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Data Preprocessing:
- Loaded the dataset and checked for missing values (there were none).
- Examined descriptive statistics to understand the dataset.
- Explored the distribution of age and duration using box plots and histograms.
- Transformed categorical variables into dummy variables.
- Converted 'pdays' values of -1 to a large value (10000) to indicate clients not previously contacted.
- Created a new column 'recent_pdays' and dropped 'pdays'.
- Combined similar job categories, education categories, and poutcome categories.
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Exploratory Data Analysis:
- Investigated the relationship between variables, such as age and balance.
- Analyzed the characteristics of people who signed up for a term deposit, including their age, balance, duration of the last contact, etc.
- Explored specific scenarios, like people with loans or credit defaults who signed up for term deposits.
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Data Visualization:
- Utilized bar charts to visualize the relationship between job category, previous outcome, and deposit subscription.
- Examined the correlation between the duration of the call and the previous outcome.
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Classification:
- Calculated the correlation matrix to understand the relationships between variables.
- Prepared the dataset for classification by creating dummy variables.
- Identified correlations between various features and the target variable ('deposit_cat').
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Model Building:
- Implemented a decision tree classifier to predict whether a client will subscribe to a term deposit.
- Split the dataset into training and testing sets.
- Trained the decision tree model and evaluated its performance using metrics like accuracy, precision, recall, and F1-score.