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