A practical walkthrough of the real steps, tools, and pitfalls in machine learning development from my experience
1. Why Most ML Projects Fail Before They Start
In my years working on ML projects, I’ve noticed that many stumble right out of the gate because people underestimate data preparation. Getting your data clean and structured isn’t glamorous, but it’s the foundation. Skipping this is like trying to build a house on sand.
2. The Data Cleaning Routine I Never Skip
Here’s the exact routine I use before any modeling:
- Check for missing data and fill or remove as necessary
- Normalize numerical features to a consistent scale
- Convert categories with one-hot encoding or embeddings
- Identify and remove outliers that skew results
Example:
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformerdf = pd.read_csv('data.csv')
num_features =…
