Program 1 Machine Learning Dataset import pandas as pd import numpy as np from sklearn import linear_model import pickle df=pd.read_excel(“D://scikit_data/emp/employee.xlsx”) print(df) model=linear_model.LinearRegression() model.fit(df.drop(‘salary’,axis=’columns’),df.salary) print(model) print(“Predication with Model: “,model.predict([[7]])) print(“Coficient with Model: “,model.coef_) print(“Intercepet with...
Program 1 Machine Learning Dataset import pandas as pd # Version 2.2.2 import numpy as np # Version 1.24.3 import matplotlib.pyplot as plt # Version 3.7.1 import os from sklearn import linear_model # 1.5.11import...
Program 1 Machine Learning Dataset import pandas as pd # Version 2.2.2 import numpy as np # Version 1.24.3 import matplotlib.pyplot as plt # Version 3.7.1 from sklearn import linear_model # 1.5.1 import joblib...
Program 1 Machine Learning Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model mydf=pd.read_csv(“D://scikit_data/home/homeprices.csv”) mydf.bedrooms=mydf.bedrooms.fillna(mydf.bedrooms.median()) print(mydf) model=linear_model.LinearRegression() # print(model) model.fit(mydf.drop(‘price’,axis=’columns’),mydf.price) print(model) sq=int(input(“Enter area: “)) bdr=int(input(“Enter No...
Program 1 Machine Learning Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model df=pd.read_excel(“D://scikit_data/newemp/employee.xlsx”) df=df.rename(columns={‘test_score(out of 10)’:’TestScore’}) df=df.rename(columns={‘interview_score(out of 10)’:’InteviewScore’}) df=df.rename(columns={‘salary($)’:’Salary’}) my_dict={‘zero’:0,’one’:1,’two’:2,’three’:3,’four’:4,’five’:5,’six’:6,’seven’:7,’eight’:8,’nine’:9,’ten’:10} df[‘experience’]=df[‘experience’].map(my_dict) df.experience=df.experience.fillna(2) df.TestScore=df.TestScore.fillna(df.TestScore.median()) #...
Program 1 Machine Learning Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score df=pd.read_csv(“D://scikit_data/insurancedata/insurance.csv”) map_dict={‘female’:0,’male’:1} df[‘sex’]=df[‘sex’].map(map_dict) map_dict={‘southwest’:0,’southeast’:1,’northwest’:2,’northeast’:3} df[‘region’]=df[‘region’].map(map_dict)...
Program 1 Machine Learning Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model mydf=pd.read_excel(“D://MLFile/employee.xlsx”) print(mydf) mymodel=linear_model.LinearRegression() mymodel.fit(mydf.drop(‘salary’,axis=’columns’),mydf.salary) #print((mymodel)) #print(mymodel.predict([[7.5]])) new_df=pd.read_excel(“D://MLFile/empexp.xlsx”) sal=mymodel.predict((new_df)) new_df[‘salary’]=sal print(new_df) new_df.to_excel(“D://MLFile/employeenew.xlsx”) plt.scatter(new_df.experience,new_df.salary,color=”green”,marker=’+’) plt.plot(new_df.experience,new_df.salary,color=”blue”)...
Program 1 Machine Learning Dataset # Machine Learning model for student marks predication import pandas as pd import numpy as np import matplotlib.pyplot as plt import os from sklearn import linear_model mydf=pd.read_excel(“D://MLFile/result.xlsx”) mymodel=linear_model.LinearRegression() mymodel.fit(mydf.drop(‘marks’,axis=’columns’),mydf.marks)...
Program 1 Employee Dataset # ML Model for Salary Predication import pandas as pd import numpy as np import matplotlib.pyplot as mlt from sklearn import linear_model mydf=pd.read_excel(“D://MLFile/employee.xlsx”) print(mydf) mymodel=linear_model.LinearRegression() mymodel.fit(mydf.drop(‘salary’,axis=’columns’),mydf.salary) ex=float(input(“Enter an Experience: “))...
Program 1 Machine Learning Dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model mydf=pd.read_excel(“homeprice.xlsx”) # Data Frame with Area and Price print(mydf) plt.xlabel(‘Area’) plt.ylabel(‘Price’) plt.scatter(mydf.area,mydf.price,color=’blue’,marker=’*’) plt.show()...
