- Python 2.7
- Numpy >= 1.14.2
- Matplotlib >= 2.2.0
- Pandas >= 0.22.0
- Scikit-Learn >= 0.19.1
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Bank Marketing dataset is collected from direct marketing campaign of a bank institution from Portuguese.
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Marketing campaign can be understood as phone calls to the clients to convince them accept to make a term deposit with their bank.
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After each call, they are being noted as to no - being the client did not make a deposit and yes - being the client on call accepted to make a deposit.
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The purpose of this project is to predict if the client on call would accept to make a term deposit or not based on the information of the clients.
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The Bank Marketing Data Set considered for this project is a small portion (10%) of the entire available data set. The data set has about 4119 rows of data with 19 features and 1 column of Class information.
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The main issues of the dataset are:
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Preprocessing required to fill unknown values in the dataset
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Preprocessing required to decide on usage of categorical data along with continuous data
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The data is class imbalanced (Number of class 1 (yes) is very low when compared to the number of Class 0 (no))
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Data Analysis Work done for this analysis include:
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Understanding of features
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Preprocessing of features
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K-Nearest Neighbor Classifier
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Logistic Regression
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Naïve Bayes
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SVM
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Perceptrons
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Random Forest Classifier
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Dimensionality Reduction
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