mydata.py

#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn
import seaborn as sns
data=pd.read_csv("data.csv")
data


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data.shape


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data.info()


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data.columns


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data.isnull().sum()# m= malignant خبيث b=benignحميد


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#data.drop('id',axis=1,inplace=True)
data.drop('Unnamed: 32',axis=1,inplace=True)


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data.isnull().sum()


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data


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data.diagnosis.unique()


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data["diagnosis"].value_counts()


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data['diagnosis'] = data['diagnosis'].map({'M':1,'B':0})#manuplution sample #rating
data.head()#عشان ارجع ب اول خمس قيم ف العمود 


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data.describe() #describe columns of numbers only


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data.groupby(by="diagnosis").count()


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l=data.groupby(by="diagnosis")
B=l.get_group(0)
B


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h=data.groupby(by="diagnosis")
M=l.get_group(1)
M


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data.groupby(by=["diagnosis","radius_mean"]).count()


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data[["diagnosis","radius_mean","texture_mean"]].sort_values(by="radius_mean")


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data[["diagnosis","radius_mean","texture_mean"]].sort_values(by="texture_mean")#عشان أثبت ان حجم النسيج بيعتمد علي نصف قطره اما بيبقي قليل التاني بيبقي قليل


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data.loc[data.radius_mean<10]#عشان أثبت ان اللي اقل من عشره ف نصف قطر الخليه دايما خبيث


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data.loc[data.radius_mean>10]#عشان اثبت ان اللي اكبر من عشره نصف قطره بيبقي حميد


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my_labels=data.diagnosis.unique().tolist()
my_labels


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data.diagnosis.value_counts()


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values=data.diagnosis.value_counts().tolist()
values


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data.hist()
plt.show()


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data.radius_mean.hist()


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plt.bar(data.radius_mean,data.texture_mean)
plt.show()


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data.plot(kind="bar")
plt.show()


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my_explode=[0,.1]# pipe chart
my_labels=['B','M']
my_colors=["green","red"]
plt.pie(values,labels=my_labels,startangle=90,explode=my_explode,colors=my_colors)
plt.show()


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x=data.loc[:301,["radius_mean"]]#independent #input   # linear    regression  supervised continous
y=data.loc[:301,["perimeter_mean"]]# depndent dataمعتمده علي اللي فوقها   #y=a+bx #output
x_test=data.loc[:302,["radius_mean"]]
y_desired=data.loc[:302,["perimeter_mean"]]#actual predicted data
x


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plt.scatter(x,y)
plt.show()


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from sklearn.linear_model import LinearRegression#calling model


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model=LinearRegression()#object from linear


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model.fit(x,y)#training


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y_predicted=model.predict(x_test)#test for predictiong
y_predicted #الداتا اللي المودل توقعها ليا 


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plt.scatter(x_test,y_predicted)
plt.show()


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plt.scatter(x_test,y_desired)
plt.show()


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model.intercept_ #التقاطع


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model.coef_ 


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import sklearn.metrics as mc


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dir(mc)  


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mc.mean_absolute_error(y_desired,y_predicted)#الفرق بين الاتنين ويجمعهم ويحطهم جوا المقياس ويقسم التموسط علي عددهم


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mc.mean_squared_error(y_desired,y_predicted)# الفرق بينهم ويجمعهم ويربعهم علي عددهم


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#polynomial


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from sklearn.preprocessing import PolynomialFeatures 


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polyF=PolynomialFeatures(degree=3)


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x_poly=polyF.fit_transform(x) 


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model=LinearRegression()


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model.fit(x_poly,y)


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y_poly_predict=model.predict(x_poly)


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plt.scatter(x,y)
plt.plot(x,y_poly_predict)


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from sklearn.pipeline import Pipeline #2 الطريقه المختصره للبولي 


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input=[('polynomial',PolynomialFeatures(degree=3)),('model',LinearRegression())]


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pip=Pipeline (input)


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pip.fit(x,y)


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y_poly_predict=pip.predict(x)
y_poly_predict


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plt.scatter(x,y)
plt.plot(x,y_poly_predict)


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# mulite linerregression


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x=data[["radius_mean","perimeter_mean"]]#y=a+b1x1+b2x2 ....  # input
y=data.area_mean # اللي هتوقعها #output


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from sklearn.model_selection import train_test_split


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x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.3)
x_train


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sns.pairplot(data,x_vars=["radius_mean","perimeter_mean"],y_vars=["area_mean"],diag_kind=None)
plt.show()


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from sklearn.linear_model import LinearRegression


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model=LinearRegression()#object from linear


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model.fit(x,y)#training


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y_predicted=model.predict(x_test)#test for predictiong
y_predicted #الداتا اللي المودل توقعها 


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x_test


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y_test


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model.intercept_


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model.coef_ 


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#statemodels


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import statsmodels.formula.api as sm


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lr=sm.ols(formula='area_mean ~ radius_mean +perimeter_mean',data=data[:600]).fit() 
# x=["radius_mean","perimeter_mean"]    y=["area_mean"] y~x #multilinear regression by formulaالطريقه التانيه لل


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#scaling


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#y=data["diagnosis"]
#x=data.radius_mean


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from sklearn.model_selection import train_test_split  #standarization


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x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2)


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from sklearn.preprocessing import StandardScaler # mean , standard divission


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sc=StandardScaler()
x_train_scale=sc.fit_transform(x_train)
x_train_scale


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x_test_scale=sc.transform(x_test)
z=sc.inverse_transform(x_test_scale)
z


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from sklearn.preprocessing import MinMaxScaler # min max normalization


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MNS=MinMaxScaler()
x_train_MN=MNS.fit_transform(x_train) 
x_train_MN


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#classifiation


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from sklearn.neighbors import KNeighborsClassifier


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model=KNeighborsClassifier(n_neighbors=5) #numbers of neighbors where i select


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#model.fit(x_train_scale,y_train)


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#y_predict=model.predict(x_train_scale)
#y_predict


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#navie bayes classifier


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from sklearn.naive_bayes import GaussianNB


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GNB=GaussianNB()


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#GNB.fit(x_train,y_train)


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#y_predict=GNB.predict(x_test)
#y_predict


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#svm


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from sklearn import svm


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classifer =svm.SVC(kernel='linear')


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#classifer.fit(x_train,y_train)


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#y_svm_predict=classifer.predict(x_test)
#y_svm_predict


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