I use various Data Science and machine learning techniques to analyze customer data using STP framework. I preprocessed the data, performed segmentation, hierarchical clustering, k-means, PCA techniques with a lot of visualizations to segment and understand customer data. I have performed Purchase Analytics (both descriptive analysis and predictive analysis). Used deep learning to enhance my model.

All files save in this repository consist of R, python and sql scripts to solve for marketing problems centered around 4P's i.e. product, price, promotion and placement (distribution) and 5C's i.e. customer, company, competition, collaborators and context leveraging analytics

For this project, you will assume the role of a Data Scientist / Data Analyst working for a new startup investment firm that helps customers invest their money in stocks. Your job is to extract financial data like historical share price and quarterly revenue reportings from various sources using Python libraries and webscraping on popular stocks. After collecting this data you will visualize it in a dashboard to identify patterns or trends. The stocks we will work with are Tesla, Amazon, AMD, and GameStop. Dashboard Analytics Displayed A dashboard often provides a view of key performance indicators in a clear way. Analyzing a data set and extracting key performance indicators will be practiced. Prompts will be used to support learning in accessing and displaying data in dashboards. Learning how to display key performance indicators on a dashboard will be included in this assignment. We will be using Plotly in this course for data visualization and is not a requirement to take this course. Watson Studio In the Python for Data Science, AI and Development course you utilized Skills Network Labs for hands-on labs. For this project you will use Skills Network Labs and Watson Studio. Skills Network Labs is a sandbox environment for learning and completing labs in courses. Whereas Watson Studio, a component of IBM Cloud Pak for Data, is a suite of tools and a collaborative environment for data scientists, data analysts, AI and machine learning engineers and domain experts to develop and deploy your projects. Review criteria There are two hands-on labs on Extracting Stock Data and one assignment to complete. You will be judged by completing two quizzes and one peer review assignment. The quizzes will test you based on the output of the hands-on labs. In the peer review assignment you will share and take screen shots of the outcomes of your assignment.

DataCamp course excercises

Key: clustering, using logistic regression to build elasticity modeling for purchase probability, brand choice, and purchase quantity & deep neural network to build a black-box model to predict future customer behaviors.

🌟 A modern, open-source ISP billing system with dark-mode UI, built in Python. Features smart invoice generation, customer management, real-time analytics, and GST compliance. Perfect for ISPs seeking a free, feature-rich billing solution.

AdventureWorks is a fictitious multinational manufacturing company. The data set used for this report is from the AdventureWorks2014 version. The goal of this project is to demonstrate how building powerful and beautiful (I hope :)) reports in Power BI by following the data visualization best practices and the data modelling patterns, showing the awesome features of Power BI like Report Page Tooltip, Calculated Groups, Forecasting, What-If parameters, Time Intelligence Functions, complex DAX code, custom charts and how it is easy to integrate Power BI with Python/R script in order to build machine learning models and so creating advanced and predictive analytics reports. Reports Pages are as follow: - P&L Overview: Profit & Loss Report, in which I analyzed the economic status of the company from the point of view of Revenues and Expenditures. In this report page I compared the trend of actual amount and budget amount, unfortunately for 2011 year only (missing data for other years). You can notice the powerful feature of “report page tooltip” in this page, by hovering over the bar charts. - Internet Sales Overview: in this report page I analyzed the sales of products to customers via Internet. You can notice the forecasting feature of Power BI and the use of what-if parameters in order to build advanced analytics reports. Also I made use of calculated groups (for further info about visit https://www.sqlbi.com/articles/introducing-calculation-groups/). - Reseller Sales Overview: similar to the previous one, but this time the focus is on the sales of products to resellers. Things to notice in this report page are the comparison of current sales vs last year sales, the comparison of actual sales amount vs budget/quota sales amount, and the running total and moving/running average charts. - Product Inventory Overview: inventory management analysis of the company. I analyzed the current stock on hold vs the recent sales revenue, showing the value of current stock, the current units in stock and the stock ratio. - Products Overview: in this report page I analyzed the products by category, color, size, “ABC” class too. Two very interesting and powerful things to notice: the basket analysis using DAX and the basket/association rules analysis using R script and apriori machine learning model. I made use of the custom chart called “Network Chart” to represent the association rules between products. Through the basket analysis the user could know which product is likely to be bought with another one. - Customers Overview: customer analysis by age group, location, status, and so on. The customer could be classified as “Bike Buyer” and “Non Bike Buyer”. So I decided to build a machine learning model (in this case I used XGBoost algorithm in Python) in order to predict if a (new) customer would be a bike buyer or not. The model has a prediction accuracy of about 84% which it is not bad. - Employees Overview: employee analysis by sales location, title, age. Thing to notice is the ribbon chart through which I can analyze the performance of employees over time. - Reseller Overview: analysis of the performance of the resellers. Important dimensions to analyze are the business type, the product line, the reseller location.

I use various Data Science and machine learning techniques to analyze customer data using STP framework. I preprocessed the data, performed segmentation, hierarchical clustering, k-means, PCA techniques with a lot of visualizations to segment and understand customer data. I have performed Purchase Analytics (both descriptive analysis and predict…

A complete Retail analytics project using SQL and Python (Machine Learning) to analyze customer behavior and profitability, visualized in an interactive Power BI dashboard. End-to-end retail analysis: from SQL querying and Python ML models (segmentation, forecasting) to a final business intelligence dashboard in Power BI.

Hotel cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer cancelling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices. Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel — allowing a hotel chain to adjust its marketing strategy accordingly. To investigate how machine learning can aid in this task, the ExtraTreesClassifer, logistic regression, and support vector machine models were employed in Python to determine whether cancellations can be accurately predicted with this model. For this example, both hotels are based in Portugal. The Algarve Hotel dataset available from Science Direct was used to train and validate the model, and then the logistic regression was used to generate predictions on a second dataset for a hotel in Lisbon.

Tech career has taken over the world with an exponential increase in opportunities in the job market. Niche fields like data science and cybersecurity are booming with hefty salary packages. Every industry in the world is facing some technological disruption degree but, Information Technology is a diverse industry that can take careers in n number of directions. Which career path to follow in the world is not an easy determination? Here are the top 10 tech careers in Australia that are booming and taking off now, according to the Asia-Pacific Tech and Executive Talent Specialists Findings. Java, Python & GoLang Developers Their primary platforms work well with algorithms required to operate or build AI functionalities and Machine Learning. So they are in current top trends now. Experts in these are difficult to find by as the experience is mostly coming from the startup space, not from the larger employers. JavaScript Developers (with Angular or React Expertise) JavaScript Developers are among the top 10 tech careers in Australia. It’s a versatile language used across a variety of businesses so will continue to be in demand for their talents. The larger number of frameworks within the language makes it a talent pool segmented across niche expertise. DevOps (Cloud) & DevSecOps (Security) & Cloud Security Engineers These are required to maintain and automate the developer’s environment. It’s growing as businesses are becoming more reliant on high volumes of data. DevOps engineers speed up the deployment rate and ensure the minimal downtime of products. DevSecOps Engineers perform same with a focus on security code level security. Cloud Security Engineers instruct on maintaining the quality of coding methods which promote a secure cloud-based environment. Cyber Threat Intelligence / Cyber Emergency Response (CERT) Managers These specialists act as a bullet in the world of cyber attackers. They identify and mitigate risk and respond to cyber-attacks allowing businesses to maintain service, productivity, and data security. Automation Engineers They speed up the market and reallocate the resources by automating human capital savings and processes saving cost. Data Scientists These jobs are booming as businesses seek to better understand and derive value from the data they have generated from their customers. More enterprises are consequently turning to data scientists to apply data-driven strategy decisions and allocate resources more efficiently for a better forecast. Data Engineers They are the critical members for an enterprise as a data analytics team. Their demand is high as more and more businesses are becoming reliant on manipulation and collection of high volumes of data. Agile Coaches / Scrum Masters They are responsible for teaching and running methodological processes and frameworks in the search for efficiency. So demand for these roles will continue to grow. CX/UX Researchers and Designers They are responsible for collecting and making design decisions based on customer experience data and allow the businesses to create better efficiencies across the organization by optimizing their product to something that serves the requirements of the customers. Tech Sales Executives (Enterprise Level) Enterprise-level Tech Sales Executives will always be in demand as they are responsible for an efficient path towards the capital growth of the business. The salesperson who can build strong relationships and grow steadily are in constant demand.

Diwali Sales Analytics in Python: EDA on customer & sales data with visualizations to uncover trends, customer behavior, and regional sales insights.

End-to-end data analytics project showcasing an industry-standard analysis of customer shopping trends in retail data using Python, SQL and Power BI.

In today's competitive market, retaining customers is crucial for the success of subscription-based services. Churn prediction models help identify at-risk customers, allowing companies to implement targeted retention strategies. This project support data analytics and machine learning through methodologies: Python, MS Excel, MySQL. and Tableau.

This repository features the Retail sales and customer insights dashboard demonstrating data cleaning and analysis using Excel, Python, and SQL, along with interactive visualization in Power BI. Explore the code and reports to gain insights into sales performance, customer behavior, and product trends. A complete end-to-end data analytics workflow

A comprehensive data analytics case study using Python, Excel, Power BI, MySQL, and Tableau, completed in the TATA Data Visualization Program 2025. It analyzes revenue trends, customer segmentation, product demand, and country-level insights, featuring interactive dashboards, time series analysis, and actionable business insights.

Sales Analysis is a data analytics project in Python that examines sales data from an e-commerce platform during the Diwali festival. The goal is to gain insights into customer behavior, sales trends, and purchasing patterns to optimize marketing strategies and boost sales during the festive season

Built an end-to-end data analytics project using Python, MySQL, and Power BI. Cleaned and processed raw data with Pandas, stored it in MySQL using SQL queries, and created an interactive Power BI dashboard to visualize customer demographics, spending patterns, and product trends for better insights.

It is no wonder that how our favorite coffee shop Starbucks employs data analytics and business intelligence techniques to deliver excellent customer service. This is the largest and famous coffee chain which has become one of the places which uses data analytics and enterprise applications in intersection. This report illustrates how behind a freshly prepared cup of coffee there is an insightful corporate tactic and how factors like weather conditions and twitter sentiments affect the location and stocks of Starbuck stores. Predicting stock prices based on twitter sentiments data would produce strong buy or not is still a debatable topic over the years and making it more difficult to forecast accurately. Data analytics also plays a key role in determining the best location for new stores. In this study, for data extraction APIs were used to extract Twitter Sentiment and weather condition and Starbucks Location dataset was taken from Kaggle in csv format. After Data Transformations and Data Loading, a data warehouse was created for further analysis. Using our analysis, a significant dependency of all these datasets is identified using python libraries. For data storage MongoDB and SQL were used.It is no wonder that how our favorite coffee shop Starbucks employs data analytics and business intelligence techniques to deliver excellent customer service. This is the largest and famous coffee chain which has become one of the places which uses data analytics and enterprise applications in intersection. This report illustrates how behind a freshly prepared cup of coffee there is an insightful corporate tactic and how factors like weather conditions and twitter sentiments affect the location and stocks of Starbuck stores. Predicting stock prices based on twitter sentiments data would produce strong buy or not is still a debatable topic over the years and making it more difficult to forecast accurately. Data analytics also plays a key role in determining the best location for new stores. In this study, for data extraction APIs were used to extract Twitter Sentiment and weather condition and Starbucks Location dataset was taken from Kaggle in csv format. After Data Transformations and Data Loading, a data warehouse was created for further analysis. Using our analysis, a significant dependency of all these datasets is identified using python libraries. For data storage MongoDB and SQL were used.

Using the collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio. Resources AvailableThe historical data for this project is available in filehttps://archive.ics.uci.edu/ml/datasets/Bank+MarketingDeliverable –1(Exploratory data quality report reflecting the following)1.Univariate analysisa.Univariate analysis –data types and description of the independent attributes which should include (name, meaning, range of values observed, central values (mean and median), standard deviation and quartiles, analysis of the body of distributions / tails, missing values, outliers.2.Multivariate analysisa.Bi-variate analysis between the predictor variables and target column. Comment on your findings in terms of their relationship and degree of relation if any. Presence of leverage points. Visualize the analysis using boxplots and pair plots, histograms or density curves. Select the most appropriate attributes. 3.Strategies to address the different data challenges such as data pollution, outliers and missing values. Deliverable –2(Prepare the data for analytics)1.Load the data into a data-frame. The data-frame should have data and column description.2.Ensure the attribute types are correct. If not, take appropriate actions.3.Transform the data i.e. scale / normalize if required4.Create the training set and test set in ration of 70:30Deliverable –3(create the ensemble model)1.Write python code using scikitlearn, pandas, numpy and othersin Jupyter notebook to train and test the ensemble model.2.First create a model using standard classification algorithm. Note the model performance.3.Use appropriate algorithms and explain why that algorithm in the comment lines.4.Evaluate the model. Use confusion matrix to evaluate class level metrics i.e..Precision and recall. Also reflect the overall score of the model.5.Advantages and disadvantages of the algorithm.6.Build the ensemble models and compare the results with the base model. Note: Random forest can be used only with Decision trees.

A MOOC from Udemy about data analytic

Customer analytics and in particular A/B Testing are crucial parts of leveraging quantitative know-how to help make business decisions that generate value.

Kaggle challenge / Google Analytics Customer Revenue Prediction in Python with LightGBM, XGBoost, CatBoost

Segmenting Financial Customers with KMeans and Visual Analytics using python and to visually represent the data in powerBI.

Capstone project for the Google Data Analytics Certificate. Analysis of customer churn in streaming services using SQL, Python, and R.

Customer segmentation using K-Means (Python) + Business Dashboard in Power BI. Full project with EDA, clustering, insights and real-world analytics.

My data analytics portfolio showcasing projects in sales forecasting, customer segmentation, churn analysis, and dashboard development using Python, Power BI, and SQL.

A collection of data analytics projects using Excel and Python. Includes an interactive Sales Dashboard in Excel and a Customer Insights Analysis in Python. Showcasing skills in data cleaning, visualization, and business insights.

Customer Segmentation & Behavior Analytics – SQL | Python | Power BI This project analyzes customer shopping behavior using SQL and Python, and visualizes key insights in Power BI. It includes customer segmentation, revenue trends, category-wise performance, and behavior patterns to support data-driven decision-making.

Customer segmentation using KMeans clustering in Python. Includes data preprocessing, Silhouette evaluation, PCA visualization and an interactive Streamlit dashboard. Machine learning & marketing analytics project.