Dataset: This dataset contains missing values, categorical variables, and requires data standardization.Requirements:Data Preparation:Remove rows with too many missing values.Remove columns with too many missing values.Impute missing values appropriately.Encode categorical variables $($dummy or one$-$hot encoding$).$Standardize the numerical variables.Model Building:Use scikit$-$learn's MLPClassifier to build your predictive model.Perform hyperparameter tuning to optimize your model.Use techniques like GridSearchCV or RandomizedSearchCV.Adjust your search strategy to account for computational time and resources.Document your hyperparameter selection process and justify your choices.Evaluation:Discuss and justify the evaluation metrics you consider important $($e$.$g$.,$ accuracy, precision, recall, F$1-$score$).$Include details around your chosen steps, such as data split ratios, addressing data imbalance, or using stratified splits.Documentation:Create markdown cells to explain each of your steps.Present a professional document with an introduction, methodology, results, and conclusion. You must have markdown to sufficiently describe your reasoning for each of the steps $($for example, why you choice the train test ratio that you did$).$ You must demonstrate knowledge of the material and methods covered in the course.