Machine Learning Task: Iris Species Classification

This task has been prepared for the Machine Learning Engineer Virtual Internship Program in Intern2Grow.

Objective

Your task is to build a machine learning model that can classify the species of iris flowers based on their features.

Dataset

You are provided with a dataset in CSV format with the following columns:

• Sepal_Length: The length of the sepal.
• Sepal_Width: The width of the sepal.
• Petal_Length: The length of the petal.
• Petal_Width: The width of the petal.
• Class: The species of the iris (e.g., setosa, versicolor, virginica).

Task Breakdown

1. Data Preparation: Load the dataset from the CSV file. Perform any necessary data cleaning and preprocessing tasks, such as dealing with missing values, outliers, or encoding categorical variables.

2. Exploratory Data Analysis (EDA): Analyze the dataset to gain insights about the distribution of variables, correlation between features, etc. This may involve creating visualizations such as scatter plots, histograms, or box plots.

3. Feature Selection: Decide which features from the dataset you will use to train your model. The goal is to predict the class of the iris, so this is your target variable.

4. Model Selection: Choose an appropriate machine learning algorithm for this classification task. Justify your choice.

5. Model Training: Split the dataset into a training set and a test set. Use the training set to train your model.

6. Model Evaluation: Use the test set to evaluate the performance of your model. You may use appropriate metrics for classification tasks such as accuracy, precision, recall, or F1-score.

7. Prediction: Finally, use your trained model to predict the class of a new iris flower given its features.

Deliverable

Submit a report detailing your approach, methodology, and results. Your report should include:

• An explanation of your data cleaning and preprocessing steps.
• The features you selected and your rationale behind this selection.
• The machine learning algorithm you chose and why you chose it.
• The results of your model evaluation, including the metrics you used.
• A discussion of any challenges you faced and how you overcame them.

Also, submit your code files along with your report. Your code should be well-commented and easy to understand.

Tips

• Pay attention to the distribution of the classes. If the classes are imbalanced, you may need to use techniques such as resampling or choose a model that can handle imbalanced classes.
• Consider using regularization to prevent overfitting if necessary.
• Remember to standardize or normalize your features if you're using a model that's sensitive to the scale of the features, such as k-nearest neighbors or support vector machines.