Machine learning basics, Schemes and Mind Maps of Data Warehousing

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UNIT - II Statistics for Data Science SCHOOL OF mechanical ENGINEERING AND TECHNOLOGY

Data Science

T. Y. BTECH UNIT III

UNIT III-Syllabus

• (^) Introduction to machine learning
• (^) Supervised and Unsupervised Learning
• (^) Splitting datasets: Training and Testing,
• (^) Regression: Simple Linear Regression
• (^) Classification: Naïve Bayes classifier
• (^) Clustering: K-means
• (^) Evaluating model performance
• (^) Python libraries for machine learning

Data preprocessing

Machine Learning Process Steps in Data Preprocessing

• (^) Step 1 : Import the libraries
• (^) Step 2 : Import the data-set
• (^) Step 3 : Check out the missing values
• (^) Step 4 : See the Categorical Values
• (^) Step 5 : Splitting the data-set into Training and Test Set
• (^) Step 6 : Feature Scaling

Step 2 : Import the Dataset

Step 3 : Check out the Missing Values

Step 4 : See the Categorical Values

Dummy Variables in Data Preprocessing

• (^) Dummy Variables is one that takes the value 0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift the outcome
• (^) To create dummy variable
  • (^) OneHotEncoder Class from sklearn.preprocessing
  • (^) Use pandas get dummies method.

Step 5 : Splitting the data-set into Training and Test Set

Step 6 : Feature Scaling

• Feature scaling is the method to

limit the range of variables so that

they can be compared on common

grounds.

Machine Learning

Supervised Algorithms

• There are two types of supervised machine

learning algorithms:

– Regression : predicts continuous value output

– Classification: predicts discrete outputs

– For Example , predicting the price of a house in

dollars is a regression problem whereas predicting

whether a tumor is malignant or benign is a

classification problem.

What is Regression