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Canadian Open stats & predictions

Overview of the Upcoming Canadian Open Tennis Matches

The Canadian Open is set to captivate tennis enthusiasts tomorrow with an array of thrilling matches. As the tournament progresses, fans and experts alike are eagerly anticipating the performances of top-seeded players and emerging talents. This year's event promises a blend of intense competition and strategic gameplay, making it a must-watch for tennis aficionados. With several high-profile matches lined up, the day is poised to be a highlight of the tennis calendar. Stay tuned for expert betting predictions and in-depth analyses as we delve into the key matchups.

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Key Matchups to Watch

Tomorrow's schedule is packed with exciting matchups that are sure to keep spectators on the edge of their seats. Among the most anticipated encounters is the clash between Player A and Player B. Both players have shown remarkable form throughout the tournament, and this match is expected to be a showcase of skill and strategy. Additionally, Player C, known for their aggressive playstyle, will face off against Player D, who excels in defensive tactics. This matchup promises to be a thrilling battle of contrasting styles.

Expert Betting Predictions

As the Canadian Open progresses, betting enthusiasts are keenly analyzing odds and player statistics to make informed predictions. Based on recent performances and head-to-head records, experts have provided insights into some of the key matches:

  • Match 1: Player A vs Player B

    • Experts predict a closely contested match, with Player A having a slight edge due to their superior serve. However, Player B's resilience on clay courts could turn the tide in their favor.

  • Match 2: Player C vs Player D

    • This matchup is expected to be a nail-biter, with odds favoring Player C due to their recent winning streak. Nonetheless, Player D's ability to counter aggressive playstyles makes them a formidable opponent.

  • Match 3: Player E vs Player F

    • With both players known for their consistency, this match is predicted to be a tactical battle. Experts suggest betting on Player E, who has demonstrated exceptional form in recent tournaments.

In-Depth Analysis of Top Players

The Canadian Open features some of the world's best tennis players, each bringing their unique strengths to the court. Here's a closer look at a few top contenders:

  • Player A

    • Known for their powerful serve and baseline dominance, Player A has consistently performed well in major tournaments. Their ability to maintain focus under pressure makes them a favorite among fans.

  • Player B

    • With an impressive record on clay courts, Player B excels in long rallies and strategic point construction. Their mental toughness and adaptability have been key factors in their success.

  • Player C

    • Renowned for their aggressive playstyle, Player C often overwhelms opponents with powerful groundstrokes. Their confidence and energy on the court make them a crowd favorite.

Tournament Highlights and Key Statistics

As we approach tomorrow's matches, here are some key statistics and highlights from the Canadian Open so far:

  • Average Match Length: Matches have averaged around 90 minutes, showcasing the competitive nature of the tournament.
  • Serve Success Rate: Top players have maintained an impressive serve success rate of over 70%, highlighting the importance of serving well in crucial points.
  • Break Points Won: Players who excel at converting break points have had a significant advantage, with some converting over 50% of their opportunities.

Tips for Watching Tomorrow's Matches

For those planning to watch tomorrow's matches live or on broadcast, here are some tips to enhance your viewing experience:

  • Follow Live Scores: Keep track of live scores through official apps or websites to stay updated on match progress.
  • Analyze Player Form: Pay attention to players' recent performances and any injuries that might affect their game.
  • Enjoy Commentary: Listen to expert commentary for insights into player strategies and potential turning points in matches.

The Role of Weather Conditions

Weather conditions can significantly impact tennis matches, affecting players' performance and strategies. Tomorrow's forecast predicts mild temperatures with occasional cloud cover, which could influence ball speed and bounce. Players may need to adjust their tactics accordingly, especially those who rely heavily on spin or serve-and-volley techniques.

Fans' Expectations and Excitement

Tennis fans are buzzing with excitement as they anticipate tomorrow's matches at the Canadian Open. Social media platforms are abuzz with discussions about favorite players and predictions for upcoming games. Fans are eager to see if any dark horses will emerge or if established champions will continue their dominance.

Historical Context: Past Performances at the Canadian Open

Jahnavi-Pujari/Book-Recommendation-System<|file_sep|>/README.md # Book-Recommendation-System A book recommendation system which recommends books based on user preferences. ## Features 1. It uses K-means clustering algorithm which takes into account ratings given by users for books as input. 2. Based on these ratings it creates clusters. 3. It also takes into account user preferences while recommending books. ## Installation Install dependencies using pip sh pip install -r requirements.txt ## Usage 1) Run book_recommendation_system.py file sh python book_recommendation_system.py ## Authors * **Jahnavi Pujari** <|repo_name|>Jahnavi-Pujari/Book-Recommendation-System<|file_sep|>/book_recommendation_system.py from sklearn.cluster import KMeans from sklearn.metrics import pairwise_distances_argmin_min import numpy as np import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import linear_kernel from nltk.stem.snowball import SnowballStemmer # Function that takes in users preferences as input def get_user_input(): # Get user preferences print("Please enter your name : ") name = input() # Enter name in list of users users.append(name) print("Please enter your age : ") age = input() # Enter age in list of ages ages.append(age) print("Please enter your gender (M/F) : ") gender = input() # Enter gender in list of genders genders.append(gender) print("Please enter your country : ") country = input() # Enter country in list of countries countries.append(country) print("Please enter your occupation : ") occupation = input() # Enter occupation in list of occupations occupations.append(occupation) print("Please enter your favorite book : ") favorite_book = input() # Enter favorite book in list of favorite books favorite_books.append(favorite_book) print("Please enter number of books you want us to recommend : ") num_books = int(input()) return name def get_user_input_1(): # Get user preferences print("Please enter your name : ") name = input() # Enter name in list of users users.append(name) print("Please enter your age : ") age = input() # Enter age in list of ages ages.append(age) print("Please enter your gender (M/F) : ") gender = input() # Enter gender in list of genders genders.append(gender) print("Please enter your country : ") country = input() # Enter country in list of countries countries.append(country) print("Please enter your occupation : ") occupation = input() # Enter occupation in list of occupations occupations.append(occupation) print("Enter book ID you want us to recommend similar books for : ") user_book_id = int(input()) return user_book_id def recommend_books(user): user_id = [] for i in range(len(users)): if users[i] == user: user_id.append(i) break similar_user_ids = [] for i in range(len(users)): if i != user_id[0]: if ages[i] == ages[user_id[0]]: if genders[i] == genders[user_id[0]]: if countries[i] == countries[user_id[0]]: if occupations[i] == occupations[user_id[0]]: similar_user_ids.append(i) break if len(similar_user_ids) != 0: similar_user_ids = np.array(similar_user_ids) else: similar_user_ids = [] for i in range(len(book_ratings)): sum_ratings = 0 for j in range(len(similar_user_ids)): sum_ratings += book_ratings[i][similar_user_ids[j]] if sum_ratings > 0: break if sum_ratings > 0: user_cluster_rating[user_id[0]][i] += sum_ratings for i in range(len(book_ratings)): user_cluster_rating[user_id[0]][i] /= len(similar_user_ids) if len(similar_user_ids) != 0: similar_user_cluster_rating[user_id[0]] = np.array(user_cluster_rating[similar_user_ids]) else: similar_user_cluster_rating[user_id[0]] = np.array([]) clusters_centroids[user_id[0]] = kmeans.cluster_centers_[np.argmin(pairwise_distances_argmin_min(kmeans.transform(user_cluster_rating[user_id[0]].reshape(1,-1)),kmeans.cluster_centers_)[1])] closest_cluster_index[user_id[0]] = np.argmin(pairwise_distances_argmin_min(kmeans.transform(user_cluster_rating[user_id[0]].reshape(1,-1)),kmeans.cluster_centers_)[1]) closest_cluster_members[user_id[0]] = np.where(kmeans.labels_ == closest_cluster_index[user_id[0]])[0] closest_clusters_centroids[user_id[0]] = clusters_centroids[user_id[0]] closest_clusters_centroids_with_memberships[user_id[0]] = [closest_clusters_centroids[user_id[0]], closest_cluster_members[user_id[0]]] if len(similar_user_cluster_rating[user_id[0]]) != 0: similar_users_closest_clusters_centroids_with_memberships[user_id[0]] = [] else: similar_users_closest_clusters_centroids_with_memberships[user_id[0]] = np.array([]) for i in range(len(similar_user_ids)): similar_users_closest_clusters_centroids_with_memberships[user_id[0]].append([clusters_centroids[similar_user_ids[i]], np.where(kmeans.labels_ == closest_cluster_index[similar_user_ids[i]])[0]]) recommended_books_list = [] for i in range(len(closest_clusters_centroids_with_memberships[user_id[0]][1])): recommended_books_list.append(closest_clusters_centroids_with_memberships[user_id][1][i]) if len(recommended_books_list) == num_books_to_recommend: break recommended_books_list.sort() return recommended_books_list def recommend_similar_books(user_book): user_book_index_in_ratings_df = [] for i in range(len(book_ratings_df.index.values)): if book_ratings_df.index.values[i] == user_book: user_book_index_in_ratings_df.append(i) break user_book_vectorizer_result=vectorizer.transform([user_books_df['books'][user_book_index_in_ratings_df]]).toarray() user_similarities=linear_kernel(user_book_vectorizer_result,vectorizer_matrix).flatten() similarity_indices=[i for i in user_similarities.argsort()[::-1] if i!=user_book_index_in_ratings_df] user_similarities=user_similarities[similarity_indices] top_ten_similarities=user_similarities.argsort()[::-1][:10] top_ten_similarities=list(top_ten_similarities) top_ten_similarities=[similarity_indices[index]for index in top_ten_similarities] recommended_books=[] for i in top_ten_similarities: recommended_books.append(book_ratings_df.index.values[i]) return recommended_books if __name__=="__main__": users=[] user_cluster_rating=[] similar_users_closest_clusters_centroids_with_memberships=[] closest_users_closest_clusters_centroids_with_memberships=[] closest_clusters_centroids=[] closest_clusters_centroids_with_memberships=[] closest_cluster_members=[] closest_cluster_index=[] num_books_to_recommend=5 users_to_recommend=10 df=pd.read_csv('BX-Book-Ratings.csv',sep=';',error_bad_lines=False) users=pd.read_csv('BX-Users.csv',sep=';',error_bad_lines=False) df.columns=['user','isbn','rating'] df['isbn']=df['isbn'].astype('str') users.columns=['user','age','gender','country','profession'] df=df.dropna(axis=0,subset=['user']) df['user']=df['user'].astype('str') isbn_list=df['isbn'].unique().tolist() isbn_list.sort() isbn_dict={} for i,j in zip(isbn_list,list(range(978,'978'+str(len(isbn_list)+978),1))): isbn_dict[i]=j df=df.replace({'isbn':isbn_dict}) ratings=np.zeros((len(isbn_dict),len(df['user'].unique().tolist()))) for i,j,k in zip(df['isbn'],df['rating'],df['user']): ratings[int(i)-978][int(k)-1]=j num_users=len(df['user'].unique().tolist()) num_items=len(isbn_dict.keys()) kmeans=KMeans(n_clusters=users_to_recommend) kmeans.fit(ratings.T) ratings_df=pd.DataFrame(ratings,index=isbn_dict.keys(),columns=list(range(1,num_users+1))) ratings_df=ratings_df.transpose() ratings_df.columns=pd.MultiIndex.from_tuples([(x,) for x in ratings_df.columns]) ratings_df.index.names=['user'] ratings_df.index=pd.to_numeric(ratings_df.index) ratings_df=ratings_df.sort_index() for i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i1,j1,k1,l1,m1,n1,o1,p1,q1,r1,s1,t1,u1,v1,w1,x1,y1,z1,a1,b1,c1,d1,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z],names=['users']) users_to_drop=[x for x in ratings_df.index if x not in df['user']] ratings_df.drop(users_to_drop,inplace=True) ratings_df.reset_index(inplace=True) ratings_df.rename(columns={'index':'users'},inplace=True) num_users=len(ratings_df['users']) num_items=len(isbn_dict.keys()) ratings=np.zeros((num_items,num_users)) isbns=list(isbn_dict.keys()) isbns.sort() isbn_dict={} for i,j in zip(isbns,list(range(978,'978'+str(len(isbns)+978),1))): isbn_dict[j]=i isbn_dict_rev={} for i,j in isbn_dict.items(): isbn_dict_rev[j]=i users=[] user_cluster_rating=np.zeros((num_users,num_items)) similar_users_closest_clusters_centroids_with_memberships=np.zeros((num_users,num_items)) closest_users_closest_clusters_centroids_with_memberships=np.zeros((num_users,num_items)) closest_clusters_centroids=np.zeros(num_items) closest_clusters_centroids_with_memberships=[] closest_cluster_members=[] closest_cluster_index=[] df=pd.read_csv('BX-Books.csv',sep=';',error_bad_lines=False) df.columns=['ISBN','bookTitle','bookAuthor','yearOfPublication','publisher','imageUrlS','imageUrlM','imageUrlL'] isbns=df['ISBN'].