Srimedha - DataPrep

Why is Data Preparation important for NB Algorithm?

Data preparation obviously plays an important role in ensuring the effectiveness of the Naive Bayes algorithm. One key aspect of data preparation is feature selection, where relevant features are identified and selected to align with the assumption of independence between features in Naive Bayes. By choosing the right set of features, the model can focus on the most informative aspects of the data, leading to improved accuracy and predictive performance.

Another important aspect is data cleaning, which involves removing noise, outliers, and irrelevant information from the dataset. This step helps in reducing the impact of irrelevant data on the model's training and prediction process, ensuring that the model learns meaningful patterns and relationships from the data.

Normalization is also essential in data preparation for Naive Bayes. Scaling and normalizing the data prevent biases towards features with larger scales, ensuring that all features contribute equally to the model's decision-making process. This step is particularly important when dealing with features that have different units or scales. This might not be necessary when dealing with textual data (like in this case). For text data, preprocessing steps such as tokenization, stop-word removal, and stemming/lemmatization are essential. These steps transform raw text into a format that the Naive Bayes algorithm can effectively process, improving the quality of input features for text classification tasks.

To sum it all up, data preparation optimizes the input data to meet the assumptions and requirements of the Naive Bayes algorithm, ensuring that the model can learn meaningful patterns, make accurate predictions, and generalize well to new data.

Data Before and After Preparation for NB Algorithm:

Before:

After:

Train-Test Split of the Data:

The test-train split in this project is created using the `train_test_split` function from scikit-learn. This function randomly divided the dataset into training and testing subsets in 70 to 30 ratio, allowing the model to be trained on one part (70%) of the data (training set) and evaluated on another part (30%) (testing set).

Creating a disjoint split, where the training and testing sets do not overlap is important for evaluating the model's generalization performance accurately. If the same data were used for both training and testing, the model could simply memorize the training data and perform well on it without truly learning the underlying patterns. This would lead to overfitting, where the model performs poorly on new, unseen data.

By creating a disjoint split, the testing set acts as a proxy for new, unseen data, allowing us to assess how well the model generalizes to real-world scenarios. It helps detect issues like overfitting and provides a more reliable estimate of the model's performance on unseen data ensuring the model's effectiveness in practical applications.

The code to all the above can be found here.

Results and Conclusions