Should I add csv as text in SO question? There's lot more data.
%matplotlib inline
plt.xlabel('Year')
plt.ylabel('Income($US)')
plt.scatter(df.year,df.income,color='red',marker='+')
reg = linear_model.LinearRegression()
reg.fit(df[['year']],df.income)
Output : LinearRegression()
reg.predict('10000')
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) in ----> 1 reg.predict('10000')
~/.local/lib/python3.9/site-packages/sklearn/linear_model/_base.py in predict(self, X) 236 Returns predicted values. 237 """ --> 238 return self._decision_function(X) 239 240 _preprocess_data = staticmethod(_preprocess_data)
~/.local/lib/python3.9/site-packages/sklearn/linear_model/base.py in decision_function(self, >X) 218 check_is_fitted(self) 219 --> 220 X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) 221 return safe_sparse_dot(X, self.coef.T, 222 dense_output=True) + self.intercept
~/.local/lib/python3.9/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs) 61 extra_args = len(args) - len(all_args) 62 if extra_args <= 0: ---> 63 return f(*args, **kwargs) 64 65 # extra_args > 0
~/.local/lib/python3.9/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) 628 # If input is scalar raise error 629 if array.ndim == 0: --> 630 raise ValueError( 631 "Expected 2D array, got scalar array instead:\narray={}.\n" 632 "Reshape your data either using array.reshape(-1, 1) if "
ValueError: Expected 2D array, got scalar array instead: array=10000. 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 am not sure why I am getting above error. I have income lists of some years. So, I graph it. When I was trying to predict a data from linearRegression I got the error. I am new to ML(Machine Learning)
How to solve it? What am I missing?
