In This Blog , I will write About How to Learn and Master Machine Learning , It will takes from me N-1 Days , and it will be divided into 6 Parts (Data Preprocessing , Classification , Regression , Clustering , Artificial Neural networks , Reinforcement learning )
Day 2 : Classification
In This part I will explain for you what is Classification in Machine learning ?
What is Classification ?
Classification is one of the most widely used techniques in machine learning, with a broad array of applications, including sentiment analysis, ad targeting, Spam detection, risk assessment, medical diagnosis and image classification.
The core goal of classification is to predict a category or class y from some inputs x.
Let’s start Building our Model
Our Problem is To Predict if the Customer will purchase our Product or Not ?
We Have the dataset of the customers with all the necessary data (Age , Estimated_Salary , Gender ,User_Id, and Purchased or not )
Our Mission is Train Machine learning Model with Logistic_Regression to predict the action of the customers (To Buy or not To Buy .. )
What is Logistic Regression ?
. A Logistic regression maps a continuous x to a “binary” y
. We can use Logistic regression to make “category” or “true/false” decisions from data.
Example of application :
Logistic Regression: Given a set of medical inputs, does a patient have cancer? (Yes/No)
Don’t’ Worry if you don’t like Maths , you are not supposed to know the Mathematics equations
All What we have to do it to know how to implement the Logistic_Regression Classifier with scikit-learn
Let’ start Coding
Import the necessary Libraries
# Imporitng the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
Load the Dataset (Social_Network_Ads.csv)
dataset = pd.read_csv('Social_Network_Ads.csv');
X = dataset.iloc[:,[2,3]].values
Y = dataset.iloc[:,4].values
We need to Provide 2 Features for our Model : Age && Estimated_Salary
# Splitting the Data into Training Set and Testing Setfrom sklearn.model_selection import train_test_split
X_Train ,X_Test , Y_Train,Y_Test= train_test_split(X,Y, test_size=0.25,random_state=0)
Now let’s Scale our Data
# Scaling the data *************from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_Train)
Now let’s Prepare our Classifier :
and Train it with Our Data (X_Train Y_Train )
from sklearn import linear_model
#Prepare our Classifier*********
classifier = linear_model.LogisticRegression(C=1e5)#Train our classifier
Now let’s Make Our Prediction with Classifier.predict
y_predicted = classifier.predict(X_Test)
I tried in This Blog to Explain for You How To make a Machine Learning Model , Train it with Data , and make your Prediction
I have used Logistic_Regression as a Classifier , there is other Classifier we can use them for the classification Task like (SVM, Naive-Bayes , Decision_Tree , K-Nearest-Neighbor and Random Forest )
Making mistakes is a lot better than not doing anything
You Can fin the Full code source Here :
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