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

**I am making laptop price prediction application with ML and there are many examples of this topic. Although I write the codes exactly the same as them, I get such errors and I don't know how to fix them.
these are my codes:
**

step1 = ColumnTransformer(transformers=[
    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])],remainder='passthrough')

step2 = LinearRegression()

pipe = Pipeline([
    ('step1',step1),
    ('step2',step2)
])

pipe.fit(X_train,y_train)

y_pred = pipe.predict(X_test)

print('R2 score',r2_score(y_test,y_pred))
print('MAE',mean_absolute_error(y_test,y_pred))

AND Output:

Output exceeds the size limit. Open the full output data in a text editor---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[94], line 12
      5 step2 = LinearRegression()
      7 pipe = Pipeline([
      8     ('step1',step1),
      9     ('step2',step2)
     10 ])
---> 12 pipe.fit(X_train,y_train)
     14 y_pred = pipe.predict(X_test)
     16 print('R2 score',r2_score(y_test,y_pred))

File c:\Users\...\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\pipeline.py:405, in Pipeline.fit(self, X, y, **fit_params)
    403     if self._final_estimator != "passthrough":
    404         fit_params_last_step = fit_params_steps[self.steps[-1][0]]
--> 405         self._final_estimator.fit(Xt, y, **fit_params_last_step)
    407 return self

File c:\Users\...\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\linear_model\_base.py:648, in LinearRegression.fit(self, X, y, sample_weight)
    644 n_jobs_ = self.n_jobs
    646 accept_sparse = False if self.positive else ["csr", "csc", "coo"]
--> 648 X, y = self._validate_data(
    649     X, y, accept_sparse=accept_sparse, y_numeric=True, multi_output=True
    650 )
    652 sample_weight = _check_sample_weight(
...
--> 185     array = numpy.asarray(array, order=order, dtype=dtype)
    186     return xp.asarray(array, copy=copy)
    187 else:

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

I think I need tyour texto do type conversion when I do research on the internet, but frankly, I don't know where to do it.

It's a bit hard to see what exactly is happening without more code but from what I see in your error messages, you define X, Y but where are X_train, X_test, y_train, and y_test instantiated?

from scipy.stats import chi2
from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import SelectPercentile
from sklearn.impute import SimpleImputer
from sklearn.metrics import r2_score, mean_absolute_error
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
import pandas as pd


df = pd.read_csv('laptop_data.csv')
# define your X and Y

# numeric data
numeric_features = ["Screen Size", "RAM"]
numeric_transformer = Pipeline(
    steps=[
        ("imputer", SimpleImputer(strategy="median")),
        ("scaler", StandardScaler())]
)
# categorical data
categorical_features = ["CPU"]
categorical_transformer = Pipeline(
    steps=[
        ("encoder", OneHotEncoder(handle_unknown="ignore")),
        ("selector", SelectPercentile(chi2, percentile=50)),
    ]
)
preprocessor = ColumnTransformer(
    transformers=[
        ("num", numeric_transformer, numeric_features),
        ("cat", categorical_transformer, categorical_features),
    ]
)
pipe = Pipeline([
    ("preprocessor", preprocessor),
    ("classifier", LinearRegression())
])

X_train, X_test, y_train, y_test = \
    train_test_split(X, Y, test_size=0.2, random_state=30, stratify=Y)

pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)

print('R2 score', r2_score(y_test, y_pred))
print('MAE', mean_absolute_error(y_test, y_pred))

Edit: I don't know what your data looks like but you'll probably want to create preprocessing pipelines for both numeric and categorical data. I just used CPU as a categorical assuming that was the column containing 'Intel' and 'AMD'