Welcome to the new era. One of the biggest challenges when studying the technical skills of data science is understanding how those skills and concepts translate into real jobs, like growth marketing. The main idea is to demonstrate how with Python skills you can make the best marketing decisions based on data. In this project, through Python, using packages such as pandas, I perform an analysis of marketing campaigns using machine learning, taking into account the different metrics such as CTR, conversion rate, or retention rate of each social network, to learn how to analyze campaign performance, measure customer engagement, and predict customer churn, to improve company's marketing strategy.

A production-scale customer segmentation system using RFM Analysis implemented in PostgreSQL and Python. The project analyzes 151K+ e-commerce records to segment 5,000 customers into 8 behavioral groups, enabling data-driven marketing strategies and identifying high-value and churn-risk customers.

This project utilizes Python, employing regression and correlation analysis to optimize marketing campaigns. By analyzing sales impact across different hospital account types, it unveils key strategies, culminating in actionable recommendations and a comprehensive ROI table for effective decision-making.

A data analysis project using Python and Power BI to explore and predict customer behavior in a bank's term deposit marketing campaign.

Data science portfolio featuring projects in machine learning, sentiment analysis, predictive modeling, marketing analytics, and data engineering. Developed using Python, R, and SQL, and grounded in a strong academic foundation in Business Management and Business Analytics. Continuously updated to reflect ongoing growth.

This learning project is designed to demonstrate skills in data analysis using Python and MySQL. The primary goal is to analyze sales data from an online store, providing insights into product performance, customer behavior, and the effectiveness of marketing campaigns.

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

Association rule mining is a rule-based machine learning method which is used for discovering relationships and patterns between various items in large datasets. For example, association rule mining discovers regularities between products in large scale transactions, as we can see in point-of-sale systems of supermarkets. This will help extensively in marketing activities such as ‘product placements’ and ‘pricing’.Association rule mining also has other applications such as web usage mining, intrusion detection, bioinformatics etc.In this project, we have discussed association rule mining and its application for market basket analysis. We have discussed the calculation and importance of various metrics like support, confidence, lift, all-confidence, conviction. A case study was done, using Python programming language to analyse a departmental store data consisting of 7501 records and found the association rules with their corresponding metrics. We have used the apriori function for the process. For better understanding and visualisation, we have plotted the rules and made a combined effort to infer the best possible rule.

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.

The Marketing Department of a global retail store is running a customer appreciation campaign on the occasion of Christmas and New Year. They need to deploy appropriate marketing program for each customer group and exploit potential customers to become loyal customers. This Python project provided a segmentation analysis base on RFM Model.

Machine Learning projects in Python covering Linear & Logistic Regression, with real-world datasets — Swiggy, Loan Approval, Customer Churn, Retail Store & Marketing Sales Analysis.

This end-to-end marketing analysis project involved preprocessing and querying data using SQL, running sentiment analysis in Python, building an interactive dashboard in Power BI, and presenting key findings in a PowerPoint presentation.

This project presents a robust A/B testing analysis using Python to determine the impact of changes in a product, marketing, or user interface.

This project involves using python and various API’s like Clarifai, Paralleldots for analysis of popular hash-tag on the Instagram which helps in marketing.

This project employs Python to assess and optimize marketing strategies, leveraging data analysis to enhance targeting, engagement, and overall effectiveness in the food and beverage industry.

An in-depth analysis of customer acquisition cost (CAC) across marketing channels in 2023, using Python for data processing and visualization. The project evaluates performance metrics like CAC, conversion efficiency, and spend effectiveness to inform marketing strategy and budget optimization.

This project utilizes K-means clustering in Python to segment customers based on their purchasing behavior and demographic data. The analysis identifies distinct customer groups, enabling the development of personalized marketing campaigns and improved customer engagement.

Uncover valuable insights into customer purchasing behavior with this Market Basket Analysis project in Python. Implementing the Apriori Algorithm, it efficiently identifies frequent itemsets, generating association rules to optimize product placement and marketing strategies. Boost your business intelligence in just a few lines of code.

Build a Car Sales Prediction Model using Machine Learning in Python. This project covers data cleaning, exploratory analysis, feature engineering, model training, and evaluation to forecast car sales. The model helps dealerships make data-driven decisions on inventory, pricing, and marketing.

In this project I used RFM Analysis with SQL and K-Means Clustering with Python to segment the customers of e-commerce retail store. I developed marketing strategies to increase customer lifetime value based on the purchasing beviour of customer segments identified.

End-to-end data analysis case study in the on-demand delivery industry. Using a fictitious company (OnTime), this project covers data cleaning, advanced SQL queries, Python prototyping, Tableau dashboard development, and stakeholder-ready presentations focused on marketing performance, attribution, retention, and ROI.

This Sales Analysis project was designed to uncover meaningful insights from historical sales data to support better decision-making in areas like marketing, sales, and operations. By using Python and its powerful data analytics libraries, we turned raw transactional data into valuable business intelligence.

This project build using python language on jupyter notebook .Firstly imports the data then analysis on different datasets. Conducted EDA on credit card data to identify patterns and trends in customer spending behavior .Analyzed credit card data to develop customer segmentation and targeted marketing strategies.

Data science project for AAA Northeast: Predictive modeling and value-based segmentation to identify high-value members, optimize marketing ROI, and forecast 5-year revenue growth. Features look-alike modeling, cost concentration analysis (38.8% in top 10%), and actionable segmentation framework. Built with Python, scikit-learn (AUC 0.68-0.74).

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Project in the Python course of Marketing Technology Data Analysis

Internship project performing customer segmentation using RFM (Recency, Frequency, Monetary) analysis in Python to derive marketing insights.

Portfolio showcasing data analysis projects in Finance, HR & Marketing using SQL, Python, Power BI, Tableau and Excel

This project automates the handling and analysis of weekly marketing data using Python, enhancing efficiency in data processing and enabling better decision-making in marketing strategies.

Marketing analytics portfolio with churn prediction, funnel analysis, and cohort retention dashboard projects built in Python, SQL, and Streamlit.