Count not conver string to float using iris dataset

Question

So I am using iris dataset on my sample linear regression code. But when I tried to train/fit the model. I get an error

ValueError: could not convert string to float: 'setosa'

This bugs and I could not find the fix for this one. Below is the code that I am using.

iris_df = pd.read_csv(r'C:\Users\Admin\iris.csv')
iris_df.describe()

# Variables
X= iris_df.drop(labels= 'sepal length in cm', axis= 1)
y= iris_df['sepal length in cm']

# Splitting the Dataset 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.47, random_state= 42)

# Instantiating LinearRegression() Model
lr = LinearRegression()

# Training/Fitting the Model
lr.fit(X_train, y_train)

Answer 1

As it is written in the example you are using, you need to transform your data first:

# Converting Objects to Numerical dtype
iris_df.drop('species', axis= 1, inplace= True)
target_df = pd.DataFrame(columns= ['species'], data= iris.target)
iris_df = pd.concat([iris_df, target_df], axis= 1)

# Variables
X= iris_df.drop(labels= 'sepal length (cm)', axis= 1)
y= iris_df['sepal length (cm)']

# Splitting the Dataset 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.33, random_state= 101)

# Instantiating LinearRegression() Model
lr = LinearRegression()

# Training/Fitting the Model
lr.fit(X_train, y_train)

# Making Predictions
lr.predict(X_test)
pred = lr.predict(X_test)

# Evaluating Model's Performance
print('Mean Absolute Error:', mean_absolute_error(y_test, pred))
print('Mean Squared Error:', mean_squared_error(y_test, pred))
print('Mean Root Squared Error:', np.sqrt(mean_squared_error(y_test, pred)))