Thera bank case study using python

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Thera bank case study using python

The most effective way to learn data science is by solving data science related problems. Reading, listening and taking notes is valuable, but once you work through a problem, concepts solidify from abstractions into tools you feel confident using. Generally a full cycle data science project includes the following stages:. In this case study, we will walk through the Analysis, Modelling and Communication part of the workflow. The general steps involved for solving a data science problem are as follows:.

Those of you who are not familiar with the field of Data Science and Python programming language can still follow through the article as it will give an high level overview about how these kind of problems are approached and solved. While some code snippets are included within the blog, for the full code you can check out this Jupyter Notebook. So our objective is to build a model that automatically suggests the right product prices to the sellers. We are provided with the following information for each product:.

This type of problem lies under the category of Supervised Regression Machine Learning:. EDA is an open-ended process where we calculate statistics and make figures to find trends, anomalies, patterns, or relationships within the data.

In short, the goal of EDA is to learn what our data can tell us. It generally starts out with a high level overview, then narrows in to specific areas as we find interesting parts of the data. The findings may be interesting in their own right, or they can be used to inform our modeling choices, such as by helping us decide which features to use.

In a hurry to get to the machine learning stage, some data scientists either entirely skip the exploratory process or do a very perfunctory job but in reality EDA is one of the most crucial steps in solving a data science related problem. The data set can be downloaded from Kaggle. As a common practice, we will conduct EDA on the train data set only. There are around 1.

Features and variables mean the same thing here, so they might be used interchangeably within the blog. A common problem when dealing with real-world data is missing values.

thera bank case study using python

These can arise for many reasons and have to be either filled in or removed before we train a machine learning model. Some of the approaches that are usually considered while dealing with missing values are:. In this project, we will go forward with the third approach. Now we will start analyzing the features one by one. We will first go through the target variable, Price, and then start analyzing the predictor variables individually and also see how it interacts with the Price variable.

The shipping fee for Normally when we buy products online, we need to pay for shipping or delivery of products which are below a certain price.

But here the trend is kind of opposite since the median price of items for which the seller pays the shipping fees is lower than the median price of the items for which the buyer pays the shipping fees. Lower the number, better the condition of the item. For comparing and visualizing the relation between a categorical variable and a numerical variable, Box-Plots are very helpful. The code below is used for plotting Box-Plots.Improve marketing campaign of a Portuguese bank by analyzing their past marketing campaign data and recommending which customer to target.

Use of machine learning to predict whether a customer would subscribe to a term deposit. The data is related with direct marketing campaigns phone calls of a Portuguese banking institution.

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The classification goal is to predict if the client will subscribe a term deposit. Marketing refers to activities undertaken by a company to promote the buying or selling of a product or service. Marketing includes advertising, selling, and delivering products to consumers or other businesses.

Our data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. The classification goal is to predict if the client will subscribe a term deposit variable y. Add a description, image, and links to the bank-marketing-analysis topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the bank-marketing-analysis topic, visit your repo's landing page and select "manage topics.

Learn more. Skip to content. Here are 7 public repositories matching this topic Language: All Filter by language. All 7 Jupyter Notebook 6 Python 1. Star Code Issues Pull requests. Updated May 26, Jupyter Notebook.

Star 5. Updated Jan 11, Jupyter Notebook. Star 4.Mathematicians often conduct competitions for the most beautiful formulae of all. The first position, almost every time, goes to the formula discovered by Leonhard Euler.

Displayed below is the formula. This formula is phenomenal because it is a combination of the five most important constants in mathematics i. It is just beautiful how such a simple equation links these fundamental constants in mathematics. The name is an apt choice for another reason — Euler is considered the most prolific mathematician of all time. He used to produce novel mathematics at an exponential rate. This is particularly startling since Euler was partially blind for more than half his life and completely blind for around last two decades of his life.

Incidentally, he was producing a high-quality scientific paper a week for a significant period when he was completely blind. The bank had disbursed auto loans in the quarter between April—June Additionally, you had noticed around 2. Now, you want to create a simple logistic regression model with just age as the variable. If you recall, you have observed the following normalized histogram for age overlaid with bad rates. We shall use this plot for creating the coarse classes to run a simple logistic regression.

However, the idea over here is to learn the nuances of logistic regression. Hence, let us first go through some basic concepts in logistic regression. In a previous article Logistic Regressionwe have discussed some of the aspects of logistic regression.

Let me reuse a picture from the same article. I would recommend that you read that article, as it would be helpful while understanding some of the concepts mentioned here.

This keeps the bounds of probability within 0 and 1 on either side at infinity. If you have ever indulged in betting of any sorts, the bets are placed in terms of odds. Mathematically, odds are defined as the probability of winning divided by the probability of losing. If we calculate the odds for our problem, we will get the following equation. Now, let create coarse classes from the data-set we have seen in the first article of this series for age groups.

Coarse classes are formed by combining the groups that have similar bad rates while maintaining the overall trend for bad rates. We have done the same thing for age groups as shown below.The most effective way to learn data science is by solving data science related problems.

Reading, listening and taking notes is valuable, but once you work through a problem, concepts solidify from abstractions into tools you feel confident using.

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Generally a full cycle data science project includes the following stages:. In this case study, we will walk through the Analysis, Modelling and Communication part of the workflow. The general steps involved for solving a data science problem are as follows:.

Those of you who are not familiar with the field of Data Science and Python programming language can still follow through the article as it will give an high level overview about how these kind of problems are approached and solved. While some code snippets are included within the blog, for the full code you can check out this Jupyter Notebook. So our objective is to build a model that automatically suggests the right product prices to the sellers.

We are provided with the following information for each product:. This type of problem lies under the category of Supervised Regression Machine Learning:. EDA is an open-ended process where we calculate statistics and make figures to find trends, anomalies, patterns, or relationships within the data.

Making Predictions with Data and Python : Predicting Credit Card Default - gwapestarfile.online

In short, the goal of EDA is to learn what our data can tell us. It generally starts out with a high level overview, then narrows in to specific areas as we find interesting parts of the data. The findings may be interesting in their own right, or they can be used to inform our modeling choices, such as by helping us decide which features to use.

In a hurry to get to the machine learning stage, some data scientists either entirely skip the exploratory process or do a very perfunctory job but in reality EDA is one of the most crucial steps in solving a data science related problem.

The data set can be downloaded from Kaggle. As a common practice, we will conduct EDA on the train data set only. There are around 1. Features and variables mean the same thing here, so they might be used interchangeably within the blog.

thera bank case study using python

A common problem when dealing with real-world data is missing values. These can arise for many reasons and have to be either filled in or removed before we train a machine learning model. Some of the approaches that are usually considered while dealing with missing values are:. In this project, we will go forward with the third approach.

Now we will start analyzing the features one by one. We will first go through the target variable, Price, and then start analyzing the predictor variables individually and also see how it interacts with the Price variable.

The shipping fee for Normally when we buy products online, we need to pay for shipping or delivery of products which are below a certain price. But here the trend is kind of opposite since the median price of items for which the seller pays the shipping fees is lower than the median price of the items for which the buyer pays the shipping fees.

Lower the number, better the condition of the item. For comparing and visualizing the relation between a categorical variable and a numerical variable, Box-Plots are very helpful. The code below is used for plotting Box-Plots.Keeping you updated with latest technology trends, Join DataFlair on Telegram. Python does not have a simple switch case construct.

K-Nearest Neighbors Case Study 1

But Python does not have this. We can also specify what to do when none is met. You can refer to the following link to find out what happened:. One way out would be to implement an if-elif-else ladder. Rather, we can use a dictionary to map cases to their functionality. Here, we define a function week to tell us which day a certain day of the week is.

A switcher is a dictionary that performs this mapping. This is because we tell it to do so using the get method of a dictionary. We can also use functions and lambdas in the dictionary. Using this concept with classes lets us choose a method at runtime. Hence, we conclude that Python does not have an in-built switch-case construct, we can use a dictionary instead.

We Respect Your Opinion! The two ways we choose are 1. Python Functions 2. Python classes. You do not need to import any module for this. This is not very useful to me! To understand how to implement a switch case we look at an easy explanation. You are right this would be more helpful when one wants to perform multiple set of operations. So by understanding this one can easily use switch case as per their requirements.This case is about a bank Thera Bank which has a growing customer base.

Majority of these customers are liability customers depositors with varying size of deposits. The number of customers who are also borrowers asset customers is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers while retaining them as depositors.

This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget. The department wants to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan.

This will increase the success ratio while at the same time reduce the cost of the campaign. The dataset has data on customers.

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The data include customer demographic information age, income, etc. You are brought in as a consultant and your job is to build the best model which can classify the right customers who have a higher probability of purchasing the loan.

You are expected to do the following: EDA of the data available. Interpret all the model outputs and do the necessary modifications wherever eligible such as pruning Check the performance of all the models that you have built test and train. Use all the model performance measures you have learned so far. Share your remarks on which model performs the best. Our Assignment Writing Experts are efficient to provide a fresh solution to this question.

Be it a used or new solution, the quality of the work submitted by our assignment experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively.

thera bank case study using python

You could choose a new assignment solution file to get yourself an exclusive, plagiarism with free Turnitin fileexpert quality assignment or order an old solution file that was considered worthy of the highest distinction.

FAQs Pricing Login. Order New Solution. Order New Solution Download Now.Sidharth Macherla has over 12 years of experience in data science and his current area of focus is Natural Language Processing.

Let's Get Connected: LinkedIn. Hi sir, I keep on follow this site. I am a post graduate in statistics. Now I am working as MIS executive.

Logistic Regression in Python - Case Study

I willing to learn machine learning languages of any these SASR or Python Can u plz advise me that will add my career. In this article, we will walk you through an application of topic modelling and sentiment analysis to solve a real world business problem.

This approach has a onetime effort of building a robust taxonomy and allows it to be regularly updated as new topics emerge. This approach is widely used in topic mapping tools. Please note that this is not a replacement of the topic modelling methodologies such as Latent Dirichlet allocation LDA and it is beyond them.

thera bank case study using python

Data Structure. The customer review data consists of a serial number, an arbitrary identifier to identify each review uniquely and a text field that has the customer review. Example : Sentiment Analysis.

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Steps to topic mapping and sentiment analysis 1. Identify Topics and Sub Topics 2. Build Taxonomy 3. Map customer reviews to topics 4.

Map customer reviews to sentiment Step 1 : Identifying Topics. The first step is to identify the different topics in the reviews. You can use simple approaches such as Term Frequency and Inverse Document Frequency or more popular methodologies such as LDA to identify the topics in the reviews. In addition, it is a good practice to consult a subject matter expert in that domain to identify the common topics.

Step 2 : Build Taxonomy I. Build Topic Hierarchy. Based on the topics from Step 1, Build a Taxonomy. A Taxonomy can be considered as a network of topics, sub topics and key words. Snapshot of sample taxonomy:. Sample Taxonomy. If you need to add a phrase or any keyword with a special character in it, you can wrap it in quotes. For novel keywords that are similar to the topics but may come up in the future are not identified.

There could be use cases where businesses want to track certain topics that may not always be identified as topics by the topic modelling approaches.

Step 3 : Map customer reviews to topic Each customer comment is mapped to one or more sub topics. Some of the comments may not be mapped to any comment. Such instances need to be manually inspected to check if we missed any topics in the taxonomy so that it can be updated.

The rest of the comments could be vague. Snapshot of how the topics are mapped:. Below is the python code that helps in mapping reviews to categories. Firstly, import all the libraries needed for this task. Install these libraries if needed. Download Datafiles Customer Review Taxonomy Download Python Code If you copy-paste the code from the article, some of the lines of code might not work as python follows indentation very strictly so download python code from the link below.

The code is built in Python 2.


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