battery_power	bluetooth	clock_speed	dual_sim	frontcamerapixel	4G	memorysize	weight	cores	primarycamerapixel	pixelresolutionheight	pixelresolutionwidth	ram	screenheight	screenwidth	talk_time	3G	touch_screen	wifi	price_range
842	0	2.2	0	1	0	7	188	2	2	20	756	2549	9	7	19	0	0	1	1
1021	1	0.5	1	0	1	53	136	3	6	905	1988	2631	17	3	7	1	1	0	2
563	1	0.5	1	2	1	41	145	5	6	1263	1716	2603	11	2	9	1	1	0	2

Examine neural networks using Python. The dataset has 2,000 records and 19 different attributes about smartphone specifications. The goal in this analysis is to predict the price of a smartphone based on its attributes using neural network techniques. This data set has the following price range: 0 for low, 1 for middle, 2 for expensive, and 3 for very expensive. Preparing the data for a neural network is very important. The data needs to be divided into training (70%) and testing (30%) datasets. A. Analyze the data using neural network and interpret the results with an appropriate validation by applying the evaluate () method and reporting the accuracy. B. Predict the following smart phone's price range. ( 30pts) To print out the most likely class of prediction, please use the following method from the numpy package: np.argmax("yourprediction" [0]) - This smartphone's battery power is 1500 , and it has a Bluetooth function. - The clock speed is 2.1, and the user can use dual sims. - The front camera pixel size is 10, and it supports 4G. - The memory size of the model is 45 , and the weight is 148. - It has eight cores, and the primary camera pixel is 7. - The pixel resolution height and width are 1216 by 1786. - The ram size is 3763 . The screen height and width are 14 by 9. - The maximum talk time is 13 , and it supports 3G. - It also provides both touch screen and wifi. Hint: The data used in this assignment is not image-related data. Therefore, we need use Conv1D() and MaxPooling1D() when developing the CNN model. The following example can be used: yourmodel.add (keras. layers. Conv1D (filters="some number", kernel size= "number", activation='relu', input shape= the shap of data(In this assignment, it would be (19,1) ))) yourmodel.add (keras.layers.MaxPooling1D (pool size = "some number", strides = "number"))