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Prediction with Logistic Regression

Projects:

Predicting whether the bank client has subscribed for the term deposit or not.

Objectives:

Exploring Insights/Inferences by performing EDA on the given data. Relevant graphs were plotted to get some insights on data using seaborn package. Model fitting via Logistic Regression by Importing sklearn package.

Python Libraries Used:

  • Pandas
  • Numpy
  • Matplotlib
  • Seaborn
  • Scikit learn
  • Joblib

Methodology:

  1. Data copying and cleaning:

    • Read the csv file
    • copy the data
    • check for null values and other informations

  2. Exploratory Data Analysis:

    • Conduct all the necessary EDA using various graphs on the dataset
    • interpret the graphs
    • check for outliers and correlation among the coloumns
    • perform one hot encoding in case of categorical columns

  3. Sampling of data:

    • Divide the data into x and y
    • standardize the data using StandardScaler lib
    • import test_train_split from sklearn.model_selection
    • divide the data into training and testing

  4. Modelling of data:

    • import LogisticRegression and initialize it
    • fit the model
    • predict the model

  5. Model validation (Error Calculation):

    • From sklearn.metris import accuracy_score, precision_score, confusion_matrix
    • check the accuracy of the model

  6. Save the Model:

    • import joblib
    • save the model

Bank-Deposit data Probelm:

File name: Bank-Deposit-data-files

EDA Inferences:

  • Toatl Bank clients are 45211 , out of which 39922 did not subscribed for term deposit.
  • There are large number of clients with manangement and blue-collar job profiles and are more likely to opt for term deposit.
  • Clients with secondary education type are more compared to other and are the one opted for term deposited in large number.
  • Bank has more married clients followed by single and divorced.
  • From analysing job, education and martial data, clients with more balance are more likely to opt for term deposit.
  • The same is true in case of duration of call, the clients with maximum call duration are more likely to opt for term deposit.
  • The data is normally distributed for age and Age and day features.
  • Balance, duration, campaign, pdays, previous are skewed towards left and might have outliers present.
  • Salary and Age of the customers are correalted with the Iphone purchase.
  • There are outliers present in all the features of the dataset except day feature.

Logistic Regression Model Results:

The accuracy of the model is came out to be:

Accuracy Score: 0.90

Precision Score:0.65

Contribution

Still Learning,

So feel free, Anything You wanna contirubute.