Copa Uruguay stats & predictions

This match-up is a classic encounter that never fails to deliver excitement. Club Nacional has been in impressive form, boasting a solid defensive record and an attack that has found its rhythm in recent weeks. Peñarol, on the other hand, is known for its resilience and ability to turn games around.

Prediction: Over 2.5 goals – Both teams have shown they can score and defend well, making a high-scoring game likely.

Betting Tip: Back Club Nacional to win – Their recent performances give them the edge in this clash.

This fixture promises a tactical battle as both teams have been focusing on strengthening their defensive setups. Defensor Sporting has been consistent at home, while Montevideo Wanderers have shown they can be unpredictable on their travels.

Prediction: Under 2.5 goals – Expect a tightly contested match with few goals.

Betting Tip: Draw no bet on Defensor Sporting – They are likely to secure at least a point at home.

River Plate enters this match as favorites, thanks to their strong attacking lineup and recent victories. Danubio will be looking to upset the odds with their disciplined approach and counter-attacking strategy.

Prediction: Both teams to score – Danubio has been efficient in exploiting defensive gaps.

Betting Tip: Back River Plate to win – Their attacking prowess should see them through against Danubio.

Tactical Overview

Club Nacional is likely to adopt a compact formation, focusing on quick transitions from defense to attack. Peñarol may counter with a high press, aiming to disrupt Nacional's build-up play.

Key Players

• Luis Aguiar (Club Nacional): A pivotal figure in Nacional's attack, known for his vision and goal-scoring ability.
• Gastón Rodríguez (Peñarol): The creative midfielder who can change the game with his precise passing and dribbling skills.

Possible Lineups

• Club Nacional:
  • GK: Mathías Laborda
  • Defense: Joaquín Piquerez, Guillermo de los Santos, Bruno Méndez, Agustín Oliveros
  • Midfield: Gabriel Neves, Brian Ocampo, Agustín Canobbio
  • Attack: Gonzalo Bergessio, Luis Aguiar
• Peñarol:
  • GK: Kevin Dawson
  • Defense: Fabricio Formiliano, Gary Kagelmacher, Ignacio Laquintana, Giovanni González
  • Midfield: Juan Ramírez, Facundo Torres, Gastón Rodríguez
  • Attack: Agustín Álvarez Martínez, Facundo Torres (or Gonzalo Vega)

Betting Insights

Nacional's home advantage and current form make them slight favorites. However, Peñarol's experience in high-pressure games could lead to an upset if they capitalize on set-piece opportunities.

Potential Outcomes

• Nacional wins with both teams scoring – A balanced approach from both sides could lead to an entertaining match with multiple goals.
• A draw with under 2.5 goals – Defensive solidity may prevail over attacking flair.
• An upset by Peñarol – Capitalizing on any slip-ups by Nacional could swing the result in their favor.

Injury Concerns and Suspensions

• Nacional: No major injury concerns reported.
• Peñarol: Facundo Torres is doubtful due to a minor hamstring issue but is expected to play.

Historical Context

This rivalry has seen many memorable encounters over the years. Recent clashes have been closely contested, with both teams securing victories at each other's grounds. The psychological edge could play a significant role in tomorrow's match.

Betting Market Trends

The betting market favors Club Nacional slightly due to their home advantage and current form. However, odds for over/under goals are evenly split, reflecting the unpredictable nature of this rivalry.

Possible Game-Changing Moments

• A goal from Luis Aguiar could boost Nacional's confidence early on.
• A red card or injury for a key player like Gastón Rodríguez could tilt the balance in favor of Nacional.

Defensor Sporting vs. Montevideo Wanderers

Tactical Overview

Defensor Sporting will likely employ a solid defensive strategy while looking for quick counter-attacks through their pacey forwards. Montevideo Wanderers might opt for a more possession-based approach to control the game tempo.

Key Players

Damián Frascarelli (Defensor Sporting): Known for his leadership qualities and ability to score crucial goals.
José María Franco (Montevideo Wanderers): The experienced midfielder who orchestrates play from deep positions.

Possible Lineups

Defensor Sporting:

• GK: Kevin Dawson
  
• D: Gastón Silva, Leandro Cabrera, Joaquín Piquerez
  
• MF: Federico Pereyra
  
• FW: Damián Frascarelli
  

Montevideo Wanderers:

• GK: Manuel Banguera
  
• D: Nicolás Correa
  
• MF: José María Franco
  
• FW: Nicolás Albarracín
  

Betting Insights

The odds suggest a close game with potential for few goals due to both teams' focus on defense over attack.

Potential Outcomes

• A narrow victory for Defensor Sporting leveraging home advantage.
  
• A stalemate if both sides fail to break through each other's defenses.
  
• An unexpected win for Montevideo Wanderers capitalizing on set pieces.
  

Injury Concerns and Suspensions

• No major injuries reported for either team.
  

Historical Context

This matchup often ends in low-scoring draws due to tactical conservatism from both sides.

Betting Market Trends

The market shows slight favoritism towards Defensor Sporting but remains cautious due to potential stalemate outcomes.

Possible Game-Changing Moments

• A red card early in the game could drastically affect team dynamics.
  
• An early goal by Damián Frascarelli might shift momentum towards Defensor Sporting.
  

River Plate vs. Danubio

Tactical Overview

River Plate will likely dominate possession while employing swift counter-attacks through their fast wingers. Danubio will aim for a compact shape defensively and exploit spaces left by River Plate’s attacking plays.

Key Players

Gabriel Neves (River Plate): The midfield maestro who controls tempo and creates scoring opportunities.
Daniel Néculman (Danubio): A seasoned striker capable of breaking defensive lines with his sharp finishing.

Possible Lineups

River Plate:

• GK: Kevin Dawson
  
• D: Joaquín Piquerez
  
• MF: Gabriel Neves
  
• FW: Rodrigo Mora
  

Danubio:

• GK: Carlos Kiese Thiel
  
• D: Fernando Gorriarán
  
• MF: Emiliano Albín
  
• FW: Daniel Néculman
  

Betting Insights

River Plate is favored due to their attacking prowess; however, Danubio’s disciplined defense makes it an intriguing contest.

Potential Outcomes

• A convincing win for River Plate if they exploit Danubio’s defensive gaps effectively.
  
• A narrow victory or draw if Danubio manages to withstand River Plate’s attacks.
  
• An upset by Danubio if they capitalize on counter-attacking opportunities.
  

Injury Concerns and Suspensions

No significant injury concerns or suspensions reported for either team.

Historical Context <|repo_name|>mohammadfahad24/Business-Credit-Card-Fraud-Detection<|file_sep|>/README.md # Business Credit Card Fraud Detection ### Problem Statement: Businesses use credit cards daily for purchases ranging from travel expenses all the way up to millions of dollars in capital equipment purchases or services rendered. These transactions are extremely vulnerable as fraudulent actors have gotten very sophisticated in mimicking legitimate business spending patterns. ### Objective: We are provided transaction data containing fraudulent transactions along with legitimate transactions. The objective is predict whether any given transaction is fraudulent or not. ### Dataset: The dataset used was obtained from [kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud). It contains details about transactions made using credit cards in September of year two thousand fifteen. The data contains only numerical input variables which are the result of PCA transformation. As this information was confidentially shared by an European bank only two features are not transformed: Time - number of seconds elapsed between each transaction and first transaction in dataset Amount - transaction amount Class - whether or not transaction was fraudulent ### Approach: Since there were only two features that were not transformed we used those two features along with principal component analysis (PCA) components. Since this problem was classification we used logistic regression as our baseline model. Then we used various classification models including XGBoost (Extreme Gradient Boosting), SVM (Support Vector Machine), Decision Tree Classifier etc. We also tried different variations of feature selection techniques such as RFE (Recursive Feature Elimination) and L1 regularization. Finally we tried ensemble methods such as voting classifier. ### Results: 1) Logistic Regression Accuracy : **0.9988** Precision : **0.80** Recall : **0.06** F1-score : **0.11** ROC-AUC Score : **0.95** 2) XGBoost Accuracy : **0.9999** Precision : **0.99** Recall : **0.26** F1-score : **0.41** ROC-AUC Score : **0.99** 3) Decision Tree Classifier Accuracy : **0.9999** Precision : **1** Recall : **0** F1-score : **0** ROC-AUC Score : **0.96** ### Conclusion: Based on ROC-AUC score XGBoost performed better than other models. However since it had low recall we further experimented with hyperparameter tuning using GridSearchCV which improved recall from **0**% --> **26**% but reduced precision from **1**% --> **99**% and accuracy remained almost same i.e., **99**%. <|repo_name|>mohammadfahad24/Business-Credit-Card-Fraud-Detection<|file_sep|>/code.py #!/usr/bin/env python # coding: utf-8 # In[ ]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix , classification_report , accuracy_score , roc_auc_score , roc_curve , auc , precision_score , recall_score , f1_score , plot_roc_curve , plot_precision_recall_curve , precision_recall_curve # In[ ]: df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv') # In[ ]: df.head() # In[ ]: df.info() # In[ ]: df.describe() # In[ ]: df.isnull().sum() # ### Distribution Plot # In[ ]: sns.distplot(df['Time'], bins=100) # ### Scatter Plot # In[ ]: sns.scatterplot(x='Time', y='Amount', data=df) # ### Histograms # In[ ]: df.hist(bins=50 , figsize=(20 ,20)) plt.show() # ### Correlation Heatmap # In[ ]: corrmat = df.corr() top_corr_features = corrmat.index plt.figure(figsize=(20 ,20)) g = sns.heatmap(df[top_corr_features].corr(),annot=True,cmap="RdYlGn") # ### Checking Outliers using Boxplot # In[ ]: fig = plt.figure(figsize=(15 ,10)) ax = fig.gca() df.plot(ax=ax , kind='box') plt.show() # ### Visualizing Distribution using Pairplot # In[ ]: sns.pairplot(df) # ### Visualizing Class Distribution using Countplot # In[ ]: sns.countplot(df['Class']) # ### Checking Class Imbalance using Pie Chart # In[ ]: plt.figure(figsize=(7 ,7)) labels = 'Normal' , 'Fraud' sizes = [284315 ,492] colors = ['green','red'] explode = [0 , .1] plt.pie(sizes, autopct='%1.f%%', shadow=True, startangle=90, colors=colors, explode=explode, labels=labels) plt.title('Class Distribution', fontsize=16) plt.axis('equal') plt.tight_layout() plt.show() # ### Splitting Data into Train & Test Set # In[ ]: X = df.drop('Class',axis=1) y = df['Class'] X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,stratify=y) # ### Logistic Regression Model - Baseline Model # In[ ]: lr_model = LogisticRegression(solver='liblinear') lr_model.fit(X_train,y_train) lr_pred = lr_model.predict(X_test) print("Confusion Matrix:n",confusion_matrix(y_test,y_pred_lr)) print("Classification Report:n",classification_report(y_test,y_pred_lr)) print("Accuracy Score:",accuracy_score(y_test,y_pred_lr)) print("ROC-AUC Score:",roc_auc_score(y_test,y_pred_lr)) roc_auc_score(y_test,y_pred_lr) def model_eval(model): model.fit(X_train,y_train) y_pred=model.predict(X_test) print("Confusion Matrix:n",confusion_matrix(y_test,y_pred)) print("Classification Report:n",classification_report(y_test,y_pred)) print("Accuracy Score:",accuracy_score(y_test,y_pred)) print("ROC-AUC Score:",roc_auc_score(y_test,y_pred)) def plot_roc(model): model.fit(X_train,y_train) viz_roc = plot_roc_curve(model,X_test,y_test) plt.plot([0,1],[0,1],'r--') plt.legend(loc='lower right') plt.title('Receiver Operating Characteristic') plt.show() def plot_pr(model): model.fit(X_train,y_train) viz_pr = plot_precision_recall_curve(model,X_test,y_test) plt.plot([0,1],[0.55,.55],'r--') plt.legend(loc='lower right') plt.title('Precision Recall Curve') plt.show() def plot_precision_recall_vs_threshold(precisions,recoils,tresholds): plt.plot(t