LINEAR REGRESSION IN MACHINE LEARNING/PYTHON/ARTIFICIAL INTELLIGENCE

Linear Regression

• Use Regression Analysis
• Assumptions of Linear Regression
• Types of Linear Regression
• Evaluation Metrics for Linear Regression
• Advantages of Linear Regression
• Disadvantages of Linear Regression

This section focuses on linear regression, a fundamental component of supervised machine learning falling under the umbrella of ML regression models or analysis. Linear regression machine learning serves as a basic yet crucial algorithm within this domain. Most algorithms within supervised learning are geared towards handling continuous data, aiming to forecast outcomes in scenarios such as predicting stock prices or estimating car values. In linear regression analysis, we have one or more independent variables x that try to predict an outcome y.

Supervised learning encompasses two primary branches: classification and regression. Classification involves predicting the category or class of a dataset based on independent variables. It yields discrete outcomes, typically binary choices like 'yes' or 'no', '1' or '0', or specific categories such as dog breeds or car models.

In contrast, regression, another form of supervised learning, focuses on predicting continuous output variables based on independent input variables. This methodology is instrumental in forecasting scenarios like housing prices or stock values. Essentially, regression involves examining correlations between variables to forecast continuous outcomes. It is heavily utilized in applications like forecasting and time series modeling. Regression, put simply, is the act of reducing the vertical distance on a graph that displays the relationship between the target and predictor variables between data points and a line or curve that is plotted through them.

Overall, linear regression plays a pivotal role in predictive modeling, particularly in scenarios involving continuous data prediction. Its simplicity and effectiveness make it a staple in various predictive analytics tasks, offering valuable insights into variable relationships and enabling accurate forecasts. In this blog, we learn linear regression in Python regression analysis.

Things that are related to regression analysis

Dependent variable – the dependent variable is that variable which we want to predict or guess, it is also called the target variable.

Independent variables – these are called features which mainly affect the dependent variable. The model is trained over these independent variables they are also called predictors.

Outliers –is a very low or very high value that does not match with other values if we remove it from the dataset then it does not very much affect the result but if we do not remove it from the training or dataset then there is a chance that it might lead to decrease performance of the model so therefore, we need to remove it before training.

Multicollinearity – "Multicollinearity" describes a situation in which the variables are highly associated with one another. It is a bad thing for the dataset because it creates problems when we try to rank the most affecting variables.

Overfitting and underfitting also need to be a concern in machine learning regression or any other machine learning model.

Linear Regression

A supervised machine learning method called linear regression can be used to determine the relationship between one or more independent features and a dependent variable. When there is just one independent variable, univariate linear regression analysis is utilized; however, multivariate linear regression is employed when there are several independent factors.

Why should we use regression analysis?

As we know regression analysis mainly uses continuous variables and there are many situations in the real world where we need continuous results or predictions to make a good decision. For such problems, we need such type of technique that can predict continuous values and regression is good in this. There are some other reasons also available for regression analysis which are given below: -

It predicts the relationship between the dependent and the independent variable.

It can find how data points are related.

It can also help to find the predicted real/continuous values

By using linear regression we can detect the most important features,  also the least important features, by using this we can also check or know how one feature affects the other feature.

Real-world Example of Linear Regression

Let's suppose we have to bake cookies, but we don’t know how much dough each batch needs. To overcome this, we need data like tracked flour cups (independent variable) and cookies made by them (dependent variable).

Let's suppose we have data in the below table

Flour (cups)	Cookies
1.5	12
2	16
2.5	20

 From the above data, we need to use some sort of method to understand how many cups of flour we need to bake the desired number of cookies. In a problem like this linear regression works like magic or we can say in this case magic recipe. Now let’s create an equation that fits this data. The equation builds a straight line that shows the relationship. Let’s suppose the equation is solved by a computer for our help and it gives an equation:

Cookies = 8 * Flour + 4

The above formula reveals that 8 more cookies for every extra cup of flour. The number 4 is the starting number of cookies (with no flour!).

Now let’s suppose someone asked us about how many cookies we can bake using 3.5 cups of flour. Now we can confidently tell them the answer using the above equation:

Cookies = (8 * 3.5) + 4 = 28 + 4 = 32 cookies.

Linear regression gives us knowledge that if someone asks us how many cookies, we can make with certain cups of flour we can answer them using the above equation.

However, this will not work with all the cookie recipes, if we made chocolate chip cookies then there might be different formulas applied to it for better results.

The assumption for the linear regression model

linearity - it means that the independent and dependent variables are in some sort of relationship. which means that change in the independent variable(s) can affect the dependent variable linearly. it also means that we can draw a straight line through data points.

The idea behind the independence, which holds that features in a dataset are unrelated to one another, is crucial to linear regression. This suggests that one observation's dependent variable value is independent of another observation's dependent variable value. Dependencies between attributes or observations may jeopardize the linear model's correctness.

Homoscedasticity refers to the consistent variance of errors across all levels of the independent variable(s). This means that regardless of the values that independent variable(s) have, the variability of the errors remains uniform. Maintaining constant variance in the residuals is essential because any deviation from homoscedasticity can lead to inaccuracies in the model's predictions.

Normality – This means that the residuals should follow that bell-shaped curve; otherwise, the linear model will not work. 

Types of Linear Regression

There are many types of linear regression but two of them are the most prominent they are: -

Simple Linear Regression – It is the most fundamental and often used version of linear regression available. For this regression, all we need is one dependent variable and one independent variable. Its formula is written below:

y=β_0+β_1 X

Here:

y is the dependent variable

X is the independent variable

β_0 is the intercept

β_1 is the slope

Multiple linear regression – in this regression there is more than one independent variable available and one dependent variable available. The equation for this regression method is:

y=β_0+β_1 X+β_2 X+⋯.+β_n X

Here:

y is the dependent variable

X1, X2, …, Xp are the independent variables

β_0 is the intercept β_1  ,β_2,….,β_n are the slopes

Some other types are regression also available and they are: -

Polynomial regression improves upon linear regression by adding higher-order polynomial terms (i.e., independent variables) to the model. This allows for more flexible and complex relationships to be captured between the variables.

Ridge regression is a regularization technique for linear regression models that helps avoid overfitting; it performs best when there are several independent variables to take into account. Ridge regression drives the model toward solutions with lower coefficients by adding many terms to the least squares objective function, improving model stability and reducing the impact of multicollinearity.

Lasso regression is an additional regularization method that employs an L1 penalty term to zero out of the non-significant independent variable coefficients. This effectively performs feature selection, enabling the model to focus on the most relevant predictors and disregard irrelevant ones.

Elastic Net regression merges the regularization penalties of both ridge and lasso regression techniques. By striking a balance between their strengths, elastic net regression offers enhanced flexibility and robustness in handling multicollinearity and feature selection challenges commonly encountered in regression analysis.

Best fit line

Linear regression algorithms aim to determine the optimal equation for a best-fit line, this line can accurately predict values, and these values are based on independent variables. The major objective is to minimize the error margin between the predicted values of the model and the actual values that are acquired. The relationship between the actual and predicted variables in the dataset is displayed by the best-fit line, which is often a straight line.

Within this line, the slope plays a crucial role, indicating the rate of change of the predicted variable in response to a unit change in the actual variable(s). Understanding the kind and intensity of the link between the two classes may be gained by quantifying the impact of the independent factors on the outcome variable.

In the diagram provided, the variable Y shows the dependent variable, while X means the independent variable(s), also known as features or predictors of Y. Making predictions about the dependent variable Y based on the values of the independent variable or variables X is one of the primary goals of linear regression. This predictive relationship is represented by a straight line, hence the term "linear" regression.

Linear regression models use optimization techniques like gradient descent to lower the mean squared error (MSE) on a training dataset by iteratively changing the model's parameters. The goal is to reduce the values of parameters, often denoted as θ_1 (theta subscript 1) and θ_2(theta subscript 2), to maximize the model's performance and to get the best-fit line. Gradient descent facilitates this process by iteratively updating the parameters to gradually converge toward the optimal values that minimize the cost function, ultimately leading to the creation of an accurate linear regression model.

Evaluation Metrics for linear regression

The evaluation metrics are used to check how well our linear regression model performs. They help us to understand how well the model can detect or give the observed outputs.

The most common measurements are: -

Coefficient of determination (R-squared): It is static, primarily indicating the degree of variation that the generated model can account for or describe. It always lies between 0 and 1. If the model is good then it is much closer to 1 and vice versa. Its mathematical expression is as follows: -

R^2=1-(RSS/TSS)

Residual sum of Squares (RSS) – The total of all the residuals for every data point in the graph or information set is known as the residual sum of squares, or RSS. This metric may be used to calculate the deviation between the expected and observed outputs RSS =

Total Sum of Squares (TSS) – The total sum of squares (TSS) is the sum of all the data value deviations from the response variable's standard deviation. 

Root Mean Squared Error (RMSE): It is computed as the variance of the residuals squared. The degree to which the actual data point agrees with the expected values characterizes the absolute fit of our model to the data. It might also be expressed as

To produce an unbiased estimate, we can divide the sum of the squared residuals from the above equation instead of dividing the whole number of data points from the module that have the number of degrees of freedom. This is then referred to as the Residual Standard Error (RSE). It can be represented as 

The R-squared method is superior to the RSME. Since the Root Mean Squared Error's value depends on the units of the variables, it may change when the variables' units change.

Linear Regression Line

The linear regression line serves as a powerful tool for understanding the relationship among two variables. It typically shows the optimal line that best describes how the predicted variable (Y) adapts to fluctuations in an actual variable (X). The general pattern is captured in this line, which shows how changes in the independent variable affect the dependent variable. Important information about the direction and strength of the link between the variables may be obtained by studying the slope and intercept of the regression line. All things considered, the underlying dynamics between the two variables under examination are concisely and clearly shown by the linear regression line.

• When the predicted value (X) and the actual value (Y) correlate positively, then the linear regression line is positive. This means that as X's value increases, Y grows as well, and vice versa when X's drops. The positive linear regression line shows a good correlation between the variables visually by sloping upward from left to right.
• When an expected variable (Y) is negative and an actual variable (X) is positive, we say that the two variables are inversely related. This system is supposed to work as follows: as X increases, Y decreases, and vice versa. Negative linear regression lines slope negatively, slanting from left to right to show a negative correlation between the variables.

Advantages of Linear Regression

• Comparing linear regression to its more complex parents, it is a straightforward and widely used method in regression analysis. The coefficients in the linear regression model indicate how much a change in the dependent variable corresponds to a change of one unit in the independent variable and are highly interpretable and provide significant information about the correlations between variables.
• Scalability and computing efficiency of linear regression are two of its main advantages; these allow it to handle big datasets with ease. Real-time applications, where rapid model deployment is critical, are especially well-suited for its capacity to be quickly trained on large datasets.
• Furthermore, compared to other machine learning techniques, linear regression has resilience against outliers, i.e., these anomalies have a very little effect on the model's overall performance. This robustness helps linear regression to be stable and reliable under different conditions.
• Moreover, linear regression functions as a fundamental model and is frequently used as a benchmark to assess the effectiveness of increasingly sophisticated machine learning algorithms. Its accessibility and usefulness in a wide range of applications are further enhanced by its simplicity and well-established character, which make it a widely available choice across many machine learning libraries and software packages.

Disadvantages of Linear Regression

• Despite its simplicity and efficiency, linear regression exhibits certain limitations that can affect its performance in certain scenarios. One significant drawback is its reliance on assuming a linear relationship between independent and dependent variables. When such a linear relationship doesn't exist within the dataset, linear regression tends to perform poorly, leading to inaccurate predictions and inadequate model fitting.
• Another challenge is its sensitivity to multicollinearity, a situation where independent variables display a high correlation with each other. This can lead to instability in the model estimates and affect the interpretation of individual coefficients.
• Furthermore, linear regression assumes that the features are already formatted correctly for the model. Thus, to convert the features into a format that the model can use efficiently, feature engineering is frequently required, which complicates the modeling process.
• To make matters worse, before the model is run, linear regression assumes that the features are adequately constructed. Consequently, the modeling process becomes more complex as feature engineering is frequently required to convert the features into a format that the model can use efficiently.
• Furthermore, linear regression has limitations in providing explanatory relationships between variables, particularly in cases where the relationships are complex or nonlinear. In such instances, more advanced machine-learning techniques may be required to uncover deeper insights and nuances within the data.

Conclusion

Linear regression is a very basic and fundamental machine learning algorithm that is widely used for simple datasets and benchmarking for the other models’ performance. It is widely used because of its simplicity, interpretability, and efficiency. It very useful tool especially when it comes to understanding the relationship between variables and making predictions in a variety of applications. However, we must also know its limitations like it cannot work very well when there is no linear correlation between independent and dependent variable(s). It is sensitive to multicollinearity also.

Python code

Here is the linear regression model Python code: -

Machine Learning

• Definition
• History
• Types 
• Lifecycle
• Real-World Example 
• Main challenges

When we think about machine learning we may think of situations like those described in The Terminator movie in which machine starts to think on their own like humans and start a war against humanity, but things like this are still far away from reality because till now machines can only do one task at a time with precision even when they are trained on similar data or situation for a long time or with many data. Therefore, when we think about machine learning operations we think that machines can think or be able to learn by themselves which is not fully true. Machine learning is a subfield of artificial intelligence we can say that artificial is a superset then machine learning is only a part or subset of artificial intelligence but they are both often discussed together. And many times artificial intelligence and machine learning are used interchangeably It allows the systems or computers to automatically learn from already available data. To do this machine learning uses various algorithms and mathematical formulas. In this, we will look at what is machine learning and its types and learn about them, in other words, we learn machine learning basics or we can say it is a machine learning for beginners blog. 

First, we need to know what is machine learning.

AI and Machine learning is an area of study in computer science that allows computers the capability to which the computers can learn without being heavily programmed before doing some task. It tries to make computers act like or behave similarly to humans which is the ability to learn. It is very extensively used in today’s world. The term machine learning has been available since around 1959 when it was first used by Arthur Samuel. 

Machine learning is the science or we also can say the art of programming computers which makes them learn from data. The more general definition would be: that machine learning is the field of study that gives computers the ability to learn without being explicitly programmed (Arthur Samuel; 1959). For a more engineering-oriented definition, we can say that: - A computer program is said to learn from experience E concerning some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E (Tom Mitchell, 1977).

Machine Learning  Examples

Let’s look at a real-world example to better understand machine learning. There is a megacity, where there is a hospital in which a nurse Sarah is working, and she finds herself grappling with a common healthcare challenge: patient readmissions. Despite the hospital’s best efforts to provide quality care, some patients returned shortly after discharge, mainly due to complications that could have been prevented with timely interventions.

One Sarah attended a workshop on machine learning applications in healthcare. Excited by the potential of this technology, she started a journey to explore how it could help to reduce the readmissions at her hospital.

Sarah arrived at the hospital's data science department with a can-do attitude, determined to use machine learning algorithms to crunch reams of patient data. The data science department and Sarah examined factors like patient’s medical histories, treatment plans, demographics, and post-discharge follow-up procedures.

As they went deeper into the data, patterns started to emerge. The machine learning algorithms or machine learning system design identify several key predictors of readmission, including the patient’s age, previous hospitalizations, chronic conditions, and adherence to medication regimens. Moreover, they find a nuanced relationship between these variables, allowing for a more comprehensive understanding of readmission risk factors.

With these insights, Sarah and the data science team devised a proactive strategy to prevent readmissions. They implemented personalized care plans tailored to each patient’s unique needs, leveraging machine learning algorithms to predict and mitigate potential risks. For instance, high-risk patients received additional post-discharged support such as home visits from healthcare professionals, remote monitoring devices, and regular check-in calls.

How does machine learning work?

Mainly machine learning models try to predict the results, for doing prediction it first trains on previous data and learns from it then tries to predict the new outcome for unseen data. The more the amount of data the better the model predicts generally, that is the models’ accuracy increases. By hard coding the problem we might face many challenges like it is a very complex task and also if we need to update the code then we need to go through all the written code for updating it which is costly and time-consuming. In machine learning without writing the code, we just need to provide the data into the model’s algorithms, which automatically able to build the logic based on the data and also able to predict the output. With this process, we can easily update the model if we have new data when we need to update the model.

Why we should use machine learning?

We should use machine learning because it reduces the time and cost of tasks that previously took many lines of code and lots of effort. For example, now our email filters can filter ham and spam mail without our guidance which is possible because of machine learning if we try to do this task using the traditional coding approach it would take lots of time and hard coding even after that if the new type of spam arrived we need to update the code manually which is a very difficult task and therefore if we use machine learning we can do this much easily and also for a new type of spam we need to just train our model with a new type of data which have these type of spam mails. This is only one example of why we should use machine learning there are countless options or applications available where we use machine learning or we might be able to use them in the future. Below are some key points that show the importance of Machine learning: - 

1. Instant increment in the production of data.
2. Solving complex problems, which are difficult for humans like finding patterns in a dataset which have 1000 features.
3. Ability to decide on many sectors like finance, fraud, and anomaly detection.
4. Finding hidden patterns and extracting useful information from data.

Examples of machine learning applications

1. An example of anomaly detection is the examination of product images on a production line to automatically classify them and identify defective products. This process may also include image classification techniques to determine product type or potential defects.
2. Detecting tumors in brain scans. It is an example of semantic segmentation in which every pixel of an image (or perhaps a medical image) is classified. 
3. Automatically classify new articles. It is a natural language process (NLP), in which it can further be classified as a text classifier it can use recurrent neural networks or transformers. 
4. With the help of machine learning we can automatically detect offensive comments in forums which is achieved by text classification techniques, often using natural language processing (NLP) tools. In this process, we analyze comments to determine if they have offensive language or inappropriate content. 
5. Automatic summarization of long documents is a natural language processing (NLP) feature that involves compressing large texts into shorter, more concise versions. This process, called text summarization, aims to extract important information from a document while preserving its main points. 
6. Building a personal assistant or chatbot. It involves several NLP components, such as question-answering modules and natural language understanding (NLU).
7. Predicting our company's profit for the next year is also based on various performance metrics achieved by the ML model this type of ML is predictive analytics. In this we need to use multiple regression algorithms, such as linear regression and polynomial regression models, to analyze historical data and predict future revenue trends.
8. Developing an application that responds to voice commands requires the introduction of speech recognition technology. This process involves analyzing sound samples to interpret spoken commands. Due to the complexity and long duration of sound sequences, speech recognition is mostly based on deep learning models such as RNNs, CNNs, or transformers.
9. Detecting credit card fraud can also done via machine learning methods. It is an example of anomaly detection in which we try to detect uneven patterns in someone’s credit card transaction

Features of Machine Learning

• It can learn from past data and improve its performance accordingly.
• It can detect many or different patterns from data.
• It is data-driven technology.
• It is very much similar to data mining because it can deal with huge amounts of data.

Types of Machine Learning

There are many different classifications of machine learning but at the broader level we can classify them into four types:

·       Supervised learning

·       Unsupervised learning

·       Reinforcement learning

·       Semi-supervised learning

Supervised learning

In this, the training set we feed to the algorithm has solutions these solutions are also called labels. We can also say that in supervised learning, labeled datasets are given to the machine learning system for training, and after the training, the system can predict the results or outcomes. 

Gender	Age	Weight	Label
M	47	68	Sick
M	68	70	Sick
F	58	56	Healthy
M	49	67	Sick
F	32	60	Healthy
M	34	65	Healthy
M	21	74	healthy

In above table contains the patient’s information that also has a labels data set which is also called "label" in this and has two options Sick and Healthy.

At very basic supervised learning can be divided into 2 sub-classes called ‘classification’ and ‘regression’. One good example of classification is filtering spam and spam emails. In this supervised learning model, we first trained the model with a large number of example emails with their labels which makes the model able to learn how to differentiate new emails. Another common task is to predict the continuous values like the price of a car, house, or stock price prediction. Let’s look at the most common supervised learning algorithms: -

•         K-Nearest Neighbors
•         Linear Regression
•         Logistic Regression
•         Support Vector Machines
•         Decision Trees and Random Forests
•         Neural Networks and deep learning.

Unsupervised Learning

In this process, we have a dataset but don’t have the labels that is we have unlabeled data. In its algorithms, classification or categorization is not added. We can also say that in unsupervised learning the machine tries to learn from itself without the help of supervision. In this, the machine tries to find useful insights or patterns from the data. Many recommendation systems are used to recommend movies and songs, next purchases are based on unsupervised learning. Clustering is also a good example of unsupervised learning. In this, we can say that the machine uses information that does not have labels or classifiers to allow it to act on that guidance or categories. In this, the main goal of the machine is to group the unorganized information according to similarities, patterns, and differences without any previous training of data. 

Gender	Age	Weight
M	47	68
M	68	70
F	58	56
M	49	67
F	32	60
M	34	65
M	21	74

In the above table, we have a dataset but don’t have any labels that help us in supervised learning. Now in the above dataset, we can try to find the patterns or clusters among the data. One such cluster can be dividing or sorting the data according to Gender or also can be done using sorting the data into different age groups. Here are the most common unsupervised learning and their algorithms: -

·       Clustering

o   K Means

o   DBSCAN

o   Hierarchical Cluster Analysis (HCA)

·       Anomaly detection and novelty detection

o   One-class SVM

o   Isolation Forest

·       Visualization and dimensionality reduction

o   Principal Component Analysis (PCA)

o   Kernel PCA

o   Locally Linear Embedding (LLE)

o   t-Distributed Stochastic NeighborEmbedding (t-SNE)

·       Association rule learning

o   Apriori

o   Eclat

Reinforcement Learning

It is very different from both supervised learning and unsupervised learning. In this learning system, we have an agent or bot that observes the environment and selects an action or takes an action, this action then either gets a reward or penalty (penalty meaning negative reward) the model must learn by itself which is the best strategy for it and maximizes the reward. For example – like when we train an animal, we reward them for doing a good thing or following our orders and punish or do not reward them when they don’t follow the order this, we train the animal. We can imagine the same thing for reinforcement learning. When the bot or agent behaves well, it gets rewarded; when it behaves poorly, it gets penalized. One excellent example of reinforcement learning is the AlphaGo software developed by DeepMind. It is a Go player that made waves in newspapers in 2017 when it defected the world champion. Through the analysis and self-play of games millions of times, it learns its winning policies. It is different from supervised learning in such a way that supervised learning is trained using the labels or answers which is already available in training data; in contrast, in reinforcement learning, the model does not have any answers. The model agent chooses how to proceed with the assigned task. It employs what is known as the trial-and-error approach. Algorithms that use reinforcement learning are capable of learning from results and choosing the best course of action. Every time it takes an action, it evaluates it and gets input from the algorithm to help it decide if the decision is right or wrong. 

Semi-Supervised Learning

Semi-supervised learning is a type of machine learning that falls between supervised and unsupervised learning methods and offers a solution for situations where there is few labeled data compared to unlabeled data. This approach merges a small set of labeled data with a larger set of unlabeled data during its training process. A notable example is Google Photos, where the system independently detects faces from uploaded images, a form of unsupervised learning. However, it asks users to tag these faces to improve accuracy and user experience, thus adding supervised learning elements. This combination allows us to more efficiently organize photos and search based on identified individuals.

Most algorithms that fall within the category of semi-supervised learning incorporate aspects of both unsupervised and supervised learning. Deep belief networks (DBNs) are composed of layers of unsupervised restricted Boltzmann machines (RBMs). Before being refined as a whole using supervised learning techniques, RBMs go through several rounds of unsupervised training.

There are also other types of machine learning types available like instance-based, model-based, batch learning, online learning, etc.

Machine Learning Lifecycle

The Machine learning lifecycle involves a series of steps which are: -

Understanding the problem – it is the first step of the machine-learning process. In this process, we first try to understand the business problem and define its objective which is what the model must do.

Data Collection – Once the problem statement is established, the next step involves gathering the relevant information needed to build the model. These data sets can be obtained from a variety of channels, including databases, sensors, application interfaces (APIs), or web capture technologies.

Data Preparation – only collecting data will not help to make the machine learning model work properly that is when our data is collected it is necessary to check that is, is the data is proper and then convert it into the desired format so we can use it in machine learning model and the model will able to find the hidden patterns. This process has its small sub-processes these are: -

• Data cleaning
• Data Transformation
• Explanatory data analysis and feature engineering
• Split the dataset for training and testing

Model Selection – Selecting the optimal machine-learning algorithm to address the problem comes next after preprocessing the data. Making this decision requires knowledge of the advantages and disadvantages of various algorithms. Many times, it is required to apply many models, evaluate their results, and then choose the best algorithm according to the particular needs of the our job.

Model building and training – when we select the proper algorithm for our model we need to build the model. It can be done using below three methods: -

1. In the traditional machine learning building approach, we just need to fine-tune some hyperparameter tunings.
2. In the field of deep learning, the first step involves sketching the architecture of each layer. This includes defining details such as input and output dimensions, number of nodes in each layer, choice of loss function, optimization of gradient descent, and other related parameters needed to build the neural network.
3. At last, after the model is trained using the preprocessed dataset.

Model Evaluation – Once the training phase is completed, assessing the model's efficacy involves evaluating its performance on the test dataset. This assessment aids in gauging accuracy and effectiveness through various methodologies such as generating a classification report, calculating metrics like F1 score, precision, and recall, and examining performance indicators like the ROC Curve, Mean Square Error, and Absolute Error.

Model tuning – after the training and testing are done, we have the results of the model on the unseen dataset, now we might need to tune or optimize the algorithms’ hyperparameter so we can get the optimized result or performance.

Deployment – Once the model is optimized and its performance meets our expectations, we deploy it to a production environment where it can make predictions based on fresh, never-before-seen data. In this implementation phase, the model is integrated into the existing software infrastructure, or a new system is developed that is specifically adapted to implement the model.

Monitoring and Maintenance – Following deployment, it's critical to track the model's performance over time, keep an eye on the dataset in the production environment, and do any necessary maintenance. In this process, the model is updated if new data becomes available, retrained as necessary, and data drift is monitored for.

Main Challenges of Machine Learning

In the realm of machine learning, two critical factors often lead to suboptimal outcomes: flawed algorithms and inadequate data. Let's delve into each aspect separately.

Insufficient Training Data:

To effectively train a machine learning model, a substantial amount of data is essential. Simple tasks, such as distinguishing between an apple and a ball, may require thousands of examples, while more complex endeavors like speech recognition might demand millions. Unfortunately, numerous fields suffer from limited or unexplored data, hindering the development of robust machine-learning models.

Nonrepresentative Training Data:

The quality of training data is paramount for successful machine learning. It's imperative that the data provided for training accurately represents the scenarios the model will encounter in real-world applications. Failure to ensure representativeness can result in a model that struggles to generalize effectively, leading to poor predictions or outputs.

Poor-Quality Data: 

A model cannot identify important patterns if it is trained on data that is full of mistakes, outliers, or noise. To improve model performance, time and effort must thus be spent cleaning and improving training data.

Irrelevant Features: 

Feeding a model with relevant features is as important as saying "Garbage in, garbage out". Selected, extracted, and created features that allow the model to successfully understand the underlying patterns in the data are mostly dependent on feature engineering.

Overfitting: 

When a model too closely fits the training data—including noise and outliers—it is said to be overfit. It may do well in training, but in real-world situations, it performs worse since it finds it difficult to generalize to unknown input.

Underfitting: 

It occurs when a model does not identify important patterns in the training data, which leads to worse performance during training and when presented with new data..

Additional Limitations:

Machine learning thrives on data diversity and heterogeneity. Algorithms struggle to derive meaningful insights without a sufficient range of variations within the data. For efficient model training, sufficient sample sizes are usually at least 20 observations per group. Moreover, the availability of training data determines the usefulness of machine learning; in the absence of it, the model is idle. Machine learning projects are often hampered by the dearth or lack of variety in data.

In conclusion, successful implementation of machine learning in many fields depends critically on resolving problems with algorithmic defects and data constraints.