Write Algorithm for Round robin tournament including a scoring for the match results. Round-Robin-Tournament(P) in: sequence P of n players (1 ≤ n) out: sequence R of n players with attribute score(i) constant: score points for a winner w, for a loser
, for a tie t local: number of rounds t

def solve_n_queens(n):    def is_safe(board, row, col):        # Verificar la columna        for i in range(row):            if board[i][col] == 1:                return False               # Verificar la diagonal izquierda        i, j = row, col        while i >= 0 and j >= 0:            if board[i][j] == 1:                return False            i -= 1            j -= 1        # Verificar la diagonal derecha        i, j = row, col        while i >= 0 and j < n:            if board[i][j] == 1:                return False            i -= 1            j += 1        return True    def solve(board, row):        if row >= n:            solution = []            for r in board:                solution.append("".join(['R' if x == 1 else '.' for x in r]))            solutions.append(solution)            return               for col in range(n):            if is_safe(board, row, col):                board[row][col] = 1                solve(board, row + 1)…

def solve_n_queens(n):    def is_safe(board, row, col):        # Verificar la columna        for i in range(row):            if board[i][col] == 1:                return False               # Verificar la diagonal izquierda        i, j = row, col        while i >= 0 and j >= 0:            if board[i][j] == 1:                return False            i -= 1            j -= 1        # Verificar la diagonal derecha        i, j = row, col        while i >= 0 and j < n:            if board[i][j] == 1:                return False            i -= 1            j += 1        return True    def solve(board, row):        if row >= n:            solution = []            for r in board:                solution.append("".join(['Q' if x == 1 else '.' for x in r]))            solutions.append(solution)            return               for col in range(n):            if is_safe(board, row, col):                board[row][col] = 1                solve(board, row + 1)…

: |: Section 3 - Random Forests Decision Trees on their own are effectiev classifiers. The true power, though, comes from a forest of trees -- multiple decision trees working together as an ensemble learner. We create multiple trees, each using a subset of the available attributes, let them each make a guess at the correct classification, and then take the majority vote as the predicted class. Sounds tricky, right? Once again, sklearn to the rescue. We are going to see if a random forest can improve on our decision tree accuracy for the bank data. To begin, we are going to create a Random Forest using the wheat training and test data from the previous section. First, reload our data. 1 # reload the wheat dataset from UCI 2 3 df = pd. read_csv ("seeds_dataset.txt", sep='\\t', engine='python') 4 6 7 df.columns = ['a', 'p', 'compactness', 'length', 'width', 'coeff', 'length_g', 'type'] print (f'Our data has {df.shape [0]} rows and {df.shape [1]} columns') 8 #Mark 70% of the data for…