How to apply linear regression for this file please ?

ahmed salah

I'm beginner working on python machine learning using google colab . i face issue i can't apply linear regression on excel file

price is output or Y

other fields on excel is input

How to clean data ?

after that How to apply linear regressin for this excel file

what i try

import pandas as pd
df=pd.read_excel('Data_Train.xlsx')

file attached with post

/forums/uploadfile/e2e758/01302024213902PM/Data_Train.zip

/forums/uploadfile/e2e758/01302024213902PM/Data_Train.xlsx

3 Replies

Know the answer? Post it — somebody with the same question will find it here.

Amira Bedhiafi2y ago

Accepted answer

Try the following :


import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

df = pd.read_excel('Data_Train.xlsx')

# For example, if there are missing values, you can fill them with the mean or median.
# df.fillna(df.mean(), inplace=True)

X = df.drop('price', axis=1) # Input features
Y = df['price'] # Output variable

# Here I split the data into training and testing sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)


model = LinearRegression()
model.fit(X_train, Y_train)
predictions = model.predict(X_test)

mse = mean_squared_error(Y_test, predictions)
print(f"Mean Squared Error: {mse}")

Tuhin Paul2y ago
To apply linear regression on an Excel file using Python in Google Colab, check below:
1. Import the necessary libraries, including pandas for data manipulation and scikit-learn for machine learning algorithms.
2. Load the Excel file into a Pandas DataFrame.
3. Explore the dataset, handle missing values, and preprocess the data if needed. For linear regression, it's crucial to handle missing values and encode categorical variables if present.
4. Split the dataset into input features (X) and the output variable (Y).
5. Split the data into training and testing sets. This is essential for evaluating the model's performance.
6. Use scikit-learn's LinearRegression class to train the model.
7. Make predictions on the test set.
8. Evaluate the model's performance using metrics such as mean squared error, R-squared, etc.
+1
Rohit Gupta2y ago
Hi

Please refer to https://www.c-sharpcorner.com/article/linear-regression2/

Thanks
+4