Supervised Learning (Data Science).pptx

Supervised Machine Learning

1. WIIRTUU LEENJII KOMPIYUTERAA OITI OITI COMPUTER TRAINING CENTER ኦ፥አይ፥ቲ፥አይ የኮምፒውተር ማሰልጠኛ ማዕከል Slide 1 Advanced Data Science (Supervised Learning) Handout May 16, 2024 Burayu , Ethiopia By: Tariku Endale (MSc) 5/26/24 Prepared by: Tariku Endale (MSc) 1

2. Machine Learning (ML) 5/25/24 Prepared by: Tariku Endale (MSc) 2 Definition by Tom Mitchell (1998): Machine Learning is the study of algorithms that improve their performance P at some task T with experience E. A well-defined learning task is given by <P,T,E>. Supervised learning is a paradigm in machine learning where input objects and a desired output value train a model. The training data is processed, building a function that maps new data on expected output values( https://en.wikipedia.org/wiki/Supervised_learning ). Unsupervised learning in artificial intelligence is  a type of machine learning that learns from data without human supervision . ( https://en.wikipedia.org/wiki/Unsupervised_learning ). Semi-supervised learning is a branch of machine learning that combines supervised and unsupervised learning by using both labeled and unlabeled data to train artificial intelligence (AI) models for classification and regression tasks. https://www.ibm.com/topics/semi-supervised-learning Reinforcement learning (RL) is  a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results . It mimics the trial-and-error learning process that humans use to achieve their goals. Https://aws.amazon.com

3. Supervised Learning (ML) 5/25/24 Prepared by: Tariku Endale (MSc) 3

4. Supervised Learning (Classification & Regression)) 5/25/24 Prepared by: Tariku Endale (MSc) 4 Type Output Type Problem Nature Classification predicts a categorical value used to separate data into classes Regression predicts a continuous value used to predict a value

5. Classification Code (OITI Lab Practice) Simple Classification Import Packages (Libraries) Define and Load Dataset Create a Model, Train and Evaluate it Get the Trained Data Compare the actual and Predicted Data and write a Conclusion

6. Regression Model (SL OITI Lab Practice) Import all Packages Load Dataset Read Dataframe Understand the column list

7. Regression Model (Cont’d) Describe the dataframe Check Null Values No column has “NULL” value

8. Regression Model (Cont’d) Drop Columns that you don’t need while P rocessing and start cleaning Check Null Values again No column has “NULL” value yet Find an Outliers and try to figureout using histogram so that A ny separated distributions are an outliers. E .g. at 0,0 we have an outlier and it should be either omitted or minimized Outlier with Scatter

9. Regression Model (Cont’d) Data Cleaning from null value and assign on different data frame variable Check Null Values again and Check whether all the rows have equal rows with data. A ll columns e qual rows. 768 Apply EDA to check whether the outliers are collected together and Describe it again

10. Regression Model (Cont’d) Remove or Isolate the outlier from the dataset we are expecting to use as train and test dataset. Import the required packages to start model building: H ere we have used: DecisionTreeRegression Linear Regression XGBRegression and we will select the highest score.

11. Regression Model (Cont’d) Install packages immediately if they are not accessible Split Train and Test Dataset

12. Regression Model (Cont’d) Score of our model with DecisionTreeRegressor is: 0.9998 Score of our model with Linear Regression is: 1.0000 Score of our model with XGBRegressor is: 0.9995

13. 5/25/24 Prepared by: Tariku Endale (MSc) 13 THANK YOU!