Using the csv file nbaallelo_log.csv and the logit function, construct a logistic regression model to classify whether a team will win or lose a game based on the team's elo_i score. - Read in the file nbaaello_log.csv. - The target feature will be converted from string to a binary feature by the provided code. - Split the data into 70 percent training set and 30 percent testing set. Set random_state =0. - Use the logit function to construct a logistic regression model with wins as the target and elo_i as the predictor. - Print the coefficients of the model. Ex: If the feature pts is used as the predictor, rather than elo_i, the output is: Optimization terminated successfully. Current function value: 0.621201 Iterations 5 Intercept 5.908580 pts 0.057528 dtype: float 64 \# import the necessary libraries \# load nbaallelo_log.csv into a dataframe df= \# code to load csv file \# Converts the feature "game_result" to a binary feature and adds as new column "wins" wins =df.game_result=="W" bool_val =np.multiply( wins, 1) wins = pd.DataFrame(bool_val, columns = ["game_result" ] ) wins_new = wins.rename(columns ={ "game_result": "wins" } ) df_final = pd. concat ([df, wins_new ], axis=1 ) \# split the data df_final into training and test sets with a test size of 0.3 and random_state =0 train, test =# code to split df_final into training and test sets \# construct a logistic model with wins and the target and elo_i as the predictor, using the training se lm= \# code to construct logistic model using the logit function \# print coefficients for the model print(\# code to return coefficients)