Create boston_dataset.py

Machine_Learning_Projects/simple_linear_regression/boston_dataset.py

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+import pandas as pd
+from sklearn import datasets
+import numpy as np
+import matplotlib.pyplot as plt
+
+boton=datasets.load_boston
+print(boston.keys())
+df=pd.Dataframe(boston.data)
+df.columns=boston.frature_names
+df['PRICE']=boston.target
+df.head()
+
+cdf=df[['ZN','INDUS','CHAS','RM','TAX','PTRATIO','PRICE','CRIM']]
+cdf.head()
+
+cdf.head(11)
+
+ktr=cdf[['ZN','INDUS','RM','TAX','PTRATIO','PRICE','CRIM']]
+ktr.hist()
+plt.show()
+plt.scatter(cdf.CRIM,cdf.PRICE,color='blue')
+plt.xlabel('crime_rate')
+plt.ylabel('price of houses')
+plt.title('crime_rate vs price of houses')
+plt.show()
+
+msk=np.random.rand((len(df)))<0.8
+train=cdf[msk]
+test=cdf[~msk]
+
+plt.scatter(train.CRIM,train.PRICE,color='blue')
+plt.xlabel('crime rate')
+plt.ylabel('price of houses')
+plt.show()
+
+from sklearn import linear_model
+lr=linear_model.LinearRegression()
+x=np.asanyarray(train['CRIM'])
+print(pd.DataFrame(x))
+print(pd.DataFrame(y))
+print(x)
+y=np.asanyarray(train['PRICE'])
+a=np.reshape(x,(-1,1))
+b=np.reshape(y,(-1,1))
+lr.fit(a,b)
+print(lr.coef_)
+print(lr.intercept_)
+
+plt.scatter(a,b,color='blue')
+plt.plot(a,lr.coef_[0][0]*a+lr.intercept_[0],'-r')
+plt.xlabel("crime rate")
+plt.ylabel("price of houses")
+plt.title("crime vs price")
+plt.show()