Bank-Mareting-Data-Analysis

Requirements

• Python 2.7
• Numpy >= 1.14.2
• Matplotlib >= 2.2.0
• Pandas >= 0.22.0
• Scikit-Learn >= 0.19.1

The data was collected as a marketing campaign to predict if a customer would make a term deposit in the bank.

The dataset considered for the project is 10% of the UCI bank Marketing dataset available online. The dataset has 4119 rows with 19 features.

The issues in the dataset were as follows: -> The features had missing values which had to be imputed. -> Preprocessing involved handling categorical data. -> The dataset was imbalanaced. Number of class 1 (yes) labels were low compared to number of class 0 (no) labels.

Preprocessing

Preprocessing work done on the data included:

1. Outlier removal
2. Label and one hot encoding
3. Handling missing data by mode imputation
4. Handling imbalanced data by oversampling using SMOTE,
5. Dimensionality reduction
6. Normalization and standardization

Models

Classsifiers used:

1. Support Vector Machine (SVM)
2. Naive Bayes
3. K Nearest Neighbors
4. Random Forest
5. Perceptron

Results

Performance Evaluation Metric used:

1. F1 score
2. AUC score
3. Training and test accuracy
4. Confusion matrix
5. ROC plots