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Regression problem optimization using ML or DL

I have some data (data from sensors and etc.) from an energy system. consider the x-axis is temperature and the y-axis is energy consumption. Suppose we just have data and we don't have access to the mathematical formulation of the problem:
energy consumption vs temperature curve
In the above figure, it is absolutely obvious that the optimum point is 20. I want to predict the optimum point using ML or DL models. Based on the courses that I have taken I know that it's a regression supervised learning problem, however, I don't know how can I do optimization on this kind of problem.
I don't want you to write a code for this problem. I just want you to give me some hints and instructions about doing this optimization problem.
Also if you recommend any references or courses, I will welcome them to learn how to predict the optimum point of a regression supervised learning problem without knowing the mathematical formulation of the problem.

There are lots of ways that you can try when it comes to optimizing your model, for example, fine tuning your model. What you can do with fine tuning is to try different options that a model consists of and find the smallest errors or higher accuracy based on the actual and predicted data.
Using DecisionTreeRegressor model, you can try to use different split criterion, limit the minimum number of split & depth to see which give you the best predicted scores/errors. For neural network model, using keras, you can try different optimizers, try different loss functions, tune your parameters etc. and try all out as a combination of model.
As for resources, you can go Google, Youtube, and other platform to use keywords such as "fine tuning DNN model" and a lot of resources will pop up for your reference. The bottom line is that you will need to try out different models and fine tune your model until when you are satisfied with your results. The results will be based on your judgement and there is no right or wrong answers (i.e., errors are always there), it just completely up to you on how would you like to achieve your solution with handful of ML and DL models that you got. My advice to you is to spend more time on getting your hands dirty. It will be worth it in the long run. HFGL!

For a binary classification model based on multiple continuous variable what model should be used?

I am working on a waste water data. The data is collected every 5 min. This is the sample data.
The threshold of the individual parameters is provided. My question is what kind of models should I go for to classify it as usable or not useable and also output the anomaly because of which it is unusable (if possible since it is a combination of the variables). The column for yes/no is yet to be and will be provided to me.
The other question I have is how do I keep it running since the data is collected every 5 minutes?

Your data and use case seem fit for a decision tree classifier. Decision trees are easy to train and interpret (which is one of your requirements, since you want to know why a given sample was classified as usable or not usable), do not require large amounts of labeled data, can be trained and used for prediction on most haedware, and are well suited for structured data with no missing values and low dimensionality. They also work well without normalizing your variables.
Scikit learn is super mature and easy to use, so you should be able to get something working without too much trouble.
As regards time, I'm not sure how you or your employee will be taking samples, so I don't know. If you will be getting and reading samples at that rate, using your model to label data should not be a problem, but I'm not sure if I understood your situation.
Note stackoverflow is aimed towards questions of the form "here's my code, how do I fix this?", and not so much towards general questions such as this. There are other stackexhange sites specially dedicated to statistics and data science. If you don't find here what you need, maybe you can try those other sites!

How do I make a good feature using machine learning on a timeseries forecast that has only traffic volume as an input?

So I have a time series that only has traffic volume. I've done FB prophet and neural prophet. They work okay, but I would like to do something using machine learning. So far I have the problem of trying to make my features. Using the classical dayofyear, month, etc does not give me good results. I have tried using shift where I get the average, minimum, and max of the two previous days. However that would work, but my problem is when I try to predict days in advance the feature doesn't really work for that since I cant get the average of that day. My main concern is trying to find a good feature that my predicting future dataframe also has. A picture of my data is included. Does anyone know how I would do this?

First of all, you have to clarify some definitions. FBProphet works on a mechanism that is the same as any machine learning algorithm that is, fitting the model and then predicting the output. Being an additive regression model with a piecewise linear or logistic growth curve trend, it can be considered as a Machine Learning method that allows us to predict a continuous outcome variable.
Secondly, I think you missed the most important word that your question was about - namely: Feature engineering.
Feature Engineering includes :
Process of using domain knowledge to Extract features (characteristics, properties, attributes) from raw data.
Process of transforming raw data into features that better represent the underlying problem to the predictive models, etc..
But it's very unlikely to use Machine Learning to do Feature engineering. You do Feature engineering in order to improve your Machine Learning model. Many techniques such as imputation, handling outliers, binning, log transform, one-hot encoding, grouping operations, feature split, scaling are hybrid methods using a statistical approach and/or domain knowledge.
Regarding your data, bearing in my mind that the seasonality is already handled by FBProphet, I am not confident if feature engineering transformations such as adding the day of the week, adding holidays periods, etc... could really help improve performance...
To conclude, it is not possible to create ex-nihilo new features that would outperform your model. Whether you process/transform your data or add external domain-knowledge dataset

Best model to predict failure using time series from sensors

I'm working with a company on a project to develop ML models for predictive maintenance. The data we have is a collection of log files. In each log file we have time series from sensors (Temperature, Pressure, MototSpeed,...) and a variable in which we record the faults occurred. The aim here is to build a model that will use the log files as its input (the time series) and to predict whether there will be a failure or not. For this I have some questions:
1) What is the best model capable of doing this?
2) What is the solution to deal with imbalanced data? In fact, for some kind of failures we don't have enough data.
I tried to construct an RNN classifier using LSTM after transforming the time series to sub time series of a fixed length. The targets were 1 if there was a fault and 0 if not. The number of ones compared to the number of zeros is negligible. As a result, the model always predicted 0. What is the solution?

Mohamed, for this problem you could actually start with traditional ML models (random forest, lightGBM, or anything of this nature). I recommend you focus on your features. For example you mentioned Pressure, MototSpeed. Look at some window of time going back. Calculate moving averages, min/max values in that same window, st.dev. To tackle this problem you will need to have a set of healthy features. Take a look at featuretools package. You can either use it or get some ideas what features can be created using time series data. Back to your questions.
1) What is the best model capable of doing this? Traditional ML methods as mentioned above. You could also use deep learning models, but I would first start with easy models. Also if you do not have a lot of data I probably would not touch RNN models.
2) What is the solution to deal with imbalanced data? You may want to oversample or undersample your data. For oversampling look at the SMOTE package.
Good luck

Which data to plot to know what model suits best for the problem?

I'm sorry, i know that this is a very basic question but since i'm still a beginner in machine learning, determining what model suits best for my problem is still confusing to me, lately i used linear regression model (causing the r2_score is so low) and a user mentioned i could use certain model according to the curve of the plot of my data and when i see another coder use random forest regressor (causing the r2_score 30% better than the linear regression model) and i do not know how the heck he/she knows better model since he/she doesn't mention about it. I mean in most sites that i read, they shoved the data to some models that they think would suit best for the problem (example: for regression problem, the models could be using linear regression or random forest regressor) but in some sites and some people said firstly we need to plot the data so we can predict what exact one of the models that suit the best. I really don't know which part of the data should i plot? I thought using seaborn pairplot would give me insight of the shape of the curve but i doubt that it is the right way, what should i actually plot? only the label itself or the features itself or both? and how can i get the insight of the curve to know the possible best model after that?

This question is too general, but I will try to give an overview of how to choose the model. First of all you should that there is no general rule to choose the family of models to use, it is more a choosen by experiminting different model and looking to which one gives better results. You should also now that in general you have multi-dimensional features, thus plotting the data will not give you a full insight of the dependance of your features with the target, however to check if you want to fit a linear model or not, you can start plotting the target vs each dimension of the input, and look if there is some kind of linear relation. However I would recommand that you to fit a linear model, and check if if this is relvant from a statistical point of view (student test, smirnov test, check the residuals...). Note that in real life applications, it is not likeley that linear regression will be the best model, unless you do a lot of featue engineering. So I would recommand you to use more advanced methods (RandomForests, XGboost...)

If you are using off-the-shelf packages like sklearn, then many simple models like SVM, RF, etc, are just one-liners, so in practice, we usually try several such models at the same time.