classification model

# Import required libraries import pandas as pd import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, accuracy_score, confusion_matrix from sklearn.linear_model import LogisticRegression, LinearRegression from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.naive_bayes import GaussianNB from sklearn.svm import SVC # Load the dataset and convert to DataFrame wine = load_wine() X = pd.DataFrame(wine.data, columns=wine.feature_names) y = pd.Series(wine.target, name='target') # Train/Test split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # DataFrame to store results results = pd.DataFrame(columns=['Model', 'Accuracy']) print(results) # Helper function for confusion matrix plotting def plot_conf_matrix(y_true, y_pred, title): cm = confusion_matrix(y_true, y_pred) plt.figure(figsize=(5, 4)) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=wine.target_names, yticklabels=wine.target_names) plt.xlabel('Predicted') plt.ylabel('Actual') plt.title(f'Confusion Matrix: {title}') plt.show() # Train & evaluate models ## 1. Logistic Regression lr = LogisticRegression(max_iter=1000) lr.fit(X_train, y_train) y_pred_lr = lr.predict(X_test) print("Logistic Regression\n", classification_report(y_test, y_pred_lr)) plot_conf_matrix(y_test, y_pred_lr, "Logistic Regression") results.loc[len(results)] = ['Logistic Regression', accuracy_score(y_test, y_pred_lr)] # Task Explain the provided Python code for classifying the Wine dataset, fix any errors, complete the code to train and evaluate multiple classification models (Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine), and generate a report in markdown format with sections for Introduction, Task Completion (including screenshots of data loading/exploration, model training, and evaluation results), and Conclusion. ## Fix code ### Subtask: Address the `NameError` by adding a step to scale the features before splitting the data. **Reasoning**: The previous cell failed because `X_scaled` was not defined. I need to add a step to scale the features before splitting the data into training and testing sets. This involves importing `StandardScaler`, instantiating it, and then fitting and transforming the data. from sklearn.preprocessing import StandardScaler # Scale the features scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Convert scaled data back to a DataFrame (optional but good practice for clarity) X_scaled = pd.DataFrame(X_scaled, columns=X.columns) # Train/Test split X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42) # DataFrame to store results results = pd.DataFrame(columns=['Model', 'Accuracy']) # Helper function for confusion matrix plotting def plot_conf_matrix(y_true, y_pred, title): cm = confusion_matrix(y_true, y_pred) plt.figure(figsize=(5, 4)) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=wine.target_names, yticklabels=wine.target_names) plt.xlabel('Predicted') plt.ylabel('Actual') plt.title(f'Confusion Matrix: {title}') plt.show() # Train & evaluate models ## 1. Logistic Regression lr = LogisticRegression(max_iter=1000) lr.fit(X_train, y_train) y_pred_lr = lr.predict(X_test) print("Logistic Regression\n", classification_report(y_test, y_pred_lr)) plot_conf_matrix(y_test, y_pred_lr, "Logistic Regression") results.loc[len(results)] = ['Logistic Regression', accuracy_score(y_test, y_pred_lr)] **Reasoning**: The previous code block failed because `X` and `y` were not defined. I need to include the code from the original cell that loads the wine dataset and creates the `X` and `y` dataframes, then add the scaling step, and finally the train/test split and the rest of the model training code. **Reasoning**: Continue training and evaluating the remaining models as instructed in the subtask. ## 2. Decision Tree dt = DecisionTreeClassifier(random_state=42) dt.fit(X_train, y_train) y_pred_dt = dt.predict(X_test) print("Decision Tree\n", classification_report(y_test, y_pred_dt)) plot_conf_matrix(y_test, y_pred_dt, "Decision Tree") results.loc[len(results)] = ['Decision Tree', accuracy_score(y_test, y_pred_dt)] ## 3. Random Forest rf = RandomForestClassifier(random_state=42) rf.fit(X_train, y_train) y_pred_rf = rf.predict(X_test) print("Random Forest\n", classification_report(y_test, y_pred_rf)) plot_conf_matrix(y_test, y_pred_rf, "Random Forest") results.loc[len(results)] = ['Random Forest', accuracy_score(y_test, y_pred_rf)] ## 4. K-Nearest Neighbors knn = KNeighborsClassifier() knn.fit(X_train, y_train) y_pred_knn = knn.predict(X_test) print("K-Nearest Neighbors\n", classification_report(y_test, y_pred_knn)) plot_conf_matrix(y_test, y_pred_knn, "K-Nearest Neighbors") results.loc[len(results)] = ['K-Nearest Neighbors', accuracy_score(y_test, y_pred_knn)] ## 5. Gaussian Naive Bayes nb = GaussianNB() nb.fit(X_train, y_train) y_pred_nb = nb.predict(X_test) print("Gaussian Naive Bayes\n", classification_report(y_test, y_pred_nb)) plot_conf_matrix(y_test, y_pred_nb, "Gaussian Naive Bayes") results.loc[len(results)] = ['Gaussian Naive Bayes', accuracy_score(y_test, y_pred_nb)] ## 6. Support Vector Machine svm = SVC(random_state=42) svm.fit(X_train, y_train) y_pred_svm = svm.predict(X_test) print("Support Vector Machine\n", classification_report(y_test, y_pred_svm)) plot_conf_matrix(y_test, y_pred_svm, "Support Vector Machine") results.loc[len(results)] = ['Support Vector Machine', accuracy_score(y_test, y_pred_svm)]