amansk2050

#Import scikit-learn dataset library
from sklearn import datasets

#Load dataset
wine = datasets.load_wine()

# print the names of the 13 features
print("Features: ", wine.feature_names)

# print the label type of wine(class_0, class_1, class_2)

amansk2050

# importing libraries  
import numpy as nm  
import matplotlib.pyplot as mtp  
import pandas as pd  
#importing datasets  
data_set= pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv') 
#Extracting Independent and dependent Variable
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelEncoder
y=data_set['species'].astype(str)

amansk2050

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv("/kaggle/input/iris-flower-dataset/IRIS.csv")
dataset.head()
Features=["sepal_length","sepal_width","petal_length","petal_width"]
X=dataset[Features]
y=dataset.species
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)

amansk2050

#importing basic library
import numpy as np
import pandas as pd
#loading dataset 
data_frame= pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv')
#preparing data 
feature=['sepal_length','sepal_width','petal_length','petal_width']
X=data_frame[feature]
y=data_frame.species
#dividing into train test set

amansk2050

#importing libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn import metrics
import matplotlib.pyplot as plt
#load_dataset
dataset= pd.read_csv('/kaggle/input/usa-housing/USA_Housing.csv')

amansk2050

#importing libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#loading dataset
dataset = pd.read_csv("/kaggle/input/decision-tree-data-set-from-stack-abuse/bill_authentication.csv")
#data analysis
dataset.shape
dataset.head()

amansk2050

#import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as mtp
# loding data set
data=pd.read_csv("/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv")
#having a look on data set
data.head(15)
#extracting dependent and independent variables
Y=data["Salary"]