Project Introduction In this project you will be provided with real world data which is related with direct marketing campaigns (phone calls) of a Portuguese banking institution.
The classification goal is to predict if the client will subscribe a term deposit (variable y).
We are a data science team in thier offices and it our task to make the most sense of the data provided by the company
Let us Get Started! As you remember our task is to explore our Bank's Marketing campaign and create meaningful insights from the data
The step one should be accessing the data
Our data has been curated by UCI Machine Learning Repository which is an excellent datahouse of various real world problems!
We are going to use the Banking Data described by our friends in UCI here
The data can also be downloaded from the Tech I.S. Github Repository
Good Start! Now that you have your dataset , let us go through the problems one by one!
Section I : Data Loading Part I : Load the dataset into the notebook Part II : Explore and make note of Attribute Information from UCI Part III : What is the significance of the y column in the dataset and what are the value counts of the y column? Part IV : What is the ratio of the two classes ? Are they balanced ?
Section II : Data Cleaning Since this is real world data , A good practice is to make sure the dataset is devoid of any nuances
Part I : Get the dtypes of all the columns of our dataset Part II : Refering to the UCI data description , explore the data in your columns and check if there are any errors Part III : Make note of the deviation in the dataset compared to the description provided by UCI Part IV : Using Data Cleaning principles you learned from Pandas Tutorial) figure out the best ways to get rid of the dirty data Part V : Print the cleaned data
Section III : Exploring data with Group by In this section , we must create some primitive EDA
Use the groupby function on the mean of the following columns :
I : y II : job III : marital IV : education
Make a note of what you learn from the outputs !
Section III : Exploratory Data Analysis Let us put Matplotlib to use !
Part I : Create bar graphs to the frequency of purchase with respect to the job , martial etc Part II : Also create stacked bars to same data columns with respect to Part III : Explore the age column using a histogram and note down your observations
Section IV : Categorical Variable Encoding Part I : Create dummy variables for your categorial variables part II : Explore your new dataset with these new dummy variables !
Section V : Preliminary Training Part I : Import your Logisitc Regression libraries Part II : Split your train and test dataset and train on the data Part III : Make note of the classification report and other metrics
Section VI : Let's Improve the performance ! Part 0 : What was your answer to Section - Part IV? Do you think class imbalance affects the model performance? Explore SMOTE implementation
Part I : Make note of the performance from the last training Part II : Try implementing SMOTE to balance the two class labels Part III : Make note of the y label data now , what are the rations now ?
Section VII : Let us Re-Train! Part I : Explore what RFE means Part II : Implement your training process inside the RFE Part III : What are the best columns that your RFE found? Please make a list of it
Section VIII : Training time ! Now that you have found the best columns for this problem
Part I : Now train the model with the new data you have created after the RFE Part II : Create the prediction system to get the metrics such as accuracy
Section IX : Additional Metrics Accuracy is not always the best metric
Part I : Explore what Confusion Matrix means Part II : Create the confusion matrix for the predictions and make note of the outputs Part III : Create a classification report and make note of various outputs
Section X : What's next? Part I : Make a note of difference in performance? Part II : Can you recommend more improvements that could give much better results in all metrics?