diff --git a/charts.R b/charts.R
index c08f9ab..46c2d1c 100644
--- a/charts.R
+++ b/charts.R
@@ -1,12 +1,19 @@
+library(highcharter)
+library(tidyr)
+library(plotly)
+library(dplyr)
basedf<-read.csv("dev/matma_bayes/heart.csv", header = TRUE)
siha<-read.csv('dev/matma_bayes/sex_if_heart_attack.csv')
df2<-read.csv('dev/matma_bayes/age_sex_heart_attack.csv')
+chol<-read.csv('dev/matma_bayes/chol_sex_heart_attack.csv')
basedf$sex[basedf$sex==1]<-'Man'
basedf$sex[basedf$sex==0]<-'Woman'
df2$sex[df2$sex==1]<-'Man'
df2$sex[df2$sex==0]<-'Woman'
+chol$sex[chol$sex==1]<-'Man'
+chol$sex[chol$sex==0]<-'Woman'
df2$x<-paste(df2$sex,df2$age)
-df2$comb<-
+
basedf%>%
count(sex)%>% arrange(n)%>%
@@ -15,16 +22,10 @@ basedf%>%
siha%>%
- hchart(type = "pie", hcaes(x=sex, y = Probability, color = Probability, name='Probability')) %>%
+ hchart(type = "pie", hcaes(x=sex, y = Probability, color = Probability), name='Probability') %>%
hc_title(text="Sex if heart attack")
-basedf%>%
- group_by(sex, chol)%>%
- count()%>%
- hchart('area', hcaes(x='chol', y='n', group='sex'))%>%
- hc_xAxis(title=list(text='Cholesterol'))%>%
- hc_yAxis(title=list(text='Quantity'))%>%
- hc_title(text="Number of terrorists attacks in each year by region")
+
df2%>%
@@ -34,3 +35,20 @@ df2%>%
hc_yAxis(title=list(text='Probability'))%>%
hc_legend(enabled=F)%>%
hc_title(text="Probability of heart attack for sex and age group")
+
+
+chol %>%
+ drop_na(heart.attack)%>%
+ plot_ly(x = ~heart.attack,
+ y = ~sex,
+ type = 'bar',
+ orientation = 'h',
+ color = ~chol,
+ colors ='Spectral',
+ hoverinfo = 'text',
+ text = ~paste('Probability:', heart.attack, '
Interval:', chol),
+ xaxis=list(categoryorder='total descending')) %>%
+ layout(title = 'Chol/probability',
+ xaxis = list(title = "Probability"),
+ yaxis = list(title = "Cholesterol"))
+
diff --git a/chol_sex_heart_attack.csv b/chol_sex_heart_attack.csv
new file mode 100644
index 0000000..6e6bf5c
--- /dev/null
+++ b/chol_sex_heart_attack.csv
@@ -0,0 +1,31 @@
+chol,sex,heart attack,no heart attack
+"[120, 150]",0,0.7900763358778626,0.20992366412213742
+"[120, 150]",1,0.5057933317569164,0.4942066682430835
+"[150, 180]",0,0.7150259067357513,0.28497409326424866
+"[150, 180]",1,0.40557451649601817,0.5944254835039818
+"[180, 210]",0,0.8168701442841287,0.18312985571587126
+"[180, 210]",1,0.5481189641089703,0.4518810358910296
+"[210, 240]",0,0.7900763358778626,0.2099236641221374
+"[210, 240]",1,0.5057933317569164,0.4942066682430835
+"[240, 270]",0,0.7594186538732892,0.2405813461267109
+"[240, 270]",1,0.46189495365602473,0.5381050463439754
+"[270, 300]",0,0.5654082528533801,0.43459174714661986
+"[270, 300]",1,0.26132942377673635,0.7386705762232636
+"[300, 330]",0,0.7553882996920972,0.24461170030790286
+"[300, 330]",1,0.45644780538550794,0.5435521946144921
+"[330, 360]",0,0.5564516129032259,0.4435483870967742
+"[330, 360]",1,0.2543703175169461,0.745629682483054
+"[360, 390]",0,1.0,0.0
+"[360, 390]",1,1.0,0.0
+"[390, 420]",0,0.7150259067357513,0.2849740932642487
+"[390, 420]",1,0.4055745164960181,0.5944254835039817
+"[420, 450]",0,nan,nan
+"[420, 450]",1,nan,nan
+"[450, 480]",0,nan,nan
+"[450, 480]",1,nan,nan
+"[480, 510]",0,nan,nan
+"[480, 510]",1,nan,nan
+"[510, 540]",0,nan,nan
+"[510, 540]",1,nan,nan
+"[540, 570]",0,1.0,0.0
+"[540, 570]",1,1.0,0.0
diff --git a/first.py b/first.py
new file mode 100644
index 0000000..bf8c1d4
--- /dev/null
+++ b/first.py
@@ -0,0 +1,91 @@
+import pandas as pd
+
+
+# P(B|A)*P(A)
+#P(A|B)=-----------
+# P(B)
+
+
+data=pd.read_csv('heart.csv')
+
+print(data)
+
+men=0
+for i in range(len(data['sex'])):
+ if data['sex'][i] ==1:
+ men+=1
+print(men)
+p_men=men/len(data)
+print("Prawdopodobieństwo, że mężczyzna",p_men)
+
+chol=0
+for i in range(len(data['chol'])):
+ if data['chol'][i] >200:
+ chol+=1
+print(chol)
+p_chol=chol/len(data)
+print("Prawdopodobieństwo, że cholesterol większy niż 200",p_chol)
+
+age_over_50=0
+for i in range(len(data['age'])):
+ if data['age'][i] >50:
+ age_over_50+=1
+print(age_over_50)
+p_age_over_50=age_over_50/len(data)
+print("Prawdopodobieństwo, że wiek powyżej 50",p_age_over_50)
+
+sugar=0
+for i in range(len(data['fbs'])):
+ if data['fbs'][i] ==1:
+ sugar+=1
+print(sugar)
+p_sugar=sugar/len(data)
+print("Prawdopodobieństwo, że wysoki cukier",p_sugar)
+
+heart_attack=0
+for i in range(len(data['target'])):
+ if data['target'][i] ==1:
+ heart_attack+=1
+print(heart_attack)
+p_heart_attack=heart_attack/len(data)
+print("Prawdopodobieństwo dużego ryzyka zawału serca",p_heart_attack) #P(class)
+
+man_if_heart_attack=0
+for i in range(len(data['target'])):
+ if data['target'][i] ==1:
+ if data['sex'][i]==1:
+ man_if_heart_attack+=1
+print(man_if_heart_attack)
+p_man_if_heart_attack=man_if_heart_attack/len(data)
+p_man_if_heart_attack=p_man_if_heart_attack/p_heart_attack
+print("rawdopodobieństwo, że mężczyzna jeśli wystąpił zawał serca",p_man_if_heart_attack)
+
+over50_if_heart_attack=0
+for i in range(len(data['target'])):
+ if data['target'][i] ==1:
+ if data['age'][i]>50:
+ over50_if_heart_attack+=1
+print(over50_if_heart_attack)
+p_over50_if_heart_attack=over50_if_heart_attack/len(data)
+p_over50_if_heart_attack=p_over50_if_heart_attack/p_heart_attack
+print("Prawdopodobieństwo, że powyżej 50 lat jeżeli wystąpił zawał serca",p_over50_if_heart_attack)
+
+chol_over200_if_heart_attack=0
+for i in range(len(data['target'])):
+ if data['target'][i] ==1:
+ if data['chol'][i]>200:
+ chol_over200_if_heart_attack+=1
+print(chol_over200_if_heart_attack)
+p_chol_over200_if_heart_attack=chol_over200_if_heart_attack/len(data)
+p_chol_over200_if_heart_attack=p_chol_over200_if_heart_attack/p_heart_attack
+print("Prawdopodobieństwo, że cholestorol powyżej 200, jezeli wystąpił zawał serca",p_chol_over200_if_heart_attack)
+
+sugar_if_heart_attack=0
+for i in range(len(data['target'])):
+ if data['target'][i] ==1:
+ if data['fbs'][i]==1:
+ sugar_if_heart_attack+=1
+print(sugar_if_heart_attack)
+p_sugar_if_heart_attack=sugar_if_heart_attack/len(data)
+p_sugar_if_heart_attack=p_sugar_if_heart_attack/p_heart_attack
+print("Prawdopodbieństwo, że był wysoki poziom cukru jeżeli wystąpił zawał serca",p_sugar_if_heart_attack)