1000 Projects

Retail banking

Typical mass-market banking is used by local clients of local branches of large commercial banks. The services offered include checking and savings accounts, mortgages, personal loans, debit/credit cards. Attention is paid to the customer.

The main problems in this sector are:

Which product is right to recommend to the customer?
What is the best time to sell a product?
What is the most effective channel to communicate with the customer?

PROBLEM STATEMENT

The data in this regard are related to the direct marketing campaigns of the banking institution. Marketing campaigns are based on phone calls. Often multiple contacts with a customer were required to reach the product (term bank deposit) whether to subscribe (‘yes’) or unsubscribe (‘no’). The goal is to know in advance whether the customer will sign up for a term deposit.

ABOUT THE INFORMATION

This is a classic marketing bank data package uploaded to the UCI machine learning repository. The data set provides you with information about the financial institution’s marketing campaign, which you need to analyze to find ways to look for future strategies to improve the bank’s future marketing campaigns.

These are the columns in the data set:

Age: age of the client (quantitative)
Job: Client’s profession – (categorical) (administrator, worker, entrepreneur, housekeeper, manager, retiree, self-employed, service provider, student, technician, unemployed, unknown)
Marital: Client’s marital status – (categorical) (divorced, married, single, unknown, note: divorced means divorced or widowed)
Education: Client’s level of knowledge – (categorical)
By default: Indicates whether the customer has a debt – (categorical) (no, yes)
Balance: average annual balance, in euros (quantitative).
Housing: Does the client have a home loan? – (categorical) (no, yes)
Loan: Is the client a personal loan? – (categorical) (no, yes)
Contact: Contact type – (categorical) (unknown, mobile, phone)
Date: The date of the last contact with the customer.
Month: Last customer contact month – (categorical) (January-December)

CONCLUSION

In the real world, most classification problems are two-sided. Also, data sets have almost no meaning. In this post, we cover strategies to combat unbalanced data sets with missing values. We will also explore different ways to build packages within the sklearn. Here are some exceptions:

Accuracies compared

• K-nearest Neighbour: 75.3%
• Logistic Regression: 80.9%
• Decision Tree: 78.2%
• Random Forest Classifier: 78%
• Support vector Machine: 53%

Sometimes we may be willing to give up some model improvements if this estimate is much more complex than the percentage change in metric improvement.
When building ensemble models, try to use as many different good models as possible to minimize the correlation between basic students. We could improve the model of our concentrated ensemble by adding a dense neural network and other types of basic learners, as well as adding more layers to the concentrated model.
Easy Ensemble generally works better than other re-modeling methods.

Download the complete Predictive Analytics for Retail Banking Machine Learning Python Project Source Code, Report, PPT.

For more details about the project visit this page

Project description

In India, delays in diagnosing diseases are a major problem due to a lack of medical professionals. The typical scenario, which is mainly in rural and slightly urban areas:

1. A patient who sees a doctor with certain symptoms.
2. The doctor will perform some tests, such as blood and urine tests, depending on the symptoms.
3. The patient undergoes the above tests in the analytical laboratory.
4. The patient takes the reports back to the hospital, where they are examined and diagnosed.

The goal of this project is to reduce some of the delays caused by unnecessary detours between the hospital and the pathology laboratory. Historically, work has been done to detect the onset of heart disease, such as Parkinson’s, and machine learning algorithms have been developed to predict liver disease in patients based on a variety of characteristics.

Problem statement

The problem report is officially defined as follows:

“Considering the data set containing the various attributes of 584 Indian patients, use the functions in the data set and determine a controlled classification algorithm to determine whether a person is suffering from liver disease. This data set contains 416 liver recordings and 167 non-liver recordings. collected in northeastern Andhra Pradesh. This data set contains records of 441 male patients and 142 female patients. Each patient over the age of 89 is “90” years old.

Strategy

This seems to be a classic example of controlled learning. We are given a fixed number of functions for each data point, and our goal is to teach different controlled learning algorithms based on this data, so that when a new data point appears, our best-performing classifier can be used. information point as a positive or negative example. Detailed information on the number and types of algorithms used for training is contained in the “Algorithms and Techniques” section of the “Analysis” section.

Conclusion

Initially, the data set was studied and prepared for inclusion in the classifiers. This was achieved by removing some rows containing zero values, modifying some columns indicating the skewness, and using appropriate conversion techniques (a hot coding) to make the labels more useful for classification purposes. The performance indicators for which the models will be evaluated have been resolved. The data set was then divided into a reading and testing package.

First, a simple predictive and base model (“Logistic Regression”) was developed in the data set to determine the value of the base accuracy. The biggest challenge in implementing this project was in two areas: defining learning algorithms and selecting the appropriate parameters for precise configuration. Initially, making a decision on 3 or 4 methods out of the many choices available at sklearn was very tedious.

Algorithms and Techniques used to develop this Liver Patient Analysis Machine Learning Project are

1. Random Forest Classifier:
2. Gaussian Naive Bayes Classifier
3. Logistic Regression:

Download the complete Liver Patient Analysis Machine Learning Project Python source code, project report, PPT Presentation.

For more details about the project visit this page

The main objective of this Python project is to Analysis & Prediction the final grade of Portuguese high school students. This is Machine Learning Project and The algorithms used to implement this project are Linear Regression, ElasticNet Regression, Random Forest, Extra Trees, SVM, Gradient Boosted, Baseline.

Problem statement

The problem statement can be defined as follows: Considering the data set containing the attributes of 396 Portuguese students, the available functions of the data set were used and classification algorithms were defined to determine whether the student performed well in the final qualifying exam.

Description of the data set

These data reflect the performance of secondary school students in two Portuguese schools. Information attributes (student grades, demographic, social, and school-related characteristics) were collected using school reports and questionnaires. There are two sets of indicators for two different subjects: mathematics (math) and Portuguese (por). [Cortez and Silva, 2008], two data sets were modeled under binary / five-level classification and regression tasks. Important Note: The target attribute G3 has a strong correlation with the G2 and G1 attributes.

Methodology

As universities are prestigious universities, it is a matter of great concern that students stay at these universities. It was found that the majority of students dropped out of university in the first year due to a lack of adequate support for undergraduate courses. For this reason, the first year of a bachelor’s degree is called the “make or break” year. Without finding any support for mastering the course and its complexity, you can lower the student’s motivation and cause him to drop out of the course.

There is a great need to develop an appropriate solution to help students stay in higher education. Early grade forecasting is one of the solutions aimed at monitoring the progress of students in the undergraduate courses of the university and leads to the improvement of the learning process of students on the basis of projected grades.

The use of machine learning with the extraction of educational information improves the learning process of students. Various models can be developed to estimate a student’s grades in registered courses, which will provide valuable information to make it easier for students to stay in those courses. This information can be used for the early identification of students at risk, based on which the system can recommend teachers to pay special attention to those students. This information will also help predict the grades of students in different courses to better monitor their performance in a way that will improve student retention in universities.

Using different packages, such as cuffs, seaborn, and matplotlib, to analyze the data set to predict the final price (G3), to display the data graphically or graphically along with various attributes.

Download the Complete Student Grade Analysis & Prediction Machine Learning Python Project Source code, Report.

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