Added codes, datasets and Jupyter notebooks directory.

Codes/Code-A6 (Data Analytics-3).md

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+# A6 - Data Analytics-3
+
+✅ Tested and working as intended.
+
+---
+
+## Pre-requisites
+
+- Install required libraries: `pandas`, `numpy`, `matplotlib`, `seaborn` & `scikit-learn`
+
+```shell
+pip install pandas numpy matplotlib seaborn
+pip install -U scikit-learn
+```
+
+- Save the dataset [iris.csv](https://git.kska.io/sppu-te-comp-content/DataScienceAndBigDataAnalytics/src/branch/main/Datasets/iris.csv) in the same directory as this Jupyter notebook.
+
+---
+
+## Code blocks
+
+1. Import libraries:
+
+```python3
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score
+```
+
+2. Load the dataset from a CSV file into a Pandas DataFrame:
+
+```python3
+df = pd.read_csv("iris.csv")
+df.head()
+```
+
+3. Set independent & dependent variables; Train, test, split:
+
+```python3
+# Set independent and dependent variables
+X = df.drop('variety', axis=1) # Independent variable
+y = df['variety'] # Dependent variable
+
+# train test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+```
+
+4. Scale features:
+
+```python3
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+```
+
+5. Train Naive Bayes model:
+
+```python3
+# Train Naive Bayes model
+model = GaussianNB()
+model.fit(X_train_scaled, y_train)
+
+# Predict
+y_pred = model.predict(X_test_scaled)
+```
+
+6. Evaulate the model; Plot Confusion Matrix:
+
+```python3
+# Evaluate the model
+cm = confusion_matrix(y_test, y_pred, labels=model.classes_)
+cm_df = pd.DataFrame(cm, index=model.classes_, columns=model.classes_)
+
+# Plot Confusion Matrix
+sns.heatmap(cm_df, annot=True, cmap='Blues', fmt='d')
+plt.title('Confusion Matrix')
+plt.xlabel('Predicted')
+plt.ylabel('Actual')
+plt.show()
+```
+
+7. Set variables for accuracy, precision, recall & error rate + print em:
+
+```python3
+accuracy = accuracy_score(y_test, y_pred)
+precision = precision_score(y_test, y_pred, average='macro')
+recall = recall_score(y_test, y_pred, average='macro')
+error_rate = 1 - accuracy
+
+print(f"Accuracy: {accuracy:.2f}")
+print(f"Error Rate: {error_rate:.2f}")
+print(f"Precision (Macro): {precision:.2f}")
+print(f"Recall (Macro): {recall:.2f}")
+```
+
+---