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

A6 - Data Analytics-3

✅ Tested and working as intended.

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Pre-requisites

• Install required libraries: pandas, numpy, matplotlib, seaborn & scikit-learn

pip install pandas numpy matplotlib seaborn
pip install -U scikit-learn

• Save the dataset iris.csv in the same directory as this Jupyter notebook.

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Code blocks

1. Import libraries:

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:

df = pd.read_csv("iris.csv")
df.head()

3. Set independent & dependent variables; Train, test, split:

# 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:

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

5. Train Naive Bayes model:

# 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:

# 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:

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}")

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