Using Pycharm or Google Colab develop a code for ANN

Data set information is as follows: The NASA data set comprises different size NACA 0012 airfoils at various wind tunnel speeds and angles of attack. The span of the airfoil and the observer position were the same in all of the experiments. Attribute Information: This problem has the following inputs: 1. Frequency, in Hertzs. 2. Angle of attack, in degrees. 3. Chord length, in meters. 4. Free-stream velocity, in meters per second. 5. Suction side displacement thickness, in meters. The only output is: 6. Scaled sound pressure level, in decibels. Use ANN in Python and build a regression machine learning model to predict the Scaled sound pressure level: 1. Checking, preprocessing the data (converting the categorical data to integer if needed), analyzing and visualizing the data 2. Defining the appropriate input and output based on the results from step 1 3. Splitting the data into training set and testing set 4. Building a ANN model to predict the Scaled sound pressure level 5. Tune your model by changing the network structure (change the number of hidden layers and the number of nodes in each hidden layer; the number of nodes in the input layer is equal to the number of attributes you use) 6. Enter a new dataset (you can make up the data set) and use your model to predict