ivopbernardo

import pandas as pd
import pyodbc

#Ask for user and password input 

user = input('Provide user: \n')
pwd = input('Provide password: \n')

#Make connection to My SQL local host
mydb = pyodbc.connect("DRIVER={MySQL ODBC 8.0 ANSI Driver}; SERVER=localhost; PORT=3306;DATABASE=sakila; UID=%s; PASSWORD=%s;" % (user, pwd)) 

ivopbernardo

# Loading readxl library
library(readxl)

clean_crime_data <- function(path) {
  # Load the Data
  crime_data <- read_xls(path)
  
  # Assigning colnames
  colnames(crime_data) <- crime_data[3,]
  

ivopbernardo

# Import sklearn vectorizers and pandas
import pandas as pd
from sklearn.feature_extraction.text import (
  CountVectorizer, 
  TfidfVectorizer
)


# Defining our sentence examples
sentence_list = [

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from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer, SnowballStemmer, LancasterStemmer

porter = PorterStemmer()
snowball = SnowballStemmer(language='english')
lanc = LancasterStemmer()

sentence_example = (
  'This is definitely a controversy as the attorney labeled the case "extremely controversial"'
)

ivopbernardo

import wikipedia
import pandas as pd
import numpy as np
import string
from nltk.tokenize import word_tokenize
from sklearn.metrics.pairwise import cosine_similarity

def retrieve_page(page_name: str) -> list:
    '''
    Retrieves page data from wikipedia

ivopbernardo

# Don't forget to download the train.csv file
# to make this gist work.

# Download it at: https://www.kaggle.com/c/titanic/data?select=train.csv

# You also need to install ROCR and rpart libraries

# Reading the titanic train dataset
titanic <- read.csv('./train.csv')

ivopbernardo

# Training a Random Forest in R - used in blog post:
# https://towardsdatascience.com/data-science-tutorials-training-a-random-forest-in-r-a883cc1bacd1

library(dplyr)
library(randomForest) 
library(ranger)
library(Metrics)

# Load london bike csv
london_bike <- read.csv('./london_merged.csv')

ivopbernardo

# Training an XGBoost in R - used in blog post:
# https://towardsdatascience.com/data-science-tutorials-training-an-xgboost-using-r-cf3c00b1425

library(dplyr)
library(xgboost) 
library(Metrics)
library(ggplot2)

# Load london bike csv
london_bike <- read.csv('./london_merged.csv')

ivopbernardo

# Getting Latitude and Longitude from Nominatim

from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter

geocoder = Nominatim(user_agent="FindAddress")
geocode = RateLimiter(
    geocoder.geocode,
    min_delay_seconds = 1,
    return_value_on_exception = None

ivopbernardo

# Training a decision tree in R - used in blog post:
# https://medium.com/codex/data-science-tutorials-training-a-decision-tree-using-r-d6266936d86

library(dplyr)
library(rpart)
library(rpart.plot)
library(caret)
library(Metrics)
library(ggplot2)