machine_learning
#Intro
Examples
- CO2 emissions (Regression)
- Is this concor? (Classification)
- Bank loans (Clustering)
- Anomaly detections (cridit card fraud)
- Netflix recommendations (cridit card fraud)
#Supervised and Unsupervised
Regression
Regression predict continues values
Simple vs multiple
Regression samples
- Household Price
- Customer Satisfaction
- Sales forecast
- Employment income
Simple Linear Regression
Simple in regression means just using one independet variable to predict (just using x1 or xn)
MSE (mean square error):
hat Y is the prediction y(estimate) and (Y-hatY) is mistake of prediction and to bold the mistake we should square it and finaly sum all data:
Benfs(Proc)
Model Evaluation
Training accuracy VS Out of sample accuracy
Train/Test split
Evaluation Matrix
Mean absolute error(MAE):
And other evaluation algorithems
To learninig panda and numpy please run codes by jupyter by
python3.8 -m jupyter
python3.8 -m notebook
python -m notebook
Multiple Linear Regression
