Estadísticas y predicciones de Davis Cup Qualifiers

Juan Pablo Varillas

Juan Pablo Varillas es uno de los jugadores más prometedores de Perú. Su habilidad para adaptarse a diferentes tipos de canchas lo convierte en un adversario formidable. Con un sólido juego defensivo y una potente derecha, Varillas ha demostrado ser capaz de superar a rivales experimentados.

Sergio Galdós

calvinj/kaggle-titanic<|file_sep|>/README.md # kaggle-titanic This is my solution to the Kaggle Titanic competition. To run this code you will need the following packages: - pandas - numpy - sklearn - xgboost This code will run on Python 3.x. The `train.csv` and `test.csv` files are not included here due to their size. ## Code ### preprocessing.py This module contains functions to do data preprocessing. The `main` function in this file takes care of reading the data files and doing preprocessing. ### model.py This module contains functions to train and test models. The `main` function in this file trains the model on the preprocessed training set and writes out the predictions for the test set. <|file_sep|># -*- coding: utf-8 -*- import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from xgboost import XGBClassifier def get_title(name): """Extracts title from passenger's name""" title_search = re.search(' ([A-Za-z]+).', name) if title_search: return title_search.group(1) return "" def fill_missing_age(df): """Fills missing age values with mean age""" age_mean = df["Age"].mean() df["Age"] = df["Age"].fillna(age_mean) def fill_missing_embarked(df): """Fills missing embarked values with most common port""" mode_embarked = df["Embarked"].mode() df["Embarked"] = df["Embarked"].fillna(mode_embarked[0]) def create_dummies(df): """Creates dummy variables for categorical features""" df = pd.get_dummies(df) return df def preprocess_data(df): """Preprocesses data for modeling""" # extract titles from names df["Title"] = df["Name"].apply(get_title) # replace rare titles with more common ones title_map = { "Capt": "Officer", "Col": "Officer", "Major": "Officer", "Jonkheer": "Royalty", "Don": "Royalty", "Sir": "Royalty", "Dr": "Officer", "Rev": "Officer", "the Countess": "Royalty", "Dona": "Royalty", } for k,v in title_map.items(): df["Title"] = df["Title"].replace(k,v) df["FamilySize"] = df["SibSp"] + df["Parch"] dummies = ["Pclass", "Sex", "Title", "Embarked"] for d in dummies: df = pd.get_dummies(df, columns=[d], prefix=d) df.drop(["PassengerId", "Name", "Ticket", "Cabin"], axis=1, inplace=True) fill_missing_age(df) fill_missing_embarked(df) return df def main(): data_dir = "../data" training_file_path = data_dir + "/train.csv" test_file_path = data_dir + "/test.csv" training_data = pd.read_csv(training_file_path) test_data = pd.read_csv(test_file_path) training_data_preprocessed = preprocess_data(training_data) test_data_preprocessed = preprocess_data(test_data) y_train = training_data_preprocessed.pop("Survived") X_train, X_valid, y_train_valid, y_valid = train_test_split( training_data_preprocessed, y_train, test_size=0.1, random_state=42, shuffle=True) model = XGBClassifier() model.fit(X_train,y_train_valid) predictions_valid = model.predict(X_valid) print("Validation accuracy: {}".format(accuracy_score(y_valid,predictions_valid))) predictions_test = model.predict(test_data_preprocessed) output_file_path = data_dir + "/my_submission.csv" output_df = pd.DataFrame({ "PassengerId": test_data["PassengerId"], "Survived": predictions_test}) output_df.to_csv(output_file_path,index=False) if __name__ == "__main__": main() <|repo_name|>calvinj/kaggle-titanic<|file_sep|>/preprocessing.py # -*- coding: utf-8 -*- import pandas as pd import numpy as np import re def get_title(name): title_search = re.search(' ([A-Za-z]+).', name) if title_search: return title_search.group(1) return "" def fill_missing_age(df): df.loc[ (df.Age.isnull()) & (df.Sex=="male"), "Age"] = df[ (df.Age.notnull()) & (df.Sex=="male") ]['Age'].median() df.loc[ (df.Age.isnull()) & (df.Sex=="female"), "Age"] = df[ (df.Age.notnull()) & (df.Sex=="female") ]['Age'].median() def fill_missing_embarked(df): mode_embarked = df["Embarked"].mode() df["Embarked"] = df["Embarked"].fillna(mode_embarked[0]) def create_dummies(df): dummies_sex = pd.get_dummies(df['Sex'], prefix='Sex') dummies_pclass = pd.get_dummies(df['Pclass'], prefix='Pclass') dummies_embarked = pd.get_dummies(df['Embarked'], prefix='Embarked') df.drop(['Sex'], axis=1,inplace=True) df.drop(['Pclass'], axis=1,inplace=True) df.drop(['Embarked'], axis=1,inplace=True) df = pd.concat([df,dummies_sex,dummies_pclass,dummies_embarked],axis=1) return df def preprocess_data(df): df["Title"] = df.Name.apply(get_title) title_mapping = {"Mr." : 0, "Miss." : 1, "Mrs." : 2, "Master." : 3, "Dr." : 4, "Rev." : 5, "Col." : 6, "Major." : 7, "Mlle." : 8, "Mme." : 9, "Don." : 10, "Lady." : 11, "Countess." : 12, "Sir." : 13, Jonkheer.":14 } df['Title'] .map(title_mapping) df['FamilySize'] =df['SibSp'] + df['Parch'] df['IsAlone'] =df['FamilySize'].apply(lambda x: 1 if x==0 else 0) df.drop(['Name','Ticket','Cabin'], axis=1,inplace=True) fill_missing_age(df) fill_missing_embarked(df) return create_dummies(df) def main(): data_dir = "../data" training_file_path = data_dir + "/train.csv" test_file_path = data_dir + "/test.csv" training_data training_data_preprocessed test_data test_data_preprocessed y_train X_train X_valid y_valid model model.fit(X_train,y_train) predictions_valid print("Validation accuracy: {}".format(accuracy_score(y_valid,predictions_valid))) predictions_test output_file_path output_df output_df.to_csv(output_file_path,index=False) if __name__ == "__main__": main() <|file_sep|>#include #include #include #include #include #include using namespace std; int main() { string line; ifstream myfile("C:\Users\Karan\Desktop\code.txt"); vectorv; listL; list::iterator iter; if (myfile.is_open()) while (!myfile.eof()) { getline(myfile,line); v.push_back(line); } myfile.close(); for(int i=0;i#include using namespace std; int main() { int n,sum=0,i; cin>>n; for(i=n;i<=n+100;i++) { if(i%7==0) { sum=sum+i; } } cout<karansalve93/Programming-Languages-Codes<|file_sep|>/Java/Day_4/Day4_Q5.java import java.util.*; class Day4_Q5 { public static void main(String[] args) { Scanner s=new Scanner(System.in); String str,sentence=""; System.out.println("Enter a string"); str=s.nextLine(); for(int i=str.length()-1;i>=0;i--) sentence+=str.charAt(i); System.out.println(sentence); } }<|repo_name|>karansalve93/Programming-Languages-Codes<|file_sep|>/Java/Day_7/Day7_Q7.java import java.util.*; class Day7