Data Science & Machine Learning

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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

Сітка:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert Індія4 359 Освіта2 114...

📈 Аналітичний огляд Telegram-каналу Data Science & Machine Learning

Канал Data Science & Machine Learning (@datasciencefun) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 75 645 підписників, посідаючи 2 114 місце в категорії Освіта та 4 359 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 75 645 підписників.

За останніми даними від 11 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 911, а за останні 24 години на 29, загальне охоплення залишається високим.

Статус верифікації: Не верифікований
Рівень залученості (ER): Середній показник залученості аудиторії становить 3.63%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.36% реакцій від загальної кількості підписників.
Охоплення публікацій: В середньому кожен допис отримує 2 747 переглядів. Протягом першої доби публікація в середньому набирає 1 032 переглядів.
Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, accuracy, distribution, panda, dataset.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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”

Завдяки високій частоті оновлень (останні дані отримано 12 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.

75 645

Підписники

+2924 години

+2107 днів

+91130 день

2 747

Перегляди допису

~ 1 03224 години

~ 1 40648 годин

3.63%

Коефіцієнт залучення

~ 2

Дописів на день

Ads index

beta

Архів дописів

75 634

What is Random Forest mainly made of?

Anonymous voting

75 634

𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 - 𝗚𝗲𝘁 𝗦𝗮𝗹𝗮𝗿𝘆 𝗣𝗮𝗰𝗸𝗮𝗴𝗲 𝗨𝗽𝘁𝗼 𝟰𝟭𝗟𝗣𝗔 😍 Upskill on the most in-demand skills in the market Learn Coding & Get Placed In Top Tech Companies 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:- 💼 Avg. Package: ₹7.2 LPA | Highest: ₹41 LPA 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:- https://pdlink.in/42WOE5H Hurry! Limited seats are available.🏃‍♂️

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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])

🔹 5. Advantages ⭐ ✔ High accuracy ✔ Reduces overfitting ✔ Handles large datasets well ✔ Works for classification regression 🔹 6. Disadvantages ❌ Slower than Decision Trees ❌ Harder to interpret 🔹 7. Why Random Forest is Important? ✔ Used in real-world applications ✔ Powerful baseline ML model ✔ Frequently asked in interviews 🎯 Today’s Goal ✔ Understand ensemble learning ✔ Learn majority voting ✔ Implement Random Forest model 💬 Tap ❤️ for more!

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75 634

What type of problems can Decision Trees solve?

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75 634

Which of the following is a disadvantage of Decision Trees?

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75 634

Which library module is commonly used for Decision Trees in Python?

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75 634

What is the starting node of a Decision Tree called?

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75 634

What does a Decision Tree mainly use to make predictions?

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𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀😍 Kickstart Your Data Science Career In Top Tech Companies 💫Learn Tools, Skills & Mindset to Land your first Job 💫Join this free Masterclass for an expert-led session on Data Science Eligibility :- Students ,Freshers & Working Professionals 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 :- https://pdlink.in/42hIcpO ( Limited Slots ..Hurry Up‍ ) 🔥Date & Time :- 8th May 2026 , 7:00 PM

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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 💬 Tap ❤️ for more!

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75 634

What does a threshold (0.5) do?

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75 634

Which function is used in Logistic Regression?

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75 634

What is the range of output in Logistic Regression?

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75 634

Logistic Regression is used for which type of problem?

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✅ Logistic Regression Basics 🤖📊 👉 After predicting numbers (Linear Regression), now we predict categories. 🔹 1. What is Logistic Regression? Logistic Regression is used for classification problems. 👉 Output is NOT a number — it’s a category. Examples: ✔ Spam or Not Spam ✔ Pass or Fail ✔ Fraud or Not Fraud 🔥 2. How it Works Instead of a straight line, it uses a Sigmoid Function: \sigma(x) = 1 / (1 + e⁻)} 👉 Output is always between 0 and 1 👉 This is treated as probability 🔹 3. Decision Boundary 👉 If probability > 0.5 → Class 1 👉 If probability < 0.5 → Class 0 🔹 4. Example 👉 Predict if a student passes: Study Hours Result 2 Fail 5 Pass 👉 Model learns boundary between pass/fail. 🔹 5. Implementation

from sklearn.linear_model import LogisticRegression

# Sample data
X = [[1], [2], [3], [4]]
y = [0, 0, 1, 1]

model = LogisticRegression()
model.fit(X, y)

print(model.predict([[3]]))

🔹 6. Important Terms ⭐ ✔ Classification → Predict category ✔ Probability → Output (0–1) ✔ Threshold → Decision boundary 🔹 7. Why Logistic Regression is Important? ✔ Used in real-world classification problems ✔ Foundation for advanced classification models ✔ Easy to understand and implement 🎯 Today’s Goal ✔ Understand classification ✔ Learn sigmoid function ✔ Understand probability output 💬 Tap ❤️ for more!

75 634

Which library is used for Linear Regression in Python?

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75 634

In Linear Regression, what does y represent?

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