Adventures in Machine Learning

Master Classification with the Iris Dataset: SVM KNN Decision Tree and Random Forest

Data analysis and machine learning are becoming increasingly important in the modern world. One dataset that is commonly used for beginner-level data analysis and machine learning is the Iris dataset.

This dataset consists of 150 data points which are divided into three classes, where each class corresponds to a different species of iris flower. In this article, we will explore how to prepare the data for machine learning, implement various classification models, and compare their accuracy levels.

Data Preparation and ML Model Implementation

Importing Modules

Before we can start working with the Iris dataset, we need to import a few modules. In Python, the common modules used for data analysis and visualizations are Numpy, Pandas, and Matplotlib.

Loading and Preparing the Iris Dataset

The Iris dataset can be found on Kaggle, a popular data science community website. After downloading the dataset, we can load it into a Pandas dataframe using the read_csv() function.

The dataset consists of four features: sepal length, sepal width, petal length, and petal width. We can use the slicing operation to extract the features as well as the corresponding labels.

Split Data Into Testing and Training Data

To train and test our ML models efficiently, we need to split the data into two sets: training data and testing data. We can use the train_test_split() function from Scikit-learn to do this.

The function randomly splits the data into two sets based on a specified ratio. Normalization/Standardization of Data

Normalization and standardization are techniques used to transform the data so that it is easier to process.

We can use the StandardScaler class from Scikit-learn to normalize the features. This scales the data to have a mean of 0 and standard deviation of 1.

We can use the cross_val_score() function to check if our data has been normalized correctly.

Applying Classification ML model

We can now apply classification machine learning models to our data. Here are four different models that can be implemented:

SVM (Support Vector Machine)

SVM is a powerful classification algorithm that can be used to classify linearly separable and non-linearly separable data. It works by finding the optimal hyperplane that separates the different classes of data.

We can use the SVM classifier from Scikit-learn to classify our data. After training the model, we can check the training accuracy and testing accuracy.

KNN (K-Nearest Neighbors)

KNN is a beginner-level classification algorithm that classifies new data points based on the k nearest data points in the training data. The value of k is a hyperparameter and can be varied to get the best results.

We can use the KNN algorithm from Scikit-learn to classify our data. After training the model, we can check the testing accuracy.

Decision Tree

Decision tree is a complex ML model that can be used to classify both numerical and categorical data. It works by recursively splitting the data based on the selected criterion (e.g., Gini index) until a stopping criteria is met.

The result is a tree-like structure that can be used to classify new data points. We can use the DecisionTreeClassifier from Scikit-learn to classify our data.

After training the model, we can check the testing accuracy.

Random Forest

Random Forest is an ensemble method that combines multiple decision trees to improve the accuracy of the classification. The idea is to train multiple decision trees on different subsets of the data and then combine their results to get a final prediction.

We can use the RandomForestClassifier from Scikit-learn to classify our data. After training the model, we can check the training data accuracy and testing data accuracy.

Conclusion

In conclusion, the Iris dataset provides an excellent opportunity for beginner-level data analysis and machine learning. We have explored how to prepare the data for machine learning, implement different classification models, and compared their accuracy levels.

By following the steps outlined in this article, readers can gain a better understanding of the process of implementing classification models and how to choose the best model for their data. In this article, we have discussed how to prepare the Iris dataset for machine learning and implement various classification models like SVM, KNN,

Decision Tree, and

Random Forest.

We compared their accuracy levels and discovered that all models achieved high accuracy levels on the Iris dataset. By following the outlined steps, readers can learn how to efficiently train and test their ML models on the Iris dataset.

The importance of data preparation, normalization, and standardization were also emphasized. In conclusion, the Iris dataset is a useful tool for beginner-level data analysis and machine learning, and the implementation of classification models can help in a wide range of real-world applications.

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