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In this research paper, different functions, and packages are imported here to execute the program successfully. Besides that, the linear regression model is created by utilizing the wind dataset file. Moreover, the jupyter notebook platform is implemented to execute the entire program. The mean, median, model, as well as standard deviation, have been calculated in this paper. Scatter plots and bar charts are also generated in this program by utilizing the wind dataset file.

Technical Analysis

Task 1

Import the dataset

(Source: Generated by Learner)

The above pictures show the codes that have been utilized to import the CSV file in the jupyter notebook platforms. The df. head() function is utilized to display the first five data corresponding to the wind_dataset.csv file.

 View the dataset in a table format

(Source: Generated by Learner)

In this case, the above codes have been utilized to display the data in a table format. The tabulate package is applied to execute the program successfully. Moreover, the tabulate.tabulate(df) codes are implemented here to generate the CSV file data in a table form.

Task 2

 Code to generate the scatter plot

(Source: Generated by Learner)

The matplotlib package is utilized here to visualize the scatter plot corresponding to the task. Scatter plots are utilized to discover the connection between the variables of the wind dataset. In this paper, the scatter plot has been created between the wind and rain values of the wind_dataset.csv file. The plt.show() command is implemented to visualize the scatter plot after successfully executing the task. Furthermore, the scatter plot described that when rain wind speed values increase while rain values decrease corresponding to the task.

 Code to generate the Bar chart

(Source: Generated by Learner)

A bar chart is one type of graphical representation of the categorical data with different height and length values. In this figure, the bar chart plot is created between the IND values as well as the temperature minimum values of the wind dataset. The above-included figure shows codes that are utilized to generate the bar charts corresponding to the research paper. Moreover, the seaborn packages are imported here to display the graphs. The plt.show() command is applied here to visualize the bar chart after executing the program. Moreover, the ax.bar () is defined as the plot axis in this file.

Task 3

 Calculate the mean value

(Source: Self-generated)

The above codes are utilized to find the mean values of the wind speed corresponding to the wind dataset file. The results are also shown after executing the task successfully. The print() option is applied to generate the outcome of the mean values.

 Calculate the median value

(Source: Self-generated)

In this part, the median value is calculated for the wind dataset. The numpy.median() codes are utilized to calculate the median values of the dataset file. The outcome shows in the above-inserted figure.

 Calculate the mode value

(Source: Self-generated)

In this case, the mode value is shown for the wind dataset regarding the task. The mode results are visualized in this program after executing the program in the jupyter notebook platform.

 Calculate the range value

(Source: Self-generated)

The column range has been calculated by utilizing the above codes in the jupyter notebook platform. The result is also shown in the above picture. The range value is calculated between the wind maximum values and wind minimum values of the CSV dataset file.

 Calculate the standard value

(Source: Self-generated)

The standard deviation is calculated by utilizing the above python codes for the wind speed. The output is also shown in the figure after executing the task.

Task 4

 Code to split the dataset

(Source: Self-generated)

In this section, the wind dataset has been splited into two different categories, such as test or train. Moreover, various types of packages are imported here to execute the linear regression program as well as calculate the MSE values. The Xtrainset.head() function is utilized to display the five values of the Xtrainset. The results are also visualized after successfully executing the program. 

 Code to prepare the training set

(Source: Self-generated)

The above-included python codes are utilized to implement the train set values corresponding to the task. The MSE error has been generated for the y values and train prediction values of the dataset file. The RMSE values are created between the y and train prediction values of the wind dataset. Furthermore, the results are also visualized after successfully executing the program.

 Code to prepare the test set

(Source: Self-generated)

In this section, the test value preparation process is shown corresponding to the task. Moreover, the MSE and RMSE values are calculated regarding the prediction values as well as the y values of the dataset file. The results are also visualized in the above figure after executing the program.

Conclusion

From the above passage, it can be concluded that the linear regression model is created by utilizing the wind dataset file in the jupyter notebook platform. Moreover, the dataset file is shown in a table format in this task. The mean, median, and other values are created by utilizing the python codes as well as the wind dataset file. The dataset has been splited into two different categories, such as test or train that is applied in the linear regression model. Different types of charts, such as bar charts, and scatter plots are visualized in this program. At last, it can be concluded that the root means square error has been created for both, the train and test values of the dataset file.

References

Babatunde, G., Emmanuel, A.A., Oluwaseun, O.R., Bunmi, O.B. and Precious, A.E., 2019. Impact of climatic change on agricultural product yield using K-means and multiple linear regressions. International Journal of Education and Management Engineering, 5(8), pp.16-26. 

Lemenkova, P., 2019. Testing linear regressions by StatsModel Library of Python for oceanological data interpretation. Aquatic Sciences and Engineering, 34(2), pp.51-60.

Stan?in, I. and Jovi?, A., 2019, May. An overview and comparison of free Python libraries for data mining and big data analysis. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 977-982). IEEE.