Added code in markdown format for code a2.

Codes/Code-A1.md

@@ -15,6 +15,7 @@ Perform following tasks:
  
 ## Steps
 
+1. Importing Libraries
 1. Data Loading and Pre-processing
 2. Outlier Detection
 3. Correlation Analysis
@@ -25,6 +26,21 @@ Perform following tasks:
 
 ## Code
 
+0. Importing Libraries:
+
+```python3
+# Import necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+from math import radians, cos, sin, asin, sqrt
+```
+
 1. Data Loading & Preprocessing:
 
 ```python3
@@ -168,6 +184,6 @@ plt.show()
 
 ## Miscellaneous
 
-- [Dataset](https://www.kaggle.com/datasets/yasserh/uber-fares-dataset)
+- [Dataset source](https://www.kaggle.com/datasets/yasserh/uber-fares-dataset)
 
 ---

Codes/Code-A2.md

@@ -0,0 +1,114 @@
+# Practical-A2 (Spam Email Detection)
+
+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. 
+
+> [!NOTE]
+> Dataset available in [Datasets](../Datasets/emails.csv) directory.
+
+---
+ 
+## Steps
+
+1. Import libraries
+2. Load dataset
+3. Data splitting (training and testing)
+4. KNN
+5. SVM
+6. Plotting
+
+---
+
+## Code
+
+1. Import libraries:
+
+```python3
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+import matplotlib.pyplot as plt
+import seaborn as sns
+```
+
+2. Load dataset:
+
+```python3
+df = pd.read_csv("emails.csv", encoding="ISO-8859-1")  # Adjust path if needed
+
+# Drop unnecessary columns if present
+if "Email No." in df.columns:
+    df = df.drop(columns=["Email No."])
+
+# Ensure label is integer
+df["Prediction"] = df["Prediction"].astype(int)
+
+# Features & target
+X = df.drop(columns=["Prediction"])
+y = df["Prediction"]
+
+# Print basic info
+print(df.columns)
+print(df.head(5))
+```
+
+3. Data splitting (training and testing):
+
+```python3
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42, stratify=y
+)
+```
+
+4. KNN:
+
+```python3
+knn = KNeighborsClassifier(n_neighbors=5)
+knn.fit(X_train, y_train)
+y_pred_knn = knn.predict(X_test)
+
+print("\n--- KNN Performance ---")
+print("Accuracy:", accuracy_score(y_test, y_pred_knn))
+print("Classification Report:\n", classification_report(y_test, y_pred_knn))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_knn))
+```
+
+5. SVM:
+
+```python3
+svm = SVC(kernel='linear', random_state=42)  # Linear kernel for binary classification
+svm.fit(X_train, y_train)
+y_pred_svm = svm.predict(X_test)
+
+print("\n--- SVM Performance ---")
+print("Accuracy:", accuracy_score(y_test, y_pred_svm))
+print("Classification Report:\n", classification_report(y_test, y_pred_svm))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_svm))
+```
+
+6. Plotting:
+
+```python3
+fig, ax = plt.subplots(1, 2, figsize=(12, 5))
+
+sns.heatmap(confusion_matrix(y_test, y_pred_knn), annot=True, fmt="d", cmap="Blues", ax=ax[0])
+ax[0].set_title("KNN Confusion Matrix")
+ax[0].set_xlabel("Predicted")
+ax[0].set_ylabel("Actual")
+
+sns.heatmap(confusion_matrix(y_test, y_pred_svm), annot=True, fmt="d", cmap="Greens", ax=ax[1])
+ax[1].set_title("SVM Confusion Matrix")
+ax[1].set_xlabel("Predicted")
+ax[1].set_ylabel("Actual")
+
+plt.show()
+```
+
+---
+
+## Miscellaneous
+
+- [Dataset source](https://www.kaggle.com/datasets/balaka18/email-spam-classification-dataset-csv)
+
+---