This is a program that will help you get insights on your ads from Twitter. It is meant to be a work in progress.
Twitter Ads.xlsx is a report exported with all the possible parameters directly from TwitterAds
#Documentation This is a Python script that performs some data analysis and visualization on a data set stored in an excel file "Twitter Ads.xlsx". It uses the library pandas to read the data and create data frames, the library numpy to perform computations, the library matplotlib to create plots and graphs, and the library seaborn to style these plots.
The script starts by reading in 6 sheets of data from the excel file (Age, Language, Gender, Devices, OS, Interest) and creating a pandas data frame for each. The script then computes and appends values to lists that are then used to create a data frame for Age data.
The script then uses KMeans from scikit-learn's cluster module to cluster the data into 3 segments. The script then separates the 3 clusters into different lists based on the indices of the cluster labels assigned by KMeans. Finally, the script defines a function "GetAgeRange" which takes in a cluster list and returns the minimum and maximum values of the age ranges in the cluster.
The script ends with creating a data frame for language data and plotting the results rate by language using a bar plot.
Things to add.
Data validation: Checking the input data for validity, missing values, or other data issues that might affect the results.
Error handling: Adding error handling mechanisms to handle unexpected situations and to provide user-friendly error messages.
User interaction: Adding a user interface to the program to make it easier for users to interact with the program and input their data.
Performance improvement: Improving the performance of the program by optimizing the code, reducing the number of data manipulations, and increasing the speed of data processing.
Visualization options: Adding more visualization options to the program to allow users to visualize their results in different ways.
Export options: Adding export options to the program to allow users to save their results in different formats, such as CSV, PDF, and others.
Algorithm selection: Adding an option for users to select the appropriate clustering algorithm for their data, based on their requirements and the type of data they have.