🔵 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
| Term | Meaning |
|---|---|
| feature | Input column used for analysis or model training. |
| encoding | Converting categorical text values into numeric form. |
| scaling | Putting numeric values into a comparable range. |
| new column | new column is an important term in this topic. |
| model input | model 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 pdimports 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
- Program ko
feature-engineering.pyfile me type karke run karein. - Values change karke output compare karein.
- creating better input columns par ek छोटा example banayen.
- 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.
💻 लाइव कोड एडिटर
इस पेज के प्रोग्राम यहीं तैयार हैं — चलाएँ, बदलें और सीखें। कुछ भी इंस्टॉल किए बिना।
OneCompiler द्वारा संचालित। कोड एडिटर में अपने आप आ जाता है — Run दबाकर आउटपुट देखें। अगर एडिटर न खुले तो नए टैब में खोलें.