I was trying to fit a basic machine learning model of linear regression. My code was simple.
import pandas as pd
import numpy as np
from sklearn import linear_model
df = pd.read_csv('homeprices.csv')
reg = linear_model.LinearRegression()
reg.fit(df[['area']], df.price)
reg.predict(3300)
I got the following error.
ValueError Traceback (most recent call last) /tmp/ipykernel_10591/2177379720.py in <module> ----> 1 reg.predict(3300) ~/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py in predict(self, X) 360 Returns predicted values. 361 """ --> 362 return self._decision_function(X) 363 364 _preprocess_data = staticmethod(_preprocess_data) ~/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py in _decision_function(self, X) 343 check_is_fitted(self) 344 --> 345 X = self._validate_data(X, accept_sparse=["csr", "csc", "coo"], reset=False) 346 return safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_ 347 ~/.local/lib/python3.8/site-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params) 564 raise ValueError("Validation should be done on X, y or both.") 565 elif not no_val_X and no_val_y: --> 566 X = check_array(X, **check_params) 567 out = X 568 elif no_val_X and not no_val_y: ~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator) 759 # If input is scalar raise error 760 if array.ndim == 0: --> 761 raise ValueError( 762 "Expected 2D array, got scalar array instead:\narray={}.\n" 763 "Reshape your data either using array.reshape(-1, 1) if " ValueError: Expected 2D array, got scalar array instead: array=3300. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
I solved the issue by making the array 2D from 1D.
reg.predict([[3300]])