We are creating a classification model aimed at predicting which clients are more likely to subscribe to term deposits. This model will analyze client data and behavior to assess the probability of them opting for term deposits. By leveraging various features and machine learning techniques, we intend to provide insights into the client base, aiding marketing and client engagement strategies. This predictive tool will enable the institution to allocate resources effectively, tailor marketing campaigns, and enhance customer relationships. Ultimately, it aims to optimize term deposit subscription rates, improving the institution's financial performance and customer satisfaction.
To prepare the data for model building, we performed the following steps:
- Data encoding: Categorical variables were encoded using techniques such as one-hot encoding or label encoding.
- Handling missing values: Missing values were imputed or removed, depending on the variable and dataset size.
Our EDA process involved:
- Data visualization: We created various plots and graphs to visualize client attributes and understand the distribution of key variables.
- Statistical analysis: We used descriptive statistics to gain insights into client behavior and preferences.
In the feature importance analysis, we aimed to identify the most influential features impacting term deposit subscriptions. This helps us focus our marketing efforts on the most relevant client attributes.
For model building, we followed these steps:
- Data splitting: We divided the dataset into training and testing sets.
- Model selection: We Used RandomForestClassifier classification model
- Model evaluation: We assessed model performance using metrics like accuracy, precision, recall, and F1-score.
- Cross Validation - validating model with k-fold time to check the mean performance
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