1. somebody plz help with this
Make use of the scikit-learn (sklearn) python package in your function implementations
Complete the Following Functions in task4.py: calculate_naive_metrics
Given a train dataframe, test dataframe, target_col and naive assumption split out the target
column from the training and test dataframes to create a feature dataframes and a target series
then calculate (rounded to 4 decimal places) accuracy, recall, precision and f1 score using the
sklearn functions, the train and test target values and the naive assumption.
calculate_logistic_regression_metrics
Given a train dataframe, test dataframe, target_col and logreg_kwargs split out the target column
from the training and test dataframes to create a feature dataframes and a target series. Then train
a logistic regression model (initialized using the kwargs) on the training data and predict (both
binary predictions and probability estimates) on the training and test data. Then using those
predictions and estimates along with the target values calculate (rounded to 4 decimal places)
accuracy, recall, precision, f1 score, false positive rate, false negative rate and area under the
reciever operator curve (using probabilities for roc auc) for both training and test datasets.
For Feature Importance use the top 10 features selected by RFE and sort by absolute value of the
coefficient from biggest to smallest (make sure you use the same feature and importance column
names as ModelMetrics shows in feat_name_col and imp_col and the index is 0-9 you can do
that with `df.reset_index(drop=True)` )
calculate_random_forest_metrics
Given a train dataframe, test dataframe, target_col and rf_kwargs split out the target column
from the training and test dataframes to create a feature dataframes and a target series. Then train
a random forest model (initialized using the kwargs) on the training data and predict (both binary
predictions and probability estimates) on the training and test data. Then using those predictions
and estimates along with the target values calculate (rounded to 4 decimal places) accuracy,
recall, precision, f1 score, false positive rate, false negative rate and area under the reciever
operator curve (using probabilities for roc auc) for both training and test datasets
For Feature Importance use the top 10 features using the built in feature importance attributes as
sorted from biggest to smallest (make sure you use the same feature and importance column
names as ModelMetrics shows in feat_name_col and imp_col and the index is 0-9 you can do
that with `df.reset_index(drop=True)` )
calculate_gradient_boosting_metrics
Given a train dataframe, test dataframe, target_col and gb_kwargs split out the target column
from the training and test dataframes to create a feature dataframes and a target series. Then train
a gradient boosting model (initialized using the kwargs) on the training data and predict (both

2. binary predictions and probability estimates) on the training and test data. Then using those
predictions and estimates along with the target values calculate (rounded to 4 decimal places)
accuracy, recall, precision, f1 score, false positive rate, false negative rate and area under the
reciever operator curve (using probabilities for roc auc) for both training and test datasets
For Feature Importance use the top 10 features using the built in feature importance attributes as
sorted from biggest to smallest (make sure you use the same feature and importance column
names as ModelMetrics shows in feat_name_col and imp_col and the index is 0-9 you can do
that with `df.reset_index(drop=True)` )
import numpy as np
import pandas as pd
from sklearn.metrics import *
from sklearn.linear_model import *
from sklearn.ensemble import *
from sklearn.feature_selection import RFE
class ModelMetrics:
def __init__(self,
model_type:str,train_metrics:dict,test_metrics:dict,feature_importance_df:pd.DataFrame):
self.model_type = model_type
self.train_metrics = train_metrics
self.test_metrics = test_metrics
self.feat_imp_df = feature_importance_df
self.feat_name_col = "Feature"
self.imp_col = "Importance"
def add_train_metric(self,metric_name:str,metric_val:float):
self.train_metrics[metric_name] = metric_val
def add_test_metric(self,metric_name:str,metric_val:float):
self.test_metrics[metric_name] = metric_val
def __str__(self):
output_str = f"MODEL TYPE: {self.model_type}n"
output_str += f"TRAINING METRICS:n"
for key in sorted(self.train_metrics.keys()):
output_str += f" - {key} : {self.train_metrics[key]:.4f}n"
output_str += f"TESTING METRICS:n"
for key in sorted(self.test_metrics.keys()):
output_str += f" - {key} : {self.test_metrics[key]:.4f}n"
if self.feat_imp_df is not None:
output_str += f"FEATURE IMPORTANCES:n"
for i in self.feat_imp_df.index:
output_str += f" - {self.feat_imp_df[self.feat_name_col][i]} :

3. {self.feat_imp_df[self.imp_col][i]:.4f}n"
return output_str
def calculate_naive_metrics(train_dataset:pd.DataFrame, test_dataset:pd.DataFrame,
target_col:str, naive_assumption:int) -> ModelMetrics:
# TODO: Write the necessary code to calculate accuracy, recall, precision and fscore given a
train and test dataframe
# and a train and test target series and naive assumption
train_metrics = {
"accuracy" : 0,
"recall" : 0,
"precision" : 0,
"fscore" : 0
}
test_metrics = {
"accuracy" : 0,
"recall" : 0,
"precision" : 0,
"fscore" : 0
}
naive_metrics = ModelMetrics("Naive",train_metrics,test_metrics,None)
return naive_metrics
def calculate_logistic_regression_metrics(train_dataset:pd.DataFrame,
test_dataset:pd.DataFrame, target_col:str, logreg_kwargs) ->
tuple[ModelMetrics,LogisticRegression]:
# TODO: Write the necessary code to train a logistic regression binary classifiaction model and
calculate accuracy, recall, precision, fscore,
# false positive rate, false negative rate and area under the reciever operator curve given a train
and test dataframe and train and test target series
# and keyword arguments for the logistic regrssion model
model = LogisticRegression()
train_metrics = {
"accuracy" : 0,
"recall" : 0,
"precision" : 0,
"fscore" : 0,
"fpr" : 0,
"fnr" : 0,
"roc_auc" : 0
}
test_metrics = {
"accuracy" : 0,
"recall" : 0,
"precision" : 0,

4. "fscore" : 0,
"fpr" : 0,
"fnr" : 0,
"roc_auc" : 0
}
# TODO: Use RFE to select the top 10 features
# make sure the column of feature names is named Feature
# and the column of importances is named Importance
# and the dataframe is sorted by ascending ranking then decending absolute value of Importance
log_reg_importance = pd.DataFrame()
log_reg_metrics = ModelMetrics("Logistic
Regression",train_metrics,test_metrics,log_reg_importance)
return log_reg_metrics,model
def calculate_random_forest_metrics(train_dataset:pd.DataFrame, test_dataset:pd.DataFrame,
target_col:str, rf_kwargs) -> tuple[ModelMetrics,RandomForestClassifier]:
# TODO: Write the necessary code to train a random forest binary classification model and
calculate accuracy, recall, precision, fscore,
# false positive rate, false negative rate and area under the reciever operator curve given a train
and test dataframe and train and test
# target series and keyword arguments for the random forest model
model = RandomForestClassifier()
train_metrics = {
"accuracy" : 0,
"recall" : 0,
"precision" : 0,
"fscore" : 0,
"fpr" : 0,
"fnr" : 0,
"roc_auc" : 0
}
test_metrics = {
"accuracy" : 0,
"recall" : 0,
"precision" : 0,
"fscore" : 0,
"fpr" : 0,
"fnr" : 0,
"roc_auc" : 0
}
# TODO: Reminder DONT use RFE for rf_importance
# make sure the column of feature names is named Feature
# and the column of importances is named Importance
# and the dataframe is sorted by decending absolute value of Importance

5. rf_importance = pd.DataFrame()
rf_metrics = ModelMetrics("Random Forest",train_metrics,test_metrics,rf_importance)
return rf_metrics,model
def calculate_gradient_boosting_metrics(train_dataset:pd.DataFrame,
test_dataset:pd.DataFrame, target_col:str, gb_kwargs) ->
tuple[ModelMetrics,GradientBoostingClassifier]:
# TODO: Write the necessary code to train a gradient boosting binary classification model and
calculate accuracy, recall, precision, fscore,
# false positive rate, false negative rate and area under the reciever operator curve given a train
and test dataframe and train and test
# target series and keyword arguments for the gradient boosting model
model = GradientBoostingClassifier()
train_metrics = {
"accuracy" : 0,
"recall" : 0,
"precision" : 0,
"fscore" : 0,
"fpr" : 0,
"fnr" : 0,
"roc_auc" : 0
}
test_metrics = {
"accuracy" : 0,
"recall" : 0,
"precision" : 0,
"fscore" : 0,
"fpr" : 0,
"fnr" : 0,
"roc_auc" : 0
}
# TODO: Reminder DONT use RFE for gb_importance
# make sure the column of feature names is named Feature
# and the column of importances is named Importance
# and the dataframe is sorted by decending absolute value of Importance
gb_importance = pd.DataFrame()
gb_metrics = ModelMetrics("Gradient Boosting",train_metrics,test_metrics,gb_importance)
return gb_metrics,model