Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Machine Learning analitikasi
Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 41 257 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 163-o'rinni va Suriya mintaqasida 216-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 41 257 obunachiga ega bo‘ldi.
14 Sentabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 320 ga, so‘nggi 24 soatda esa 14 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 3.04% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.66% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 253 marta ko‘riladi; birinchi sutkada odatda 683 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 15 Sentabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
41 257
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| Sana | Obunachilarni jalb qilish | Esdaliklar | Kanallar | |
| 15 Sentabr | +12 | |||
| 14 Sentabr | +19 | |||
| 13 Sentabr | +41 | |||
| 12 Sentabr | +43 | |||
| 11 Sentabr | +5 | |||
| 10 Sentabr | +17 | |||
| 09 Sentabr | +6 | |||
| 08 Sentabr | +20 | |||
| 07 Sentabr | +21 | |||
| 06 Sentabr | +1 | |||
| 05 Sentabr | +10 | |||
| 04 Sentabr | +21 | |||
| 03 Sentabr | +14 | |||
| 02 Sentabr | +24 | |||
| 01 Sentabr | +6 |
Kanal postlari
| 2 | Matn yo'q... | 729 |
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| 20 |  📋 3 Main Steps for a Proper EDA in Python
🔑 Exploratory Data Analysis Cheat Sheet
From raw data to real insight
EXPLORE. UNDERSTAND. TURN DATA INTO INSIGHTS.
Good analysis starts with good questions.
🟦 STEP 1 — Data Collection & Structuring
Goal: Get the data in, shape it, and understand its skeleton.
1.1 Import Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Optional display settings
pd.set_option('display.max_columns', None)
sns.set_style('whitegrid')
1.2 Load the Data File
df = pd.read_csv('data.csv')
# df = pd.read_excel('data.xlsx')
# df = pd.read_sql(query, connection)
df.head() # first rows
df.info() # schema, types, nulls
df.shape # (rows, cols)
df.describe() # quick stats
🟧 STEP 2 — Issue Identification
Goal: Spot problems — missing values, duplicates, wrong types, outliers.
2.1 Missing Values
df.isna().sum() # count per column
df.isna().mean() * 100 # % missing
# Fill
df['age'] = df['age'].fillna(df['age'].median())
df['city'] = df['city'].fillna('Unknown')
# Drop
df = df.dropna(subset=['critical_col'])
2.2 Duplicates
df.duplicated().sum()
df = df.drop_duplicates()
2.3 Data Types & Conversions
df.dtypes
df['date'] = pd.to_datetime(df['date'])
df['id'] = df['id'].astype(int)
df['name'] = df['name'].str.strip().str.lower()
2.4 Outliers (IQR method)
Q1 = df['col'].quantile(0.25)
Q3 = df['col'].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df['col'] < Q1 - 1.5*IQR) | (df['col'] > Q3 + 1.5*IQR)]
sns.boxplot(x=df['col'])
🟩 STEP 3 — Understand & Decide
Goal: Reveal patterns and turn data into decisions.
3.1 Descriptive Statistics
df.describe() # numeric
df['category'].value_counts() # categorical
df['category'].value_counts().plot(kind='bar')
3.2 Data Visualization
# Histogram — distribution
df['age'].hist(bins=30)
# Countplot — categories
sns.countplot(x='category', data=df)
# Scatter — relationships
plt.scatter(df['x'], df['y'])
# Heatmap — correlations
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
3.3 Ask the Right Questions
✅ What's the distribution of each column?
✅ Are there correlations between variables?
✅ Do groups behave differently?
✅ Does the data match business reality?
✅ What story does the data tell?
🟢 Key Notes
💡 Context matters — numbers without meaning mislead
🧹 Quality over quantity — clean data beats big data
🧠 Understand before you predict — EDA is the foundation of every successful data project
🎯 The 3-Step Formula
Step
Focus
Key Action
1
Collect & Structure
Load + shape the data
2
Identify Issues
Missing, duplicates, outliers, types
3
Understand & Decide
Stats + viz + right questions
EXPLORE. UNDERSTAND. TURN DATA INTO INSIGHTS.
Better data → Better decisions* | 5 |
