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The project employs the popular scikit-learn library and explores Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and Naive Bayes algorithms to create predictive models for classifying iris flowers into three different species: Setosa, Versicolour, and Virginica.\n\n## Problem Description\nThe main objective of this project is to build a machine learning model that can accurately classify iris flowers into one of the three species based on four features: sepal length, sepal width, petal length, and petal width.\n\n## Dataset\nThe Iris Flower dataset consists of 150 samples, each with four features: sepal length, sepal width, petal length, and petal width. These samples are labeled with the corresponding species they belong to: Setosa, Versicolour, or Virginica.\n\n## Implementation\nThe project involves the following steps:\n\n1. Data Preprocessing: The dataset is loaded and preprocessed to handle any missing values or anomalies.\n\n2. Data Splitting: The dataset is divided into a training set and a test set. The training set is used to train the logistic regression model, while the test set is used to evaluate the model's performance.\n\n3. Model Training: A classification ML model is trained using the features from the training dataset and their corresponding labels.\n\n4. Model Evaluation: The accuracy of the trained model is calculated using the test dataset. This accuracy gives an indication of how well the model can classify new, unseen samples.\n\n5. Prediction: The trained model is used to predict the species of iris flowers in the test dataset.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flijesh010%2Fml_project-iris_data_species_classification_algorithms","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flijesh010%2Fml_project-iris_data_species_classification_algorithms","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flijesh010%2Fml_project-iris_data_species_classification_algorithms/lists"}