Data Science and Machine Learning Implementation in Python
Statistical Analytics with Pandas
Import libraryimport pandas as pd
Part 1: Descriptive Statistics
Load datasetdf = pd.read_csv("stat.csv")
print("First 5 rows:")
print(df.head())
Group by categorical column (income) and numeric column (age)print("\nSummary Statistics (Grouped by income):")
print(df.groupby("income")["age"].describe())
Individual Statistics
print("\nMean:")
print(df.groupby("income")["age"].mean())
print("\nMedian:")
print(df.groupby("income")["age"].median())
print("\nMin:")
print(df.groupby(
Implement BERT for Sentiment Analysis in PyTorch
This implementation demonstrates how to build a BERT-based model from scratch using PyTorch and the Hugging Face Transformers library for sentiment classification.
Required Libraries and Imports
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer
from datasets import load_dataset
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")The Self-Attention Mechanism
The core of the Transformer architecture is the Multi-Head Self-Attention mechanism,
Read MorePython Machine Learning Code Snippets Collection
Python Machine Learning Code Snippets
Hierarchical Clustering Example
This snippet demonstrates hierarchical clustering using scipy and visualizes the result as a dendrogram, followed by applying Agglomerative Clustering.
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy as sch
import numpy as np
from sklearn.cluster import AgglomerativeClustering
X = np.random.rand(50, 2)
dendrogram = sch.dendrogram(sch.linkage(X, method='ward'))
plt.title('Dendrogram')
plt.show()
hc = AgglomerativeClustering(Machine Learning Algorithms Implementation Showcase
# ============================
# 1. K-MEANS CLUSTERING
# ============================
# Create synthetic 2D data with 3 clusters
X_blobs, y_blobs = make_blobs(n_samples=300, centers=3, random_state=42)
# Fit KMeans
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
kmeans.Fit(X_blobs)
# Get cluster labels
cluster_labels = kmeans.Labels_
centers = kmeans.Cluster_centers_
print(“KMeans cluster centers:\n”, centers)
# Plot clusters
plt.
Figure(figsize=(6, 5))
plt.Scatter(X_blobs[:, 0], X_blobs[:, 1], c=cluster_
CSMA/CD Ethernet Collision Detection and LAN Access
CSMA/CD Collision Detection in Ethernet LANs
CSMA/CD (Carrier Sense Multiple Access/Collision Detection) is a media access control method that was widely used in early Ethernet technology and LANs when networks used a shared bus topology and each node (computers) was connected by coaxial cables. Today, Ethernet is typically full-duplex and the topology is either star (connected via a switch or router) or point-to-point (direct connection). Hence, CSMA/CD is not commonly used in modern switched, full‑duplex
Read MoreMachine Learning Algorithms: KNN, LWR, Random Forest, SVM
K-Nearest Neighbors (KNN) Algorithm Implementation
This section demonstrates the implementation of the K-Nearest Neighbors (KNN) algorithm using the Iris dataset in Python with scikit-learn.
Python Code for KNN
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import numpy as np
# Load the Iris dataset
dataset = load_iris()
# Split the dataset into training and test sets
X_train, X_test, y_train, y_test =