Improved formatting for markdown codes and fixed title for all.

Codes/Code-1.md

@@ -1,4 +1,4 @@
-# Practical-A1 (Uber)
+# Practical-1 (Uber)
 
 Problem Statement: Predict the price of the Uber ride from a given pickup point to the agreed drop-off location.
 Perform following tasks:
@@ -26,7 +26,7 @@ Perform following tasks:
 
 ## Code
 
-0. Importing Libraries:
+### 0. Importing Libraries:
 
 ```python3
 # Import necessary libraries
@@ -41,7 +41,7 @@ from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
 from math import radians, cos, sin, asin, sqrt
 ```
 
-1. Data Loading & Preprocessing:
+### 1. Data Loading & Preprocessing:
 
 ```python3
 # Load the dataset
@@ -72,7 +72,7 @@ df.drop(['pickup_datetime', 'key'], axis=1, inplace=True, errors='ignore')
 print("\nColumns after feature extraction:\n", df.columns)
 ```
 
-2. Outlier Detection & Removal:
+### 2. Outlier Detection & Removal:
 
 ```python3
 # Remove entries with unrealistic fares
@@ -87,7 +87,7 @@ df = df[(df['dropoff_longitude'] <= 180) & (df['dropoff_longitude'] >= -180)]
 print("Data shape after removing outliers:", df.shape)
 ```
 
-3. Feature Engineering - Distance Calculation:
+### 3. Feature Engineering - Distance Calculation:
 
 ```python3
 # Define Haversine function to calculate distance between pickup and drop-off
@@ -110,7 +110,7 @@ df['distance_km'] = df.apply(lambda x: haversine(x['pickup_latitude'], x['pickup
 df = df[df['distance_km'] > 0]
 ```
 
-4. Correlation Analysis:
+### 4. Correlation Analysis:
 
 ```python3
 plt.figure(figsize=(10, 6))
@@ -119,7 +119,7 @@ plt.title("Feature Correlation Heatmap")
 plt.show()
 ```
 
-5. Model Training:
+### 5. Model Training:
 
 ```python3
 # Define features and target
@@ -140,7 +140,7 @@ rf_model.fit(X_train, y_train)
 y_pred_rf = rf_model.predict(X_test)
 ```
 
-6. Model Evaluation:
+### 6. Model Evaluation:
 
 ```python3
 def evaluate_model(y_true, y_pred, model_name):
@@ -158,7 +158,7 @@ lr_scores = evaluate_model(y_test, y_pred_lr, "Linear Regression")
 rf_scores = evaluate_model(y_test, y_pred_rf, "Random Forest Regressor")
 ```
 
-7. Comparison:
+### 7. Comparison:
 
 ```python3
 results = pd.DataFrame({

Codes/Code-2.md

@@ -1,4 +1,4 @@
-# Practical-A2 (Spam Email Detection)
+# Practical-2 (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. 
 
@@ -20,7 +20,7 @@ Problem Statement: Classify the email using the binary classification method. Em
 
 ## Code
 
-1. Import libraries:
+### 1. Import libraries:
 
 ```python3
 import pandas as pd
@@ -32,7 +32,7 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 ```
 
-2. Load dataset:
+### 2. Load dataset:
 
 ```python3
 df = pd.read_csv("emails.csv", encoding="ISO-8859-1")  # Adjust path if needed
@@ -53,7 +53,7 @@ print(df.columns)
 print(df.head(5))
 ```
 
-3. Data splitting (training and testing):
+### 3. Data splitting (training and testing):
 
 ```python3
 X_train, X_test, y_train, y_test = train_test_split(
@@ -61,7 +61,7 @@ X_train, X_test, y_train, y_test = train_test_split(
 )
 ```
 
-4. KNN:
+### 4. KNN:
 
 ```python3
 knn = KNeighborsClassifier(n_neighbors=5)
@@ -74,7 +74,7 @@ print("Classification Report:\n", classification_report(y_test, y_pred_knn))
 print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_knn))
 ```
 
-5. SVM:
+### 5. SVM:
 
 ```python3
 svm = SVC(kernel='linear', random_state=42)  # Linear kernel for binary classification
@@ -87,7 +87,7 @@ print("Classification Report:\n", classification_report(y_test, y_pred_svm))
 print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_svm))
 ```
 
-6. Plotting:
+### 6. Plotting:
 
 ```python3
 fig, ax = plt.subplots(1, 2, figsize=(12, 5))

Codes/Code-4.md

@@ -1,4 +1,4 @@
-# Practical-A3 (Gradient Descent Algorithm)
+# Practical-4 (Gradient Descent Algorithm)
 
 Problem Statement: Implement Gradient Descent Algorithm to find the local minima of a function. For example, find the local minima of the function y=(x+3)² starting from the point x=2.
 
@@ -16,14 +16,14 @@ Problem Statement: Implement Gradient Descent Algorithm to find the local minima
 
 ## Code
 
-0. Import libraries:
+### 0. Import libraries:
 
 ```python3
 import numpy as np
 import matplotlib.pyplot as plt
 ```
 
-1. Define the function and its derivative:
+### 1. Define the function and its derivative:
 
 ```python3
 def f(x):
@@ -33,7 +33,7 @@ def grad_f(x):
     return 2 * (x + 3)  # derivative of f(x)
 ```
 
-2. Initialize parameters for Gradient Descent:
+### 2. Initialize parameters for Gradient Descent:
 
 ```python3
 x_current = 2          # starting point
@@ -43,7 +43,7 @@ max_iterations = 25    # maximum iterations
 history = [x_current]  # sotring history
 ```
 
-3. Gradient Descent Loop:
+### 3. Gradient Descent Loop:
 
 ```python3
 for i in range(max_iterations):
@@ -60,14 +60,14 @@ for i in range(max_iterations):
     print(f"Iteration {i+1}: x = {x_current:.4f}, f(x) = {f(x_current):.4f}")
 ```
 
-4. Print the result:
+### 4. Print the result:
 
 ```python3
 print("Local minima at x =", x_current)
 print("Function value at local minima y =", f(x_current))
 ```
 
-5. Plotting:
+### 5. Plotting:
 
 ```python3
 plt.plot(history, [f(val) for val in history], marker='o')

Codes/Code-6.md

@@ -1,4 +1,4 @@
-# Practical-A6 (Clustering)
+# Practical-6 (Clustering)
 
 Problem Statement: Implement K-Means clustering/ hierarchical clustering on `sales_data_sample.csv` dataset. Determine the number of clusters using the elbow method.