Vizly: PCA Analysis on Socioeconomic Factors and Dataset Compilation

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PCA Analysis on Socioeconomic Factors and Dataset Compilation

Normalized_Monthly_Road_Accident_Final (1) (1).xlsx

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Perform PCA on the socioeconomic factors alone(Sex Ratio Literacy rate Employment percentage Poverty rate Mobile Phone Availability) and give the combined dataset

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I have performed PCA on the socioeconomic factors (Sex Ratio, Literacy rate, Employment percentage, Poverty rate, Mobile Phone Availability) and combined the principal components with the original dataset. The combined dataset has been saved as 'combined_dataset_with_pca.xlsx'. You can download it using the link provided above.

what does principal componenet 1 and 2 signify?

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original variables into a new set of variables called principal components. These principal components are linear combinations of the original variables and are designed to capture the maximum variance in the data.

Principal Component 1 (PC1): This is the first principal component and it captures the largest amount of variance in the data. It is a linear combination of the original variables that explains the most variation in the dataset. In other words, PC1 is the direction in the feature space along which the data varies the most.

Principal Component 2 (PC2): This is the second principal component and it captures the second largest amount of variance in the data, subject to the constraint that it is orthogonal (uncorrelated) to PC1. PC2 is the direction in the feature space that explains the most variation in the dataset after accounting for the variation explained by PC1.

The principal components are ordered by the amount of variance they capture, with PC1 capturing the most variance, followed by PC2, and so on. By examining the principal components, we can gain insights into the underlying structure of the data and identify the most important features that contribute to the variance in the dataset.

Would you like to see the explained variance ratio for these principal components?

Can you visualize the principal components?

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Copy of Normalized_Monthly_Road_Accident_Final.xlsx

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perform pca analysis on the given socioeconoic factors based on the provided distress call data and provide the principal componenets

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I have performed PCA on the socioeconomic factors (Literacy rate, Mobile Phone Availability) based on the provided distress call data. The principal components have been added to the original dataset.

Here are the first few rows of the combined dataset:

YearMonth	Districts	Distress call	Rainfall	Literacy rate	Mobile Phone Availability	Principal Component 1	Principal Component 2
2019-01-01	Ariyalur	0.005639	0.022560	0.120638	0.668237	-0.934498	-1.095877
2019-01-01	Chennai	0.577068	0.060302	0.932357	0.710099	1.616470	1.214059
2019-01-01	Coimbatore	0.136591	0.021275	0.665231	0.963893	1.547269	-0.316401
2019-01-01	Cuddalore	0.020677	0.269454	0.409306	0.813187	0.347122	-0.648832
2019-01-01	Dharmapuri	0.013158	0.017010	0.000000	0.510727	-1.749154	-1.003649

Would you like to download the combined dataset or visualize the principal components?

combined_dataset_with_pca.xlsx

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perform pca analysis on the given socioeconomic factors and give individual pc1 and c2 values with the combined dataset according to the principal componenets

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I have performed PCA on the socioeconomic factors (Sex Ratio, Literacy rate, Employment percentage, Poverty rate, Mobile Phone Availability) and combined the principal components with the original dataset. The combined dataset has been saved as 'combined_dataset_with_pca_with_principal_components.xlsx'. You can download it using the link provided above.

Would you like to visualize the principal components or perform any other analysis?

according to the analysis what features have a significance on the distress call data?

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The PCA analysis provides insight into the significance of each feature on the distress call data. Here are the results:

Explained Variance

The explained variance ratio for the first two principal components are:

Principal Component 1: 37.00%
Principal Component 2: 29.77%

PCA Components

The contribution of each feature to the principal components are as follows:

Feature	Principal Component 1	Principal Component 2
Sex Ratio	0.210003	-0.718349
Literacy rate	0.543155	-0.405498
Employment percentage	0.623573	0.330659
Poverty rate	0.502832	0.216623
Mobile Phone Availability	0.138557	0.404086

Interpretation

Principal Component 1 is primarily influenced by Employment percentage (0.623573), Literacy rate (0.543155), and Poverty rate (0.502832).
Principal Component 2 is primarily influenced by Sex Ratio (-0.718349) and Mobile Phone Availability (0.404086).

These results suggest that Employment percentage, Literacy rate, and Poverty rate have a significant impact on the distress call data, as they contribute the most to the first principal component.

Would you like to explore further or visualize these components?