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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
Больше📈 Аналитический обзор Telegram-канала Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 75 810 подписчиков, занимая 2 118 место в категории Образование и 4 300 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 75 810 подписчиков.
Согласно последним данным от 17 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 903, а за последние 24 часа — 2, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.39%. В первые 24 часа после публикации контент обычно набирает 1.40% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 573 просмотров. В течение первых суток публикация набирает 1 064 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 4.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, accuracy, distribution, panda, dataset.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
Благодаря высокой частоте обновлений (последние данные получены 18 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
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What is a major advantage of Random Forest over Decision Trees?
Which module is used for Random Forest in scikit-learn?
How does Random Forest make the final prediction in classification?
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✅ Random Forest Basics🌲🤖
👉 Random Forest is one of the most popular and powerful Machine Learning algorithms.
It combines multiple Decision Trees to make better predictions.
🔹 1. What is Random Forest?
Random Forest = Collection of many Decision Trees
👉 Instead of relying on one tree, it takes predictions from many trees and gives the final result.
This improves:
✔ Accuracy
✔ Stability
✔ Performance
🔥 2. How Random Forest Works
Step-by-step:
1️⃣ Create multiple Decision Trees
2️⃣ Train each tree on random data samples
3️⃣ Each tree gives prediction
4️⃣ Final prediction = Majority vote (classification)
🔹 3. Example
👉 Predict if a customer will buy a product.
Tree 1 → Yes
Tree 2 → Yes
Tree 3 → No
✅ Final Prediction → Yes
🔹 4. Implementation (Python)
from sklearn.ensemble import RandomForestClassifier
# Sample data
X = [,,, ]
y = [1, 2, 3, 4, 0]
model = RandomForestClassifier()
model.fit(X, y)
print(model.predict([])[3])
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What type of problems can Decision Trees solve?
Which of the following is a disadvantage of Decision Trees?
Which library module is commonly used for Decision Trees in Python?
What is the starting node of a Decision Tree called?
What does a Decision Tree mainly use to make predictions?
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✅ Decision Trees Basics🌳🤖
👉 Decision Trees are one of the most intuitive ML algorithms — they work like a flowchart.
🔹 1. What is a Decision Tree?
A Decision Tree is a model that makes decisions by splitting data into branches.
👉 It asks questions like:
- Is age > 18?
- Is salary > 50k?
Based on answers → it predicts output.
🔥 2. Structure of a Decision Tree
🌳 Root Node → Starting point
🌿 Branches → Conditions (Yes/No)
🍃 Leaf Nodes → Final output
🔹 3. Example
👉 Predict if a person will buy a product:
Is Age > 30?
├── Yes → High Chance
└── No → Check Income
├── High → Medium Chance
└── Low → Low Chance
🔹 4. Types of Problems
✔ Classification (Yes/No)
✔ Regression (predict values)
🔹 5. Implementation (Python)
from sklearn.tree import DecisionTreeClassifier
# Sample data
X = [[25], [30], [45], [50]]
y = [0, 0, 1, 1]
model = DecisionTreeClassifier()
model.fit(X, y)
print(model.predict([[40]]))
🔹 6. Advantages ⭐
✔ Easy to understand
✔ No need for scaling
✔ Works with both numbers & categories
🔹 7. Disadvantages
❌ Can overfit (too complex tree)
❌ Sensitive to small data changes
🔹 8. Why Decision Trees are Important?
✔ Used in real-world ML systems
✔ Foundation for Random Forest & XGBoost
✔ Easy to explain to stakeholders
🎯 Today’s Goal
✔ Understand tree structure
✔ Learn splitting logic
✔ Implement basic model
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What is the range of output in Logistic Regression?
Logistic Regression is used for which type of problem?
