Beginning to build model.

Model - Data Checking

@@ -48,5 +48,41 @@ pad = np.zeros((160,4))
 temp = np.concatenate((temperature_data,pad),axis=1)
 temp = np.reshape(temp,(160,5))
 
-data_points = np.concatenate((ph,moisture_data,temp),axis=0)
+#don't need this
+# data_points = np.concatenate((ph,moisture_data,temp),axis=0)
+# print(data_points)
+
+#just put the data into a array of (160,3,5)
+    # [[[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]],
+    #   [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]]..]
+
+# print(ph[0])
+# print(temp[0])
+# print(moisture_data[0])
+
+data_points = np.zeros((160,3,5))
+# print(data_points[0])
+
+#first array set
+print(data_points[0])
+#first array - in array
+print(data_points[0][0])
+
+# data_points[0] = [ph[0],moisture_data[0],temp[0]]
+# print(data_points[0])
+
+for i in range(160):
+  data_points[i] = [ph[i],moisture_data[i],temp[i]]
+
 print(data_points)
+
+#Splitting the dataset into testing & training
+from sklearn.model_selection import train_test_split
+train_data, test_data, train_labels, test_labels = train_test_split(data_points,plants, test_size=0.3, random_state = 737)
+#print(train_data)
+
+train_data = np.asarray(train_data).astype(np.float32)
+test_data = np.asarray(test_data).astype(np.float32)
+
+print(train_labels.dtypes)
+print(train_data.dtype)