Random Forest Classifier on Car data

Load car$\_$evaluation.csv using pandas

Perform a null check on the data

Encode every column using sklearn

Create independent and target variables

Split the dataset into training and test data with $25\%$ as test data and random$\_$state $=0$

Initialize a Random Forest Classifier from sklearn with criterion $=$ "gini", randomstate $=100,$ maxdepth$=3,$ minsamplesleaf$=5$

Train the Random Forest Classifier Model

Predict using test data

Generate confusion matrix

main.py:

# loads the necessary libraries

import pandas as pd

# load the car dataset

df $=$ #read the dataset using

# Encode the data

encoder $=$ Label$\_$Encoder$()$

df $=$ #encoded dataframe

x $=$ # subset the data containing car features

y $=$ # subset the data containing the labels

x$\_$train, x$\_$test, y$\_$train, y$\_$test $=$ # splits the data into training and test sets for both x and y$,$ with random$\_$state $=0$

rfc $=$ RandomForestClassifier$()$

print$($confusion$\_$matrix$)$