How to apply decision tree regression for this file please ?

Question

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

price is output or Y

other fields on excel is input

so

How to apply decision tree regression for code below ?

i need to know how to write part related to decision tree regression after clean data ?

what i try

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)

file attached with post

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

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

Jayraj Chhaya · Answer

First you need to import the DecisionTreeRegressor class from the sklearn.tree module. You can do this by adding the following line of code at the beginning of your code: from sklearn.tree import DecisionTreeRegressor Next you can create an instance of the DecisionTreeRegressor class and specify any parameters you want to use. For example you can set the max_depth parameter to control the maximum depth of the decision tree. Here's an example of how you can create the decision tree regressor: regressor = DecisionTreeRegressor(max_depth=5) After creating the regressor you can fit it to your training data using the fit method. This will train the decision tree on your training data. Here's how you can fit the regressor: regressor.fit(X_train Y_train) Once the regressor is trained you can use it to make predictions on your test data using the predict method. Here's how you can make predictions: Y_pred = regressor.predict(X_test) Finally you can evaluate the performance of your model by calculating the mean squared error between the predicted values and the actual values. You can use the mean_squared_error function from the sklearn.metrics module to do this. Here's how you can calculate the mean squared error: mse = mean_squared_error(Y_test Y_pred) Now that we have added the decision tree regression part to your code here's the updated code: import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeRegressor 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) # Create the decision tree regressor regressor = DecisionTreeRegressor(max_depth=5) # Fit the regressor to the training data regressor.fit(X_train Y_train) # Make predictions on the test data Y_pred = regressor.predict(X_test) # Calculate the mean squared error mse = mean_squared_error(Y_test Y_pred) Now you should be able to apply decision tree regression to your Excel file and obtain predictions for the output variable. Remember to adjust the parameters of the DecisionTreeRegressor class according to your specific needs.

Tuhin Paul · Answer

To apply Decision Tree Regression on your dataset using Python and scikit-learn check once these things: Import necessary libraries. Load the dataset. Clean the data if needed (handling missing values encoding categorical variables etc.). Split the dataset into input features (X) and the output variable (Y). Split the data into training and testing sets. Train the Decision Tree model. Make predictions on the test set. Evaluate the performance of the model.