#featureengineering

[interdatavn]

Feature Engineering là gì? Vai trò & Ứng dụng trong học máy

Trong lĩnh vực Machine Learning, Feature Engineering có vai trò quyết định đến sự thành công của mô hình. Bằng cách biến dữ liệu thô thành các đặc trưng có giá trị, kỹ thuật này giúp tối ưu hóa hiệu suất và độ chính xác của mô hình. Hãy cùng khám phá Feature Engineering và cách ứng dụng nó trong các tình huống thực tế qua bài viết dưới đây.

Xem chi tiết tại: Feature Engineering là gì? Vai trò & Ứng dụng trong học máy

INTERDATA

Website: Interdata.vn Hotline: 1900-636822 Email: Info@interdata.vn VPĐD: 240 Nguyễn Đình Chính, P.11. Q. Phú Nhuận, TP. Hồ Chí Minh VPGD: Số 211 Đường số 5, KĐT Lakeview City, P. An Phú, TP. Thủ Đức, TP. Hồ Chí Minh

[daviddavi09]

Machine Learning Guide: Turn Data into Predictions Step-by-Step #shorts

Take a hands-on adventure through machine learning—no PhD or supercomputer needed! In this introduction video, you'll see how to create your first machine learning model from ground zero using only a small dataset, curiosity, and step-by-step instructions. We trace the journey of Aisha, a new data fan, as she works through a real-world problem: forecasting monthly sales for her online bookstore. From problem definition to data gathering and cleaning, exploratory data analysis, feature engineering, and baseline model training, all the crucial steps are addressed. You will also get to know how to implement a simple interactive demo that makes your model a useful tool. Whether you're using sales records, health diaries, or side projects, this video demonstrates how machine learning turns raw information into powerful predictive power. Ideal for beginners willing to dive in—watch now and get started with creating smart systems today!

[damilola-doodles]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[dammyanimation]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-ai-automation]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-warrior-mindset]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-moyo]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[data-analytics-masters]

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[mitsde123]

Core Concepts in Machine Learning: Dependent and Independent Variables, Correlations, Feature Engineering, and Regression Techniques

Machine learning is built on key concepts like dependent and independent variables, correlations, feature engineering, and regression techniques. Understanding dependent variables (what we predict) and independent variables (the features we use for predictions) is essential for model training. Correlations help identify relationships between variables, guiding feature selection and preventing multicollinearity.

Feature engineering transforms raw data into useful inputs, improving model performance. Finally, linear and logistic regression are foundational techniques for predicting continuous and categorical outcomes, respectively. Mastering these concepts is crucial for creating effective machine learning models and making informed data-driven decisions.

Read More

[datasciencewithmohsin]

Feature Engineering in Machine Learning: A Beginner's Guide

Feature Engineering in Machine Learning: A Beginner's Guide

Feature engineering is one of the most critical aspects of machine learning and data science. It involves preparing raw data, transforming it into meaningful features, and optimizing it for use in machine learning models. Simply put, it’s all about making your data as informative and useful as possible.

In this article, we’re going to focus on feature transformation, a specific type of feature engineering. We’ll cover its types in detail, including:

1. Missing Value Imputation

2. Handling Categorical Data

3. Outlier Detection

4. Feature Scaling

Each topic will be explained in a simple and beginner-friendly way, followed by Python code examples so you can implement these techniques in your projects.

What is Feature Transformation?

Feature transformation is the process of modifying or optimizing features in a dataset. Why? Because raw data isn’t always machine-learning-friendly. For example:

Missing data can confuse your model.

Categorical data (like colors or cities) needs to be converted into numbers.

Outliers can skew your model’s predictions.

Different scales of features (e.g., age vs. income) can mess up distance-based algorithms like k-NN.

1. Missing Value Imputation

Missing values are common in datasets. They can happen due to various reasons: incomplete surveys, technical issues, or human errors. But machine learning models can’t handle missing data directly, so we need to fill or "impute" these gaps.

Techniques for Missing Value Imputation

1. Dropping Missing Values: This is the simplest method, but it’s risky. If you drop too many rows or columns, you might lose important information.

2. Mean, Median, or Mode Imputation: Replace missing values with the column’s mean (average), median (middle value), or mode (most frequent value).

3. Predictive Imputation: Use a model to predict the missing values based on other features.

Python Code Example:

import pandas as pd

from sklearn.impute import SimpleImputer

# Example dataset

data = {'Age': [25, 30, None, 22, 28], 'Salary': [50000, None, 55000, 52000, 58000]}

df = pd.DataFrame(data)

# Mean imputation

imputer = SimpleImputer(strategy='mean')

df['Age'] = imputer.fit_transform(df[['Age']])

df['Salary'] = imputer.fit_transform(df[['Salary']])

print("After Missing Value Imputation:\n", df)

Key Points:

Use mean/median imputation for numeric data.

Use mode imputation for categorical data.

Always check how much data is missing—if it’s too much, dropping rows might be better.

2. Handling Categorical Data

Categorical data is everywhere: gender, city names, product types. But machine learning algorithms require numerical inputs, so you’ll need to convert these categories into numbers.

Techniques for Handling Categorical Data

1. Label Encoding: Assign a unique number to each category. For example, Male = 0, Female = 1.

2. One-Hot Encoding: Create separate binary columns for each category. For instance, a “City” column with values [New York, Paris] becomes two columns: City_New York and City_Paris.

3. Frequency Encoding: Replace categories with their occurrence frequency.

Python Code Example:

from sklearn.preprocessing import LabelEncoder

import pandas as pd

# Example dataset

data = {'City': ['New York', 'London', 'Paris', 'New York', 'Paris']}

df = pd.DataFrame(data)

# Label Encoding

label_encoder = LabelEncoder()

df['City_LabelEncoded'] = label_encoder.fit_transform(df['City'])

# One-Hot Encoding

df_onehot = pd.get_dummies(df['City'], prefix='City')

print("Label Encoded Data:\n", df)

print("\nOne-Hot Encoded Data:\n", df_onehot)

Key Points:

Use label encoding when categories have an order (e.g., Low, Medium, High).

Use one-hot encoding for non-ordered categories like city names.

For datasets with many categories, one-hot encoding can increase complexity.

3. Outlier Detection

Outliers are extreme data points that lie far outside the normal range of values. They can distort your analysis and negatively affect model performance.

Techniques for Outlier Detection

1. Interquartile Range (IQR): Identify outliers based on the middle 50% of the data (the interquartile range).

IQR = Q3 - Q1

[Q1 - 1.5 \times IQR, Q3 + 1.5 \times IQR]

2. Z-Score: Measures how many standard deviations a data point is from the mean. Values with Z-scores > 3 or < -3 are considered outliers.

Python Code Example (IQR Method):

import pandas as pd

# Example dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate IQR

Q1 = df['Values'].quantile(0.25)

Q3 = df['Values'].quantile(0.75)

IQR = Q3 - Q1

# Define bounds

lower_bound = Q1 - 1.5 * IQR

upper_bound = Q3 + 1.5 * IQR

# Identify and remove outliers

outliers = df[(df['Values'] < lower_bound) | (df['Values'] > upper_bound)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Values'] >= lower_bound) & (df['Values'] <= upper_bound)]

print("Filtered Data:\n", filtered_data)

Key Points:

Always understand why outliers exist before removing them.

Visualization (like box plots) can help detect outliers more easily.

4. Feature Scaling

Feature scaling ensures that all numerical features are on the same scale. This is especially important for distance-based models like k-Nearest Neighbors (k-NN) or Support Vector Machines (SVM).

Techniques for Feature Scaling

1. Min-Max Scaling: Scales features to a range of [0, 1].

X' = \frac{X - X_{\text{min}}}{X_{\text{max}} - X_{\text{min}}}

2. Standardization (Z-Score Scaling): Centers data around zero with a standard deviation of 1.

X' = \frac{X - \mu}{\sigma}

3. Robust Scaling: Uses the median and IQR, making it robust to outliers.

Python Code Example:

from sklearn.preprocessing import MinMaxScaler, StandardScaler

import pandas as pd

# Example dataset

data = {'Age': [25, 30, 35, 40, 45], 'Salary': [20000, 30000, 40000, 50000, 60000]}

df = pd.DataFrame(data)

# Min-Max Scaling

scaler = MinMaxScaler()

df_minmax = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)

# Standardization

scaler = StandardScaler()

df_standard = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)

print("Min-Max Scaled Data:\n", df_minmax)

print("\nStandardized Data:\n", df_standard)

Key Points:

Use Min-Max Scaling for algorithms like k-NN and neural networks.

Use Standardization for algorithms that assume normal distributions.

Use Robust Scaling when your data has outliers.

Final Thoughts

Feature transformation is a vital part of the data preprocessing pipeline. By properly imputing missing values, encoding categorical data, handling outliers, and scaling features, you can dramatically improve the performance of your machine learning models.

Summary:

Missing value imputation fills gaps in your data.

Handling categorical data converts non-numeric features into numerical ones.

Outlier detection ensures your dataset isn’t skewed by extreme values.

Feature scaling standardizes feature ranges for better model performance.

Mastering these techniques will help you build better, more reliable machine learning models.

[daviddavi09]

Machine Learning Tutorial: Build Your First Predictive Model Step-by-Step

Take a hands-on adventure through machine learning—no PhD or supercomputer needed! In this introduction video, you'll see how to create your first machine learning model from ground zero using only a small dataset, curiosity, and step-by-step instructions. We trace the journey of Aisha, a new data fan, as she works through a real-world problem: forecasting monthly sales for her online bookstore. From problem definition to data gathering and cleaning, exploratory data analysis, feature engineering, and baseline model training, all the crucial steps are addressed. You will also get to know how to implement a simple interactive demo that makes your model a useful tool. Whether you're using sales records, health diaries, or side projects, this video demonstrates how machine learning turns raw information into powerful predictive power. Ideal for beginners willing to dive in—watch now and get started with creating smart systems today!

[damilola-doodles]

Project Title: Comprehensive Predictive Maintenance Pipeline with Advanced Feature Engineering, Outlier Detection, and Model Optimization - Scikit-Learn-Exercise-003

Project Title: cddml-RZtQ3PuKxLt – “Comprehensive Predictive Maintenance Pipeline with Advanced Feature Engineering, Outlier Detection, and Model Optimization” File Name: comprehensive_predictive_maintenance_pipeline.py Below is an extensive, production-grade Python project that leverages scikit-learn and a variety of complementary modules (such as imbalanced-learn, mlflow, optuna, dask, and…

[dammyanimation]

Project Title: Comprehensive Predictive Maintenance Pipeline with Advanced Feature Engineering, Outlier Detection, and Model Optimization - Scikit-Learn-Exercise-003

Project Title: cddml-RZtQ3PuKxLt – “Comprehensive Predictive Maintenance Pipeline with Advanced Feature Engineering, Outlier Detection, and Model Optimization” File Name: comprehensive_predictive_maintenance_pipeline.py Below is an extensive, production-grade Python project that leverages scikit-learn and a variety of complementary modules (such as imbalanced-learn, mlflow, optuna, dask, and…

[damilola-ai-automation]

Project Title: Comprehensive Predictive Maintenance Pipeline with Advanced Feature Engineering, Outlier Detection, and Model Optimization - Scikit-Learn-Exercise-003

Project Title: cddml-RZtQ3PuKxLt – “Comprehensive Predictive Maintenance Pipeline with Advanced Feature Engineering, Outlier Detection, and Model Optimization” File Name: comprehensive_predictive_maintenance_pipeline.py Below is an extensive, production-grade Python project that leverages scikit-learn and a variety of complementary modules (such as imbalanced-learn, mlflow, optuna, dask, and…

[damilola-warrior-mindset]

Project Title: Comprehensive Predictive Maintenance Pipeline with Advanced Feature Engineering, Outlier Detection, and Model Optimization - Scikit-Learn-Exercise-003

Project Title: cddml-RZtQ3PuKxLt – “Comprehensive Predictive Maintenance Pipeline with Advanced Feature Engineering, Outlier Detection, and Model Optimization” File Name: comprehensive_predictive_maintenance_pipeline.py Below is an extensive, production-grade Python project that leverages scikit-learn and a variety of complementary modules (such as imbalanced-learn, mlflow, optuna, dask, and…