Below is R output of a summary of a GLM. Interpret the output below in detail.

Data: df_Final.csv from https://www.kaggle.com/jaimebadiola/bitcoin-tweets-and-price

Call: glm( formula = Close ~ Compound_Score + Total. Volume. of. Tweets + Count_Negatives + Count_Positives + Count_Neutrals + Sent_Negatives + Sent_Positives + Count_News + Count_Bots, family = Gamma(link = identity), data = bitcoindata) Deviance Residuals : Min 10 Median 30 Max -1. 00671 -0.20477 -0. 01145 0. 14972 1.50095 Coefficients : Estimate Std. Error t value Pr(>|t) (Intercept) -3427. 0057 264. 6938 -12.947 > Compound_Score 3921 . 0476 1107.7762 3. 540 0. 000402 * ** Total. Volume . of . Tweets 602. 4529 1060.2131 0. 568 0.569884 Count_Negatives -602 . 8805 1060.2103 -0. 569 0.569609 Count_Positives -595.1053 1060.2128 -0. 561 0.574598 Count_Neutrals -600. 2677 1060. 2121 -0. 566 0.571284 Sent_Negatives -1901 . 6611 462 . 2112 -4. 114 3.91e-05 *** Sent_Positives 9597.5169 665.7969 14. 415 Count News -0. 9102 0. 2746 -3. 314 0. 000921 * * * Count_Bots -595. 8745 1060.2135 -0. 562 0.574104 Signif. codes: 0 (xxx> 0.001 (*> > 0.01 > 0.05 . .' 0.1