requirements.txt

#REQUIREMENTS#
This file lists the libraries used at some stage or other during the project
in order of the stages
All versions are the latest versions imported as of 31st March 2023

###The essentials 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
import seaborn as sns
import json
import pickle
import pickle as pkl


###Geolocation and Mapping
import googlemaps
import folium
from geopy.distance import distance
from sklearn.neighbors import BallTree, KDTree
from sklearn.metrics.pairwise import haversine_distances

###DisplayCompatibility
from IPython.display import display, HTML
import ipywidgets as widgets


###Wrangling and Processing
import re
import time
from tqdm import tqdm
import random
import csv
import ast

###FeatureEngineering:Standardising Scaling,Regularizing and Transforming
import itertools
import random
simpleImputer
from sklearn.decomposition import PCA
from sklearn.preprocessing import (
    OneHotEncoder, 
    OrdinalEncoder, 
    StandardScaler,
    PowerTransformer
)

######NLP############
from cleantext import clean
Spacy used for tokenizing
from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer


########Column Transformer, Pipeline, Modeling########
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import FunctionTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import (
    RandomizedSearchCV,
    GridSearchCV,
    cross_val_score,
    cross_validate,
    train_test_split)
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import lightgbm as lgb
import xgboost as xgb

####TimesSeriesModeling######
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.seasonal import seasonal_decompose
from pmdarima import auto_arima
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tools.eval_measures import rmse

####HyperParameterTuning##########
import warnings 
warnings.filterwarnings('ignore')
from sklearn.model_selection import (
    RandomizedSearchCV,
    GridSearchCV,
    cross_val_score,
    cross_validate,
    train_test_split,
)

####ModelEvaluation##########
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.metrics import (
    classification_report,
    confusion_matrix,
    f1_score,
    recall_score,
)
from statsmodels.tools.eval_measures import rmse