Introduction to Model Selection
Model selection in machine learning is the crucial process of choosing the most suitable model from a set of candidate models for a given task and dataset. The goal is…
Data Splitting for Time-Series Considerations in Machine Learning
Time-series data is a sequence of data points collected or recorded at successive, equally spaced points in time. The defining characteristic of time-series data is its chronological order, meaning the…
Practical guide to Data Visualization with Python
Mastering Data Visualization in Python: A Comprehensive Guide Welcome to your ultimate resource for creating stunning and insightful data visualizations in Python! In this comprehensive guide, we’ll embark on a…
Handling Identifiers in EDA
how to Identify and remove columns that are likely to be identifiers or have very limited predictive power while performing Exploratory Data Analysis Identifying and removing columns with limited predictive…
Practical guide to Scaling in Machine Learning
Taming the Wild Numbers: Why Scaling Your Data is Like Fitting Shoes! Ever tried to compare the weight of an elephant in kilograms with the length of an ant in…
Dimensionality Reduction and Handle Correlation
Follow on LinkedIn Machine Learning Project Ways to reduce Correlation 1. Feature Selection Identify and removing one or more of the highly correlated features. Retain the most informative variables while…
Normalization Techniques
No worries at all! It happens. You’re right, the focus should be squarely on normalizing continuous features. Let’s dive back in with a clearer focus. Normalizing Continuous Features in Machine…
Outliers
You’re looking to identify those pesky outliers in your machine learning datasets using Python! They can really throw off your model training, so it’s a great idea to get a…
Data Preparation Python Functions
import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, LabelEncoder, OneHotEncoder, SimpleImputer, Normalizer, PowerTransformer, QuantileTransformer, PolynomialFeatures Explanation of Machine Learning Preprocessing…
Level 3: Advanced Python for Data Science!
Welcome back, Data Explorers! 🚀 Feeling comfortable with the Python fundamentals? Excellent! Now, it’s time to put on your advanced coding hats 🎩 and delve into more sophisticated techniques that…