AI_Workshop.py

# coding: utf-8

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import pandas as pd
data=pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv',index_col=0)


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X=data[["TV","radio","newspaper"]].as_matrix()
Y=data[["sales"]].as_matrix()
X.shape


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X_train=X[:150,:]
X_test=X[150:,:]

Y_train=Y[:150,:]
Y_test=Y[150:,:]


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#Regression


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import seaborn as sns
get_ipython().magic('matplotlib inline')
sns.pairplot(data,x_vars=["TV","radio","newspaper"],y_vars=["sales"],kind='reg',size=7)


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#Sci-Kit learn
from sklearn.linear_model import LinearRegression


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linreg=LinearRegression()


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#.fit---Training using the dataset including features & Labels part
#.predict----predict using features
linreg.fit(X_train,Y_train)


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Y_pred=linreg.predict(X_test)


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import numpy as np
m=np.concatenate((Y_test,Y_pred),axis=1)
m


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#Evaluation Matices
#MAE-Mean Absolute error
#MSE-Mean Squared Error
#RMSE-Root Mean Squared Error
from sklearn import metrics
np.sqrt(metrics.mean_squared_error(Y_test,Y_pred))


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from sklearn.datasets import load_iris
iris=load_iris()
X=iris.data
Y=iris.target


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y=Y.reshape(150,1)
data=np.concatenate((X,y),axis=1)
data
from random import shuffle
shuffle(data)


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data
f=data[:,:4]
l=data[:,4:]
f_train=f[:100,:]
f_test=f[100:,:]

l_train=l[:100,:]
l_test=l[100:,:]


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from sklearn.neighbors import KNeighborsClassifier
knn=KNeighborsClassifier(n_neighbors=7)
knn.fit(f_train,l_train)
y_pred=knn.predict(f_test)
count=0
for i in range(0,50):
    if (y_pred[i]==l_test[i]):
        count=count+1
        
(count/50)*100
        


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