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Preparing Data for Machine Learning

Preparing Data for Machine Learning

After exploring the dataset and understanding the relationships between variables, the next step is preparing the data for machine learning.

Machine learning algorithms require data to be organized in a specific format. Features and target variables must be separated, and the dataset must be divided into training and testing subsets. This ensures that the model learns patterns from one portion of the data and is evaluated on previously unseen data.

In this module, we will prepare the California Housing Dataset for machine learning by separating features and labels, creating training and testing datasets, and understanding the importance of data splitting.

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House Price Prediction Using Machine Learning (XGBoost)

VA
Vishalini A

House Price Prediction Using Machine Learning (XGBoost)

MODULE 1 : Introduction
lessonIntroduction of the Project
lessonWhat Is Data Science? Key Concepts and Core Features
lessonWhat Is Machine Learning?
lessonUnderstanding Regression Problems
lessonUnderstanding the House Price Prediction Problem
lessonUnderstanding the California Housing Dataset
lessonAim of the Project
MODULE 2 : Environment Setup and Exploratory Data Analysis
lessonEnvironment Setup and Exploratory Data Analysis
lessonWhy Use Google Colab?
lessonImporting Required Libraries
lessonLoading the California Housing Dataset
lessonCreating a Pandas DataFrame
lessonInspecting the Dataset
lessonUnderstanding Dataset Dimensions
lessonChecking for Missing Values
lessonStatistical Summary of the Dataset
lessonIntroduction to Exploratory Data Analysis
lessonUnderstanding Correlation
lessonCreating a Correlation Matrix
lessonVisualizing Correlations Using a Heatmap
MODULE 3 : Preparing Data for Machine Learning
lessonPreparing Data for Machine Learning
lessonWhy Data Preparation Matters
lessonUnderstanding Features and Labels
lessonSeparating Features and Target Variables
lessonUnderstanding Train-Test Split
lessonWhy We Need Unseen Data
lessonSplitting the Dataset
lessonCreating Training and Testing Sets
lessonUnderstanding the Resulting Datasets
lessonInspecting Training Data
lessonInspecting Testing Data
lessonUnderstanding Data Leakage
lessonPreparing for Model Training
MODULE 4 : Building the House Price Prediction Model Using XGBoost
lessonBuilding the House Price Prediction Model Using XGBoost
lessonWhat Is XGBoost?
lessonUnderstanding Ensemble Learning
lessonUnderstanding Decision Trees
lessonUnderstanding Gradient Boosting
lessonImporting the XGBoost Regressor
lessonCreating the Model
lessonTraining the Model
lessonHow the Model Learns
lessonUnderstanding Model Parameters
lessonWhy XGBoost Is Effective for House Price Prediction
lessonTraining Completion
MODULE 5 : Model Evaluation and Prediction
lessonModel Evaluation and Prediction
lessonMaking Predictions Using the Trained Model
lessonUnderstanding Regression Evaluation Metrics
lessonUnderstanding R square Score
lessonUnderstanding Mean Absolute Error (MAE)
lessonWhy Training Performance Alone Is Not Enough
lessonEvaluating the Model on Test Data
lessonCalculating Test R square Score
lessonUnderstanding Generalization
lessonVisualizing Actual vs Predicted Prices
MODULE 6 : Project Conclusion
lessonProject Conclusion
lessonLive Link and Output Overview
lessonMCQs
lessonResources

House Price Prediction Using Machine Learning (XGBoost)

    MODULE 1 : Introduction
    lessonIntroduction of the Project
    lessonWhat Is Data Science? Key Concepts and Core Features
    lessonWhat Is Machine Learning?
    lessonUnderstanding Regression Problems
    lessonUnderstanding the House Price Prediction Problem
    lessonUnderstanding the California Housing Dataset
    lessonAim of the Project
    MODULE 2 : Environment Setup and Exploratory Data Analysis
    lessonEnvironment Setup and Exploratory Data Analysis
    lessonWhy Use Google Colab?
    lessonImporting Required Libraries
    lessonLoading the California Housing Dataset
    lessonCreating a Pandas DataFrame
    lessonInspecting the Dataset
    lessonUnderstanding Dataset Dimensions
    lessonChecking for Missing Values
    lessonStatistical Summary of the Dataset
    lessonIntroduction to Exploratory Data Analysis
    lessonUnderstanding Correlation
    lessonCreating a Correlation Matrix
    lessonVisualizing Correlations Using a Heatmap
    MODULE 3 : Preparing Data for Machine Learning
    lessonPreparing Data for Machine Learning
    lessonWhy Data Preparation Matters
    lessonUnderstanding Features and Labels
    lessonSeparating Features and Target Variables
    lessonUnderstanding Train-Test Split
    lessonWhy We Need Unseen Data
    lessonSplitting the Dataset
    lessonCreating Training and Testing Sets
    lessonUnderstanding the Resulting Datasets
    lessonInspecting Training Data
    lessonInspecting Testing Data
    lessonUnderstanding Data Leakage
    lessonPreparing for Model Training
    MODULE 4 : Building the House Price Prediction Model Using XGBoost
    lessonBuilding the House Price Prediction Model Using XGBoost
    lessonWhat Is XGBoost?
    lessonUnderstanding Ensemble Learning
    lessonUnderstanding Decision Trees
    lessonUnderstanding Gradient Boosting
    lessonImporting the XGBoost Regressor
    lessonCreating the Model
    lessonTraining the Model
    lessonHow the Model Learns
    lessonUnderstanding Model Parameters
    lessonWhy XGBoost Is Effective for House Price Prediction
    lessonTraining Completion
    MODULE 5 : Model Evaluation and Prediction
    lessonModel Evaluation and Prediction
    lessonMaking Predictions Using the Trained Model
    lessonUnderstanding Regression Evaluation Metrics
    lessonUnderstanding R square Score
    lessonUnderstanding Mean Absolute Error (MAE)
    lessonWhy Training Performance Alone Is Not Enough
    lessonEvaluating the Model on Test Data
    lessonCalculating Test R square Score
    lessonUnderstanding Generalization
    lessonVisualizing Actual vs Predicted Prices
    MODULE 6 : Project Conclusion
    lessonProject Conclusion
    lessonLive Link and Output Overview
    lessonMCQs
    lessonResources

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    Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

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