Machine Learning by Python

Implementation of Machine Learning Algorithm

There are different types of algorithm used to implement the machine learning projects. Generally it is classified as:

1. supervised machine learning model
2. unsupervised machine learning model
3. semi -supervised machine learning model
4. deep learning model
5. reinforcement learning model and so on.

basically, to implement each model, the following steps can be needed:

steps:

1. Import Python Libraries
2. read and retrieve data set like CSV and excel file
3. visualize the data set to see their characteristics
4. find and remove the missing values
5. wrangling the data set
6. determine the independent and target variables
7. splitting the data set into train and test set
8. fitting the  train and test data into the model
9. predict the results
10. again visualize the training results and test results
11. evaluate the model
12.  by confusion matrix or accuracy score to check the accuracy of the model on the given data set.

Note:basic python libraries are: numpy, pandas, matplotlib, seaborn,scikit,tensorflow and so on.

example: KNN algorithm for SUV car purchase prediction based on age and estimated salary