Supervised Learning in Machine Learning > Regression Algoritms

Regressor Model Selection

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A set of exploratory techniques for enhancing regression models is referred to as model selection criteria. Each model selection technique entails selecting a subset of available predictor variables that nonetheless adequately account for the variation in the regression model's observed variation.

To describe the relationships between a group of independent factors and the dependent variable, use regression analysis. Regression analysis yields a regression equation, the coefficients of which show the connection between each independent variable and the dependent variable.

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where is the finaldata.csv

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great learning plateform kushal sir is really too good

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Superb course content and easy to understand.

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  1. Introduction to Machine Learning
    1m 5s
  2. Environment Setup part 1
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  3. Environment Setup part 2
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  4. Environment Setup part 3
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  1. Data Wrangling
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  3. Handling Missing Data
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    8m 3s
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    3m 38s
  8. Splitting Dataset - Practical
    9m 36s
  9. Normalizing the Data - Part 1
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  10. Normalizing the Data - Part2
    6m 58s
  11. Finding Machine Learning Datasets
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  12. Exploratory Data Analysis
    9m
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    19m 12s
  14. Plotting Graphs - Part 2
    23m 44s
  15. Distribution Models - Part 1
    33m 54s
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    13m 56s
  17. Assignment of Data Preprocessing for Machine Learning
    1m 39s

  1. Machine Learning Paradigms
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  3. Underfitting and Overfitting in Models
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    9m 49s
  5. Assignment of Machine Learning Paradigms
    3m 24s

  1. Overview of Classification
    5m 22s
  2. Logistic Regression
    4m 54s
  3. Logistic Regression - Practical
    32m 40s
  4. KNN
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  5. KNN - Practical
    35m
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  7. Decision Trees Practical - 1
    22m 41s
  8. Decision Trees Practical - 2
    20m 42s
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  10. Support Vector Machine Classifier
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  11. Support Vector Machine Classifier - Practical 1
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  12. Support Vector Machine Classifier - Practical 2
    11m 18s
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  14. Naive Bayes Classifier - Practical 1
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  18. Assignment of Supervised Learning Algorithms
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  1. Simple Linear Regression
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  3. Multi-Linear Regression
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  5. Support Vector Regressor - Practical
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  6. Decision Tree Regressor
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  7. Decision Tree Regressor - Practical
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  8. Regressor Model Selection
    9m 7s
  9. Evaluating Regression Model Performance
    8m 45s
  10. Evaluating Regression Model Performance - Practical
    11m 34s
  11. Assignment of Regression Algoritms
    1m 12s

  1. Advance Trends in Machine Learning
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  1. Course Summary
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  1. Interview Questions Part 1
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