Data Prep & Code

Data Cleaning:

During our analysis of the weather history dataset, it was crucial to conduct a thorough data cleaning process to verify that the dataset was prepared for modeling. The initial efforts focused on resolving missing values in crucial variables such as 'Precip Type', improving the overall completeness and trustworthiness of the data. In addition, we converted the textual descriptors in the 'Summary' variable into numerical codes, allowing our Decision Tree model to accurately interpret and gain knowledge from these situations. The encoding procedure preserved both the informational content of the data and conformed to the numerical input specifications of our selected model.

We improved our dataset by carefully selecting the most important features and removing those that were less relevant or redundant. This allowed us to focus our investigation on the predictors that have the most influence. An essential part of our preparation involved dividing the dataset into separate training and testing sets to ensure an impartial assessment of the model's performance on unfamiliar material. The data cleaning and preparation activities established a strong basis, allowing us to proceed with confidence to the modeling phase, knowing that our dataset is of high quality and integrity.

In our case, we used the train_test_split function from sklearn.model_selection to partition the dataset into a training set and a testing set. This is a crucial step in preparing the data for modeling with both Naïve Bayes and Decision Trees algorithms, or indeed any supervised learning model. Here's a closer look at how this was achieved and why it's important:

How the Test-Train Split Was Created:

• Selection of Features and Target Variable: Initially, we selected the relevant features (X) and the target variable (y). In our example, the features might include numeric variables such as 'Temperature (C)', 'Humidity', etc., and the target could be a binary variable representing the 'Precip Type' (e.g., rain or not).

• Applying train_test_split: We used the train_test_split function, specifying the dataset (X and y), the size of the test set (test_size), and a random_state to ensure reproducibility. The test_size parameter determines the proportion of the data that will be reserved for testing. A common choice is setting this between 0.2 and 0.3, meaning 20-30% of the data is used for testing, and the remainder is used for training.

• python

• Copy code

• from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  This line of code randomly splits the dataset into training (70%) and testing (30%) sets, with the random_state parameter ensuring that the split is reproducible across different runs.

Importance of Creating a Disjoint Split:

• Model Evaluation: The primary reason for creating a disjoint split is to evaluate the model's performance on unseen data. The training set is used to train the model, and the testing set serves to evaluate its predictive performance. This helps in assessing how well the model generalizes to new, unseen data.

• Preventing Overfitting: Overfitting occurs when a model learns the training data too well, including its noise and outliers, leading to poor performance on new data. By evaluating the model on a separate test set, we can detect overfitting early on.

• Model Tuning: The test set provides a basis for comparing different models or configurations and tuning hyperparameters. It's essential for selecting the model that performs best on unseen data.

• Fair Assessment: A disjoint split ensures that the evaluation of the model's performance is fair and unbiased. Using the same data for training and testing would give an overly optimistic estimate of the model's performance.

In summary, the train-test split is a fundamental practice in machine learning for developing models that are effective, generalizable, and robust against overfitting. It ensures that the assessment of the model's predictive power is realistic and reliable.