diff --git a/Kai_Model.py b/Kai_Model.py
new file mode 100644
index 0000000..c2ad679
--- /dev/null
+++ b/Kai_Model.py
@@ -0,0 +1,87 @@
+import numpy as np
+import pandas as pd
+import tensorflow as tf 
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_absolute_percentage_error, r2_score
+## Data Preprocessing
+# Importing the dataset
+dataset = pd.read_excel('Two_Axle_Sim_Data.xlsx')
+X = dataset.iloc[2:10000, :12].values
+Y = dataset.iloc[2:10000, [20]].values
+
+# Splitting the dataset into the Training set and Test set
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
+
+# Change it to numpy array
+X_train = np.array(X_train, dtype=np.float32)
+Y_train = np.array(Y_train, dtype=np.float32)
+
+# Feature scaling
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+sc2 = StandardScaler()
+Y_train = sc2.fit_transform(Y_train)
+Y_test = sc2.transform(Y_test)
+
+## Building the ANN
+# Initializing the ANN
+ann = tf.keras.models.Sequential()
+
+# Adding layers
+ann.add(tf.keras.layers.Dense(units=64, activation='relu'))
+ann.add(tf.keras.layers.Dense(units=64, activation='relu'))
+ann.add(tf.keras.layers.Dense(units=64, activation='relu'))
+ann.add(tf.keras.layers.Dense(units=64, activation='relu'))
+ann.add(tf.keras.layers.Dense(units=1)) 
+
+## Training the ANN
+# Compiling the ANN
+ann.compile(optimizer='adam', loss='mean_absolute_percentage_error')
+
+# Training the ANN on the Training set
+ann.fit(X_train, Y_train, batch_size = 32, epochs = 50)
+
+## Making the predictions and evaluating the model
+# Predicting the Test set results
+Y_pred = ann.predict(X_test)
+
+# Y_pred_squeezed = np.squeeze(Y_pred)
+# Y_test_squeezed = np.squeeze(Y_test)
+
+# result_df = pd.DataFrame({
+#     'Predicted Values': Y_pred_squeezed,
+#     'Actual Values': Y_test_squeezed
+# })
+
+# Create Multi-Index DataFrame for predicted and actual values
+num_samples = Y_pred.shape[0]  # Assuming same number of samples for predicted and actual
+num_features = Y_pred.shape[1]  # Assuming same feature dimensions
+
+# Flatten both arrays for better comparison
+Y_pred_flattened = Y_pred.reshape(num_samples, num_features)
+Y_test_flattened = Y_test.reshape(num_samples, num_features)
+
+# Create DataFrame
+result_df = pd.DataFrame({
+    ('Predicted', 'Feature' + str(i)): Y_pred_flattened[:, i] for i in range(num_features)
+})
+result_df = result_df.join(pd.DataFrame({
+    ('Actual', 'Feature' + str(i)): Y_test_flattened[:, i] for i in range(num_features)
+}))
+
+# Display the DataFrame
+print(result_df)
+
+## Evaluating the Model
+# Average Mean Squared Error
+mape_avg  = mean_absolute_percentage_error(Y_test, Y_pred, multioutput='uniform_average')
+print(f'Average Mean absolute Error across all outputs: {mape_avg }')
+
+# R-squared Score
+# r2 = r2_score(Y_test, Y_pred, multioutput='uniform_average')
+# print(f'R-squared score: {r2}')
+
+# Saving the model to a file
+ann.save('my_ann_model.h5')
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diff --git a/Todo.txt b/Todo.txt
new file mode 100644
index 0000000..8425c15
--- /dev/null
+++ b/Todo.txt
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+1. It can predict mutiple output but the result is very bad
+2. It is very good to predict one output
+3. If not scale and not including column 23 and 27 the result is good (0.02)
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diff --git a/Two_Axle_ANN.py b/Two_Axle_ANN.py
index 196dc2e..40f4280 100644
--- a/Two_Axle_ANN.py
+++ b/Two_Axle_ANN.py
@@ -1,7 +1,6 @@
 import numpy as np
 import pandas as pd
-import tensorflow as tf
-
+import tensorflow as tf 
 ## Data Preprocessing
 # Importing the dataset
 dataset = pd.read_excel('Two_Axle_Sim_Data.xlsx')
diff --git a/my_ann_model.h5 b/my_ann_model.h5
new file mode 100644
index 0000000..fe853c1
Binary files /dev/null and b/my_ann_model.h5 differ