Pareto Distribution

Author: heysushil

19.Chi_Square_Distribution.py

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 '''
-Chi Square Distribution using NumPy Array and Matplotlib.pyplotP:
+Chi Square Distribution using NumPy Array and Matplotlib.pyplot:
 
 Chi Square Distribution ka use kiya jata hai jab ek jaise 2 chijo ke beach me realtion nikalna ho tab is distribution ka use kiya jata hai.
 

21.Pareto_Distribution.py

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+'''
+Pareto Distribution using matplotlib.pyplot, numpy.random module and seaborn librarys by hey sushil:
+
+Pareto Distribution ka naam Itelian economist and sociologist, Vilfredo Pareto par pada hain. Isko Pareto principle bhi kahte hain.
+
+Pareto Distribution ka use hota hai:
+
+1. Social phenomena
+2. Scientific phenomena
+3. Geophysical phenomena
+
+Pareto Distribution kya hai?
+
+1. Pareto Distribution ko  80% 20% rule ke naam se jana jata hai.
+2. Pareto Distribution kisi bhi range ke andar ke situation ko bata hai.
+
+Pareto Distribution ke examples:
+
+1. Customer support: Yaha par kaha jata hai ki 80% problem kewal 20% customer ko hi hoti hai.
+2. Economics: World me 80% paisa 20% logo ke pass hi pada hai.
+3. Human Size: Cities kam hai aur villages jada hain
+4. File size: Internet me TCP Protocole use hota hai jisme few large file aur many more small files hoti hain.
+
+Is tarike ke bahot sare example bataye ja sakte hain Pareto Distribution ke jo ki 80% 20% rule me best fit baithte hain.
+
+Pareto Distribution ke best practical application:
+
+1. Business Management: Is case me business me kiya gaya 20% effort jo specific alag alag business activiy karne par 80% result deta hai. Is tarike se important segments par focuss karke business ki efficency ko badhya ja sakta hai.
+
+2. Company revenues: Is case me company dekhti hai ki 80% annual revenue kewal 20% current customers se hi aajata hai. Is tarike se company in customers ko satify karne me jada focuss karta hai. Isi tarkike se aane wale 80% compalin 20% customer se hi aate hain. Jinko resolve karne par company focuss karta hai. Aur unke satify hone par agle 20% customers ko satify karne me company lag jata hai.
+
+3. Employee Evaluation: 80-20 ratio ka use kar ke compnay apne employee ke performance par bhi focuss karta hai. Jisme ki 80% work result company ke 20% employee se aata hai. Aur is tarike se company apne best 20% employee ko motivated rakhne ke liye rewards deta rahta hai. Aur baki employees ko hard work karne ka message bhi deta hai.
+
+Is tarike se hi company me hone wali 80% problem company ke 20% employees ke dwara hi ki jati hai.
+
+Pareto Distribution ki limitations:
+
+1. 80-20 ka ratio jarui nahi hai ki hamesa 80 aur 20 me hi aaye. Ho sakta hai ki kabhi 90 aajaye to isse ye nahi ki 90+20 = 110 kar diya jaye.
+2. Means Pareto Distribution ka use kar ke ye pata chal jata hai ki ratio ke behalf par kis area par jada focuss karne ki jarurat hai.
+3. Pareto Distribution ya bahi bhi probabiliy ke case hamesa puri tarike se sahi nahi ho sakte hain.
+
+numpy.random.pareto() method ke arguments:
+
+1. a: shape parameter
+2. size: shape of return array
+
+Pareto me har value posstive hi milega.
+'''
+
+import numpy.random as r
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+pareto = r.pareto(a=1, size=(10))
+# print('\n',pareto)
+
+sns.distplot(r.pareto(a=1, size=(100)), hist=False, label=1)
+sns.distplot(r.pareto(a=2, size=(100)), hist=False, label=2)
+sns.distplot(r.pareto(a=3, size=(100)), hist=False, label=3)
+sns.distplot(r.pareto(a=4, size=(100)), hist=False, label=4)
+
+# plt
+plt.xlabel('Range X')
+plt.ylabel('Frequecy Y')
+plt.show()
+