This dataset showed the WiFi information in terms of RSSI signal. The large RSSI value means the user is closer to the access point (AP) of Wifi. Could you suggest what kind of Deep Learning algorithm I can use to train the abundant data? In this case, the data is referring to the WIFI RSSI signal collected by the smartphone. My purpose is to classify the data to locate the user and identify some landmarks such as elevators, stairs, etc and What is need to implement in python. Please help.
D E F G H 1 J K M N 0 -69 -69 -69 -72.5 -75.99 -75.12 -79 -75.56 -79 - 75.78 -79 L - 75.94 -79 -74.25 -79 -77.5 -75.89 -79 -75.97 -79 -75.99 -79 -79 -79 -79 -79 -76 -79 -76 -76 -77 -84 -84 -84 -84 -84 -84 -84 -84 -83.5 -84 -83.75 -84 -83.94 -84 -83.99 -84 -83.99 -80 -80 -80 -82 -83 -83.97 -83.99 -70 - 75.25 -75.62 -70 -69 -75.9 -70 -69 -73 -72.5 -74.5 -74.25 -84 -83.88 -75.81 -75.78 -75.81 -75.95 -75.94 -69 - 75.99 -75.99 -75.56 AB 1 00:24:6c:d4:36:50 2 00:40:5a:bf:d0:b2 3 00:40:5a:bf:do:b3 4 00:30:0d:54:a0:d3 5 00:26:66:f4:4f:80 6 00:24:6c:d4:36:53 7 00:24:6c:d4:36:52 8 00:24:6c:d4:36:51 9 70:62:b8:6f:19:60 10 70:62:b8:6f:19:6e 11 00:30:0d:54:a8:e5 12 00:30:0d:54:55:87 13 00:40:5a:bf:ff:9b 14 40:9b:cd:2a:f2:56 15 00:40:5a:bf:ff:9a -75.89 -75.97 -75.97 -75.97 -75.12 - 75.25 - 75.99 - 75.99 -75.99 -70 -70 -70 -73 -74.5 -75.9 -75.99 - 75.95 -39.97 -35 -35 -35 -37.5 -39.38 -39.85 -39.99 -40 -40 -42 -42 -42 -52.92 -52.96 -52.99 -47.5 -76.5 -38.75 -50.25 - 77.25 -82 - 75.62 -39.69 -52.32 - 77.81 -82 -73.75 -75 -75 -75 -51.63 -77.62 -82 -73.5 -77.991 -39.93 -52.83 - 77.95 -82 -73.93 -52.66 -77.9 -82 -73.87 -82 -52.98 - 77.99 -82 -73.99 -82 -82 - 77.97 -82 -73.96 -82 - 77.99 -82 -73.98 -82 -72 -72 -72 -72 -73 -73.99 -88 -88 -88 -71 -71 -71 -71 - 70.5 -70.25 - 70.12 -71.56 -72.28 -72.64 -72.82 -72.91 -72.96 D E F G H 1 J K M N 0 -69 -69 -69 -72.5 -75.99 -75.12 -79 -75.56 -79 - 75.78 -79 L - 75.94 -79 -74.25 -79 -77.5 -75.89 -79 -75.97 -79 -75.99 -79 -79 -79 -79 -79 -76 -79 -76 -76 -77 -84 -84 -84 -84 -84 -84 -84 -84 -83.5 -84 -83.75 -84 -83.94 -84 -83.99 -84 -83.99 -80 -80 -80 -82 -83 -83.97 -83.99 -70 - 75.25 -75.62 -70 -69 -75.9 -70 -69 -73 -72.5 -74.5 -74.25 -84 -83.88 -75.81 -75.78 -75.81 -75.95 -75.94 -69 - 75.99 -75.99 -75.56 AB 1 00:24:6c:d4:36:50 2 00:40:5a:bf:d0:b2 3 00:40:5a:bf:do:b3 4 00:30:0d:54:a0:d3 5 00:26:66:f4:4f:80 6 00:24:6c:d4:36:53 7 00:24:6c:d4:36:52 8 00:24:6c:d4:36:51 9 70:62:b8:6f:19:60 10 70:62:b8:6f:19:6e 11 00:30:0d:54:a8:e5 12 00:30:0d:54:55:87 13 00:40:5a:bf:ff:9b 14 40:9b:cd:2a:f2:56 15 00:40:5a:bf:ff:9a -75.89 -75.97 -75.97 -75.97 -75.12 - 75.25 - 75.99 - 75.99 -75.99 -70 -70 -70 -73 -74.5 -75.9 -75.99 - 75.95 -39.97 -35 -35 -35 -37.5 -39.38 -39.85 -39.99 -40 -40 -42 -42 -42 -52.92 -52.96 -52.99 -47.5 -76.5 -38.75 -50.25 - 77.25 -82 - 75.62 -39.69 -52.32 - 77.81 -82 -73.75 -75 -75 -75 -51.63 -77.62 -82 -73.5 -77.991 -39.93 -52.83 - 77.95 -82 -73.93 -52.66 -77.9 -82 -73.87 -82 -52.98 - 77.99 -82 -73.99 -82 -82 - 77.97 -82 -73.96 -82 - 77.99 -82 -73.98 -82 -72 -72 -72 -72 -73 -73.99 -88 -88 -88 -71 -71 -71 -71 - 70.5 -70.25 - 70.12 -71.56 -72.28 -72.64 -72.82 -72.91 -72.96
