Supervised Learning in Machine Learning > Data Preprocessing for Machine Learning

Data Wrangling

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The process of cleaning and integrating chaotic and complicated data sets for easy access and analysis is known as data wrangling. With the amount of data and data sources continually rising and expanding, it is becoming increasingly important to organize massive volumes of available data for analysis.

Multiple data sources are combined into a single dataset for analysis. Identifying data gaps (for example, empty cells in a spreadsheet) and filling or eliminating them. Delete data that is either superfluous or irrelevant to the project at hand.

Data wrangling improves data usability by converting data into a format that is suitable with the end system. It facilitates the creation of data flows within an easy user interface, as well as the scheduling and automation of data flows.

Data cleaning is concerned with removing incorrect data from your data set. Data-wrangling, on the other hand, focuses on modifying the data format by converting "raw" data into a more useable form.

There are three main types of data wrangling tools:

  • Data Preparation Tools: These tools help users prepare the data for machine learning. They include functions such as splitting a large dataset into smaller chunks and reorganizing it into a usable format.
  • Data Visualization Tools: These tools help users visualize their machine learning results, which is helpful when they have too much data or when they want to show their findings to others.
  • Machine Learning Tools: These tools help users train and test predictive models using a wide variety of algorithms such as decision trees, random forests, gradient boosting machines.

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Ankit Malik
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where is the finaldata.csv

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Vimal Bhatt
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great learning plateform kushal sir is really too good

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good

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Prabhat Yadav
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Superb course content and easy to understand.

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fahad ameer
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good

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Course Content

  1. Introduction to Machine Learning
    1m 5s
  2. Environment Setup part 1
    3m 26s
  3. Environment Setup part 2
    4m 43s
  4. Environment Setup part 3
    2m 33s

  1. Data Wrangling
    12m 26s
  2. Importing Libraries and Dataset
    11m 19s
  3. Handling Missing Data
    11m 51s
  4. Handling Missing Data - Practical
    17m 3s
  5. Encoding Categorical Data
    6m 34s
  6. Encoding Catergorical Data - Practical
    8m 3s
  7. Splitting Dataset
    3m 38s
  8. Splitting Dataset - Practical
    9m 36s
  9. Normalizing the Data - Part 1
    6m 18s
  10. Normalizing the Data - Part2
    6m 58s
  11. Finding Machine Learning Datasets
    9m 17s
  12. Exploratory Data Analysis
    9m
  13. Plotting Graphs - Part 1
    19m 12s
  14. Plotting Graphs - Part 2
    23m 44s
  15. Distribution Models - Part 1
    33m 54s
  16. Distribution Models - Part 2
    13m 56s
  17. Assignment of Data Preprocessing for Machine Learning
    1m 39s

  1. Machine Learning Paradigms
    9m 53s
  2. Sampling Methods
    19m 43s
  3. Underfitting and Overfitting in Models
    6m 7s
  4. Variance and Bias
    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
    7m 21s
  5. KNN - Practical
    35m
  6. Decision Trees for Classification
    6m 17s
  7. Decision Trees Practical - 1
    22m 41s
  8. Decision Trees Practical - 2
    20m 42s
  9. Random Forest
    8m 37s
  10. Support Vector Machine Classifier
    5m 44s
  11. Support Vector Machine Classifier - Practical 1
    16m 59s
  12. Support Vector Machine Classifier - Practical 2
    11m 18s
  13. Naive Bayes Classifier
    5m 25s
  14. Naive Bayes Classifier - Practical 1
    15m 21s
  15. Naive Bayes Classifier - Practical 2
    11m 7s
  16. Evaluating Classification Models Performance
    7m 31s
  17. Evaluating Classification Models Performance - Practical
    33m 2s
  18. Assignment of Supervised Learning Algorithms
    1m 12s

  1. Simple Linear Regression
    8m 57s
  2. Simple Linear Regression - Practical
    12m 34s
  3. Multi-Linear Regression
    3m
  4. Support Vector Regressor
    10m 47s
  5. Support Vector Regressor - Practical
    6m 47s
  6. Decision Tree Regressor
    4m 52s
  7. Decision Tree Regressor - Practical
    5m 2s
  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
    10m 13s

  1. Course Summary
    3m 51s

  1. Interview Questions Part 1
    19m 22s
  2. Interview Questions Part 2
    12m 23s
  3. Interview Questions Part 3
    15m 20s

  1. Career Guidelines
    1m 33s
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