2024-14-04

Adaboost-Step-by-Step-Guide-with-Mathematical-Insights.

machine learning

20 min read

2024-04-04

Boosting

Boosting is an ensemble of weak machine-learning models (Decision Trees) sequentially connected to obtain a single strong predictive model.

machine learning

15 min read

2024-01-20

KNN on Telecustomers categorization

hands on machine learning

25 min read

2024-01-17

Naive Bayes Classifier for Mushroom Edibility Prediction

Mushroom edibility classifier using Naive Bayes

hands on machine learning

25 min read

2024-01-16

Decision Tree VS Random Forest on Customer Churn dataset

Decision Tree VS Random Forest on Customer Churn

hands on machine learning

25 min read

2024-01-15

Decision Tree for Tennis dataset

hands on machine learning

25 min read

2024-01-14

Heart Attack Prediction using Logistic Regression

Heart Attack Prediction using Logistic Regression in Python

hands on machine learning

25 min read

2024-01-02

Housing Price Prediction using Linear Regression

Housing Price Prediction using Python

hands on machine learning

25 min read

2023-07-6

Drawing a Linear Regression Line using Python

Linear Regression

hands on machine learning

25 min read

2023-06-25

K-Nearest Neighbors

Machine learning can be a complex field, but there are some algorithms that offer a simple and straightforward approach to problem-solving. One such algorithm is K-Nearest Neighbors (KNN). In this blog post, we will decode the concept of KNN in simple terms, understanding how it works and its practical applications.

hands on machine learning

machine learning

10 min read

2023-06-11

Random Forest and Bagging

Random Forest (RF) is a popular supervised machine learning algorithm used for both classification and regression problems. The term ‘Forest’ in Random Forest refers to multiple decision trees. It is based on the concept of ensemble learning that combines multiple base models (decision trees for RF) to improve the model's performance. Random Forest contains a number of decision trees on various subsets of the given dataset and takes the majority vote to improve the predictive accuracy of that dataset.

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machine learning

10 min read

2023-06-11

Naive Bayes Classifier

Naive Bayes classifier is a probabilistic supervised machine learning algorithm used in classification problems. The term ‘Bayes’ refers to the Bayes theorem and ‘Naive’ refers to the assumption of independence among the features(columns), which means that changing a feature's values doesn’t change the value of another feature. In real-world scenarios, it is uncommon where the features are independent. But, then why this algorithm is so popular and famous?

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machine learning

10 min read

2023-06-11

Entropy, Gini Impurity and Information Gain

Let us understand the definition of entropy and Gini impurity and see how the purity of split is calculated mathematically. We will also look at how a particular feature is selected for splitting by using information gain mathematically.

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machine learning

10 min read

2023-06-11

Decision Tree

Decision trees in a way act like human brains while making a decision. The logic in decision tree is simple to understand as it is just a structured tree with a bunch of if-else statements. Due to the straightforward implementation and interpretation, decision trees are an excellent choice for a newbie in the field of machine learning.

hands on machine learning

machine learning

10 min read

2023-06-11

Confusion Matrix & Evaluation Metrices

In this blog, we will discuss some of the commonly used performance metrics for logistic regression. Once we have trained a logistic regression model on the training dataset, the next step is to evaluate its performance on the testing dataset. There are several performance metrics that can be used to evaluate the performance of the model. Let us look at each one of them and understand the concepts associated with it.

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machine learning

10 min read

2023-06-10

Sigmoid, Cost Function and Maximum Likelihood

What exactly is a sigmoid function? How does it convert the continuous linear regression line to S curve ranging from 0-1?

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machine learning

10 min read

2023-05-18

Regression

Regression is a statistical tool for finding the relationship between the continuous target variable and one or more input features.

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machine learning

20 min read

2023-05-18

Logistic Regression

Logistic Regression is a popular machine learning algorithm which comes under the wing of ‘Supervised Learning’. It is one of the predictive modelling techniques used whenever the dependent variable(Y) is categorical in nature like whether the person is diabetic or non-diabetic, whether the mail is spam or not spam etc.

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machine learning

10 min read

2023-05-18

Linear Regression

Linear Regression is used for modelling a linear relationship between the dependent variable(Y) and one or more independent variables(X). Linear regression is a powerful tool which helps in making predictions for continuous variables like housing prices, sales of a product, weather forecasting etc.

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machine learning

25 min read

2023-05-13

Types of Machine Learning

Machine learning uses different simple-to-complex algorithms to work for the data. These algorithms are first trained using train dataset, and a model is built which then provides you with the future predictions, outcomes, idea to perform a specific task.

machine learning

10 min read

2023-05-13

Introduction to AI/ML/DL

Lately, you must have heard terms like Deep Learning, Machine Learning, and Artificial Intelligence frequently in most of the ads, some online courses and mostly from people around you.

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machine learning

10 min read

2023-07-6

Drawing a Linear Regression Line using Python

hands on machine learning

25 min read

Prerequisites:Python IDE-Jupite Notebook,VS Code,Pycharm etc.

Drawing a Linear Regression Line using Python

Importing Essential Libraries:

#Importing numpy for computations
#Importing matplotlib for visual representation

import numpy as np
import matplotlib.pyplot as plt

Function for estimating coefficients b0,b1 in Y=b1*X+b0

def estimate_coefficients(x,y):
    n = np.size(x)
    mean_x, mean_y = np.mean(x), np.mean(y)
    SS_xy = np.sum(y*x - n*mean_y*mean_x)
    SS_xx = np.sum(x*x -n*mean_x*mean_x)
    b1 = SS_xy /SS_xx
    b0 = mean_y - b1*mean_x
    return(b0,b1)

Function for plotting the Linear Regression Line

def plot_regression_line(x,y,b):
    plt.scatter(x,y,color = 'm',marker="o")
    y_pred = b[0]+b[1]*x
    plt.plot(x,y_pred,color = 'g')
    plt.xlabel('Size')
    plt.ylabel('Cost')
    plt.show()

Creating Random Data

x = np.array([1,2,3,4,5,6,7,8,9,10])
y = np.array([300,350,500,700,800,850,900,900,1000,1200])

Calling the coefficients function & displaying the output

b = estimate_coefficients(x,y)
print("Estimated Coefficients: \nb0 = {} \nb1 = {}".format(b[0],b[1]))

Output

Estimated Coefficients:
b0 = -7.5
b1 = 137.72727272727272

Plotting the linear regresion line for the data

plot_regression_line(x,y,b)

Linear-Regression-Line-Using-Python