Machine Learning

 

Steps:

1.       Import the data

2.       Clean the data

3.       Split the data into training/test sets

4.       Create a model

5.       Train the model

6.       Make predictions

7.       Evaluate and improve

Libraries and tools:

-numpy

-pandas

-MatPlotLib

-Scikit-learn

 

Install anaconda and open terminal and execute the command  $jupyter notebook

It will take you to the notebook server

Importing a dataset:

-Download dataset from kaggle.com and save it to dextop

-Execute the commands on notebook server

 >import pandas as pd

>df = pd.read_csv(‘vgsales.csv’)

>df

> df.shape

> df.describe()

Cleaning/Preparing data and prediction:

Persisting Models:

In the dextop a file ‘music-recommender.joblib’ will be saved

After train the model we call the output:

 

Visualizing Decision Tree:

It will generate a dot file named ‘music_recommender.dot’ on dextop. Drag and drop the file in VSCode to visualize the graph.

After installing open the file