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Bank-Marketing-Data-Analysis

Requirements

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

Description

  1. Bank Marketing dataset is collected from direct marketing campaign of a bank institution from Portuguese.

  2. Marketing campaign can be understood as phone calls to the clients to convince them accept to make a term deposit with their bank.

  3. 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.

  4. 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.

  5. 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.

  6. The main issues of the dataset are:

    • Preprocessing required to fill unknown values in the dataset

    • Preprocessing required to decide on usage of categorical data along with continuous data

    • The data is class imbalanced (Number of class 1 (yes) is very low when compared to the number of Class 0 (no))

  7. Data Analysis Work done for this analysis include:

    • Understanding of features

    • Preprocessing of features

    • K-Nearest Neighbor Classifier

    • Logistic Regression

    • Naïve Bayes

    • SVM

    • Perceptrons

    • Random Forest Classifier

    • Dimensionality Reduction