Starting Machine Learning with Python!

Machine Learning is not a new word in this world but its highly popular. So, why is it popular so much? One answer is it ML algorithm can make predictions and take decision on its own.

This article, will give you a basic idea of how you can write a ML code of your own using Python and its modules.

We are going to use sample music dataset and do some predications. Big Shoutout to Programming with Mosh. Go check it out his video on YouTube.

Pre-requisite's

1. music dataset Click to download
2. Jupyter notebook (Anaconda)
3. Basic Python knowledge

Basic rule to follow while doing ML analysis-

1. Import the Data
2. Check null data and Clean the Data
3. Try to convert into numeric data (good for ML)
4. Split the dataset into Training and Test
5. Create a Model
6. Make Predictions
7. Evaluate and Improve

Lets Begin our ML Journey!

Open your Anaconda application and Launch Jupyter Notebook

Launch a new Python notebook

Starting with Machine Learning with Python!

Import some python ML modules

import pandas as pd

Starting with Machine Learning with Python!

Use pandas to read the musical csv files in Jupyter Notebook

df = pd.read_csv(“musical file path”)

#Check null data is present

df.isnull().sum() #0 indicate there is no null values in dataset

Starting with Machine Learning with Python!

#Split the dataset for testing and training

x = df.drop(columns =[“genre”])
y = df[“genre”]

Starting with Machine Learning with Python!

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2)

#Okay, so we are good till here. We have check the null values and split the dataset for training and testing.

#Note- Larger the training dataset more accurate will be the predictions

#Choose a Machine Learning Algorithm

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()

model.fit(x_train,y_train)

#Now, to predict the values follow the below command.

predictions = model.predict([[21,1]])

#[21,1] represents Age of 21 with gender Male as 1 = Male and 0 = Female

predictions

Starting with Machine Learning with Python!

#To check the accuracy of your ML Algo -

from sklearn.metrics import accuracy_score

#calculating accuracy
#score = accuracy_score(y_test,predictions)
#score

#If score is = 1, model is 100% accurate, 0.75 means accuracy is 75% and so on.

#Actual script for reference

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
df = pd.read_csv(“\\Users\\MYPCDELLPC\\Desktop\\Python\\Project\\music\\music.csv”)
x= df.drop(columns = [“genre”])
y = df[“genre”]

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2)

model = DecisionTreeClassifier()
model.fit(x_train,y_tra#calculating accuracy
#score = accuracy_score(y_test,predictions)
#scorei
predictions = model.predict(x_test)
predictions

Starting with Machine Learning with Python!

#Note — replace x_test value with [Age,Gender]