1019 A02 Linear Regression
#Import Dataset
import pandas as pd
pd.read_csv("/work/data/twitchdata-update.csv")
twitch = pd.read_csv("/work/data/twitchdata-update.csv")
dataframe = twitch
display(twitch)
#Import Matplotlib
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import sklearn
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#Task 3: Perform exploratory data analysis using pandas library or any library of your choice.
twitch.head(10)
#Task 3: Perform exploratory data analysis using pandas library or any library of your choice.
twitch
#Task 3: Perform exploratory data analysis using pandas library or any library of your choice.
from numpy import nan
twitch = np.array([[ nan, 0, 3 ],
[ 3, 7, 9 ],
[ 3, 5, 2 ],
[ 4, nan, 6 ],
[ 8, 8, 1 ]])
twitch
#Task 3: Perform exploratory data analysis using pandas library or any library of your choice.
twitch['Followers']
#Task 3: Perform exploratory data analysis using pandas library or any library of your choice.
twitch['Followers'].mean()
#Task 4: Build and validate the model using Scikit-learn library and make predictions on a test data set.
#Task 5: Create at least four visualizations using Matplotlib in a 2 x 2 panel to share your insights.
fig = plt.figure()
ax = fig.add_subplot(2,2,1)
ax.plot(np.random.standard_normal(50).cumsum(),color="blue",linestyle="dashed")