Assume that you are working as analyst for swig, a food delivery chain. The group has collected some interesting characteristics of customers who had purchased their food earlier. (Refer the attached customers.csv file for the same). The marketing team is planning a campaign to increase the sales of a newly launched Spanish food. Before that they want to analyze the segmentation of existing customers so that they can have the clearer picture about the customer categories.

Write a Python program that will take the "customers dataset" as input and produce the clusters which represents the customer segments present in the dataset.

• You may like to do some preprocessing on the given dataset.
• You have to write your own code matching to the problem statement.
• Add comments at appropriate place so that its easy to understand your thought process.
• You are supposed to use k-means clustering algorithm (custom implementation not from any library) for customer segmentations.
• The program should clearly output the cluster number, centroid used and number of records belonging to that cluster.

The final clusters should be preserved in such a way that it can be used in following exercises.

Your marketing team is now aware about the spending behavior of your customers. Wear hat of marketing professional and think of marketing strategy to attract these customers to your newly launched Spanish food.

As an outcome of this exercise, you will obtain clusters in the given dataset.

• Apply appropriate labels to those clusters after careful look at the points belonging to those clusters.
  • Explain your logic behind nomenclature of the clusters. Show the sample dataset.
• Based on this segmentation, think of some marketing offers that can be given to the customers.
  • Briefly explain the logic behind the offers to be made to the various customer categories.

By this time you are well aware about your existing customer base. You know the customers spending habit, you have decided upon offers to be made, now its time to test it out for the existing customers. Lets assume that they frequently visits the mall where your food shop is located. For this purpose you have to have the mechanisms through which their visits to the mall are captured.

Write a Python program that will simulate the movement of existing customers around a mall in near real time fashion.

• You can assume that customers cell phones are enabling the transfer of location data to your centralized server where its stored for further analysis.

With access to the real time movements of customers around mall, you are feeling more powerful and ready to target these customers. Your accumulated knowledge of streaming data processing and analytics can be leveraged for the same purpose. Now think for an architecture to bring it to the reality through a streaming data pipeline.

Construct a streaming data pipeline integrating the various technologies, tools and Programmes covered in the course that will harvest this real time data of customers movements and produces the offers that can be sent on the customers mobile devices. You can think of various aspects related to streaming data processing such as:

• Real time streaming data ingestion
• Datas intermittent storage
• Data preprocessing cleaning, transformations etc.
• Data processing filters, joins, windows etc.
• Business logic for placing the offers
• Final representation of the outcome

Submission requirements:

1. Python program for customer segmentation with sample input and outputs
2. Short note on criteria used for cluster nomenclature
3. Python program for customer movement simulation
4. Short note on describing

• the streaming data pipeline architecture
• components used and purposes of the same
• data flows
• business logic used

5. Programs / Queries used in streaming data pipeline
6. A short demo describing the overall thought process for approaching this problem and data flow through the pipeline.

Customer DataSet

CustomerID Gender Age Annual Income (k$) Spending Score (1-100) 1 Male 19 15 39 2 Male 21 15 81 3 Female 20 16 6 4 Female 23 16 77 5 Female 31 17 40 6 Female 22 17 76 7 Female 35 18 6 8 Female 23 18 94 9 Male 64 19 3 10 Female 30 19 72 11 Male 67 19 14 12 Female 35 19 99 13 Female 58 20 15 14 Female 24 20 77 15 Male 37 20 13 16 Male 22 20 79 17 Female 35 21 35 18 Male 20 21 66 19 Male 52 23 29 20 Female 35 23 98 21 Male 35 24 35 22 Male 25 24 73 23 Female 46 25 5 24 Male 31 25 73 25 Female 54 28 14 26 Male 29 28 82 27 Female 45 28 32 28 Male 35 28 61 29 Female 40 29 31 30 Female 23 29 87 31 Male 60 30 4 32 Female 21 30 73 33 Male 53 33 4 34 Male 18 33 92 35 Female 49 33 14 36 Female 21 33 81 37 Female 42 34 17 38 Female 30 34 73 39 Female 36 37 26 40 Female 20 37 75 41 Female 65 38 35 42 Male 24 38 92 43 Male 48 39 36 44 Female 31 39 61 45 Female 49 39 28 46 Female 24 39 65 47 Female 50 40 55 48 Female 27 40 47 49 Female 29 40 42 50 Female 31 40 42 51 Female 49 42 52 52 Male 33 42 60 53 Female 31 43 54 54 Male 59 43 60 55 Female 50 43 45 56 Male 47 43 41 57 Female 51 44 50 58 Male 69 44 46 59 Female 27 46 51 60 Male 53 46 46 61 Male 70 46 56 62 Male 19 46 55 63 Female 67 47 52 64 Female 54 47 59 65 Male 63 48 51 66 Male 18 48 59 67 Female 43 48 50 68 Female 68 48 48 69 Male 19 48 59 70 Female 32 48 47 71 Male 70 49 55 72 Female 47 49 42 73 Female 60 50 49 74 Female 60 50 56 75 Male 59 54 47 76 Male 26 54 54 77 Female 45 54 53 78 Male 40 54 48 79 Female 23 54 52 80 Female 49 54 42 81 Male 57 54 51 82 Male 38 54 55 83 Male 67 54 41 84 Female 46 54 44 85 Female 21 54 57 86 Male 48 54 46 87 Female 55 57 58 88 Female 22 57 55 89 Female 34 58 60 90 Female 50 58 46 91 Female 68 59 55 92 Male 18 59 41 93 Male 48 60 49 94 Female 40 60 40 95 Female 32 60 42 96 Male 24 60 52 97 Female 47 60 47 98 Female 27 60 50 99 Male 48 61 42 100 Male 20 61 49 101 Female 23 62 41 102 Female 49 62 48 103 Male 67 62 59 104 Male 26 62 55 105 Male 49 62 56 106 Female 21 62 42 107 Female 66 63 50 108 Male 54 63 46 109 Male 68 63 43 110 Male 66 63 48 111 Male 65 63 52 112 Female 19 63 54 113 Female 38 64 42 114 Male 19 64 46 115 Female 18 65 48 116 Female 19 65 50 117 Female 63 65 43 118 Female 49 65 59 119 Female 51 67 43 120 Female 50 67 57 121 Male 27 67 56 122 Female 38 67 40 123 Female 40 69 58 124 Male 39 69 91 125 Female 23 70 29 126 Female 31 70 77 127 Male 43 71 35 128 Male 40 71 95 129 Male 59 71 11 130 Male 38 71 75 131 Male 47 71 9 132 Male 39 71 75 133 Female 25 72 34 134 Female 31 72 71 135 Male 20 73 5 136 Female 29 73 88 137 Female 44 73 7 138 Male 32 73 73 139 Male 19 74 10 140 Female 35 74 72 141 Female 57 75 5 142 Male 32 75 93 143 Female 28 76 40 144 Female 32 76 87 145 Male 25 77 12 146 Male 28 77 97 147 Male 48 77 36 148 Female 32 77 74 149 Female 34 78 22 150 Male 34 78 90 151 Male 43 78 17 152 Male 39 78 88 153 Female 44 78 20 154 Female 38 78 76 155 Female 47 78 16 156 Female 27 78 89 157 Male 37 78 1 158 Female 30 78 78 159 Male 34 78 1 160 Female 30 78 73 161 Female 56 79 35 162 Female 29 79 83 163 Male 19 81 5 164 Female 31 81 93 165 Male 50 85 26 166 Female 36 85 75 167 Male 42 86 20 168 Female 33 86 95 169 Female 36 87 27 170 Male 32 87 63 171 Male 40 87 13 172 Male 28 87 75 173 Male 36 87 10 174 Male 36 87 92 175 Female 52 88 13 176 Female 30 88 86 177 Male 58 88 15 178 Male 27 88 69 179 Male 59 93 14 180 Male 35 93 90 181 Female 37 97 32 182 Female 32 97 86 183 Male 46 98 15 184 Female 29 98 88 185 Female 41 99 39 186 Male 30 99 97 187 Female 54 101 24 188 Male 28 101 68 189 Female 41 103 17 190 Female 36 103 85 191 Female 34 103 23 192 Female 32 103 69 193 Male 33 113 8 194 Female 38 113 91 195 Female 47 120 16 196 Female 35 120 79 197 Female 45 126 28 198 Male 32 126 74 199 Male 32 137 18 200 Male 30 137 83