🔵 Data Science  ·  Lesson 45

Feature Engineering

Feature Engineering क्या है?

Feature Engineering ka matlab hai: Feature engineering creates useful input columns from raw data to improve model performance. Simple words me, ye topic practical Python programs likhne me direct use hota hai.

Is topic ko sirf definition ke liye nahi, balki creating better input columns jaise real examples ke liye practice karein.

यह क्यों सीखना जरूरी है?

  • Ye creating better input columns me kaam aata hai.
  • Ye encoding categories se bhi connected hai.
  • Isse aap code ka output aur errors better samajh paate hain.

Important Terms

TermMeaning
featureInput column used for analysis or model training.
encodingConverting categorical text values into numeric form.
scalingPutting numeric values into a comparable range.
new columnnew column is an important term in this topic.
model inputmodel input is an important term in this topic.

Syntax / Basic Pattern

Basic idea: pehle data तैयार करें, phir Python logic apply करें, aur finally result display करें.

Basic Pattern
import pandas as pd
df = pd.DataFrame({"StudyHours": [2, 4, 6], "Attendance": [70, 85, 95]})
df["Study_Attendance_Score"] = df["StudyHours"] * df["Attendance"]
print(df)

Complete Example Program

Python – feature-engineering.py
import pandas as pd

df = pd.DataFrame({"StudyHours": [2, 4, 6], "Attendance": [70, 85, 95]})

df["Study_Attendance_Score"] = df["StudyHours"] * df["Attendance"]
print(df)

Expected Output

StudyHours Attendance Study_Attendance_Score 0 2 70 140 1 4 85 340 2 6 95 570

Program Explanation

  • import pandas as pd imports ready-made features from a module/library.
  • df = pd.DataFrame({"StudyHours": [2, 4, 6], "Attendance": [70, 85, 95]}) stores a value in df.
  • df["Study_Attendance_Score"] = df["StudyHours"] * df["Attendance"] stores a value in df["Study_Attendance_Score"].
  • print(df) displays information or calculated result on the screen.

Practical Uses

  • Creating better input columns.
  • Encoding categories.
  • Improving model performance.

Common Mistakes

  • Analysing data before checking missing values, duplicates and data types.
  • Changing original data without keeping a clean copy.
  • Creating charts without title, labels or explanation.

Practice Tasks

  1. Program ko feature-engineering.py file me type karke run karein.
  2. Values change karke output compare karein.
  3. creating better input columns par ek छोटा example banayen.
  4. Logic ko apne words me 5 lines me likhein.

सारांश

Feature Engineering ko tab complete maanenge jab aap iska meaning, example, output aur practical use clearly explain kar saken.

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