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Aishwarya 5 days python

Validating phone numbers

A valid mobile number is a ten digit number starting with 7,8,9

import re # input numbers = [input() for i in range(int(input()))] # regex regex = re.compile(r"^(7|8|9)([0-9]){9}$") # Check for number in numbers: if regex.match(number): print('YES') else: print('NO')

hyderabad DRK college of engeering and techonology student

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Akhil 1 week Finance

How do central banks control inflation?

The US Federal Reserve typically designs financial policy to achieve an inflation target of 2%. Inflation targeting is a central banking policy that revolves around adjusting monetary policy to achieve a specified annual rate of inflation. Interest rates can be seen as a mechanism or tool to achieve inflation targeting. When inflation is high, banks will raise interest rates. This has a trickle down effect starting with central banks, going down to commercial banks, and eventually down to commercial bank clients such as businesses and individual consumers.

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Aishwarya 5 weeks python ml deep learning

Automatic Number Plate Recognition

This is a manual process and you need to do it for all the images. Be careful while doing labeling because the labeling process has a direct impact on the accuracy of the model.

https://colab.research.google.com/drive/1ia2Gs4HK6wP5DpajLb5_Y_POhFGRCNh4?usp=sharing

hyderabad DRK college of engeering and techonology student

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Aishwarya 5 weeks data structures tree's

Kitty's Calculations on a Tree

The first line contains an integer,k , the size of the set. The second line contains k space-separated integers, the set's elements.

# Enter your code here. Read input from STDIN. Print output to STDOUT from itertools import combinations from queue import Queue from collections import defaultdict as dd,deque def find_shortest_path(graph, start, end): dist = {start: [start]} que = deque() que.append(start) while len(que): at = que.popleft() for next in graph[at]: if next not in dist: dist[next] = dist[at]+[next] que.append(next) return dist.get(end) def add(d,a,b): d[a].append(b) d[b].append(a) if __name__=='__main__': d=dd(list) t,q=map(int,input().split()) for _ in range(t-1): a,b=map(int,input().split()) add(d,a,b) for _ in range(q): k=int(input()) l=list(map(int,input().split())) if len(l)>1: s=list(combinations(l,2)) sum=0 for j in s: u,v=j p=find_shortest_path(d,u,v) dist=len(p)-1 sum=sum+(u*v*dist) result=sum%((10**9)+7) print(result) else: print(0) continue

Hyderabad DRK College of Engeering and Techonology Student

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Aishwarya 5 weeks , data structures sorting

Merge the tools!

we have to split into sub strings

def merge_the_tools(string, k): # your code goes here substring = f"" for position, value in enumerate(string): if value not in substring: substring += value if (position+1) % k == 0: print(substring) substring = f"" if __name__ == '__main__':

hyderabad DRK college of engeering and techonology student

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Goutham raj 5 weeks , best practices python

The Minion Game

Both players are given the same string, . Both players have to make substrings using the letters of the string . Stuart has to make words starting with consonants. Kevin has to make words starting with vowels. The game ends when both players have made all possible substrings. string: the winner's name and score, separated by a space on one line, or Draw if there is no winner

def minion_game(string: str) -> None: """Print the winner of the game and the score.""" kevin = stuart = 0 length: int = len(string) for i, char in enumerate(string): points: int = length - i if char in {"A", "E", "I", "O", "U"}: kevin += points else: stuart += points if kevin == stuart: print("Draw") else: print(*("Stuart", stuart) if stuart > kevin else ("Kevin", kevin)) if __name__ == '__main__': s = input() minion_game(s)

hyderabad Drk college of engineering and technology colab

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Goutham raj 6 weeks , classification python

titanic classification

https://colab.research.google.com/drive/1g11-keVgNNVowVW0EQZmJAbRBXogbbQl?usp=sharing

import pandas as pd df = pd.read_csv("/content/titanic.csv") df.head() inputs = df.drop(['Survived','PassengerId','Name','SibSp','Parch','Ticket','Cabin','Embarked'],axis='columns') inputs.head(10) inputs.isnull().sum() inputs['Age'].fillna(inputs['Age'].mean(),inplace=True) inputs.isnull().sum() target = df['Survived'] target.head(10) from sklearn.preprocessing import LabelEncoder le_Pclass = LabelEncoder() le_Sex = LabelEncoder() le_Age = LabelEncoder() inputs['Pclass_n'] = le_Pclass.fit_transform(inputs['Pclass']) inputs['Sex_n'] = le_Sex.fit_transform(inputs['Sex']) inputs['Age_n'] = le_Age.fit_transform(inputs['Age']) inputs inputs_n = inputs.drop(['Pclass','Sex','Age'],axis='columns') inputs_n.head(10) target from sklearn import tree model = tree.DecisionTreeClassifier() model.fit(inputs_n, target) model.score(inputs_n,target) #predicting model.predict([[53.1000,0,0,47]]) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(inputs_n,target,test_size=0.2) len(X_train) len(X_test) from sklearn import tree model = tree.DecisionTreeClassifier() model.fit(X_train,y_train) model.score(X_test,y_test) #acurcy : 78.77094972067039 from sklearn.metrics import classification_report,confusion_matrix predictions = model.predict(X_test) print(classification_report(y_test,predictions)) print(confusion_matrix(y_test,predictions))

hyderabad Drk college of engineering and technology colab

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B.Srilekha 6 weeks #cleaning the dataset

kickstarter

https://colab.research.google.com/drive/1ckA5_-B5jgvHQSlV7e6THn3F8lJZW_H7#scrollTo=udEISwth_jwn&line=1&uniqifier=1