A Basic AI Model Implementation

import pandas as pd
from sklearn.preprocessing import OrdinalEncoder 
oe = OrdinalEncoder()

data = pd.read_csv('Data/Customer Churn.csv')

data['Gender'] = oe.fit_transform(data[['Gender']])
data['EmailOptIn'] = oe.fit_transform(data[['EmailOptIn']])
data['PromotionResponse'] = oe.fit_transform(data[['PromotionResponse']])

X = data.iloc[:,:-1]
y = data.iloc[:,-1]

• Here, we first read the data that is stored in a CSV (Comma-separated Values) file.
• The data is then pre-processed into a format that can be understood and used by the model. The processing may vary depending on the model you use.
• Finally, we separate the data into the measured values and the goal variable.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
                                        X,
                                        y,
                                        test_size=0.2,
                                        stratify=X['Gender'],
                                        random_state=1
                                        )

• test_size=0.2 denotes that we are using $20\%$ of the data for the testing set.

• Here we choose to implement a Random Forrest Classifier.

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()

grid = { 
    'n_estimators': [25 * i for i in range(1, 10 + 1)], 
    'max_features': ['sqrt', 'log2'], 
    'max_depth': [3 * i for i in range(1, 5 + 1)], 
    'max_leaf_nodes': [2, 5, 10, 20, 50], 
}

• The dictionary grid contains the names of the hyper-parameters that can be tuned for this particular model.

from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RepeatedStratifiedKFold

cv = RepeatedStratifiedKFold(
    n_splits=10,
    n_repeats=3,
    random_state=1
    )
grid_search = GridSearchCV(
    estimator=model,
    param_grid=grid,
    n_jobs=-1,
    cv=cv,
    scoring='accuracy'
    )

grid_result = grid_search.fit(X_train, y_train)
print(f"Highest Accuracy: {grid_result.best_score_:.4f}")
print(f"Hyper Parameter Values: {grid_result.best_params_}")

• Here we use functions called RepeatedStratifiedKFold and GridSearchCV as RepeatedStratifiedKFold creates a validation set from the training data on its own, without any data leakage and GridSearchCV automatically varies across all the hyper-parameter values, trains the given model and evaluates the given score over the validation dataset.
• At the end, we use the last two print statements to get the highest accuracy achieved across all the hyper-parameter values and to get the respective values as well.

rf = RandomForestClassifier(
    n_estimators=200,
    max_features='log2',
    max_leaf_nodes=2,
    max_depth=12,
    random_state=1
    )

rf_model = rf.fit(X_train, y_train)