WTB coding services in Python and ML

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Looking for someone who can basically teach me how to do this coding assignment.

1. Read the data and perform EDA. Determine a metrics for model evaluation.
   
2. Based on the EDA, clean the data and perform encoding for the columns (e.g. converting categories to numbers, or multiple columns)
   
3. Select the important features for your model.
   
4. Build and train the following machine learning models (70% of the overall data):
   
   1. Decision Tree
      
   2. Random Forest
      
   3. Logistic Regression
      
   4. SVM
      
   5. Naïve Bayes
      
5. Evaluate the models (30% of the overall data not used in training) and determine the metrics of interest.
   
6. Compare the models.
   
7. In task 4, while building the models, tune different hyperparameters for the models. Use grid search and cross validation.
   
8. Is it possible to build a model with better performance if we balance the classes? If so, how? Build models and demonstrate how it helps.
   

If you can do this, please DM me.