Create a Model for Classification or Regression based on a selected dataset.

Benchmarks

A succinct and self$-$contained problem statement and motivation should be included.

b$.$ Introduction and Data Description: Summarize of pertinent information. Why is that

Is the issue significant? Why is it difficult? Describe the project's motivations.

query and the manner in which it was formulated using early EDA.

c$.$ Literature Review$/$Related Work: This could be listing any important books, articles, or websites that you have used to formulate your modeling strategy in addition to previous research that has been done on the issue. You have to provide due acknowledgment to your sources.

d$.$ Modeling Approach and Project Trajectory: Provide a description of your implementations beyond the baseline model as well as $1)$ a baseline model for comparison. Provide a succinct summary of any modifications you have made to your project's objectives or implementation strategies. These adjustments are an inevitable component of all projects, even the easiest ones at first glance. Your approach can be effectively illustrated by the narrative you tell about how you got at your outcomes.

e$.$ Findings, Conclusions, and Upcoming Projects: Display and explain your findings. Provide an overview of your findings, highlighting both your advantages and disadvantages, and provide some educated guesses on how, with more time, you might resolve the shortcomings.

$2.$ Provide Jupyter notebook code as well.

$$ Hint: Obtain your dataset from Kaggle