Needed to add more processing to the labels.

Model - Data Checking

@@ -87,6 +87,25 @@ test_data = np.asarray(test_data).astype(np.float32)
 print(train_labels.dtypes)
 print(train_data.dtype)
 
+#Need to do further data processing
+plants = np.array(plants).astype((str))
+uv = np.unique(plants)
+#getting unique values to encode
+print(uv)
+
+print(encode.transform(plants[1]))
+print(plants.shape)
+#data_points = np.zeros((160,3,5))
+#Need a 2 array 
+encoded = np.zeros((160,80))
+for i in range(160):
+  encoded[i] = encode.transform(plants[i])
+
+print(encoded)
+
+encode = preprocessing.LabelEncoder()
+encode.fit(uv)
+print(encode.transform(uv))
 
 #Splitting the dataset into testing & training
 from sklearn.model_selection import train_test_split