initcron · May 26, 2025 09:37 · May 26, 2025 · May 26, 2025
diff --git a/basic-ml.ipynb → basic-ml.ipynb.md b/basic-ml.ipynb → basic-ml.ipynb.md
@@ -1,3 +1,4 @@
+```
 # Step 0 / Cell 0
 import pandas as pd
 import numpy as np
@@ -50,4 +51,6 @@ sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
 plt.xlabel("Predicted")
 plt.ylabel("Actual")
 plt.title("🔍 Confusion Matrix")
-plt.show()
+plt.show()
+
+```
diff --git a/basic-ml.ipynb b/basic-ml.ipynb
@@ -0,0 +1,53 @@
+# Step 0 / Cell 0
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, r2_score, mean_squared_error
+
+# Step 1: Load the Iris dataset
+print("📥 Loading the Iris dataset...")
+data = load_iris()
+
+# Step 2: Explore the dataset structure
+print("\n📝 Feature names:", data.feature_names)
+print("🎯 Target classes:", data.target_names)
+print("📐 Data shape:", data.data.shape)
+
+# Step 3: Create a DataFrame for exploration
+df = pd.DataFrame(data.data, columns=data.feature_names)
+df['target'] = data.target
+print("\n🔍 First 5 rows of the dataset:")
+print(df.head())
+
+# Step 4: Define features (X) and target (y)
+X = df[data.feature_names]
+y = df['target']
+
+# Step 5: Train a Logistic Regression model
+print("\n⚙️ Training Logistic Regression model...")
+model = LogisticRegression(max_iter=200)
+model.fit(X, y)
+
+# Step 6: Make predictions
+y_pred = model.predict(X)
+
+# Step 7: Evaluate the model
+accuracy = accuracy_score(y, y_pred)
+print(f"\n📊 Accuracy Score: {accuracy:.2f}")
+
+print("\n📋 Classification Report:")
+print(classification_report(y, y_pred, target_names=data.target_names))
+
+# Step 8 : Confusion Matrix Plot
+cm = confusion_matrix(y, y_pred)
+plt.figure(figsize=(6, 4))
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+            xticklabels=data.target_names,
+            yticklabels=data.target_names)
+plt.xlabel("Predicted")
+plt.ylabel("Actual")
+plt.title("🔍 Confusion Matrix")
+plt.show()