shreya

Keep learning

[shreya @shreeeeee21]

Day 13/100

Today: Arrays + basic problem-solving in Java.Still figuring out how to approach problems, but that’s exactly what I’m here to learn.

#100DaysOfDSA #DSA #Java #LeetCode

[shreya @shreeeeee21]

Day 12/100

Finally done with the Java basics
Now starting DSA + problem solving.
I’ll keep revising Java alongside so I don’t forget the basics.

#100DaysOfDSA #Java #DSA #LeetCode #Coding

[shreya @shreeeeee21]

Day 11/100

Still stuck in Java basics 😭
Today: methods, parameters & return types.
Takeaway: Build the foundation, then build the logic.

#100DaysOfDSA #Java #DSA

[shreya @shreeeeee21]

Day 10/100

One more day focused on Java fundamentals.
Today I practiced:
• Arrays & array declaration
• Loops and array traversal
• Taking input using "Scanner"
• Basic array operations

#100DaysOfDSA #Java #DSA #Coding #LearningInPublic

[shreya @shreeeeee21]

Day 9/100

Today I continued learning Java basics and practiced Strings & basic string operations.The goal is to get comfortable with Java first.

#100DaysOfDSA #DSA #Java #LeetCode #LearningInPublic

[shreya @shreeeeee21]

Day 8/100

Today I practiced Java arrays and worked on basic array problems.
Understanding how to access, traverse, and work with elements is making DSA feel a little more practical.

#100DaysOfDSA #DSA #Java #Coding #LearningInPublic

[shreya @shreeeeee21]

Day 7/100

Today I practiced Java input/output and basic problem-solving.

#100DaysOfDSA #DSA #Java #Coding #LearningInPublic

[shreya @shreeeeee21]

Day 6/100

Today I continued with Java basics and practiced functions/methods learning how to break code into smaller, reusable parts.

#100DaysOfDSA #DSA #Java #Coding

[shreya @shreeeeee21]

Day 5/100

Today I practiced Java basics and worked on simple problems using loops, conditions, and patterns.

#100DaysOfDSA #DSA #Java #Coding #LearningInPublic

[shreya @shreeeeee21]

Day 4/100

Today I practiced more Java basics and started understanding how to write simple logic using loops and conditions.
Small concepts, but they’re building the foundation for DSA.

#100DaysOfDSA #DSA #Java #Coding #LearningInPublic

[shreya @shreeeeee21]

Day 3/100

Today I started getting comfortable with Java syntax and also explored the basics of Arrays in DSA.
Slowly understanding not just what the code does, but why it works.

#100DaysOfDSA #DSA #Java #Coding #LearningInPublic

[shreya @shreeeeee21]

Day 2/100

Today I learned about Space Complexity understanding how much extra memory an algorithm needs as input grows.

#100DaysOfDSA #DSA #Coding #LearningInPublic

[shreya @shreeeeee21]

Day 1/100

Started my 100 Days of DSA Challenge!

Today’s topic: ⏱️ Time Complexity
Learning how to analyze how efficiently an algorithm scales with input size.

One day. One concept. Let’s build consistency. 💻🔥

#100DaysOfDSA #DSA #Coding #LearningInPublic

[shreya @shreeeeee21]

Starting my 100 Days of DSA Challenge from 14 September

Looking to #connect with people who are also learning:
• DSA
• Problem Solving
• LeetCode
• Coding Interviews
If you're starting too, join me!

Let’s learn together, solve together & stay consistent.
#100DaysOfDSA #DSA

[shreya @shreeeeee21]

What matters more: Precision or Recall?

It depends on the problem. Precision focuses on avoiding false positives, while Recall focuses on finding as many true positives as possible. In ML, the “best” metric depends on what mistakes matter most. #DataScience #MachineLearning

[shreya @shreeeeee21]

What if one train-test split gives you a misleading result?
Cross-validation tests your model across multiple data splits, giving a more reliable picture of how well it generalizes. #DataScience #MachineLearning

[shreya @shreeeeee21]

Which features actually matter to an ML model?
Feature importance helps identify which inputs contribute most to predictions. Not every column in your dataset is equally useful. #DataScience #MachineLearning

[shreya @shreeeeee21]

What if your ML model secretly gets a peek at the answers?
That’s data leakage when information from the test data influences training, making performance look better than it really is. Always keep test data truly unseen. #DataScience #MachineLearning

[shreya @shreeeeee21]

What should you do when your dataset has missing values?
Don’t blindly delete them. Depending on the data, you can use methods like mean, median, or model-based imputation. Data cleaning is a key part of building reliable ML models. #DataScience #MachineLearning #X

[shreya @shreeeeee21]

Why does feature scaling matter in Machine Learning?
A model can treat ₹100,000 and 0.8 very differently because of their scales. Scaling puts features on comparable ranges, helping many ML algorithms learn more effectively. #DataScience #MachineLearning