🤯 STOP SCROLLING! Your AI Project will be 10x better if you know THIS simple secret!

Ever wondered how algorithms make decisions just like you do? 🤔
It's not magic, it's often a Decision Tree!

Think of it like a flowchart 📊
that helps AI pick the best path
based on different conditions.

It's super intuitive for college projects,
explaining complex AI easily,
and nailing those technical interview questions! 🔥

Here’s a quick peek at how to build one:

import pandas as pd
from sklearn.tree import DecisionTreeClassifier

# Imagine data for predicting if a student gets a job offer
data = {
    'GPA': [3.5, 2.8, 3.9, 3.2, 3.0],
    'Internship': ['Yes', 'No', 'Yes', 'No', 'Yes'],
    'Project_Count': [3, 1, 4, 2, 2],
    'Offer': ['Yes', 'No', 'Yes', 'No', 'Yes']
}
df = pd.DataFrame(data)

# Convert categorical data for the model
df['Internship_Num'] = df['Internship'].map({'No': 0, 'Yes': 1})

X = df[['GPA', 'Internship_Num', 'Project_Count']] # Features
y = df['Offer'].map({'No': 0, 'Yes': 1}) # Target

# Build the Decision Tree Model
model = DecisionTreeClassifier()
model.fit(X, y)

print("🚀 Decision Tree Model Trained! You just built a decision-making AI!")
# Now you can use 'model.predict()' for new students!

This simple code is the brain behind many recommendation systems and classification tasks! Get creative with your college projects! 💡

---

Q: Which of these is NOT a common metric used to split nodes in a Decision Tree for classification?
a) Gini Impurity
b) Information Gain
c) Entropy
d) Mean Squared Error (MSE)

---

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Join our community now! 👇
https://t.me/Projectwithsourcecodes

#AI #MachineLearning #Python #CodingProjects #DecisionTree #BCA #BTech #MCA #StudentLife #TechTips #CodingInterview

🤯 Stop building BASIC college projects! Want to literally WOW your professors & land that dream internship? 🚀

Short Explanation:
Forget generic CRUD apps! Learning the basics of AI/ML is your secret weapon. Even a simple predictive model can transform your project from "okay" to "AMAZING" and prove you're future-ready. It's not just about code; it's about solving real-world problems. Think predicting sales, automating tasks, or personalizing user experiences! ✨

Code Snippet:
Here's a super simple Python example using scikit-learn to predict student marks based on study hours. Imagine extending this for your own project – predicting customer churn, disease spread, anything!

import numpy as np
from sklearn.linear_model import LinearRegression

# Sample Data: Study Hours vs. Marks
study_hours = np.array([2, 3, 4, 5, 6]).reshape(-1, 1) # X (features)
marks = np.array([50, 60, 70, 80, 90])                # y (target)

# Create and Train the Model
model = LinearRegression()
model.fit(study_hours, marks) # This is where the magic happens! 🧙‍♂️

# Make a Prediction
new_study_hours = np.array([[7]]) # Someone studied 7 hours
predicted_marks = model.predict(new_study_hours)

print(f"Predicted marks for 7 hours of study: {predicted_marks[0]:.2f}")
# Output: Predicted marks for 7 hours of study: 100.00

🔥 Interview Tip: Always be ready to explain why you chose a particular model and how it works, not just what it does!

Coding Question:
What is the primary purpose of the .fit() method in the LinearRegression model above?
A) To make predictions on new data.
B) To initialize the model with default parameters.
C) To train the model using the provided study_hours and marks data.
D) To print the model's coefficients.

CTA:
Got a project idea? Need source code? Join our community! 👇
https://t.me/Projectwithsourcecodes

#AIProjects #MachineLearning #PythonForStudents #CollegeHacks #CodingInterview #ProjectIdeas #BCA #BTech #MCA #MScCS #CSStudent

🎯 TOP 15 Python Interview Questions Asked in 2026
(TCS • Infosys • Wipro • Startups — ye sab poochte hain!)

Save karo — interview se pehle zaroor padho! 📌

━━━━━━━━━━━━━━━━━━━━━━
BASICS (Freshers ke liye must!)

1️⃣ List vs Tuple?
→ List = mutable | Tuple = immutable
→ Tuple is faster & used for fixed data

2️⃣ What are Python decorators?
→ Functions jo dusre functions wrap karte hain
→ @staticmethod, @classmethod, @property
→ Flask mein @app.route bhi ek decorator hai

3️⃣ deepcopy vs shallow copy?
→ Shallow = reference copy (nested objects share)
→ Deep = completely new independent copy
→ import copy → copy.deepcopy(obj)

4️⃣ What is GIL in Python?
→ Global Interpreter Lock
→ Only ONE thread runs at a time in CPython
→ Use multiprocessing for true parallelism

5️⃣ What are generators?
→ Use yield instead of return
→ Memory efficient — values on demand
→ Perfect for large datasets

━━━━━━━━━━━━━━━━━━━━━━
INTERMEDIATE (Most asked in tech rounds!)

6️⃣ List comprehension vs map() — which is faster?
→ List comprehension is more readable & Pythonic
→ map() slightly faster for simple functions

7️⃣ What is *args and **kwargs?
→ *args = variable positional arguments
→ **kwargs = variable keyword arguments

8️⃣ Mutable vs Immutable data types?
→ Mutable: list, dict, set
→ Immutable: int, str, tuple, float

9️⃣ What is __init__ vs __new__?
→ __new__ creates the object
→ __init__ initializes it (constructor)

🔟 Python memory management?
→ Reference counting + Garbage collector
→ del keyword decrements reference count

━━━━━━━━━━━━━━━━━━━━━━
ADVANCED (For senior/experienced roles!)

1️⃣1️⃣ Lambda function?
→ Anonymous one-line function
→ square = lambda x: x**2

1️⃣2️⃣ OOPS in Python?
→ Class, Object, Inheritance, Polymorphism
→ Always give a real example (Bank, Car etc.)

1️⃣3️⃣ is vs == difference?
→ == checks value | is checks memory identity
→ [] == [] is True but [] is [] is False!

1️⃣4️⃣ Exception handling?
→ try / except / else / finally
→ Always catch specific exceptions first

1️⃣5️⃣ @staticmethod vs @classmethod?
→ @staticmethod: no self or cls — utility method
→ @classmethod: gets class as first arg (cls)

━━━━━━━━━━━━━━━━━━━━━━

🔥 BONUS Interview Tips:
→ Always explain with a real-world example
→ Say 'I used this in my project' — instant +1!
→ Mention time/space complexity when possible
→ If unsure — explain what you DO know confidently

📂 Get Python Projects with Source Code:
👉 https://t.me/Projectwithsourcecodes

📢 Save this post + Share with your batch!

#PythonInterview #InterviewQuestions #Python2026
#TCSInterview #InfosysInterview #PlacementPrep
#Freshers2026 #CodingInterview #BTech #MCA #BCA
#PythonDeveloper #TechInterview #ProjectWithSourceCodes
#StudentsOfIndia #CampusPlacement #OffCampus

DSA CHEAT SHEET — Save This Post!
Most Asked Patterns in TCS Infosys Amazon Interviews!

====================================

90% of coding interviews use THESE 10 patterns.
Master these = crack any tech interview!

====================================
PATTERN 1: TWO POINTERS
Use when: Sorted array, find pairs, remove duplicates
Problems: Two Sum, Reverse String, 3Sum
Logic: left=0, right=n-1, move based on condition
Companies: Amazon, Microsoft, TCS

PATTERN 2: SLIDING WINDOW
Use when: Subarray/substring with condition
Problems: Max sum subarray, Longest substring
Logic: Expand right, shrink left when invalid
Companies: Infosys, Wipro, Google

PATTERN 3: BINARY SEARCH
Use when: Sorted array, find position/condition
Problems: Search in rotated array, Find peak
Logic: mid = (lo+hi)//2, eliminate half each time
Companies: Amazon, Flipkart, Accenture

PATTERN 4: LINKED LIST (Fast & Slow Pointer)
Use when: Cycle detection, find middle
Problems: Detect cycle, Find middle, Palindrome
Logic: slow moves 1 step, fast moves 2 steps
Companies: TCS, Infosys, HCL

PATTERN 5: TREE BFS (Level Order)
Use when: Level-by-level traversal, shortest path
Problems: Level order, Zigzag, Right side view
Logic: Use queue, process level by level
Companies: Amazon, Cognizant, Capgemini

====================================
PATTERN 6: TREE DFS
Use when: Path sum, depth, validate BST
Problems: Max depth, Path sum, Inorder traversal
Logic: Recursion — visit node, left, right
Companies: Microsoft, Wipro, IBM

PATTERN 7: DYNAMIC PROGRAMMING
Use when: Optimization, count ways, max/min
Problems: Fibonacci, Knapsack, LCS, Coin change
Logic: Break into subproblems, store results
Companies: Amazon, Goldman Sachs, Barclays

PATTERN 8: STACK
Use when: Matching brackets, next greater element
Problems: Valid Parentheses, Stock Span, Min Stack
Logic: Push/pop based on LIFO order
Companies: TCS, Accenture, Infosys

PATTERN 9: HASHING (HashMap)
Use when: Count frequency, find duplicates, grouping
Problems: Two Sum, Anagram, Group Anagrams
Logic: key=element, value=count/index
Companies: Google, Amazon, Flipkart

PATTERN 10: GREEDY
Use when: Local optimal = global optimal
Problems: Activity selection, Jump game, Intervals
Logic: Always pick the best option at each step
Companies: Wipro, HCL, Mindtree

====================================
MUST KNOW COMPLEXITY:

Array access -> O(1)
Binary Search -> O(log n)
Linear Search -> O(n)
Bubble Sort -> O(n2)
Merge/Quick Sort-> O(n log n)
HashMap get/put -> O(1) average
BFS/DFS -> O(V + E)

====================================
30-DAY DSA PLAN FOR PLACEMENTS:

Week 1: Arrays + Strings + Hashing
Week 2: Linked List + Stack + Queue
Week 3: Trees + Binary Search
Week 4: DP + Greedy + Mock Tests

Practice on: LeetCode / GeeksForGeeks
Target: 2 problems daily = 60 problems/month

====================================
SAVE this post now!
You will need it before every interview!

Want FREE projects for your resume too?
https://t.me/Projectwithsourcecodes

Share with your placement batch!

#DSA #DataStructures #Algorithms #CodingInterview
#TCS #Infosys #Wipro #Amazon #Microsoft #Google
#LeetCode #PlacementPrep #CampusPlacement
#BTech2026 #MCA2026 #BCA2026 #OffCampus
#DynamicProgramming #BinarySearch #LinkedList
#ProjectWithSourceCodes #StudentsOfIndia #Coding

GIT CHEAT SHEET — Save This!
Commands Every Developer Must Know!

====================================

Git is asked in EVERY tech interview!
TCS, Wipro, Infosys, startups — all use Git.
Master these commands before placement!

====================================
SETUP (Do This First!)

git config --global user.name 'Your Name'
git config --global user.email 'you@email.com'
-> Set your identity for commits

====================================
STARTING A PROJECT

git init
-> Initialize new repo in current folder

git clone <url>
-> Copy remote repo to your machine

====================================
DAILY COMMANDS (Use Every Day!)

git status
-> See changed/untracked files

git add .
-> Stage ALL changed files

git add filename.py
-> Stage specific file only

git commit -m 'Your message here'
-> Save staged changes with a message

git push origin main
-> Upload commits to GitHub

git pull origin main
-> Download latest changes from GitHub

====================================
BRANCHING (Important for Team Projects!)

git branch
-> List all branches

git branch feature-login
-> Create new branch

git checkout feature-login
-> Switch to that branch

git checkout -b feature-login
-> Create AND switch in one command!

git merge feature-login
-> Merge branch into current branch

====================================
UNDO MISTAKES (Life Savers!)

git restore filename.py
-> Undo unsaved changes in a file

git reset HEAD~1
-> Undo last commit (keep changes)

git revert <commit-id>
-> Safely undo a pushed commit

git stash
-> Temporarily hide current changes

git stash pop
-> Bring back stashed changes

====================================
VIEWING HISTORY

git log
-> Full commit history

git log --oneline
-> Short commit history (one line each)

git diff
-> See exactly what changed in files

====================================
GIT INTERVIEW QUESTIONS:

1. git merge vs git rebase — difference?
2. What is a pull request?
3. How to resolve a merge conflict?
4. What is git stash used for?
5. Difference: git reset vs git revert?

====================================
Save this post before your next interview!
Get FREE projects with Git setup guides:
https://t.me/Projectwithsourcecodes

Share with your batchmates!

#GitCheatSheet #Git #GitHub #VersionControl
#GitCommands #DevTools #BTech2026 #MCA2026
#BCA2026 #PlacementPrep #CodingInterview
#SoftwareEngineer #OpenSource #GitTips
#ProjectWithSourceCodes #StudentsOfIndia

PYTHON CHEAT SHEET — Save This!
Most Asked Python in Tech Interviews!

====================================

Python is #1 language for AI, Data Science,
Backend & Automation roles. Master this!

====================================
DATA TYPES & BASICS

x = 10 # int
y = 3.14 # float
s = 'hello' # string
b = True # boolean
l = [1,2,3] # list (mutable)
t = (1,2,3) # tuple (immutable)
d = {'a': 1} # dictionary
st = {1,2,3} # set (unique values)

====================================
STRINGS — Most Asked!

s = 'Hello World'
s.upper() # 'HELLO WORLD'
s.lower() # 'hello world'
s.split(' ') # ['Hello', 'World']
s.replace('o','0') # 'Hell0 W0rld'
s.strip() # remove whitespace
len(s) # 11
s[0:5] # 'Hello' (slicing)
s[::-1] # reverse string!
f'Name: {s}' # f-string formatting

====================================
LIST OPERATIONS

l = [3, 1, 4, 1, 5]
l.append(9) # add to end
l.insert(0, 7) # insert at index 0
l.remove(1) # remove first '1'
l.pop() # remove last element
l.sort() # sort in place
sorted(l) # returns new sorted list
l.reverse() # reverse in place
len(l) # length of list
sum(l) # sum of all elements
max(l), min(l) # max and min value

====================================
LIST COMPREHENSION — Interviewers Love!

squares = [x**2 for x in range(10)]
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

evens = [x for x in range(20) if x%2==0]
# [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

====================================
DICTIONARY TRICKS

d = {'name': 'Rahul', 'age': 22}
d['name'] # 'Rahul'
d.get('city', 'N/A') # safe get
d.keys() # all keys
d.values() # all values
d.items() # key-value pairs
d.update({'city': 'Delhi'}) # add/update

====================================
FUNCTIONS & LAMBDA

def add(a, b):
return a + b

# Lambda (one-line function)
square = lambda x: x**2
square(5) # 25

# *args and **kwargs
def greet(*names):
for name in names:
print(f'Hi {name}')

====================================
MUST-KNOW PYTHON CONCEPTS:

List vs Tuple -> mutable vs immutable
Deep vs Shallow copy -> copy.deepcopy()
Global vs Local -> variable scope
try/except -> error handling
with open() -> file handling
OOP: class, __init__, self, inheritance

====================================
TOP 5 PYTHON INTERVIEW QUESTIONS:

1. Difference: list vs tuple vs set vs dict?
2. What is a lambda function?
3. How does Python handle memory management?
4. What are decorators in Python?
5. Difference: deep copy vs shallow copy?

====================================
PRACTICE FREE ON:
HackerRank -> hackerrank.com/domains/python
LeetCode -> leetcode.com
W3Schools -> w3schools.com/python

====================================
Save this before your next interview!
Get FREE Python projects with source code:
https://t.me/Projectwithsourcecodes

Share with your placement batch!

#PythonCheatSheet #Python #PythonInterview
#DataScience #MachineLearning #PythonDeveloper
#BTech2026 #MCA2026 #BCA2026 #PlacementPrep
#CodingInterview #TechInterview #LearnPython
#ProjectWithSourceCodes #StudentsOfIndia

DSA CHEAT SHEET — Save This!
Data Structures Asked in Every Interview!

====================================

DSA is tested in ALL product company interviews!
Amazon, Google, Microsoft, Flipkart, Adobe —
ALL start with DSA rounds. Master this!

====================================
ARRAYS — Most Basic, Most Asked!

Find max/min in array -> O(n) linear scan
Reverse an array -> two pointer approach
Find duplicates -> use HashSet O(n)
Rotate array by k -> reverse technique
Two Sum problem -> HashMap O(n)
Sliding Window -> for subarray problems

====================================
STRINGS

Palindrome check -> two pointers
Anagram check -> sort both or HashMap
Longest common prefix -> vertical scan
Count vowels/consonants -> loop + set
String reversal -> s[::-1] in Python

====================================
LINKED LIST — Very Frequently Asked!

Reverse a linked list -> 3 pointer trick
Detect cycle -> Floyd's algo (slow/fast)
Find middle node -> slow/fast pointers
Merge 2 sorted lists -> compare & merge
Remove Nth node from end -> two pass

====================================
STACK & QUEUE

Stack: LIFO — use for:
-> Valid parentheses check
-> Next Greater Element
-> Undo/Redo operations

Queue: FIFO — use for:
-> BFS (level order traversal)
-> Sliding window maximum

====================================
TREES — 30% of Interview Questions!

Inorder: Left -> Root -> Right
Preorder: Root -> Left -> Right
Postorder: Left -> Right -> Root

Level Order -> use Queue (BFS)
Height of tree -> recursion
Check BST -> inorder should be sorted
LCA of two nodes -> recursive approach

====================================
SORTING ALGORITHMS

Bubble Sort -> O(n²) | Simple
Selection Sort -> O(n²) | Simple
Insertion Sort -> O(n²) | Best for small
Merge Sort -> O(nlogn) | Stable sort
Quick Sort -> O(nlogn) | In-place

For interviews: Know Merge Sort well!

====================================
SEARCHING

Linear Search -> O(n) | Unsorted array
Binary Search -> O(logn) | Sorted array only

Binary Search template:
low, high = 0, len(arr)-1
while low <= high:
mid = (low + high) // 2
if arr[mid] == target: return mid
elif arr[mid] < target: low = mid+1
else: high = mid-1

====================================
TIME COMPLEXITY QUICK REFERENCE:

O(1) -> Constant | Array index access
O(logn) -> Log | Binary search
O(n) -> Linear | Single loop
O(nlogn) -> Linearithmic | Merge sort
O(n²) -> Quadratic | Nested loops

====================================
TOP DSA PLATFORMS TO PRACTICE:
LeetCode -> leetcode.com (must!)
HackerRank -> hackerrank.com
GeeksForGeeks -> geeksforgeeks.org
Codeforces -> codeforces.com

====================================
Save this post — revise before every interview!
Get FREE projects with DSA implementation:
https://t.me/Projectwithsourcecodes

Share with your placement batch!

#DSA #DataStructures #Algorithms #LeetCode
#CodingInterview #TechInterview #Placements
#BTech2026 #MCA2026 #BCA2026 #CompetitiveCoding
#Java #Python #DSACheatSheet #FAANG
#ProjectWithSourceCodes #StudentsOfIndia

DSA CHEAT SHEET - Save This!
Data Structures Asked in Every Tech Interview!

====================================

DSA is tested at Amazon, Google, Microsoft,
Flipkart, Adobe, Uber, Swiggy — ALL of them!
Master these before your placement rounds!

====================================
ARRAYS - Most Basic, Most Asked!

Two Sum -> HashMap O(n)
Find max/min -> linear scan O(n)
Reverse array -> two pointers O(n)
Find duplicates -> HashSet O(n)
Rotate by k -> reverse technique O(n)
Subarray sum -> sliding window O(n)
Merge sorted arrays -> two pointer O(n+m)

====================================
STRINGS

Palindrome check -> two pointers O(n)
Anagram check -> sort or HashMap O(n)
Longest substring no repeat -> sliding window
String reversal -> s[::-1] in Python
Count char frequency -> HashMap O(n)

====================================
LINKED LIST - Very Frequently Asked!

Reverse linked list -> 3 pointer trick O(n)
Detect cycle -> Floyd's slow/fast O(n)
Find middle -> slow/fast pointers O(n)
Merge 2 sorted lists -> compare & link O(n)
Remove Nth from end -> two pass O(n)

====================================
STACK & QUEUE

Stack (LIFO) - use for:
-> Valid parentheses {[()]}
-> Next Greater Element
-> Undo/Redo operations

Queue (FIFO) - use for:
-> BFS (level order tree traversal)
-> Sliding window maximum

====================================
TREES - 30% of Interview Questions!

Inorder: Left Root Right
Preorder: Root Left Right
Postorder: Left Right Root
Level Order: BFS using Queue

Height of tree -> recursion O(n)
Check BST valid -> inorder sorted check
Lowest Common Ancestor -> recursive O(n)
Path sum root to leaf -> DFS O(n)

====================================
GRAPHS

BFS -> Queue, shortest path unweighted
DFS -> Stack/Recursion, path finding
Detect cycle undirected -> Union Find
Detect cycle directed -> DFS + visited
Topological Sort -> Kahn's algo (BFS)

====================================
DYNAMIC PROGRAMMING

Fibonacci -> memoization O(n)
0/1 Knapsack -> 2D DP O(n*W)
Longest Common Subsequence -> 2D DP
Coin Change -> bottom-up DP O(n*amount)
Climb Stairs -> DP same as Fibonacci

====================================
TIME COMPLEXITY QUICK REFERENCE:

O(1) Constant | Array index
O(logn) Log | Binary search
O(n) Linear | Single loop
O(nlogn) Linearithmic | Merge sort
O(n2) Quadratic | Nested loops
O(2n) Exponential | Recursion tree

====================================
TOP DSA PRACTICE PLATFORMS:
LeetCode -> leetcode.com (must!)
GeeksForGeeks -> geeksforgeeks.org
HackerRank -> hackerrank.com
Codeforces -> codeforces.com

====================================
Save this - revise before every interview!
Get FREE projects with DSA implementations:
https://t.me/Projectwithsourcecodes

Share with your placement batch!

#DSACheatSheet #DSA #DataStructures #Algorithms
#LeetCode #CodingInterview #Placements #FAANG
#BTech2026 #MCA2026 #BCA2026 #CompetitiveCoding
#Java #Python #TechInterview #DynamicProgramming
#ProjectWithSourceCodes #StudentsOfIndia

GIT CHEAT SHEET - Save This!
Commands Every Developer Must Know!

====================================

Git is asked in EVERY tech interview!
TCS, Wipro, Infosys, startups, product cos
ALL expect you to know Git. Master these!

====================================
FIRST TIME SETUP

git config --global user.name 'Your Name'
git config --global user.email 'you@email.com'
-> Run once after installing Git

====================================
STARTING A PROJECT

git init
-> Start tracking a new project folder

git clone <url>
-> Download a repo from GitHub to your PC

====================================
DAILY COMMANDS (Use Every Day!)

git status
-> See which files changed or are new

git add .
-> Stage ALL changed files for commit

git add filename.py
-> Stage one specific file only

git commit -m 'Your message here'
-> Save your staged changes permanently

git push origin main
-> Upload your commits to GitHub

git pull origin main
-> Download latest changes from GitHub

====================================
BRANCHING - Important for Team Work!

git branch
-> List all branches in your repo

git branch feature-login
-> Create a new branch called feature-login

git checkout feature-login
-> Switch to that branch

git checkout -b feature-login
-> Create AND switch in ONE command!

git merge feature-login
-> Merge branch into your current branch

git branch -d feature-login
-> Delete branch after merging

====================================
UNDO MISTAKES - Life Savers!

git restore filename.py
-> Undo unsaved changes in a file

git reset HEAD~1
-> Undo last commit but keep the changes

git revert <commit-id>
-> Safely undo a commit already pushed

git stash
-> Temporarily hide your current changes

git stash pop
-> Bring back your stashed changes

====================================
VIEWING HISTORY

git log
-> Full commit history with details

git log --oneline
-> Short commit history (one line each)

git diff
-> See exactly what changed line by line

git blame filename.py
-> See who changed which line and when

====================================
TOP 5 GIT INTERVIEW QUESTIONS:

1. What is the difference between git merge
and git rebase?
2. What is a pull request and how does it work?
3. How do you resolve a merge conflict?
4. What is git stash and when do you use it?
5. Difference between git reset and git revert?

====================================
Save this post - revise before every interview!
Get FREE projects with Git setup included:
https://t.me/Projectwithsourcecodes

Share with your placement batch!

#GitCheatSheet #Git #GitHub #VersionControl
#GitCommands #DevTools #GitBranching
#BTech2026 #MCA2026 #BCA2026 #PlacementPrep
#CodingInterview #TechInterview #OpenSource
#ProjectWithSourceCodes #StudentsOfIndia

5 GITHUB REPOS TO CRACK CODING INTERVIEWS!
DSA - System Design - Get the Job

Placement season is coming. These free
GitHub repos have everything you need to
prepare and land your dream job. Links below!

#CodingInterview #DSA #Placement #GitHub
#BTech2026 #MCA2026 #BCA2026
#ProjectWithSourceCodes #StudentsOfIndia