Jasminewakini/WK14-IP-R

You are a Data analyst at Carrefour Kenya and are currently undertaking a project that will inform the marketing department on the most relevant marketing strategies that will result in the highest no. of sales (total price including tax). Your project has been divided into four parts where you'll explore a recent marketing dataset by performing various unsupervised learning techniques and later providing recommendations based on your insights. # Moringa_Data_Science_Core_WK14-IP-R-Prog_Independent_Project_2022_04_Jasmine_Wakini_R_Markdown #### {R Programmming Data Science Project}, {April, 2022} #### By **{Jasmine Wakini}** ## Description {You are a Data analyst at Carrefour Kenya and are currently undertaking a project that will inform the marketing department on the most relevant marketing strategies that will result in the highest no. of sales (total price including tax). Your project has been divided into four parts where you'll explore a recent marketing dataset by performing various unsupervised learning techniques and later providing recommendations based on your insights. Part 1: Dimensionality Reduction This section of the project entails reducing your dataset to a low dimensional dataset using the t-SNE algorithm or PCA. You will be required to perform your analysis and provide insights gained from your analysis. Part 2: Feature Selection This section requires you to perform feature selection through the use of the unsupervised learning methods learned earlier this week. You will be required to perform your analysis and provide insights on the features that contribute the most information to the dataset. Part 3: Association Rules This section will require that you create association rules that will allow you to identify relationships between variables in the dataset. You are provided with a separate dataset that comprises groups of items that will be associated with others. Just like in the other sections, you will also be required to provide insights for your analysis. Part 4: Anomaly Detection You have also been requested to check whether there are any anomalies in the given sales dataset. The objective of this task being fraud detection. ## Experimental Procedure: 1. Define the question, the metric for success, the context, experimental design taken. 2. Read and explore the given dataset. 3. Define the appropriateness of the available data to answer the given question. ## Resea …