TBL stats & predictions

Cosa include esattamente un pronostico personalizzato?

I pronostici personalizzati includono analisi dettaglia delle squadre selezionate dal cliente, statistiche aggiornate sui giocatori chiave e suggerimenti specifici basati sulla storia delle performance delle squadre coinvolte nelle partite prescelte. Inoltre, vengono forniti consigli su eventuali fattori esterni che potrebbero influenzare l'esito delle partite.

Come posso verificare l'affidabilità dei vostri pronostici?

Grazie alla trasparenza nei confronti dei nostri clienti, pubblichiamo regolarmente rapporti sui risultati dei nostri pronostici. Questo ti permette di verificare direttamente l'affidabilità dei servizi che offriamo confrontando i risultati realizzati con quelli previsti dai nostri esperti.

Come funzionano gli aggiornamenti giornalieri?

Grazie all'integrazione con piattaforme digital avanzate e all'impiego continuo dei migliori strumenti tecnologici disponibili nel settore dello sport business analytics (SBA), siamo in grado di fornire agli utenti aggiornamenti quotidian [0]: #!/usr/bin/env python [1]: import sys [2]: import os [3]: import argparse [4]: from datetime import datetime [5]: import numpy as np [6]: from scipy.stats import norm [7]: from sklearn.model_selection import train_test_split [8]: from sklearn.preprocessing import MinMaxScaler [9]: from tensorflow.keras.models import Sequential [10]: from tensorflow.keras.layers import Dense [11]: from tensorflow.keras.layers import LSTM [12]: from tensorflow.keras.callbacks import EarlyStopping [13]: def get_data(path): [14]: # load data and drop NA values [15]: df = pd.read_csv(path) [16]: df = df.dropna() [17]: # extract target variable and convert to numpy array [18]: target = df['price'] [19]: # drop columns we don't want to use for training [20]: df = df.drop(['date', 'price', 'symbol'], axis=1) [21]: # convert dataframe to numpy array for modeling [22]: data = df.values [23]: return data,target [24]: def get_dataset(data,target): [25]: # split dataset into train and test set [26]: X_train,X_test,y_train,y_test=train_test_split(data,target,test_size=0.2) [27]: # create scaler object and fit on train set [28]: scaler=MinMaxScaler() [29]: # fit on train set only and transform both train and test set [30]: X_train=scaler.fit_transform(X_train) [31]: X_test=scaler.transform(X_test) [32]: y_train=scaler.fit_transform(y_train.values.reshape(-1,1)) [33]: return X_train,X_test,y_train,y_test ***** Tag Data ***** ID: 1 description: Function that splits the dataset into training and testing sets using MinMaxScaler for normalization. start line: 24 end line: 33 dependencies: - type: Function name: get_data start line: 13 end line: 23 context description: This function is crucial for preparing the data for machine learning. It splits the data into training and testing sets and normalizes them using MinMaxScaler. algorithmic depth: 4 algorithmic depth external: N obscurity: 2 advanced coding concepts: 4 interesting for students: 5 self contained: Y ************ ## Challenging aspects ### Challenging aspects in above code: 1. **Data Handling and Preprocessing**: - **NA Values**: The original function `get_data` drops NA values without any checks or warnings which might lead to loss of critical data if NA values are not missing at random. - **Normalization**: The `MinMaxScaler` is fit on the training data and then applied to both the training and test data separately. This ensures no information leakage but requires careful handling to maintain consistency across splits. 2. **Train/Test Split**: - The split ratio of `0.2` is fixed which might not be optimal for all datasets; determining an optimal split can be complex depending on the size and nature of the dataset. - The split should ensure that both training and testing sets have similar distributions of the target variable. 3. **Feature Engineering**: - Dropping specific columns (`date`, `price`, `symbol`) without any analysis might lead to loss of potentially useful features that could improve model performance. ### Extension: 1. **Handling Time Series Data**: - If the dataset represents time-series data (which is likely given the presence of a `date` column), splitting based on time rather than random sampling is crucial to avoid temporal leakage. 2. **Dynamic Feature Selection**: - Instead of hardcoding columns to drop or keep (`date`, `price`, `symbol`), dynamically determine which features are useful based on their statistical properties or correlations with the target variable. 3. **Outlier Detection and Handling**: - Implement mechanisms to detect and handle outliers which could skew the scaling process or affect model performance. 4. **Cross-validation**: - Introduce cross-validation techniques to ensure model robustness rather than relying solely on a single train/test split. ## Exercise ### Problem Statement: Expand the given [SNIPPET] to handle time-series data appropriately by implementing time-based splitting instead of random splitting while maintaining normalization consistency across splits. Additionally: - Implement dynamic feature selection based on correlation with the target variable. - Add outlier detection using the Z-score method and handle them by capping/flooring values within a specified range. - Integrate cross-validation for robust model evaluation. ### Requirements: 1. **Time-based Splitting**: Modify the `get_dataset` function to ensure that training data precedes testing data temporally. 2. **Dynamic Feature Selection**: Automatically select features based on their correlation with the target variable before splitting. 3. **Outlier Detection**: Detect outliers using Z-score method (considering points beyond ±3 standard deviations as outliers) and handle them by capping/flooring within ±2 standard deviations. 4. **Cross-validation**: Implement K-Fold cross-validation within the training set to evaluate model performance. python import pandas as pd import numpy as np from sklearn.model_selection import train_test_split, TimeSeriesSplit from sklearn.preprocessing import MinMaxScaler def get_data(path): # load data and drop NA values df = pd.read_csv(path) df = df.dropna() # extract target variable and convert to numpy array target = df['price'] # drop columns we don't want to use for training dynamically based on correlation with target variable corr_threshold = 0.1 correlations = df.corr()['price'].abs() features_to_keep = correlations[(correlations > corr_threshold) & (correlations.index != 'price')].index.tolist() # add 'date' back since it's important for time series splitting but drop it from actual features used for modeling features_to_keep.append('date') # filter dataframe based on selected features df = df[['date'] + features_to_keep] # convert date