--- a
+++ b/MOA/Pressure.py
@@ -0,0 +1,86 @@
+import numpy as np
+import pandas as panda
+import matplotlib.pyplot as plt
+from scipy.stats import norm
+import statistics
+
+
+# Consider Gluc Values > 1 Is A Diabetes
+
+db = panda.read_csv('cardio_train.csv')
+
+UpPressure = db.loc[db.cardio == 1].ap_hi
+DownPressure = db.loc[db.cardio == 1].ap_lo
+
+UpPressureQ25,UpPressureQ75 = np.percentile(UpPressure,[25,75])
+
+IQR = UpPressureQ75 - UpPressureQ25
+
+
+plt.boxplot(UpPressure)
+plt.title("Before Cleaning")
+plt.show()
+
+# Cleaning Data
+UpPressure = UpPressure.loc[UpPressure >= (UpPressureQ25 - 1.5 * IQR)].loc[UpPressure <= (UpPressureQ75 + 1.5 * IQR)]
+
+
+plt.boxplot(UpPressure)
+plt.title("After Cleaning")
+plt.show()
+
+UpPressureMean = UpPressure.mean()
+UpPressureMedian = UpPressure.median()
+UpPressureMode = UpPressure.mode()
+UpPressureSD = UpPressure.std()
+
+
+print("Systolic blood pressure Mean = " + str(UpPressureMean))
+print("Systolic blood pressure Median = " + str(UpPressureMedian))
+# print("Systolic blood pressure Mode = " + str(UpPressureMode))
+print("Systolic blood pressure Standard Deviation = " + str(UpPressureSD))
+
+# Hypothesis: Abnormal Systolic blood Pressure is a sign of heart disease
+# Check If Probabilty Of Having Both Normal blood pressure and heart disease is greater than having abnormal blood pressure
+
+
+NormalPressure = UpPressure.loc[UpPressure >= 120].loc[UpPressure < 130]
+UpLowPressure = UpPressure.loc[UpPressure < 120]
+UpHighPressure = UpPressure.loc[UpPressure >= 130]
+
+
+PNormalPressure = (NormalPressure.count()) * 100 / UpPressure.count()
+PabNormalPressure = (UpLowPressure.count() + UpHighPressure.count()) * 100 / UpPressure.count()
+
+print("Propability of Of Having Both Normal blood pressure and heart disease = " + str(PNormalPressure))
+print("Propability of Of Having Both Abnormal blood pressure and heart disease = " + str(PabNormalPressure))
+
+plt.pie([PNormalPressure,PabNormalPressure],labels= ["Normal Pressure", "Abnormal Pressure"])
+plt.title("Probability of having")
+plt.show()
+
+#=====================================================================================================
+
+# DownPressureQ25,DownPressureQ75 = np.percentile(DownPressure,[25,75])
+# IQR = DownPressureQ75 - DownPressureQ25
+
+# plt.boxplot(UpPressure)
+# plt.title("Before Cleaning")
+# plt.show()
+
+# Cleaning Data
+# DownPressure = DownPressure.loc[DownPressure >= (DownPressureQ25 - 1.5 * IQR)].loc[DownPressure <= (DownPressureQ75 + 1.5 * IQR)]
+
+# DownPressureMean = DownPressure.mean()
+# DownPressureMedian = DownPressure.median()
+# DownPressureMode = DownPressure.mode()
+# DownPressureSD = DownPressure.std()
+
+# # plt.boxplot(UpPressure)
+# # plt.title("After Cleaning")
+# # plt.show()
+
+
+# NormalPressure = UpPressure.loc[UpPressure]
+
+