How can I train test validation split?

In the context of data science, you can decide on the train test split ratio for the particular dataset by considering the following factors:

Dataset size

With a dataset of 10,000 records, you have moderately large datasets. A common split ratio could be 80% for the training and 20% for the testing. This can ensure that you have enough data for the training while still having a reasonable amount for testing so that you can evaluate model performance.

Model complexity

If the model is complex or even tends to overfit a larger test set can be beneficial for providing a more reliable estimate of the generalization error.

Data quality

You should try to ensure that both training and testing the sets would represent the overall distribution of the data. It can random shuffling before splitting helps in achieving this.

Here is how you can implement this in Python programming language by using the sci-kit learn:

From sklearn.model_selection import train_test_split

Import pandas as pd

# Assuming df is your DataFrame containing the dataset

# X contains features, y contains target variable (churn in this case)

X = df.drop(‘churn’, axis=1)

Y = df[‘churn’]

# Splitting the dataset with 80% training and 20% testing

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Now X_train and y_train are the training sets, and X_test and y_test are the test sets

You can adjust the “test size” parameters based on your specific requirements and the considerations that have been discussed earlier.

Here is a detailed Python-based code that includes loading a dataset preprocessing steps and splitting the data into the train and testing sets by using the sci-kit learn:

  # Importing necessary libraries

Import pandas as pd

From sklearn.model_selection import train_test_split

From sklearn.preprocessing import StandardScaler

From sklearn.linear_model import LogisticRegression

From sklearn.metrics import accuracy_score, classification_report

# Assuming you have a dataset in a CSV file named ‘telecom_churn.csv’

# Load the dataset

Df = pd.read_csv(‘telecom_churn.csv’)

# Assuming ‘churn’ is the target variable

# Separate features (X) and target variable (y)

X = df.drop(‘churn’, axis=1)

Y = df[‘churn’]

# Perform any necessary preprocessing, such as scaling numerical features

# Example: Scaling numerical features using StandardScaler

Scaler = StandardScaler()

X_scaled = scaler.fit_transform(X)

# Split the dataset into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# Example model fitting and evaluation

# Assuming a simple logistic regression model

Model = LogisticRegression(max_iter=1000)

Model.fit(X_train, y_train)

# Predict on the test set

Y_pred = model.predict(X_test)

# Evaluate model performance

Accuracy = accuracy_score(y_test, y_pred)

Print(f’Accuracy on test set: {accuracy:.2f}’)

# Additional evaluation metrics

Print(classification_report(y_test, y_pred))

# Optionally, you can also explore feature importance or other model diagnostics

# Example of feature importance (if applicable to your model)

If hasattr(model, ‘coef_’):

    Feature_importance = pd.Series(model.coef_[0], index=X.columns)

    Print(‘Feature importance:’)

    Print(feature_importance.sort_values(ascending=False))

# Further analysis or adjustments to the model based on evaluation results