import string import pandas as pd from sklearn.model_selection import train_test_split from sklearn import preprocessing import wget import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.optimizers import Adam from tensorflow.keras.layers import Dropout from tensorflow.keras.callbacks import EarlyStopping from sklearn.metrics import mean_squared_error, mean_absolute_error movies_data = pd.read_csv('train.csv', error_bad_lines=False) movies_data.drop(movies_data.columns[0], axis=1, inplace=True) movies_data.dropna(inplace=True) X = movies_data.drop("rating", axis=1) Y = movies_data["rating"] print(X, Y.values) # Split set to train/test 8:2 ratio X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state=42 ) # Set up model model = Sequential() model.add(Dense(8, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(3, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(1)) model.compile(optimizer="adam", loss="mse") early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10) model.fit( x=X_train.values, y=Y_train.values, validation_data=(X_test, Y_test.values), batch_size=128, epochs=400, callbacks=[early_stop], ) model.save('model_movies')