import pandas as pd import numpy as np import pickle

import matplotlib.pyplot as plt

car_df = pd.read_csv("Car_Purchasing_Data.csv",encoding = 'ISO-8859-1')

X = car_df.drop (['Customer Name','Customer e-mail','Country','Car Purchase Amount'],axis = 1) y = car_df['Car Purchase Amount']

from sklearn.preprocessing import MinMaxScaler

scaler_x = MinMaxScaler() X_scaled = scaler_x.fit_transform(X)

y = y.values.reshape(-1,1)

scaler_y = MinMaxScaler()

y_scaled = scaler_y.fit_transform(y)

from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(X_scaled,y_scaled,test_size = 0.25)

import tensorflow.keras from Keras.models import Sequential from Keras. layers import Dense from sklearn. preprocessing import MinMaxScaler

model = Sequential() model.add(Dense(25,input_dim = 5,activation='relu')) model.add(Dense(25,input_dim = 5 ,activation = 'relu')) model.add(Dense(1,activation ='linear')) model.summary()

model.compile(optimizer = 'adam',loss='mean_squared_error')

epochs_hist = model.fit(X_train,y_train,epochs=10,batch_size=25,verbose =1,validation_split = 0.2)

X_test_sample = np.array([[1, 55, 300000, 400000, 300500]]) y_predict_sample = model.predict(X_test_sample) print('Expected Purchase Amount',y_predict_sample)

with open("model.pkl","wb") as f: pickle.dump(model,f)

with open("model.pkl","rb") as f: mp = pickle.load(f)

mp.predict([[X_test_sample]]) model = pickle.load(open('model.pkl','rb'))

print(model.predict([[X_test_sample]]))

I couldn't fetch my model into flask or joblib because I am getting the below error

ValueError Traceback (most recent call last)

in ----> 1 jm.predict([[1,50,30000,50000,40000]]) ~\Anaconda3\lib\site-packages\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) 1439 1440 # Case 2: Symbolic tensors or Numpy array-like. -> 1441 x, _, _ = self._standardize_user_data(x) 1442 if self.stateful: 1443 if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0: ~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size) 577 feed_input_shapes, 578 check_batch_axis=False, # Don't enforce the batch size. --> 579 exception_prefix='input') 580 581 if y is not None: ~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 143 ': expected ' + names[i] + ' to have shape ' + 144 str(shape) + ' but got array with shape ' + --> 145 str(data_shape)) 146 return data 147 ValueError: Error when checking input: expected dense_1_input to have the shape (5,) but got array with shape (1,) **Could anyone please help me in deploying this model..!!**

ROBOTIS-GIT / turtlebot3_machine_learning

Value Error #39

import matplotlib.pyplot as plt

print(model.predict([[X_test_sample]]))