Data Science and Machine Learning: Key Concepts and Applications Case Study

Exploring Data Science and Machine Learning Techniques

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Introduction

In general, automated learning operations are nothing more than human activities of the same kind. Similar to how humans usually gain knowledge from their experiences and mistakes, a machine learning model is developed after it has been trained using a particular set of data. These platforms collect consumer usage information and give customers correct data and demands based on their prior behavior. Thus, these factors improve client convenience and give them more precise services.

Task 1:

(a)

Data science is nothing but a lifestyle which allows an individual or an analyst to use the data or various statistics in order to get inside details or critical information from it. Data science is a combination of statistics, machine learning, calculations and several analysis related activities which allow us to perform and gather proper insights and solutions for a particular subject matter.

The data science in terms of the healthcare industry has been used for purposes such as analyzing the medical images and data of the patients or certain medical conditions where the prediction of the disease or the issue in patients is pre-gathered or identified.

In the field of manufacturing there are several aspects their several manufacturing organizations are using in order to optimize their performance in and applying model solutions into their functionalities. It has been used for prize optimization activity, fault prevention, real time data performance and many more quality improvement related activities (Halbouni et al. 2022).

Nowadays even the educational industries are using the potential model for the prediction of the marks as well as the activities of the students for their personal growth and enhancement. Such types of activities are also helping students to identify and predict their qualification related data which somehow directly and directly contribute towards the education.

In the finance department there are several activities which are related to uncertainty and frauds, so this data science helps in managing the risk, detecting the frauds, consumer analytics, automation activity for pricing and many more.

(b)

Machine learning activities are nothing but the same type of activity which is done by humans in their general terms. Humans generally learn from the experience or the mistakes which have been made by them, similarly in machine learning also a model is created where it is been trained with certain types of data and as per the training and evaluation done into that model the prediction of the future is done. Machine learning is a modern technique of using computers and all the resources where several statistics, calculation, patterns and analysis is done to perform the ideal prediction.

For the purpose of customer experience machine learning has been very efficient as it helps the customers in every possible way. Several popular business models such as Netflix, Amazon and every other popular social media platform are using it and have seen huge success. These apps allow the customer to perform efficient personalization such as using the data of the customers and as per the activities of the customers into the application or the platform. These platforms are gathering the usage data of the customer and providing accurate information and requirements to the customer as per their previous activities. So, these things are increasing the convenience of customers and providing more accurate services to them.

The two practical uses of deep learning are:

• Translation of language
• Chatbots

The language translation is one of the most efficient and required activities as it allows people from different places or communities to eliminate the language barrier. And, for the purpose of chatbots it is used by every big platform and it is seen that these chatbots are solving the maximum number of queries of the customer on their own.

(c)

Linear regression is a very popular and the most used algorithm in machine learning analysis. In this type of analysis, the prediction is done on the basis of value present in another component or variable. In this concept there are several independent variables which are used to be calculated or analyzed in order to predict the main output variable or the dependent variable. In this process the model can support one or more number of independent variables for the performance of best data prediction. Linear regression using a particular mathematical formula for its functioning which is y= c+b*x, y is the dependent variable, x is the independent variable, c is the constant and b is the coefficient of the regression.

The threes uses of linear regression are:

• Visualization or forecasting of the data
• Data analysis
• Prediction of the future action of data

(d)

• Correlation: it is nothing but a statistical approach or the technique that implies the performance changes of a variable when the other connected variable is changing (Ardabili et al. 2022). This technique helps in gathering the induct information about the relationship or the degree of relationship between the two variables. Basically, correlation helps in identifying the bond or the dependency of a variable into another variable and how it changes as per the modifications happening into the connected variable.
• Causality: it can be termed as an event or a process through which a particular affect a change happening into the data or variable. Such an effect of a particular process or an event is known as causality.
• Confounding variable: it is known as the variable which is affecting the whole machine learning process externally. Certainly, these are the variables which manipulate or affect the dependent variable and contribute towards incorrect outputs or results.
• Multicollinearity: it is a concept which occurs when two or more than two variables which are independent are correlated with one another in a model, mainly a regression type (Raschka et al. 2022).

Task-2

(a)

In the above figure it can be seen that the data set of historicalemission in the data frame and by using the head () it can be seen that the data is being shown. Later on, another data frame is used and in the data frame the "average" column is sorted out in an ascending order and is resettled. After that it can be seen that the top 5 least CO2 emitting countries have been extracted.

In the above figure the library is used d matplotlib which is converted into "plt". It can be seen that the figure size is modified by using the bar plot for the two columns which are "country" and "average". The graph title is set along with the x and y labels. From the above visualization it can be seen that Bhutan is the country which is emitting the least amount of CO2 and after that Uruguay, Latvia country, Fiji and Montenegro.

(b)

In the above figure it can be seen that the melting action is done where certain columns of the year from 2001 to 2006 are melted. It can be seen that the melted values are being soon into the emission column. Can be seen that after the melting function is done certain columns are dropped from the data frame which are "gas" and "unit". As it can be seen that those variables do not have direct connection with the output which are required.

In the above figure the variables of the data frame are extracted and it can be seen that apart from the emission column all the columns are in object data type and then by using the isna() the null values are checked. Along with that shaping, description is also done and the output can be seen in the figure.

Here another data frame has been created where only the country and the emission column are imported. These countries are later on sub-classified into descending order and then among those descending values the top ten most emitted countries are gathered. Here also using the same plotting structure, the top 10 most emitting countries are visualized.

(c)

The figure provides the information about the imputation of the dataset d Mercedes Benz into the data frame and also the data in the data frame is also extracted. After the insertion of the data set in the next cell it can be seen that the max (), min () along with the null values checking is done for the c-class vehicle.

In the above again the matplotlib library is been used for the data visualization and the dispersion and average rate is also calculated where the average is 34727.9 and dispersion is 2.3.

(d)

• Sum of squared errors: it is nothing but you taking it is used to get the difference or the gap between the actual and the presented values and once the difference is identified then the estimation is done about what the output should be through comparison and evaluation (Aldhyani et al. 2022).
• Total sum of squares: the total sum of squares represents the square output of the dependent variable along with the dependent variable mean.
• R²: this is known as the coefficient for the case of a greater number of determinations.

(e)

Initially the solar radiation data set is imported into the data frame and by using the head function the data is extracted. Here it can be seen that in the next cell the columns are dropped from the data frame as the independent variable pressure and humidity is to be used and for the dependent variable radiation is to be used. Here the radiation column has been categorized as linear regression is to be used for the predicational activity and the categorization is done through cat codes. After that the X and y classification is done.

The classification of X and y is done then the data is split into two parts: train and test. Now the linear regression model is being imported into a variable and then x train and train variables are imported into the model and the mean square error between the real and predicted values is gathered which is 40% and here the R² is also extracted.

(f)

• Confusion matrix: it is nothing but a concept of getting the details regarding the performance of the classification model when a certain type of prediction activity is happening (Chiche and Yitagesu, 2022).
• Sensitivity and specificity: in general terms sensitivity is mainly an activity where the measure of the model performance is understood.

Specificity is a term which analyzes and evaluates the performance of the model or the algorithm.

(g)

The provider analysis details the credit card client data set which has been imported into a data frame and extracted. Using the info function details of the columns is been extracted.

First it can be seen that the ID column has been dropped as it does not carry a certain valuation into this calculation. The next cell can be seen that the X and y classification is done.

The first cell contains the information of classifying or splitting the X and y values into further categories which are train and test categories which are divided into 80:20 percent respectively. After that the logistic regression library is imported and settled as a model where the x train and y train values are fitted initially.

Model is ready with the train values then the x test value is imported for the prediction process. When the accuracy score is calculated between the extracted values with the actual values it is the same that there is 77% acquisition model. Later on, the confusion matrix is also extracted.

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Conclusion

In the above report it has been concluded that, Data science is nothing more than a way of life that enables a person or an analyst to use data or various in order to glean inside information or crucial information. Data science combines math, statistics, machine learning, and various analysis-related tasks. Machine learning has proven to be very effective for improving customer experience because it assists customers in every manner. It is used by many well-known business models and has been extremely successful, including Netflix, Amazon, and every other well-known social media site. The most widely used method in the field of machine learning analysis is linear regression.

References

Aldhyani, T.H., Alsubari, S.N., Alshebami, A.S., Alkahtani, H. and Ahmed, Z.A., 2022. Detecting and analyzing suicidal ideation on social media using deep learning and machine learning models. International journal of environmental research and public health, 19(19), p.12635.

Ardabili, S., Abdolalizadeh, L., Mako, C., Torok, B. and Mosavi, A., 2022. Systematic review of deep learning and machine learning for building energy. Frontiers in Energy Research, 10, p.254.

Chiche, A. and Yitagesu, B., 2022. Part of speech tagging: a systematic review of deep learning and machine learning approaches. Journal of Big Data, 9(1), pp.1-25.

Halbouni, A., Gunawan, T.S., Habaebi, M.H., Halbouni, M., Kartiwi, M. and Ahmad, R., 2022. Machine learning and deep learning approaches for cybersecuriy: A review. IEEE Access.

Raschka, S., Liu, Y.H., Mirjalili, V. and Dzhulgakov, D., 2022. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. Packt Publishing Ltd.