This week, I encountered a challenge while working on the Titanic competition on Kaggle. The challenge revolved around handling missing data in the dataset. Some features had significant missing values, and deciding on the appropriate strategy to impute or drop these values was a key decision point. After researching different imputation methods and consulting Kaggle discussions, I decided to use a combination of statistical imputation and dropping columns with a high percentage of missing values. This resolved the challenge and allowed me to proceed with further analysis.

New Skills Acquired

One of the main skills I focused on this week was feature engineering. I delved into creating new features from existing ones to potentially enhance the predictive power of the model. This involved understanding the domain of the problem, identifying relevant patterns, and transforming the data accordingly. I also explored techniques like one$-$hot encoding for categorical variables to ensure compatibility with machine learning algorithms. The resources I found particularly helpful for this were Kaggle kernels shared by experienced participants, providing insights into feature engineering strategies.

Reviewed Resources

In addition to Kaggle kernels, I reviewed documentation on the scikit$-$learn library for implementing machine learning algorithms and techniques. The scikit$-$learn documentation provided clarity on the usage of various functions and classes, aiding in the implementation of my predictive model. Additionally, I participated in discussions on the Kaggle forums, gaining insights into different approaches taken by participants and learning from their experiences.

New Goals for Future Weeks

Looking ahead, my goals include refining the feature engineering process and experimenting with different machine learning algorithms to improve model performance. I plan to explore ensemble methods and hyperparameter tuning to optimize the model further. Additionally, I aim to contribute to the Kaggle community by sharing my findings and insights in the form of a notebook.