Create app.yml

.githubworkflows/app.yml

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split   
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, confusion_matrix
+from sklearn.metrics import precision_score, recall_score, f1_score
+
+sns.set(style='white')
+
+data = pd.read_csv("iris.csv")
+X = data.drop('Species', axis=1)  # Features
+y = data['Species']  # Target
+
+
+# Print the first few rows to check the data
+print(data.head())
+
+# Check for any missing values
+print("Missing values in the dataset:\n", data.isnull().sum())
+
+# Ensure that the data types are correct (convert to numeric if necessary)
+data = data.apply(pd.to_numeric, errors='coerce')  # Convert to numeric, non-numeric will become NaN
+
+# Drop rows with NaN values
+data = data.dropna()
+
+
+# Check the shape of the dataset
+print(f"Dataset shape: {data.shape}")
+print(f"Features shape: {X.shape}")
+print(f"Target shape: {y.shape}")
+
+# Split into train and test sets (80% train, 20% test)
+X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80,test_size=0.20, random_state=0)
+
+# Check the sizes of the train/test sets
+print(f"Training set size: {X_train.shape}")
+print(f"Test set size: {X_test.shape}")
+
+# Initialize Logistic Regression model
+model = LogisticRegression(max_iter=200)
+model.fit(X_train, y_train)
+
+# Predictions
+y_pred = model.predict(X_test)
+
+# Calculate accuracy
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Model accuracy: {accuracy:.4f}")
+
+bias = model.score(X_train, y_train)
+variance = model.score(X_test, y_test)
+
+print(bias)
+print(variance)
+
+# Save the results to a text file
+with open('results.txt', 'w') as f:
+    f.write(f"Model accuracy: {accuracy:.4f}\n")
+
+# Calculate precision, recall, and F1 score
+precision = precision_score(y_test, y_pred, average='micro')
+recall = recall_score(y_test, y_pred, average='micro')
+f1 = f1_score(y_test, y_pred, average='micro')
+
+# Write the metrics to the results file
+with open('results.txt', 'a') as f:
+    f.write(f"Precision: {precision:.4f}\n")
+    f.write(f"Recall: {recall:.4f}\n")
+    f.write(f"F1 Score: {f1:.4f}\n")
+
+# Plot and save the confusion matrix
+cm = confusion_matrix(y_test, y_pred)
+plt.figure(figsize=(8, 6))
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=model.classes_, yticklabels=model.classes_)
+plt.title('Confusion Matrix')
+plt.xlabel('Predicted')
+plt.ylabel('True')
+plt.tight_layout()
+plt.savefig('confusion_matrix.png')
+
+# Plot and save feature importance (coefficients for Logistic Regression)
+plt.figure(figsize=(10, 6))
+plt.barh(X.columns, model.coef_[0])
+plt.xlabel('Importance')
+plt.title('Feature Importance - Logistic Regression')
+plt.tight_layout()
+plt.savefig('feature_importance.png')