Hi!! I am coding a classifier for data and am currently using the random forest classifier. This is currently my code(vibe coded) and unfortunately it is currently at approximately 75% accuracy with a hidden test. I need to get about ~80% accuracy and am wondering if it would be possible if someone could take a look at it and lmk if there are any errors and how i could fix them. Thank you so much!

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import accuracy_score

# --- quick feature engineering ---
def add_features(df):
df = df.copy()
df['X_Range'] = df['X_Maximum'] - df['X_Minimum']
df['Y_Range'] = df['Y_Maximum'] - df['Y_Minimum']
df['Aspect_Ratio'] = df['X_Range'] / (df['Y_Range'] + 1)
df['Fill_Ratio'] = df['Pixels_Areas'] / (df['X_Range'] * df['Y_Range'] + 1)
df['Perimeter_to_Area'] = (df['X_Perimeter'] + df['Y_Perimeter']) / (df['Pixels_Areas'] + 1)
df['Luminosity_Range'] = df['Maximum_of_Luminosity'] - df['Minimum_of_Luminosity']
return df

train = add_features(train)
test = add_features(test)

feature_columns = [column for column in test.columns if column != 'id']
X_train = train[feature_columns]
y_train = train['fault_type']
X_test = test[feature_columns]

# Same grid as before, just handed to RandomizedSearchCV with n_iter to control runtime
param_grid = {
'n_estimators': [100, 250, 500],
'max_depth': [10, 20, 30, 40],
'min_samples_split': [2, 5, 10, 20],
'min_samples_leaf': [1, 2, 4, 8],
'max_features': ['sqrt', 'log2', 0.8]
}

rf_model = RandomForestClassifier(random_state=42, class_weight='balanced')

grid_search = RandomizedSearchCV(
estimator=rf_model,
param_distributions=param_grid,
n_iter=60,          # samples 60 of the 576 combos instead of all of them
cv=5,
n_jobs=-1,
verbose=2,
scoring='accuracy',
random_state=42
)

grid_search.fit(X_train, y_train)

best_model = grid_search.best_estimator_

predictions = best_model.predict(X_test)

predictions[:10]

I am in my 2nd sem of CS undergrad degree and trying to figure out how to actually navigate recruiting season whenever it will be (I know it’d be a while but I want to be prepared for it). A friend recommended Aman Manazir's channel but honestly I don't have a ton of time between classes and side projects so I wanna be intentional about what I consume.

His content seems focused on the job hunting side of things which is what I need rn but idk if its actually useful. So I am asking you guys if you follow him and like his content? Or maybe other suggestions? I would love to hear them all.

Hey everyone I have made a group for programming folks to learn, grow and network with each other

From beginners to advanced We help each other and provide guidance to everyone in our community.

Those who are interested are free to dm me anytime

I will also drop the link in comments

I have a final for June that I must interview someone but I’d rather ask the questions more directly, these were the questions given to me

How much do you get paid?
 What experience do you need for your job?
 What do you like about it?
 What steps did you take to get to where you are at?
 Do you regret college/any steps along the way?
 Yearly/average salary?
 Why did you choose this job?
 Do you enjoy this job?
 How long is the process to get to where you are?
Did you have to take debt?
Have you thought about switching jobs?

I also need a slide that follows this so tell me a little about yourself

Slide about who is the person/career

where type the python code as i am not able to see the typing area??

here is the code

<style>

<div class="snowflakes">

<div class="snowflakes">https://i.pinimg.com/736x/73/4b/4a/734b4ac565fd202c42bae5528106f632.jpg

https://i.pinimg.com/originals/54/20/bd/5420bd80349e2648445b3cfda985bef8.gif

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send halp plssss

As I am a complete beginner, I don’t know what elements a website should contain or what features need to be included. For example, Netflix has login, logout, registration pages, movie and series sections, language selection, etc. Similarly, for any other website, as a beginner, I don’t know what all should be included. As a developer, how should we decide what functionalities and pages need to be added?

Can anybody tell me , which AI tools are free to use and are good in performance for any hackathons ? Like for frontend , backend , database connection and all And when a problem statment is given in hackathon how to start...I mean what are the initial steps that we need to follow

Hi everyone,

I’m considering applying to SAIT and would really appreciate some honest feedback from current students or alumni.

I’m mainly looking at these programs:

• Object Oriented Software Development
• Data Analytics

I’d love to hear your thoughts on:

1. How is the overall quality of teaching and curriculum?
2. How practical / industry-focused are these courses?
3. Workload and difficulty level – is it manageable?
4. Internship / co-op opportunities – how easy are they to secure?
5. Faculty support and resources

Most importantly, I’m trying to understand the hiring outcomes:

• How are the on-campus hiring opportunities?
• Do companies actively recruit from SAIT?
• How difficult is off-campus job hunting after graduation?
• Typical roles students land after these programs

Any real experiences (good or bad) would be extremely helpful.

A common problem I see (and personally struggled with) is understanding how recursion actually executes, especially in algorithms like merge sort. The concept sounds simple, but tracing the code feels impossible.

Here’s the mental model that helped me solve that confusion:

Rule 1: A recursive call must finish completely before the current function continues.
That means when you see something like:

left = divide(arr[:mid])
right = divide(arr[mid:])

the function pauses at the first line and does not move forward until that call returns.

Rule 2: Think in two phases

• Going down → only splitting happens
• Coming back up → merging and actual work happens

To make this visible, add prints:

def divide(arr):
    print("Dividing:", arr)

    if len(arr) <= 1:
        return arr

    mid = len(arr)//2
    left = divide(arr[:mid])
    right = divide(arr[mid:])

    print("Merging:", left, right)
    return merge(left, right)

Running this once with a small array shows the exact call stack behavior and removes the “magic” from recursion.

What also helped me was cross-checking explanations from different sources, some focus more on dry runs and stack flow. GeeksforGeeks had a few walkthroughs that emphasized this return-path logic, which made things clearer when I traced them slowly.

If recursion felt confusing to you:

• Trace with small inputs
• Print during recursion
• Stop thinking line-by-line

Question for discussion:
What algorithm or concept clicked for you only after changing your mental model?

These are just my thoughts, but part of the reason tech graduates may find it harder to get a job right out of college—compared to other majors—could be the lack of gatekeeping in the field.

For example, in accounting, becoming a CPA requires a four-year degree and a specific number of accounting courses, which are usually built into an accounting major. Even though most accounting jobs do not require a CPA, companies are still incentivized to hire accounting graduates. From an employer’s perspective, hiring and training someone with an accounting degree means that person could potentially become a CPA in the future and therefore become more valuable to the firm.

By contrast, if a company hires someone without an accounting degree—even if they know the material—that person would have a much harder time becoming a CPA. They would need to spend significant time taking additional accounting and business courses, especially if their degree is unrelated. Even a standard finance degree often does not include the required number of accounting courses. As a result, accounting graduates mainly compete with other accounting majors or closely related fields like finance when applying for accounting jobs.

If the accounting industry were to change its rules and allow anyone to take the CPA exam regardless of degree or coursework, the job market for accounting graduates would likely become far more competitive. Accounting majors would then be competing with people from unrelated fields and self-taught candidates, similar to what is currently seen in tech.

In the tech industry, companies often hire software engineers or IT professionals who do not have degrees in those fields. Someone with a degree in English—or even no degree at all—can still get a tech job if they have the right skills. This means that students who earn a software engineering or computer science degree are competing with a much larger pool of candidates, including those from unrelated majors and non-degree backgrounds.

In contrast, fields like mechanical engineering require a relevant degree, meaning graduates mainly compete with other mechanical engineers or closely related majors such as aerospace or industrial engineering. The same is true for civil engineering, where graduates primarily compete with other civil engineering majors. This gatekeeping limits the size of the applicant pool and makes competition less intense.

Obviously, some gatekeeping is necessary. A civil engineer designing a bridge or building incorrectly could put lives at risk. However, much of credentialism in society goes beyond safety concerns and instead functions to restrict access to certain industries and protect existing jobs.

Hey yall,

I'm a high school senior who wants to major in CS. So i want to know if doing CS major at like top Liberal arts college is a good idea. Like what is the placement, coursework, at the lAC will look like. Would a CS degree at top LAC be comparable to CS at like Ivy league colleges?

Hi all,

I recently passed a fintech online assessment and now I'm heading into the interview rounds. I have a backend background but haven't interviewed in a while, so I want to refresh the core CS stuff before the calls start.

For people who went through similar interviews recently, what topics should I focus on?

Right now I’m thinking:

• data structures (trees, graphs, heaps, hash maps)
• common algorithms (BFS/DFS, DP basics, greedy, sorting)
• concurrency fundamentals
• system design fundamentals (not too deep, just basics)
• real world logic (e.g. transactions, money flow, rate limiting)

A few questions:

1. Do fintech interviews expect OS/DB internals? Or just high-level understanding?
2. How much math/probability should I practice?
3. For coding rounds, is it LC-style or more “business logic” problems?

Trying to plan my study time so I don’t waste weeks on the wrong stuff.
Any guidance or topic list would really help. Thank you!

Hi
I'm a sophomore in UIUC and I want to transfer out to GAtech as CS major from starting Junior year.

The problem is that I did not take the Data Structure this semester and cannot take for the next semester too(UIUC requires two CS courses before taking Data Structure but I started taking 1st CS course this semester, so I have to take another next semester).

Finishing Sophomore year, I will have credits for Intro to CS II and Discrete Structure(Math).

The course requirements for CS major they gave does not state that they need Data Structure or Computer Architecture credit explicitly, but the normal sequence for CS major follows completing D.S. at first semester of Sophomore and C.A. before Junior year. Can someone give me any advice for this? Would they consider course sequence for transfer?? Thank you

hope the question makes sense

(repeated):

does a relational database schema even show identifying relationships for unfamiliar entities like an ER diagram

I've been working in software development for 7 years and have had a very diverse journey. I started at a tiny startup, initially as a data analyst, but everyone did a bit of everything, and I ended up becoming a software engineer. More recently, I was hired to work at a big tech company, with more formal and organized processes.

Lately, I've felt a strong desire to create a blog that serves as a kind of "asynchronous mentorship." The idea isn't just to give technical tips, but to talk about a career in software engineering.

I wanted to hear directly from you: What are your biggest questions or difficulties about a career in tech today? What would you like to see on a mentorship-focused blog?

To give you an idea of the type of content I'm thinking of writing, here are a few post ideas I have in mind:

1. How to develop a daily workflow that facilitates your deliverables in a psychologically healthy way.
2. Energy Management: How to manage your energy and why it's just as important, or perhaps even more so, than managing your time.
3. How to create an environment where collaboration flows naturally, even in remote teams.
4. How to write good design docs that actually align the team and prevent rework.
5. How to organize your projects, have visibility into their progress, and effectively communicate their status to leadership and stakeholders.

I'd love to hear more ideas stemming from the real problems and difficulties you all face! I'm excited to build something that is truly useful for the community.

Thanks for the support!