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import java.sql.Statement;
import java.sql.Connection;
import java.sql.Driver;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.sql.Date;
public class jdbcInsert {
    public static void main(String[] args) throws ClassNotFoundException,SQLException{
        Connection conn = null;
        Statement st = null;
        try
        {
            conn = DriverManager.getConnection("jdbc:oracle:thin:@//localhost:1521/XE", "system", "1808");
            st = conn.createStatement();
            int row= st.executeUpdate("Insert into employee values (7990,'manish','salesman',7755,TO_DATE('17-DEC-24', 'DD-MON-RRRR'),1000,30,25)");
            ResultSet rs1 = st.executeQuery("select * from employee");
            while (rs1.next()) {
                int id=rs1.getInt(1);
                String ename = rs1.getString(2);
                String job = rs1.getString(3);
                int mgr = rs1.getInt(4);
                Date hiredate = rs1.getDate(5);
                int sal = rs1.getInt(6);
                int com = rs1.getInt(7);
                int deptno = rs1.getInt(8);
                System.out.println(id+" "+ename+" "+job+" "+mgr+" "+hiredate+" "+sal + " "+com+" "+deptno);
            }
        }catch(SQLException e)
        {
            e.printStackTrace();
        }
    }
}

Statement Select

@login123

import java.sql.Statement;
import java.sql.Connection;
import java.sql.Date;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.SQLException;

public class jdbcselect {
    public static void main(String[] args) {
        Connection conn = null;
        Statement st = null;
        try{
            conn = DriverManager.getConnection("jdbc:oracle:thin:@//localhost:1521/XE", "system", "1808");
            st = conn.createStatement();
            ResultSet rs1 = st.executeQuery("select * from employee");
            while(rs1.next())
            {
                int id = rs1.getInt(1);
                String name = rs1.getString(2);
                String job = rs1.getString(3);
                int mgr = rs1.getInt(4);
                Date date = rs1.getDate(5);
                int sal = rs1.getInt(6);
                int com = rs1.getInt(7);
                int deptno = rs1.getInt(8);
                System.out.println(
                        id + "," + name + "," + job + "," + mgr + "," + date + "," + sal + "," + com + "," + deptno); 
            }
        }catch(SQLException e)
        {
            e.printStackTrace();
        }
    }
}

#html

Grid-Box Layout

@signup_returns

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Simple Grid Layout</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            margin: 0;
            padding: 0;
        }
        header {
            background-color: #4CAF50;
            color: white;
            padding: 15px;
            text-align: center;
        }
        .grid-container {
            display: grid;
            grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
            gap: 10px;
            padding: 20px;
        }
        .card {
            background-color: #f9f9f9;
            border: 1px solid #ccc;
            border-radius: 5px;
            padding: 20px;
            text-align: center;
            box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
        }
    </style>
</head>
<body>
    <header>
        <h1>Simple Grid Layout</h1>
    </header>
    <main>
        <div class="grid-container">
            <div class="card">
                <h2>Card 1</h2>
                <p>This is a simple card.</p>
            </div>
            <div class="card">
                <h2>Card 2</h2>
                <p>This is another simple card.</p>
            </div>
            <div class="card">
                <h2>Card 3</h2>
                <p>This is yet another simple card.</p>
            </div>
            <div class="card">
                <h2>Card 4</h2>
                <p>This is an additional card.</p>
            </div>
        </div>
    </main>
</body>
</html>

#python

1 and 2 #pca and #rfe

@varuntej

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import seaborn as sns

df_petrol = pd.read_csv('petrol_consumption.csv')
print(df_petrol.head())
df_petrol.info()
#
df_petrol.isnull().sum()
#
df_petrol.fillna(df_petrol.mean(),inplace=True)
#
sns.pairplot(df_petrol,hue="Petrol_Consumption")
plt.show()
#
#PCA
scaler = StandardScaler()
df = scaler.fit_transform(df)

pca = PCA(n_components=2)
df_pca = pca.fit_transform(df)
df = pd.DataFrame(data = df_pca,columns=['PC1','PC2'])
df["Petrol_Consumption"] = df_petrol["Petrol_Consumption"]
sns.scatterplot(x='PC1',y='PC2',data = df,hue="Petrol_Consumption")
plt.show()

#Rfe
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

df = pd.read_csv("Fish.csv")
print(df.head())
df.info()
#
X = df.drop('Species',axis=1)
y = df['Species']

y.info()
#
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=42)

dtc = DecisionTreeClassifier()
dtc.fit(X_train,y_train)
y_pred = dtc.predict(X_test)
print(y_pred)
print(y_test)

accuracy = accuracy_score(y_test,y_pred)
print(accuracy)
#
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder

df = pd.read_csv('Iris.csv')
df.info()
#
encoder = LabelEncoder()
df["Species"] = encoder.fit_transform(df["Species"])
#
df.info()
#
X = df.iloc[:,:-1] #df.drop(columns=["speices"])
y = df.iloc[:,-1] #df["species"]
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
dtc = DecisionTreeClassifier()
dtc.fit(X_train,y_train)
y_pred = dtc.predict(X_test)
print(y_test,y_pred)
#
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix

accuracy = accuracy_score(y_pred,y_test)
print(accuracy)
print(classification_report(y_pred,y_test))
print(confusion_matrix(y_pred,y_test))
#
# prompt: Visualize insights of Above decision tree classification on iris dataset

from sklearn import tree
import matplotlib.pyplot as plt

plt.figure(figsize=(15,10))
tree.plot_tree(dtc,filled=True,feature_names=X.columns,class_names=['0','1','2'])
plt.show()
#from sklearn.neighbors import KNeighborsClassifier

df = pd.read_csv('diabetes.csv')
df.info()
#
X = df.iloc[:,:-1] #df.drop("Outcome",axis=1)
y = df.iloc[:,-1]
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
knn = KNeighborsClassifier(n_neighbors=2)
knn.fit(X_train,y_train)
y_pred = knn.predict(X_test)
print(y_test,y_pred)
#
accuracy = accuracy_score(y_pred,y_test)
print(accuracy)
print(classification_report(y_pred,y_test))
print(confusion_matrix(y_pred,y_test))

#html

Flex-Box Layout

@signup_returns

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Flexbox Layout Example</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            margin: 0;
            padding: 0;
        }
        header {
            background-color: #4CAF50;
            color: white;
            padding: 15px;
            display: flex;
            justify-content: space-between;
            align-items: center;
        }
        nav {
            display: flex;
            gap: 15px;
        }
        .container {
            display: flex;
            justify-content: space-around;
            margin: 20px;
            flex-wrap: wrap;
        }
        .card {
            background-color: #f9f9f9;
            border: 1px solid #ccc;
            border-radius: 5px;
            padding: 20px;
            width: 30%;
            box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
            text-align: center;
            margin: 10px;
        }
        @media (max-width: 768px) {
            .card {
                width: 100%; /* Full width on small screens */
            }
        }
    </style>
</head>
<body>
    <header>
        <h1>Flexbox Layout</h1>
        <nav>
            <a href="#" style="color: white;">Home</a>
            <a href="#" style="color: white;">About</a>
            <a href="#" style="color: white;">Services</a>
            <a href="#" style="color: white;">Contact</a>
        </nav>
    </header>
    <main>
        <div class="container">
            <div class="card">
                <h2>Card 1</h2>
                <p>This is a simple card.</p>
            </div>
            <div class="card">
                <h2>Card 2</h2>
                <p>This is another simple card.</p>
            </div>
            <div class="card">
                <h2>Card 3</h2>
                <p>This is yet another simple card.</p>
            </div>
        </div>
    </main>
</body>
</html>

#html

Internal, External, Inline CSS

@signup_returns

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>CSS Demo</title>
    <link rel="stylesheet" href="styles.css"> <!-- Link to external CSS -->
    <style>
        /* Internal CSS */
        h2 {
            color: green;
            text-align: center;
        }
    </style>
</head>
<body>
    <h1 style="color: blue; text-align: center;">Welcome to CSS Demo</h1> <!-- Inline CSS -->

    <h2>Internal CSS Example</h2>
    <p>This paragraph demonstrates the use of internal CSS for styling headings.</p>

    <h2 style="color: orange;">Inline CSS Example</h2>
    <p>This paragraph is styled using inline CSS for the heading.</p>

    <h2 class="external-header">External CSS Example</h2>
    <p>This paragraph demonstrates the use of external CSS for styling.</p>
</body>
</html>


/* External CSS */
body {
    background-color: #f4f4f4;
    font-family: Arial, sans-serif;
}

.external-header {
    color: purple;
    text-align: center;
}

#python

5.Decision Tree Classification AND K-Neareast Nrighbour

@varuntej

#Decision Tree Classification

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
import pandas as pd

iris = load_iris()
df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df['species'] = iris.target
print(df.head())
print(df.info())
X = df.drop('species', axis=1)
y = df['species']
x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
model = DecisionTreeClassifier()
model.fit(x_train,y_train)
y_pred = model.predict(x_test)
accuracy = accuracy_score(y_test,y_pred)
print("Accuracy:",accuracy)
print("Classification Report:\n",classification_report(y_test,y_pred))
print("Confusion Matrix:\n",confusion_matrix(y_test,y_pred))
output:
   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \
0                5.1               3.5                1.4               0.2   
1                4.9               3.0                1.4               0.2   
2                4.7               3.2                1.3               0.2   
3                4.6               3.1                1.5               0.2   
4                5.0               3.6                1.4               0.2   

   species  
0        0  
1        0  
2        0  
3        0  
4        0  
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   sepal length (cm)  150 non-null    float64
 1   sepal width (cm)   150 non-null    float64
 2   petal length (cm)  150 non-null    float64
 3   petal width (cm)   150 non-null    float64
 4   species            150 non-null    int64  
dtypes: float64(4), int64(1)
memory usage: 6.0 KB
None
Accuracy: 1.0
Classification Report:
               precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00         9
           2       1.00      1.00      1.00        11

    accuracy                           1.00        30
   macro avg       1.00      1.00      1.00        30
weighted avg       1.00      1.00      1.00        30

Confusion Matrix:
 [[10  0  0]
 [ 0  9  0]
 [ 0  0 11]]
2.#K-Neareast Nrighbour
#K-Neareast Nrighbour
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
df = pd.DataFrame(data=iris.data,columns=iris.feature_names)
df['species']=iris.target
print(df.head())
print(df.info())
x = df.drop('species',axis=1)
y = df['species']
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(x_train,y_train)
y_pred = knn.predict(x_test)
accuracy = accuracy_score(y_test,y_pred)
print("Accuracy:",accuracy)
print("Classification Report:\n",classification_report(y_test,y_pred))
print("Confusion Matrix:\n",confusion_matrix(y_test,y_pred))
output:
]
#K-Neareast Nrighbour
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
df = pd.DataFrame(data=iris.data,columns=iris.feature_names)
df['species']=iris.target
print(df.head())
…print("Confusion Matrix:\n",confusion_matrix(y_test,y_pred))
   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \
0                5.1               3.5                1.4               0.2   
1                4.9               3.0                1.4               0.2   
2                4.7               3.2                1.3               0.2   
3                4.6               3.1                1.5               0.2   
4                5.0               3.6                1.4               0.2   

   species  
0        0  
1        0  
2        0  
3        0  
4        0  
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   sepal length (cm)  150 non-null    float64
 1   sepal width (cm)   150 non-null    float64
 2   petal length (cm)  150 non-null    float64
 3   petal width (cm)   150 non-null    float64
 4   species            150 non-null    int64  
dtypes: float64(4), int64(1)
memory usage: 6.0 KB
None
Accuracy: 1.0
Classification Report:
               precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00         9
           2       1.00      1.00      1.00        11

    accuracy                           1.00        30
   macro avg       1.00      1.00      1.00        30
weighted avg       1.00      1.00      1.00        30

Confusion Matrix:
 [[10  0  0]
 [ 0  9  0]
 [ 0  0 11]]

#python

7. K MAEANS AND HIERARCHIAL CLUSTERING

@varuntej

1.
from sklearn.datasets import load_iris
import pandas as pd
iris=load_iris()
df=pd.DataFrame(data=iris.data, columns=iris.feature_names)
print("DataFrame Head:\n",df.head())
print("DataInfo:\n",df.info())

output
DataFrame Head:
    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 4 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   sepal length (cm)  150 non-null    float64
 1   sepal width (cm)   150 non-null    float64
 2   petal length (cm)  150 non-null    float64
 3   petal width (cm)   150 non-null    float64
dtypes: float64(4)
memory usage: 4.8 KB
DataInfo:
 None
2.
from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
x_scaled=scaler.fit_transform(df)
from sklearn.cluster import KMeans
Kmeans=KMeans(n_clusters=3, random_state=42)
Kmeans.fit((x_scaled))
clusters_labels=Kmeans.labels_
df['cluster']=clusters_labels
cluster_centers=Kmeans.cluster_centers_
print("cluster centers:\n",cluster_centers)

ouput

cluster centers:
 [[ 1.13597027  0.08842168  0.99615451  1.01752612]
 [-1.01457897  0.85326268 -1.30498732 -1.25489349]
 [-0.05021989 -0.88337647  0.34773781  0.2815273 ]]

3.

from sklearn.metrics import adjusted_rand_score,silhouette_score
true_labels=iris.target
ari=adjusted_rand_score(true_labels,clusters_labels)
print(f"Adjusted Rand_Index(ARI):{ari}")
silhouette_avg=silhouette_score(x_scaled,clusters_labels)
print(f"sithouette score : {silhouette_avg}")

ouput

Adjusted Rand_Index(ARI):0.6201351808870379
sithouette score : 0.45994823920518635

ll.1

#Hierarchial clustering
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from scipy.cluster.hierarchy import dendrogram,linkage
from scipy.cluster.hierarchy import fcluster
iris=load_iris()
data=pd.DataFrame(iris.data,columns=iris.feature_names)
scaler=StandardScaler()
scaled_data=scaler.fit_transform(data)
z=linkage(scaled_data,method="ward")
plt.figure(figsize=(10,7))
plt.title("Dendogram for Iris dataset")
dendrogram(z,labels=iris.target)
plt.show()
clusters=fcluster(z,3,criterion="maxclust")
data['cluster']=clusters
data['species']=iris.target
print(data.groupby(['cluster','species']).size())
silhouette_avg=silhouette_score(scaled_data,clusters)
print(f"Silhouette score: {silhouette_avg}")

output
 

cluster  species
1        0          49
2        0           1
         1          27
         2           2
3        1          23
         2          48
dtype: int64
Silhouette score: 0.446689041028591

#python

9.Design and implement Hierarchical clustering to cluster species of flower. Estimate the accuracy of the model. Also write the program to visualize insights of the Iris dataset.

@varuntej

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 
from sklearn.preprocessing import StandardScaler 
from sklearn.cluster import AgglomerativeClustering 
from sklearn.metrics import accuracy_score, confusion_matrix 
from scipy.cluster.hierarchy import dendrogram, linkage 
from sklearn.decomposition import PCA 
# Load the Iris dataset 
df = pd.read_csv('Iris.csv') 
# Display the first few rows to understand the structure (optional) 
print(df.head()) 
# Remove the 'Id' column if present 
df = df.drop(columns=['Id']) 
# Separate features and the actual labels (for comparison) 
X = df.drop(columns=['Species']) 
y_true = df['Species'] 
# Standardize the features 
scaler = StandardScaler() 
X_scaled = scaler.fit_transform(X) 
# Generate a dendrogram to visualize hierarchical clustering structure 
plt.figure(figsize=(10, 7)) 
linked = linkage(X_scaled, method='ward') 
dendrogram(linked, labels=y_true.values, orientation='top', distance_sort='descending', show_leaf_counts=True) 
plt.title("Dendrogram for Iris Dataset") 
plt.xlabel("Samples") 
plt.ylabel("Euclidean Distance") 
plt.show() 
# Apply Hierarchical Clustering 
hc = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward') 
y_hc = hc.fit_predict(X_scaled) 
# Map cluster labels to actual classes for accuracy estimation 
y_hc_mapped = np.zeros_like(y_hc) 
for i in range(3): 
mask = (y_hc == i) 
y_hc_mapped[mask] = np.bincount(y_true[mask].factorize()[0]).argmax() 
# Estimate accuracy by comparing clusters to actual labels 
accuracy = accuracy_score(y_true.factorize()[0], y_hc_mapped) 
print("Estimated Accuracy of Hierarchical Clustering:", accuracy) 
# Confusion matrix to visualize clustering performance 
conf_matrix = confusion_matrix(y_true.factorize()[0], y_hc_mapped) 
plt.figure(figsize=(6, 4)) 
sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=['Setosa', 'Versicolor', 'Virginica'], 
yticklabels=['Setosa', 'Versicolor', 'Virginica']) 
plt.xlabel("Cluster Label") 
plt.ylabel("True Label") 
plt.title("Confusion Matrix of Hierarchical Clustering") 
plt.show() 
# Visualize clusters using PCA (reduce to 2D for plotting) 
pca = PCA(n_components=2) 
X_pca = pca.fit_transform(X_scaled) 
# Plot the clusters and actual labels 
plt.figure(figsize=(12, 5)) 
# Plot hierarchical clusters 
plt.subplot(1, 2, 1) 
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_hc, cmap="viridis", s=50) 
plt.title("Hierarchical Clustering on Iris Dataset (PCA-reduced)") 
plt.xlabel("Principal Component 1") 
plt.ylabel("Principal Component 2") 
plt.colorbar(label="Cluster") 
# Plot actual species 
species_to_num = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2} 
y_true_numeric = y_true.map(species_to_num) 
plt.subplot(1, 2, 2) 
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_true_numeric, cmap="viridis", s=50) 
plt.title("Actual Species Labels (PCA-reduced)") 
plt.xlabel("Principal Component 1") 
plt.ylabel("Principal Component 2") 
plt.colorbar(label="Species") 
plt.tight_layout() 
plt.show()

#python

8.Design and implement k-Means clustering to cluster species of flower. Estimate the accuracy of the model. Also write the program to visualize insights of the Iris dataset.

@varuntej

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 
from sklearn.preprocessing import StandardScaler 
from sklearn.cluster import KMeans 
from sklearn.metrics import accuracy_score, confusion_matrix 
from sklearn.decomposition import PCA 
# Load the Iris dataset 
df = pd.read_csv('Iris.csv') 
# Display the first few rows (optional) 
print(df.head()) 
# Remove the 'Id' column if present 
df = df.drop(columns=['Id']) 
# Separate features and the actual labels (for comparison) 
X = df.drop(columns=['Species']) 
y_true = df['Species'] 
# Standardize the features 
scaler = StandardScaler() 
X_scaled = scaler.fit_transform(X) 
# Apply k-Means clustering 
kmeans = KMeans(n_clusters=3, random_state=42) 
kmeans.fit(X_scaled) 
y_kmeans = kmeans.labels_ 
# Map cluster labels to the actual classes for accuracy estimation 
# Since k-means labels are arbitrary, we map them using the most common species in each cluster. 
y_kmeans_mapped = np.zeros_like(y_kmeans) 
for i in range(3): 
mask = (y_kmeans == i) 
y_kmeans_mapped[mask] = np.bincount(y_true[mask].factorize()[0]).argmax() 
# Estimate accuracy by comparing clusters to actual labels 
accuracy = accuracy_score(y_true.factorize()[0], y_kmeans_mapped) 
print("Estimated Accuracy of k-Means Clustering:", accuracy) 
# Confusion matrix to visualize clustering performance 
conf_matrix = confusion_matrix(y_true.factorize()[0], y_kmeans_mapped) 
plt.figure(figsize=(6, 4)) 
sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=['Setosa', 'Versicolor', 'Virginica'], 
yticklabels=['Setosa', 'Versicolor', 'Virginica']) 
plt.xlabel("Cluster Label") 
plt.ylabel("True Label") 
plt.title("Confusion Matrix of k-Means Clustering") 
plt.show() 
# Visualize clusters using PCA (reduce to 2D for plotting) 
pca = PCA(n_components=2) 
X_pca = pca.fit_transform(X_scaled) 
# Plot the clusters and actual labels 
plt.figure(figsize=(12, 5)) 
# Plot k-Means clusters 
plt.subplot(1, 2, 1) 
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_kmeans, cmap="viridis", s=50) 
plt.title("k-Means Clustering on Iris Dataset (PCA-reduced)") 
plt.xlabel("Principal Component 1") 
plt.ylabel("Principal Component 2") 
plt.colorbar(label="Cluster") 
# Plot actual species 
species_to_num = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2} 
y_true_numeric = y_true.map(species_to_num) 
plt.subplot(1, 2, 2) 
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_true_numeric, cmap="viridis", s=50) 
plt.title("Actual Species Labels (PCA-reduced)") 
plt.xlabel("Principal Component 1") 
plt.ylabel("Principal Component 2") 
plt.colorbar(label="Species") 
plt.tight_layout() 
plt.show()

#python

7.Design and implement a Random Forest Classification model to predict if a loan will get approved or not for a bank customer dataset. Estimate the accuracy of the model. Also write the program to visualize insights of the dataset.

@varuntej

import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns 
from sklearn.model_selection import train_test_split 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix 
from sklearn.preprocessing import StandardScaler 
# Load the dataset 
df = pd.read_csv('bank_loans.csv') 
# Display the first few rows to understand the structure (optional) 
print(df.head()) 
print(df.info()) 
# Check for missing values (optional) 
print("Missing values:\n", df.isnull().sum()) 
# Handle missing values if any (optional, assuming numerical columns filled with mean) 
df.fillna(df.mean(), inplace=True) 
# Encode categorical variables (assuming 'Gender', 'Married', etc. as example categorical features) 
df = pd.get_dummies(df, drop_first=True) 
# Separate features and target variable 
X = df.drop(columns=['Loan_Status'])  # Assuming 'Loan_Status' is the target column (1 = Approved, 0 = Not Approved) 
y = df['Loan_Status'] 
# Split the data into training and testing sets 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 
# Standardize the features 
scaler = StandardScaler() 
X_train_scaled = scaler.fit_transform(X_train) 
X_test_scaled = scaler.transform(X_test) 
# Initialize and train the Random Forest Classifier 
rf_model = RandomForestClassifier(n_estimators=100, random_state=42) 
rf_model.fit(X_train_scaled, y_train) 
# Predict on the test set 
y_pred = rf_model.predict(X_test_scaled) 
# Calculate accuracy and other performance metrics 
accuracy = accuracy_score(y_test, y_pred) 
print("Random Forest Model Accuracy:", accuracy) 
print("\nClassification Report:\n", classification_report(y_test, y_pred)) 
# Plot confusion matrix for better insight into model performance 
conf_matrix = confusion_matrix(y_test, y_pred) 
plt.figure(figsize=(6, 4)) 
sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=['Not Approved', 'Approved'], 
yticklabels=['Not Approved', 'Approved']) 
plt.xlabel("Predicted") 
plt.ylabel("Actual") 
plt.title("Confusion Matrix") 
plt.show() 
# Feature importance visualization 
feature_importances = rf_model.feature_importances_ 
features = X.columns 
# Create a dataframe for feature importances 
feature_df = pd.DataFrame({'Feature': features, 'Importance': feature_importances}) 
feature_df = feature_df.sort_values(by='Importance', ascending=False) 
# Plot feature importances 
plt.figure(figsize=(10, 6)) 
sns.barplot(x='Importance', y='Feature', data=feature_df, palette="viridis") 
plt.title("Feature Importances in Random Forest Model") 
plt.xlabel("Importance") 
plt.ylabel("Feature") 
plt.show()

#python

6.Write a program for k-NN classifier to predict the class of the person on available attributes. Consider diabetes.cs dataset. Also calculate the performance measures of the model

@varuntej

import pandas as pd 
from sklearn.model_selection import train_test_split 
from sklearn.preprocessing import StandardScaler 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report 
# Load the dataset 
df = pd.read_csv('diabetes.csv') 
# Display the first few rows to understand the dataset structure (optional) 
print(df.head()) 
# Separate features and target 
X = df.drop(columns=['Outcome'])  # Assuming 'Outcome' is the target column (0 = No Diabetes, 1 = Diabetes) 
y = df['Outcome'] 
# Split the data into training and testing sets 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 
# Standardize the features 
scaler = StandardScaler() 
X_train_scaled = scaler.fit_transform(X_train) 
X_test_scaled = scaler.transform(X_test) 
# Initialize and train the k-NN classifier 
k = 5  # You can tune this value 
knn = KNeighborsClassifier(n_neighbors=k) 
knn.fit(X_train_scaled, y_train) 
# Predict on the test set 
y_pred = knn.predict(X_test_scaled) 
# Calculate performance measures 
accuracy = accuracy_score(y_test, y_pred) 
precision = precision_score(y_test, y_pred) 
recall = recall_score(y_test, y_pred) 
f1 = f1_score(y_test, y_pred) 
# Display the performance measures 
print("Performance Measures for k-NN Classifier:") 
print(f"Accuracy: {accuracy:.4f}") 
print(f"Precision: {precision:.4f}") 
print(f"Recall: {recall:.4f}") 
print(f"F1-Score: {f1:.4f}") 
# Detailed classification report 
print("\nClassification Report:\n", classification_report(y_test, y_pred))

#python

5.Implement the Decision tree Classification model on Iris.est dataset. Estimate the accuracy of the model. Also write the program to visualize insights of the dataset.

@varuntej

import pandas as pd 
# Load the dataset 
df = pd.read_csv('Iris.csv') 
# Display the first few rows and basic information 
print(df.head()) 
print(df.describe()) 
print(df.info()) 
from sklearn.model_selection import train_test_split 
from sklearn.preprocessing import LabelEncoder 
# Encode the target variable 
label_encoder = LabelEncoder() 
df['Species'] = label_encoder.fit_transform(df['Species']) 
# Separate features and target variable 
X = df.drop(columns=['Species']) 
y = df['Species'] 
# Split the data into training and testing sets 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 
from sklearn.tree import DecisionTreeClassifier 
from sklearn.metrics import accuracy_score 
# Initialize the Decision Tree Classifier 
model = DecisionTreeClassifier(random_state=42) 
model.fit(X_train, y_train) 
# Predict on the test set 
y_pred = model.predict(X_test) 
# Estimate the accuracy of the model 
accuracy = accuracy_score(y_test, y_pred) 
print("Accuracy of Decision Tree Classifier:", accuracy) 
from sklearn.tree import plot_tree 
import matplotlib.pyplot as plt 
# Plot the decision tree 
plt.figure(figsize=(12, 8)) 
plot_tree(model, feature_names=X.columns, class_names=label_encoder.classes_, filled=True) 
plt.title("Decision Tree Structure") 
plt.show() 
importances = model.feature_importances_ 
feature_names = X.columns 
# Plot feature importances 
plt.figure(figsize=(8, 6)) 
plt.barh(feature_names, importances, color='skyblue') 
plt.xlabel("Feature Importance") 
plt.ylabel("Feature") 
plt.title("Feature Importances in Decision Tree Model") 
plt.show() 
import numpy as np 
from matplotlib.colors import ListedColormap 
# Define a function to plot decision boundaries 
def plot_decision_boundary(model, X, y): 
# Set up the grid for plotting decision boundaries 
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), 
np.arange(y_min, y_max, 0.02)) 
# Predict over the grid 
Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) 
Z = Z.reshape(xx.shape) 
# Plot the contour and training examples 
plt.figure(figsize=(10, 6)) 
plt.contourf(xx, yy, Z, alpha=0.3, cmap=ListedColormap(('red', 'green', 'blue'))) 
scatter = plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=ListedColormap(('red', 'green', 'blue'))) 
plt.xlabel("Petal Length") 
plt.ylabel("Petal Width") 
plt.title("Decision Boundary (Petal Length vs Petal Width)") 
plt.legend(handles=scatter.legend_elements()[0], labels=label_encoder.classes_) 
plt.show() 
# Extract PetalLength and PetalWidth for visualization 
X_2d_train = X_train[['PetalLengthCm', 'PetalWidthCm']].values 
y_train_2d = y_train.values 
# Train a new model on these two features for simplicity 
model_2d = DecisionTreeClassifier(random_state=42) 
model_2d.fit(X_2d_train, y_train_2d) 
# Plot the decision boundary 
plot_decision_boundary(model_2d, X_2d_train, y_train_2d)

#python

4.Implement Regression model and compare the performance of the model with Dimensionality reduction and without Dimensionality reduction. Consider student_scores.csv dataset.

@varuntej

import pandas as pd 
# Load the dataset 
df = pd.read_csv('student_scores.csv') 
# Display the first few rows and basic information 
print(df.head()) 
print(df.describe()) 
print(df.info()) 
from sklearn.model_selection import train_test_split 
# Separate features and target 
X = df.drop(columns=['Score'])  # Assuming 'Score' is the target column 
y = df['Score'] 
# Split into training and testing sets 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 
from sklearn.preprocessing import StandardScaler 
# Initialize and fit the scaler on training data 
scaler = StandardScaler() 
X_train_scaled = scaler.fit_transform(X_train) 
X_test_scaled = scaler.transform(X_test) 
from sklearn.linear_model import LinearRegression 
from sklearn.metrics import mean_absolute_error, mean_squared_error 
import numpy as np 
# Initialize and train the model 
model = LinearRegression() 
model.fit(X_train_scaled, y_train) 
# Predict on the test set 
y_pred = model.predict(X_test_scaled) 
# Evaluate performance 
mae = mean_absolute_error(y_test, y_pred) 
rmse = np.sqrt(mean_squared_error(y_test, y_pred)) 
print("Without Dimensionality Reduction") 
print("Mean Absolute Error (MAE):", mae) 
print("Root Mean Squared Error (RMSE):", rmse) 
from sklearn.decomposition import PCA 
# Initialize PCA and fit on the scaled training data 
pca = PCA(n_components=0.95)  # Retain 95% variance 
X_train_pca = pca.fit_transform(X_train_scaled) 
X_test_pca = pca.transform(X_test_scaled) 
# Check the number of components 
print("Number of components selected:", pca.n_components_) 
# Train the regression model on PCA-reduced data 
model_pca = LinearRegression() 
model_pca.fit(X_train_pca, y_train) 
# Predict on the PCA-transformed test set 
y_pred_pca = model_pca.predict(X_test_pca) 
# Evaluate performance 
mae_pca = mean_absolute_error(y_test, y_pred_pca) 
rmse_pca = np.sqrt(mean_squared_error(y_test, y_pred_pca)) 
print("With Dimensionality Reduction (PCA)") 
print("Mean Absolute Error (MAE):", mae_pca) 
print("Root Mean Squared Error (RMSE):", rmse_pca) 
print("Comparison of Model Performance:") 
print("Without Dimensionality Reduction - MAE:", mae, ", RMSE:", rmse) 
print("With Dimensionality Reduction (PCA) - MAE:", mae_pca, ", RMSE:", rmse_pca)

#python

3.Design and Demonstrate Regression model to predict the rent of a house. Evaluate the performance of the model. Consider Pune_rent.csv dataset.

@varuntej

import pandas as pd 
# Load the dataset 
df = pd.read_csv('Pune_rent.csv') 
# Display the first few rows and basic information 
print(df.head()) 
print(df.describe()) 
print(df.info()) 
# Check for missing values 
print(df.isnull().sum()) 
# Convert categorical variables to dummy/indicator variables if necessary 
# For demonstration, assuming 'location', 'house_type' are categorical variables 
df = pd.get_dummies(df, columns=['location', 'house_type'], drop_first=True) 
from sklearn.model_selection import train_test_split 
# Separate features and target 
X = df.drop(columns=['Rent']) 
y = df['Rent'] 
# Split into training and testing sets 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 
from sklearn.preprocessing import StandardScaler 
# Standardize the features 
scaler = StandardScaler() 
X_train_scaled = scaler.fit_transform(X_train) 
X_test_scaled = scaler.transform(X_test) 
from sklearn.linear_model import LinearRegression 
# Initialize and train the model 
model = LinearRegression() 
model.fit(X_train_scaled, y_train) 
# Predict on the test set 
y_pred = model.predict(X_test_scaled) 
from sklearn.metrics import mean_absolute_error, mean_squared_error 
import numpy as np 
# Calculate MAE and RMSE 
mae = mean_absolute_error(y_test, y_pred) 
rmse = np.sqrt(mean_squared_error(y_test, y_pred)) 
print("Mean Absolute Error (MAE):", mae) 
print("Root Mean Squared Error (RMSE):", rmse)

#python

2.Implement the Dimensionality Reduction using Recursive Feature Elimination method and analyze the results with any one classifier. Consider Fish.cs dataset.

@varuntej

import pandas as pd 
# Load the dataset 
df = pd.read_csv('Fish.csv') 
# Display the first few rows and basic information 
print(df.head()) 
print(df.describe()) 
print(df.info()) 
from sklearn.model_selection import train_test_split 
from sklearn.preprocessing import LabelEncoder 
# Encode the target variable if it's categorical 
label_encoder = LabelEncoder() 
df['Species'] = label_encoder.fit_transform(df['Species']) 
# Separate the features and target variable 
X = df.drop(columns=['Species']) 
y = df['Species'] 
# Split into training and testing sets 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) 
from sklearn.preprocessing import StandardScaler 
# Standardize the features 
scaler = StandardScaler() 
X_train_scaled = scaler.fit_transform(X_train) 
X_test_scaled = scaler.transform(X_test) 
from sklearn.svm import SVC 
from sklearn.feature_selection import RFE 
# Initialize the classifier and RFE 
svc = SVC(kernel="linear", random_state=42) 
rfe = RFE(estimator=svc, n_features_to_select=3)  # Choose 3 features for demonstrate on 
# Fit RFE 
rfe.fit(X_train_scaled, y_train) 
# Print the ranking of features 
print("Feature ranking (1 indicates selected features):", rfe.ranking_) 
print("Selected features:", X.columns[rfe.support_]) 
from sklearn.metrics import accuracy_score, classification_report 
# Select the features indicated by RFE 
X_train_rfe = X_train_scaled[:, rfe.support_] 
X_test_rfe = X_test_scaled[:, rfe.support_] 
# Train the classifier on the selected features 
svc.fit(X_train_rfe, y_train) 
# Make predictions and evaluate performance 
y_pred = svc.predict(X_test_rfe) 
print("Accuracy:", accuracy_score(y_test, y_pred)) 
print("Classification Report:\n", classification_report(y_test, y_pred))

#python

1.Implement Dimensionality reduction by Principal Component Analysis and analyze the results of both methods. Consider petrol _consumption.cs dataset. Also write the program to visualize insights of the dataset.

@varuntej

import pandas as pd 
# Load the dataset 
df = pd.read_csv('petrol_consumption.csv') 
# Display basic information about the dataset 
print(df.head()) 
print(df.describe()) 
print(df.info()) 
from sklearn.preprocessing import StandardScaler 
# Separate the features and target variable if there's one 
X = df.drop(columns=['Petrol_Consumption'])  # Assuming 'Petrol_Consumption' is the target variable 
y = df['Petrol_Consumption'] 
# Standardize the features 
scaler = StandardScaler() 
X_scaled = scaler.fit_transform(X) 
from sklearn.decomposition import PCA 
# Apply PCA 
pca = PCA(n_components=2) 
X_pca = pca.fit_transform(X_scaled) 
# Check the explained variance 
print("Explained variance by each component:", pca.explained_variance_ratio_) 
print("Cumulative explained variance:", pca.explained_variance_ratio_.cumsum())

#c

6.OBST

@varuntej

Task-6
Implement OBST using dynamic programming
Algorithm:
Algorithm OBST(p, q, n)
// Given n distinct identifiers a1 < a2 < ··· < an and probabilities
// p[i], 1 ≤ i ≤ n, and q[i], 0 ≤ i ≤ n, this algorithm computes
// the cost c[i, j] of optimal binary search tree for identifiers
// a_i, ..., a_j. It also computes r[i, j], the root of t[i, j].
// w[i, j] is the weight of t[i, j].
{
  for i := 0 to n do
  { 
    // Initialize.
    w[i, i] := q[i]; r[i, i] := 0; c[i, i] := 0.0;
    // Optimal trees with one node
    w[i, i + 1] := q[i] + q[i + 1] + p[i + 1];
    c[i, i + 1] := w[i, i + 1]; r[i, i + 1] := i + 1;
  }
  
  for m := 2 to n do // Find optimal trees with m nodes.
    for i := 0 to n - m do
    {
      j := i + m;
      w[i, j] := w[i, j - 1] + p[j] + q[j];
      c[i, j] := ∞; // Solve 5.12 using Knuth's result.
      k := Find(c, r, i, j);
      // A value of k in the range [r[i, j - 1], r[i + 1, j]] that minimizes c[i, k - 1] + c[k, j];
      c[i, j] := w[i, j] + c[i, k - 1] + c[k, j];
      r[i, j] := k;
    }
  write (c[0, n], w[0, n], r[0, n]);
}

Algorithm Find(c, r, i, j)
{
  min := ∞;
  for m := r[i, j - 1] to r[i + 1, j] do
    if (c[i, m - 1] + c[m, j] < min) then
    {
      min := c[i, m - 1] + c[m, j];
      l_i := m;
    }
  return l_i;
}

Program:
#include <stdio.h>
#define MAX 10
#define INF 300000

void main() {
    char ele[MAX][MAX];
    int w[MAX][MAX], c[MAX][MAX], r[MAX][MAX];
    int p[MAX], q[MAX];
    int temp, min, min1;
    int i, j, k, b, n;

    // Input number of elements
    printf("Enter the number of elements: ");
    scanf("%d", &n);

    // Input probabilities for elements p[i]
    for (i = 1; i <= n; i++) {
        printf("Enter the element P(%d): ", i);
        scanf("%d", &p[i]);
    }
    printf("\n");

    // Input probabilities for dummy keys q[i]
    for (i = 0; i <= n; i++) {
        printf("Enter the element q(%d): ", i);
        scanf("%d", &q[i]);
    }
    printf("\n");

    printf("W \t\t C \t\t R\n");

    // Initialization of w, c, and r matrices for single elements
    for (i = 0; i <= n; i++) {
        for (j = 0; j <= n; j++) {
            if (i == j) {
                w[i][i] = q[i];
                c[i][i] = 0;
                r[i][i] = 0;
                printf("w[%d][%d] : %d \t c[%d][%d]: %d \t r[%d][%d] : %d\n", i, j, w[i][j], i, j, c[i][j], i, j, r[i][j]);
            }
        }
    }
    printf("\n");

    // Fill w, c, and r matrices for ranges [i, j]
    for (b = 0; b < n; b++) {
        for (i = 0, j = b + 1; j <= n; i++, j++) {
            if (i != j && i < j) {
                // Calculate w[i][j]
                w[i][j] = w[i][j - 1] + p[j] + q[j];
                min = INF;

                // Find minimum cost and root for this subproblem
                for (k = i + 1; k <= j; k++) {
                    min1 = c[i][k - 1] + c[k][j] + w[i][j];
                    if (min > min1) {
                        min = min1;
                        temp = k;
                    }
                }

                // Store minimum cost and root for this range
                c[i][j] = min;
                r[i][j] = temp;
            }
            // Print w[i][j], c[i][j], and r[i][j]
            printf("w[%d][%d] : %d \t c[%d][%d]: %d \t r[%d][%d] : %d\n", i, j, w[i][j], i, j, c[i][j], i, j, r[i][j]);
        }
        printf("\n");
    }

    // Print minimum cost for the entire tree
    printf("Minimum cost: %d\n", c[0][n]);
}
Output:
/tmp/VsdNZE6fYV.o
Enter the number of elements: 4
Enter the element P(1): 3
Enter the element P(2): 3
Enter the element P(3): 1
Enter the element P(4): 1

Enter the element q(0): 2
Enter the element q(1): 3
Enter the element q(2): 1
Enter the element q(3): 1
Enter the element q(4): 1

W 		 C 		 R
w[0][0] : 2 	 c[0][0]: 0 	 r[0][0] : 0
w[1][1] : 3 	 c[1][1]: 0 	 r[1][1] : 0
w[2][2] : 1 	 c[2][2]: 0 	 r[2][2] : 0
w[3][3] : 1 	 c[3][3]: 0 	 r[3][3] : 0
w[4][4] : 1 	 c[4][4]: 0 	 r[4][4] : 0

w[0][1] : 8 	 c[0][1]: 8 	 r[0][1] : 1
w[1][2] : 7 	 c[1][2]: 7 	 r[1][2] : 2
w[2][3] : 3 	 c[2][3]: 3 	 r[2][3] : 3
w[3][4] : 3 	 c[3][4]: 3 	 r[3][4] : 4

w[0][2] : 12 	 c[0][2]: 19 	 r[0][2] : 1
w[1][3] : 9 	 c[1][3]: 12 	 r[1][3] : 2
w[2][4] : 5 	 c[2][4]: 8 	 r[2][4] : 3

w[0][3] : 14 	 c[0][3]: 25 	 r[0][3] : 2
w[1][4] : 11 	 c[1][4]: 19 	 r[1][4] : 2

w[0][4] : 16 	 c[0][4]: 32 	 r[0][4] : 2

Minimum cost: 32


=== Code Exited With Errors ===

External

@signup1

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>External Style Example</title>
    <!-- Link to the external CSS file -->
    <link rel="stylesheet" href="styles.css">
</head>
<body>
    <div class="container">
        <h1 class="heading">Welcome to External CSS Styling</h1>
        <p class="text">This is an example using external styling. All CSS rules are in a separate file.</p>
        <p class="text highlight">This paragraph uses both the `text` and `highlight` classes.</p>
    </div>
</body>
</html>
------
CSS (styles.css):
/* External CSS file: styles.css */

body {
    font-family: Arial, sans-serif;
    background-color: #f0f0f0;
    margin: 0;
    padding: 0;
}

.container {
    padding: 40px;
    background-color: #ffffff;
    border: 1px solid #ddd;
    max-width: 800px;
    margin: 20px auto;
}

.heading {
    color: #333;
    text-align: center;
    padding: 20px;
}

.text {
    color: #666;
    font-size: 18px;
    line-height: 1.6;
    margin: 20px;
}

.highlight {
    color: #0056b3;
    font-weight: bold;
}

#c

5.Shortest Path

@varuntej

Task-5
Implement Dijkstra's algorithm to compute the shortest path through a graph.
Algorithm:
Algorithm ShortestPaths(v, cost, dist, n)
// dist[j], 1 ≤ j ≤ n, is set to the length of the shortest
// path from vertex v to vertex j in a digraph G with n
// vertices. dist[v] is set to zero. G is represented by its
// cost adjacency matrix cost[1 : n, 1 : n].
{
  for i := 1 to n do
  { // Initialize S.
    S[i] := false; dist[i] := cost[v, i];
  }
  S[v] := true; dist[v] := 0.0; // Put v in S.
  for num := 2 to n − 1 do
  {
    // Determine n − 1 paths from v.
    Choose u from among those vertices not
    in S such that dist[u] is minimum;
    S[u] := true; // Put u in S.
    for (each w adjacent to u with S[w] = false) do
    // Update distances.
    if (dist[w] > dist[u] + cost[u, w]) then
      dist[w] := dist[u] + cost[u, w];
  }
}
Program:
#include <stdio.h> 
#include <limits.h>
#define M 8

int n;

void spath(int graph[n][n], int u) {
    int dist[M];
    int s[M] = {0};
    for (int i = 0; i < n; i++) {
        dist[i] = INT_MAX;
    }
    dist[u] = 0;
    for (int i = 0; i < n - 1; i++) {
        int v;
        int min = INT_MAX;
        for (int j = 0; j < n; j++) {
            if (dist[j] < min && s[j] == 0) {
                min = dist[j];
                v = j;
            }
        }
        s[v] = 1;
        for (int j = 0; j < n; j++) {
            if (graph[v][j] != 0 && s[j] == 0) {
                if (dist[j] > dist[v] + graph[v][j]) {
                    dist[j] = dist[v] + graph[v][j];
                }
            }
        }
    }
    for (int i = 0; i < n; i++) {
        printf("%d ", dist[i]);
    }
    printf("\n");
}

int main() {
    printf("Enter the number of vertices (up to %d): ", M);
    scanf("%d", &n);

    int graph[M][M];
    printf("Enter the adjacency matrix (use 0 for no direct path):\n");
    for (int i = 0; i < n; i++) {
        for (int j = 0; j < n; j++) {
            scanf("%d", &graph[i][j]);
        }
    }

    int startNode;
    printf("Enter the starting node (0 to %d): ", n - 1);
    scanf("%d", &startNode);

    spath(graph, startNode);
    return 0;
}
Output:
/tmp/q6mNdkXwgB.o
Enter the number of vertices (up to 8): 8
Enter the adjacency matrix (use 0 for no direct path):
0 0 0 0 0 0 0 0
300 0 0 0 0 0 0 0
1000 800 0 0 0 0 0 0
0 0 1200 0 0 0 0 0
0 0 0 1500 0 250 0 0
0 0 0 1000 0 0 900 1400
0 0 0 0 0 0 0 1000
1700 0 0 0 0 0 0 0
Enter the starting node (0 to 7): 4
3350 3250 2450 1250 0 250 1150 1650 


=== Code Execution Successful ===

#c

4.Fractional Knapsack

@varuntej

Task-4
Implement Fractional Knapsack Algorithm
Algorithm:
Algorithm Sort(p, w, n){
  int r[n], index[n];
  int p[n], w[n];
  
  for i := 1 to n
    r[i] := p[i] / w[i];
  
  for i := n to n do{
    j := i;
    for k := i + 1 to n do
      if (r[k] <= r[j]) then 
	j := k;
    if(j!-i){
     index[i] := j;
     t := r[i];
     r[i] := r[j];
     r[j] := t;
    }
  }
  
  for i := 1 to n
  {
    p[i] := p[index[i]];
    w[i] := w[index[i]];
  }
}
Program:
#include <stdio.h>

void knapsack(int n, float weight[], float profit[], float capacity) {
    float tp = 0;
    int i, u;
    u = capacity;
    for (i = 0; i < n; i++) {
        if (weight[i] > u)
            break;
        else {
            tp = tp + profit[i];
            u = u - weight[i];
        }
    }
    if (i < n)
        tp = tp + (u / weight[i] * profit[i]);
    printf("\nMaximum profit is:- %f", tp);
}

int main() {
    float weight[20], profit[20], capacity;
    int num, i, j;
    float ratio[20], temp;

    printf("\nEnter the no. of objects:- ");
    scanf("%d", &num);

    printf("\nEnter the wts and profits of each object:-\n");
    for (i = 0; i < num; i++) {
        scanf("%f %f", &weight[i], &profit[i]);
    }

    printf("\nEnter the capacity of knapsack:- ");
    scanf("%f", &capacity);

    // Calculate profit/weight ratio
    for (i = 0; i < num; i++) {
        ratio[i] = profit[i] / weight[i];
    }

    // Sort items by profit/weight ratio in descending order
    for (i = 0; i < num; i++) {
        for (j = i + 1; j < num; j++) {
            if (ratio[i] < ratio[j]) {
                // Swap ratios
                temp = ratio[j];
                ratio[j] = ratio[i];
                ratio[i] = temp;

                // Swap weights and profits to keep arrays aligned
                temp = weight[j];
                weight[j] = weight[i];
                weight[i] = temp;

                temp = profit[j];
                profit[j] = profit[i];
                profit[i] = temp;
            }
        }
    }

    knapsack(num, weight, profit, capacity);
    return 0;
}
Output:
Enter the no. of objects:- 5
Enter the wts and profits of each object:-
4 12
1 2
2 2
1 1
4 10
Enter the capacity of knapsack:- 15
Maximum profit is:- 17.333334

=== Code Execution Successful ===

internal

@signup1

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Internal Style Example</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            background-color: #f0f0f0;
            margin: 0;
            padding: 0;
        }
        
        #main-content {
            padding: 40px;
            background-color: #ffffff;
            border: 1px solid #ddd;
            max-width: 800px;
            margin: 20px auto;
        }
        
        h1 {
            color: #333;
            text-align: center;
            padding: 20px;
        }
        
        p {
            color: #666;
            font-size: 18px;
            line-height: 1.6;
            margin: 20px;
        }
        
        p.highlight {
            color: #0056b3;
            font-weight: bold;
        }
    </style>
</head>
<body>
    <div id="main-content">
        <h1>Welcome to Internal CSS Styling</h1>
        <p>This is an example of internal styling. All CSS rules are within a style block in the head section.</p>
        <p class="highlight">This paragraph has specific styling defined in the internal CSS.</p>
    </div>
</body>
</html>

inline

@signup1

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Inline Style Example</title>
</head>
<body style="font-family: Arial, sans-serif; background-color: #f0f0f0; margin: 0; padding: 0;">
    <div style="padding: 40px; background-color: #ffffff; border: 1px solid #ddd; max-width: 800px; margin: 20px auto;">
        <h1 style="color: #333; text-align: center; padding: 20px;">Welcome to Inline CSS Styling</h1>
        <p style="color: #666; font-size: 18px; line-height: 1.6; margin: 20px;">
            This is an example of inline styling. All CSS rules are directly within the HTML elements.
        </p>
        <p style="color: #0056b3; font-weight: bold; margin: 20px;">
            This paragraph uses inline styling for specific text color and font weight.
        </p>
    </div>
</body>
</html>

#kotlin

2. ARTICULATION POINTS

@varuntej

Task-2
Write a program to identify the articulation points present in a graph.
Algorithm:
Algorithm Art(u, v)
// u is a start vertex for depth first search. v is its parent if any
// in the depth first spanning tree. It is assumed that the global
// array dfn is initialized to zero and that the global variable
// num is initialized to 1. n is the number of vertices in G.
{
    dfn[u] := num; L[u] := num; num := num + 1;
    for each vertex w adjacent from u do`
    {
        if (dfn[w] = 0) then
        {
            Art(w, u);     // w is unvisited.
            L[u] := min(L[u], L[w]);
        }
        else if (w ≠ v) then L[u] := min(L[u], dfn[w]);
    }
}
Program:
#include <stdio.h>

int dfn[10], a[10][10], l[10], n, num = 1, children[10];
int artp[10]; // Array to store articulation points

// Function to find the minimum of two values
int min(int a, int b) {
    return (a > b ? b : a);
}

// Depth-first search to find articulation points
void art(int u, int v) {
    dfn[u] = num;
    l[u] = num;
    num++;
    children[u] = 0;

    for (int i = 1; i <= n; i++) {
        if (a[u][i] == 1) { // Check if there is an edge from u to i
            int w = i;
            if (dfn[w] == 0) { // w is unvisited
                children[u]++;
                art(w, u);

                // Update the low value of u for parent function calls
                l[u] = min(l[u], l[w]);

                // Check articulation point conditions
                if (v != -1 && l[w] >= dfn[u]) {
                    artp[u] = 1; // Mark u as an articulation point
                }
            } else if (w != v) { // Update low value of u for back edges
                l[u] = min(l[u], dfn[w]);
            }
        }
    }
}

// Main function
int main() {
    printf("Enter no. of vertices: ");
    scanf("%d", &n);

    int s;
    printf("Enter root vertex: ");
    scanf("%d", &s);

    printf("Enter adjacency matrix:\n");
    for (int i = 1; i <= n; i++) {
        for (int j = 1; j <= n; j++) {
            scanf("%d", &a[i][j]);
        }
    }

    // Initialize arrays
    for (int i = 0; i <= n; i++) {
        dfn[i] = 0;
        l[i] = 0;
        children[i] = 0;
        artp[i] = 0;
    }

    printf("Articulation points are:\n");
    
    // Run DFS from the root vertex
    art(s, -1);

    // Check if the root is an articulation point (special case)
    if (children[s] > 1) {
        printf("%d\n", s);
    }

    // Print other articulation points
    for (int i = 1; i <= n; i++) {
        if (artp[i]) {
            printf("%d\n", i);
        }
    }

    return 0;
}
Output:
/tmp/96iN6R0Irx.o
Enter no. of vertices: 6
Enter root vertex: 1
Enter adjacency matrix:
0 1 0 1 0 0
1 0 1 0 0 0
0 1 0 1 1 1
1 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
Articulation points are:
3


=== Code Execution Successful ===

#c

1.MERGE AND QUICK SORT

@varuntej

Task-1
a) Implement Merge sort algorithm and plot its time complexity with reference to the size of the input.
Algorithm:
Algorithm MergeSort(low, high)
// a[low : high] is a global array to be sorted.
// Small(P) is true if there is only one element
// to sort. In this case the list is already sorted.
{
    if (low < high) then     // If there are more than one element
    {
        // Divide P into subproblems.
        // Find where to split the set.
        mid := ⌊(low + high) / 2⌋;
        // Solve the subproblems.
        MergeSort(low, mid);
        MergeSort(mid + 1, high);
        // Combine the solutions.
        Merge(low, mid, high);
    }
}
Algorithm Merge(low, mid, high)
// a[low : high] is a global array containing two sorted
// subsets in a[low : mid] and in a[mid + 1 : high]. The goal
// is to merge these two sets into a single set residing
// in a[low : high]. b[ ] is an auxiliary global array.
{
  h := low; i := low; j := mid + 1;
  while ((h ≤ mid) and (j ≤ high)) do
  {
    if (a[h] ≤ a[j]) then
    {
      b[i] := a[h]; h := h + 1;
    }
    else
    {
      b[i] := a[j]; j := j + 1;
    }
    i := i + 1;
  }
  if (h > mid) then
    for k := j to high do
    {
      b[i] := a[k]; i := i + 1;
    }
  else
    for k := h to mid do
    {
      b[i] := a[k]; i := i + 1;
    }
  for k := low to high do a[k] := b[k];
}

Program:
#include <stdio.h>
#include <time.h>
void merge(int a[],int i1,int j1,int i2,int j2) {
	int i,j,k=0;
	i=i1;
	j=i2;
	int temp[100];
	while(i<=j1 && j<=j2) {
		if(a[i]<a[j])
			temp[k++]=a[i++];
		else
			temp[k++]=a[j++];
	}
	while(i<=j1)
		temp[k++]=a[i++];
	while(j<=j2)
		temp[k++]=a[j++];
	for(i=i1,j=0; i<=j2; i++,j++)
		a[i]=temp[j];
}
void partition(int a[],int l,int h) {
	if(l<h) {
		int mid=(l+h)/2;
		partition(a,l,mid);
		partition(a,mid+1,h);
		merge(a,l,mid,mid+1,h);
	}
}
int main() {
	int n;
	printf("enter the size of array: ");
	scanf("%d",&n);
	int a[n];
	for(int i=0; i<n; i++)
		a[i] = n - i;
	clock_t t = clock();
	partition(a,0,n-1);
	t = clock() - t;
	printf("Time taken: %f \n",((float)t*1000)/CLOCKS_PER_SEC);
	for(int i=0; i<n; i++)
		printf("%d ",a[i]);
	return 0;
}
Output:
enter the size of array: 5
Time taken: 0.002000 
1 2 3 4 5 

=== Code Execution Successful ===
b) Implement Quick sort algorithm and plot its time complexity regarding asymptotic notations (Best, average, and worst).
Algorithm:
Algorithm QuickSort(p, q)
// Sorts the elements a[p], ..., a[q] which reside in the global
// array a[1 : n] into ascending order; a[n + 1] is considered to
// be defined and must be ≥ all the elements in a[1 : n].
{
    if (p < q) then     // If there are more than one element
    {
        // divide P into two subproblems.
        j := Partition(a, p, q + 1);
        // j is the position of the partitioning element.
        // Solve the subproblems.
        QuickSort(p, j - 1);
        QuickSort(j + 1, q);
        // There is no need for combining solutions.
    }
}
Algorithm Partition(a, m, p)
// Within a[m], a[m + 1], ..., a[p − 1] the elements are
// rearranged in such a manner that if initially t = a[m],
// then after completion a[q] = t for some q between m
// and p − 1, a[k] ≤ t for m ≤ k < q, and a[k] ≥ t
// for q < k < p. q is returned. Set a[p] = ∞.
{
  v := a[m]; i := m; j := p;
  repeat
  {
    repeat
      i := i + 1;
    until (a[i] ≥ v);
    
    repeat
      j := j − 1;
    until (a[j] ≤ v);
    
    if (i < j) then Interchange(a, i, j);
  } until (i ≥ j);
  
  a[m] := a[j]; a[j] := v; return j;
}

Algorithm Interchange(a, i, j)
// Exchange a[i] with a[j].
{
  p := a[i];
  a[i] := a[j]; a[j] := p;
}

Program:
#include <stdio.h>
#include <time.h>

void quicksort(int number[25], int first, int last) {
    int i, j, pivot, temp;
    if (first < last) {
        pivot = first;
        i = first;
        j = last;
        while (i < j) {
            while (number[i] <= number[pivot] && i < last)
                i++;
            while (number[j] > number[pivot])
                j--;
            if (i < j) {
                temp = number[i];
                number[i] = number[j];
                number[j] = temp;
            }
        }
        temp = number[pivot];
        number[pivot] = number[j];
        number[j] = temp;
        quicksort(number, first, j - 1);
        quicksort(number, j + 1, last);
    }
}

int main() {
    int i, count, number[25];
    printf("SIZE OF ARRAY?: ");
    scanf("%d", &count);

    printf("Enter array elements: ");
    for (i = 0; i < count; i++) {
        scanf("%d", &number[i]);
    }

    clock_t t = clock(); // Start time
    quicksort(number, 0, count - 1);
    t = clock() - t; // End time

    printf("Time taken: %f ms\n", ((float)t * 1000) / CLOCKS_PER_SEC);
    printf("Sorted elements:");
    for (i = 0; i < count; i++) {
        printf(" %d", number[i]);
    }
    printf("\n");

    return 0;
}
Output:

SIZE OF ARRAY?: 5
Enter array elements: 5 4 3 2 1
Time taken: 0.002000 ms
Sorted elements: 1 2 3 4 5


=== Code Execution Successful ===

vertical_navbar

@signup1

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Vertical Navigation Bar</title>
    <link rel="stylesheet" href="styles.css">
</head>
<body>
    <div class="sidebar">
        <a href="#home" class="active">Home</a>
        <a href="#news">News</a>
        <a href="#contact">Contact</a>
        <a href="#about">About</a>
    </div>

    <div class="content">
        <h1>Welcome to My Website</h1>
        <p>This is a simple vertical navigation bar example.</p>
    </div>
</body>
</html>

* {
    box-sizing: border-box;
}

body {
    font-family: Arial, sans-serif;
}

.sidebar {
    height: 100%; /* Full-height */
    width: 200px; /* Set the width of the sidebar */
    position: fixed; /* Fixed Sidebar (stay in place) */
    background-color: #333; /* Sidebar color */
}

.sidebar a {
    padding: 15px; /* Padding */
    text-decoration: none; /* No underline */
    font-size: 17px; /* Font size */
    color: #f2f2f2; /* Text color */
    display: block; /* Make links appear below each other */
}

.sidebar a:hover {
    background-color: #ddd; /* Add a hover effect */
    color: black; /* Text color on hover */
}

.sidebar a.active {
    background-color: #04AA6D; /* Active link color */
    color: white; /* Active link text color */
}

.content {
    margin-left: 220px; /* Margin to the left of the sidebar */
    padding: 20px; /* Padding for content */
}

horizontal_navbar

@signup1

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Horizontal Navigation Bar</title>
    <link rel="stylesheet" href="styles.css">
</head>
<body>
    <nav class="navbar">
        <a href="#home" class="active">Home</a>
        <a href="#news">News</a>
        <a href="#contact">Contact</a>
        <a href="#about">About</a>
    </nav>
</body>
</html>

* {
    box-sizing: border-box;
}

body {
    font-family: Arial, sans-serif;
}

.navbar {
    background-color: #333;
    overflow: hidden;
}

.navbar a {
    float: left;
    color: #f2f2f2;
    text-align: center;
    padding: 14px 16px;
    text-decoration: none;
    font-size: 17px;
}

.navbar a:hover {
    background-color: #ddd;
    color: black;
}

.navbar a.active {
    background-color: #04AA6D;
    color: white;
}

gridbox

@signup1

//grid.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Grid Layout Example</title>
    <link rel="stylesheet" href="gridstyles.css">
</head>
<body>
    <div class="grid-container">
        <div class="grid-item">1</div>
        <div class="grid-item">2</div>
        <div class="grid-item">3</div>
        <div class="grid-item">4</div>
        <div class="grid-item">5</div>
        <div class="grid-item">6</div>
    </div>
</body>
</html>
//gridstyles.css
* {
    box-sizing: border-box;
}

body {
    font-family: Arial, sans-serif;
}

.grid-container {
    display: grid;
    grid-template-columns: repeat(3, 1fr);
    gap: 10px; /* Space between grid items */
    padding: 20px;
}

.grid-item {
    background-color: #2196F3;
    color: white;
    padding: 20px;
    text-align: center;
}

flexbox

@signup1

//flex.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Flexbox Layout Example</title>
    <link rel="stylesheet" href="flexstyles.css">
</head>
<body>
    <div class="flex-container">
        <div class="flex-item">Item 1</div>
        <div class="flex-item">Item 2</div>
        <div class="flex-item">Item 3</div>
        <div class="flex-item">Item 4</div>
        <div class="flex-item">Item 5</div>
    </div>
</body>
</html>
//flexstyles.css
* {
    box-sizing: border-box;
}

body {
    font-family: Arial, sans-serif;
}

.flex-container {
    display: flex;
    flex-wrap: wrap;
    justify-content: space-between;
    align-items: center;
    background-color: #f4f4f4;
    padding: 20px;
}

.flex-item {
    background-color: #009688;
    color: white;
    padding: 20px;
    margin: 10px;
    flex: 1 1 30%; /* Grow, shrink, and set base width */
    text-align: center;
}

JDBC programs on 1. Update the database 2. Insert rows of servlet.

@signup1

//First, create a database named user_db and a table named users
sql:
CREATE DATABASE user_db;

USE user_db;

CREATE TABLE users (
    id INT AUTO_INCREMENT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    password VARCHAR(50) NOT NULL
);
//index.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>User Registration</title>
</head>
<body>
    <h2>User Registration</h2>
    <form action="InsertUserServlet" method="post">
        Username: <input type="text" name="username" required><br><br>
        Password: <input type="password" name="password" required><br><br>
        <input type="submit" value="Register">
    </form>

    <h2>Update Password</h2>
    <form action="UpdateUserServlet" method="post">
        Username: <input type="text" name="username" required><br><br>
        New Password: <input type="password" name="newPassword" required><br><br>
        <input type="submit" value="Update Password">
    </form>
</body>
</html>

//InsertUserServlet.java
import java.io.IOException;
import java.io.PrintWriter;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;

@WebServlet("/InsertUserServlet")
public class InsertUserServlet extends HttpServlet {
    private static final long serialVersionUID = 1L;

    protected void doPost(HttpServletRequest request, HttpServletResponse response)
            throws ServletException, IOException {
        String username = request.getParameter("username");
        String password = request.getParameter("password");

        String jdbcURL = "jdbc:mysql://localhost:3306/user_db";
        String dbUser = "root"; // Change as needed
        String dbPassword = "password"; // Change as needed

        try (Connection conn = DriverManager.getConnection(jdbcURL, dbUser, dbPassword)) {
            String sql = "INSERT INTO users (username, password) VALUES (?, ?)";
            PreparedStatement statement = conn.prepareStatement(sql);
            statement.setString(1, username);
            statement.setString(2, password);
            statement.executeUpdate();

            PrintWriter out = response.getWriter();
            out.println("<html><body><b>User registered successfully!</b></body></html>");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}
//UpdateUserServlet.java
import java.io.IOException;
import java.io.PrintWriter;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;

@WebServlet("/UpdateUserServlet")
public class UpdateUserServlet extends HttpServlet {
    private static final long serialVersionUID = 1L;

    protected void doPost(HttpServletRequest request, HttpServletResponse response)
            throws ServletException, IOException {
        String username = request.getParameter("username");
        String newPassword = request.getParameter("newPassword");

        String jdbcURL = "jdbc:mysql://localhost:3306/user_db";
        String dbUser = "root"; // Change as needed
        String dbPassword = "password"; // Change as needed

        try (Connection conn = DriverManager.getConnection(jdbcURL, dbUser, dbPassword)) {
            String sql = "UPDATE users SET password = ? WHERE username = ?";
            PreparedStatement statement = conn.prepareStatement(sql);
            statement.setString(1, newPassword);
            statement.setString(2, username);
            int rowsUpdated = statement.executeUpdate();

            PrintWriter out = response.getWriter();
            if (rowsUpdated > 0) {
                out.println("<html><body><b>Password updated successfully!</b></body></html>");
            } else {
                out.println("<html><body><b>User not found!</b></body></html>");
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}
//web.xml
<web-app xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xmlns="http://xmlns.jcp.org/xml/ns/javaee"
         xsi:schemaLocation="http://xmlns.jcp.org/xml/ns/javaee http://xmlns.jcp.org/xml/ns/javaee/web-app_3_1.xsd"
         version="3.1">

    <servlet>
        <servlet-name>InsertUserServlet</servlet-name>
        <servlet-class>InsertUserServlet</servlet-class>
    </servlet>

    <servlet-mapping>
        <servlet-name>InsertUserServlet</servlet-name>
        <url-pattern>/InsertUserServlet</url-pattern>
    </servlet-mapping>

    <servlet>
        <servlet-name>UpdateUserServlet</servlet-name>
        <servlet-class>UpdateUserServlet</servlet-class>
    </servlet>

    <servlet-mapping>
        <servlet-name>UpdateUserServlet</servlet-name>
        <url-pattern>/UpdateUserServlet</url-pattern>
    </servlet-mapping>

</web-app>

Cookies

@signup1

//cookies1
import java.io.IOException;
import java.io.PrintWriter;
 
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.Cookie;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;
 
/**
 * Servlet implementation class CookieServlet1      */
 
@WebServlet("/Cookie1")
public class cookie1 extends HttpServlet {
	private static final long serialVersionUID = 1L;
       
    
    public cookie1() {
        super();
        // TODO Auto-generated constructor stub
    }
 
	
	protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
		// TODO Auto-generated method stub
		//response.getWriter().append("Served at: ").append(request.getContextPath());
	
		response.setContentType("text/html;charset=UTF-8");
		PrintWriter out=response.getWriter();
		
        String name = request.getParameter("username");
        String pass = request.getParameter("password");
        String email=request.getParameter("email");
        
            Cookie ck = new Cookie("username", name);
            Cookie ck1=new Cookie("emailaddr",email);
            
            response.addCookie(ck);
            response.addCookie(ck1);
            out.println("<h1> Hello, welcome " + name 
                    + "!!! </h1>"); 
        out.println( 
            "<h1><a href =\"Cookie2\">Course Details</a></h1>");
 
        	     		}
 
	/**
	 * @see HttpServlet#doPost(HttpServletRequest request, HttpServletResponse response)
	 */
	protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
		// TODO Auto-generated method stub
		doGet(request, response);
	}
 
}
 
//cooki2
import java.io.IOException;
import java.io.PrintWriter;
 
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.Cookie;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;
 
/**
 * Servlet implementation class CookieServlet2     */
@WebServlet("/Cookie2")
public class CookieServlet2 extends HttpServlet {
	private static final long serialVersionUID = 1L;
       
    /**
     * @see HttpServlet#HttpServlet()
     */
    public CookieServlet2() {
        super();
        // TODO Auto-generated constructor stub
    }
 
	/**
	 * @see HttpServlet#doGet(HttpServletRequest request, HttpServletResponse response)
	 */
	protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
		// TODO Auto-generated method stub
		//response.getWriter().append("Served at: ").append(request.getContextPath());
 
		response.setContentType("text/html;charset=UTF-8");
        PrintWriter out = response.getWriter();
        response.setContentType("text/html");
        out.println("<h1> Welcome Back!!!</h1>");
        
        Cookie[] cks = request.getCookies();
        
        for(int i=0;i<cks.length;i++)
        out.println("<h1> "+cks[i].getName()+": "+ cks[i].getValue()+"</h1>");
	}
 
	/**
	 * @see HttpServlet#doPost(HttpServletRequest request, HttpServletResponse response)
	 */
	protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
		// TODO Auto-generated method stub
		doGet(request, response);
	}
 
}

//index.html
<!DOCTYPE html>
<html>
<head>
<meta charset="ISO-8859-1">
<title>LoginPage</title>
<style >
div{
display:block;
height:600px;
color:white;
}
</style> 
</head> 
<body style="text-align:center;background-color:green;">
<div >
 
<form action="Cookie1" method="post" >
 
<h1> Login to Cookie Application </h1>
 
<label>Username:</label><input type="text" name="username" ><br>
 
<label>Password:</label><input type="password" name="password"><br>
 
<label>email: </label><input type="text" name="email"><br>

<button type="submit" value="Login" >Submit</button>
</form>
</div>
</body>
</html>

Destructuring on arrays and objects

@signup1

// Array Destructuring Example

const fruits = ["Apple", "Banana", "Cherry"];
const [firstFruit, secondFruit] = fruits;

console.log("First Fruit:", firstFruit);   // Output: Apple
console.log("Second Fruit:", secondFruit);  // Output: Banana

// Skipping Elements Example
const numbers = [1, 2, 3, 4, 5];
const [one, , three] = numbers;
console.log("One:", one);                   // Output: 1
console.log("Three:", three);               // Output: 3

// Rest Parameter Example
const colors = ["Red", "Green", "Blue", "Yellow"];
const [primaryColor, ...otherColors] = colors;
console.log("Primary Color:", primaryColor);      // Output: Red
console.log("Other Colors:", otherColors);        // Output: ["Green", "Blue", "Yellow"]

// Function Returning Array Example
function getCoordinates() {
    return [10, 20];
}

const [x, y] = getCoordinates();
console.log("X Coordinate:", x);                // Output: 10
console.log("Y Coordinate:", y);                // Output: 20


// Object Destructuring Example

const person = {
    name: "John",
    age: 30,
    city: "New York"
};

const { name, age } = person;
console.log("Name:", name);                     // Output: John
console.log("Age:", age);                       // Output: 30

// Renaming Variables Example
const { name: fullName, age: years } = person;
console.log("Full Name:", fullName);           // Output: John
console.log("Years:", years);                   // Output: 30

// Default Values Example
const { country = "USA" } = person;
console.log("Country:", country);               // Output: USA (default value)

// Nested Destructuring Example
const user = {
    id: 1,
    details: {
        name: "Alice",
        age: 25,
        address: {
            city: "Los Angeles",
            zipCode: "90001"
        }
    }
};

const { details: { name:userName, address:{ city:userCity } } } = user;
console.log("User Name:", userName);           // Output: Alice
console.log("User City:", userCity);           // Output: Los Angeles

String build in methods in js

@signup1

// Initialize a string for demonstration
let originalString = "  Hello, World!  ";

console.log("Original String:", originalString);

// 1. charAt()
console.log("Character at index 0:", originalString.charAt(0)); // H

// 2. concat()
let greeting = originalString.concat(" Welcome to JavaScript.");
console.log("Concatenated String:", greeting);

// 3. includes()
console.log("Includes 'World':", originalString.includes("World")); // true

// 4. indexOf()
console.log("Index of 'o':", originalString.indexOf("o")); // 4

// 5. lastIndexOf()
console.log("Last index of 'o':", originalString.lastIndexOf("o")); // 8

// 6. replace()
let replacedString = originalString.replace("World", "JavaScript");
console.log("Replaced String:", replacedString); // Hello, JavaScript!

// 7. split()
let fruitsString = "Apple,Banana,Cherry";
let fruitsArray = fruitsString.split(",");
console.log("Fruits Array:", fruitsArray); // ["Apple", "Banana", "Cherry"]

// 8. toLowerCase()
console.log("Lowercase String:", originalString.toLowerCase()); // hello, world!

// 9. toUpperCase()
console.log("Uppercase String:", originalString.toUpperCase()); // HELLO, WORLD!

// 10. trim()
console.log("Trimmed String:", originalString.trim()); // Hello, World!

/*OUTPUT:
Original String:    Hello, World!  
Character at index 0:  
Concatenated String:    Hello, World!  Welcome to JavaScript.
Includes 'World': true
Index of 'o': 4
Last index of 'o': 8
Replaced String:    Hello, JavaScript!  
Fruits Array: [ 'Apple', 'Banana', 'Cherry' ]
Lowercase String:    hello, world!  
Uppercase String:    HELLO, WORLD!  
Trimmed String: Hello, World!*/

Built-in methods of Arrays in JavaScript

@signup1

//JavaScript Program
// Initialize an array of fruits
let fruits = ["Banana", "Orange", "Apple"];

// 1. push() - Add an element to the end of the array
console.log("Initial fruits:", fruits);
fruits.push("Mango");
console.log("After push('Mango'):", fruits); // Output: ["Banana", "Orange", "Apple", "Mango"]

// 2. pop() - Remove the last element from the array
const lastFruit = fruits.pop();
console.log("After pop():", fruits); // Output: ["Banana", "Orange", "Apple"]
console.log("Removed fruit:", lastFruit); // Output: "Mango"

// 3. shift() - Remove the first element from the array
const firstFruit = fruits.shift();
console.log("After shift():", fruits); // Output: ["Orange", "Apple"]
console.log("Removed fruit:", firstFruit); // Output: "Banana"

// 4. unshift() - Add an element to the beginning of the array
fruits.unshift("Banana");
console.log("After unshift('Banana'):", fruits); // Output: ["Banana", "Orange", "Apple"]

// 5. splice() - Remove and add elements in the array
fruits.splice(1, 1, "Kiwi"); // Removes 1 element at index 1 and adds "Kiwi"
console.log("After splice(1, 1, 'Kiwi'):", fruits); // Output: ["Banana", "Kiwi", "Apple"]

// 6. slice() - Create a shallow copy of a portion of the array
const citrus = fruits.slice(1, 3);
console.log("Slice from index 1 to 3:", citrus); // Output: ["Kiwi", "Apple"]

// 7. concat() - Merge two or more arrays
const moreFruits = ["Pineapple", "Grapes"];
const allFruits = fruits.concat(moreFruits);
console.log("After concat(moreFruits):", allFruits); // Output: ["Banana", "Kiwi", "Apple", "Pineapple", "Grapes"]

// 8. forEach() - Execute a function for each element in the array
console.log("Listing all fruits:");
allFruits.forEach(function(item, index) {
    console.log(index + ": " + item);
});

// Final output of all operations
console.log("Final fruits array:", allFruits);

Session Mgmt

@signup1

//login.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Login Page</title>
</head>
<body>
    <h2>Login</h2>
    <form action="LoginServlet" method="post">
        Username: <input type="text" name="username" required><br><br>
        <input type="submit" value="Login">
    </form>
</body>
</html>

//Login Servlet.java
import java.io.IOException;
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;
import javax.servlet.http.HttpSession;

@WebServlet("/LoginServlet")
public class LoginServlet extends HttpServlet {
    private static final long serialVersionUID = 1L;

    protected void doPost(HttpServletRequest request, HttpServletResponse response)
            throws ServletException, IOException {
        String username = request.getParameter("username");

        // Create a new session or retrieve existing one
        HttpSession session = request.getSession();
        
        // Store the username in the session
        session.setAttribute("username", username);

        // Redirect to welcome page
        response.sendRedirect("welcome.jsp");
    }
}
//welcome.jsp
<%@ page language="java" contentType="text/html; charset=UTF-8" pageEncoding="UTF-8"%>
<%@ page import="javax.servlet.http.HttpSession" %>
<%
    HttpSession session = request.getSession(false); // Get existing session
    String username = null;

    if (session != null) {
        username = (String) session.getAttribute("username");
    }

    if (username == null) {
        response.sendRedirect("login.html"); // Redirect to login if not logged in
    }
%>
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Welcome</title>
</head>
<body>
    <h2>Welcome, <%= username %>!</h2>
    <a href="LogoutServlet">Logout</a> <!-- Link to logout -->
</body>
</html>
//logout.java

import java.io.IOException;
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;
import javax.servlet.http.HttpSession;

@WebServlet("/LogoutServlet")
public class LogoutServlet extends HttpServlet {
    private static final long serialVersionUID = 1L;

    protected void doGet(HttpServletRequest request, HttpServletResponse response)
            throws ServletException, IOException {
        // Invalidate the session
        HttpSession session = request.getSession(false);
        if (session != null) {
            session.invalidate(); // Remove all attributes and invalidate the session
        }
        
        // Redirect to login page
        response.sendRedirect("login.html");
    }
}

Servlet accepting request parameters

@signup1

//index.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Parameter Input Form</title>
</head>
<body>
    <h2>Enter Your Details</h2>
    <form action="ParameterServlet" method="post">
        Name: <input type="text" name="name" required><br><br>
        Age: <input type="text" name="age" required><br><br>
        <input type="submit" value="Submit">
    </form>
</body>
</html>

//ParameterServlet.java
import java.io.IOException;
import java.io.PrintWriter;
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;

@WebServlet("/ParameterServlet")
public class ParameterServlet extends HttpServlet {
    private static final long serialVersionUID = 1L;

    protected void doPost(HttpServletRequest request, HttpServletResponse response)
            throws ServletException, IOException {
        // Retrieve parameters from the request
        String name = request.getParameter("name");
        String age = request.getParameter("age");

        // Set response content type
        response.setContentType("text/html");
        PrintWriter out = response.getWriter();

        // Generate response
        out.println("<html><body>");
        out.println("<h2>Your Details:</h2>");
        out.println("<p>Name: " + name + "</p>");
        out.println("<p>Age: " + age + "</p>");
        out.println("</body></html>");
        
        out.close();
    }

    protected void doGet(HttpServletRequest request, HttpServletResponse response)
            throws ServletException, IOException {
        // Redirect to doPost for GET requests
        doPost(request, response);
    }
}

ServletLifeCycleMethods

@signup1

//Servlet Class
import java.io.IOException;
import java.io.PrintWriter;
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;

// Annotate the servlet with a URL pattern
@WebServlet("/lifecycle")
public class ServletLifeCycleExample extends HttpServlet {

    private static final long serialVersionUID = 1L;

    // Constructor
    public ServletLifeCycleExample() {
        System.out.println("Constructor called: Servlet instance created.");
    }

    // init method called once when the servlet is loaded
    @Override
    public void init() throws ServletException {
        System.out.println("Init method called: Servlet initialized.");
    }

    // service method called for each request
    @Override
    protected void service(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
        System.out.println("Service method called: Handling request.");

        // Set response content type
        response.setContentType("text/html");
        PrintWriter out = response.getWriter();
        
        // Write response to client
        out.println("<html><body>");
        out.println("<h1>Servlet Lifecycle Example</h1>");
        out.println("<p>This is a response from the service method.</p>");
        out.println("</body></html>");
        
        out.close();
    }

    // destroy method called once when the servlet is taken out of service
    @Override
    public void destroy() {
        System.out.println("Destroy method called: Servlet is being destroyed.");
    }
} 

//web.xml
<web-app xmlns="http://xmlns.jcp.org/xml/ns/javaee" 
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://xmlns.jcp.org/xml/ns/javaee 
                             http://xmlns.jcp.org/xml/ns/javaee/web-app_3_1.xsd"
         version="3.1">

    <servlet>
        <servlet-name>lifecycleServlet</servlet-name>
        <servlet-class>ServletLifeCycleExample</servlet-class>
    </servlet>
    
    <servlet-mapping>
        <servlet-name>lifecycleServlet</servlet-name>
        <url-pattern>/lifecycle</url-pattern>
    </servlet-mapping>
</web-app>


//OUTPUT
Constructor called: Servlet instance created.
Init method called: Servlet initialized.
Service method called: Handling request.
Destroy method called: Servlet is being destroyed.

Prepared Statements

@signup1

//SQL:
CREATE TABLE Users (
    id INT PRIMARY KEY AUTO_INCREMENT,
    username VARCHAR(50) NOT NULL,
    password VARCHAR(50) NOT NULL,
    email VARCHAR(100) NOT NULL
);

//INSERT
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;

public class InsertUser {
    private static final String INSERT_USER_SQL = "INSERT INTO Users (username, password, email) VALUES (?, ?, ?)";

    public static void main(String[] args) {
        try (Connection connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/mydatabase", "root", "password");
             PreparedStatement preparedStatement = connection.prepareStatement(INSERT_USER_SQL)) {

            preparedStatement.setString(1, "john_doe");
            preparedStatement.setString(2, "securepassword");
            preparedStatement.setString(3, "john@example.com");

            int rowsAffected = preparedStatement.executeUpdate();
            System.out.println(rowsAffected + " row(s) inserted.");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

//SELECT
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;

public class SelectUsers {
    private static final String SELECT_ALL_USERS_SQL = "SELECT * FROM Users";

    public static void main(String[] args) {
        try (Connection connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/mydatabase", "root", "password");
             PreparedStatement preparedStatement = connection.prepareStatement(SELECT_ALL_USERS_SQL);
             ResultSet resultSet = preparedStatement.executeQuery()) {

            while (resultSet.next()) {
                System.out.println("ID: " + resultSet.getInt("id") +
                                   ", Username: " + resultSet.getString("username") +
                                   ", Email: " + resultSet.getString("email"));
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

//UPDATE
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;

public class UpdateUserEmail {
    private static final String UPDATE_EMAIL_SQL = "UPDATE Users SET email = ? WHERE username = ?";

    public static void main(String[] args) {
        try (Connection connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/mydatabase", "root", "password");
             PreparedStatement preparedStatement = connection.prepareStatement(UPDATE_EMAIL_SQL)) {

            preparedStatement.setString(1, "new_email@example.com");
            preparedStatement.setString(2, "john_doe");

            int rowsAffected = preparedStatement.executeUpdate();
            System.out.println(rowsAffected + " row(s) updated.");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

//OUTPUT:
ID: 1, Username: john_doe, Email: john@example.com

Sort and reverse on arrays

@login123

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Sort and Reverse Array Methods</title>
</head>
<body>

<h1>JavaScript Array Sorting and Reversing</h1>

<script>
   
    const fruits = ["Banana", "Apple", "Orange", "Mango"];
    console.log("Original Fruits Array:", fruits);
    fruits.sort();
    console.log("Sorted Fruits Array:", fruits); 

    
    const numbers = [10, 2, 5, 30];
    console.log("\nOriginal Numbers Array:", numbers);
    numbers.sort((a, b) => a - b); 
    console.log("Sorted Numbers (Ascending):", numbers); 

   
    numbers.sort((a, b) => b - a); 
    console.log("Sorted Numbers (Descending):", numbers); 

    
    const letters = ["a", "b", "c", "d"];
    console.log("\nOriginal Letters Array:", letters);
    letters.reverse();
    console.log("Reversed Letters Array:", letters); 


    const mixNumbers = [15, 3, 25, 8];
    mixNumbers.sort((a, b) => a - b).reverse(); 
    console.log("\nMixed Numbers Sorted in Descending Order:", mixNumbers); // Output: [25, 15, 8, 3]
</script>

</body>
</html>

WT git

@abhigna

https://github.com/cvrcoe26/wt

String built in methods in js

@login123

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>String Methods in JavaScript</title>
</head>
<body>

<h1>JavaScript String Methods</h1>


<script>
    const str = "   Hello, JavaScript World!   ";

    console.log("Original String:", str);

    
    console.log("\ntrim() method:");
    const trimmedStr = str.trim();
    console.log("Trimmed String:", trimmedStr);

  
    console.log("\ntoUpperCase() method:");
    console.log("Uppercase:", trimmedStr.toUpperCase());

  
    console.log("\ntoLowerCase() method:");
    console.log("Lowercase:", trimmedStr.toLowerCase());

    
    console.log("\nslice() method:");
    console.log("Sliced:", trimmedStr.slice(7, 17)); // Extracts "JavaScript"

    console.log("\nreplace() method:");
    console.log("Replaced:", trimmedStr.replace("JavaScript", "Coding"));

    
    console.log("\nincludes() method:");
    console.log("Includes 'World':", trimmedStr.includes("World")); // true


    console.log("\nsplit() method:");
    console.log("Split by space:", trimmedStr.split(" ")); // ["Hello,", "JavaScript", "World!"]


    console.log("\nrepeat() method:");
    console.log("Repeated String:", "Hi! ".repeat(3));

    
</script>

</body>
</html>

Built -in methods of Arrays

@login123

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Array Methods Demo</title>
</head>
<body>
    <h1>Array Methods in JavaScript</h1>

    <script>


        console.log("push() method:");
        const fruits = ["Apple", "Banana"];
        fruits.push("Orange");

        console.log("\npop() method:");
        const lastFruit = fruits.pop();
        console.log(lastFruit); 
        console.log(fruits);    

        console.log("\nshift() method:");
        const firstFruit = fruits.shift();
        console.log(firstFruit); 
        console.log(fruits);    

        console.log("\nunshift() method:");
        fruits.unshift("Apple");
        console.log(fruits);

        console.log("\nsplice() method:");
        const newFruits = ["Apple", "Banana", "Orange", "Mango"];
        const removedFruits = newFruits.splice(1, 2, "Grapes");
        console.log("Removed Fruits:", removedFruits); 
        console.log("Updated Fruits:", newFruits);     

        console.log("\nslice() method:");
        const citrus = newFruits.slice(0, 2);
        console.log("Sliced Fruits:", citrus);
        console.log("Original Fruits:", newFruits); 

        console.log("\nmap() method:");
        const numbers = [1, 2, 3, 4];
        const doubled = numbers.map(num => num * 2);
        console.log("Doubled Numbers:", doubled);

        console.log("\nfilter() method:");
        const evenNumbers = numbers.filter(num => num % 2 === 0);
        console.log("Even Numbers:", evenNumbers); 
    </script>
</body>
</html>

Destructuring on arrays and objects

@login123

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Destructuring in JavaScript</title>
</head>
<body>
  <h1>JavaScript Destructuring Examples</h1>
  

  <script>
    // ARRAY DESTRUCTURING

    // 1. Basic Array Destructuring
    const fruits = ["Apple", "Banana", "Cherry"];
    const [firstFruit, secondFruit, thirdFruit] = fruits;
    console.log("Basic Array Destructuring:");
    console.log(firstFruit);  
    console.log(secondFruit); 
    console.log(thirdFruit);  
    console.log("------------");

    // 2. Skipping Elements
    const numbers = [1, 2, 3, 4, 5];
    const [, secondNum, , fourthNum] = numbers;
    console.log("Skipping Elements:");
    console.log(secondNum);
    console.log(fourthNum); 
    console.log("------------");

    // 3. Default Values
    const colors = ["Red"];
    const [primaryColor, secondaryColor = "Green"] = colors;
    console.log("Default Values:");
    console.log(primaryColor); 
    console.log(secondaryColor); 
    console.log("------------");

    // 4. Rest Operator with Arrays
    const languages = ["JavaScript", "Python", "Ruby", "C++"];
    const [firstLang, ...otherLangs] = languages;
    console.log("Rest Operator with Arrays:");
    console.log(firstLang);    
    console.log(otherLangs);   
    console.log("------------");

    // OBJECT DESTRUCTURING

    // 1. Basic Object Destructuring
    const person = {
      name: "Alice",
      age: 25,
      country: "USA"
    };
    const { name, age, country } = person;
    console.log("Basic Object Destructuring:");
    console.log(name);    
    console.log(age);     
    console.log(country); 
    console.log("------------");

    // 2. Renaming Variables
    const student = {
      fullName: "John Doe",
      grade: "A"
    };
    const { fullName: studentName, grade: finalGrade } = student;
    console.log("Renaming Variables:");
    console.log(studentName);   
    console.log(finalGrade);    
    console.log("------------");

    // 3. Default Values in Objects
    const settings = {
      theme: "dark"
    };
    const { theme, fontSize = 16 } = settings;
    console.log("Default Values in Objects:");
    console.log(theme);    
    console.log(fontSize); 
    console.log("------------");

    // 4. Rest Operator with Objects
    const car = {
      make: "Toyota",
      model: "Corolla",
      year: 2021,
      color: "Blue"
    };
    const { make, model, ...otherDetails } = car;
    console.log("Rest Operator with Objects:");
    console.log(make);          
    console.log(model);         
    console.log(otherDetails);  
    console.log("------------");

    // FUNCTION PARAMETERS DESTRUCTURING

    // 1. Array Destructuring in Function Parameters
    function printFirstTwo([first, second]) {
      console.log("Array Destructuring in Function Parameters:");
      console.log("First:", first);
      console.log("Second:", second);
    }
    printFirstTwo(["Apple", "Banana", "Cherry"]);
    console.log("------------");

    // 2. Object Destructuring in Function Parameters
    function displayUserInfo({ username, email }) {
      console.log("Object Destructuring in Function Parameters:");
      console.log("Username:", username);
      console.log("Email:", email);
    }
    displayUserInfo({ username: "johndoe", email: "johndoe@example.com" });
    console.log("------------");

  </script>
</body>
</html>

Navbar

@login123

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>College Website</title>
    <style>
        
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        html, body {
            height: 100%;
            display: flex;
            flex-direction: column;
        }

      
        header {
            background-color: #4CAF50;
            color: white;
            padding: 10px;
            text-align: center;
        }

        /* Navigation styling */
        nav {
            display: flex;
            background-color: #333;
        }
        nav a {
            color: white;
            text-align: center;
            padding: 14px 20px;
            text-decoration: none;
            flex: 1;
        }
        nav a:hover {
            background-color: #ddd;
            color: black;
        }

        /* Main layout */
        .container {
            display: flex;
            flex: 1;
            padding: 20px;
        }

        /* Sidebar styling */
        .sidebar {
            width: 25%;
            background-color: #f4f4f4;
            padding: 20px;
            border-right: 1px solid #ccc;
        }

        /* Content styling */
        .content {
            flex: 1;
            display: flex;
            flex-direction: column;
            padding: 20px;
        }

        /* Courses section */
        .courses {
            display: flex;
            flex-direction: row;
            flex-wrap: wrap;
            gap: 20px;
            margin-top: 20px;
        }
        .box {
            background-color: #ddd;
            border: 1px solid #ccc;
            padding: 20px;
            width: calc(33.33% - 20px);
        }

        /* Footer styling */
        footer {
            background-color: #333;
            color: white;
            text-align: center;
            padding: 10px;
        }
    </style>
</head>
<body>

    <!-- Header -->
    <header>
        <h1>University Name</h1>
    </header>

    <!-- Navigation Bar -->
    <nav>
        <a href="#home">Home</a>
        <a href="#about">About</a>
        <a href="#courses">Courses</a>
        <a href="#contact">Contact</a>
    </nav>

    <!-- Main Container -->
    <div class="container">

        <!-- Left Sidebar -->
        <div class="sidebar">
            <h2>Menu</h2>
            <ul>
                <li><a href="#menu1">Menu Item 1</a></li>
                <li><a href="#menu2">Menu Item 2</a></li>
                <li><a href="#menu3">Menu Item 3</a></li>
            </ul>
        </div>

        <!-- Main Content -->
        <div class="content">
            <h2>Main Content</h2>
            <p>Welcome to our university's website. Here you can explore the various courses we offer and learn more about our campus life.</p>
            
            <!-- Courses Section -->
            <div class="courses">
                <div class="box">Course 1</div>
                <div class="box">Course 2</div>
                <div class="box">Course 3</div>
                <div class="box">Course 4</div>
                <div class="box">Course 5</div>
                <div class="box">Course 6</div>
            </div>
        </div>

        <!-- Right Sidebar (Optional) -->
        <div class="sidebar">
            <h2>Promotions</h2>
            <p>Check out our latest programs and offers for students.</p>
        </div>
    </div>

    <!-- Footer -->
    <footer>
        <p>&copy; 2024 University Name. All rights reserved.</p>
    </footer>

</body>
</html>

Grid layout(sample)

@login123

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Document</title>
    <style>
        .container{
            border: 2px solid red;
            display: grid;
            grid-template-columns:100px 120px 100px;
            grid-template-rows: 100px 100px 100px;
            background-color: rgb(166, 120, 209);
                     
            
        }
        .item{
            height: 45px;
            width: 45px;
            border: 5px solid black;
            margin: 5px;

        }
    </style>
</head>
<body>
    <div class="container">
        <div class="item">1</div>
        <div class="item">2</div>
        <div class="item">3</div>
        <div class="item">4</div>
        <div class="item">5</div>
        <div class="item">6</div>
        <div class="item">7</div>
        <div class="item">8</div>

    </div>
</body>
</html>

Grid layout

@login123

<!DOCTYPE html>
<html>
<head>
    <title>Grid Layout</title>
    <style>
        .container {
            display: grid;
            grid-template-columns: repeat(3, 1fr);
            /* Define 2 rows, first row 50% of height, second row auto */
            grid-template-rows: 1fr auto;
            gap: 20px;
            background-color: cornflowerblue;
            padding: 20px;
            height: 100vh;
            width: 100%;
        }

        .container > div {
            background-color: rgb(138, 138, 182);
            border: 1px solid black;
            padding: 20px;
            text-align: center;
            font-size: 20px;
        }

        /* Example of grid item properties */
        .item1 {
            grid-column: 1 / span 2; /* Spans across 2 columns */
            grid-row: 1; /* Positions in the first row */
        }

        .item2 {
            grid-column: 3; /* Positions in the third column */
            grid-row: 1; /* Positions in the first row */
        }

        .item3 {
            grid-column: 2 / span 2; /* Spans across columns 2 and 3 */
            grid-row: 2; /* Positions in the second row */
        }
    </style>
</head>
<body>
    <h1 style="text-align: center;">This is my first Grid Layout</h1>
    <div class="container">
        <div class="item1">
            <p>Name: S</p>
            <p>Roll No: 22B81A0</p>
            <p>Branch: CSE</p>
            <p>Section: E</p>
            <p>CGPA: 9.7</p>
        </div>
        <div class="item2">
            <p>Name: S</p>
            <p>Roll No: 22B81A0</p>
            <p>Branch: CSE</p>
            <p>Section: E</p>
            <p>CGPA: 9.0</p>
        </div>
        <div class="item3">
            <p>Name:</p>
            <p>Roll No: 22B81A0</p>
            <p>Branch: CSE</p>
            <p>Section: E</p>
            <p>CGPA: 9.5</p>
        </div>
    </div>
</body>
</html>

#php

Array value retrieve with jquery.

@Sifat_H

var TYPR_CHD_NO_TWO = $('input[name="TYPR_CHD_NO_TWO[]"]').map(function() {
                return $(this).val();
            }).get();

FlexBox

@login123

<!DOCTYPE html>
<html>
<head>
    <title>Flex Item</title>
    <style>
        .container {
            display: flex;
            flex-wrap: wrap;
            flex-direction: row;
            align-items: center;
            justify-content: space-evenly;
            background-color: cornflowerblue;
            height: 100vh;
            width: 100%;
            position: fixed;
        }

        .item{
            color: black;
            border: 1px solid black;
            padding: 10px;
            margin: 10px;
            background-color: lavender;
            text-align: center;
            padding-top: 50px;
            font-size: 20px;
        }
    </style>
</head>
<body>
    <h1 style="text-align: center;">This is my first Flex</h1>
    <div class="container">
        <div class ="item">
            <p>Name: S</p>
            <p>Roll No: 22B81A05</p>
            <p>Branch: CSE</p>
            <p>Section: E</p>
            <p>CGPA: 9.7</p>
        </div>
        <div class ="item">
            <p>Name: S</p>
            <p>Roll No: 22B81A05</p>
            <p>Branch: CSE</p>
            <p>Section: E</p>
            <p>CGPA: 9.0</p>
        </div>
        <div class ="item">
            <p>Name: S</p>
            <p>Roll No: 22B81A05</p>
            <p>Branch: CSE</p>
            <p>Section: E</p>
            <p>CGPA: 9.5</p>
        </div>
    </div>
</body>
</html>

Inline style sheets

@login123

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Inline Style Example</title>
</head>
<body style="font-family: Arial, sans-serif; 
            background-color: #f0f0f0; 
            margin: 0; 
            padding: 0;">


    <div id="main-content" style="padding: 40px; 
            background-color: #ffffff; 
            border: 1px solid #ddd; 
            max-width: 800px; 
            margin: 20px auto;">
        <h1 style="color: #333; 
            text-align: center; 
            padding: 20px;">Welcome to Inline CSS Styling</h1>


        <p style="color: #666; 
            font-size: 18px; 
            line-height: 1.6; 
            margin: 20px;">
            This is an example of inline styling. Each HTML element has its own <code>style</code> attribute for defining CSS rules.
        </p>
        <p style="color: #0056b3;  
            font-weight: bold; 
            margin: 20px;">
            This paragraph uses an inline style to set specific text color and font weight.
        </p>
    </div>

</body>
</html>

External style sheets

@login123

HTML:
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>External Style Sheet Example</title>
    <link rel="stylesheet" href="styles.css">
</head>
<body>

    <div id="main-content">
        <h1>Welcome to External CSS Styling</h1>
        <p>This is an example of an external style sheet. The styles are stored in a separate file named <code>styles.css</code>.</p>
        <p class="highlight">This paragraph has additional styling with a specific class.</p>
    </div>

</body>
</html>



CSS:
/* styles.css */

body {
    font-family: Arial, sans-serif;
    background-color: #f0f0f0;
    margin: 0;
    padding: 0;
}

h1 {
    color: #333;
    text-align: center;
    padding: 20px;
}

p {
    color: #666;
    font-size: 18px;
    line-height: 1.6;
    margin: 20px;
}

.highlight {
    color: #0056b3;
    font-weight: bold;
}

#main-content {
    padding: 40px;
    background-color: #ffffff;
    border: 1px solid #ddd;
    max-width: 800px;
    margin: 20px auto;
}

Statement Insert

Statement Select

Grid-Box Layout

1 and 2 #pca and #rfe

Flex-Box Layout

Internal, External, Inline CSS

5.Decision Tree Classification AND K-Neareast Nrighbour

7. K MAEANS AND HIERARCHIAL CLUSTERING

9.Design and implement Hierarchical clustering to cluster species of flower. Estimate the accuracy of the model. Also write the program to visualize insights of the Iris dataset.

8.Design and implement k-Means clustering to cluster species of flower. Estimate the accuracy of the model. Also write the program to visualize insights of the Iris dataset.

7.Design and implement a Random Forest Classification model to predict if a loan will get approved or not for a bank customer dataset. Estimate the accuracy of the model. Also write the program to visualize insights of the dataset.

6.Write a program for k-NN classifier to predict the class of the person on available attributes. Consider diabetes.cs dataset. Also calculate the performance measures of the model

5.Implement the Decision tree Classification model on Iris.est dataset. Estimate the accuracy of the model. Also write the program to visualize insights of the dataset.

4.Implement Regression model and compare the performance of the model with Dimensionality reduction and without Dimensionality reduction. Consider student_scores.csv dataset.

3.Design and Demonstrate Regression model to predict the rent of a house. Evaluate the performance of the model. Consider Pune_rent.csv dataset.

2.Implement the Dimensionality Reduction using Recursive Feature Elimination method and analyze the results with any one classifier. Consider Fish.cs dataset.

1.Implement Dimensionality reduction by Principal Component Analysis and analyze the results of both methods. Consider petrol _consumption.cs dataset. Also write the program to visualize insights of the dataset.

6.OBST

External

5.Shortest Path

4.Fractional Knapsack

internal

inline

2. ARTICULATION POINTS

1.MERGE AND QUICK SORT

vertical_navbar

horizontal_navbar

gridbox

flexbox

JDBC programs on 1. Update the database 2. Insert rows of servlet.

Cookies

Destructuring on arrays and objects

String build in methods in js

Built-in methods of Arrays in JavaScript

Session Mgmt

Servlet accepting request parameters

ServletLifeCycleMethods

Prepared Statements

Sort and reverse on arrays

WT git

String built in methods in js

Built -in methods of Arrays

Destructuring on arrays and objects

Navbar

Grid layout(sample)

Grid layout

Array value retrieve with jquery.

FlexBox

Inline style sheets

External style sheets

Save snippets that work with our extensions