Embedded Files
Steps in Machine Learning
Import suitable libraries & Datasets
Import suitable libraries & Datasets
import seaborn as sns
import pandas as pd
iris = sns.load_dataset('iris')
Data Preprocessing
Data Preprocessing
View and examine the dataset (using .head / .describe /.info)
Check for missing values
Eliminate unnecessary rows/column
Find for x and y values
Split data into training and testing
Split data into training and testing
from sklearn.model_selection import train_test_split
Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,random_state=1)
Start model training
Start model training
Choose suitable model
Choose suitable model
Import model, fit the model to the training data.
How many models we can choose?
Train the model
Train the model
Apply the model to the testing data to predict
Evaluate the model
Evaluate the model
To test the accuracy, obtain classification report & confusion matrix
Parameter Tuning
Parameter Tuning
Check the accuracy/change the models to optimize it! Then finally can use it for predictions! A Good model produced!
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