To determine class or cateogry of flower which its belong to base on their 4 features or parameters such as sepal length,sepal width, petal length and petal width. In this dataset there are toatl 3 category of flowers such as(setosa,virginica,versicolor)
I have taken IRIS dataset from Kaggle https://www.kaggle.com/datasets/uciml/iris/
Dataset consists of total 5 Columns
- Sepal length
- Sepal Width
- Petal length
- Petal Width
- Species has 3 categories(setosa,virginica,versicolor)
- I have made this model which will predict cateogry of flower which its belong to base on their 4 features such as sepal length,sepal width, petal length and petal width.
- I have done stepwise EDA (Exploratory Data Analysis) then visualization to get some idea about imp features and correlation sepal length,sepal width, petal length and petal width with output feature Species
- Train model with multiples classification algorithms
- Analysed & compare performance of differents models based of accuracy and complexity
- After traning with mulptiples algo SVM and KNN had gievn best performace
- Then I have vary value of K still accuracy was same around 97.2 which was appro equal to SVM then after cross validation SVM accuracy was better then KNN
- Finally Build web application in python using streamlit library and then deploy the model
- https://karanchinch10-streamlit-iris-app-0k57bb.streamlitapp.com// works too. Must be used for explicit links.
- Technical tools or library used --Python,numpy,pandas,sklearn,matplotllib,html,css,streamlit