Srimedha - DataPrep

Data preparation is crucial before running the decision tree algorithm for several reasons:

1. Handling Missing Values: Decision trees cannot directly handle missing values. Data preparation involves strategies such as imputation (replacing missing values with estimated values) or removing instances with missing values to ensure the dataset is complete before training the decision tree.

2. Encoding Categorical Variables: Decision trees typically work with numerical data. Categorical variables need to be encoded into numerical format through techniques like one-hot encoding or label encoding to make them compatible with the decision tree algorithm.

3. Normalization and Scaling: Decision trees are not sensitive to the scale of numerical features. However, normalization or scaling may still be beneficial, especially if other algorithms or ensemble methods (like Random Forests) are used alongside decision trees.

4. Handling Outliers: Outliers can skew the decision tree's splitting process, leading to suboptimal splits. Data preparation may involve identifying and handling outliers appropriately, either by removing them or applying robust techniques that are less sensitive to outliers.

5. Feature Selection: Preprocessing can involve feature selection techniques to identify and retain only relevant features for the decision tree model. This helps in reducing noise and improving the tree's performance and interpretability.

6. Dealing with Imbalanced Data: If the dataset is imbalanced (i.e., one class significantly outnumbers the others), data preparation techniques like resampling (oversampling or under sampling) or using class weights can help address the imbalance and improve the decision tree's ability to classify minority classes accurately.

7. Improving Performance: Properly prepared data leads to a more robust and accurate decision tree model. By cleaning the data, handling missing values, encoding variables correctly, and optimizing feature selection, the decision tree can focus on learning meaningful patterns and making better predictions.

In summary, data preparation ensures that the input data is in a suitable format and quality for the decision tree algorithm to learn effectively, generalize well, and make accurate predictions or classifications. It plays a crucial role in the overall performance and reliability of the decision tree model.

The Dataset Before Preparation:

After:

Train-Test Split of the Data:

The `train_test_split` function from scikit-learn is used in this project to create the test-train split. This function allowed the model to be trained on 70% of the data (the training set) and evaluated on 30% of the data (the testing set) by randomly dividing the dataset into training and testing subsets in a 70 to 30 ratio.

To appropriately assess the model's generalization ability, a disjoint split—where the training and testing sets do not overlap—must be created. The model could simply learn the training data by heart and perform well on it without really understanding the underlying patterns if the same data were used for both testing and training. Overfitting would result from this, making the model underperform on fresh, untested data.

The testing set serves as a stand-in for fresh, unused data by generating a disjoint split, which enables us to evaluate how effectively the model generalizes to actual situations. It ensures that the model is useful in real-world applications by assisting in the detection of problems like overfitting and offering a more trustworthy estimate of the model's performance on unseen data.

The code to all the above can be found here.

Results and Conclusions