Perform data cleaning and exploratory analysis on the bank marketing dataset from a Portuguese banking institution. Analyze the data to develop a Decision Tree model that predicts whether a client will subscribe to a term deposit based on various features.

To build a predictive model for term deposit subscriptions by performing data cleaning, exploratory data analysis (EDA), and hyperparameter tuning. The goal is to develop a high-performing model optimized to achieve the best possible recall and accuracy.

• Numpy: For data manipulation and handling missing values.
• Pandas: For data manipulation and cleaning.
• Matplotlib: For data visualization.
• Seaborn: For data visualization.
• Scikit-learn: For building and evaluating machine learning models.

1. Importing the Libraries
2. Importing the Dataset
3. Data Understanding
   • Description of dataset features and their definitions.
   • Initial exploration of data distribution and statistics.
4. Data Cleaning
   • 4.1. Handling Missing Values - Dropping: Dropping rows or columns with excessive missing values.
   • 4.2. Handling Missing Values - Imputing: Imputing missing values with appropriate methods (e.g., mode for categorical variables).
   • 4.3. Outliers : Finding the outliers in the data and treating them appropriately
5. Exploratory Data Analysis
   • 5.1. Bivariate analysis
   • 5.2. Multivariate analysis
     • Recommendation
6. Data Preparation for Modeling
   • 6.1. One-Hot Encoding : Encoding categorical variables.
   • 6.2. Label Encoding : Encoding categorical variables.
   • 6.3. Train-Test Split : Spliting the data for training and test purposes.
   • 6.4. Resampling Class_Imbalance : Identifying the class imbalance in the training data.
     • 6.4.1. SMOTE Algorithm
7. Building decision Tree
   • 7.1. Visualizing Decision Tree
   • 7.2. Hyperparameter Tuning using GridSearchCV
   • 7.3. Precision-Recall Curve
8. Evaluating the Model
   • Prediction and Metrics
9. Conclusion