Standardization is quite different: first it subtracts the mean value (so standardized
values always have a zero mean), and then it divides by the standard deviation so that
the resulting distribution has unit variance. Unlike min-max scaling, standardization
does not bound values to a specific range, which may be a problem for some algo‐
rithms (e.g., neural networks often expect an input value ranging from 0 to 1).

Just opened instagram after a while and this the first reel that showed up and jeez I really needed this

Feels like a personal attack, but yes

What’s wrong with this country, fr? I was genuinely shocked when I heard that @A2SVOfficial has run out of funding. Emre (the founder) is doing everything he can to keep it running, but no one is willing to help. Now they’re moving to Rwanda. It’s honestly so sad and disappointing. This guy is changing lives and has raised the bar for so many developers’ expectations and ambitions.

@selfmadecoder

Hey 👋

ያልጠፋነው ከእግዚአብሔር ምሕረት የተነሣ ነው ርኅራኄው አያልቅምና።
ማለዳ ማለዳ ዐዲስ ነው ታማኝነትህ ብዙ ነው።

ሰቈቃወ 3:23

So today let's talk about the most important real world data science questions

"How do I know which features matter and which ones don't"

Let's break this down step by step, nice and clear

🔗 1. How to Find Correlation Between Features

Correlation shows how strongly two variables move together.
In pandas, the easiest way:

 python
import pandas as pd

# Load your dataset
df = pd.read_csv("your_data.csv")

# Get correlation matrix
correlation_matrix = df.corr(numeric_only=True)

# View it
print(correlation_matrix)

📊 Visualize it:

 python
import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
plt.noscript("Correlation Matrix")
plt.show()

Values close to 1 = strong positive correlation
Values close to -1 = strong negative correlation
Close to 0 = no linear relationship

2. How to Know Which Columns Are Useful?
Here are every possible way to decide which features matter:

A. Correlation with Target Variable (for regression)

Check how each feature correlates with the target (label).

df.corr()['target_column'].sort_values(ascending=False)

→ The ones with strong correlation (positive or negative) are often useful.

[The Best Programmers I Know]

I just packed my bag, hyped myself up to study my academic courses, and went to the library feeling like a scholar. mann Guess what?
I forgot everything notebooks even my pen... just vibes
me here Sitting like a monk with no scrolls then Started learning ML out of spite 😂

Sometimes forgetfulness leads to unexpected learning paths. Let’s roll with it😁
#MLChoseMe

lemme pretend this was the plan😁

lets do simple questions related to "preparing data for training"

profound

It’s one of those nights where everything’s just… still. No chaos. No overthinking. Just peace. I needed this so bad and I don’t want it to end.

They say I’m strange for loving the night,
for walking alone with no one around,
for enjoying silence more than noise.
But I’m not strange. I just found peace where most people never look within.

Alrighttt the break is over lemme get back to tuning them hyperparameters

Good night y'all

The rain >>>