What more machine learning algorithms can be applied in temperature prediction models apart from linear and polynomial regression for better accuracy and results ?

- Decision Trees

2.Random forest

3.Support Vector Machines (SVMs) - Neural network
- Time series Analysis
- Gradient Boosting

1.Multilabel classificattion

2. Logistic Regression and clustering

3. KNN and Decision Tree

4. XG Boot

5.Neural Network

6.Random Forest

**Regression and classification algorithms** are the most popular options for predicting values, identifying similarities, and discovering unusual data patterns.

but these need some more independent variables . so how to proceed

There are several other machine learning algorithms that can be used for temperature prediction models. Some of these algorithms are:

- Support Vector Machines (SVM): SVM is a popular algorithm used for classification and regression problems. It can handle both linear and non-linear data and can be used to predict temperature based on historical data.
- Random Forest: Random Forest is an ensemble algorithm that uses multiple decision trees to make predictions. It can handle both numerical and categorical data and can be used to predict temperature based on various factors such as humidity, wind speed, etc.
- Gradient Boosting: Gradient Boosting is another ensemble algorithm that uses multiple weak learners (such as decision trees) to make predictions. It can be used for regression problems and can be used to predict temperature based on various factors such as time of day, season, etc.
- Neural Networks: Neural Networks are a popular class of algorithms used for both classification and regression problems. They are particularly useful when dealing with complex data and can be used to predict temperature based on various factors such as geographical location, altitude, etc.
- Long Short-Term Memory (LSTM): LSTMs are a type of neural network that are particularly useful for time-series data. They can be used to predict temperature based on historical data, such as temperature readings from the past week, month, or year.
- K-Nearest Neighbors (KNN): KNN is a simple algorithm that is often used for regression problems. It works by finding the k nearest data points to a given input and then using their values to make a prediction. KNN can be used to predict temperature based on historical data from nearby weather stations or sensors.

Overall, the choice of algorithm will depend on the specific requirements of the problem at hand, such as the amount and quality of available data, the complexity of the relationships between the variables, and the desired level of accuracy.

- neural network
- decision tree
- support vector machine

There are various machine learning algorithms that can be used for temperature prediction models apart from linear and polynomial regression. Some of the commonly used algorithms are:

- Decision Trees: Decision trees are used for both classification and regression problems. They can be used for temperature prediction models by identifying the key variables that influence temperature and creating a decision tree to predict future temperatures.
- Random Forests: Random forests are an ensemble learning method that combines multiple decision trees to improve prediction accuracy. They can be used for temperature prediction models by building multiple decision trees with different subsets of data and then combining the results to get more accurate predictions.
- Support Vector Machines (SVMs): SVMs are used for both linear and nonlinear regression problems. They can be used for temperature prediction models by identifying the key variables that influence temperature and creating a nonlinear model to predict future temperatures.
- Neural Networks: Neural networks are a popular machine learning algorithm for regression problems. They can be used for temperature prediction models by training the network on historical temperature data and then using it to make predictions about future temperatures.
- Time Series Analysis: Time series analysis is a statistical method for analyzing time series data to extract meaningful insights and make predictions. It can be used for temperature prediction models by identifying trends and patterns in historical temperature data and using them to make predictions about future temperatures.
- K-Nearest Neighbors (KNN): KNN is a non-parametric machine learning algorithm used for both classification and regression problems. It can be used for temperature prediction models by identifying the k-nearest data points and using their average value to predict future temperatures.

These are just a few of the machine learning algorithms that can be used for temperature prediction models. The choice of algorithm depends on the nature of the data, the problem at hand, and the specific goals of the prediction model.

SVM

Decision Trees

Random Forest