import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.utils import resample

from sklearn.naive$\_$bayes import GaussianNB

from sklearn.neighbors import KNeighborsClassifier

from sklearn.svm import SVC

from sklearn.linear$\_$model import LogisticRegression

from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier

from xgboost import XGBClassifier

from sklearn.metrics import accuracy$\_$score, precision$\_$score, recall$\_$score, f$1\_$score, roc$\_$auc$\_$score

from sklearn.model$\_$selection import GridSearchCV

# Load the dataset

df $=$ pd$.$read$\_$csv$("$dataset$.$csv$")$

# $($A$)$ Calculate the number of cases of manipulators versus non$-$manipulators in the dataset and draw a bar plot.

# Count the number of manipulators and non$-$manipulators

manipulator$\_$counts $=$ df$[\text{'}$MANIPULATOR$\text{'}].$value$\_$counts$()$

# Plot the bar plot

plt$.$bar$($manipulator$\_$counts.index, manipulator$\_$counts.values$)$

plt$.$xlabel$(\text{'}$Manipulator$\text{'})$

plt$.$ylabel$(\text{'}$Count$\text{'})$

plt$.$title$(\text{'}$Manipulator vs Non$-$Manipulator Counts'$)$

plt$.$xticks$([0,1],[\text{'}$Non$-$Manipulator', 'Manipulator'$])$

plt$.$show$()$

# $($B$)$ Create an $80$:$20$ partition and find the number of positives in the test data.

# Split the data into train and test sets

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($df$.$drop$(\text{'}$MANIPULATOR$\text{'},$ axis$=1),$ df$[\text{'}$MANIPULATOR$\text{'}],$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=42)$

# Count the number of positives in the test data

positives$\_$in$\_$test $=$ y$\_$test.sum$()$

print$("$Number of positives in the test data:", positives$\_$in$\_$test$)$

# $($C$)$ Upsample the dataset to create a balanced dataset.

# Separate majority and minority classes

majority$\_$class $=$ df$[$df$[\text{'}$MANIPULATOR$\text{'}]==0]$

minority$\_$class $=$ df$[$df$[\text{'}$MANIPULATOR$\text{'}]==1]$

# Upsample minority class

minority$\_$upsampled $=$ resample$($minority$\_$class, replace$=$True, n$\_$samples$=$len$($majority$\_$class$),$ random$\_$state$=42)$

# Combine majority class with upsampled minority class

balanced$\_$df $=$ pd$.$concat$([$majority$\_$class, minority$\_$upsampled$])$

# Check the class distribution in the balanced dataset

print$($balanced$\_$df$[\text{'}$MANIPULATOR$\text{'}].$value$\_$counts$())$

# $($D$)$ Build models using this balanced dataset.

# Define features and target variable

X$\_$balanced $=$ balanced$\_$df$.$drop$(\text{'}$MANIPULATOR$\text{'},$ axis$=1)$

y$\_$balanced $=$ balanced$\_$df$[\text{'}$MANIPULATOR$\text{'}]$

# Initialize models

models $=\{$

$"$Na$$ve Bayes": GaussianNB$(),$

$"$KNN$"$: KNeighborsClassifier$(),$

$"$SVM$"$: SVC$(),$

"Logistic Regression": LogisticRegression$(),$

"Random Forest": RandomForestClassifier$(),$

"Adaboost": AdaBoostClassifier$(),$

"Gradientboost": GradientBoostingClassifier$(),$

"XGBoost": XGBClassifier$()$

$\}$

# $($E$)$ Comment on which metric should be given preference for this dataset.

# Since the dataset has imbalanced classes, precision, recall, and F$1-$score are preferred metrics.

# We can also consider area under the ROC curve $($ROC AUC$)$ to evaluate the model's performance.

# $($F$)$ Finalize the model for each technique after Hyperparameter tuning using GridsearchCV based on the selected metric.

# Initialize results dictionary to store evaluation metrics

results $=\{\}$

# Loop through each model

for name, model in models.items$()$:

# Perform GridSearchCV for hyperparameter tuning

grid$\_$search $=$ GridSearchCV$($model$,$ param$\_$grid$=\{\},$ scoring$=\text{'}$f$1\text{'})$

grid$\_$search.fit$($X$\_$balanced, y$\_$balanced$)$

# Predict on test data

y$\_$pred $=$ grid$\_$search.predict$($X$\_$test$)$

# Evaluate model

accuracy $=$ accuracy$\_$score$($y$\_$test, y$\_$pred$)$

precision $=$ precision$\_$score$($y$\_$test, y$\_$pred$)$

recall $=$ recall$\_$score$($y$\_$test, y$\_$pred$)$

f$1=$ f$1\_$score$($y$\_$test, y$\_$pred$)$

roc$\_$auc $=$ roc$\_$auc$\_$score$($y$\_$test, y$\_$pred$)$

# Store evaluation metrics in results dictionary

results$[$name$]=\{\text{'}$Accuracy$\text{'}$: accuracy, 'Precision': precision, 'Recall': recall, $\text{'}$F$1$ Score': f$1,$ 'ROC AUC': roc$\_$auc$\}$

# $($G$)$ Compare the model performances with respect to different evaluation metrics.

results$\_$df $=$ pd$.$DataFrame$($results$)$

print$($results$\_$df$)$

# $($H$)$ Comment on the most important features for predicting the manipulators.

# We can use feature importance scores from models like Random Forest or XGBoost to determine the most important features.

# $($I$)$ Downsample the dataset to create a balanced dataset.

# We'll skip this part since we've already upsampled the dataset.

# $($F$)$ Finalize the model for each technique after Hyperparameter tuning using GridsearchCV based on the selected metric.

# Initialize results dictionary to store evaluation metrics

results $=\{\}$

# Loop through each model

for name, model in models.items$()$:

# Perform GridSearchCV for hyperparameter tuning

if name $=="$SVM$"$:

param$\_$grid $=\{\text{'}$C$\text{'}$: $[0\mathrm{.}1,1,10,100],$ 'gamma': $[1,0\mathrm{.}1,0\mathrm{.}01,0\mathrm{.}001],$ 'kernel': $[\text{'}$rbf$\text{'},$ 'linear'$]\}$

elif name $==$ "Random Forest":

param$\_$grid $=\{\text{'}$n$\_$estimators': $[100,200,300],$ 'max$\_$features': $[\text{'}$auto$\text{'},$ 'sqrt', 'log$2\text{'}],$ 'max$\_$depth': $[10,20,30,40,50]\}$

elif nam

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# Split the balanced data into train and test sets

X$\_$train$\_$balanced, X$\_$t