Nov05

New York,Los Angeles,2778
Chicago,Houston,940
San Francisco,Seattle,1094
Miami,Atlanta,663
Washington DC,Boston,394
Dallas,Austin,195
Phoenix,Denver,586
Detroit,Minneapolis,539
Charlotte,Raleigh,172
Philadelphia,Pittsburgh,305

Nov05

Time,Cnt,Sales,%Sales
10:00AM-10:30AM,7,"$108.60",0.2
10:30AM-11:00AM,62,"$970.59",1.84
11:00AM-11:30AM,102,"$1,399.29",2.66
11:30AM-12:00PM,174,"$2,592.46",4.94
12:00PM-12:30PM,210,"$2,847.98",5.42
12:30PM-1:00PM,189,"$2,609.96",4.97
1:00PM-1:30PM,163,"$2,388.46",4.55
1:30PM-2:00PM,168,"$2,222.29",4.23
2:00PM-2:30PM,104,"$1,430.59",2.72

Nov05

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models


class EncoderCNN(nn.Module):
    def __init__(self, embed_size):
        # super(EncoderCNN, self).__init__()
        super().__init__()

Nov05

Find the elements that appear in both lists.

import time
with open('names_1.txt', 'r') as f:
    names_1 = f.read().split("\n")  # List containing 10000 names
with open('names_2.txt', 'r') as f:
    names_2 = f.read().split("\n")  # List containing 10000 names

Nov05

'''
Load Yelp JSON files and spit out CSV files
Does not try to reinvent the wheel and uses pandas json_normalize
Kinda hacky and requires a bit of RAM. But works, albeit naively.

Tested with Yelp JSON files in dataset challenge round 12:
https://www.yelp.com/dataset/challenge
'''

import json

Nov05

Baseline

# baseline score
X_train, X_val, y_train, y_val = train_test_split(
    X_trainval, y_trainval, test_size=0.33, random_state=42)
print("train mean velocity:", y_train.mean())
y_val_pred = [y_train.mean()] * len(y_val)
print('baseline error score:', mean_squared_error(y_val, y_val_pred))

Nov05

!pip install colorlover
# Successfully installed colorlover-0.3.0

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

Nov05

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
import pandas_profiling
import plotly
import plotly.graph_objects as go
from sklearn.preprocessing import MinMaxScaler

Nov05

2019-08-05
Anaconda Logs

Unit 3 Module 1 Python Packages and Environments
https://github.com/Nov05/DS-Unit-3-Sprint-1-Software-Engineering/tree/master/module1-python-modules-packages-and-environments

Managing Environments (Anaconda)
https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

Windows 10, Anaconda Prompt

Nov05

2019-08-01

Check Heroku Logs and Debug

Dash App on Heroku
https://hours-estimate.herokuapp.com/
GitHub Repo
https://github.com/Nov05/DS-Unit-2-Sprint-4-Project

• navigate: Heroku > hours-estimate (or your own app name) > Activity > View build log