When teams talk about MLOps, the focus usually goes straight to model training, deployment, or monitoring. But there's a quiet hero in the ML pipeline that often gets overlooked: The Feature Store. You might ask - why all the fuss about storing featu...
Data preprocessing is the essential art of refining raw, unpolished data into a clean, structured format that machine learning models can effectively utilize. This process encompasses a variety of tasks, such as addressing missing values, converting ...
Feature Engineering is the process of transforming raw data into features that improve a model’s performance. Imagine a scenario; where you are training a model for prediction of house price. The dataset contains “Year Built,” but this doesn’t tell u...
Mathematical transformations are essential in feature engineering, a key step in the machine learning process. Before using data in a model, raw datasets often need preprocessing to improve their quality, clarity, and predictive power. Many real-worl...
The big secret is in your data! Bad Data = Bad Model (No Matter How Fancy Your Algorithm Is) Let’s use a real-life example. As data scientists or machine learning engineers, you’ve been asked to build a machine learning model to predict customer spen...
The French electrical grid operator RTE (Réseau de Transport d'Électricité) uses a system of PP1 and PP2 days to signal periods of high stress on the electrical grid. These designations typically occur during intense cold spells or peak consumption p...
Original Dataset import pandas as pd import numpy as np # Step 1: Create a sample dataset data = { "A": [1, 2, np.nan, 4, 5], "B": [np.nan, 2, 3, np.nan, 5], "C": ["cat", "dog", np.nan, "cat", "dog"], "D": [10, 20, 30, 40, np.nan] } ...
Let me just give the answer away in the first sentence: “No amount of feature is too much features.” I know it's sad to hear, but stay with me for a while... I would answer this question with questions to give you clarity. Feature selection involves ...
Feature engineering is the backbone of machine learning. Transforming raw data into a format that models can understand is crucial for building efficient and robust systems. In this blog, we’ll explore key aspects of feature engineering, including ha...
Welcome to Day 11! Today, we’re focusing on feature engineering, one of the most creative and impactful stages in any data science project. Feature engineering involves creating, modifying, or selecting features to improve the performance of machine ...
