I am getting an error on features_test_scaled that says:
Traceback (most recent call last):
** File “life_expectancy.py”, line 36, in **
** features_test_scaled = ct.transform(features_test)**
** File “/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py”, line 580, in transform**
** if self._n_features > X.shape[1]:**
IndexError: tuple index out of range
I feel like I’ve looked over this quite a few times. Has anyone else had a similar issue?
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
import tensorflow
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import InputLayer, Dense
from tensorflow.keras.optimizers import Adam
dataset = pd.read_csv(‘life_expectancy.csv’)
#print(dataset.head())
#print(dataset.describe())
df = dataset.copy()
df.drop(‘Country’, axis=1)
features = df.iloc[:, :-1]
labels = df.iloc[:,-1]
#print(features)
features = pd.get_dummies(features)
num_features = features.select_dtypes(include=[‘float64’, ‘int64’])
num_cols = num_features.columns
print(features.sum())
features_train, labels_train, features_test, labels_test = train_test_split(features, labels, random_state=27, test_size=0.3)
ct = ColumnTransformer([(‘only_numeric’, StandardScaler(), num_cols)], remainder=‘passthrough’)
features_train_scaled = ct.fit_transform(features_train)
features_test_scaled = ct.transform(features_test)
my_model = Sequential()
input = InputLayer(input_shape = (features.shape[1], ))
my_model.add(input)
my_model.add(Dense(64, activation=‘relu’))
my_model.add(Dense(1))
print(my_model.summary())
opt = Adam(learning_rate= 0.01)
my_model.compile(loss=‘mse’, metrics=‘mae’, opt=opt)
my_model.fit(features_train_scaled, labels_train, epochs= 20, batch_size= 5, verbose=1)
res_mse, res_mae = my_model.evaluate(features_train_scaled, labels_test, verbose=0)
print(res_mse, res_mae)
Thank you!