EDA for Machine Learning: Understand Your Data Before You Model It (EP 06)
Before you train any model, you need to understand your data.
Skip this step and you'll spend hours debugging the wrong thing.
In this episode of ML & AI From Scratch, we walk through a complete
Exploratory Data Analysis (EDA) workflow using pandas and matplotlib
on the California Housing dataset.
By the end of this video you'll know how to:
✅ Inspect any DataFrame in 5 commands (.shape, .info, .describe...)
✅ Find and fix missing values — with the right strategy each time
✅ Visualise distributions with histograms and boxplots
✅ Build a correlation heatmap to find the most useful features
✅ Use scatter plots to spot linear relationships before modelling
✅ Avoid 4 common data pitfalls that trip up even experienced devs
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💻 EDA STARTER CODE (run this on any dataset):
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df.shape
df.info()
df.describe()
df.isnull().sum()
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.show()
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💬 What's the strangest thing EDA revealed in your data? Drop it below!
📌 NEXT EPISODE → Model Evaluation: Overfitting, Underfitting & Metrics (EP 07)
⬅️ PREVIOUS EPISODE → Linear Regression: Your First ML Model (EP 05)
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💻 GitHub (code + notebooks) → https://github.com/shahmi0519
🔗 LinkedIn → linkedin.com/in/ahamed-shahmi-abduljabbar
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#EDA #pandas #matplotlib #MachineLearning #DataScience
#Python #AIForBeginners #ExploratoryDataAnalysis #MLTutorial