Prediction with Logistic Regression

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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.

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Python Libraries Used:

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

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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

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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

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Contribution

Still Learning,

So feel free, Anything You wanna contirubute.

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