Factor Analysis using Python

I chose a dataset that can help me apply factor analysis, i first cleaned the data by removing nulls , then applied the adquecy test . I tried to calculate the bartlett sphericity by getting the chi squared and p-value. The Bartlett's test for sphericity is significant as (chi-square = 502.32, p < 0.05) this shows that the correlation matrix is not an identity matrix. I then calculated the kmo of the data to see if it is more than 0.6; therefore, it would be fit for factor analysis. As the Kmo value is greater than 0.6, it is generally considered adequate as the closer to 1 the more likely factor analysis would be suitable for the data. Then, I calculated the eigenvalues and it shows 4 values are greater than 1 ; therefore, this indicates that the number of factors needed would be 4. Next, I applied the factor analysis algorithm .Factor 1 has the highest variance (2.8574), followed by Factor 2 (2.3193), Factor 3 (2.1422), and Factor 4 (1.8134). Each column represents a latent factor. The values in each column indicate the strength or intensity of the association between the observation and the corresponding latent factor.

(-1.3419) is suggesting that Factor 1 has the most influence on the first observation

A communality value close to 1 indicates that a large proportion of the variable's variance is explained by the factors. Conversely, a communality close to 0 suggests that the factor model does not account for much of the variance in that variable.