Hello everyone for school i need to make a machine learning project where i have to predict the rainfall
my excell looks like this:
and this is my code:
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
df = pd.read_excel('Month.xlsx', usecols='W, AJ')
data = pd.read_excel('Month.xlsx')
df
x = df.iloc[:,0]
y = df.iloc[:,1]
model = LinearRegression()
model.fit(x, y)
y_pred = model.predict(x)
# Visualisatie van de regressielijn
plt.scatter(x, y, color='gray')
plt.plot(x, y_pred, color='red', linewidth=2)
plt.xlabel('Luchtvochtigheid')
plt.ylabel('Regenval')
plt.show()
but i keep getting an error with "ValueError: could not convert string to float: ''. This error is given on the line where i try to model.fit(x,y)
I have tried to change the x and y values to a float with a new code like Xdata = float(x), but then i got a whole different error: cannot convert the series to <class 'float'>
>Solution :
This means that in your df you have some observations that are empty strings like this: '' that of course cannot be converted to a number.
In this case you have to decide what’s the best option between replace them with a number (like zero, the mean, the mean divided by the standard deviation etc) or just drop the rows with the empty string / None values.
Before implementing an ML algorithm you should always do some data preprocessing (that is also the difficult part in a data science project), that is totally missing in your code.
Also, you are not splitting X and y into train and test, hence you are using for predictions the same data you used for training, that is an error, since the model will be completely useless.
I suggest you to take a look at some documentation on how to implement a data science project, because your approach is lacking a lot of basic stuff.