Python Interviews
Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free For collaborations: @coderfun
Ko'proq ko'rsatishđ Telegram kanali Python Interviews analitikasi
Python Interviews (@pythoninterviews) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 28 837 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 4 609-o'rinni va Hindiston mintaqasida 14 423-o'rinni egallagan.
đ Auditoriya koârsatkichlari va dinamika
ĐŊĐĩвŅĐ´ĐžĐŧĐž sanasidan buyon loyiha tez oâsib, 28 837 obunachiga ega boâldi.
27 Iyul, 2026 dagi oxirgi maâlumotlarga koâra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 96 ga, soânggi 24 soatda esa 11 ga oâzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oârtacha 2.48% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.57% ini tashkil etuvchi reaksiyalarni toâplaydi.
- Post qamrovi: Har bir post oârtacha 715 marta koâriladi; birinchi sutkada odatda 163 ta koârish yigâiladi.
- Reaksiyalar va oâzaro taâsir: Auditoriya faol: har bir postga oârtacha 2 ta reaksiya keladi.
- Tematik yoânalishlar: Kontent |--, link:-, learning, sql, analytic kabi asosiy mavzularga jamlangan.
đ Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taâriflaydi:
âJoin this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free
For collaborations: @coderfunâ
Yuqori yangilanish chastotasi (oxirgi maâlumot 28 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boâlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taâsir nuqtasiga aylantirishini koârsatadi.
âHow can I represent the real world in numbers, without losing its meaning?âExample: â âDate of birthâ â Age (time-based insight) â âText reviewâ â Sentiment score (emotional signal) â âPriceâ â log(price) (stabilized distribution) Every transformation teaches your model how to see the world more clearly. âī¸ Why It Matters More Than the Model You canât outsmart bad features. A simple linear model trained on smartly engineered data will outperform a deep neural net trained on noise. Kaggle winners know this. They spend 80% of their time creating and refining features not tuning hyperparameters. Why? Because models donât create intelligence, They extract it from what you feed them. đ§Š The Core Idea: Add Signal, Remove Noise Feature engineering is about sculpting your data so patterns stand out. You do that by: âī¸ Transforming data (scale, encode, log). âī¸ Creating new signals (ratios, lags, interactions). âī¸ Reducing redundancy (drop correlated or useless columns). Every step should make learning easier not prettier. â ī¸ Beware of Data Leakage Hereâs the silent trap: using future information when building features. For example, when predicting loan default, if you include âpayment status after 90 days,â your model will look brilliant in training and fail in production. Golden rule: đ A feature is valid only if itâs available at prediction time. đ§ Think Like a Domain Expert Anyone can code transformations. But great data scientists understand context. They ask: âWhat actually influences this outcome in real life? âHow can I capture that influence as a feature? When you merge domain intuition with technical precision, feature engineering becomes your superpower. âĄī¸ Final Takeaway The model is the student. The features are the teacher. And no matter how capable the student if the teacher explains things poorly, learning fails.
Feature engineering isnât preprocessing. Itâs the art of teaching your model how to understand the world.
