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Machine learning basics, Schemes and Mind Maps of Data Warehousing

An introduction to machine learning and data preprocessing. It covers topics such as supervised and unsupervised learning, splitting datasets, regression, classification, clustering, and evaluating model performance. The document also includes steps for data preprocessing, such as importing libraries, importing datasets, checking for missing values, and feature scaling. Additionally, it discusses methods for rescaling, mean normalization, and standardization. code examples in Python using libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.

Typology: Schemes and Mind Maps

2021/2022

Available from 06/25/2023

parth-pethkar
parth-pethkar 🇮🇳

6 documents

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UNIT - II
Statistics for Data Science
SCHOOL OF mechanical ENGINEERING AND TECHNOLOGY
Data Science
T. Y. BTECH
UNIT III
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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