardization of numeric features

  - `LabelEncoder`: for encoding oyr categorical features

  - `RandomForestClassifier, SVC, LogisticRegression, DecisionTreeClassifier`: ML algorithm for classification problems

  

* Evaluation Metrics:

  - `Accuracy Score`: Number of correctly predicted class over the total classes 

  - `Precision`: ratio of correctly predicted positive classes over the total positive classes

  - `Recall`: ratio of correctly predicted positive class over the total classes

  - `Classification report`: a report showing precision, recall and F-1 score 

  - `ROC Curve`: a plot showing the true positive rate(TPR) over false positive rate(FPR)

  - `Confusion matrix`: a table for assessing the quality of our classification model prediction

    

* Deployment: `Streamlit`

  

[Deployment Video](https://drive.google.com/file/d/1USr4u_sPX2mlkSUDhbb4Ctf8C_pTy3kT/view?usp=drive_link)

[Streamlit App](https://iris-flower-specie-classifier.streamlit.app/)