Feedback on Classification Assignment

• Well constructed arguments but lots of bullets. Try to write full sentences.
• Some comments in code
• "Imbalance leads to a low overall accuracy and poor reliability for bearish predictions." Thus…?
• Clear observations
• kNN only with k=1

• Little analysis
• Formatting and layout need attention

• Make a new repo for each assignment please.
• Report well written and formatted
• Correlations well argued. Observation, hypothesis, test.
• Good use of LLMs, but advice not always followed
  • Hints for transforming variables
  • Handling nonpositive values
  • Better model overview
  • Pandas vs Polars
• "The model predicts Up most of the times, meaning we have a lot of false positives." Thus unbalanced data. Thus…?
• Good plot of error rate of kNN v Logistic Regression
• Use of LLM's
  • model transformation explanation
  • add column transformation

  • code error solving and git problems

    Nice — that’s a clean and systematic way to explore transformations for your predictors!
    
    Here’s what your code is doing, step by step:
    
    ...
    
    ⚠️ Potential Improvements / Notes
    
    ...
    

    That error —
    
    ValueError: endog has evaluated to an array with multiple columns that has shape (605, 2)
    This occurs when the variable converted to endog is non-numeric (e.g., bool or str).
    
    — means your response variable (Direction) is categorical (e.g. "Up"/"Down"), and
    statsmodels.ols is trying to treat it as numeric, which OLS can’t handle.
    
    

    TypeError: DataFrame object does not support Series assignment by index
    ChatGPT said:
    
    Good catch — that error usually means something like this happened:
    
        ❌ You tried to assign a Pandas Series to a Polars DataFrame using weekly_pd[...] = ....
    
    🔍 What’s going on
    
    Your earlier code suggests you’re using Polars, not Pandas — for example from
    your earlier messages (weekly_pd looks like a Polars DataFrame).
    
    But here:
    
    weekly_pd[f"log_{col}"] = np.log(weekly_pd[col] + 1e-6)
    
    that’s Pandas-style assignment — it won’t work with Polars.
    Polars uses expression syntax instead of direct column assignment.
    
    ✅ How to fix it
    
    ...
    

    If you want, I can rewrite your full loop for all transformations (log, sqrt, x^2)
    and automatically print a confusion matrix for each, using only the libraries you
    listed. This will give you a clean, reproducible workflow.
    
    Do you want me to do that?
    
    

    You said: yes please

    I see exactly what’s happening here:
    
    👉 The error comes from this line:
    python
    
    X_train, X_test = X.loc[train], X.loc[~train]
    
    The problem is that X is a NumPy array (because you used .to_numpy() earlier).
    NumPy arrays don’t have .loc (that’s a pandas method).
    
    

• Good correlation plot.
• Give your plot files sensible names.
• Note how to do math in markdown using LaTeX notation.
• Better explanation and reasoning would be helpful.

• Good explanation and reasoning about the parts that you did.
• Note how to do math in markdown using LaTeX notation.
• Good interpretation of kNN tuning plot

• ChatGPT said

  There’s no real problem here 🙂 — your commits and push worked perfectly.
  

  • when i add .idea/ to my gitigonre i dont have to commit right?

  ChatGPT said:
  
  Good question — but actually, yes, you do need to commit 😄
  
  Here’s why:
  
  Adding .idea/ to your .gitignore just changes a local text file called .gitignore.
  
  Git treats that file like any other tracked file — so if you want that ignore
  rule to apply for everyone who clones the repo (and to keep it saved permanently),
  you must commit the change.
  

• what are these problems

  ChatGPT said:
  
  Excellent question — these warnings are actually quite common when you use LDA
  or QDA in scikit-learn, and they indicate numerical or collinearity problems in
  your data. Let’s break each part down clearly 👇
  
  

• whats the function for KNN in python
• how do you put bold text in markdown

• whats a k tunning plot

  ChatGPT said:
  
  Excellent question 👏 — you’re probably referring to a “k tuning plot” (also
  called a KNN tuning curve) used when training a K-Nearest Neighbors (KNN)
  model.
  
  Let’s go over it clearly 👇
  

• how do i cite a chat gpt conversation in a README in markdown language

• All commits by Matúš with input from Madalina
• Data in repo
• Good explations of your understanding of what you are seeing.
• The volume data appear to follow exponential/ polynomial trend. Once we take the cube root, we can see the data shape changing/ becoming more linear.
• The following table was generated using ChatGPT (link above), I struggled with splitting the data for training:

Accuracy: 0.994

Accuracy is fairly high => not trustworthy at all, perhaps due to the high number of predictors.
Multicolinearity is highly unlikely since no correlation could be observed between the
remaining variables.

• "Volume increases exponentially with year." If that is so then log(Volume) should be approximately linear. That is something you could test!
• Try a tuning plot for kNN

• Your layout and formatting need attention
• Nice to see you citing resources other than LLMs!

• All Roman?
• Commit messages a bit monotonous.
• Good use of plots in the data exploration phase.
• Clear statement of observations and hyphotheses/questions.
• Limit float precision.

• does this mean that in 2009 and 2010 the predictions were very good or no

  ChatGPT said:
  
  Excellent question — and it shows you’re thinking critically about
  what the results actually mean 👏
  
  Let’s unpack this clearly:
  
  ...
  

  • DeprecationWarning

  ChatGPT said:
  
  Ah good catch 🔥 — you’re on Polars 1.0+, and both of those warnings are about API changes.
  Let me rewrite the snippet in the new Polars style so it’s clean and future-proof:
  

• so I want the same plots but the year one the line is weird

  ChatGPT said:
  
  Excellent question — that “weird” line in your Volume vs Year plot
  happens because your data has many rows per year (e.g., weekly
  observations), so Matplotlib connects all those points in order
  — making the line zig-zag heavily.
  
  You’ve got two clean ways to fix that, depending on what you want to show 👇
  
  ...