Projects6 Analyzing US Economic Data - House Sales in King County, USA 1. import libraries import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn as sns from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler,PolynomialFeatures %matplotlib inline 2. import the data and show the first five rows # importing the data file_name='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass.. 2021. 6. 28. Which is the best model to be able to predict car price based on the dataset? (1) import libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt (2) get the data # path of data path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv' df = pd.read_csv(path) df.head() (3) load the modules & create the linear regression object from sklearn.linear_m.. 2021. 6. 5. What are the main characteristics which have the most impact on the car price? import pandas as pd import numpy as np 1) import data from the external source path='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv' df = pd.read_csv(path) df.head() 2) install seaborn and matplotlib for visualization %%capture ! pip install seaborn import matplotlib.pyplot as plt import seab.. 2021. 5. 24. Socioeconomic Indicators, Schools & Crimes in Chicago Data Analysis (using sqlite3) 보호되어 있는 글 입니다. 2021. 4. 25. Socioeconomic Indicators in Chicago Data Analysis (using sqlite3) The city of Chicago released a dataset of socioeconomic data to the Chicago City Portal. This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” for each Chicago community area, for the years 2008 – 2012. Scores on the hardship index can range from 1 to 100, with a higher index number representing a greater level of hardship. A deta.. 2021. 4. 25. Analyzing Historical Stock/Revenue Data and Building a Dashboard - 직접 주식 data web scraping해서 history 변화 graph나타내는 project 하기! (toy project) 모든 주식 - selenium 배우기 * Define Graphing Function def make_graph(stock_data, revenue_data, stock): fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3) fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data.Date, infer_datetime_format=Tr.. 2021. 4. 7. 이전 1 다음
