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task2.py

+# Importing libraries
+import pandas as pd
+import seaborn as sns
+from sklearn.linear_model import LogisticRegression
+from sklearn import datasets
+import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score,f1_score
+from sklearn.inspection import DecisionBoundaryDisplay
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+# Importing the csv files
+testDF= pd.read_csv('TestingDataMulti.csv', header=None)
+trainDF= pd.read_csv('TrainingDataMulti.csv', header=None)
+# Use of Random forest model function
+randomfunc = RandomForestClassifier()
+# Getting the data reading for fitting
+X = trainDF.iloc[:,:-1]
+y = trainDF.iloc[:,-1]
+#Creating the variables for both testing and training by splitting the data into 80% for training and 20% for testing.
+X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=.2, random_state=21)
+randomfunc.fit(X_train, y_train)
+y_predictDF = randomfunc.predict(X_test)
+# Computing the prediction error
+prediction_error = 1 - accuracy_score(y_test, y_predictDF)
+print("Prediction Error:", prediction_error)
+# Predicting using the X_test variable
+accuracy = accuracy_score(y_test, y_predictDF)
+print("Accuracy:", accuracy)
+# Evaluating and Displaying the accuracy of our predictions
+label = randomfunc.predict(testDF)
+# By utilizing the testing data, we determine the anticipated values/predictions.
+Output = pd.DataFrame(label, columns=['Prediction'])
+# Saving the predictions to a CSV file
+testDF['Prediction'] = Output["Prediction"].tolist()
+testDF.to_csv("TestingResultsMulti.csv", index=False)
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