Difference between linear regression and logistic regression 👇👇

Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.

Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.

Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.

Data Science Interview Resources
👇👇
https://topmate.io/coding/914624

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🚀 Complete Roadmap to Become a Data Scientist in 5 Months

📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.

🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.

🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.

🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.

🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.

🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.

📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).

🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).

🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).

🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.

🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.

💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.

🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.

👨‍💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.

🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.

🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.

🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.

🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!

🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥

Python Learning Plan in 2025

|-- Week 1: Introduction to Python
|   |-- Python Basics
|   |   |-- What is Python?
|   |   |-- Installing Python
|   |   |-- Introduction to IDEs (Jupyter, VS Code)
|   |-- Setting up Python Environment
|   |   |-- Anaconda Setup
|   |   |-- Virtual Environments
|   |   |-- Basic Syntax and Data Types
|   |-- First Python Program
|   |   |-- Writing and Running Python Scripts
|   |   |-- Basic Input/Output
|   |   |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
|   |-- Control Structures
|   |   |-- Conditional Statements (if, elif, else)
|   |   |-- Loops (for, while)
|   |   |-- Comprehensions
|   |-- Functions
|   |   |-- Defining Functions
|   |   |-- Function Arguments and Return Values
|   |   |-- Lambda Functions
|   |-- Modules and Packages
|   |   |-- Importing Modules
|   |   |-- Standard Library Overview
|   |   |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
|   |-- Data Structures
|   |   |-- Lists, Tuples, and Sets
|   |   |-- Dictionaries
|   |   |-- Collections Module
|   |-- File Handling
|   |   |-- Reading and Writing Files
|   |   |-- Working with CSV and JSON
|   |   |-- Context Managers
|   |-- Error Handling
|   |   |-- Exceptions
|   |   |-- Try, Except, Finally
|   |   |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
|   |-- OOP Basics
|   |   |-- Classes and Objects
|   |   |-- Attributes and Methods
|   |   |-- Inheritance
|   |-- Advanced OOP
|   |   |-- Polymorphism
|   |   |-- Encapsulation
|   |   |-- Magic Methods and Operator Overloading
|   |-- Design Patterns
|   |   |-- Singleton
|   |   |-- Factory
|   |   |-- Observer
|
|-- Week 5: Python for Data Analysis
|   |-- NumPy
|   |   |-- Arrays and Vectorization
|   |   |-- Indexing and Slicing
|   |   |-- Mathematical Operations
|   |-- Pandas
|   |   |-- DataFrames and Series
|   |   |-- Data Cleaning and Manipulation
|   |   |-- Merging and Joining Data
|   |-- Matplotlib and Seaborn
|   |   |-- Basic Plotting
|   |   |-- Advanced Visualizations
|   |   |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
|   |-- Web Development
|   |   |-- Flask Basics
|   |   |-- Django Basics
|   |-- Data Science and Machine Learning
|   |   |-- Scikit-Learn
|   |   |-- TensorFlow and Keras
|   |-- Automation and Scripting
|   |   |-- Automating Tasks with Python
|   |   |-- Web Scraping with BeautifulSoup and Scrapy
|   |-- APIs and RESTful Services
|   |   |-- Working with REST APIs
|   |   |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
|   |-- Capstone Project
|   |   |-- Project Planning
|   |   |-- Data Collection and Preparation
|   |   |-- Building and Optimizing Models
|   |   |-- Creating and Publishing Reports
|   |-- Case Studies
|   |   |-- Business Use Cases
|   |   |-- Industry-specific Solutions
|   |-- Integration with Other Tools
|   |   |-- Python and SQL
|   |   |-- Python and Excel
|   |   |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
|   |-- Python for Automation
|   |   |-- Automating Daily Tasks
|   |   |-- Scripting with Python
|   |-- Advanced Python Topics
|   |   |-- Asyncio and Concurrency
|   |   |-- Advanced Data Structures
|   |-- Continuing Education
|   |   |-- Advanced Python Techniques
|   |   |-- Community and Forums
|   |   |-- Keeping Up with Updates
|
|-- Resources and Community
|   |-- Online Courses (Coursera, edX, Udemy)
|   |-- Books (Automate the Boring Stuff, Python Crash Course)
|   |-- Python Blogs and Podcasts
|   |-- GitHub Repositories
|   |-- Python Communities (Reddit, Stack Overflow)

Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

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Hope it helps :)

Where Each Programming Language Shines 🚀👨🏻‍💻

❯ C ➟ OS Development, Embedded Systems, Game Engines
❯ C++ ➟ Game Development, High-Performance Applications, Financial Systems
❯ Java ➟ Enterprise Software, Android Development, Backend Systems
❯ C# ➟ Game Development (Unity), Windows Applications, Enterprise Software
❯ Python ➟ AI/ML, Data Science, Web Development, Automation
❯ JavaScript ➟ Frontend Web Development, Full-Stack Apps, Game Development
❯ Golang ➟ Cloud Services, Networking, High-Performance APIs
❯ Swift ➟ iOS/macOS App Development
❯ Kotlin ➟ Android Development, Backend Services
❯ PHP ➟ Web Development (WordPress, Laravel)
❯ Ruby ➟ Web Development (Ruby on Rails), Prototyping
❯ Rust ➟ Systems Programming, High-Performance Computing, Blockchain
❯ Lua ➟ Game Scripting (Roblox, WoW), Embedded Systems
❯ R ➟ Data Science, Statistics, Bioinformatics
❯ SQL ➟ Database Management, Data Analytics
❯ TypeScript ➟ Scalable Web Applications, Large JavaScript Projects
❯ Node.js ➟ Backend Development, Real-Time Applications
❯ React ➟ Modern Web Applications, Interactive UIs
❯ Vue ➟ Lightweight Frontend Development, SPAs
❯ Django ➟ Scalable Web Applications, AI/ML Backend
❯ Laravel ➟ Full-Stack PHP Development
❯ Blazor ➟ Web Apps with .NET
❯ Spring Boot ➟ Enterprise Java Applications, Microservices
❯ Ruby on Rails ➟ Startup Web Apps, MVP Development
❯ HTML/CSS ➟ Web Design, UI Development
❯ GIT ➟ Version Control, Collaboration
❯ Linux ➟ Server Management, Security, DevOps
❯ DevOps ➟ Infrastructure Automation, CI/CD
❯ CI/CD ➟ Continuous Deployment & Testing
❯ Docker ➟ Containerization, Cloud Deployments
❯ Kubernetes ➟ Scalable Cloud Orchestration
❯ Microservices ➟ Distributed Systems, Scalable Backends
❯ Selenium ➟ Web Automation Testing
❯ Playwright ➟ Modern Browser Automation

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Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables

2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries

3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages

2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets

3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting

2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)

3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards

Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)

2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX

3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes

Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.

Show some ❤️ if you're ready to elevate your data science game! 📊

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ML interview Question 📚

What is Quantization in machine learning?

Quantization the process of reducing the precision of the numbers used to represent a model's parameters, such as weights and activations. This is often done by converting 32-bit floating-point numbers (commonly used in training) to lower precision formats, like 16-bit or 8-bit integers.

Quantization is primarily used during model inference to:
1. Reduce model size: Lower precision numbers require less memory.
2. Improve computational efficiency: Operations on lower-precision data types are faster and require less power.
3. Speed up inference: Smaller models can be loaded faster, improving performance on edge devices like smartphones or IoT devices.

Quantization can lead to a small loss in model accuracy, as reducing precision can introduce rounding errors. But in many cases, the trade-off between accuracy and efficiency is worthwhile, especially for deployment on resource-constrained devices.

There are different types of quantization:
1. Post-training quantization: Applied after the model has been trained.
2.Quantization-aware training (QAT): Takes quantization into account during the training process to minimize the accuracy drop.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

Data Scientist Roadmap 📈

📂 Python Basics
∟📂 Numpy & Pandas
 ∟📂 Data Cleaning
  ∟📂 Data Visualization (Seaborn, Plotly)
   ∟📂 Statistics & Probability
    ∟📂 Machine Learning (Sklearn)
     ∟📂 Deep Learning (TensorFlow / PyTorch)
      ∟📂 Model Deployment
       ∟📂 Real-World Projects
        ∟✅ Apply for Data Science Roles

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✅ 8-Week Beginner Roadmap to Learn Data Science 📊🚀

🗓️ Week 1: Python Basics
Goal: Understand basic Python syntax & data types
Topics: Variables, lists, dictionaries, loops, functions
Tools: Jupyter Notebook / Google Colab
Mini Project: Calculator or number guessing game

🗓️ Week 2: Python for Data
Goal: Learn data manipulation with NumPy & Pandas
Topics: Arrays, DataFrames, filtering, groupby, joins
Tools: Pandas, NumPy
Mini Project: Analyze a CSV (e.g., sales or weather data)

🗓️ Week 3: Data Visualization
Goal: Visualize data trends & patterns
Topics: Line, bar, scatter, histograms, heatmaps
Tools: Matplotlib, Seaborn
Mini Project: Visualize COVID or stock market data

🗓️ Week 4: Statistics & Probability Basics
Goal: Understand core statistical concepts
Topics: Mean, median, mode, std dev, probability, distributions
Tools: Python, SciPy
Mini Project: Analyze survey data & generate insights

🗓️ Week 5: Exploratory Data Analysis (EDA)
Goal: Draw insights from real datasets
Topics: Data cleaning, outliers, correlation
Tools: Pandas, Seaborn
Mini Project: EDA on Titanic or Iris dataset

🗓️ Week 6: Intro to Machine Learning
Goal: Learn ML workflow & basic algorithms
Topics: Supervised vs unsupervised, train/test split
Tools: Scikit-learn
Mini Project: Predict house prices (Linear Regression)

🗓️ Week 7: Classification Models
Goal: Understand and apply classification
Topics: Logistic Regression, KNN, Decision Trees
Tools: Scikit-learn
Mini Project: Titanic survival prediction

🗓️ Week 8: Capstone Project + Deployment
Goal: Apply all concepts in one end-to-end project
Ideas: Sales prediction, Movie rating analysis, Customer churn detection
Tools: Streamlit (for simple web app)
Bonus: Upload your project on GitHub

💡 Tips:
⦁ Practice daily on platforms like Kaggle or Google Colab
⦁ Join beginner projects on GitHub
⦁ Share progress on LinkedIn or X (Twitter)

💬 Tap ❤️ for the detailed explanation of each topic!

🗓️ Python Basics You Should Know 🐍

✅ 1. Variables & Data Types 
Variables store data. Data types show what kind of data it is.

# String (text)
name = "Alice"

# Integer (whole number)
age = 25

# Float (decimal)
height = 5.6

# Boolean (True/False)
is_student = True

🔹 Use type() to check data type:

print(type(name))  # <class 'str'>

✅ 2. Lists and Tuples
⦁ List = changeable collection

fruits = ["apple", "banana", "cherry"]
print(fruits)  # banana
fruits.append("orange")  # add item

⦁ Tuple = fixed collection (cannot change items)

colors = ("red", "green", "blue")
print(colors)  # red

✅ 3. Dictionaries 
Store data as key-value pairs.

person = {
  "name": "John",
  "age": 22,
  "city": "Seoul"
}
print(person["name"])  # John

✅ 4. Conditional Statements (if-else) 
Make decisions.

age = 20
if age >= 18:
    print("Adult")
else:
    print("Minor")

🔹 Use elif for multiple conditions:

if age < 13:
    print("Child")
elif age < 18:
    print("Teenager")
else:
    print("Adult")

✅ 5. Loops 
Repeat code.

⦁ For Loop – fixed repeats

for i in range(3):
    print("Hello", i)

⦁ While Loop – repeats while true

count = 1
while count <= 3:
    print("Count is", count)
    count += 1

✅ 6. Functions 
Reusable code blocks.

def greet(name):
    print("Hello", name)

greet("Alice")  # Hello Alice

🔹 Return result:

def add(a, b):
    return a + b

print(add(3, 5))  # 8

✅ 7. Input / Output 
Get user input and show messages.

name = input("Enter your name: ")
print("Hi", name)

🧪 Mini Projects

1. Number Guessing Game

import random
num = random.randint(1, 10)
guess = int(input("Guess a number (1-10): "))
if guess == num:
    print("Correct!")
else:
    print("Wrong, number was", num)

2. To-Do List

todo = []
todo.append("Buy milk")
todo.append("Study Python")
print(todo)

🛠️ Recommended Tools
⦁ Google Colab (online)
⦁ Jupyter Notebook
⦁ Python IDLE or VS Code

💡 Practice a bit daily, start simple, and focus on basics — they matter most!

Data Science Roadmap: https://t.me/datasciencefun/3730

Double Tap ♥️ For More

Python for Data Science: NumPy & Pandas 📊🐍

🧮 Step 1: Learn NumPy (for numbers and arrays)

What is NumPy? 
A fast Python library for working with numbers and arrays.

➤ 1. What is an array? 
Like a list of numbers: [1, 2, 3, 4]

import numpy as np
a = np.array([1, 2, 3, 4])

➤ 2. Why NumPy over normal lists? 
Faster for math operations:

a * 2  # array([2, 4, 6, 8])

➤ 3. Cool NumPy tricks:

a.mean()        # average  
np.max(a)       # max number  
np.min(a)       # min number  
a[0:2]          # slicing → [1, 2]

Key Topics:
⦁ Arrays are like faster, memory-efficient lists
⦁ Element-wise operations: a + b, a * 2
⦁ Slicing and indexing: a[0:2], a[:,1]
⦁ Broadcasting: operations on arrays with different shapes
⦁ Useful functions: np.mean(), np.std(), np.linspace(), np.random.randn()

————————

📊 Step 2: Learn Pandas (for tables like Excel)

What is Pandas? 
Python tool to read, clean & analyze data — like Excel but supercharged.

➤ 1. What’s a DataFrame? 
Like an Excel sheet, rows & columns.

import pandas as pd
df = pd.read_csv("sales.csv")
df.head()  # first 5 rows

➤ 2. Check data info:

df.info()       # rows, columns, missing data  
df.describe()   # stats like mean, min, max

➤ 3. Get a column:

df['product']

➤ 4. Filter rows:

df[df['price'] > 100]

➤ 5. Group data: 
Average price by category:

df.groupby('category')['price'].mean()

➤ 6. Merge datasets:

merged = pd.merge(df1, df2, on='customer_id')

➤ 7. Handle missing data:

df.isnull()      # where missing  
df.dropna()      # drop missing rows  
df.fillna(0)     # fill missing with 0

————————

💡 Beginner Tips:
⦁ Use Google Colab (free, no setup)
⦁ Try small tasks like:
  ⦁  Show top products
  ⦁  Filter sales > $500
  ⦁  Find missing data
⦁ Practice daily, don’t just memorize

————————

🛠️ Mini Project: Analyze Sales Data
1. Load a CSV
2. Check number of rows
3. Find best-selling product
4. Calculate total revenue
5. Get average sales per region

Data Science Roadmap: 
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/1210

Double Tap ♥️ For More

Commonly used Power BI DAX functions:

DATE AND TIME FUNCTIONS:
- CALENDAR
- DATEDIFF
- TODAY, DAY, MONTH, QUARTER, YEAR

AGGREGATE FUNCTIONS:
- SUM, SUMX, PRODUCT
- AVERAGE
- MIN, MAX
- COUNT
- COUNTROWS
- COUNTBLANK
- DISTINCTCOUNT

FILTER FUNCTIONS:
- CALCULATE
- FILTER
- ALL, ALLEXCEPT, ALLSELECTED, REMOVEFILTERS
- SELECTEDVALUE

TIME INTELLIGENCE FUNCTIONS:
- DATESBETWEEN
- DATESMTD, DATESQTD, DATESYTD
- SAMEPERIODLASTYEAR
- PARALLELPERIOD
- TOTALMTD, TOTALQTD, TOTALYTD

TEXT FUNCTIONS:
- CONCATENATE
- FORMAT
- LEN, LEFT, RIGHT

INFORMATION FUNCTIONS:
- HASONEVALUE, HASONEFILTER
- ISBLANK, ISERROR, ISEMPTY
- CONTAINS

LOGICAL FUNCTIONS:
- AND, OR, IF, NOT
- TRUE, FALSE
- SWITCH

RELATIONSHIP FUNCTIONS:
- RELATED
- USERRELATIONSHIP
- RELATEDTABLE

Remember, DAX is more about logic than the formulas.

✅ Data Visualization with Matplotlib 📊

🛠 Tools:
⦁ matplotlib.pyplot – Basic plots
⦁ seaborn – Cleaner, statistical plots

1️⃣ Line Chart – to show trends over time

import matplotlib.pyplot as plt

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
sales = [200, 450, 300, 500, 650]

plt.plot(days, sales, marker='o')
plt.title('Daily Sales')
plt.xlabel('Day')
plt.ylabel('Sales')
plt.grid(True)
plt.show()

2️⃣ Bar Chart – compare categories

products = ['A', 'B', 'C', 'D']
revenue = [1000, 1500, 700, 1200]

plt.bar(products, revenue, color='skyblue')
plt.title('Revenue by Product')
plt.xlabel('Product')
plt.ylabel('Revenue')
plt.show()

3️⃣ Pie Chart – show proportions

labels = ['iOS', 'Android', 'Others']
market_share = [40, 55, 5]

plt.pie(market_share, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title('Mobile OS Market Share')
plt.axis('equal')  # perfect circle
plt.show()

4️⃣ Histogram – frequency distribution

ages = [22, 25, 27, 30, 32, 35, 35, 40, 45, 50, 52, 60]

plt.hist(ages, bins=5, color='green', edgecolor='black')
plt.title('Age Distribution')
plt.xlabel('Age Groups')
plt.ylabel('Frequency')
plt.show()

5️⃣ Scatter Plot – relationship between variables

income = [30, 35, 40, 45, 50, 55, 60]
spending = [20, 25, 30, 32, 35, 40, 42]

plt.scatter(income, spending, color='red')
plt.title('Income vs Spending')
plt.xlabel('Income (k)')
plt.ylabel('Spending (k)')
plt.show()

6️⃣ Heatmap – correlation matrix (with Seaborn)

import seaborn as sns
import pandas as pd

data = {'Math': [90, 80, 85, 95],
        'Science': [85, 89, 92, 88],
        'English': [78, 75, 80, 85]}

df = pd.DataFrame(data)
corr = df.corr()

sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Subject Score Correlation')
plt.show()

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