Credit card fraud poses significant financial and reputational risks to individuals, financial institutions, and payment processors. This project delivers an end-to-end fraud detection solution that transitions from experimental model development in Jupyter notebooks to production-ready inference through an intuitive Streamlit dashboard.

This project implements an end-to-end customer churn analysis and prediction pipeline leveraging SQL Server for data preparation, Power BI for interactive visualization, and Python(Jupyter Notebook) for machine learning modeling. It combines database management, business intelligence, and predictive analytics to deliver actionable insights

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.

This project delivers a Jupyter Notebook with analysis on Austin's housing prices and demographic data.