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2. Data Preprocessing.pdf

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Jyoti Yadav
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Machine Learning- Practical using Python Dr. Jyoti Yadav Department of Computer Science SPPU
Practicals on Machine Learning 1. Write a python program to Prepare Scatter Plot (Use Forge Dataset / Iris Dataset) 2. Write a python program to find all null values in a given data set and remove them. 3. Write a python program the Categorical values in numeric format for a given dataset. 4. Write a python program to implement simple Linear Regression for predicting house price. 5. Write a python program to implement multiple Linear Regression for a given dataset. 6. Write a python program to implement Polynomial Regression for given dataset. 7. Write a python program to Implement Naïve Bayes. 2
Practicals on Machine Learning 8. Write a python program to Implement Decision Tree whether or not to play tennis. 9. Write a python program to implement linear SVM. 10. Write a python program to find Decision boundary by using a neural network with 10 hidden units on two moons dataset 11. Write a python program to transform data with Principal Component Analysis (PCA) 12. Write a python program to implement k-nearest Neighbors ML algorithm to build prediction model (Use Forge Dataset) 13. Write a python program to implement k-means algorithm on a synthetic dataset. 14. Write a python program to implement Agglomerative clustering on a synthetic dataset. 3
4 Overview of Python Libraries for Machine Learning Reading Data; Selecting and Filtering the Data; Data manipulation, sorting, grouping, rearranging Plotting the data Descriptive statistics Inferential statistics
Python Libraries/Toolboxes • IDLE - Jupyter Notebook, Spyder • NumPy -Numerical computations /Vectors, Arrays • SciPy - Scientific computations • Pandas - Data structures operations / DataFrames() • SciKit-Learn -sklearn() Machine Learning Algorithms • StatsModels - Statistical Modeling Visualization libraries - 2D plotting Library • Matplotlib • Seaborn and many more … 5
Descriptive Statistics Types of Descriptive Statistics: • Organize Data • Tables • Graphs • Summarize Data • Central Tendency • Variation • Tables • Frequency Distributions • Relative Frequency Distributions • Graphs • Bar Chart or Histogram • Stem and Leaf Plot • Frequency Polygon • Central Tendency (or Groups’ “Middle Values”) • Mean • Median • Mode • Variation (or Summary of Differences Within Groups) • Range • Interquartile Range • Variance • Standard Deviation
1 Data Preprocessing
• Get the dataset • Import the libraries • Importing the dataset • Missing Data • Categorical Data • Splitting the data into the training set and test set • Feature Scaling
Get the Data set Importing a Library Country Age Salary Purchased France 44 72000 No Spain 27 48000 Yes Germany 30 54000 No Spain 38 61000 No Germany 40 Yes France 35 58000 Yes Spain 52000 No France 48 79000 Yes Germany 50 83000 No France 37 67000 Yes
Importing a Dataset
Handling Missing Values
Missing Values overwritten with mean
Categorical Variables
Dummy Encoding
Encoding Categorical Variables
Encoding Categorical Variable- X
Encoding Categorical Variables-Y
Splitting Data into Training set and Test Set
Feature Scaling
Feature Scaling
2. Data Preprocessing.pdf
2. Data Preprocessing.pdf

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2. Data Preprocessing.pdf

  • 1. Machine Learning- Practical using Python Dr. Jyoti Yadav Department of Computer Science SPPU
  • 2. Practicals on Machine Learning 1. Write a python program to Prepare Scatter Plot (Use Forge Dataset / Iris Dataset) 2. Write a python program to find all null values in a given data set and remove them. 3. Write a python program the Categorical values in numeric format for a given dataset. 4. Write a python program to implement simple Linear Regression for predicting house price. 5. Write a python program to implement multiple Linear Regression for a given dataset. 6. Write a python program to implement Polynomial Regression for given dataset. 7. Write a python program to Implement Naïve Bayes. 2
  • 3. Practicals on Machine Learning 8. Write a python program to Implement Decision Tree whether or not to play tennis. 9. Write a python program to implement linear SVM. 10. Write a python program to find Decision boundary by using a neural network with 10 hidden units on two moons dataset 11. Write a python program to transform data with Principal Component Analysis (PCA) 12. Write a python program to implement k-nearest Neighbors ML algorithm to build prediction model (Use Forge Dataset) 13. Write a python program to implement k-means algorithm on a synthetic dataset. 14. Write a python program to implement Agglomerative clustering on a synthetic dataset. 3
  • 4. 4 Overview of Python Libraries for Machine Learning Reading Data; Selecting and Filtering the Data; Data manipulation, sorting, grouping, rearranging Plotting the data Descriptive statistics Inferential statistics
  • 5. Python Libraries/Toolboxes • IDLE - Jupyter Notebook, Spyder • NumPy -Numerical computations /Vectors, Arrays • SciPy - Scientific computations • Pandas - Data structures operations / DataFrames() • SciKit-Learn -sklearn() Machine Learning Algorithms • StatsModels - Statistical Modeling Visualization libraries - 2D plotting Library • Matplotlib • Seaborn and many more … 5
  • 6. Descriptive Statistics Types of Descriptive Statistics: • Organize Data • Tables • Graphs • Summarize Data • Central Tendency • Variation • Tables • Frequency Distributions • Relative Frequency Distributions • Graphs • Bar Chart or Histogram • Stem and Leaf Plot • Frequency Polygon • Central Tendency (or Groups’ “Middle Values”) • Mean • Median • Mode • Variation (or Summary of Differences Within Groups) • Range • Interquartile Range • Variance • Standard Deviation
  • 8. • Get the dataset • Import the libraries • Importing the dataset • Missing Data • Categorical Data • Splitting the data into the training set and test set • Feature Scaling
  • 9. Get the Data set Importing a Library Country Age Salary Purchased France 44 72000 No Spain 27 48000 Yes Germany 30 54000 No Spain 38 61000 No Germany 40 Yes France 35 58000 Yes Spain 52000 No France 48 79000 Yes Germany 50 83000 No France 37 67000 Yes
  • 15.
  • 19. Splitting Data into Training set and Test Set
  • 20.