Iris Species Classification using Machine Learning

We are using Classification techniques like ID3,CART, Logistic Regression, Random Forest,Neural networks to classify the Iris species.

Modules involved:

1. Loading Data , pre-processing our data
2. Splitting the data into training samples and testing samples
3. Using classification techniques and finding the accuracy of the model
4. Analyzing different classification metrics like MSE, RMSE , Precision , Recall , Accuracy etc.
5. Concluding the best model.

DATASET

Dimensions: 150 rows × 6 columns

Attribute Information:

Using Radial basis function in SVM for classifying Iris Species

1)Iris-setosa

2)Iris-versicolor

3)Iris-virginica

4)Id

5)SepalLengthCm

6)SepalWidthCm

7)PetalLengthCm

8)PetalWidthCm

9)Species

1. Loading & pre-processing our dataset

As all the values are numeric, we can proceed to normalize the values

Divided the given dataset into train and test data with a 7:3 ratio using Sklearn’s train_test_split function.

3.Using classification techniques and finding the accuracy of the model

ID3

CART

Logistic Regression

Random Forest

Neural Network

4. Analyzing different classification metrics like MSE, RMSE , Precision , Recall , Accuracy etc.

we already analyzed them in the previous steps.

5. Concluding the best model.

Among all the classifiers we choose CART, ID3, Logistic regression as they have the highest accuracy later we choose Random forest.