# regression-problem KNN Algorithm in Machine Learning

K-Nearest Neighbor is a supervised learning algorithm that can be used to solve classification and regression problems. This algorithm learns without explicitly mapping input variables to the target variables. It is probably the first machine learning algorithm, and due to its simplicity, it is still accepted in solving many industrial problems. Linear Regression in Machine Learning

Linear Regression is a supervised machine learning algorithm used to solve regression problems. In this blog, we have discussed: 1) What is linear regression? 2) Various types 3) The loss function for linear regression 4) Ordinary Least Squares (OLS) method 5) Polynomial regression 6) Python implementation of linear regression. Evaluation Metrics for Regression Models

We evaluate the performance of our regression models in Machine learning using standard metrics. In this article, we will be discussing all the standard evaluation metrics namely MAE, MAPE, MSE, RMSE, R-Squared and Adjusted R-Squared, for regression models used to compare two different models on the same dataset. Wine Quality Prediction Using Machine Learning

Prediction of wine quality can be made easy with machine learning and data science techniques. In this blog, we have discussed: 1) Why do we need a machine learning model for wine quality assessment? 2) Factors that affect wine quality 3) Various ML models to predict wine quality 4) Implementation of predicting wine quality using k-NN regressor Loss and Cost Function in Machine Learning

Optimization of error function is the respiratory process for machine learning algorithms. But this error function varies for classification and regression problems. In this blog, we have discussed: 1) Definition and importance of loss function 2) Loss functions used for regression 3) Loss functions used for binary classification 4) Loss functions used for multiple classification, etc. Classification and Regression in Machine Learning

We categorize supervised learning into two different classes: Classification Problems and Regression Problems. Both classification and regression in machine learning deal with the problem of mapping a function from input to output. However, in classification problems, the output is a discrete (non-continuous) class label or categorical output, whereas, in regression problems, the output is continuous. Guide to Implement Machine Learning Model using Python

These days, the support of libraries and frameworks is easily accessible in machine learning. But in this article, we will implement a basic machine learning project without using frameworks like Scikit-learn, Keras, or Pytorch. We will use two python libraries: NumPy library for numerical operations and Matplotlib library to visualize graphs. Boston House Price Prediction Using Machine Learning

Machine learning techniques can accurately predict the house price by considering all important features. In this blog, we have discussed: 1) Why do we need machine learning models for house price prediction? 2) What are the factors that affect house prices? 3) Implementation of predicting house prices using Support Vector Regressor Life Expectancy Prediction Using Machine Learning

Using machine learning, we can predict the life expectancy of a person. In this blog, we will explore parameters affecting the lifespan of individuals living in different countries and learn how life span can be estimated with the help of machine learning models. We will also focus on the application of linear regression in predicting life expectancy. How Machine Learns in Machine Learning?

In Machine Learning, a machine learns by using algorithms and statistical models to identify patterns in data. Here the process of learning begins with feeding a large amount of training data to the algorithm. The algorithm then uses this data to make predictions or take actions based on the patterns it has identified. In other words, the algorithm constantly adjusts its parameters to minimize the difference between its predictions and actual outcomes.