$1-$ Explain your dataset briefly. What is your aim about this dataset. Read the dataset$\mathrm{.}2-$ List the first $10$ data$\mathrm{.}3-$ Find out how many data there are. Show the number of rows and columns$\mathrm{.}4-$ Display the number $0$ and $1$ in the predict column $($target column$)\mathrm{.}5-$ Display data types of all parameters$\mathrm{.}6-$ Display statistical information about the data set$\mathrm{.}7-$ Display missing data in dataset$\mathrm{.}8-$ Suppose there is missing data in the dataset, show the solution to the problem$9-$ Find the mean of any column of numerical data in the dataset$10-$ Find the highest value in any column of numerical data in the dataset$11-$ Find the mean value by grouping them according to the column of categorical data$\mathrm{.}12-$ Find how many different data are in the column consisting of categorical data$\mathrm{.}13-$ Draw the correlation matrix$14-$ Decide about independent and dependent variables. Create your x and y$\text{'}$ variables in your dataset$\mathrm{.}15-$ Divide $($split$)$ the data set as test and train$16-$ Apply normalization to the dataset$\mathrm{.}17-$ Apply one of the Machine Learning algorithms $($Random Forest or Support Vector Machine$)-$ Briefly explain the relevant algorithm you used in your project and what you understand from the algorithm$\mathrm{.}18-$ Create a confusion matrix$19-$ Find the values of accuracy, sensitivity$,$ recall and f$1$ score. Make comment about the Accuracy score. Why do we find out these metrics, explain briefly.