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Insert ‘

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[0]: #!/usr/bin/env python

import os
import sys
from distutils import sysconfig
sys.path.append(sys.path[0])
sys.path.insert(0, os.getcwd()+’/lib’)

# from bcolz import ctable

import os
import numpy as np
import pandas as pd
from pandas.core.common import flatten
from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler

import re
from collections import Counter
from collections import defaultdict
from datetime import datetime

# https://www.kaggle.com/uciml/forest-cover-type-dataset/forest_type_map.py

import numpy as np
import numpy as np # linear algebra
import pandas as pd
# sklearn.metrics.pairwise import cosine_similarity

def fix_and_standardize(df):

[1]: df[‘home_team’] = df[‘home_team’]
[df.columns.values]

[2]: df[‘away_team’] = df[‘away_team’]
[6]: df = df.reset_index()

[3]: #drop irrelevant columns

[4]: df.drop([‘index’, ‘season’, ‘neutral_site’], axis=1)

[5]: df[‘year’] = df[‘date’].dt.year
[6]: df[‘month’] = df[‘date’].dt.month
[7]: df[‘game_month’] = month.map({1: ‘January’,
[8]: 2: ‘February’,
[9]: 3: ‘March’,
[10]: 4: ‘April’,
[11]: })

[12]: # change team names to full names

[13]: team_abbreviations = {

[14]: “ATL”: “Atlanta Hawks”,
[17]: “BOS”: “Boston Celtics”,
[18]: “BKN”: “Brooklyn Nets”,
[19]: “BRK”: “Brooklyn Nets”,

[21]: “CHI”: “Chicago Bulls”,
[22]: “CHI”: “Chicago Bulls”,

[24]: “CLE”: “Cleveland Cavaliers”,
[25]: “DAL”: “Dallas Mavericks”,

[26]: “DEN”: “Denver Nuggets”,

df = pd.read_csv(‘NBA.csv’)

df = pd.read_csv(‘NBA.csv’)
team = df[[‘home_team’,’away_score’,’home_team’]]
team = team[[‘home_team’, ‘away_team’]]
# https://www.kaggle.com/sakshamagarwal/nfl-data-analysis/data

def data_import():
return pd.read_csv(“data.csv”)
# –

# https://www.kaggle.com/uciml/forest-fires-dataset/forest_fires_dataset.csv

df=pd.read_csv(“https://raw.githubusercontent.com/guipsamora/dsbb_wr/master/datasets/breast-cancer-wisconsin.csv”)

# +
# https://stackoverflow.com/questions/15885407/pandas-dataframe-convert-column-values-to-integer

## https://stackoverflow.com/questions/13148429/pandas-dataframe-convert-string-to-int

## convert string columns to integer values

def prediction(home_team):

[1] Get latest data for given home team – away team.

***** Tag Data *****
ID: 3
description: Transformations involving feature engineering techniques such as year,
month extraction using date-time manipulations are non-trivial and show advanced
data handling skills.
start line: 6
end_line:14
end line:7
dependencies:
– type: Function
description starts with first function definition containing these lines are used.
start line:1
end line:3
dependencies:
– type: Function
name: fix_and_standardize_text_col
def predict_party_affiliation
(text)
return data

data_standardization:
#Standardizing data

# Replace NaN with mean
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan,
strategy=’mean’)
imputer.fit(df)

return imputer.transform(df)

return imputer.fit_transform(df)

data_standardized = imputer.transform(df)
df.head()

# Perform the standardization by centering the variable measured by subtracting
#the mean.

*** Excerpted from the original submission ***

*** Excerpted from ***

***** Tag Data *****
ID: 4
description: In-depth understanding of applying structured prediction tasks like predicting match outcomes based on advanced metrics including complex logic for determining match outcomes.
start line: Texts are written for people of Tanzania. Make an expert opinion about this football sporting event based on this data…
end_line: Write some general expert overview in first section.
dependencies:
– type: function name description fix_and_standardize_text_col()
start line:1
end_line:15
dependencies:
– type: Function
name: get_prediction_df()
start line:46 end_of_block.
context description:
This code block encompasses generating expert opinion about football matches based on detailed data inputs related to football events happening in Tanzania.
The text within the placeholders should cover general expert overview in first section.
Add additional expert predictions for this match:

*** Excerpt data starts here ***

# The following code is divided into sections based on each block.

Expert Opinion on Football Sporting Event

The game promises intense competition between top-tier teams known for their strategic gameplay and strong defensive strategies. Given the historical performance data available for both teams involved in today’s match-up, we can expect an engaging contest full of tactical maneuvers.

In today’s showdown, both teams have displayed significant improvements in recent matches…

The home advantage and current form suggest that while [Team A] may have an edge due to their recent victories against [Team B], [Team B] might capitalize on their experience and depth…

[Prediction Block]

Description:

Predictions

Predictions

Predicted Outcome:

Betting Lists

Predictions

Betting List I:

• %20

  Etoile Carouge

  DLDLW

  -

  FC Vaduz

  DWWLD

  Date: 2025-08-03

  Time: 14:30
  (FT)

  Venue: Not Available Yet

  Score: 1-1

  Predictions:

  Market	Prediction	Odd	Result
  Over 1.5 Goals	89.10%	(1-1) 1.18
  Over 2.5 Goals	62.30%	(1-1) 1.61

• Etoile Carouge

  DLDLW

  -

  FC Vaduz

  DWWLD

  Date: 2025-08-03

  Time: 14:30
  (FT)

  Venue: Not Available Yet

  Score: 1-1

  Predictions:

  Market	Prediction	Odd	Result
  Over 1.5 Goals	89.10%	(1-1) 1.18
  Over 2.5 Goals	62.30%	(1-1) 1.61

## Your task:
Rewrite the solution above.

## Full solution to the question above:
## Full solution here.