<jupyter_start><jupyter_text>Coding Problems  1. Data Preprocessing 1. Load Data - Wine Dataset from Sklearn2. Check for missing values3. Encode numerical column   - Packages that can be used    - sklearn: LabelEncoder, OneHotEncoder    - pandas: map with dict4. Stratify train test split -> 70% Train and 30% Test  2. Feature Selection  1. Train the Random Forest model on the train data  2. Select top 5 features by look at the feature importance of the RF for both train and test set  3. Dimension Reduction  1. Standarize the train and test data  2. Using PCA to transform the X into 2 components     - visualize the component with scatter plot and color label         4. Model Training  1. Select 2 Models for the dataset   2. Train the 2 models  3. Compare the model performance    - look over the accuracy, Precision and recall value  4. Which model is better and why    5. Pipeline  1. Create the pipeline that consist data transformer and selected model from above  2. Pass the pipeline into GridsearchCV  3. Find the best hyperparameter of the selected model<jupyter_code><jupyter_output><empty_output>
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