Program 1 Seaborn Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df_tips=pd.read_csv(“D://scikit_data/Billing/tips.csv”) df_tips.head() df_tips.shape df_tips.isnull().sum() df_tips.info() sns.displot(x=’total_bill’,data=df_tips) sns.displot(x=’total_bill’,data=df_tips,bins=100) sns.displot(x=’total_bill’,data=df_tips) sns.displot(x=’total_bill’,data=df_tips,kind=’hist’) # # kde=Karnel Denstity Estimeter...
Program 1 Seaborn Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df_tips=pd.read_csv(“D://scikit_data/Billing/tips.csv”) df_tips.head() sns.distplot(df_tips[‘total_bill’],bins=70) sns.distplot(df_tips[‘total_bill’],kde=False) sns.distplot(df_tips[‘total_bill’],hist=False) plt.xlabel(“Total Bill”) plt.ylabel(“Count”) plt.title(“Billing System”) sns.distplot(df_tips[‘total_bill’],hist=False,xlabel=”Total Bill”) sns.distplot(df_tips[‘total_bill’]) sns.distplot(df_tips[‘total_bill’],color=’r’)...
Program 1 import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df_tips=pd.read_csv(“D://scikit_data/Billing/tips.csv”) df_tips sns.lineplot(x=’size’,y=’total_bill’,data=df_tips,hue=’day’,style=’day’,palette=’rocket’, dashes=False,legend=’brief’,markers=[‘.’,’*’,’>’,'<‘]) plt.title(“Bill Vs Size”) plt.xlabel(“Size”,fontsize=15) plt.ylabel(“Totl Bill”,fontsize=15) plt.show() df_tips.shape
Program 1 Seaborn Dataset # # Applcation of Seaborn Line plot using Seaborn gitgub import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df_tips=sns.load_dataset(“tips”) df_tips.head() df_tips.info() df_tips.isnull().sum()...
Program 1 Seaborn Dataset # # Application of SeaBorn import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns #using Matplot lib days=[1,2,3,4,5,6,7,8,9,10] ind_temp=[34.4,23.4,45.4,24.4,34.4,36.2,44.4,33.2,28.7,33.5] plt.plot(days,ind_temp) plt.xlabel(“Days”); plt.ylabel(“Temperature”); plt.title(“Indore...
Program 1 Loan Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split import seaborn as sn from sklearn.metrics import accuracy_score #pandas 2.2.2 #numpy 1.24.3 #matplotlib 3.7.1...
Program 1 Machine Learning Dataset import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics #pandas 2.2.2 #numpy 1.24.3 #matplotlib...
Program 1 Machine Learning Dataset import pandas as pd from matplotlib import pyplot as plt df = pd.read_csv(“D://scikit_data/Employee/HR_comma_sep.csv”) df.head(10) left = df[df.left==1] left.shape left.shape left retained = df[df.left==0] retained.shape retained pd.crosstab(df.salary,df.left).plot(kind=’bar’) pd.crosstab(df.Department,df.left).plot(kind=’bar’) subdf =...
Program 1 Machine Learning Dataset import pandas as pd import numpy as np from matplotlib import pyplot as plt df=pd.read_csv(“D://scikit_data/insurancedata/insurance_data.csv”) df.head(10) plt.scatter(df.age,df.bought_insurance,marker=’*’,color=’blue’) plt.show() from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test=train_test_split(df[[‘age’]],df.bought_insurance,test_size=0.1) len(x_train) x_train len(x_test) x_test from sklearn.linear_model...
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