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For Data classification you can use the Sklearn libraries, which contains a range of useful algorithms that can easily be implemented and setting for the purposes of classification and other machine learning tasks You can also use different libraries for Python up to you.1. Load the DataFrame, which provided or Generate a random DataFrame for classification problem with your own setting (used method make_classification()).2. Generate descriptive statistics. Group DataFrame using a Series of columns up to you. Create two count plots up to you.3. Choose a Classification method (K-Nearest Neighbors, Support Vector Machines, Decision Tree Classifiers, Naive Bayes, Linear Discriminant Analysis, Logistic Regression and so on) and perform necessary data-preparation for this model. Justify the need for choosing preprocessing steps.4. Separate the dataset into feature columns and target column. Create the testing and training splits (import train_test_split).5. Perform an Exploratory Data Analysis (import pyplot, import seaborn).6. Analyze the model settings. Create classification models with different settings. For each model with different settings:6.1 Estimate the model on Testing dataset.6.2 Make a classification report (import ConfusionMatrixDisplay, roc_curve, auc, classification_report,accuracy_score, confusion_matrix).Visualize a confusion matrix. Plot ROC curve.7. Additional step. Compare the quality of the model at different settings. Present the result of comparing as a barplot. Print the value of the criteria upper the every barplot.Output:The program code.
