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Coding Projects

Coding Projects

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Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data

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📈 Análisis del canal de Telegram Coding Projects

El canal Coding Projects (@programming_experts) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 932 suscriptores, ocupando la posición 1 914 en la categoría Tecnologías y Aplicaciones y el puesto 4 746 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 932 suscriptores.

Según los últimos datos del 28 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 157, y en las últimas 24 horas de -8, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.29%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.04% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 2 236 visualizaciones. En el primer día suele acumular 709 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
  • Intereses temáticos: El contenido se centra en temas clave como |--, algorithm, array, framework, javascript.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 29 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

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67 932
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+15730 días
Archivo de publicaciones
✅ Coding A-Z: Your Essential Guide 💻 ✨ 🅰️ Algorithm: A step-by-step procedure for solving a problem. The backbone of every program. 🅱️ Boolean: A data type with only two possible values: true or false. The foundation of logic in code. ©️ Class: A blueprint for creating objects, encapsulating data and methods. Central to object-oriented programming. 🅳 Data Structure: A way of organizing and storing data for efficient access and modification (e.g., arrays, linked lists, trees). 🅴 Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions (handle them!). 🅵 Function: A block of organized, reusable code that performs a specific task. A building block of modular code. 🅶 Git: A distributed version control system for tracking changes in source code during software development. Essential for collaboration. 🅷 HTTP (Hypertext Transfer Protocol): The foundation of data communication on the World Wide Web. 🅸 IDE (Integrated Development Environment): A software application that provides comprehensive facilities to computer programmers for software development (e.g., VS Code, IntelliJ). 🅹 JSON (JavaScript Object Notation): A lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. 🅺 Keyword: A reserved word in a programming language that has a special meaning and cannot be used as an identifier. 🅻 Loop: A sequence of instructions that is continually repeated until a certain condition is reached (e.g., for loop, while loop). 🅼 Method: A function that is associated with an object. They define the behavior of objects. 🅽 Null: Represents the absence of a value or a non-existent object pointer. 🅾️ Object: A fundamental concept in object-oriented programming, it is an instance of a class, containing data (attributes) and code (methods). 🅿️ Polymorphism: The ability of different classes to respond to the same method call in their own specific way. 🆀 Query: A request for data from a database. 🆁 Recursion: A function that calls itself to solve a smaller instance of the same problem. Useful for problems with self-similar substructures. 🆂 String: A sequence of characters, used to represent text. 🆃 Thread: A small unit of CPU execution, that can be executed concurrently with other units of the same program. 🆄 Unicode: A character encoding standard that provides a unique number for every character, regardless of the platform, program, or language. 🆅 Variable: A named storage location in the computer's memory that can hold a value. 🆆 While Loop: A control flow statement that allows code to be executed repeatedly based on a given boolean condition. 🆇 XML (Extensible Markup Language): A markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. 🆈 YAML (YAML Ain't Markup Language): A human-readable data serialization language often used for configuration files and in applications where data is being stored or transmitted. 🆉 Zero-Based Indexing: A way of indexing an array where the first element has an index of zero. Tap ❤️ for more!

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✅ 5 Power BI Projects for Beginners 📊🟡 1️⃣ Sales Dashboard → Track revenue, profit, top products & sales by region → Practice: bar charts, slicers, KPIs, date filters 2️⃣ Customer Analysis Report → Analyze customer demographics, behavior, and retention → Practice: pie charts, filters, clustering 3️⃣ HR Analytics Dashboard → Monitor employee count, attrition rate, department stats → Practice: cards, stacked bars, trend lines 4️⃣ Financial Statement Report → Visualize income, expenses, cash flow trends → Practice: waterfall chart, time intelligence 5️⃣ Social Media Performance Dashboard → Track engagement, followers, reach by platform → Practice: multi-page reports, custom visuals, drill-through 💡 Tip: Use sample datasets from Kaggle, Microsoft, or mock Excel files. 👍 Tap ❤️ if you found this helpful!

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✅ Data Science Project Series: Part 1 - Loan Prediction. Project goal Predict loan approval using applicant data. Business value - Faster decisions - Lower default risk - Clear interview story Dataset Use the common Loan Prediction dataset from analytics practice platforms. Target Loan_Status Y approved N rejected Tech stack - Python - Pandas - NumPy - Matplotlib - Seaborn - Scikit-learn Step 1. Import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
Step 2. Load data
df = pd.read_csv("loan_prediction.csv")
df.head()
Step 3. Basic checks
df.shape
df.info()
df.isnull().sum()
Step 4. Data cleaning Fill missing values
df['LoanAmount'].fillna(df['LoanAmount'].median(), inplace=True)
df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mode()[0], inplace=True)
df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)
categorical_cols = ['Gender','Married','Dependents','Self_Employed']
for col in categorical_cols:
    df[col].fillna(df[col].mode()[0], inplace=True)
Step 5. Exploratory Data Analysis Credit history vs approval
sns.countplot(x='Credit_History', hue='Loan_Status', data=df)
plt.show()
Income distribution.python
sns.histplot(df['ApplicantIncome'], kde=True)
plt.show()
Insight Applicants with credit history have far higher approval rates. Step 6. Feature engineering Create total income.
df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']

# Log transform loan amount
df['LoanAmount_log'] = np.log(df['LoanAmount'])
Step 7. Encode categorical variables
le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
    df[col] = le.fit_transform(df[col])
Step 8. Split features and target
X = df.drop('Loan_Status', axis=1)
y = df['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)
Step 9. Build model Logistic Regression.
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)
Step 10. Predictions
y_pred = model.predict(X_test)
Step 11. Evaluation
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
confusion_matrix(y_test, y_pred)
Classification report.python
print(classification_report(y_test, y_pred))
Typical result - Accuracy around 80 percent - Strong precision for approved loans - Recall needs focus for rejected loans Step 12. Model improvement ideas - Use Random Forest - Tune hyperparameters - Handle class imbalance - Track recall for rejected cases Resume bullet example - Built loan approval prediction model using Logistic Regression - Achieved ~80 percent accuracy - Identified credit history as top approval driver Interview explanation flow - Start with bank risk problem - Explain feature impact - Justify Logistic Regression - Discuss recall vs accuracy Double Tap ♥️ For More

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Famous programming languages and their frameworks 1. Python: Frameworks: Django Flask Pyramid Tornado 2. JavaScript: Frameworks (Front-End): React Angular Vue.js Ember.js Frameworks (Back-End): Node.js (Runtime) Express.js Nest.js Meteor 3. Java: Frameworks: Spring Framework Hibernate Apache Struts Play Framework 4. Ruby: Frameworks: Ruby on Rails (Rails) Sinatra Hanami 5. PHP: Frameworks: Laravel Symfony CodeIgniter Yii Zend Framework 6. C#: Frameworks: .NET Framework ASP.NET ASP.NET Core 7. Go (Golang): Frameworks: Gin Echo Revel 8. Rust: Frameworks: Rocket Actix Warp 9. Swift: Frameworks (iOS/macOS): SwiftUI UIKit Cocoa Touch 10. Kotlin: - Frameworks (Android): - Android Jetpack - Ktor 11. TypeScript: - Frameworks (Front-End): - Angular - Vue.js (with TypeScript) - React (with TypeScript) 12. Scala: - Frameworks: - Play Framework - Akka 13. Perl: - Frameworks: - Dancer - Catalyst 14. Lua: - Frameworks: - OpenResty (for web development) 15. Dart: - Frameworks: - Flutter (for mobile app development) 16. R: - Frameworks (for data science and statistics): - Shiny - ggplot2 17. Julia: - Frameworks (for scientific computing): - Pluto.jl - Genie.jl 18. MATLAB: - Frameworks (for scientific and engineering applications): - Simulink 19. COBOL: - Frameworks: - COBOL-IT 20. Erlang: - Frameworks: - Phoenix (for web applications) 21. Groovy: - Frameworks: - Grails (for web applications)

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🔤 A–Z of Programming 💻 A – Array A data structure that stores a collection of elements of the same type, accessed by index. B – Binary A base-2 number system using 0s and 1s, the foundation of how computers represent data and perform operations. C – Class A blueprint in object-oriented programming for creating objects, defining attributes and methods. D – Data Structure An organization of data for efficient access and modification, like lists or trees. E – Exception An error or unexpected event during program execution that can be handled to prevent crashes. F – Function A reusable block of code that performs a specific task, often taking inputs and returning outputs. G – Git A version control system for tracking changes in code, enabling collaboration and history management. H – HashMap/Hash Table A data structure storing key-value pairs for fast lookups using hashing. I – Inheritance A mechanism where a class inherits properties and methods from a parent class in OOP. J – JavaScript A versatile language for web development, handling client-side interactivity and server-side with Node.js. K – Keyword A reserved word in a language with special meaning, like "if" or "for", not usable as variable names. L – Loop A control structure repeating code until a condition is met, such as for or while loops. M – Modulus An operator (%) returning the remainder of division, useful for cycles or checks. N – Null A special value indicating absence of data or no object reference. O – Object An instance of a class containing data (attributes) and behavior (methods) in OOP. P – Pointer A variable storing the memory address of another variable for direct access. Q – Queue A FIFO (First-In-First-Out) data structure for processing items in order. R – Recursion A function calling itself to solve smaller instances of a problem. S – Stack A LIFO (Last-In-First-Out) data structure, like a stack of plates. T – Testing Verifying a program's correctness through unit tests, integration, and more. U – Unicode A standard encoding characters from all writing systems for global text handling. V – Variable A named storage for data that can change during program execution. W – While Loop Repeats code while a condition remains true, offering flexible iteration. X – XOR A logical operator true if operands differ, used in cryptography and checks. Y – Yield A keyword returning a value from a generator, enabling lazy iteration. Z – Zeroes (numpy.zeros) Creates an array filled with zeros, useful for initialization. Double Tap ♥️ For More

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✅ Coding Interview Prep Guide 💻🔥 1️⃣ Core Programming Fundamentals • Variables, data types, operators • Control flow (loops, conditions) • Functions recursion • Time space complexity basics • Debugging mindset 2️⃣ Data Structures (High Priority) • Arrays Strings • Linked Lists • Stacks Queues • HashMaps / Dictionaries • Trees Binary Trees • Heaps Priority Queues • Graphs (BFS, DFS) 3️⃣ Algorithms You MUST Know • Searching (Binary Search) • Sorting (Quick, Merge, Heap) • Recursion Backtracking • Greedy algorithms • Dynamic Programming • Sliding Window • Two Pointers • Prefix Sum 4️⃣ Problem-Solving Patterns • Brute force → optimized approach • Hashing for lookups • Divide and conquer • Recursion → DP conversion • Space–time tradeoffs 5️⃣ Language-Specific Prep • Python / Java / C++ fundamentals • Built-in data structures • Edge cases constraints • Writing clean, readable code • Input/output handling 6️⃣ Coding Interview Expectations • Explain approach before coding • Write code step-by-step • Handle edge cases • Analyze time space complexity • Optimize if asked 7️⃣ Common Interview Questions • Reverse a string / array • Find duplicates • Two Sum / Subarray problems • Palindrome checks • Tree traversal • LRU Cache • Longest substring problems 8️⃣ Where to Practice • LeetCode (Top priority) • HackerRank • Codeforces • CodeChef • GeeksforGeeks 9️⃣ Mock Interview Focus • Think out loud • Don’t panic on hard questions • Ask clarifying questions • Partial solutions still matter • Correct approach > perfect code 🔟 Pro Tips ✔️ Master patterns, not random problems ✔️ Revise mistakes weekly ✔️ Practice writing code without IDE help ✔️ Speed improves with consistency ✔️ Interviews test thinking, not memory Double Tap ♥️ For More

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🌐 Complete Roadmap to Become a Web Developer 📂 1. Learn the Basics of the Web – How the internet works – What is HTTP/HTTPS, DNS, Hosting, Domain – Difference between frontend & backend 📂 2. Frontend Development (Client-Side) ∟📌 HTML – Structure of web pages ∟📌 CSS – Styling, Flexbox, Grid, Media Queries ∟📌 JavaScript – DOM Manipulation, Events, ES6+ ∟📌 Responsive Design – Mobile-first approach ∟📌 Version Control – Git & GitHub 📂 3. Advanced Frontend ∟📌 JavaScript Frameworks/Libraries – React (recommended), Vue or Angular ∟📌 Package Managers – npm or yarn ∟📌 Build Tools – Webpack, Vite ∟📌 APIs – Fetch, REST API integration ∟📌 Frontend Deployment – Netlify, Vercel 📂 4. Backend Development (Server-Side) ∟📌 Choose a Language – Node.js (JavaScript), Python, PHP, Java, etc. ∟📌 Databases – MongoDB (NoSQL), MySQL/PostgreSQL (SQL) ∟📌 Authentication & Authorization – JWT, OAuth ∟📌 RESTful APIs / GraphQL ∟📌 MVC Architecture 📂 5. Full-Stack Skills ∟📌 MERN Stack – MongoDB, Express, React, Node.js ∟📌 CRUD Operations – Create, Read, Update, Delete ∟📌 State Management – Redux or Context API ∟📌 File Uploads, Payment Integration, Email Services 📂 6. Testing & Optimization ∟📌 Debugging – Chrome DevTools ∟📌 Performance Optimization ∟📌 Unit & Integration Testing – Jest, Cypress 📂 7. Hosting & Deployment ∟📌 Frontend – Netlify, Vercel ∟📌 Backend – Render, Railway, or VPS (e.g. DigitalOcean) ∟📌 CI/CD Basics 📂 8. Build Projects & Portfolio – Blog App – E-commerce Site – Portfolio Website – Admin Dashboard 📂 9. Keep Learning & Contributing – Contribute to open-source – Stay updated with trends – Practice on platforms like LeetCode or Frontend Mentor ✅ Apply for internships/jobs with a strong GitHub + portfolio! 👍 Tap ❤️ for more!

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