Bank-Marketing-Effectiveness-Prediction

Problem statement :-

• One of the Portuguese banking institution conducted a marketing campaign based on phone calls. The records of their efforts are available in the form of a dataset. The objective here is to apply machine learning techniques to analyse the dataset and figure out most effective tactics that will help the bank in next campaign to persuade more customers to subscribe to banks term deposit.
• Bank profitability depends on long-term deposits. Targeted marketing tactics that let customers interact with banks directly are the main focus of bank marketing.

Project Summary :-

• Banks are required to sell more long-term bank deposits to enhance their cash reserves right ?
• Bank profitability depends on long-term deposits and Marketing executives are therefore under pressure to persuade the general public to buy long-term deposits. To increase the positive response rate, marketing managers should build better use of their limited resources by making fewer calls to customers while closing more sales. Because they already contain data from prior campaigns to examine, managers may utilize data classification techniques to determine clients in near future.
• The dataset class is labelled as ‘yes’ or ‘no’ depending on whether the contacted client has subscribed to the term deposit or not. It is a marketing problem and the outcome will largely influence the future strategies of bank. Banking institute has a very large client base and even larger target clients.
• In real world , less clients will respond positively to marketing campaign and most of them will say no. Contacting all of them is time consuming task and demands tremendous time and efforts. To manage the human resource in efficient way, it is necessary to correctly identify those clients who have more chances of saying yes. This is where machine learning comes into picture.

Objective :-

• The main objective is to build a predictive model that predicts whether a new client will subscribe to a term deposit or not, based on data from previous marketing campaigns.

Dataset Info :-

• Number of records: 45,211
• Number of features: 17

The dataset contains features like :-

Bank Client data :

Age :- This is age of client.

Job :- The job of clients. It has 12 categories including unknown.

Marital :- Marital status of the customer. It has 3 categories.

Education :- The level of education of the customer.

Default :- It tells whether the customer has credit in default or not.

Balance :- It specifies the account balance of the customer.

Housing :- Whether the customer has a housing loan or not.

Loan :- Whether the customer has a personal loan or not.

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Related with the last contact of the current campaign :

Contact :- The method of communication used to contact the customer.

Month :- The month of the year on which the customer was last contacted.

Day :- The day of the month on which the customer was last contacted.

Duration :- The duration of the last contact with the customer in seconds.

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Other attributes :

Campaign :- The number of contacts performed during the current marketing campaign for this customer.

Pdays :- Number of days passed by after the customer was last contacted from a previous campaign. some value is -1 if the client was not contacted previously.

Previous :- Number of times this client was contacted before this campaign.

Poutcome :-The outcome of the previous marketing campaign for this customer.

Deposit(y) :- Whether the customer has subscribed to a term deposit Yes or No. (the target variable)

Project Work flow :-

• Importing Neccessary Libraries
• Data Wrangling

      ▪ Gathering Data 
      ▪ Assessing Data
      ▪ Cleaning Data 

• EDA

      ▪ Univariate Analysis
      ▪ Bivariate Analysis
      ▪ Multivariate Analysis
 

• Features Engineering

      ▪ check Outliers
      ▪ features transformation
      ▪ features construction
      ▪ features selection

• Remove Multicollinearity
• pre-processing

      ▪ preprocessing columns
      ▪ Splitting Dependent and Independent Variables
      ▪ Handling imbalance class
            

• Model implementation and HyperParameter Tuning

      ▪ Train, Test and Split
      ▪ make pipeline using different algorithms

• Final selection of the model
• Conclusion

Algorithms used for ML model implementation :-

• K-nearest Neighbour Classifier (KNN)
• Logistic Regression (LR)
• Logistic Regression with Polynomial Features (degree=2)
• Gaussion Naive Bayes
• SVM Classifier
• RandomForest Classifier

Best Models to Achieve our Objective :-

🥇 RandomForestClassifier with hyperparameter tuning 🥇

Final Summary of Conclusion :-

Experiment & Discussion

• Six different algorithms are used to solve this problem. Various results have been compared at the end of each model train. A plot is used to compare ROC curves and auc score. Recall score is used as one of the performance matrix.

Why Recall score ?

• This is a marketing problem that involves significant resources, making it crucial to optimize results and save resources. The target variable is the Term deposit, which indicates whether a potential client says yes or no to a term deposit subscription following a phone call. In order to save time and effort, the objective is to focus on those clients who have a higher probability of subscribing to the term deposit. the aim is to predict as many true positives(For Yes(1) Class) as possible from the dataset, making recall score a crucial performance metric.

• so, here we are more interested in correctly predicting the customers who said yes to the term deposit. the proportion of actual positives(Yes) that are correctly classified by the model. In our case, a high recall score for Yes means that we are correctly identifying customers who are more likely to subscribe to the term deposit. so, we need to focus more on recall score of Yes (1) and then precision, F1-Score and also AUC score.

Conclusion

• After evaluating the performance of various machine learning algorithms on the given dataset, it was found that the Random Forest Classifier outperformed other algorithms such as KNN, SVM, Naive Bayes, and Logistic Regression. The hyperparameters of the Random Forest Classifier were tuned using GridSearchCV to find the best combination of parameters for optimal performance.

• The cross-validation score for the Random Forest Classifier was found to be 0.937, indicating that the model generalizes well to unseen data. The accuracy score for the test data was 0.94, which is a good indication of the model's ability to perform well on new, unseen data. Additionally, the accuracy score for the train data was 0.97, which indicates that the model did not overfit to the training data.

• Moreover, the Random Forest Classifier achieved a recall score of 0.92 and an F1-score of 0.94, which are slightly better than the corresponding scores for KNN and SVM. Therefore, based on the evaluation metrics and performance results, it can be concluded that the Random Forest Classifier is a suitable algorithm for this problem.

• Using these results bank can specifically target clients and gain higher success in their endeavours. Saving a lot of time by not focusing on clients with less probability is yet another advantages of this project.

click here To access the solution of the Capstone Project on Bank Marketing Effectiveness Prediction Supervised Learning, which includes the usage of 6 different algorithms with detailed explanations and conclusions.

Apologies for the inconvenience caused. Due to some technical issues with the .ipynb file, I have attached a Google Drive link of .ipynb file and also atteched pdf file of google colab for you to view the file. Thank you.