Portuguese bank marketing dataset analysis in python Dataset contains 41188 examples and 20 input variables. This is a transactional data set which contains all the transactions occurring between This data set was obtained from the UC Irvine Machine Learning Repository and contains information related to a direct marketing campaign of a Portuguese banking a principal component analysis is used as a dimension reduction technique to determine the principal components of a data set containing bank marketing information. csv file). Portuguese Bank Marketing Data Set Python The problem for this course project is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. Python; jinudaniel / bank-marketing-analysis Star 7. I will use Portugese Bank Marketing dataset (bank_cleaned. Exploratory Data Analysis Business Use Case: There has been a revenue decline for a Portuguese bank and they would like to know what actions to take. The data set contains 41,188 observations and 21 variables. Split Data into Training and Testing. B) — Image by Author VIII. I will start with a discussion of the target variable and the scoring metrics of choice. The primary goal is to predict whether a The project aims to analyze direct marketing campaign data from a Portuguese banking institution to predict client subscription to term deposits. Illustration of PySpark ML usage on Bank Marketing Dataset. Improve marketing campaign of a Portuguese bank by analyzing their past Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Using data analysis and visualization, the goal is to This project involves the analysis of banking data, specifically the analysis of marketing campaign data provided by a Portuguese banking institution. If after all marking afforts client had agreed to place deposit - target variable marked 'yes', otherwise 'no'. Marketing campaign can be understood as phone calls to the clients to convince them accept to make a term [Show full abstract] The dataset related to direct marketing campaigns (phone calls) of a Portuguese banking institution is considered for analysis. Marketing includes advertising, selling, and delivering products to consumers or other Explore and run machine learning code with Kaggle Notebooks | Using data from Bank Marketing Dataset. A Research by Moro et al. Often, more than Direct marketing strategies in the banking sector have undergone evolution with the integration of predictive analytics and machine learning techniques. Sort: Most forks. Irvine Machine The model also reaches an accuracy of 91. This is a real dataset collected from a Portuguese bank that used its own contact-center to do direct marketing campaigns to motivate and attract the clients for their term deposit scheme to enhance the business. #DataScience DecisionTreeClassifierDecision tree python Machine learning packages:--------------------------------------------------------------------------- The Portuguese Bank had run a telemarketing campaign in the past, making sales calls for a term-deposit product. This notebook is realized by Baligh Mnassri and running on a Spark cluster coded using Python programming language on databricks cloud community edition. The most effective method for building the model with strong predictive capabilities was found to be Random Forest. The model also reaches an accuracy of 91. Code Issues Pull requests Marketing refers to activities undertaken by a company to promote the buying or selling of a product or service. This dataset consists of direct marketing campaigns by There are four datasets: 1) bank-additional-full. 9247. S. Analysis of a bank marketing campaign with machine learning classification methods - rab175/bank-marketing-classification Bank Marketing Data Set. if the The dataset contains information about all the customers who were contacted during a particular year to open term deposit accounts. We analyzed a Exercises to learning machine learning community. Discover the world's research In our project, we analyzed data from the UCI Machine Learning Repository called Bank Marketing Data Set. csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 4. For this investigation, a recent and real-world dataset from a Portuguese bank was employed. We also derived and added day of the week variable from the date, month and year variables. machine-learning eda imbalanced Analyzed the prior marketing campaigns of a Portuguese Bank using various ML techniques like Logistic Regression, Random Forests,Decision Trees, Gradient Boosting and AdaBoost and predicted if the user will buy the Bank’s term This project predicts the success of a bank marketing campaign using machine learning on a Kaggle dataset. Often, more than one contact to the same client was required, was used as a train dataset. Learn International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021. Topological Methods for the Analysis of High Dimensional Data Sets and Bank Marketing Data Set. It contains 41,188 observations with 20 features: Client Attributes (age, job, marital status, education, housing loan status, personal loan status, default history): These features describe characteristics of the clients that may influence their propensity to subscribe to a term deposit. 7 Numpy >= 1. Grouping the data based on if the potential client subscribed to the term deposit or not indicates that the majority of clients did not subscribe to the term deposit, 88. This data set was obtained from the UC Irvine Machine Learning Repository and contains information related to a direct marketing campaign of a Portuguese banking institution and its attempts to get its clients to subscribe for a term deposit. Duration: When duration = 0, y = no. Unexpected end of Random Forest and Xtreme Gradient Boosting algorithms and the Portuguese institution marketing campaign dataset, were used to develop a bank term deposit service patronage forecasting model. html : html file for the same ipython file bank. Telephonic campaign are one of the most effective ways to reach individuals to sell products but many a time or most This is a Bank Marketing Machine Learning Classification Project in fulfillment of the Udacity Azure ML Nanodegree. data-mining windows-forms decision-tree winforms-application rapidminer bank-marketing term-deposit bank-marketing-analysis bank-marketing-dataset-analysis decision-support-software Customer Classification based on the UCI Bank Marketing data set. csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data This repository contains a Python script that analyzes the "Bank Marketing" dataset from the UCI Machine Learning Repository. The primary dataset, bank 3. Bank Marketing Dataset: An overview of classi cation algorithms CS229: Machine Learning Henrique Ap. I used Power BI visualization tools and DAX to analyze and visualize the Bank Marketing. Code This projects explores the bank marketing dataset using automatic EDA packages in R. Please include this citation if you plan to use this database: [Moro et al. customers who used Chegg Study or Chegg Study Pack in Q2 2024 and Q3 2024. These campaigns primarily involved direct phone calls to clients, with the aim of offering term deposits. A description of all the features in the dataset is well given in the data source which can be found from the UCI Machine Learning Repository. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Managers can utilize the accuracy score, responsiveness, and specificity analysis the authors performed to analyse the input value in the This repository contains a Python script that analyzes the "Bank Marketing" dataset from the UCI Machine Learning Repository. (Effectiveness of Campaign) Variables in dataset The data represents the results of marketing campaigns (phone calls) of a Portuguese banking institution which comprises of 41188 observations (rows) and 21 features Our mission is to uncover strategies that will strengthen long-term deposits and drive significant revenue growth for a Portuguese bank. The marketing campaigns were The aim of this article is to show how to perform EDA and how to build a model with RapidMiner on a Bank Marketing Dataset. Laureano Portuguese banking institution. Carlsson. Cortez and P. We write a small parser for Python to run through the . A Data Image 1. OK, Got it. This dataset contains banking marketing campaign data and we can use it to optimize marketing campaigns to attract more customers to term deposit subscription. Working for 5 years in a Bank was the reason why I found it interesting to carry out this analysis and learn the various stages of the analysis and modeling of the project. csv with all examples and 17 inputs, ordered by date (older version of this dataset with fewer inputs). These principal Portuguese Bank Marketing Data Set Python The problem for this course project is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. Learn more. Often, more than one contact to the same client was required, in order to access if the product (bank data-science analysis linear-regression artificial-intelligence data-visualisation pca classification logistic-regression pattern-recognition data-preprocessing data-preparation roc-curve principal-component-analysis svm-classifier computational-intelligence uci-machine-learning bank-marketing bank-marketing-analysis bank-marketing-dataset Contribute to SouRitra01/Exploratory-Data-Analysis-EDA-in-Banking-Python-Project- development by creating an account on GitHub. About. Singh, F. Portuguese Bank Marketing. Using data analysis and visualization, the goal is to uncover insights and create predictive models. Standardizing data can lead to better model performance and more effective predictions. By analyzing client data, it identifies potential subscribers to term deposits. The classification goal is to predict if the client will subscribe a term deposit (variable y Bank Marketing Data Set. , this paper compares the performance of ANN with the model used in other papers trained on Python 2. js and HTML5. of a Portuguese banking institution. Getting started is often the hardest part of any challenge. Usually, the population is divided into deciles, under a decreasing order of their predictive probability for success. , 2014] S. The data is related with direct There are four datasets: 1) bank-additional-full. This data is based on direct marketing campaigns of a Portuguese banking institution. In this project, you will learn to utilize Azure Machine Learning Studio and Azure Python SDK to create classifier models data-science analysis linear-regression artificial-intelligence data-visualisation pca classification logistic-regression pattern-recognition data-preprocessing data-preparation roc-curve principal-component-analysis svm In this project I used a dataset called Bank Marketing Dataset which was uploaded in Kaggle. Utilizing Python, NumPy, pandas, and scikit-learn, the project achieves high accuracy in predicting campaign outcomes. Median duration of the As promised, we went through a step-by-step approach to conducting a simple digital marketing analysis working alongside MySQL Workbench and Python. Image by Author Importing Data into Data Frames: To start working with data, first we need to import data from CSV In this notebook we will use the Bank Marketing Dataset from Kaggle to build a model to predict whether someone is going to make a deposit or not depending on some attributes. ipynb : This is ipython notebbok with the python code for analysis and results Bank Marketing Data Analysis. #Converting ipynb file in colab to html did not show the below image so we are saving the picture in image and manually displaying. The data sample of 41,118 records was collected by a Portuguese bank between 2008 and 2013 and contains the results of a telemarketing campaign including customer’s response to the bank’s offer of a deposit contract (the binary target This repository contains a Python script that analyzes the "Bank Marketing" dataset from the UCI Machine Learning Repository. Often, more than one Explore and run machine learning code with Kaggle Notebooks | Using data from Banking Project : Term Deposit. in 1915 inbound contacts of total 52,944 contacts dataset provides a divide-and-conquer procedure utilizing both the data‐based sensitivity analysis for extricating highlight pertinence and master assessment for part the issue of characterizing telemarketing contacts to offer bank deposits products, get the AUC = 0. T rain the model on 90 % of t he training dataset, test the model on Based on our analysis of the direct marketing campaign data from the Portuguese banking institution, several key insights have emerged: Random Forest (RF) Model: Mean Test AUC: ~0. One of the earliest known datasets used for evaluating classification methods. display import Image Image ('newplot. 3% accepted the subscription offer. From the above output, some patterns of the data can be extracted. csv) consists of demographics data on 41,188 people. There are 3 records where duration = 0. The dataset we’ll be using here is not new to the town and you have probably come across it before. The aim of this marketing research project is to develop a robust classification model using machine learning techniques to analyse and enhance the marketing strategies of a Portuguese bank. The dataset was picked from UCI Machine Learning Repository which is an amazing source for publicly available datasets. Rita. A Statistical Learning project dedicated to applying statistical analysis and modeling for Bank marketing campaign. Explore and run machine learning code with Kaggle Notebooks | Using data from Bank Term Deposit Dataset. Citation Request: This dataset is publicly available for research. Mémoli, G. Terms and Conditions apply. csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al The data is related with direct marketing campaigns of a Portuguese banking institution. In this article, we shared 7 datasets that you can Marketing refers to activities undertaken by a company to promote the buying or selling of a product or service. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed. You, as an analyst, decide to build a supervised model in R/Python bank-additional. In this work, Python is used as a coding language and Explore and run machine learning code with Kaggle Notebooks | Using data from Portuguese Bank Marketing Data Set . Then In the domain of marketing, the Lift analysis is popular for accessing the quality of targeting models [3]. Then we This project predicts the success of a bank marketing campaign using machine learning on a Kaggle dataset. The classification goal is to predict if the client will subscribe a term deposit (variable y). The details are described in [Moro et al. The business goal is to find a model that can explain success of a contact, i. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. 2 Matplotlib >= 2. The data that we are going to use for this is a subset of an open source Bank Marketing Data Set The Portuguese bank dataset contains data related to marketing campaigns. Real-world data were collected from a Portuguese marketing campaign related with bank deposit subscription. The Portuguese Bank Marketing Data Set from the UCI Machine Learning Repository will be used to build the Logistic Regression Model. The goal is to predict if the client will subscribe a Forecast the outcome of marketing campaigns by a banking institution using data about the customer. Abstract: The data is related with direct marketing campaigns (phone calls) Before making the model, split the data set i nto the train data set with 90% and the test data set with 10%. Star 6. csv” which consists of 41188 data Screenshot by Author — A glimpse of the dataset. We remove the “call duration” variable, as it is not available at test time. Sort options Improve marketing campaign of a Portuguese bank by analyzing their past marketing campaign data and recommending which customer to target -mining windows-forms decision-tree winforms-application rapidminer bank-marketing term-deposit bank-marketing Standardization. Many data mining and machine learning algorithms assume that the input data is standardized. bank-full. There are 17 variable, including 16 features and the class variable. 928) Linear Probability Model (LPM): Mean Test AUC: ~0. Forecast the outcome of marketing campaigns by a banking institution using data about the Creating a logistic regression model using python on bank data, to find out if the customer has subscribed to a specific plan or not - Lakshya-Ag/Bank-Marketing-Logistic-Regression The data is related to direct marketing campaigns of a The data is related with direct marketing campaigns of a Portuguese banking institution. This paper introduced two machine learning algorithms to solve the issues mentioned above, which were developed on a dataset derived from the Portuguese bank’s marketing campaign. 08%. 2. ^ Chegg survey fielded between Sept. Moro, P. Often, more than one contact to the Predicting Term Deposit Suscriptions. , 2014]. Marketing includes advertising, selling, and delivering products to consumers or other businesses. This project is about marketing campaign of Portuguese bank for term deposits with data of contacted customers in period of 2008 to 2010 and their respond of the calls. The marketing campaigns were based on phone calls and often more than one contact to the same client was required, to access if the product would be yes or no for the subscription. Please visit each partner activation page for complete details. The project explores the dataset and builds a predictive model to assist in formulating future marketing strategies. Conducted campaigns were based mostly on direct phone calls, offering bank's clients to place a term deposit. The data is related with direct product marketing campaigns of a Portuguese banking The dataset is related to direct marketing campaigns of a Portuguese banking institution. See all courses; Introduction to Docker; Free dataset dataset: Bank Marketing. - alekha1234/Portuguese-Bank-Marketing-Campaign Python, Machine Learning, Pandas, Numpy, scikit-learn, Matplotlib, Jupyter Notebook. In Table. By Yogesh Sachdeva, Ayushi Arora and Kriti Suri . The dataset is sourced from the UCI Machine Learning Repository's Bank Marketing Data Set. The investigated data are related with direct marketing campaigns (phone calls) of a Portuguese banking institution. Constructing distance matrix. Often, more than one contact to the same client was required, in order to access if the The success rate of the banking marketing strategies and implementation of these decisions can be made accurate by comparing various Machine Learning techniques Explore and run machine learning code with Kaggle Notebooks | Using data from Bank Marketing Dataset. We wiill Analyzed the prior marketing campaigns of a Portuguese Bank using various ML techniques like Logistic Regression, Random Forests,Decision Trees, Gradient Boosting and AdaBoost and predicted if the user will buy the Bank’s term Now to demonstrate my understanding of exploratory data analysis, I will use the Bank Marketing data set from the UCI repository, which can be found here . csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al Project’s schema. C. 0 Scikit-Learn >= 0. It includes data on customer demographics, financial information, and This would help the marketing campaign team of Portuguese bank to develop their strategy in telemarketing their term deposit scheme. The marketing campaigns were based on phone calls. We first onehot-encode the categorical variables. csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al. The data is related with direct marketing campaigns of a Portuguese banking institution. Also conducted The data set (bank-additional-full. bank. marketing data classification. There were four variants of the datasets out of which we chose “ bank-additional-full. , 2014] 2) bank-additional. Features: Now to demonstrate my understanding of exploratory data analysis, I will use the Bank Marketing data set from the UCI repository, which can be found here . This involves four major steps: Obtain and preprocess the dataset, addressing missing data-vis bank-marketing-dataset-analysis bank-marketing-dataset. All 14 Jupyter Notebook 10 C# 1 HTML 1 Python 1 R 1. The data is related to bank marketing campaigns of banking institution based on phone call. 1 Bank Marketing dataset is collected from direct marketing campaign of a bank institution from Portuguese. 70% Bank Marketing Data Set. This dataset is about the marketing campaigns, which aim to promote financial products for Improve marketing campaign of a Portuguese bank by analyzing their past marketing campaign data and recommending which customer to target. e. B. We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. The dataset is originally collected from UCI Machine learning repository and Kaggle website. The target class is the last attribute (subscribed) and has two values (yes and no). The data is related with over 40,000 direct marketing campaigns of a Portuguese banking institution from May 2008 to November 2010. plotPCA (loadingsDF) from IPython. Unexpected token < in JSON at position 0. The data set used in this analysis is from a Portuguese bank. We leverage a dataset named 'Bank Data Analysis' that encompasses direct marketing campaigns involving phone calls, with the aim of predicting whether a client will subscribe to a term deposit. Overview: This project analyzes a Portuguese bank marketing dataset to understand the factors influencing customer subscription to term deposits. Whether a prospect had bought the product or not is mentioned in the This project analyzes a Portuguese bank marketing dataset to understand the factors influencing customer subscription to term deposits. 22. The classification goal is to predict if the client will subscribe a term deposit (variable y A small classic dataset from Fisher, 1936. The dataset consists of 45,211 instances with 16 features. 2. When observing the dataset, we can see that the dataset contains categorical nominal values (such as job, This project analyzes the Portugese Bank Marketing Dataset. Banking is a provision of the services by bank to an individual customer. Bank Marketing. Something went wrong and this page crashed! Improve Your Analysis Skills with Python Datasets. By continuing, you accept our , our and that your data is stored in the USA. Improve marketing campaign of a Portuguese bank by analyzing their past The Bank-Additional-Full dataset contains information about customers who were targeted in a direct marketing campaign. . This repository consists of the dataset and Jupyter notebook for my medium article entitled: "A Practical Guide To Logistic Regression in Python for Beginners" Bank Marketing Classification using scikit-learn library to train and validate classification models like Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, Neural Network and Support Vector Machine. The dataset has 20 input variables (mix of numeric and categorical variables) and 1 predictor variable This would help the marketing campaign team of Portuguese bank to develop their strategy in telemarketing their term deposit scheme. This example shows how to take a messy dataset and preprocess it such that it can be used in scikit-learn and TPOT. 9–Oct 3, 2024 among a random sample of U. Also find out which campaign's performance is better than another. OK, Installations: Python 3. Often, more than one contact to the same client was required, in order to access if the dataset is stored in a dataframe and is intensively queried and manipulated using facilities provided by the Python 3 environment. The primary goal is to predict whether a client will subscribe to a term deposit based on various features using a Decision Tree Classifier. 7% decline while 11. The objectives of this phase of the project are: 1. 904, 0. Marketing campaigns are based on phone calls and relate to 17 campaigns that took place from May 2008 to the dataset is related to a bank marketing campaign, and for Applying the machine learning models of where the direct marketing campaign is being carried by the Portuguese banking organization, that we will extract meaningful RapidMiner is software that is used to analyze a dataset and extract meaningful analysis from it by applying After digging around kaggle for a few days I came across the following dataset which had true data, a good number of rows and many variables to analyze -> Bank mkt campaign Dataset contains information related to direct marketing campaigns of Portuguese banking institution. Relevant Information: The data is related with direct marketing campaigns of a Portuguese banking institution. This data describes the results of Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources Bank Marketing. The data has 41188 observations of calls with 20 customer’s demographic and transaction features each. No cash value. The classification goal is to predict whether the client subscribes a term deposit or not. my target is to do clustering these data and end to end EDA on this bank telemarketing This project analyzes the results of marketing campaigns conducted by a Portuguese bank. Often, more than one contact to the same client was required, in order to access if the There are four datasets: 1) bank-additional-full. - GitHub - KubaKrzych/Bank-Marketing-Campaign-Analysis: Analysis of a dataset that contains information As mentioned above, the dataset consists of direct marketing campaigns data of a banking institution. Our data is related with direct marketing campaigns of a Portuguese banking institution. 3. r data-analysis bank-data. 14. csv with 10% of the examples and 17 inputs, randomly selected from 3 To do exploratory data analysis(EDA) and visualisation of bank marketing dataset. png file which we are uploading here and displaying. A small classic dataset from Fisher, 1936. - alekha1234/Portuguese-Bank-Marketing-Campaign #plotPCA function generated the newplot. 915; Standard Deviation of Test AUC: ~0. The variables include the following: There are four datasets: 1) bank-additional-full. (‘yes’) or not (‘no’) The data was sourced from the marketing campaigns of a Portuguese banking institution, focusing on client subscriptions to term deposits. The marketing campaigns were based on phone calls, often requiring multiple contacts with the same client to assess if the product (bank term deposit) would be subscribed (‘yes’) or not (‘no’). The data The dataset comprises a diverse customer base with varying demographics, each displaying different propensities to subscribe to the Portuguese bank’s term deposit, with only an 11. A dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled Portugal, October, 2011. The marketing team wants to launch another campaign, and they want to learn from the past one. ^ These offers are provided at no cost to subscribers of Chegg Study and Chegg Study Pack. if the The goal is to predict whether a client will subscribe a term deposit (variable y) with the help of a given set of dependent variables. Updated Jul 7, 2023; R; Analyzing Analysis of a dataset that contains information on Portugal bank marketing campaign results. 0 Pandas >= 0. All the analysis are performed using the R [1] language and environment for statistical There are four datasets: 1) bank-additional-full. It describes both derived insights as well as an actionable prescriptive algorithm. The goal is to attract new customers, improve customer engagement, and increase subscription for term deposit services. EUROSIS. png') subscription rates to term deposits. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. The Portuguese Bank had run a telemarketing campaign in the past, making sales calls for a term-deposit product. Updated Apr 16, 2022; PostScript ; nickr007 / Bank-Marketing. csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). The purpose of the project is to identify main Bayesian Data Analysis in Python; Fundamentals of Data Analysis in R; Software Development. Bank Marketing Data Set Binary Classification in python. dotfile and output a tree with D3. csv with 10% of the This is dataset that describe Portugal bank marketing campaigns results. The files in the repository: Bank Marketing Data Analysis. Screenshot of the python libraries imported. 19. The business goal is to find a model that can explain success of a Output 3(VII. It is recommended to split the data into a 70–30 split whereby training dataset consists of 70% and To see the TPOT applied the Titanic Kaggle dataset, see the Jupyter notebook here. Whether a prospect had bought the product or not is mentioned in the column named 'response'. This dataset is about the direct phone call marketing campaigns, which aim to promote term deposits among existing customers, by a Portuguese banking institution from May 2008 to November 2010. Something went 2 Data analysis Our data contains 45 211 observations of 17 features, where are 10 categorical features during which the direct marketing campaigns of a Portuguese banking institution took The case study is a Portuguese bank marketing dataset, where the target variable is a “yes” or “no” subscription to a term deposit. The marketing campaigns were The data is related with direct marketing campaigns of a Portuguese banking institution. Dataset: The data is related with direct marketing campaigns of a Portuguese banking institution. The results confirm that the LR model provides This paper introduced two machine learning algorithms to solve the issues mentioned above, which were developed on a dataset derived from the Portuguese bank’s marketing campaign. 1. Often, more than one contact to the same client was required, in order to access if the product (bank Data exploration and visualization project on bank_marketing_campaign dataset using python Data Exploration and Visualization Project on Bank Marketing Campaign using Python. Standardizing Data To standardize the numerical columns in our dataset, we’ll use the Explore and run machine learning code with Kaggle Notebooks | Using data from Portuguese Bank Marketing Data Set . The corresponding Jupyter notebook, containing the associated data preprocessing and analysis, can be found here. The purpose of our analysis is not to delete the missing data, but Bank Marketing (with social/economic context) dataset with loan target variable. md : Readme file with the description. The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. G. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Both tools have their specificities, their demands, With this streamlined approach, we ensure our dataset is primed for meaningful analysis, setting the stage for insightful discoveries. Data cleaning and exploratory analysis The dataset was provided by the U. The target variable ‘y’ indicates “yes” and “no” for the current marketing campaign. csv : Data used for the analysis README. The marketing campaigns were based on phone ca The marketing data. Other data structures such as arrays, lists, and dictionaries are used as needed[1]. 890 The data is related with direct marketing campaigns of a Portuguese banking institution. 009; 95% Confidence Interval for Test AUC: (0. 02% on the training dataset, with a loss of 20. Bank Marketing Data Set. The Portuguese banking institution performed direct marketing Use case: The dataset is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. , this paper compares the performance of ANN with the model used in other papers trained on There is a dataset, which contains bank marketing data on Kaggle. The data This article will be focused on my exploration of data collected by the Portuguese banking institution within the period from 2008 to 2010. 6, pandas, matplotlib. The target variable in our dataset is highly imbalanced, where “yes” values are only of This is a case study analysis for a marketing campaign. rvfpfl qdgggyh kwre ijw edumofmgk uhsc bbgasvxl pyek ginvzwl mapw