Introduction: Hey there!As part of my ML learning journey, I recently implemented a Random Forest Regression model — and I have to say, it was quite the experience! In this post, I’ll walk you through what Random Forest Regression is, how it improves...
Introduction: Hey everyone! 👋I recently completed a hands-on project using Decision Tree Regression, and I was amazed by how simple yet powerful this model is. In this article, I’ll explain what Decision Tree Regression is, why it works so well for...
Introduction: Hello!I recently completed a hands-on project using Support Vector Regression (SVR), one of the coolest and most powerful regression techniques in the ML toolkit. In this post, I’ll walk you through what SVR is, why it’s different from...
When computing TF-IDF, Scikit-Learn applies certain adjustments that may differ from the standard textbook approach. While the traditional TF-IDF calculation involves computing raw term frequency (TF) and inverse document frequency (IDF) separately b...
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] } ...
Linear Regression Math Suppose we have a small dataset of points showing the relationship between study hours ( \( x \) ) and test scores ( \( y \) ): Study Hours \( x \)Test Score \( y \) 12 23 35 We want to find the line of best fit to p...
1. Model Selection (Splitting) 📝 Boilerplate Code: from sklearn.model_selection import train_test_split Use Case: Split your data into two groups: one for training the model and another for testing how well it performs. 📚🎓 Goal: Ensure the model ...
Data cleaning is an essential step in the data preprocessing pipeline, accounting for the majority of the time spent on data-related tasks. Dirty data—missing values, incorrect formats, duplicates, and outliers—can significantly affect machine learni...
from sklearn.metrics import accuracy_score, precision_score, recall_score Imagine you run a clothing store and are trying to predict whether a customer will buy a certain type of clothing item based on their income and age. Income: This represents ...
We'll use a school grading system across different subjects as our analogy. import numpy as np import pandas as pd from sklearn.preprocessing import StandardScaler # Example data: test scores in different subjects data = { 'math_score': [65, 70,...
