code-ml

#importing important libraries
import pandas as pd
import numpy as np
import sklearn.model_selection as sl
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
employment={'Salaried':0, 'Self employed':1, ' ':1}
#importing dataset
dataset=pd.read_csv('CreditCardData.csv')

code-ml

#importing important libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.model_selection as sl
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error, r2_score
k=['linear','poly','rbf']
col=['red','blue','green']

code-ml

#importing libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, r2_score

#importing dataset
dataset=pd.read_csv('Climate.csv')

code-ml

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sklearn.model_selection as sl
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score

dataset=pd.read_csv('Climate.csv')
dataset=dataset.drop(columns=['stationname','month','period','year'])
factors=dataset.iloc[:,0:1].values

code-ml

#importing important libraries and creating dictionaries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn.model_selection as sl
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, r2_score
incomeType={'Wage':0, 'OtherSources':1, 'EntrepreneurialIncome':2}

#importing dataset and creating training and testing datasets

code-ml

#importing important libraries 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.model_selection as sl
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
col=['red','blue','green','yellow','cyan','magenta']

code-ml

#importing important libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn.model_selection as sl
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score