--- a
+++ b/Model_Training.py
@@ -0,0 +1,92 @@
+import pandas as pd 
+import tensorflow as tf 
+import matplotlib as plt
+import glob
+import numpy as np 
+from tensorflow import keras
+from keras import Sequential
+from sklearn.utils import shuffle
+import sklearn.model_selection
+
+
+STEP_SIZE = 20
+SENSOR_NUM = 6
+NUM_CLASSESS = 7
+
+df = pd.concat([pd.read_csv(f) for f in glob.glob('./train_new/*.csv')], ignore_index = True)
+print(df)
+
+Label = { 'STD':0, 'WAL':1, 'JOG':2 , 'JUM':3, 'FALL':4 , 'LYI':5,'RA':6} 
+class_names = { 0:'STD', 1:'WAL', 2:'JOG' , 3:'JUM', 4:'FALL', 5:'LYI',6:'RA'}
+
+dataSet = df[["acc_x", "acc_y", "acc_z", "gyro_x","gyro_y","gyro_z",  "label"]]
+
+dataSet.label = [Label[item] for item in dataSet.label]
+
+
+print(dataSet)
+
+x = np.array(dataSet.drop(["label"],1))
+y = np.array(dataSet["label"])
+
+modDataset = []
+modTruth =[]
+
+for i in range(len(x)-STEP_SIZE):
+    temp = []
+    for j in range(i, i+STEP_SIZE):
+        temp.append(x[j])
+    modDataset.append(temp)
+
+for i in range(len(y)-STEP_SIZE):
+    temp = []
+    for j in range(i, i+STEP_SIZE):
+        temp.append(y[j])
+    
+    most_common_item = max(temp, key = temp.count)
+
+    modTruth.append(most_common_item)
+
+print(len(modDataset))
+print(len(modTruth))
+
+print(len(modDataset[0]))
+print(modDataset[1])
+
+modDataset = np.array(modDataset).reshape(-1, STEP_SIZE, SENSOR_NUM)
+
+print(modDataset)
+
+
+
+y = np.array(modTruth)
+x = modDataset
+
+x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x,y,test_size = 0.3)
+
+print(x_train)
+print(y_train)
+
+model = Sequential()
+model.add(keras.layers.Flatten(input_shape=(STEP_SIZE, SENSOR_NUM)))
+model.add(keras.layers.Dense(128, activation='relu'))
+model.add(keras.layers.Dropout(0.3))
+model.add(keras.layers.Dense(128, activation='relu'))
+model.add(keras.layers.Dropout(0.3))
+model.add(keras.layers.Dense(NUM_CLASSESS, activation='softmax'))   
+
+model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) 
+model.summary()
+
+model.fit(x_train,y_train, epochs=30, validation_split =0.1)
+
+model.save('./model_x/')
+
+pred = model.predict(x_test)
+results = np.argmax(pred, axis=1)
+
+for i in range(50) :
+    if class_names[y_test[i]] == class_names[results[i]]:
+        print("prediction: ", class_names[results[i]], "    actual: ", class_names[y_test[i]], "prediction: Correct!!!" )
+    else:
+        print("prediction: ", class_names[results[i]], "    actual: ", class_names[y_test[i]], "prediction: Wrong :( " )
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