How to apply linear regression for this file please ?

Question

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

so

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

Amira Bedhiafi · 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(fMean Squared Error: {mse})

Rohit Gupta · Answer

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

Tuhin Paul · Answer

To apply linear regression on an Excel file using Python in Google Colab check below: Import the necessary libraries including pandas for data manipulation and scikit-learn for machine learning algorithms. Load the Excel file into a Pandas DataFrame. 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. Split the dataset into input features (X) and the output variable (Y). Split the data into training and testing sets. This is essential for evaluating the model's performance. Use scikit-learn's LinearRegression class to train the model. Make predictions on the test set. Evaluate the model's performance using metrics such as mean squared error R-squared etc.