115 lines
2.8 KiB
Markdown
115 lines
2.8 KiB
Markdown
# Practical-2 (Spam Email Detection)
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Problem Statement: Classify the email using the binary classification method. Email Spam detection has two states: a) Normal State – Not Spam, b) Abnormal State – Spam. Use K-Nearest Neighbors and Support Vector Machine for classification. Analyze their performance.
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> [!NOTE]
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> Dataset available in [Datasets](../Datasets/emails.csv) directory.
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---
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## Steps
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1. Import libraries
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2. Load dataset
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3. Data splitting (training and testing)
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4. KNN
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5. SVM
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6. Plotting
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---
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## Code
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### 1. Import libraries:
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```python3
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.svm import SVC
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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import matplotlib.pyplot as plt
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import seaborn as sns
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```
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### 2. Load dataset:
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```python3
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df = pd.read_csv("emails.csv", encoding="ISO-8859-1") # Adjust path if needed
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# Drop unnecessary columns if present
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if "Email No." in df.columns:
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df = df.drop(columns=["Email No."])
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# Ensure label is integer
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df["Prediction"] = df["Prediction"].astype(int)
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# Features & target
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X = df.drop(columns=["Prediction"])
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y = df["Prediction"]
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# Print basic info
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print(df.columns)
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print(df.head(5))
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```
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### 3. Data splitting (training and testing):
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```python3
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42, stratify=y
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)
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```
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### 4. KNN:
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```python3
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knn = KNeighborsClassifier(n_neighbors=5)
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knn.fit(X_train, y_train)
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y_pred_knn = knn.predict(X_test)
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print("\n--- KNN Performance ---")
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print("Accuracy:", accuracy_score(y_test, y_pred_knn))
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print("Classification Report:\n", classification_report(y_test, y_pred_knn))
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print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_knn))
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```
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### 5. SVM:
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```python3
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svm = SVC(kernel='linear', random_state=42) # Linear kernel for binary classification
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svm.fit(X_train, y_train)
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y_pred_svm = svm.predict(X_test)
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print("\n--- SVM Performance ---")
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print("Accuracy:", accuracy_score(y_test, y_pred_svm))
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print("Classification Report:\n", classification_report(y_test, y_pred_svm))
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print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_svm))
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```
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### 6. Plotting:
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```python3
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fig, ax = plt.subplots(1, 2, figsize=(12, 5))
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sns.heatmap(confusion_matrix(y_test, y_pred_knn), annot=True, fmt="d", cmap="Blues", ax=ax[0])
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ax[0].set_title("KNN Confusion Matrix")
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ax[0].set_xlabel("Predicted")
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ax[0].set_ylabel("Actual")
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sns.heatmap(confusion_matrix(y_test, y_pred_svm), annot=True, fmt="d", cmap="Greens", ax=ax[1])
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ax[1].set_title("SVM Confusion Matrix")
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ax[1].set_xlabel("Predicted")
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ax[1].set_ylabel("Actual")
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plt.show()
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```
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---
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## Miscellaneous
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- [Dataset source](https://www.kaggle.com/datasets/balaka18/email-spam-classification-dataset-csv)
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---
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