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PYTHON WORLD ( PYTHON | JAVA | C | C++ | HTML )

PYTHON WORLD ( PYTHON | JAVA | C | C++ | HTML )

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Senior Analyst at Capgemini Channel link :- https://t.me/python_world_in (You can share with friends) 👉 Get materials for MNC's. 👉 Off campus recruitment updates. 👉 Interview Experiences. 👉 And all the placement related information at

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📈 Telegram 频道 PYTHON WORLD ( PYTHON | JAVA | C | C++ | HTML ) 的分析概览

频道 PYTHON WORLD ( PYTHON | JAVA | C | C++ | HTML ) (@python_world_in) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 14 430 名订阅者,在 技术与应用 类别中位列第 8 953,并在 印度 地区排名第 30 065

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 14 430 名订阅者。

根据 13 六月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 -196,过去 24 小时变化为 -7,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 14.23%。内容发布后 24 小时内通常能获得 2.15% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 2 054 次浏览,首日通常累积 311 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 0
  • 主题关注点: 内容集中在 internship, engineer, graduate, intern, analytic 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
Senior Analyst at Capgemini Channel link :- https://t.me/python_world_in (You can share with friends) 👉 Get materials for MNC's. 👉 Off campus recruitment updates. 👉 Interview Experiences. 👉 And all the placement related information at

凭借高频更新(最新数据采集于 14 六月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。

14 430
订阅者
-724 小时
-427
-19630
帖子存档
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PYTHON LECTURES TOPICS 30 + Hour python Course at only 49rs😯 https://python-world-in.mojo.page/pythonlectures- Introduction About Python Setup & Installation Basic of Python Data Type Operation on String Variables and Memory Management Comments and Constant Operators in Python Input Function and Math Module Introduction eval(), Command line Argument and Specifiers Decision Control Statements Pycharm Setup, Installation and Loops User Defined Functions List Tuple String Dictionary Exception handling Operator Overloading Database Programming File Handling Modules 30 + Hour python Course at only 49rs 😯 https://python-world-in.mojo.page/pythonlectures-

106 : Collaborative Filtering Model 107 : Consumer Complaints Analysis 108 : Customer Segmentation (Online) 109 : Customer Segmentation (Who...) 110 : Employee Turnover 111 : Granger Causality Test 112 : H2O Higgs Boson Analysis 113 : Machine Learning for Diabetes 114 : Movie Recommendation System 115 : Language Translation Model using ML 116 : U.S. Gasoline and Diesel Retail Prices Analysis 117 : Twitter Sentiment Analysis 118 : Image Captioning Project 119 : FIFA 21 Data Cleaning 120 : Drive Data Analysis 121 : Advanced Uses of SHAP Values 122 : Social Network Ads Result Analysis 123 : Captcha Recognition 124 : QR Code Readability 125 : Tokyo 2020 Monitor Tweets Analysis 126 : Tesla Car Price Prediction More +++ https://python-world-in.mojo.page/-machine-learning-project- Project

All 150 ML projects cost only 299rs https://python-world-in.mojo.page/-machine-learning-project- 1 : Adult Salary Prediction 2 : Bitcoin Price Prediction 3 : Boston Housing Analysis 4 : Indian-classical-dance problem using Machine 5 : Income Classification using ML 6 : IMDb Sentiment Review Analysis using ML 7 : Advanced Hyperparameter Tuning 8 : House Price Prediction Detailed Analysis 9 : (HealthCare) Heart Disease Prediction 10 : Titanic Problem Handling Missing Values 11 : Credit Card Fraud Problem Handling Imbalance 12 : Hand Digit Recognition Using ML 13 : Gold Price Prediction 14 : Fraud Detection Using ML 15 : Forecasting using ARIMA 16 : Flower Species Classification 17 : Flight Fare Prediction 18 : FIFA Data Analysis 19 : Fashion MNIST Data Analysis Using ML 20 : Face Mask Detection using ML 21 : Face Expression Identifier 22 : Employee Turnover Prediction 23 : Earthquake Prediction 24 : Dog Vs Cat Classification 25 : Advanced Credit Card Fraud Detection 26 : Cotton Disease Prediction 27 : NLP Disaster or Not Disaster 28 : Outliers Detection 29 : Plant Diseases Recognition 30 : Pneumonia Classification 31 : In-Depth Principal Component Analysis 32 : Rainfall Prediction 33 : (Conceptual Analysis) Select Right Threshold Value 34 : Live Smile Detector 35 : Spam Classifier 36 : Student Performance Analysis 37 : Text Summarization (Medium) 38 : Time Series Forecasting 39 : Time Series With LSTM 40 : Titanic Survival Prediction 41 : Tracking of COVID-19 42 : Traffic Sign Recognition 43 : Wine Quality Prediction 44 : XGBoost Algorithm 45 : Customer Segmentation 46 : Breast Cancer Detection 47 : Car And Pedestrian Tracker 48 : Car Price Prediction 49 : CIFAR-10 Classification 50 : 50 Startups Success Rate Prediction 51 : BigMart Sales Prediction Analysis - Regression 52 : Wine Quality Prediction Analysis - Classification 53 : Turkiye Student Evaluation Analysis - Clustering 54 : Traffic Forecast - Time Series Analysis 55 : Million Songs Dataset - Recommendation Engine 56 : Loan Prediction Analysis - Classification 57 : Iris Dataset Analysis - Classification 58 : Image to Text Conversion - OCR 59 : Cross Language Information Retrieval 60 : Digit Recognition (MNIST Sequence) 61 : Stock Market Analysis for Tech Stocks 62 : Titanic Dataset - Exploratory Analysis 63 : Medical Cost Personal 64 : Drinking Water Potability 65 : Groundhog Day Predictions 66 : Mall Customer Segmentation Data 67 : Wine Production Analysis 68 : Marketing Campaign Result Prediction 69 : Regression - First Innings Score Prediction (IPL) 70 : Image to Text Conversion - OCR 71 : Autoencoder Fashion MNIST 72 : Movie Recommendation Engine 73 : Spam Email Classification 74 : Traffic Sign Recognizer 75 : Regression - Black Friday Sales Prediction Analysis 76 : California Housing 77 : Ad Demand Forecast Avito 78 : Articles Recommendation System 79 : Autoencoder for Customer Churn 80 : Bayesian Logistic Regression (Bank Marketing) 81 : Bayesian Statistics with Python (PyMC3, ArviZ) 82 : BOW, TFIDF, XGBoost Update 83 : Breast Cancer Prediction 84 : (Conceptual) Machine Learning with spaCy 85 : Mercari Price Suggestion using LightGBM 86 : Modeling House Price with Regularized Linear Model 87 : (Conceptual) NHANES Confidence Intervals 88 : (Conceptual) NHANES Hypothesis Testing 89 : (Conceptual) Practical Statistics for Housing (Python Update) 90 : Price Elasticity of Demand Analysis 91 : Promotional Time Series 92 : PySpark Basic DataFrame Operations 93 : Recommender Systems - The Fundamentals 94 : (Conceptual) Regression Diagnostics 95 : Seattle Hotels Recommender 96 : Solving a Simple Classification Problem with Python 97 : Spark DataFrames Project Exercise (Udemy) 98 : (Conceptual) Supervised Learning - Part I 99 : Weather Data Clustering using ML 100 : (Conceptual) Working with Data 101 : (Conceptual) XGBoost with BOW, TFIDF 102 : Building Recommended System 103 : Building Recommender System 104 : CLV (Non-Contractual) 105 : CLV (Online Retail)

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