[FIXED] 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

Issue

While i am predicting the one sample from my data it gives reshape error but my model have equal no of rows, what is problem guys, found similar question but different unexplained.

import pandas as pd
from sklearn.linear_model import LinearRegression
import numpy as np
x = np.array([2.0 , 2.4, 1.5, 3.5, 3.5, 3.5, 3.5, 3.7, 3.7])
y = np.array([196, 221, 136, 255, 244, 230, 232, 255, 267])

lr = LinearRegression()
lr.fit(x,y)

print(lr.predict(2.4))

Error is

"if it contains a single sample.".format(array)) ValueError: Expected 2D array, got scalar array instead: array=2.4. 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.

Solution

You should reshape your X to be a 2D array not 1D array. Fitting a model requires requires a 2D array. i.e (n_samples, n_features)

x = np.array([2.0 , 2.4, 1.5, 3.5, 3.5, 3.5, 3.5, 3.7, 3.7])
y = np.array([196, 221, 136, 255, 244, 230, 232, 255, 267])

lr = LinearRegression()
lr.fit(x.reshape(-1, 1), y)

print(lr.predict([[2.4]]))

Answered By - Abhi